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Data Analysis in Research: Types & Methods

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Why analyze data in research?

Types of data in research, finding patterns in the qualitative data, methods used for data analysis in qualitative research, preparing data for analysis, methods used for data analysis in quantitative research, considerations in research data analysis, what is data analysis in research.

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense. 

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

LEARN ABOUT: Research Process Steps

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

Researchers rely heavily on data as they have a story to tell or research problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring.

Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased toward unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected when initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research. 

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Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

• Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
• Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
• Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words. 

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find  “food”  and  “hunger” are the most commonly used words and will highlight them for further analysis.

LEARN ABOUT: Level of Analysis

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.  

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended  text analysis  methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other. 

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

• Content Analysis:  It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
• Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and  surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
• Discourse Analysis:  Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
• Grounded Theory:  When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

• Fraud: To ensure an actual human being records each response to the survey or the questionnaire
• Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
• Procedure: To ensure ethical standards were maintained while collecting the data sample
• Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

• Count, Percent, Frequency
• It is used to denote home often a particular event occurs.
• Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

• Mean, Median, Mode
• The method is widely used to demonstrate distribution by various points.
• Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

• Range, Variance, Standard deviation
• Here the field equals high/low points.
• Variance standard deviation = difference between the observed score and mean
• It is used to identify the spread of scores by stating intervals.
• Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

• Percentile ranks, Quartile ranks
• It relies on standardized scores helping researchers to identify the relationship between different scores.
• It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided  sample  without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected  sample  to reason that about 80-90% of people like the movie. 

Here are two significant areas of inferential statistics.

• Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
• Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

• Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
• Cross-tabulation: Also called contingency tables,  cross-tabulation  is used to analyze the relationship between multiple variables.  Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
• Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
• Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
• Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection methods , and choose samples.

LEARN ABOUT: Best Data Collection Tools

• The primary aim of data research and analysis is to derive ultimate insights that are unbiased. Any mistake in or keeping a biased mind to collect data, selecting an analysis method, or choosing  audience  sample il to draw a biased inference.
• Irrelevant to the sophistication used in research data and analysis is enough to rectify the poorly defined objective outcome measurements. It does not matter if the design is at fault or intentions are not clear, but lack of clarity might mislead readers, so avoid the practice.
• The motive behind data analysis in research is to present accurate and reliable data. As far as possible, avoid statistical errors, and find a way to deal with everyday challenges like outliers, missing data, data altering, data mining , or developing graphical representation.

LEARN MORE: Descriptive Research vs Correlational Research The sheer amount of data generated daily is frightening. Especially when data analysis has taken center stage. in 2018. In last year, the total data supply amounted to 2.8 trillion gigabytes. Hence, it is clear that the enterprises willing to survive in the hypercompetitive world must possess an excellent capability to analyze complex research data, derive actionable insights, and adapt to the new market needs.

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Data Analysis

• Introduction to Data Analysis
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What is Data Analysis?

According to the federal government, data analysis is "the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data" ( Responsible Conduct in Data Management ). Important components of data analysis include searching for patterns, remaining unbiased in drawing inference from data, practicing responsible  data management , and maintaining "honest and accurate analysis" ( Responsible Conduct in Data Management ). 

In order to understand data analysis further, it can be helpful to take a step back and understand the question "What is data?". Many of us associate data with spreadsheets of numbers and values, however, data can encompass much more than that. According to the federal government, data is "The recorded factual material commonly accepted in the scientific community as necessary to validate research findings" ( OMB Circular 110 ). This broad definition can include information in many formats. 

Some examples of types of data are as follows:

• Photographs 
• Hand-written notes from field observation
• Machine learning training data sets
• Ethnographic interview transcripts
• Sheet music
• Scripts for plays and musicals 
• Observations from laboratory experiments ( CMU Data 101 )

Thus, data analysis includes the processing and manipulation of these data sources in order to gain additional insight from data, answer a research question, or confirm a research hypothesis. 

Data analysis falls within the larger research data lifecycle, as seen below. 

( University of Virginia )

Why Analyze Data?

Through data analysis, a researcher can gain additional insight from data and draw conclusions to address the research question or hypothesis. Use of data analysis tools helps researchers understand and interpret data. 

What are the Types of Data Analysis?

Data analysis can be quantitative, qualitative, or mixed methods. 

Quantitative research typically involves numbers and "close-ended questions and responses" ( Creswell & Creswell, 2018 , p. 3). Quantitative research tests variables against objective theories, usually measured and collected on instruments and analyzed using statistical procedures ( Creswell & Creswell, 2018 , p. 4). Quantitative analysis usually uses deductive reasoning. 

Qualitative  research typically involves words and "open-ended questions and responses" ( Creswell & Creswell, 2018 , p. 3). According to Creswell & Creswell, "qualitative research is an approach for exploring and understanding the meaning individuals or groups ascribe to a social or human problem" ( 2018 , p. 4). Thus, qualitative analysis usually invokes inductive reasoning. 

Mixed methods  research uses methods from both quantitative and qualitative research approaches. Mixed methods research works under the "core assumption... that the integration of qualitative and quantitative data yields additional insight beyond the information provided by either the quantitative or qualitative data alone" ( Creswell & Creswell, 2018 , p. 4). 

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• Last Updated: Jun 25, 2024 10:23 AM
• URL: https://guides.library.georgetown.edu/data-analysis

Quantitative Data Analysis 101

The lingo, methods and techniques, explained simply.

By: Derek Jansen (MBA)  and Kerryn Warren (PhD) | December 2020

Quantitative data analysis is one of those things that often strikes fear in students. It’s totally understandable – quantitative analysis is a complex topic, full of daunting lingo , like medians, modes, correlation and regression. Suddenly we’re all wishing we’d paid a little more attention in math class…

The good news is that while quantitative data analysis is a mammoth topic, gaining a working understanding of the basics isn’t that hard , even for those of us who avoid numbers and math . In this post, we’ll break quantitative analysis down into simple , bite-sized chunks so you can approach your research with confidence.

Overview: Quantitative Data Analysis 101

• What (exactly) is quantitative data analysis?
• When to use quantitative analysis
• How quantitative analysis works

The two “branches” of quantitative analysis

• Descriptive statistics 101
• Inferential statistics 101
• How to choose the right quantitative methods
• Recap & summary

What is quantitative data analysis?

Despite being a mouthful, quantitative data analysis simply means analysing data that is numbers-based – or data that can be easily “converted” into numbers without losing any meaning.

For example, category-based variables like gender, ethnicity, or native language could all be “converted” into numbers without losing meaning – for example, English could equal 1, French 2, etc.

This contrasts against qualitative data analysis, where the focus is on words, phrases and expressions that can’t be reduced to numbers. If you’re interested in learning about qualitative analysis, check out our post and video here .

What is quantitative analysis used for?

Quantitative analysis is generally used for three purposes.

• Firstly, it’s used to measure differences between groups . For example, the popularity of different clothing colours or brands.
• Secondly, it’s used to assess relationships between variables . For example, the relationship between weather temperature and voter turnout.
• And third, it’s used to test hypotheses in a scientifically rigorous way. For example, a hypothesis about the impact of a certain vaccine.

Again, this contrasts with qualitative analysis , which can be used to analyse people’s perceptions and feelings about an event or situation. In other words, things that can’t be reduced to numbers.

How does quantitative analysis work?

Well, since quantitative data analysis is all about analysing numbers , it’s no surprise that it involves statistics . Statistical analysis methods form the engine that powers quantitative analysis, and these methods can vary from pretty basic calculations (for example, averages and medians) to more sophisticated analyses (for example, correlations and regressions).

Sounds like gibberish? Don’t worry. We’ll explain all of that in this post. Importantly, you don’t need to be a statistician or math wiz to pull off a good quantitative analysis. We’ll break down all the technical mumbo jumbo in this post.

Need a helping hand?

As I mentioned, quantitative analysis is powered by statistical analysis methods . There are two main “branches” of statistical methods that are used – descriptive statistics and inferential statistics . In your research, you might only use descriptive statistics, or you might use a mix of both , depending on what you’re trying to figure out. In other words, depending on your research questions, aims and objectives . I’ll explain how to choose your methods later.

So, what are descriptive and inferential statistics?

Well, before I can explain that, we need to take a quick detour to explain some lingo. To understand the difference between these two branches of statistics, you need to understand two important words. These words are population and sample .

First up, population . In statistics, the population is the entire group of people (or animals or organisations or whatever) that you’re interested in researching. For example, if you were interested in researching Tesla owners in the US, then the population would be all Tesla owners in the US.

However, it’s extremely unlikely that you’re going to be able to interview or survey every single Tesla owner in the US. Realistically, you’ll likely only get access to a few hundred, or maybe a few thousand owners using an online survey. This smaller group of accessible people whose data you actually collect is called your sample .

So, to recap – the population is the entire group of people you’re interested in, and the sample is the subset of the population that you can actually get access to. In other words, the population is the full chocolate cake , whereas the sample is a slice of that cake.

So, why is this sample-population thing important?

Well, descriptive statistics focus on describing the sample , while inferential statistics aim to make predictions about the population, based on the findings within the sample. In other words, we use one group of statistical methods – descriptive statistics – to investigate the slice of cake, and another group of methods – inferential statistics – to draw conclusions about the entire cake. There I go with the cake analogy again…

With that out the way, let’s take a closer look at each of these branches in more detail.

Branch 1: Descriptive Statistics

Descriptive statistics serve a simple but critically important role in your research – to describe your data set – hence the name. In other words, they help you understand the details of your sample . Unlike inferential statistics (which we’ll get to soon), descriptive statistics don’t aim to make inferences or predictions about the entire population – they’re purely interested in the details of your specific sample .

When you’re writing up your analysis, descriptive statistics are the first set of stats you’ll cover, before moving on to inferential statistics. But, that said, depending on your research objectives and research questions , they may be the only type of statistics you use. We’ll explore that a little later.

So, what kind of statistics are usually covered in this section?

Some common statistical tests used in this branch include the following:

• Mean – this is simply the mathematical average of a range of numbers.
• Median – this is the midpoint in a range of numbers when the numbers are arranged in numerical order. If the data set makes up an odd number, then the median is the number right in the middle of the set. If the data set makes up an even number, then the median is the midpoint between the two middle numbers.
• Mode – this is simply the most commonly occurring number in the data set.
• In cases where most of the numbers are quite close to the average, the standard deviation will be relatively low.
• Conversely, in cases where the numbers are scattered all over the place, the standard deviation will be relatively high.
• Skewness . As the name suggests, skewness indicates how symmetrical a range of numbers is. In other words, do they tend to cluster into a smooth bell curve shape in the middle of the graph, or do they skew to the left or right?

Feeling a bit confused? Let’s look at a practical example using a small data set.

On the left-hand side is the data set. This details the bodyweight of a sample of 10 people. On the right-hand side, we have the descriptive statistics. Let’s take a look at each of them.

First, we can see that the mean weight is 72.4 kilograms. In other words, the average weight across the sample is 72.4 kilograms. Straightforward.

Next, we can see that the median is very similar to the mean (the average). This suggests that this data set has a reasonably symmetrical distribution (in other words, a relatively smooth, centred distribution of weights, clustered towards the centre).

In terms of the mode , there is no mode in this data set. This is because each number is present only once and so there cannot be a “most common number”. If there were two people who were both 65 kilograms, for example, then the mode would be 65.

Next up is the standard deviation . 10.6 indicates that there’s quite a wide spread of numbers. We can see this quite easily by looking at the numbers themselves, which range from 55 to 90, which is quite a stretch from the mean of 72.4.

And lastly, the skewness of -0.2 tells us that the data is very slightly negatively skewed. This makes sense since the mean and the median are slightly different.

As you can see, these descriptive statistics give us some useful insight into the data set. Of course, this is a very small data set (only 10 records), so we can’t read into these statistics too much. Also, keep in mind that this is not a list of all possible descriptive statistics – just the most common ones.

But why do all of these numbers matter?

While these descriptive statistics are all fairly basic, they’re important for a few reasons:

• Firstly, they help you get both a macro and micro-level view of your data. In other words, they help you understand both the big picture and the finer details.
• Secondly, they help you spot potential errors in the data – for example, if an average is way higher than you’d expect, or responses to a question are highly varied, this can act as a warning sign that you need to double-check the data.
• And lastly, these descriptive statistics help inform which inferential statistical techniques you can use, as those techniques depend on the skewness (in other words, the symmetry and normality) of the data.

Simply put, descriptive statistics are really important , even though the statistical techniques used are fairly basic. All too often at Grad Coach, we see students skimming over the descriptives in their eagerness to get to the more exciting inferential methods, and then landing up with some very flawed results.

Don’t be a sucker – give your descriptive statistics the love and attention they deserve!

Branch 2: Inferential Statistics

As I mentioned, while descriptive statistics are all about the details of your specific data set – your sample – inferential statistics aim to make inferences about the population . In other words, you’ll use inferential statistics to make predictions about what you’d expect to find in the full population.

What kind of predictions, you ask? Well, there are two common types of predictions that researchers try to make using inferential stats:

• Firstly, predictions about differences between groups – for example, height differences between children grouped by their favourite meal or gender.
• And secondly, relationships between variables – for example, the relationship between body weight and the number of hours a week a person does yoga.

In other words, inferential statistics (when done correctly), allow you to connect the dots and make predictions about what you expect to see in the real world population, based on what you observe in your sample data. For this reason, inferential statistics are used for hypothesis testing – in other words, to test hypotheses that predict changes or differences.

Of course, when you’re working with inferential statistics, the composition of your sample is really important. In other words, if your sample doesn’t accurately represent the population you’re researching, then your findings won’t necessarily be very useful.

For example, if your population of interest is a mix of 50% male and 50% female , but your sample is 80% male , you can’t make inferences about the population based on your sample, since it’s not representative. This area of statistics is called sampling, but we won’t go down that rabbit hole here (it’s a deep one!) – we’ll save that for another post .

What statistics are usually used in this branch?

There are many, many different statistical analysis methods within the inferential branch and it’d be impossible for us to discuss them all here. So we’ll just take a look at some of the most common inferential statistical methods so that you have a solid starting point.

First up are T-Tests . T-tests compare the means (the averages) of two groups of data to assess whether they’re statistically significantly different. In other words, do they have significantly different means, standard deviations and skewness.

This type of testing is very useful for understanding just how similar or different two groups of data are. For example, you might want to compare the mean blood pressure between two groups of people – one that has taken a new medication and one that hasn’t – to assess whether they are significantly different.

Kicking things up a level, we have ANOVA, which stands for “analysis of variance”. This test is similar to a T-test in that it compares the means of various groups, but ANOVA allows you to analyse multiple groups , not just two groups So it’s basically a t-test on steroids…

Next, we have correlation analysis . This type of analysis assesses the relationship between two variables. In other words, if one variable increases, does the other variable also increase, decrease or stay the same. For example, if the average temperature goes up, do average ice creams sales increase too? We’d expect some sort of relationship between these two variables intuitively , but correlation analysis allows us to measure that relationship scientifically .

Lastly, we have regression analysis – this is quite similar to correlation in that it assesses the relationship between variables, but it goes a step further to understand cause and effect between variables, not just whether they move together. In other words, does the one variable actually cause the other one to move, or do they just happen to move together naturally thanks to another force? Just because two variables correlate doesn’t necessarily mean that one causes the other.

Stats overload…

I hear you. To make this all a little more tangible, let’s take a look at an example of a correlation in action.

Here’s a scatter plot demonstrating the correlation (relationship) between weight and height. Intuitively, we’d expect there to be some relationship between these two variables, which is what we see in this scatter plot. In other words, the results tend to cluster together in a diagonal line from bottom left to top right.

As I mentioned, these are are just a handful of inferential techniques – there are many, many more. Importantly, each statistical method has its own assumptions and limitations .

For example, some methods only work with normally distributed (parametric) data, while other methods are designed specifically for non-parametric data. And that’s exactly why descriptive statistics are so important – they’re the first step to knowing which inferential techniques you can and can’t use.

How to choose the right analysis method

To choose the right statistical methods, you need to think about two important factors :

• The type of quantitative data you have (specifically, level of measurement and the shape of the data). And,
• Your research questions and hypotheses

Let’s take a closer look at each of these.

Factor 1 – Data type

The first thing you need to consider is the type of data you’ve collected (or the type of data you will collect). By data types, I’m referring to the four levels of measurement – namely, nominal, ordinal, interval and ratio. If you’re not familiar with this lingo, check out the video below.

Why does this matter?

Well, because different statistical methods and techniques require different types of data. This is one of the “assumptions” I mentioned earlier – every method has its assumptions regarding the type of data.

For example, some techniques work with categorical data (for example, yes/no type questions, or gender or ethnicity), while others work with continuous numerical data (for example, age, weight or income) – and, of course, some work with multiple data types.

If you try to use a statistical method that doesn’t support the data type you have, your results will be largely meaningless . So, make sure that you have a clear understanding of what types of data you’ve collected (or will collect). Once you have this, you can then check which statistical methods would support your data types here .

If you haven’t collected your data yet, you can work in reverse and look at which statistical method would give you the most useful insights, and then design your data collection strategy to collect the correct data types.

Another important factor to consider is the shape of your data . Specifically, does it have a normal distribution (in other words, is it a bell-shaped curve, centred in the middle) or is it very skewed to the left or the right? Again, different statistical techniques work for different shapes of data – some are designed for symmetrical data while others are designed for skewed data.

This is another reminder of why descriptive statistics are so important – they tell you all about the shape of your data.

Factor 2: Your research questions

The next thing you need to consider is your specific research questions, as well as your hypotheses (if you have some). The nature of your research questions and research hypotheses will heavily influence which statistical methods and techniques you should use.

If you’re just interested in understanding the attributes of your sample (as opposed to the entire population), then descriptive statistics are probably all you need. For example, if you just want to assess the means (averages) and medians (centre points) of variables in a group of people.

On the other hand, if you aim to understand differences between groups or relationships between variables and to infer or predict outcomes in the population, then you’ll likely need both descriptive statistics and inferential statistics.

So, it’s really important to get very clear about your research aims and research questions, as well your hypotheses – before you start looking at which statistical techniques to use.

Never shoehorn a specific statistical technique into your research just because you like it or have some experience with it. Your choice of methods must align with all the factors we’ve covered here.

Time to recap…

You’re still with me? That’s impressive. We’ve covered a lot of ground here, so let’s recap on the key points:

• Quantitative data analysis is all about  analysing number-based data  (which includes categorical and numerical data) using various statistical techniques.
• The two main  branches  of statistics are  descriptive statistics  and  inferential statistics . Descriptives describe your sample, whereas inferentials make predictions about what you’ll find in the population.
• Common  descriptive statistical methods include  mean  (average),  median , standard  deviation  and  skewness .
• Common  inferential statistical methods include  t-tests ,  ANOVA ,  correlation  and  regression  analysis.
• To choose the right statistical methods and techniques, you need to consider the  type of data you’re working with , as well as your  research questions  and hypotheses.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

77 Comments

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8 Types of Data Analysis

The different types of data analysis include descriptive, diagnostic, exploratory, inferential, predictive, causal, mechanistic and prescriptive. Here’s what you need to know about each one.

Data analysis is an aspect of data science and  data analytics that is all about analyzing data for different kinds of purposes. The data analysis process involves inspecting, cleaning, transforming and  modeling data to draw useful insights from it.

Types of Data Analysis

• Descriptive analysis
• Diagnostic analysis
• Exploratory analysis
• Inferential analysis
• Predictive analysis
• Causal analysis
• Mechanistic analysis
• Prescriptive analysis

With its multiple facets, methodologies and techniques, data analysis is used in a variety of fields, including energy, healthcare and marketing, among others. As businesses thrive under the influence of technological advancements in data analytics, data analysis plays a huge role in decision-making , providing a better, faster and more effective system that minimizes risks and reduces human biases .

That said, there are different kinds of data analysis with different goals. We’ll examine each one below.

Two Camps of Data Analysis

Data analysis can be divided into two camps, according to the book R for Data Science :

• Hypothesis Generation: This involves looking deeply at the data and combining your domain knowledge to generate  hypotheses about why the data behaves the way it does.
• Hypothesis Confirmation: This involves using a precise mathematical model to generate falsifiable predictions with statistical sophistication to confirm your prior hypotheses.

More on Data Analysis: Data Analyst vs. Data Scientist: Similarities and Differences Explained

Data analysis can be separated and organized into types, arranged in an increasing order of complexity.  

1. Descriptive Analysis

The goal of descriptive analysis is to describe or summarize a set of data . Here’s what you need to know:

• Descriptive analysis is the very first analysis performed in the data analysis process.
• It generates simple summaries of samples and measurements.
• It involves common, descriptive statistics like measures of central tendency, variability, frequency and position.

Descriptive Analysis Example

Take the Covid-19 statistics page on Google, for example. The line graph is a pure summary of the cases/deaths, a presentation and description of the population of a particular country infected by the virus.

Descriptive analysis is the first step in analysis where you summarize and describe the data you have using descriptive statistics, and the result is a simple presentation of your data.

2. Diagnostic Analysis  

Diagnostic analysis seeks to answer the question “Why did this happen?” by taking a more in-depth look at data to uncover subtle patterns. Here’s what you need to know:

• Diagnostic analysis typically comes after descriptive analysis, taking initial findings and investigating why certain patterns in data happen. 
• Diagnostic analysis may involve analyzing other related data sources, including past data, to reveal more insights into current data trends.  
• Diagnostic analysis is ideal for further exploring patterns in data to explain anomalies .  

Diagnostic Analysis Example

A footwear store wants to review its  website traffic levels over the previous 12 months. Upon compiling and assessing the data, the company’s marketing team finds that June experienced above-average levels of traffic while July and August witnessed slightly lower levels of traffic. 

To find out why this difference occurred, the marketing team takes a deeper look. Team members break down the data to focus on specific categories of footwear. In the month of June, they discovered that pages featuring sandals and other beach-related footwear received a high number of views while these numbers dropped in July and August. 

Marketers may also review other factors like seasonal changes and company sales events to see if other variables could have contributed to this trend.    

3. Exploratory Analysis (EDA)

Exploratory analysis involves examining or  exploring data and finding relationships between variables that were previously unknown. Here’s what you need to know:

• EDA helps you discover relationships between measures in your data, which are not evidence for the existence of the correlation, as denoted by the phrase, “ Correlation doesn’t imply causation .”
• It’s useful for discovering new connections and forming hypotheses. It drives design planning and data collection .

Exploratory Analysis Example

Climate change is an increasingly important topic as the global temperature has gradually risen over the years. One example of an exploratory data analysis on climate change involves taking the rise in temperature over the years from 1950 to 2020 and the increase of human activities and industrialization to find relationships from the data. For example, you may increase the number of factories, cars on the road and airplane flights to see how that correlates with the rise in temperature.

Exploratory analysis explores data to find relationships between measures without identifying the cause. It’s most useful when formulating hypotheses. 

4. Inferential Analysis

Inferential analysis involves using a small sample of data to infer information about a larger population of data.

The goal of statistical modeling itself is all about using a small amount of information to extrapolate and generalize information to a larger group. Here’s what you need to know:

• Inferential analysis involves using estimated data that is representative of a population and gives a measure of uncertainty or  standard deviation to your estimation.
• The accuracy of inference depends heavily on your sampling scheme. If the sample isn’t representative of the population, the generalization will be inaccurate. This is known as the central limit theorem .

Inferential Analysis Example

A psychological study on the benefits of sleep might have a total of 500 people involved. When they followed up with the candidates, the candidates reported to have better overall attention spans and well-being with seven to nine hours of sleep, while those with less sleep and more sleep than the given range suffered from reduced attention spans and energy. This study drawn from 500 people was just a tiny portion of the 7 billion people in the world, and is thus an inference of the larger population.

Inferential analysis extrapolates and generalizes the information of the larger group with a smaller sample to generate analysis and predictions. 

5. Predictive Analysis

Predictive analysis involves using historical or current data to find patterns and make predictions about the future. Here’s what you need to know:

• The accuracy of the predictions depends on the input variables.
• Accuracy also depends on the types of models. A linear model might work well in some cases, and in other cases it might not.
• Using a variable to predict another one doesn’t denote a causal relationship.

Predictive Analysis Example

The 2020 United States election is a popular topic and many prediction models are built to predict the winning candidate. FiveThirtyEight did this to forecast the 2016 and 2020 elections. Prediction analysis for an election would require input variables such as historical polling data, trends and current polling data in order to return a good prediction. Something as large as an election wouldn’t just be using a linear model, but a complex model with certain tunings to best serve its purpose.

6. Causal Analysis

Causal analysis looks at the cause and effect of relationships between variables and is focused on finding the cause of a correlation. This way, researchers can examine how a change in one variable affects another. Here’s what you need to know:

• To find the cause, you have to question whether the observed correlations driving your conclusion are valid. Just looking at the surface data won’t help you discover the hidden mechanisms underlying the correlations.
• Causal analysis is applied in randomized studies focused on identifying causation.
• Causal analysis is the gold standard in data analysis and scientific studies where the cause of a phenomenon is to be extracted and singled out, like separating wheat from chaff.
• Good data is hard to find and requires expensive research and studies. These studies are analyzed in aggregate (multiple groups), and the observed relationships are just average effects (mean) of the whole population. This means the results might not apply to everyone.

Causal Analysis Example  

Say you want to test out whether a new drug improves human strength and focus. To do that, you perform randomized control trials for the drug to test its effect. You compare the sample of candidates for your new drug against the candidates receiving a mock control drug through a few tests focused on strength and overall focus and attention. This will allow you to observe how the drug affects the outcome. 

7. Mechanistic Analysis

Mechanistic analysis is used to understand exact changes in variables that lead to other changes in other variables . In some ways, it is a predictive analysis, but it’s modified to tackle studies that require high precision and meticulous methodologies for physical or engineering science. Here’s what you need to know:

• It’s applied in physical or engineering sciences, situations that require high  precision and little room for error, only noise in data is measurement error.
• It’s designed to understand a biological or behavioral process, the pathophysiology of a disease or the mechanism of action of an intervention. 

Mechanistic Analysis Example

Say an experiment is done to simulate safe and effective nuclear fusion to power the world. A mechanistic analysis of the study would entail a precise balance of controlling and manipulating variables with highly accurate measures of both variables and the desired outcomes. It’s this intricate and meticulous modus operandi toward these big topics that allows for scientific breakthroughs and advancement of society.

8. Prescriptive Analysis  

Prescriptive analysis compiles insights from other previous data analyses and determines actions that teams or companies can take to prepare for predicted trends. Here’s what you need to know: 

• Prescriptive analysis may come right after predictive analysis, but it may involve combining many different data analyses. 
• Companies need advanced technology and plenty of resources to conduct prescriptive analysis. Artificial intelligence systems that process data and adjust automated tasks are an example of the technology required to perform prescriptive analysis.  

Prescriptive Analysis Example

Prescriptive analysis is pervasive in everyday life, driving the curated content users consume on social media. On platforms like TikTok and Instagram,  algorithms can apply prescriptive analysis to review past content a user has engaged with and the kinds of behaviors they exhibited with specific posts. Based on these factors, an  algorithm seeks out similar content that is likely to elicit the same response and  recommends it on a user’s personal feed. 

More on Data Explaining the Empirical Rule for Normal Distribution

When to Use the Different Types of Data Analysis  

• Descriptive analysis summarizes the data at hand and presents your data in a comprehensible way.
• Diagnostic analysis takes a more detailed look at data to reveal why certain patterns occur, making it a good method for explaining anomalies. 
• Exploratory data analysis helps you discover correlations and relationships between variables in your data.
• Inferential analysis is for generalizing the larger population with a smaller sample size of data.
• Predictive analysis helps you make predictions about the future with data.
• Causal analysis emphasizes finding the cause of a correlation between variables.
• Mechanistic analysis is for measuring the exact changes in variables that lead to other changes in other variables.
• Prescriptive analysis combines insights from different data analyses to develop a course of action teams and companies can take to capitalize on predicted outcomes. 

A few important tips to remember about data analysis include:

• Correlation doesn’t imply causation.
• EDA helps discover new connections and form hypotheses.
• Accuracy of inference depends on the sampling scheme.
• A good prediction depends on the right input variables.
• A simple linear model with enough data usually does the trick.
• Using a variable to predict another doesn’t denote causal relationships.
• Good data is hard to find, and to produce it requires expensive research.
• Results from studies are done in aggregate and are average effects and might not apply to everyone.​

Frequently Asked Questions

What is an example of data analysis.

A marketing team reviews a company’s web traffic over the past 12 months. To understand why sales rise and fall during certain months, the team breaks down the data to look at shoe type, seasonal patterns and sales events. Based on this in-depth analysis, the team can determine variables that influenced web traffic and make adjustments as needed.

How do you know which data analysis method to use?

Selecting a data analysis method depends on the goals of the analysis and the complexity of the task, among other factors. It’s best to assess the circumstances and consider the pros and cons of each type of data analysis before moving forward with a particular method.

Recent Data Science Articles

What is Data Analysis? (Types, Methods, and Tools)

• Couchbase Product Marketing December 17, 2023

Data analysis is the process of cleaning, transforming, and interpreting data to uncover insights, patterns, and trends. It plays a crucial role in decision making, problem solving, and driving innovation across various domains. 

In addition to further exploring the role data analysis plays this blog post will discuss common data analysis techniques, delve into the distinction between quantitative and qualitative data, explore popular data analysis tools, and discuss the steps involved in the data analysis process. 

By the end, you should have a deeper understanding of data analysis and its applications, empowering you to harness the power of data to make informed decisions and gain actionable insights.

Why is Data Analysis Important?

Data analysis is important across various domains and industries. It helps with:

• Decision Making : Data analysis provides valuable insights that support informed decision making, enabling organizations to make data-driven choices for better outcomes.
• Problem Solving : Data analysis helps identify and solve problems by uncovering root causes, detecting anomalies, and optimizing processes for increased efficiency.
• Performance Evaluation : Data analysis allows organizations to evaluate performance, track progress, and measure success by analyzing key performance indicators (KPIs) and other relevant metrics.
• Gathering Insights : Data analysis uncovers valuable insights that drive innovation, enabling businesses to develop new products, services, and strategies aligned with customer needs and market demand.
• Risk Management : Data analysis helps mitigate risks by identifying risk factors and enabling proactive measures to minimize potential negative impacts.

By leveraging data analysis, organizations can gain a competitive advantage, improve operational efficiency, and make smarter decisions that positively impact the bottom line.

Quantitative vs. Qualitative Data

In data analysis, you’ll commonly encounter two types of data: quantitative and qualitative. Understanding the differences between these two types of data is essential for selecting appropriate analysis methods and drawing meaningful insights. Here’s an overview of quantitative and qualitative data:

Quantitative Data

Quantitative data is numerical and represents quantities or measurements. It’s typically collected through surveys, experiments, and direct measurements. This type of data is characterized by its ability to be counted, measured, and subjected to mathematical calculations. Examples of quantitative data include age, height, sales figures, test scores, and the number of website users.

Quantitative data has the following characteristics:

• Numerical : Quantitative data is expressed in numerical values that can be analyzed and manipulated mathematically.
• Objective : Quantitative data is objective and can be measured and verified independently of individual interpretations.
• Statistical Analysis : Quantitative data lends itself well to statistical analysis. It allows for applying various statistical techniques, such as descriptive statistics, correlation analysis, regression analysis, and hypothesis testing.
• Generalizability : Quantitative data often aims to generalize findings to a larger population. It allows for making predictions, estimating probabilities, and drawing statistical inferences.

Qualitative Data

Qualitative data, on the other hand, is non-numerical and is collected through interviews, observations, and open-ended survey questions. It focuses on capturing rich, descriptive, and subjective information to gain insights into people’s opinions, attitudes, experiences, and behaviors. Examples of qualitative data include interview transcripts, field notes, survey responses, and customer feedback.

Qualitative data has the following characteristics:

• Descriptive : Qualitative data provides detailed descriptions, narratives, or interpretations of phenomena, often capturing context, emotions, and nuances.
• Subjective : Qualitative data is subjective and influenced by the individuals’ perspectives, experiences, and interpretations.
• Interpretive Analysis : Qualitative data requires interpretive techniques, such as thematic analysis, content analysis, and discourse analysis, to uncover themes, patterns, and underlying meanings.
• Contextual Understanding : Qualitative data emphasizes understanding the social, cultural, and contextual factors that shape individuals’ experiences and behaviors.
• Rich Insights : Qualitative data enables researchers to gain in-depth insights into complex phenomena and explore research questions in greater depth.

In summary, quantitative data represents numerical quantities and lends itself well to statistical analysis, while qualitative data provides rich, descriptive insights into subjective experiences and requires interpretive analysis techniques. Understanding the differences between quantitative and qualitative data is crucial for selecting appropriate analysis methods and drawing meaningful conclusions in research and data analysis.

Types of Data Analysis

Different types of data analysis techniques serve different purposes. In this section, we’ll explore four types of data analysis: descriptive, diagnostic, predictive, and prescriptive, and go over how you can use them.

Descriptive Analysis

Descriptive analysis involves summarizing and describing the main characteristics of a dataset. It focuses on gaining a comprehensive understanding of the data through measures such as central tendency (mean, median, mode), dispersion (variance, standard deviation), and graphical representations (histograms, bar charts). For example, in a retail business, descriptive analysis may involve analyzing sales data to identify average monthly sales, popular products, or sales distribution across different regions.

Diagnostic Analysis

Diagnostic analysis aims to understand the causes or factors influencing specific outcomes or events. It involves investigating relationships between variables and identifying patterns or anomalies in the data. Diagnostic analysis often uses regression analysis, correlation analysis, and hypothesis testing to uncover the underlying reasons behind observed phenomena. For example, in healthcare, diagnostic analysis could help determine factors contributing to patient readmissions and identify potential improvements in the care process.

Predictive Analysis

Predictive analysis focuses on making predictions or forecasts about future outcomes based on historical data. It utilizes statistical models, machine learning algorithms, and time series analysis to identify patterns and trends in the data. By applying predictive analysis, businesses can anticipate customer behavior, market trends, or demand for products and services. For example, an e-commerce company might use predictive analysis to forecast customer churn and take proactive measures to retain customers.

Prescriptive Analysis

Prescriptive analysis takes predictive analysis a step further by providing recommendations or optimal solutions based on the predicted outcomes. It combines historical and real-time data with optimization techniques, simulation models, and decision-making algorithms to suggest the best course of action. Prescriptive analysis helps organizations make data-driven decisions and optimize their strategies. For example, a logistics company can use prescriptive analysis to determine the most efficient delivery routes, considering factors like traffic conditions, fuel costs, and customer preferences.

In summary, data analysis plays a vital role in extracting insights and enabling informed decision making. Descriptive analysis helps understand the data, diagnostic analysis uncovers the underlying causes, predictive analysis forecasts future outcomes, and prescriptive analysis provides recommendations for optimal actions. These different data analysis techniques are valuable tools for businesses and organizations across various industries.

Data Analysis Methods

In addition to the data analysis types discussed earlier, you can use various methods to analyze data effectively. These methods provide a structured approach to extract insights, detect patterns, and derive meaningful conclusions from the available data. Here are some commonly used data analysis methods:

Statistical Analysis 

Statistical analysis involves applying statistical techniques to data to uncover patterns, relationships, and trends. It includes methods such as hypothesis testing, regression analysis, analysis of variance (ANOVA), and chi-square tests. Statistical analysis helps organizations understand the significance of relationships between variables and make inferences about the population based on sample data. For example, a market research company could conduct a survey to analyze the relationship between customer satisfaction and product price. They can use regression analysis to determine whether there is a significant correlation between these variables.

Data Mining

Data mining refers to the process of discovering patterns and relationships in large datasets using techniques such as clustering, classification, association analysis, and anomaly detection. It involves exploring data to identify hidden patterns and gain valuable insights. For example, a telecommunications company could analyze customer call records to identify calling patterns and segment customers into groups based on their calling behavior. 

Text Mining

Text mining involves analyzing unstructured data , such as customer reviews, social media posts, or emails, to extract valuable information and insights. It utilizes techniques like natural language processing (NLP), sentiment analysis, and topic modeling to analyze and understand textual data. For example, consider how a hotel chain might analyze customer reviews from various online platforms to identify common themes and sentiment patterns to improve customer satisfaction.

Time Series Analysis

Time series analysis focuses on analyzing data collected over time to identify trends, seasonality, and patterns. It involves techniques such as forecasting, decomposition, and autocorrelation analysis to make predictions and understand the underlying patterns in the data.

For example, an energy company could analyze historical electricity consumption data to forecast future demand and optimize energy generation and distribution.

Data Visualization

Data visualization is the graphical representation of data to communicate patterns, trends, and insights visually. It uses charts, graphs, maps, and other visual elements to present data in a visually appealing and easily understandable format. For example, a sales team might use a line chart to visualize monthly sales trends and identify seasonal patterns in their sales data.

These are just a few examples of the data analysis methods you can use. Your choice should depend on the nature of the data, the research question or problem, and the desired outcome.

How to Analyze Data

Analyzing data involves following a systematic approach to extract insights and derive meaningful conclusions. Here are some steps to guide you through the process of analyzing data effectively:

Define the Objective : Clearly define the purpose and objective of your data analysis. Identify the specific question or problem you want to address through analysis.

Prepare and Explore the Data : Gather the relevant data and ensure its quality. Clean and preprocess the data by handling missing values, duplicates, and formatting issues. Explore the data using descriptive statistics and visualizations to identify patterns, outliers, and relationships.

Apply Analysis Techniques : Choose the appropriate analysis techniques based on your data and research question. Apply statistical methods, machine learning algorithms, and other analytical tools to derive insights and answer your research question.

Interpret the Results : Analyze the output of your analysis and interpret the findings in the context of your objective. Identify significant patterns, trends, and relationships in the data. Consider the implications and practical relevance of the results.

Communicate and Take Action : Communicate your findings effectively to stakeholders or intended audiences. Present the results clearly and concisely, using visualizations and reports. Use the insights from the analysis to inform decision making.

Remember, data analysis is an iterative process, and you may need to revisit and refine your analysis as you progress. These steps provide a general framework to guide you through the data analysis process and help you derive meaningful insights from your data.

Data Analysis Tools

Data analysis tools are software applications and platforms designed to facilitate the process of analyzing and interpreting data . These tools provide a range of functionalities to handle data manipulation, visualization, statistical analysis, and machine learning. Here are some commonly used data analysis tools:

Spreadsheet Software

Tools like Microsoft Excel, Google Sheets, and Apple Numbers are used for basic data analysis tasks. They offer features for data entry, manipulation, basic statistical functions, and simple visualizations.

Business Intelligence (BI) Platforms

BI platforms like Microsoft Power BI, Tableau, and Looker integrate data from multiple sources, providing comprehensive views of business performance through interactive dashboards, reports, and ad hoc queries.

Programming Languages and Libraries

Programming languages like R and Python, along with their associated libraries (e.g., NumPy, SciPy, scikit-learn), offer extensive capabilities for data analysis. They provide flexibility, customizability, and access to a wide range of statistical and machine-learning algorithms.

Cloud-Based Analytics Platforms

Cloud-based platforms like Google Cloud Platform (BigQuery, Data Studio), Microsoft Azure (Azure Analytics, Power BI), and Amazon Web Services (AWS Analytics, QuickSight) provide scalable and collaborative environments for data storage, processing, and analysis. They have a wide range of analytical capabilities for handling large datasets.

Data Mining and Machine Learning Tools

Tools like RapidMiner, KNIME, and Weka automate the process of data preprocessing, feature selection, model training, and evaluation. They’re designed to extract insights and build predictive models from complex datasets.

Text Analytics Tools

Text analytics tools, such as Natural Language Processing (NLP) libraries in Python (NLTK, spaCy) or platforms like RapidMiner Text Mining Extension, enable the analysis of unstructured text data . They help extract information, sentiment, and themes from sources like customer reviews or social media.

Choosing the right data analysis tool depends on analysis complexity, dataset size, required functionalities, and user expertise. You might need to use a combination of tools to leverage their combined strengths and address specific analysis needs. 

By understanding the power of data analysis, you can leverage it to make informed decisions, identify opportunities for improvement, and drive innovation within your organization. Whether you’re working with quantitative data for statistical analysis or qualitative data for in-depth insights, it’s important to select the right analysis techniques and tools for your objectives.

To continue learning about data analysis, review the following resources:

• What is Big Data Analytics?
• Operational Analytics
• JSON Analytics + Real-Time Insights
• Database vs. Data Warehouse: Differences, Use Cases, Examples
• Couchbase Capella Columnar Product Blog
• Posted in: Analytics , Application Design , Best Practices and Tutorials
• Tagged in: data analytics , data visualization , time series

Posted by Couchbase Product Marketing

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Data & Finance for Work & Life

Data Analysis: Types, Methods & Techniques (a Complete List)

( Updated Version )

While the term sounds intimidating, “data analysis” is nothing more than making sense of information in a table. It consists of filtering, sorting, grouping, and manipulating data tables with basic algebra and statistics.

In fact, you don’t need experience to understand the basics. You have already worked with data extensively in your life, and “analysis” is nothing more than a fancy word for good sense and basic logic.

Over time, people have intuitively categorized the best logical practices for treating data. These categories are what we call today types , methods , and techniques .

This article provides a comprehensive list of types, methods, and techniques, and explains the difference between them.

For a practical intro to data analysis (including types, methods, & techniques), check out our Intro to Data Analysis eBook for free.

Descriptive, Diagnostic, Predictive, & Prescriptive Analysis

If you Google “types of data analysis,” the first few results will explore descriptive , diagnostic , predictive , and prescriptive analysis. Why? Because these names are easy to understand and are used a lot in “the real world.”

Descriptive analysis is an informational method, diagnostic analysis explains “why” a phenomenon occurs, predictive analysis seeks to forecast the result of an action, and prescriptive analysis identifies solutions to a specific problem.

That said, these are only four branches of a larger analytical tree.

Good data analysts know how to position these four types within other analytical methods and tactics, allowing them to leverage strengths and weaknesses in each to uproot the most valuable insights.

Let’s explore the full analytical tree to understand how to appropriately assess and apply these four traditional types.

Tree diagram of Data Analysis Types, Methods, and Techniques

Here’s a picture to visualize the structure and hierarchy of data analysis types, methods, and techniques.

If it’s too small you can view the picture in a new tab . Open it to follow along!

Note: basic descriptive statistics such as mean , median , and mode , as well as standard deviation , are not shown because most people are already familiar with them. In the diagram, they would fall under the “descriptive” analysis type.

Tree Diagram Explained

The highest-level classification of data analysis is quantitative vs qualitative . Quantitative implies numbers while qualitative implies information other than numbers.

Quantitative data analysis then splits into mathematical analysis and artificial intelligence (AI) analysis . Mathematical types then branch into descriptive , diagnostic , predictive , and prescriptive .

Methods falling under mathematical analysis include clustering , classification , forecasting , and optimization . Qualitative data analysis methods include content analysis , narrative analysis , discourse analysis , framework analysis , and/or grounded theory .

Moreover, mathematical techniques include regression , Nïave Bayes , Simple Exponential Smoothing , cohorts , factors , linear discriminants , and more, whereas techniques falling under the AI type include artificial neural networks , decision trees , evolutionary programming , and fuzzy logic . Techniques under qualitative analysis include text analysis , coding , idea pattern analysis , and word frequency .

It’s a lot to remember! Don’t worry, once you understand the relationship and motive behind all these terms, it’ll be like riding a bike.

We’ll move down the list from top to bottom and I encourage you to open the tree diagram above in a new tab so you can follow along .

But first, let’s just address the elephant in the room: what’s the difference between methods and techniques anyway?

Difference between methods and techniques

Though often used interchangeably, methods ands techniques are not the same. By definition, methods are the process by which techniques are applied, and techniques are the practical application of those methods.

For example, consider driving. Methods include staying in your lane, stopping at a red light, and parking in a spot. Techniques include turning the steering wheel, braking, and pushing the gas pedal.

Data sets: observations and fields

It’s important to understand the basic structure of data tables to comprehend the rest of the article. A data set consists of one far-left column containing observations, then a series of columns containing the fields (aka “traits” or “characteristics”) that describe each observations. For example, imagine we want a data table for fruit. It might look like this:

Now let’s turn to types, methods, and techniques. Each heading below consists of a description, relative importance, the nature of data it explores, and the motivation for using it.

Quantitative Analysis

• It accounts for more than 50% of all data analysis and is by far the most widespread and well-known type of data analysis.
• As you have seen, it holds descriptive, diagnostic, predictive, and prescriptive methods, which in turn hold some of the most important techniques available today, such as clustering and forecasting.
• It can be broken down into mathematical and AI analysis.
• Importance : Very high . Quantitative analysis is a must for anyone interesting in becoming or improving as a data analyst.
• Nature of Data: data treated under quantitative analysis is, quite simply, quantitative. It encompasses all numeric data.
• Motive: to extract insights. (Note: we’re at the top of the pyramid, this gets more insightful as we move down.)

Qualitative Analysis

• It accounts for less than 30% of all data analysis and is common in social sciences .
• It can refer to the simple recognition of qualitative elements, which is not analytic in any way, but most often refers to methods that assign numeric values to non-numeric data for analysis.
• Because of this, some argue that it’s ultimately a quantitative type.
• Importance: Medium. In general, knowing qualitative data analysis is not common or even necessary for corporate roles. However, for researchers working in social sciences, its importance is very high .
• Nature of Data: data treated under qualitative analysis is non-numeric. However, as part of the analysis, analysts turn non-numeric data into numbers, at which point many argue it is no longer qualitative analysis.
• Motive: to extract insights. (This will be more important as we move down the pyramid.)

Mathematical Analysis

• Description: mathematical data analysis is a subtype of qualitative data analysis that designates methods and techniques based on statistics, algebra, and logical reasoning to extract insights. It stands in opposition to artificial intelligence analysis.
• Importance: Very High. The most widespread methods and techniques fall under mathematical analysis. In fact, it’s so common that many people use “quantitative” and “mathematical” analysis interchangeably.
• Nature of Data: numeric. By definition, all data under mathematical analysis are numbers.
• Motive: to extract measurable insights that can be used to act upon.

Artificial Intelligence & Machine Learning Analysis

• Description: artificial intelligence and machine learning analyses designate techniques based on the titular skills. They are not traditionally mathematical, but they are quantitative since they use numbers. Applications of AI & ML analysis techniques are developing, but they’re not yet mainstream enough to show promise across the field.
• Importance: Medium . As of today (September 2020), you don’t need to be fluent in AI & ML data analysis to be a great analyst. BUT, if it’s a field that interests you, learn it. Many believe that in 10 year’s time its importance will be very high .
• Nature of Data: numeric.
• Motive: to create calculations that build on themselves in order and extract insights without direct input from a human.

Descriptive Analysis

• Description: descriptive analysis is a subtype of mathematical data analysis that uses methods and techniques to provide information about the size, dispersion, groupings, and behavior of data sets. This may sounds complicated, but just think about mean, median, and mode: all three are types of descriptive analysis. They provide information about the data set. We’ll look at specific techniques below.
• Importance: Very high. Descriptive analysis is among the most commonly used data analyses in both corporations and research today.
• Nature of Data: the nature of data under descriptive statistics is sets. A set is simply a collection of numbers that behaves in predictable ways. Data reflects real life, and there are patterns everywhere to be found. Descriptive analysis describes those patterns.
• Motive: the motive behind descriptive analysis is to understand how numbers in a set group together, how far apart they are from each other, and how often they occur. As with most statistical analysis, the more data points there are, the easier it is to describe the set.

Diagnostic Analysis

• Description: diagnostic analysis answers the question “why did it happen?” It is an advanced type of mathematical data analysis that manipulates multiple techniques, but does not own any single one. Analysts engage in diagnostic analysis when they try to explain why.
• Importance: Very high. Diagnostics are probably the most important type of data analysis for people who don’t do analysis because they’re valuable to anyone who’s curious. They’re most common in corporations, as managers often only want to know the “why.”
• Nature of Data : data under diagnostic analysis are data sets. These sets in themselves are not enough under diagnostic analysis. Instead, the analyst must know what’s behind the numbers in order to explain “why.” That’s what makes diagnostics so challenging yet so valuable.
• Motive: the motive behind diagnostics is to diagnose — to understand why.

Predictive Analysis

• Description: predictive analysis uses past data to project future data. It’s very often one of the first kinds of analysis new researchers and corporate analysts use because it is intuitive. It is a subtype of the mathematical type of data analysis, and its three notable techniques are regression, moving average, and exponential smoothing.
• Importance: Very high. Predictive analysis is critical for any data analyst working in a corporate environment. Companies always want to know what the future will hold — especially for their revenue.
• Nature of Data: Because past and future imply time, predictive data always includes an element of time. Whether it’s minutes, hours, days, months, or years, we call this time series data . In fact, this data is so important that I’ll mention it twice so you don’t forget: predictive analysis uses time series data .
• Motive: the motive for investigating time series data with predictive analysis is to predict the future in the most analytical way possible.

Prescriptive Analysis

• Description: prescriptive analysis is a subtype of mathematical analysis that answers the question “what will happen if we do X?” It’s largely underestimated in the data analysis world because it requires diagnostic and descriptive analyses to be done before it even starts. More than simple predictive analysis, prescriptive analysis builds entire data models to show how a simple change could impact the ensemble.
• Importance: High. Prescriptive analysis is most common under the finance function in many companies. Financial analysts use it to build a financial model of the financial statements that show how that data will change given alternative inputs.
• Nature of Data: the nature of data in prescriptive analysis is data sets. These data sets contain patterns that respond differently to various inputs. Data that is useful for prescriptive analysis contains correlations between different variables. It’s through these correlations that we establish patterns and prescribe action on this basis. This analysis cannot be performed on data that exists in a vacuum — it must be viewed on the backdrop of the tangibles behind it.
• Motive: the motive for prescriptive analysis is to establish, with an acceptable degree of certainty, what results we can expect given a certain action. As you might expect, this necessitates that the analyst or researcher be aware of the world behind the data, not just the data itself.

Clustering Method

• Description: the clustering method groups data points together based on their relativeness closeness to further explore and treat them based on these groupings. There are two ways to group clusters: intuitively and statistically (or K-means).
• Importance: Very high. Though most corporate roles group clusters intuitively based on management criteria, a solid understanding of how to group them mathematically is an excellent descriptive and diagnostic approach to allow for prescriptive analysis thereafter.
• Nature of Data : the nature of data useful for clustering is sets with 1 or more data fields. While most people are used to looking at only two dimensions (x and y), clustering becomes more accurate the more fields there are.
• Motive: the motive for clustering is to understand how data sets group and to explore them further based on those groups.
• Here’s an example set:

Classification Method

• Description: the classification method aims to separate and group data points based on common characteristics . This can be done intuitively or statistically.
• Importance: High. While simple on the surface, classification can become quite complex. It’s very valuable in corporate and research environments, but can feel like its not worth the work. A good analyst can execute it quickly to deliver results.
• Nature of Data: the nature of data useful for classification is data sets. As we will see, it can be used on qualitative data as well as quantitative. This method requires knowledge of the substance behind the data, not just the numbers themselves.
• Motive: the motive for classification is group data not based on mathematical relationships (which would be clustering), but by predetermined outputs. This is why it’s less useful for diagnostic analysis, and more useful for prescriptive analysis.

Forecasting Method

• Description: the forecasting method uses time past series data to forecast the future.
• Importance: Very high. Forecasting falls under predictive analysis and is arguably the most common and most important method in the corporate world. It is less useful in research, which prefers to understand the known rather than speculate about the future.
• Nature of Data: data useful for forecasting is time series data, which, as we’ve noted, always includes a variable of time.
• Motive: the motive for the forecasting method is the same as that of prescriptive analysis: the confidently estimate future values.

Optimization Method

• Description: the optimization method maximized or minimizes values in a set given a set of criteria. It is arguably most common in prescriptive analysis. In mathematical terms, it is maximizing or minimizing a function given certain constraints.
• Importance: Very high. The idea of optimization applies to more analysis types than any other method. In fact, some argue that it is the fundamental driver behind data analysis. You would use it everywhere in research and in a corporation.
• Nature of Data: the nature of optimizable data is a data set of at least two points.
• Motive: the motive behind optimization is to achieve the best result possible given certain conditions.

Content Analysis Method

• Description: content analysis is a method of qualitative analysis that quantifies textual data to track themes across a document. It’s most common in academic fields and in social sciences, where written content is the subject of inquiry.
• Importance: High. In a corporate setting, content analysis as such is less common. If anything Nïave Bayes (a technique we’ll look at below) is the closest corporations come to text. However, it is of the utmost importance for researchers. If you’re a researcher, check out this article on content analysis .
• Nature of Data: data useful for content analysis is textual data.
• Motive: the motive behind content analysis is to understand themes expressed in a large text

Narrative Analysis Method

• Description: narrative analysis is a method of qualitative analysis that quantifies stories to trace themes in them. It’s differs from content analysis because it focuses on stories rather than research documents, and the techniques used are slightly different from those in content analysis (very nuances and outside the scope of this article).
• Importance: Low. Unless you are highly specialized in working with stories, narrative analysis rare.
• Nature of Data: the nature of the data useful for the narrative analysis method is narrative text.
• Motive: the motive for narrative analysis is to uncover hidden patterns in narrative text.

Discourse Analysis Method

• Description: the discourse analysis method falls under qualitative analysis and uses thematic coding to trace patterns in real-life discourse. That said, real-life discourse is oral, so it must first be transcribed into text.
• Importance: Low. Unless you are focused on understand real-world idea sharing in a research setting, this kind of analysis is less common than the others on this list.
• Nature of Data: the nature of data useful in discourse analysis is first audio files, then transcriptions of those audio files.
• Motive: the motive behind discourse analysis is to trace patterns of real-world discussions. (As a spooky sidenote, have you ever felt like your phone microphone was listening to you and making reading suggestions? If it was, the method was discourse analysis.)

Framework Analysis Method

• Description: the framework analysis method falls under qualitative analysis and uses similar thematic coding techniques to content analysis. However, where content analysis aims to discover themes, framework analysis starts with a framework and only considers elements that fall in its purview.
• Importance: Low. As with the other textual analysis methods, framework analysis is less common in corporate settings. Even in the world of research, only some use it. Strangely, it’s very common for legislative and political research.
• Nature of Data: the nature of data useful for framework analysis is textual.
• Motive: the motive behind framework analysis is to understand what themes and parts of a text match your search criteria.

Grounded Theory Method

• Description: the grounded theory method falls under qualitative analysis and uses thematic coding to build theories around those themes.
• Importance: Low. Like other qualitative analysis techniques, grounded theory is less common in the corporate world. Even among researchers, you would be hard pressed to find many using it. Though powerful, it’s simply too rare to spend time learning.
• Nature of Data: the nature of data useful in the grounded theory method is textual.
• Motive: the motive of grounded theory method is to establish a series of theories based on themes uncovered from a text.

Clustering Technique: K-Means

• Description: k-means is a clustering technique in which data points are grouped in clusters that have the closest means. Though not considered AI or ML, it inherently requires the use of supervised learning to reevaluate clusters as data points are added. Clustering techniques can be used in diagnostic, descriptive, & prescriptive data analyses.
• Importance: Very important. If you only take 3 things from this article, k-means clustering should be part of it. It is useful in any situation where n observations have multiple characteristics and we want to put them in groups.
• Nature of Data: the nature of data is at least one characteristic per observation, but the more the merrier.
• Motive: the motive for clustering techniques such as k-means is to group observations together and either understand or react to them.

Regression Technique

• Description: simple and multivariable regressions use either one independent variable or combination of multiple independent variables to calculate a correlation to a single dependent variable using constants. Regressions are almost synonymous with correlation today.
• Importance: Very high. Along with clustering, if you only take 3 things from this article, regression techniques should be part of it. They’re everywhere in corporate and research fields alike.
• Nature of Data: the nature of data used is regressions is data sets with “n” number of observations and as many variables as are reasonable. It’s important, however, to distinguish between time series data and regression data. You cannot use regressions or time series data without accounting for time. The easier way is to use techniques under the forecasting method.
• Motive: The motive behind regression techniques is to understand correlations between independent variable(s) and a dependent one.

Nïave Bayes Technique

• Description: Nïave Bayes is a classification technique that uses simple probability to classify items based previous classifications. In plain English, the formula would be “the chance that thing with trait x belongs to class c depends on (=) the overall chance of trait x belonging to class c, multiplied by the overall chance of class c, divided by the overall chance of getting trait x.” As a formula, it’s P(c|x) = P(x|c) * P(c) / P(x).
• Importance: High. Nïave Bayes is a very common, simplistic classification techniques because it’s effective with large data sets and it can be applied to any instant in which there is a class. Google, for example, might use it to group webpages into groups for certain search engine queries.
• Nature of Data: the nature of data for Nïave Bayes is at least one class and at least two traits in a data set.
• Motive: the motive behind Nïave Bayes is to classify observations based on previous data. It’s thus considered part of predictive analysis.

Cohorts Technique

• Description: cohorts technique is a type of clustering method used in behavioral sciences to separate users by common traits. As with clustering, it can be done intuitively or mathematically, the latter of which would simply be k-means.
• Importance: Very high. With regard to resembles k-means, the cohort technique is more of a high-level counterpart. In fact, most people are familiar with it as a part of Google Analytics. It’s most common in marketing departments in corporations, rather than in research.
• Nature of Data: the nature of cohort data is data sets in which users are the observation and other fields are used as defining traits for each cohort.
• Motive: the motive for cohort analysis techniques is to group similar users and analyze how you retain them and how the churn.

Factor Technique

• Description: the factor analysis technique is a way of grouping many traits into a single factor to expedite analysis. For example, factors can be used as traits for Nïave Bayes classifications instead of more general fields.
• Importance: High. While not commonly employed in corporations, factor analysis is hugely valuable. Good data analysts use it to simplify their projects and communicate them more clearly.
• Nature of Data: the nature of data useful in factor analysis techniques is data sets with a large number of fields on its observations.
• Motive: the motive for using factor analysis techniques is to reduce the number of fields in order to more quickly analyze and communicate findings.

Linear Discriminants Technique

• Description: linear discriminant analysis techniques are similar to regressions in that they use one or more independent variable to determine a dependent variable; however, the linear discriminant technique falls under a classifier method since it uses traits as independent variables and class as a dependent variable. In this way, it becomes a classifying method AND a predictive method.
• Importance: High. Though the analyst world speaks of and uses linear discriminants less commonly, it’s a highly valuable technique to keep in mind as you progress in data analysis.
• Nature of Data: the nature of data useful for the linear discriminant technique is data sets with many fields.
• Motive: the motive for using linear discriminants is to classify observations that would be otherwise too complex for simple techniques like Nïave Bayes.

Exponential Smoothing Technique

• Description: exponential smoothing is a technique falling under the forecasting method that uses a smoothing factor on prior data in order to predict future values. It can be linear or adjusted for seasonality. The basic principle behind exponential smoothing is to use a percent weight (value between 0 and 1 called alpha) on more recent values in a series and a smaller percent weight on less recent values. The formula is f(x) = current period value * alpha + previous period value * 1-alpha.
• Importance: High. Most analysts still use the moving average technique (covered next) for forecasting, though it is less efficient than exponential moving, because it’s easy to understand. However, good analysts will have exponential smoothing techniques in their pocket to increase the value of their forecasts.
• Nature of Data: the nature of data useful for exponential smoothing is time series data . Time series data has time as part of its fields .
• Motive: the motive for exponential smoothing is to forecast future values with a smoothing variable.

Moving Average Technique

• Description: the moving average technique falls under the forecasting method and uses an average of recent values to predict future ones. For example, to predict rainfall in April, you would take the average of rainfall from January to March. It’s simple, yet highly effective.
• Importance: Very high. While I’m personally not a huge fan of moving averages due to their simplistic nature and lack of consideration for seasonality, they’re the most common forecasting technique and therefore very important.
• Nature of Data: the nature of data useful for moving averages is time series data .
• Motive: the motive for moving averages is to predict future values is a simple, easy-to-communicate way.

Neural Networks Technique

• Description: neural networks are a highly complex artificial intelligence technique that replicate a human’s neural analysis through a series of hyper-rapid computations and comparisons that evolve in real time. This technique is so complex that an analyst must use computer programs to perform it.
• Importance: Medium. While the potential for neural networks is theoretically unlimited, it’s still little understood and therefore uncommon. You do not need to know it by any means in order to be a data analyst.
• Nature of Data: the nature of data useful for neural networks is data sets of astronomical size, meaning with 100s of 1000s of fields and the same number of row at a minimum .
• Motive: the motive for neural networks is to understand wildly complex phenomenon and data to thereafter act on it.

Decision Tree Technique

• Description: the decision tree technique uses artificial intelligence algorithms to rapidly calculate possible decision pathways and their outcomes on a real-time basis. It’s so complex that computer programs are needed to perform it.
• Importance: Medium. As with neural networks, decision trees with AI are too little understood and are therefore uncommon in corporate and research settings alike.
• Nature of Data: the nature of data useful for the decision tree technique is hierarchical data sets that show multiple optional fields for each preceding field.
• Motive: the motive for decision tree techniques is to compute the optimal choices to make in order to achieve a desired result.

Evolutionary Programming Technique

• Description: the evolutionary programming technique uses a series of neural networks, sees how well each one fits a desired outcome, and selects only the best to test and retest. It’s called evolutionary because is resembles the process of natural selection by weeding out weaker options.
• Importance: Medium. As with the other AI techniques, evolutionary programming just isn’t well-understood enough to be usable in many cases. It’s complexity also makes it hard to explain in corporate settings and difficult to defend in research settings.
• Nature of Data: the nature of data in evolutionary programming is data sets of neural networks, or data sets of data sets.
• Motive: the motive for using evolutionary programming is similar to decision trees: understanding the best possible option from complex data.
• Video example :

Fuzzy Logic Technique

• Description: fuzzy logic is a type of computing based on “approximate truths” rather than simple truths such as “true” and “false.” It is essentially two tiers of classification. For example, to say whether “Apples are good,” you need to first classify that “Good is x, y, z.” Only then can you say apples are good. Another way to see it helping a computer see truth like humans do: “definitely true, probably true, maybe true, probably false, definitely false.”
• Importance: Medium. Like the other AI techniques, fuzzy logic is uncommon in both research and corporate settings, which means it’s less important in today’s world.
• Nature of Data: the nature of fuzzy logic data is huge data tables that include other huge data tables with a hierarchy including multiple subfields for each preceding field.
• Motive: the motive of fuzzy logic to replicate human truth valuations in a computer is to model human decisions based on past data. The obvious possible application is marketing.

Text Analysis Technique

• Description: text analysis techniques fall under the qualitative data analysis type and use text to extract insights.
• Importance: Medium. Text analysis techniques, like all the qualitative analysis type, are most valuable for researchers.
• Nature of Data: the nature of data useful in text analysis is words.
• Motive: the motive for text analysis is to trace themes in a text across sets of very long documents, such as books.

Coding Technique

• Description: the coding technique is used in textual analysis to turn ideas into uniform phrases and analyze the number of times and the ways in which those ideas appear. For this reason, some consider it a quantitative technique as well. You can learn more about coding and the other qualitative techniques here .
• Importance: Very high. If you’re a researcher working in social sciences, coding is THE analysis techniques, and for good reason. It’s a great way to add rigor to analysis. That said, it’s less common in corporate settings.
• Nature of Data: the nature of data useful for coding is long text documents.
• Motive: the motive for coding is to make tracing ideas on paper more than an exercise of the mind by quantifying it and understanding is through descriptive methods.

Idea Pattern Technique

• Description: the idea pattern analysis technique fits into coding as the second step of the process. Once themes and ideas are coded, simple descriptive analysis tests may be run. Some people even cluster the ideas!
• Importance: Very high. If you’re a researcher, idea pattern analysis is as important as the coding itself.
• Nature of Data: the nature of data useful for idea pattern analysis is already coded themes.
• Motive: the motive for the idea pattern technique is to trace ideas in otherwise unmanageably-large documents.

Word Frequency Technique

• Description: word frequency is a qualitative technique that stands in opposition to coding and uses an inductive approach to locate specific words in a document in order to understand its relevance. Word frequency is essentially the descriptive analysis of qualitative data because it uses stats like mean, median, and mode to gather insights.
• Importance: High. As with the other qualitative approaches, word frequency is very important in social science research, but less so in corporate settings.
• Nature of Data: the nature of data useful for word frequency is long, informative documents.
• Motive: the motive for word frequency is to locate target words to determine the relevance of a document in question.

Types of data analysis in research

Types of data analysis in research methodology include every item discussed in this article. As a list, they are:

• Quantitative
• Qualitative
• Mathematical
• Machine Learning and AI
• Descriptive
• Prescriptive
• Classification
• Forecasting
• Optimization
• Grounded theory
• Artificial Neural Networks
• Decision Trees
• Evolutionary Programming
• Fuzzy Logic
• Text analysis
• Idea Pattern Analysis
• Word Frequency Analysis
• Nïave Bayes
• Exponential smoothing
• Moving average
• Linear discriminant

Types of data analysis in qualitative research

As a list, the types of data analysis in qualitative research are the following methods:

Types of data analysis in quantitative research

As a list, the types of data analysis in quantitative research are:

Data analysis methods

As a list, data analysis methods are:

• Content (qualitative)
• Narrative (qualitative)
• Discourse (qualitative)
• Framework (qualitative)
• Grounded theory (qualitative)

Quantitative data analysis methods

As a list, quantitative data analysis methods are:

Tabular View of Data Analysis Types, Methods, and Techniques

About the Author

Noah is the founder & Editor-in-Chief at AnalystAnswers. He is a transatlantic professional and entrepreneur with 5+ years of corporate finance and data analytics experience, as well as 3+ years in consumer financial products and business software. He started AnalystAnswers to provide aspiring professionals with accessible explanations of otherwise dense finance and data concepts. Noah believes everyone can benefit from an analytical mindset in growing digital world. When he's not busy at work, Noah likes to explore new European cities, exercise, and spend time with friends and family.

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Data collection

data analysis

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• Table Of Contents

data analysis , the process of systematically collecting, cleaning, transforming, describing, modeling, and interpreting data , generally employing statistical techniques. Data analysis is an important part of both scientific research and business, where demand has grown in recent years for data-driven decision making . Data analysis techniques are used to gain useful insights from datasets, which can then be used to make operational decisions or guide future research . With the rise of “Big Data,” the storage of vast quantities of data in large databases and data warehouses, there is increasing need to apply data analysis techniques to generate insights about volumes of data too large to be manipulated by instruments of low information-processing capacity.

Datasets are collections of information. Generally, data and datasets are themselves collected to help answer questions, make decisions, or otherwise inform reasoning. The rise of information technology has led to the generation of vast amounts of data of many kinds, such as text, pictures, videos, personal information, account data, and metadata, the last of which provide information about other data. It is common for apps and websites to collect data about how their products are used or about the people using their platforms. Consequently, there is vastly more data being collected today than at any other time in human history. A single business may track billions of interactions with millions of consumers at hundreds of locations with thousands of employees and any number of products. Analyzing that volume of data is generally only possible using specialized computational and statistical techniques.

The desire for businesses to make the best use of their data has led to the development of the field of business intelligence , which covers a variety of tools and techniques that allow businesses to perform data analysis on the information they collect.

For data to be analyzed, it must first be collected and stored. Raw data must be processed into a format that can be used for analysis and be cleaned so that errors and inconsistencies are minimized. Data can be stored in many ways, but one of the most useful is in a database . A database is a collection of interrelated data organized so that certain records (collections of data related to a single entity) can be retrieved on the basis of various criteria . The most familiar kind of database is the relational database , which stores data in tables with rows that represent records (tuples) and columns that represent fields (attributes). A query is a command that retrieves a subset of the information in the database according to certain criteria. A query may retrieve only records that meet certain criteria, or it may join fields from records across multiple tables by use of a common field.

Frequently, data from many sources is collected into large archives of data called data warehouses. The process of moving data from its original sources (such as databases) to a centralized location (generally a data warehouse) is called ETL (which stands for extract , transform , and load ).

• The extraction step occurs when you identify and copy or export the desired data from its source, such as by running a database query to retrieve the desired records.
• The transformation step is the process of cleaning the data so that they fit the analytical need for the data and the schema of the data warehouse. This may involve changing formats for certain fields, removing duplicate records, or renaming fields, among other processes.
• Finally, the clean data are loaded into the data warehouse, where they may join vast amounts of historical data and data from other sources.

After data are effectively collected and cleaned, they can be analyzed with a variety of techniques. Analysis often begins with descriptive and exploratory data analysis. Descriptive data analysis uses statistics to organize and summarize data, making it easier to understand the broad qualities of the dataset. Exploratory data analysis looks for insights into the data that may arise from descriptions of distribution, central tendency, or variability for a single data field. Further relationships between data may become apparent by examining two fields together. Visualizations may be employed during analysis, such as histograms (graphs in which the length of a bar indicates a quantity) or stem-and-leaf plots (which divide data into buckets, or “stems,” with individual data points serving as “leaves” on the stem).

Data analysis frequently goes beyond descriptive analysis to predictive analysis, making predictions about the future using predictive modeling techniques. Predictive modeling uses machine learning , regression analysis methods (which mathematically calculate the relationship between an independent variable and a dependent variable), and classification techniques to identify trends and relationships among variables. Predictive analysis may involve data mining , which is the process of discovering interesting or useful patterns in large volumes of information. Data mining often involves cluster analysis , which tries to find natural groupings within data, and anomaly detection , which detects instances in data that are unusual and stand out from other patterns. It may also look for rules within datasets, strong relationships among variables in the data.

Data Analysis Techniques in Research – Methods, Tools & Examples

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Data analysis techniques in research are essential because they allow researchers to derive meaningful insights from data sets to support their hypotheses or research objectives.

Data Analysis Techniques in Research : While various groups, institutions, and professionals may have diverse approaches to data analysis, a universal definition captures its essence. Data analysis involves refining, transforming, and interpreting raw data to derive actionable insights that guide informed decision-making for businesses.

A straightforward illustration of data analysis emerges when we make everyday decisions, basing our choices on past experiences or predictions of potential outcomes.

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Table of Contents

What is Data Analysis?

Data analysis is the systematic process of inspecting, cleaning, transforming, and interpreting data with the objective of discovering valuable insights and drawing meaningful conclusions. This process involves several steps:

• Inspecting : Initial examination of data to understand its structure, quality, and completeness.
• Cleaning : Removing errors, inconsistencies, or irrelevant information to ensure accurate analysis.
• Transforming : Converting data into a format suitable for analysis, such as normalization or aggregation.
• Interpreting : Analyzing the transformed data to identify patterns, trends, and relationships.

Types of Data Analysis Techniques in Research

Data analysis techniques in research are categorized into qualitative and quantitative methods, each with its specific approaches and tools. These techniques are instrumental in extracting meaningful insights, patterns, and relationships from data to support informed decision-making, validate hypotheses, and derive actionable recommendations. Below is an in-depth exploration of the various types of data analysis techniques commonly employed in research:

1) Qualitative Analysis:

Definition: Qualitative analysis focuses on understanding non-numerical data, such as opinions, concepts, or experiences, to derive insights into human behavior, attitudes, and perceptions.

• Content Analysis: Examines textual data, such as interview transcripts, articles, or open-ended survey responses, to identify themes, patterns, or trends.
• Narrative Analysis: Analyzes personal stories or narratives to understand individuals’ experiences, emotions, or perspectives.
• Ethnographic Studies: Involves observing and analyzing cultural practices, behaviors, and norms within specific communities or settings.

2) Quantitative Analysis:

Quantitative analysis emphasizes numerical data and employs statistical methods to explore relationships, patterns, and trends. It encompasses several approaches:

Descriptive Analysis:

• Frequency Distribution: Represents the number of occurrences of distinct values within a dataset.
• Central Tendency: Measures such as mean, median, and mode provide insights into the central values of a dataset.
• Dispersion: Techniques like variance and standard deviation indicate the spread or variability of data.

Diagnostic Analysis:

• Regression Analysis: Assesses the relationship between dependent and independent variables, enabling prediction or understanding causality.
• ANOVA (Analysis of Variance): Examines differences between groups to identify significant variations or effects.

Predictive Analysis:

• Time Series Forecasting: Uses historical data points to predict future trends or outcomes.
• Machine Learning Algorithms: Techniques like decision trees, random forests, and neural networks predict outcomes based on patterns in data.

Prescriptive Analysis:

• Optimization Models: Utilizes linear programming, integer programming, or other optimization techniques to identify the best solutions or strategies.
• Simulation: Mimics real-world scenarios to evaluate various strategies or decisions and determine optimal outcomes.

Specific Techniques:

• Monte Carlo Simulation: Models probabilistic outcomes to assess risk and uncertainty.
• Factor Analysis: Reduces the dimensionality of data by identifying underlying factors or components.
• Cohort Analysis: Studies specific groups or cohorts over time to understand trends, behaviors, or patterns within these groups.
• Cluster Analysis: Classifies objects or individuals into homogeneous groups or clusters based on similarities or attributes.
• Sentiment Analysis: Uses natural language processing and machine learning techniques to determine sentiment, emotions, or opinions from textual data.

Also Read: AI and Predictive Analytics: Examples, Tools, Uses, Ai Vs Predictive Analytics

Data Analysis Techniques in Research Examples

To provide a clearer understanding of how data analysis techniques are applied in research, let’s consider a hypothetical research study focused on evaluating the impact of online learning platforms on students’ academic performance.

Research Objective:

Determine if students using online learning platforms achieve higher academic performance compared to those relying solely on traditional classroom instruction.

Data Collection:

• Quantitative Data: Academic scores (grades) of students using online platforms and those using traditional classroom methods.
• Qualitative Data: Feedback from students regarding their learning experiences, challenges faced, and preferences.

Data Analysis Techniques Applied:

1) Descriptive Analysis:

• Calculate the mean, median, and mode of academic scores for both groups.
• Create frequency distributions to represent the distribution of grades in each group.

2) Diagnostic Analysis:

• Conduct an Analysis of Variance (ANOVA) to determine if there’s a statistically significant difference in academic scores between the two groups.
• Perform Regression Analysis to assess the relationship between the time spent on online platforms and academic performance.

3) Predictive Analysis:

• Utilize Time Series Forecasting to predict future academic performance trends based on historical data.
• Implement Machine Learning algorithms to develop a predictive model that identifies factors contributing to academic success on online platforms.

4) Prescriptive Analysis:

• Apply Optimization Models to identify the optimal combination of online learning resources (e.g., video lectures, interactive quizzes) that maximize academic performance.
• Use Simulation Techniques to evaluate different scenarios, such as varying student engagement levels with online resources, to determine the most effective strategies for improving learning outcomes.

5) Specific Techniques:

• Conduct Factor Analysis on qualitative feedback to identify common themes or factors influencing students’ perceptions and experiences with online learning.
• Perform Cluster Analysis to segment students based on their engagement levels, preferences, or academic outcomes, enabling targeted interventions or personalized learning strategies.
• Apply Sentiment Analysis on textual feedback to categorize students’ sentiments as positive, negative, or neutral regarding online learning experiences.

By applying a combination of qualitative and quantitative data analysis techniques, this research example aims to provide comprehensive insights into the effectiveness of online learning platforms.

Also Read: Learning Path to Become a Data Analyst in 2024

Data Analysis Techniques in Quantitative Research

Quantitative research involves collecting numerical data to examine relationships, test hypotheses, and make predictions. Various data analysis techniques are employed to interpret and draw conclusions from quantitative data. Here are some key data analysis techniques commonly used in quantitative research:

1) Descriptive Statistics:

• Description: Descriptive statistics are used to summarize and describe the main aspects of a dataset, such as central tendency (mean, median, mode), variability (range, variance, standard deviation), and distribution (skewness, kurtosis).
• Applications: Summarizing data, identifying patterns, and providing initial insights into the dataset.

2) Inferential Statistics:

• Description: Inferential statistics involve making predictions or inferences about a population based on a sample of data. This technique includes hypothesis testing, confidence intervals, t-tests, chi-square tests, analysis of variance (ANOVA), regression analysis, and correlation analysis.
• Applications: Testing hypotheses, making predictions, and generalizing findings from a sample to a larger population.

3) Regression Analysis:

• Description: Regression analysis is a statistical technique used to model and examine the relationship between a dependent variable and one or more independent variables. Linear regression, multiple regression, logistic regression, and nonlinear regression are common types of regression analysis .
• Applications: Predicting outcomes, identifying relationships between variables, and understanding the impact of independent variables on the dependent variable.

4) Correlation Analysis:

• Description: Correlation analysis is used to measure and assess the strength and direction of the relationship between two or more variables. The Pearson correlation coefficient, Spearman rank correlation coefficient, and Kendall’s tau are commonly used measures of correlation.
• Applications: Identifying associations between variables and assessing the degree and nature of the relationship.

5) Factor Analysis:

• Description: Factor analysis is a multivariate statistical technique used to identify and analyze underlying relationships or factors among a set of observed variables. It helps in reducing the dimensionality of data and identifying latent variables or constructs.
• Applications: Identifying underlying factors or constructs, simplifying data structures, and understanding the underlying relationships among variables.

6) Time Series Analysis:

• Description: Time series analysis involves analyzing data collected or recorded over a specific period at regular intervals to identify patterns, trends, and seasonality. Techniques such as moving averages, exponential smoothing, autoregressive integrated moving average (ARIMA), and Fourier analysis are used.
• Applications: Forecasting future trends, analyzing seasonal patterns, and understanding time-dependent relationships in data.

7) ANOVA (Analysis of Variance):

• Description: Analysis of variance (ANOVA) is a statistical technique used to analyze and compare the means of two or more groups or treatments to determine if they are statistically different from each other. One-way ANOVA, two-way ANOVA, and MANOVA (Multivariate Analysis of Variance) are common types of ANOVA.
• Applications: Comparing group means, testing hypotheses, and determining the effects of categorical independent variables on a continuous dependent variable.

8) Chi-Square Tests:

• Description: Chi-square tests are non-parametric statistical tests used to assess the association between categorical variables in a contingency table. The Chi-square test of independence, goodness-of-fit test, and test of homogeneity are common chi-square tests.
• Applications: Testing relationships between categorical variables, assessing goodness-of-fit, and evaluating independence.

These quantitative data analysis techniques provide researchers with valuable tools and methods to analyze, interpret, and derive meaningful insights from numerical data. The selection of a specific technique often depends on the research objectives, the nature of the data, and the underlying assumptions of the statistical methods being used.

Also Read: Analysis vs. Analytics: How Are They Different?

Data Analysis Methods

Data analysis methods refer to the techniques and procedures used to analyze, interpret, and draw conclusions from data. These methods are essential for transforming raw data into meaningful insights, facilitating decision-making processes, and driving strategies across various fields. Here are some common data analysis methods:

• Description: Descriptive statistics summarize and organize data to provide a clear and concise overview of the dataset. Measures such as mean, median, mode, range, variance, and standard deviation are commonly used.
• Description: Inferential statistics involve making predictions or inferences about a population based on a sample of data. Techniques such as hypothesis testing, confidence intervals, and regression analysis are used.

3) Exploratory Data Analysis (EDA):

• Description: EDA techniques involve visually exploring and analyzing data to discover patterns, relationships, anomalies, and insights. Methods such as scatter plots, histograms, box plots, and correlation matrices are utilized.
• Applications: Identifying trends, patterns, outliers, and relationships within the dataset.

4) Predictive Analytics:

• Description: Predictive analytics use statistical algorithms and machine learning techniques to analyze historical data and make predictions about future events or outcomes. Techniques such as regression analysis, time series forecasting, and machine learning algorithms (e.g., decision trees, random forests, neural networks) are employed.
• Applications: Forecasting future trends, predicting outcomes, and identifying potential risks or opportunities.

5) Prescriptive Analytics:

• Description: Prescriptive analytics involve analyzing data to recommend actions or strategies that optimize specific objectives or outcomes. Optimization techniques, simulation models, and decision-making algorithms are utilized.
• Applications: Recommending optimal strategies, decision-making support, and resource allocation.

6) Qualitative Data Analysis:

• Description: Qualitative data analysis involves analyzing non-numerical data, such as text, images, videos, or audio, to identify themes, patterns, and insights. Methods such as content analysis, thematic analysis, and narrative analysis are used.
• Applications: Understanding human behavior, attitudes, perceptions, and experiences.

7) Big Data Analytics:

• Description: Big data analytics methods are designed to analyze large volumes of structured and unstructured data to extract valuable insights. Technologies such as Hadoop, Spark, and NoSQL databases are used to process and analyze big data.
• Applications: Analyzing large datasets, identifying trends, patterns, and insights from big data sources.

8) Text Analytics:

• Description: Text analytics methods involve analyzing textual data, such as customer reviews, social media posts, emails, and documents, to extract meaningful information and insights. Techniques such as sentiment analysis, text mining, and natural language processing (NLP) are used.
• Applications: Analyzing customer feedback, monitoring brand reputation, and extracting insights from textual data sources.

These data analysis methods are instrumental in transforming data into actionable insights, informing decision-making processes, and driving organizational success across various sectors, including business, healthcare, finance, marketing, and research. The selection of a specific method often depends on the nature of the data, the research objectives, and the analytical requirements of the project or organization.

Also Read: Quantitative Data Analysis: Types, Analysis & Examples

Data Analysis Tools

Data analysis tools are essential instruments that facilitate the process of examining, cleaning, transforming, and modeling data to uncover useful information, make informed decisions, and drive strategies. Here are some prominent data analysis tools widely used across various industries:

1) Microsoft Excel:

• Description: A spreadsheet software that offers basic to advanced data analysis features, including pivot tables, data visualization tools, and statistical functions.
• Applications: Data cleaning, basic statistical analysis, visualization, and reporting.

2) R Programming Language:

• Description: An open-source programming language specifically designed for statistical computing and data visualization.
• Applications: Advanced statistical analysis, data manipulation, visualization, and machine learning.

3) Python (with Libraries like Pandas, NumPy, Matplotlib, and Seaborn):

• Description: A versatile programming language with libraries that support data manipulation, analysis, and visualization.
• Applications: Data cleaning, statistical analysis, machine learning, and data visualization.

4) SPSS (Statistical Package for the Social Sciences):

• Description: A comprehensive statistical software suite used for data analysis, data mining, and predictive analytics.
• Applications: Descriptive statistics, hypothesis testing, regression analysis, and advanced analytics.

5) SAS (Statistical Analysis System):

• Description: A software suite used for advanced analytics, multivariate analysis, and predictive modeling.
• Applications: Data management, statistical analysis, predictive modeling, and business intelligence.

6) Tableau:

• Description: A data visualization tool that allows users to create interactive and shareable dashboards and reports.
• Applications: Data visualization , business intelligence , and interactive dashboard creation.

7) Power BI:

• Description: A business analytics tool developed by Microsoft that provides interactive visualizations and business intelligence capabilities.
• Applications: Data visualization, business intelligence, reporting, and dashboard creation.

8) SQL (Structured Query Language) Databases (e.g., MySQL, PostgreSQL, Microsoft SQL Server):

• Description: Database management systems that support data storage, retrieval, and manipulation using SQL queries.
• Applications: Data retrieval, data cleaning, data transformation, and database management.

9) Apache Spark:

• Description: A fast and general-purpose distributed computing system designed for big data processing and analytics.
• Applications: Big data processing, machine learning, data streaming, and real-time analytics.

10) IBM SPSS Modeler:

• Description: A data mining software application used for building predictive models and conducting advanced analytics.
• Applications: Predictive modeling, data mining, statistical analysis, and decision optimization.

These tools serve various purposes and cater to different data analysis needs, from basic statistical analysis and data visualization to advanced analytics, machine learning, and big data processing. The choice of a specific tool often depends on the nature of the data, the complexity of the analysis, and the specific requirements of the project or organization.

Also Read: How to Analyze Survey Data: Methods & Examples

Importance of Data Analysis in Research

The importance of data analysis in research cannot be overstated; it serves as the backbone of any scientific investigation or study. Here are several key reasons why data analysis is crucial in the research process:

• Data analysis helps ensure that the results obtained are valid and reliable. By systematically examining the data, researchers can identify any inconsistencies or anomalies that may affect the credibility of the findings.
• Effective data analysis provides researchers with the necessary information to make informed decisions. By interpreting the collected data, researchers can draw conclusions, make predictions, or formulate recommendations based on evidence rather than intuition or guesswork.
• Data analysis allows researchers to identify patterns, trends, and relationships within the data. This can lead to a deeper understanding of the research topic, enabling researchers to uncover insights that may not be immediately apparent.
• In empirical research, data analysis plays a critical role in testing hypotheses. Researchers collect data to either support or refute their hypotheses, and data analysis provides the tools and techniques to evaluate these hypotheses rigorously.
• Transparent and well-executed data analysis enhances the credibility of research findings. By clearly documenting the data analysis methods and procedures, researchers allow others to replicate the study, thereby contributing to the reproducibility of research findings.
• In fields such as business or healthcare, data analysis helps organizations allocate resources more efficiently. By analyzing data on consumer behavior, market trends, or patient outcomes, organizations can make strategic decisions about resource allocation, budgeting, and planning.
• In public policy and social sciences, data analysis is instrumental in developing and evaluating policies and interventions. By analyzing data on social, economic, or environmental factors, policymakers can assess the effectiveness of existing policies and inform the development of new ones.
• Data analysis allows for continuous improvement in research methods and practices. By analyzing past research projects, identifying areas for improvement, and implementing changes based on data-driven insights, researchers can refine their approaches and enhance the quality of future research endeavors.

However, it is important to remember that mastering these techniques requires practice and continuous learning. That’s why we highly recommend the Data Analytics Course by Physics Wallah . Not only does it cover all the fundamentals of data analysis, but it also provides hands-on experience with various tools such as Excel, Python, and Tableau. Plus, if you use the “ READER ” coupon code at checkout, you can get a special discount on the course.

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Data Analysis Techniques in Research FAQs

What are the 5 techniques for data analysis.

The five techniques for data analysis include: Descriptive Analysis Diagnostic Analysis Predictive Analysis Prescriptive Analysis Qualitative Analysis

What are techniques of data analysis in research?

Techniques of data analysis in research encompass both qualitative and quantitative methods. These techniques involve processes like summarizing raw data, investigating causes of events, forecasting future outcomes, offering recommendations based on predictions, and examining non-numerical data to understand concepts or experiences.

What are the 3 methods of data analysis?

The three primary methods of data analysis are: Qualitative Analysis Quantitative Analysis Mixed-Methods Analysis

What are the four types of data analysis techniques?

The four types of data analysis techniques are: Descriptive Analysis Diagnostic Analysis Predictive Analysis Prescriptive Analysis

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Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

• Qualitative vs. quantitative : Will your data take the form of words or numbers?
• Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
• Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

• For quantitative data, you can use statistical analysis methods to test relationships between variables.
• For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

• From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
• Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

• During an experiment .
• Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square test of independence
• Statistical power
• Descriptive statistics
• Degrees of freedom
• Pearson correlation
• Null hypothesis
• Double-blind study
• Case-control study
• Research ethics
• Data collection
• Hypothesis testing
• Structured interviews

Research bias

• Hawthorne effect
• Unconscious bias
• Recall bias
• Halo effect
• Self-serving bias
• Information bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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• University Libraries
• Research Guides
• Topic Guides
• Research Methods Guide
• Data Analysis

Research Methods Guide: Data Analysis

• Introduction
• Research Design & Method
• Survey Research
• Interview Research
• Resources & Consultation

Tools for Analyzing Survey Data

• R (open source)
• Stata 
• DataCracker (free up to 100 responses per survey)
• SurveyMonkey (free up to 100 responses per survey)

Tools for Analyzing Interview Data

• AQUAD (open source)
• NVivo 

Data Analysis and Presentation Techniques that Apply to both Survey and Interview Research

• Create a documentation of the data and the process of data collection.
• Analyze the data rather than just describing it - use it to tell a story that focuses on answering the research question.
• Use charts or tables to help the reader understand the data and then highlight the most interesting findings.
• Don’t get bogged down in the detail - tell the reader about the main themes as they relate to the research question, rather than reporting everything that survey respondents or interviewees said.
• State that ‘most people said …’ or ‘few people felt …’ rather than giving the number of people who said a particular thing.
• Use brief quotes where these illustrate a particular point really well.
• Respect confidentiality - you could attribute a quote to 'a faculty member', ‘a student’, or 'a customer' rather than ‘Dr. Nicholls.'

Survey Data Analysis

• If you used an online survey, the software will automatically collate the data – you will just need to download the data, for example as a spreadsheet.
• If you used a paper questionnaire, you will need to manually transfer the responses from the questionnaires into a spreadsheet.  Put each question number as a column heading, and use one row for each person’s answers.  Then assign each possible answer a number or ‘code’.
• When all the data is present and correct, calculate how many people selected each response.
• Once you have calculated how many people selected each response, you can set up tables and/or graph to display the data.  This could take the form of a table or chart.
• In addition to descriptive statistics that characterize findings from your survey, you can use statistical and analytical reporting techniques if needed.

Interview Data Analysis

• Data Reduction and Organization: Try not to feel overwhelmed by quantity of information that has been collected from interviews- a one-hour interview can generate 20 to 25 pages of single-spaced text.   Once you start organizing your fieldwork notes around themes, you can easily identify which part of your data to be used for further analysis.
• What were the main issues or themes that struck you in this contact / interviewee?"
• Was there anything else that struck you as salient, interesting, illuminating or important in this contact / interviewee? 
• What information did you get (or failed to get) on each of the target questions you had for this contact / interviewee?
• Connection of the data: You can connect data around themes and concepts - then you can show how one concept may influence another.
• Examination of Relationships: Examining relationships is the centerpiece of the analytic process, because it allows you to move from simple description of the people and settings to explanations of why things happened as they did with those people in that setting.
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• v.60(9); 2016 Sep

Basic statistical tools in research and data analysis

Zulfiqar ali.

Department of Anaesthesiology, Division of Neuroanaesthesiology, Sheri Kashmir Institute of Medical Sciences, Soura, Srinagar, Jammu and Kashmir, India

S Bala Bhaskar

1 Department of Anaesthesiology and Critical Care, Vijayanagar Institute of Medical Sciences, Bellary, Karnataka, India

Statistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings. The statistical analysis gives meaning to the meaningless numbers, thereby breathing life into a lifeless data. The results and inferences are precise only if proper statistical tests are used. This article will try to acquaint the reader with the basic research tools that are utilised while conducting various studies. The article covers a brief outline of the variables, an understanding of quantitative and qualitative variables and the measures of central tendency. An idea of the sample size estimation, power analysis and the statistical errors is given. Finally, there is a summary of parametric and non-parametric tests used for data analysis.

INTRODUCTION

Statistics is a branch of science that deals with the collection, organisation, analysis of data and drawing of inferences from the samples to the whole population.[ 1 ] This requires a proper design of the study, an appropriate selection of the study sample and choice of a suitable statistical test. An adequate knowledge of statistics is necessary for proper designing of an epidemiological study or a clinical trial. Improper statistical methods may result in erroneous conclusions which may lead to unethical practice.[ 2 ]

Variable is a characteristic that varies from one individual member of population to another individual.[ 3 ] Variables such as height and weight are measured by some type of scale, convey quantitative information and are called as quantitative variables. Sex and eye colour give qualitative information and are called as qualitative variables[ 3 ] [ Figure 1 ].

Classification of variables

Quantitative variables

Quantitative or numerical data are subdivided into discrete and continuous measurements. Discrete numerical data are recorded as a whole number such as 0, 1, 2, 3,… (integer), whereas continuous data can assume any value. Observations that can be counted constitute the discrete data and observations that can be measured constitute the continuous data. Examples of discrete data are number of episodes of respiratory arrests or the number of re-intubations in an intensive care unit. Similarly, examples of continuous data are the serial serum glucose levels, partial pressure of oxygen in arterial blood and the oesophageal temperature.

A hierarchical scale of increasing precision can be used for observing and recording the data which is based on categorical, ordinal, interval and ratio scales [ Figure 1 ].

Categorical or nominal variables are unordered. The data are merely classified into categories and cannot be arranged in any particular order. If only two categories exist (as in gender male and female), it is called as a dichotomous (or binary) data. The various causes of re-intubation in an intensive care unit due to upper airway obstruction, impaired clearance of secretions, hypoxemia, hypercapnia, pulmonary oedema and neurological impairment are examples of categorical variables.

Ordinal variables have a clear ordering between the variables. However, the ordered data may not have equal intervals. Examples are the American Society of Anesthesiologists status or Richmond agitation-sedation scale.

Interval variables are similar to an ordinal variable, except that the intervals between the values of the interval variable are equally spaced. A good example of an interval scale is the Fahrenheit degree scale used to measure temperature. With the Fahrenheit scale, the difference between 70° and 75° is equal to the difference between 80° and 85°: The units of measurement are equal throughout the full range of the scale.

Ratio scales are similar to interval scales, in that equal differences between scale values have equal quantitative meaning. However, ratio scales also have a true zero point, which gives them an additional property. For example, the system of centimetres is an example of a ratio scale. There is a true zero point and the value of 0 cm means a complete absence of length. The thyromental distance of 6 cm in an adult may be twice that of a child in whom it may be 3 cm.

STATISTICS: DESCRIPTIVE AND INFERENTIAL STATISTICS

Descriptive statistics[ 4 ] try to describe the relationship between variables in a sample or population. Descriptive statistics provide a summary of data in the form of mean, median and mode. Inferential statistics[ 4 ] use a random sample of data taken from a population to describe and make inferences about the whole population. It is valuable when it is not possible to examine each member of an entire population. The examples if descriptive and inferential statistics are illustrated in Table 1 .

Example of descriptive and inferential statistics

Descriptive statistics

The extent to which the observations cluster around a central location is described by the central tendency and the spread towards the extremes is described by the degree of dispersion.

Measures of central tendency

The measures of central tendency are mean, median and mode.[ 6 ] Mean (or the arithmetic average) is the sum of all the scores divided by the number of scores. Mean may be influenced profoundly by the extreme variables. For example, the average stay of organophosphorus poisoning patients in ICU may be influenced by a single patient who stays in ICU for around 5 months because of septicaemia. The extreme values are called outliers. The formula for the mean is

where x = each observation and n = number of observations. Median[ 6 ] is defined as the middle of a distribution in a ranked data (with half of the variables in the sample above and half below the median value) while mode is the most frequently occurring variable in a distribution. Range defines the spread, or variability, of a sample.[ 7 ] It is described by the minimum and maximum values of the variables. If we rank the data and after ranking, group the observations into percentiles, we can get better information of the pattern of spread of the variables. In percentiles, we rank the observations into 100 equal parts. We can then describe 25%, 50%, 75% or any other percentile amount. The median is the 50 th percentile. The interquartile range will be the observations in the middle 50% of the observations about the median (25 th -75 th percentile). Variance[ 7 ] is a measure of how spread out is the distribution. It gives an indication of how close an individual observation clusters about the mean value. The variance of a population is defined by the following formula:

where σ 2 is the population variance, X is the population mean, X i is the i th element from the population and N is the number of elements in the population. The variance of a sample is defined by slightly different formula:

where s 2 is the sample variance, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. The formula for the variance of a population has the value ‘ n ’ as the denominator. The expression ‘ n −1’ is known as the degrees of freedom and is one less than the number of parameters. Each observation is free to vary, except the last one which must be a defined value. The variance is measured in squared units. To make the interpretation of the data simple and to retain the basic unit of observation, the square root of variance is used. The square root of the variance is the standard deviation (SD).[ 8 ] The SD of a population is defined by the following formula:

where σ is the population SD, X is the population mean, X i is the i th element from the population and N is the number of elements in the population. The SD of a sample is defined by slightly different formula:

where s is the sample SD, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. An example for calculation of variation and SD is illustrated in Table 2 .

Example of mean, variance, standard deviation

Normal distribution or Gaussian distribution

Most of the biological variables usually cluster around a central value, with symmetrical positive and negative deviations about this point.[ 1 ] The standard normal distribution curve is a symmetrical bell-shaped. In a normal distribution curve, about 68% of the scores are within 1 SD of the mean. Around 95% of the scores are within 2 SDs of the mean and 99% within 3 SDs of the mean [ Figure 2 ].

Normal distribution curve

Skewed distribution

It is a distribution with an asymmetry of the variables about its mean. In a negatively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the right of Figure 1 . In a positively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the left of the figure leading to a longer right tail.

Curves showing negatively skewed and positively skewed distribution

Inferential statistics

In inferential statistics, data are analysed from a sample to make inferences in the larger collection of the population. The purpose is to answer or test the hypotheses. A hypothesis (plural hypotheses) is a proposed explanation for a phenomenon. Hypothesis tests are thus procedures for making rational decisions about the reality of observed effects.

Probability is the measure of the likelihood that an event will occur. Probability is quantified as a number between 0 and 1 (where 0 indicates impossibility and 1 indicates certainty).

In inferential statistics, the term ‘null hypothesis’ ( H 0 ‘ H-naught ,’ ‘ H-null ’) denotes that there is no relationship (difference) between the population variables in question.[ 9 ]

Alternative hypothesis ( H 1 and H a ) denotes that a statement between the variables is expected to be true.[ 9 ]

The P value (or the calculated probability) is the probability of the event occurring by chance if the null hypothesis is true. The P value is a numerical between 0 and 1 and is interpreted by researchers in deciding whether to reject or retain the null hypothesis [ Table 3 ].

P values with interpretation

If P value is less than the arbitrarily chosen value (known as α or the significance level), the null hypothesis (H0) is rejected [ Table 4 ]. However, if null hypotheses (H0) is incorrectly rejected, this is known as a Type I error.[ 11 ] Further details regarding alpha error, beta error and sample size calculation and factors influencing them are dealt with in another section of this issue by Das S et al .[ 12 ]

Illustration for null hypothesis

PARAMETRIC AND NON-PARAMETRIC TESTS

Numerical data (quantitative variables) that are normally distributed are analysed with parametric tests.[ 13 ]

Two most basic prerequisites for parametric statistical analysis are:

• The assumption of normality which specifies that the means of the sample group are normally distributed
• The assumption of equal variance which specifies that the variances of the samples and of their corresponding population are equal.

However, if the distribution of the sample is skewed towards one side or the distribution is unknown due to the small sample size, non-parametric[ 14 ] statistical techniques are used. Non-parametric tests are used to analyse ordinal and categorical data.

Parametric tests

The parametric tests assume that the data are on a quantitative (numerical) scale, with a normal distribution of the underlying population. The samples have the same variance (homogeneity of variances). The samples are randomly drawn from the population, and the observations within a group are independent of each other. The commonly used parametric tests are the Student's t -test, analysis of variance (ANOVA) and repeated measures ANOVA.

Student's t -test

Student's t -test is used to test the null hypothesis that there is no difference between the means of the two groups. It is used in three circumstances:

where X = sample mean, u = population mean and SE = standard error of mean

where X 1 − X 2 is the difference between the means of the two groups and SE denotes the standard error of the difference.

• To test if the population means estimated by two dependent samples differ significantly (the paired t -test). A usual setting for paired t -test is when measurements are made on the same subjects before and after a treatment.

The formula for paired t -test is:

where d is the mean difference and SE denotes the standard error of this difference.

The group variances can be compared using the F -test. The F -test is the ratio of variances (var l/var 2). If F differs significantly from 1.0, then it is concluded that the group variances differ significantly.

Analysis of variance

The Student's t -test cannot be used for comparison of three or more groups. The purpose of ANOVA is to test if there is any significant difference between the means of two or more groups.

In ANOVA, we study two variances – (a) between-group variability and (b) within-group variability. The within-group variability (error variance) is the variation that cannot be accounted for in the study design. It is based on random differences present in our samples.

However, the between-group (or effect variance) is the result of our treatment. These two estimates of variances are compared using the F-test.

A simplified formula for the F statistic is:

where MS b is the mean squares between the groups and MS w is the mean squares within groups.

Repeated measures analysis of variance

As with ANOVA, repeated measures ANOVA analyses the equality of means of three or more groups. However, a repeated measure ANOVA is used when all variables of a sample are measured under different conditions or at different points in time.

As the variables are measured from a sample at different points of time, the measurement of the dependent variable is repeated. Using a standard ANOVA in this case is not appropriate because it fails to model the correlation between the repeated measures: The data violate the ANOVA assumption of independence. Hence, in the measurement of repeated dependent variables, repeated measures ANOVA should be used.

Non-parametric tests

When the assumptions of normality are not met, and the sample means are not normally, distributed parametric tests can lead to erroneous results. Non-parametric tests (distribution-free test) are used in such situation as they do not require the normality assumption.[ 15 ] Non-parametric tests may fail to detect a significant difference when compared with a parametric test. That is, they usually have less power.

As is done for the parametric tests, the test statistic is compared with known values for the sampling distribution of that statistic and the null hypothesis is accepted or rejected. The types of non-parametric analysis techniques and the corresponding parametric analysis techniques are delineated in Table 5 .

Analogue of parametric and non-parametric tests

Median test for one sample: The sign test and Wilcoxon's signed rank test

The sign test and Wilcoxon's signed rank test are used for median tests of one sample. These tests examine whether one instance of sample data is greater or smaller than the median reference value.

This test examines the hypothesis about the median θ0 of a population. It tests the null hypothesis H0 = θ0. When the observed value (Xi) is greater than the reference value (θ0), it is marked as+. If the observed value is smaller than the reference value, it is marked as − sign. If the observed value is equal to the reference value (θ0), it is eliminated from the sample.

If the null hypothesis is true, there will be an equal number of + signs and − signs.

The sign test ignores the actual values of the data and only uses + or − signs. Therefore, it is useful when it is difficult to measure the values.

Wilcoxon's signed rank test

There is a major limitation of sign test as we lose the quantitative information of the given data and merely use the + or – signs. Wilcoxon's signed rank test not only examines the observed values in comparison with θ0 but also takes into consideration the relative sizes, adding more statistical power to the test. As in the sign test, if there is an observed value that is equal to the reference value θ0, this observed value is eliminated from the sample.

Wilcoxon's rank sum test ranks all data points in order, calculates the rank sum of each sample and compares the difference in the rank sums.

Mann-Whitney test

It is used to test the null hypothesis that two samples have the same median or, alternatively, whether observations in one sample tend to be larger than observations in the other.

Mann–Whitney test compares all data (xi) belonging to the X group and all data (yi) belonging to the Y group and calculates the probability of xi being greater than yi: P (xi > yi). The null hypothesis states that P (xi > yi) = P (xi < yi) =1/2 while the alternative hypothesis states that P (xi > yi) ≠1/2.

Kolmogorov-Smirnov test

The two-sample Kolmogorov-Smirnov (KS) test was designed as a generic method to test whether two random samples are drawn from the same distribution. The null hypothesis of the KS test is that both distributions are identical. The statistic of the KS test is a distance between the two empirical distributions, computed as the maximum absolute difference between their cumulative curves.

Kruskal-Wallis test

The Kruskal–Wallis test is a non-parametric test to analyse the variance.[ 14 ] It analyses if there is any difference in the median values of three or more independent samples. The data values are ranked in an increasing order, and the rank sums calculated followed by calculation of the test statistic.

Jonckheere test

In contrast to Kruskal–Wallis test, in Jonckheere test, there is an a priori ordering that gives it a more statistical power than the Kruskal–Wallis test.[ 14 ]

Friedman test

The Friedman test is a non-parametric test for testing the difference between several related samples. The Friedman test is an alternative for repeated measures ANOVAs which is used when the same parameter has been measured under different conditions on the same subjects.[ 13 ]

Tests to analyse the categorical data

Chi-square test, Fischer's exact test and McNemar's test are used to analyse the categorical or nominal variables. The Chi-square test compares the frequencies and tests whether the observed data differ significantly from that of the expected data if there were no differences between groups (i.e., the null hypothesis). It is calculated by the sum of the squared difference between observed ( O ) and the expected ( E ) data (or the deviation, d ) divided by the expected data by the following formula:

A Yates correction factor is used when the sample size is small. Fischer's exact test is used to determine if there are non-random associations between two categorical variables. It does not assume random sampling, and instead of referring a calculated statistic to a sampling distribution, it calculates an exact probability. McNemar's test is used for paired nominal data. It is applied to 2 × 2 table with paired-dependent samples. It is used to determine whether the row and column frequencies are equal (that is, whether there is ‘marginal homogeneity’). The null hypothesis is that the paired proportions are equal. The Mantel-Haenszel Chi-square test is a multivariate test as it analyses multiple grouping variables. It stratifies according to the nominated confounding variables and identifies any that affects the primary outcome variable. If the outcome variable is dichotomous, then logistic regression is used.

SOFTWARES AVAILABLE FOR STATISTICS, SAMPLE SIZE CALCULATION AND POWER ANALYSIS

Numerous statistical software systems are available currently. The commonly used software systems are Statistical Package for the Social Sciences (SPSS – manufactured by IBM corporation), Statistical Analysis System ((SAS – developed by SAS Institute North Carolina, United States of America), R (designed by Ross Ihaka and Robert Gentleman from R core team), Minitab (developed by Minitab Inc), Stata (developed by StataCorp) and the MS Excel (developed by Microsoft).

There are a number of web resources which are related to statistical power analyses. A few are:

• StatPages.net – provides links to a number of online power calculators
• G-Power – provides a downloadable power analysis program that runs under DOS
• Power analysis for ANOVA designs an interactive site that calculates power or sample size needed to attain a given power for one effect in a factorial ANOVA design
• SPSS makes a program called SamplePower. It gives an output of a complete report on the computer screen which can be cut and paste into another document.

It is important that a researcher knows the concepts of the basic statistical methods used for conduct of a research study. This will help to conduct an appropriately well-designed study leading to valid and reliable results. Inappropriate use of statistical techniques may lead to faulty conclusions, inducing errors and undermining the significance of the article. Bad statistics may lead to bad research, and bad research may lead to unethical practice. Hence, an adequate knowledge of statistics and the appropriate use of statistical tests are important. An appropriate knowledge about the basic statistical methods will go a long way in improving the research designs and producing quality medical research which can be utilised for formulating the evidence-based guidelines.

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Research Methods

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Data Analysis & Interpretation

• Quantitative Data

Qualitative Data

• Mixed Methods

You will need to tidy, analyse and interpret the data you collected to give meaning to it, and to answer your research question.  Your choice of methodology points the way to the most suitable method of analysing your data.

If the data is numeric you can use a software package such as SPSS, Excel Spreadsheet or “R” to do statistical analysis.  You can identify things like mean, median and average or identify a causal or correlational relationship between variables.  

The University of Connecticut has useful information on statistical analysis.

If your research set out to test a hypothesis your research will either support or refute it, and you will need to explain why this is the case.  You should also highlight and discuss any issues or actions that may have impacted on your results, either positively or negatively.  To fully contribute to the body of knowledge in your area be sure to discuss and interpret your results within the context of your research and the existing literature on the topic.

Data analysis for a qualitative study can be complex because of the variety of types of data that can be collected. Qualitative researchers aren’t attempting to measure observable characteristics, they are often attempting to capture an individual’s interpretation of a phenomena or situation in a particular context or setting.  This data could be captured in text from an interview or focus group, a movie, images, or documents.   Analysis of this type of data is usually done by analysing each artefact according to a predefined and outlined criteria for analysis and then by using a coding system.  The code can be developed by the researcher before analysis or the researcher may develop a code from the research data.  This can be done by hand or by using thematic analysis software such as NVivo.

Interpretation of qualitative data can be presented as a narrative.  The themes identified from the research can be organised and integrated with themes in the existing literature to give further weight and meaning to the research.  The interpretation should also state if the aims and objectives of the research were met.   Any shortcomings with research or areas for further research should also be discussed (Creswell,2009)*.

For further information on analysing and presenting qualitative date, read this article in Nature .

Mixed Methods Data

Data analysis for mixed methods involves aspects of both quantitative and qualitative methods.  However, the sequencing of data collection and analysis is important in terms of the mixed method approach that you are taking.  For example, you could be using a convergent, sequential or transformative model which directly impacts how you use different data to inform, support or direct the course of your study.

The intention in using mixed methods is to produce a synthesis of both quantitative and qualitative information to give a detailed picture of a phenomena in a particular context or setting. To fully understand how best to produce this synthesis it might be worth looking at why researchers choose this method.  Bergin**(2018) states that researchers choose mixed methods because it allows them to triangulate, illuminate or discover a more diverse set of findings.  Therefore, when it comes to interpretation you will need to return to the purpose of your research and discuss and interpret your data in that context. As with quantitative and qualitative methods, interpretation of data should be discussed within the context of the existing literature.

Bergin’s book is available in the Library to borrow. Bolton LTT collection 519.5 BER

Creswell’s book is available in the Library to borrow.  Bolton LTT collection 300.72 CRE

For more information on data analysis look at Sage Research Methods database on the library website.

*Creswell, John W.(2009)  Research design: qualitative, and mixed methods approaches.  Sage, Los Angeles, pp 183

**Bergin, T (2018), Data analysis: quantitative, qualitative and mixed methods. Sage, Los Angeles, pp182

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Data analysis and findings

Data analysis is the most crucial part of any research. Data analysis summarizes collected data. It involves the interpretation of data gathered through the use of analytical and logical reasoning to determine patterns, relationships or trends. 

Data Analysis Checklist

Cleaning  data

* Did you capture and code your data in the right manner?

*Do you have all data or missing data?

* Do you have enough observations?

* Do you have any outliers? If yes, what is the remedy for outlier?

* Does your data have the potential to answer your questions?

Analyzing data

* Visualize your data, e.g. charts, tables, and graphs, to mention a few.

*  Identify patterns, correlations, and trends

* Test your hypotheses

* Let your data tell a story

Reports the results

* Communicate and interpret the results

* Conclude and recommend

* Your targeted audience must understand your results

* Use more datasets and samples

* Use accessible and understandable data analytical tool

* Do not delegate your data analysis

* Clean data to confirm that they are complete and free from errors

* Analyze cleaned data

* Understand your results

* Keep in mind who will be reading your results and present it in a way that they will understand it

* Share the results with the supervisor oftentimes

Past presentations

• PhD Writing Retreat - Analysing_Fieldwork_Data by Cori Wielenga A clear and concise presentation on the ‘now what’ and ‘so what’ of data collection and analysis - compiled and originally presented by Cori Wielenga.

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• Qualitative analysis of interview data: A step-by-step guide
• Qualitative Data Analysis - Coding & Developing Themes

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Recommended Qualitative Data Analysis books

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Home » Quantitative Research – Methods, Types and Analysis

Quantitative Research – Methods, Types and Analysis

Table of Contents

Quantitative Research

Quantitative research is a type of research that collects and analyzes numerical data to test hypotheses and answer research questions . This research typically involves a large sample size and uses statistical analysis to make inferences about a population based on the data collected. It often involves the use of surveys, experiments, or other structured data collection methods to gather quantitative data.

Quantitative Research Methods

Quantitative Research Methods are as follows:

Descriptive Research Design

Descriptive research design is used to describe the characteristics of a population or phenomenon being studied. This research method is used to answer the questions of what, where, when, and how. Descriptive research designs use a variety of methods such as observation, case studies, and surveys to collect data. The data is then analyzed using statistical tools to identify patterns and relationships.

Correlational Research Design

Correlational research design is used to investigate the relationship between two or more variables. Researchers use correlational research to determine whether a relationship exists between variables and to what extent they are related. This research method involves collecting data from a sample and analyzing it using statistical tools such as correlation coefficients.

Quasi-experimental Research Design

Quasi-experimental research design is used to investigate cause-and-effect relationships between variables. This research method is similar to experimental research design, but it lacks full control over the independent variable. Researchers use quasi-experimental research designs when it is not feasible or ethical to manipulate the independent variable.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This research method involves manipulating the independent variable and observing the effects on the dependent variable. Researchers use experimental research designs to test hypotheses and establish cause-and-effect relationships.

Survey Research

Survey research involves collecting data from a sample of individuals using a standardized questionnaire. This research method is used to gather information on attitudes, beliefs, and behaviors of individuals. Researchers use survey research to collect data quickly and efficiently from a large sample size. Survey research can be conducted through various methods such as online, phone, mail, or in-person interviews.

Quantitative Research Analysis Methods

Here are some commonly used quantitative research analysis methods:

Statistical Analysis

Statistical analysis is the most common quantitative research analysis method. It involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis can be used to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.

Regression Analysis

Regression analysis is a statistical technique used to analyze the relationship between one dependent variable and one or more independent variables. Researchers use regression analysis to identify and quantify the impact of independent variables on the dependent variable.

Factor Analysis

Factor analysis is a statistical technique used to identify underlying factors that explain the correlations among a set of variables. Researchers use factor analysis to reduce a large number of variables to a smaller set of factors that capture the most important information.

Structural Equation Modeling

Structural equation modeling is a statistical technique used to test complex relationships between variables. It involves specifying a model that includes both observed and unobserved variables, and then using statistical methods to test the fit of the model to the data.

Time Series Analysis

Time series analysis is a statistical technique used to analyze data that is collected over time. It involves identifying patterns and trends in the data, as well as any seasonal or cyclical variations.

Multilevel Modeling

Multilevel modeling is a statistical technique used to analyze data that is nested within multiple levels. For example, researchers might use multilevel modeling to analyze data that is collected from individuals who are nested within groups, such as students nested within schools.

Applications of Quantitative Research

Quantitative research has many applications across a wide range of fields. Here are some common examples:

• Market Research : Quantitative research is used extensively in market research to understand consumer behavior, preferences, and trends. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform marketing strategies, product development, and pricing decisions.
• Health Research: Quantitative research is used in health research to study the effectiveness of medical treatments, identify risk factors for diseases, and track health outcomes over time. Researchers use statistical methods to analyze data from clinical trials, surveys, and other sources to inform medical practice and policy.
• Social Science Research: Quantitative research is used in social science research to study human behavior, attitudes, and social structures. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform social policies, educational programs, and community interventions.
• Education Research: Quantitative research is used in education research to study the effectiveness of teaching methods, assess student learning outcomes, and identify factors that influence student success. Researchers use experimental and quasi-experimental designs, as well as surveys and other quantitative methods, to collect and analyze data.
• Environmental Research: Quantitative research is used in environmental research to study the impact of human activities on the environment, assess the effectiveness of conservation strategies, and identify ways to reduce environmental risks. Researchers use statistical methods to analyze data from field studies, experiments, and other sources.

Characteristics of Quantitative Research

Here are some key characteristics of quantitative research:

• Numerical data : Quantitative research involves collecting numerical data through standardized methods such as surveys, experiments, and observational studies. This data is analyzed using statistical methods to identify patterns and relationships.
• Large sample size: Quantitative research often involves collecting data from a large sample of individuals or groups in order to increase the reliability and generalizability of the findings.
• Objective approach: Quantitative research aims to be objective and impartial in its approach, focusing on the collection and analysis of data rather than personal beliefs, opinions, or experiences.
• Control over variables: Quantitative research often involves manipulating variables to test hypotheses and establish cause-and-effect relationships. Researchers aim to control for extraneous variables that may impact the results.
• Replicable : Quantitative research aims to be replicable, meaning that other researchers should be able to conduct similar studies and obtain similar results using the same methods.
• Statistical analysis: Quantitative research involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis allows researchers to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.
• Generalizability: Quantitative research aims to produce findings that can be generalized to larger populations beyond the specific sample studied. This is achieved through the use of random sampling methods and statistical inference.

Examples of Quantitative Research

Here are some examples of quantitative research in different fields:

• Market Research: A company conducts a survey of 1000 consumers to determine their brand awareness and preferences. The data is analyzed using statistical methods to identify trends and patterns that can inform marketing strategies.
• Health Research : A researcher conducts a randomized controlled trial to test the effectiveness of a new drug for treating a particular medical condition. The study involves collecting data from a large sample of patients and analyzing the results using statistical methods.
• Social Science Research : A sociologist conducts a survey of 500 people to study attitudes toward immigration in a particular country. The data is analyzed using statistical methods to identify factors that influence these attitudes.
• Education Research: A researcher conducts an experiment to compare the effectiveness of two different teaching methods for improving student learning outcomes. The study involves randomly assigning students to different groups and collecting data on their performance on standardized tests.
• Environmental Research : A team of researchers conduct a study to investigate the impact of climate change on the distribution and abundance of a particular species of plant or animal. The study involves collecting data on environmental factors and population sizes over time and analyzing the results using statistical methods.
• Psychology : A researcher conducts a survey of 500 college students to investigate the relationship between social media use and mental health. The data is analyzed using statistical methods to identify correlations and potential causal relationships.
• Political Science: A team of researchers conducts a study to investigate voter behavior during an election. They use survey methods to collect data on voting patterns, demographics, and political attitudes, and analyze the results using statistical methods.

How to Conduct Quantitative Research

Here is a general overview of how to conduct quantitative research:

• Develop a research question: The first step in conducting quantitative research is to develop a clear and specific research question. This question should be based on a gap in existing knowledge, and should be answerable using quantitative methods.
• Develop a research design: Once you have a research question, you will need to develop a research design. This involves deciding on the appropriate methods to collect data, such as surveys, experiments, or observational studies. You will also need to determine the appropriate sample size, data collection instruments, and data analysis techniques.
• Collect data: The next step is to collect data. This may involve administering surveys or questionnaires, conducting experiments, or gathering data from existing sources. It is important to use standardized methods to ensure that the data is reliable and valid.
• Analyze data : Once the data has been collected, it is time to analyze it. This involves using statistical methods to identify patterns, trends, and relationships between variables. Common statistical techniques include correlation analysis, regression analysis, and hypothesis testing.
• Interpret results: After analyzing the data, you will need to interpret the results. This involves identifying the key findings, determining their significance, and drawing conclusions based on the data.
• Communicate findings: Finally, you will need to communicate your findings. This may involve writing a research report, presenting at a conference, or publishing in a peer-reviewed journal. It is important to clearly communicate the research question, methods, results, and conclusions to ensure that others can understand and replicate your research.

When to use Quantitative Research

Here are some situations when quantitative research can be appropriate:

• To test a hypothesis: Quantitative research is often used to test a hypothesis or a theory. It involves collecting numerical data and using statistical analysis to determine if the data supports or refutes the hypothesis.
• To generalize findings: If you want to generalize the findings of your study to a larger population, quantitative research can be useful. This is because it allows you to collect numerical data from a representative sample of the population and use statistical analysis to make inferences about the population as a whole.
• To measure relationships between variables: If you want to measure the relationship between two or more variables, such as the relationship between age and income, or between education level and job satisfaction, quantitative research can be useful. It allows you to collect numerical data on both variables and use statistical analysis to determine the strength and direction of the relationship.
• To identify patterns or trends: Quantitative research can be useful for identifying patterns or trends in data. For example, you can use quantitative research to identify trends in consumer behavior or to identify patterns in stock market data.
• To quantify attitudes or opinions : If you want to measure attitudes or opinions on a particular topic, quantitative research can be useful. It allows you to collect numerical data using surveys or questionnaires and analyze the data using statistical methods to determine the prevalence of certain attitudes or opinions.

Purpose of Quantitative Research

The purpose of quantitative research is to systematically investigate and measure the relationships between variables or phenomena using numerical data and statistical analysis. The main objectives of quantitative research include:

• Description : To provide a detailed and accurate description of a particular phenomenon or population.
• Explanation : To explain the reasons for the occurrence of a particular phenomenon, such as identifying the factors that influence a behavior or attitude.
• Prediction : To predict future trends or behaviors based on past patterns and relationships between variables.
• Control : To identify the best strategies for controlling or influencing a particular outcome or behavior.

Quantitative research is used in many different fields, including social sciences, business, engineering, and health sciences. It can be used to investigate a wide range of phenomena, from human behavior and attitudes to physical and biological processes. The purpose of quantitative research is to provide reliable and valid data that can be used to inform decision-making and improve understanding of the world around us.

Advantages of Quantitative Research

There are several advantages of quantitative research, including:

• Objectivity : Quantitative research is based on objective data and statistical analysis, which reduces the potential for bias or subjectivity in the research process.
• Reproducibility : Because quantitative research involves standardized methods and measurements, it is more likely to be reproducible and reliable.
• Generalizability : Quantitative research allows for generalizations to be made about a population based on a representative sample, which can inform decision-making and policy development.
• Precision : Quantitative research allows for precise measurement and analysis of data, which can provide a more accurate understanding of phenomena and relationships between variables.
• Efficiency : Quantitative research can be conducted relatively quickly and efficiently, especially when compared to qualitative research, which may involve lengthy data collection and analysis.
• Large sample sizes : Quantitative research can accommodate large sample sizes, which can increase the representativeness and generalizability of the results.

Limitations of Quantitative Research

There are several limitations of quantitative research, including:

• Limited understanding of context: Quantitative research typically focuses on numerical data and statistical analysis, which may not provide a comprehensive understanding of the context or underlying factors that influence a phenomenon.
• Simplification of complex phenomena: Quantitative research often involves simplifying complex phenomena into measurable variables, which may not capture the full complexity of the phenomenon being studied.
• Potential for researcher bias: Although quantitative research aims to be objective, there is still the potential for researcher bias in areas such as sampling, data collection, and data analysis.
• Limited ability to explore new ideas: Quantitative research is often based on pre-determined research questions and hypotheses, which may limit the ability to explore new ideas or unexpected findings.
• Limited ability to capture subjective experiences : Quantitative research is typically focused on objective data and may not capture the subjective experiences of individuals or groups being studied.
• Ethical concerns : Quantitative research may raise ethical concerns, such as invasion of privacy or the potential for harm to participants.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Study Research Psychology and Data Analysis at Azusa Pacific University

You’ll gain a competitive edge when pursuing doctoral programs and careers in psychological research: equipped with hands-on training with faculty mentors, research experiences, and a publishable manuscript. When you graduate, you’ll have the research and statistical methods necessary to lead change in the field-–including training with popular statistical software. Hone your expertise even further with an optional 9-unit concentration in counseling psychology, data analytics, industrial/organizational psychology, or public health.

See Admission Requirements

Program at a Glance

Application deadline.

Fall Admission Deadlines

• Priority: April 1 
• Extended: May 31
• Final: July 15 

Spring Admission Deadlines

• Priority: October 1 
• Extended: November 1 
• Final: December 1 

Program Information

• Program Units: 36
• Cost per Unit: $752
• Base Cost: $27,072*
• Azusa (Main Campus)

Yellow Ribbon and Military Friendly School

Gain Hands-on Experience

• Analyze data using popular statistical software, such as SPSS, R, and JMP.
• Collaborate on a publishable research manuscript with a faculty advisor.
• Complete a master’s thesis in your area of interest. 
• Showcase your skills with an optional JMP/SAS Joint Certificate in Research Methods.

By the Numbers

Get started, program details.

The Master of Science in Research Psychology and Data Analysis provides research and statistical experience needed to be competitive for doctoral programs and pursue, or advance within, careers related to psychological research. The program consists of a rigorous 36-unit curriculum that can be completed in one academic year, 18 months, or 2 academic years. 

Browse the tabs below—if you have questions, visit the  Student Services Center  page, and we’ll make sure you get the info you need.

Admission Requirements—Domestic Applicants

The  university graduate  and program admission requirements below must be met before an application is considered complete.

Applicants must submit:

• Graduate application for admission
• $45 nonrefundable application fee
• Official transcripts from all institutions leading to and awarding the applicant’s bachelor’s degree from a regionally accredited institution and all postbaccalaureate study An official transcript is one that Azusa Pacific University receives unopened in an envelope sealed by the former institution and that bears the official seal of the college or university. A cumulative minimum 3.0 (on a 4.0 scale) grade-point average on the posted degree is required for regular admission status. Provisional admittance may be granted to individuals with a lower GPA if competency can be shown through multiple measures.
• Two letters of recommendation
• Current résumé showing professional, academic, and volunteer experiences
• For thesis track students:
• Why do you want to complete the thesis track?
• A description of the specific type of research you’re interested in focusing on for your thesis project.
• Select 1-2 APU  program faculty  with a similar area of research for your thesis advisor in the program.
• For internship track students:
• Why do you want to complete the internship track?
• What career do you want to pursue during this program and/or after you have graduated from this program?
• A description of your areas of interest in one or more of the following: research, statistics, data analysis.
• Why you’re interested in APU’s MS in Research Psychology and Data Analysis program.
• Which track (thesis or internship track) you would like to complete and why.

Send official transcripts to:

What to expect.

After all admission materials have been received by the  Student Services Center , the faculty or department chair reviews the applicant’s file. The applicant is notified in writing of the admission decision.

Admission Requirements—International Applicants

Azusa Pacific University is authorized under federal law by the U.S. Citizen and Immigration Services and the U.S. Department of State to enroll nonimmigrant, alien undergraduate and graduate students. APU issues and administers both the I-20 and DS 2019 (F-1 and J-1 status documents respectively).

To apply for a graduate program at APU, the following requirements must be fulfilled in addition to meeting the domestic applicant and  program-specific admission requirements  specified above.

International applicants must also:

• Demonstrate proficiency in English through a placement test with the  Academic Success Center , or through a TOEFL/IELTS score that meets program-specific requirements. Refer to APU’s  English proficiency requirements  to learn more. Request that official test scores be sent to APU. All other forms of proof indicated in the English proficiency requirements must be submitted directly to International Services.
• Provide a  Graduate Affidavit of Financial Support (PDF)  and a bank statement from within the last six months proving financial ability to pay for education costs through personal, family, or sponsor resources.
• Submit a copy of a valid passport showing biographical data, including your name, country of citizenship, date of birth, and other legal information.
• Get foreign transcripts evaluated. International credentials (transcripts, certificates, diplomas, and degrees) must be evaluated by a foreign transcript evaluation agency. View our policy and a list of approved  Foreign Transcript Evaluation Agencies .

View a complete list of   university fees .

All stated financial information is subject to change. Contact the   Student Services Center at (626) 815-2020 for more information.

Several types of   financial aid   are available to graduate students. The resources range from federal loans and state grants to, for some graduate programs, fellowships and scholarships. For details about financial aid available for your program, please contact the   Student Services Center   at   (626) 815-2020 .

Military Benefits

Military members—and in some cases their spouses and dependents—qualify for   financial assistance   covering tuition, housing, and books. Azusa Pacific is a Yellow Ribbon University and Military Friendly School, so you can be confident that you’ll receive the benefits and flexibility you need to complete your education.

Program-Specific Aid

APU also provides information on numerous financial aid options for prospective School of Behavioral and Applied Sciences students. Learn more   about the program-specific aid available .

Graduate Teaching Assistantships

Students can apply for Graduate Teaching Assistantship positions to work alongside faculty in evaluating student work, administering courses, providing assistance in research experience labs, and gaining opportunities for teaching, if desired. These positions can fund up to $4,000 toward tuition. Email   [email protected]   to learn more.

School of Behavioral and Applied Sciences Alumni Tuition Discounts

APU’s   School of Behavioral and Applied Sciences (BAS)   offers an alumni tuition discount for APU alumni starting in the following graduate programs:

• Adapted Physical Education Added Authorization
• Master of Arts in Clinical Psychology: Marriage and Family Therapy (MFT)
• Master of Arts in Physical Education
• Master of Science in Child Life
• Master of Science in Physical Education
• Master of Science in Research Psychology and Data Analysis
• Master of Science in Counseling Psychology with Specialization in Children and Adolescents

Students must be in good academic standing and maintain satisfactory academic progress with a minimum cumulative grade-point average (GPA) of 3.0. Students must be enrolled at least half-time. If qualifications are met, the discount may be renewed each term. APU will pay $500 per term (up to $2,000 over the course of the program). This discount cannot be combined with other institutional aid. Contact   Student Financial Services   for complete eligibility details.

Note: Award and eligibility are applicable for the 2022-23 academic year and apply to students admitted beginning fall 2020. The discount is subject to change.

Program Requirements

Who is This Program For?

This program is best suited for students bound for either PhD programs or employment in a research-related career.

PhD programs can be extremely competitive. Undergraduate students are often not accepted because they do not have the necessary research experience, GPA, or statistics background. This program helps students gain the necessary research experience, data analysis skills, and confidence to pursue their goal of earning a doctorate.

With only an undergraduate degree, pursuing a research-related career in psychology can be difficult. Employers expect new hires to be comfortable conducting research and analyzing data. With an MS in Research Psychology and Data Analysis, you gain the necessary experience in research and data analysis to separate yourself from the pack.

How Long Does This Program Take?

This 36-unit program can be completed in 11, 18, or 24 months, depending on the chosen track. If desired, students can add a concentration of 9 additional units that will add to the completion time for 11-month students; because of this, concentrations are recommended only for students on an 18- or 24-month track, or 11-month students willing to extend their time in the program. With the support of a faculty advisor, students are expected to balance their coursework with progress toward completion of a master’s thesis.

What Optional Concentrations are Offered?

The following optional concentrations are available:

Counseling Psychology Concentration

This concentration combines an emphasis on evidence-based research with extensive clinical training, preparing students to work with children and adolescents using a wide scope of practice, and considering their development physically, psychologically, and socially.

View counseling psychology concentration coursework

Data Analytics Concentration

Students in this concentration learn statistical and data visualization techniques. Coursework develops students’ skill in statistical programming, including R and SAS, data visualization using Tableau, and data mining, preparing students to pursue careers in business and/or data analytics.

View data analytics concentration coursework

Industrial/Organizational Psychology Concentration

Organizational psychology, often called industrial/organizational (I/O) psychology, considers the attitudes, feelings, and behaviors of people in the workplace. In this concentration at the intersection of business and psychology, coursework equips students to diagnose organizational health; develop and implement change initiatives; administer and create job satisfaction surveys; increase employee morale and expand retention strategies; advance performance management programs; hire, train, and develop talent; and advise, coach, and build successful teams.

View I/O psychology concentration coursework

Marketing Research Concentration

This concentration equips students with conceptual and applied knowledge of marketing processes and the evaluation of products, services, and consumer behavior. Coursework introduces marketing strategy, and students learn to apply quantitative research methods and data analysis techniques within business and marketing contexts, using relevant marketing terminology and statistical software. Such market research skills are in high demand and prepare students to pursue applied psychology careers related to marketing.

View marketing research concentration coursework

Note: The cost per unit for the concentration is based on the cost of the concentration courses, and may be different from the per-unit cost for the MS in Research Psychology and Data Analysis.

Can I Add a Concentration after I Have Been Admitted?

This is not a problem, and we encourage students to do so. You will just need to submit a   Graduate Admissions Change of Program Form (PDF)   through the Department of Psychology.

Note: Adding a concentration may increase the length of time in the program. Students should be aware of the additional 9-unit workload of the concentration, and that the restrictions of course offerings may impact the time it takes to complete the program.

How Many Units Do I Take at a Time?

Depending on the length of track the student chooses, students take 3-7 units every 8-week term.

What Am I Getting From This Program?

You will benefit from a mentorship model where you learn to master research methods, data analysis, and writing skills—key skills necessary for a research psychology career or pursuing a PhD You will also gain valuable skills in SPSS, SAS, and R that will make you competitive for doctoral programs and research careers. These skills are reflected in every course you take and during weekly one-on-one meetings with your thesis advisor.

What Courses Will I Take?

Coursework includes, in part: Experimental Research Methods, Non-experimental Research Methods, Theory Research and Practice in Psychology, Analysis of Variance, Regression, Psychometrics: Assessment and Measurement, Program Evaluation, and Statistical Labs.   View the full list of courses.

How Long is a Term?

Each term is 8 weeks. There are two 8-week terms per semester.

How Many Evenings Will I Be on Campus?

Classes are held 1-2 evenings a week. Time and duration of each class varies with your chosen track.

How Long Are the Classes?

Each class runs approximately 4 hours, in addition to outside classwork to prepare you for class.

Can I Work While Completing the Program?

Many students decide to work throughout the program. However, it is up to you to balance outside work with program requirements. If you are enrolled in the 11-month track, we recommend working no more than 20 hours per week. The 18- and 24-month tracks are amenable to full-time employment. If you plan on working during the program, you’re encouraged to consult with your faculty advisor because certain thesis projects may require more time commitment.

How Do I Choose a Faculty Thesis Advisor?

You will choose their faculty thesis advisor based on area of interest. View our program faculty and see who has expertise in your area of interest. If you would like guidance in selecting a faculty thesis advisor, please contact the department for further assistance.

What Type of Research Can I Do?

Our 22 full-time faculty have expertise in a many different areas (e.g., social, cultural, clinical, educational, cognitive, neuro, developmental, quantitative, substance abuse) and in many different types of research methodology (e.g., eye-tracking, facial recognition, focus groups, survey design, physiological measures).

What Do We Do in Lab Courses?

The lab courses are designed to give you a hands-on opportunity to apply your statistical knowledge using popular statistical software (e.g., SPSS, SAS, and R). Labs are integrated with, and supplemental to, seminar-style courses. Labs are required to be taken during the same semester as the lecture portion of the class.

What Kind of Research Will I Conduct During This Program?

Your thesis is entirely up to you! Each area of psychology and faculty advisor asks different research questions, and uses different methodology, depending on their area of expertise. Your thesis may be a continuation or adaptation of the faculty advisor’s ongoing research or it may be an independent project related to their area of expertise. During the program, you’ll collaborate with your advisor via 12 units of coursework (allocated as thesis research units), culminating in a thesis project.

APU’s MS in Research Psychology and Data Analysis program offers a highly accessible and comprehensive distance learning option, allowing you to earn your degree in a flexible environment without sacrificing the high-quality instruction and professor-student interaction for which APU is known.

What is Distance Learning?

Distance learning courses are different from traditional online courses in that students join a live face-to-face course remotely through online video technology. You experience the lectures and in-class discussions, and are able to ask questions of the professor, all in real time. This platform allows us to offer the program to those who do not live in the area, and allows you to be part of the classroom and cohort.

For more information about the distance learning option, contact Jacquelyn Babikian at   [email protected] .

Distance Learning Policy

Students interested in applying for the distance learning modality are encouraged to review the   distance learning policy .

If you have questions, we’re here to help! Connect with a representative who can walk you through the program details and application process.

Contact Your Admissions Representative

Additional Program Contact

Dr. Matthew Heller, program director,   [email protected] ,  (626) 815-6000 x2413

Department of Psychology

(626) 815-6000, Ext. 2420

Monday-Friday, 8:30 a.m.-4:30 p.m.

Student Services Center

Contact (626) 812-3016  or [email protected] , and visit  apu.edu/ssc  for details on the admissions process, financial aid, and class registration.

International Services

International students should contact +1-626-812-3055 or  [email protected] , and visit  apu.edu/international/ .

Azusa Pacific University is accredited by the WASC Senior College and University Commission (WSCUC).

Featured Faculty

Holly Holloway-Friesen

Professor, Department of Psychology; Adjunct Professor, Department of Higher Education

Matthew Heller

Program Director of MS in Research Psychology; Associate Professor, Department of Psychology

Rachel Castaneda

Professor, Department of Psychology and Department of Clinical Psychology

See All Faculty

Career Outlook and Outcomes

Career Paths for Psychology Majors to Consider

Learn more about the career options available to psychology majors, whether you earn an undergraduate degree or go on to pursue a graduate degree.

Read the Article

Related Programs

Clinical psychology (psyd), applied statistics and data science, ms.

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Biostatistics, Business Analytics

Organizational Psychology, MS

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Harris Energizes Democrats in Transformed Presidential Race

1. the presidential matchup: harris, trump, kennedy, table of contents.

• Other findings: Both Harris and Trump are viewed more favorably than a few months ago
• Voting preferences among demographic groups
• How have voters shifted their preferences since July?
• Harris’ supporters back her more strongly than Biden’s did last month
• Large gap in motivation to vote emerges between the candidates’ younger supporters
• Harris and Trump have gained ground with their own coalitions
• Share of ‘double negatives’ drops significantly with change in presidential candidates
• Views of Biden have changed little since his withdrawal from the 2024 presidential race
• Acknowledgments
• The American Trends Panel survey methodology

Nationally, Vice President Kamala Harris and former President Donald Trump are essentially tied among registered voters in the current snapshot of the presidential race: 46% prefer Harris, 45% prefer Trump and 7% prefer Robert F. Kennedy Jr.

Following Biden’s exit from the race, Trump’s support among voters has remained largely steady (44% backed him in July against Biden, while 45% back him against Harris today). However, Harris’ support is 6 percentage points higher than Biden’s was in July . In addition to holding on to the support of those who backed Biden in July, Harris’ bump has largely come from those who had previously said they supported or leaned toward Kennedy.

Harris performs best among the same demographic groups as Biden. But this coalition of voters is now much more likely to say they strongly support her: In July, 43% of Biden’s supporters characterized their support as strong – today, 62% of Harris’ do.

Overall, many of the same voting patterns that were evident in the Biden-Trump matchup from July continue to be seen today. Harris fares better than Trump among younger voters, Black voters, Asian voters and voters with college degrees. By comparison, the former president does better among older voters, White voters and voters without a college degree.

But Harris performs better than Biden across many of these groups – making the race tighter than it was just a few weeks ago.

• In July, women’s presidential preferences were split: 40% backed Biden, 40% preferred Trump and 17% favored Kennedy. With Harris at the top of the ticket, 49% of women voters now support her, while 42% favor Trump and 7% back Kennedy.
• Among men, Trump draws a similar level of support as he did in the race against Biden (49% today, compared with 48% in July). But the share of men who now say they support Harris has grown (to 44% today, up from 38% last month). As a result, Trump’s 10-point lead among men has narrowed to a 5-point lead today.

Race and ethnicity

Harris has gained substantial ground over Biden’s position in July among Black, Hispanic and Asian voters. Most of this movement is attributable to declining shares of support for Kennedy. Trump performs similarly among these groups as he did in July.

• 77% of Black voters support or lean toward Harris. This compares with 64% of Black voters who said they backed Biden a few weeks ago. Trump’s support is unchanged (13% then vs. 13% today). And while 21% of Black voters supported Kennedy in July, this has dropped to 7% in the latest survey.
• Hispanic voters now favor Harris over Trump by a 17-point margin (52% to 35%). In July, Biden and Trump were tied among Hispanic voters with 36% each.
• By about two-to-one, Asian voters support Harris (62%) over Trump (28%). Trump’s support among this group is essentially unchanged since July, but the share of Asian voters backing Harris is 15 points higher than the share who backed Biden in July.
• On balance, White voters continue to back Trump (52% Trump, 41% Harris), though that margin is somewhat narrower than it was in the July matchup against Biden (50% Trump, 36% Biden).

While the age patterns present in the Harris-Trump matchup remain broadly the same as those in the Biden-Trump matchup in July, Harris performs better across age groups than Biden did last month. That improvement is somewhat more pronounced among voters under 50 than among older voters.

• Today, 57% of voters under 30 say they support Harris, while 29% support Trump and 12% prefer Kennedy. In July, 48% of these voters said they backed Biden. Trump’s support among this group is essentially unchanged. And 12% now back Kennedy, down from 22% in July.
• Voters ages 30 to 49 are now about evenly split (45% Harris, 43% Trump). This is a shift from a narrow Trump lead among this group in July.
• Voters ages 50 and older continue to tilt toward Trump (50% Trump vs. 44% Harris).

With Harris now at the top of the Democratic ticket, the race has become tighter.

Much of this is the result of shifting preferences among registered voters who, in July, said they favored Kennedy over Trump or Biden.

Among the same group of voters surveyed in July and early August, 97% of those who backed Biden a few weeks ago say they support or lean toward Harris today. Similarly, Trump holds on to 95% of those who supported him a few weeks ago.

But there has been far more movement among voters who previously expressed support for Kennedy. While Kennedy holds on to 39% of those who backed him in July, the majority of these supporters now prefer one of the two major party candidates: By about two-to-one, those voters are more likely to have moved to Harris (39%) than Trump (20%). This pattern is evident across most voting subgroups.

In July, Trump’s voters were far more likely than Biden’s voters to characterize their support for their candidate as “strong” (63% vs. 43%). But that gap is no longer present in the Harris-Trump matchup.

Today, 62% of Harris voters say they strongly support her, while about a third (32%) say they moderately support her. Trump’s voters are just about as likely to say they strongly back him today as they were in July (64% today, 63% then).

Kennedy’s voters make up a smaller share of voters today than a month ago – and just 18% of his voters say they strongly support him, similar to the 15% who said the same in July.

Across demographic groups, strong support for Harris is higher than it was for Biden

Among women voters who supported Biden in July, 45% said they did so strongly. That has grown to 65% today among women voters who support Harris.

Increased intensity of support is similar among men voters who back the Democratic candidate: In July, 42% of men voters who supported Biden said they did so strongly. This has since grown to 59% of Harris’ voters who are men.

Across racial and ethnic groups, Harris’ supporters are more likely than Biden’s were to say they back their candidates strongly.

Among White voters, 43% who supported Biden in July did so strongly. Today, Harris’ strong support among White voters sits at 64%.

A near identical share of Harris’ Black supporters (65%) characterize their support for her as strong today. This is up from the 52% of Biden’s Black supporters who strongly backed him in July. Among Harris’ Hispanic supporters, 56% support her strongly, while 45% of Asian Harris voters feel the same. Strong support for Harris among these voters is also higher than it was for Biden in July.

Across all age groups, Harris’ strength of support is higher than Biden’s was. But the shift from Biden is less pronounced among older Democratic supporters than among younger groups.

Still, older Harris voters are more likely than younger Harris voters to describe their support as strong. For instance, 51% of Harris’ voters under 50 say they strongly support her, while 71% of Harris supporters ages 50 and older characterize their support as strong.

Today, about seven-in-ten of both Trump supporters (72%) and Harris supporters (70%) say they are extremely motivated to vote.

Motivation to vote is higher in both the Democratic and Republican coalitions than it was in July .

These shifts have occurred across groups but are more pronounced among younger voters.

Today, half of voters under 30 say they are extremely motivated to vote, up 16 points since July. Motivation is up 11 points among voters ages 30 to 49 and 50 to 64, and up 6 points among those ages 65 and older.

Among the youngest voters, the increased motivation to vote is nearly all driven by shifts among Democratic supporters.

• In July, 38% of 18- to 29-year-old Trump voters said they were extremely motivated to vote. Today, a similar share of his voters (42%) report that level of motivation.
• But 18- to 29-year-old Harris supporters are far more likely to say they are extremely motivated to vote than Biden’s supporters in this age group were about a month ago. Today, 61% of Harris’ voters under 30 say this. In July, 42% of voters under 30 who supported Biden said they were extremely motivated to vote.

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Trump's claims of a migrant crime wave are not supported by national data

WASHINGTON — When Donald Trump speaks at the southern border in Texas on Thursday, you can expect to hear him talk about “migrant crime,” a category he has coined and defined as a terrifying binge of criminal activity committed by undocumented immigrants spreading across the country.

“You know, in New York, what’s happening with crime is it’s through the roof, and it’s called ‘migrant,’” the former president said at a rally in Michigan earlier this month. “They beat up police officers. You’ve seen that they go in, they stab people, hurt people, shoot people. It’s a whole new form, and they have gangs now that are making our gangs look like small potatoes.”

Trump has undoubtedly tapped into the rising anger over crimes allegedly committed by undocumented migrants that have gained national attention — most recently, the killing of college student Laken Riley in Georgia last week, after which an undocumented migrant from Venezuela was arrested and charged with her murder, and the much-reported fight between New York police officers and a group of migrant teens.

According to a recent Pew  poll , 57% of Americans said that a large number of migrants seeking to enter the country leads to more crime. Republicans (85%) overwhelmingly say the migrant surge leads to increased crime in the U.S. A far smaller share of Democrats (31%) say the same. The poll found that 63% of Democrats say it does not have much of an impact.

But despite the former president’s campaign rhetoric, expert analysis and available data from major-city police departments show that despite several horrifying high-profile incidents, there is no evidence of a migrant-driven crime wave in the United States.

That won’t change the way Trump talks about immigrants in his bid to return to the White House, as he argues that President Joe Biden’s immigration policies are making Americans less safe. Trump says voters should hold Biden personally responsible for every crime committed by an undocumented immigrant.

An NBC News review of available 2024 crime data from the cities targeted by Texas’ “Operation Lone Star,” which buses or flies migrants from the border to major cities in the interior — shows overall crime levels dropping in those cities that have received the most migrants.

Overall crime is down year over year in  Philadelphia ,  Chicago , Denver ,  New York  and Los Angeles. Crime has risen in  Washington, D.C ., but local officials do not attribute the spike to migrants.

“This is a public perception problem. It’s always based upon these kinds of flashpoint events where an immigrant commits a crime,” explains Graham Ousey, a professor at the College of William & Mary and the co-author of “Immigration and Crime: Taking Stock.” “There’s no evidence for there being any relationship between somebody’s immigrant status and their involvement in crime.”

Ousey notes the emotional toll these incidents have taken and how they can inform public perception, saying, “They can be really egregious acts of criminality that really draw lots of attention that involve somebody who happens to be an immigrant. And if you have leaders, political leaders who are really pushing that narrative, I think that would have the tendency to sort of push up the myth.”

“At least a couple of recent studies show that undocumented immigrants are also not more likely to be involved in crime,” Ousey says — in part because of caution about their immigration status. “The individual-level studies actually show that they’re less involved than native-born citizens or second-generation immigrants.”

Another misconception often cited by critics is that crime is more prevalent in “sanctuary cities.” But a Department of Justice report found that “there was no evidence that the percentage of unauthorized or authorized immigrant population at the city level impacted shifts in the homicide rates and no evidence that immigration is connected to robbery at the city level.”

Trump’s campaign claims without evidence that those statistics obscure the problem.

“Democrat cities purposefully do not document when crimes are committed by illegal immigrants, because they don’t want American citizens to know the truth about the dangerous impact Joe Biden’s open border is having on their communities,” Karoline Leavitt, Trump campaign press secretary, said in a statement. “Nevertheless, Americans know migrant crime is a serious and growing threat; and the murder, rape, or abuse of one innocent citizen at the hands of an illegal immigrant is one too many.”

Trump has been pushing the argument that immigrants bring crime since launching his first campaign in 2015, often featuring at his rallies the family members of those who were killed by undocumented immigrants who had been drinking and driving. And his arguments are not new — opponents of immigration have long tried to make the case that migrants bring crime.

National crime data, especially pertaining to undocumented immigrants, is notoriously incomplete. The national data comes in piecemeal and can only be evaluated holistically when the annual data is released.

The data is incomplete on how many crimes each year are committed by migrants, primarily because most local police don’t record immigration status when they make arrests. But the studies that have been done on this, most recently by the University of Wisconsin-Madison, show that in Texas, where police do record immigration status, migrants commit fewer crimes per capita.

In December 2020, researchers studying Texas crime statistics found that “contrary to public perception, we observe considerably lower felony arrest rates among undocumented immigrants compared to legal immigrants and native-born U.S. citizens and find no evidence that undocumented criminality has increased in recent years.”

Olympia Sonnier is a field producer for NBC News. 

Garrett Haake is NBC News' senior Capitol Hill correspondent. He also covers the Trump campaign.

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• Volume 33, Issue 9
• Patient safety in remote primary care encounters: multimethod qualitative study combining Safety I and Safety II analysis
• Article Text
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• Rebecca Payne 1 ,
• Aileen Clarke 1 ,
• Nadia Swann 1 ,
• Jackie van Dael 1 ,
• Natassia Brenman 1 ,
• Rebecca Rosen 2 ,
• Adam Mackridge 3 ,
• Lucy Moore 1 ,
• Asli Kalin 1 ,
• Emma Ladds 1 ,
• Nina Hemmings 2 ,
• Sarah Rybczynska-Bunt 4 ,
• Stuart Faulkner 1 ,
• Isabel Hanson 1 ,
• Sophie Spitters 5 ,
• http://orcid.org/0000-0002-7758-8493 Sietse Wieringa 1 , 6 ,
• Francesca H Dakin 1 ,
• Sara E Shaw 1 ,
• Joseph Wherton 1 ,
• Richard Byng 4 ,
• Laiba Husain 1 ,
• http://orcid.org/0000-0003-2369-8088 Trisha Greenhalgh 1
• 1 Nuffield Department of Primary Care Health Sciences , University of Oxford , Oxford , UK
• 2 Nuffield Trust , London , UK
• 3 Betsi Cadwaladr University Health Board , Bangor , UK
• 4 Peninsula Schools of Medicine and Dentistry , University of Plymouth , Plymouth , UK
• 5 Wolfson Institute of Population Health , Queen Mary University of London , London , UK
• 6 Sustainable Health Unit , University of Oslo , Oslo , Norway
• Correspondence to Professor Trisha Greenhalgh; trish.greenhalgh{at}phc.ox.ac.uk

Background Triage and clinical consultations increasingly occur remotely. We aimed to learn why safety incidents occur in remote encounters and how to prevent them.

Setting and sample UK primary care. 95 safety incidents (complaints, settled indemnity claims and reports) involving remote interactions. Separately, 12 general practices followed 2021–2023.

Methods Multimethod qualitative study. We explored causes of real safety incidents retrospectively (‘Safety I’ analysis). In a prospective longitudinal study, we used interviews and ethnographic observation to produce individual, organisational and system-level explanations for why safety and near-miss incidents (rarely) occurred and why they did not occur more often (‘Safety II’ analysis). Data were analysed thematically. An interpretive synthesis of why safety incidents occur, and why they do not occur more often, was refined following member checking with safety experts and lived experience experts.

Results Safety incidents were characterised by inappropriate modality, poor rapport building, inadequate information gathering, limited clinical assessment, inappropriate pathway (eg, wrong algorithm) and inadequate attention to social circumstances. These resulted in missed, inaccurate or delayed diagnoses, underestimation of severity or urgency, delayed referral, incorrect or delayed treatment, poor safety netting and inadequate follow-up. Patients with complex pre-existing conditions, cardiac or abdominal emergencies, vague or generalised symptoms, safeguarding issues, failure to respond to previous treatment or difficulty communicating seemed especially vulnerable. General practices were facing resource constraints, understaffing and high demand. Triage and care pathways were complex, hard to navigate and involved multiple staff. In this context, patient safety often depended on individual staff taking initiative, speaking up or personalising solutions.

Conclusion While safety incidents are extremely rare in remote primary care, deaths and serious harms have resulted. We offer suggestions for patient, staff and system-level mitigations.

• Primary care
• Diagnostic errors
• Safety culture
• Qualitative research
• Prehospital care

Data availability statement

Data are available upon reasonable request. Details of real safety incidents are not available for patient confidentiality reasons. Requests for data on other aspects of the study from other researchers will be considered.

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See:  https://creativecommons.org/licenses/by/4.0/ .

https://doi.org/10.1136/bmjqs-2023-016674

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WHAT IS ALREADY KNOWN ON THIS TOPIC

Safety incidents are extremely rare in primary care but they do happen. Concerns have been raised about the safety of remote triage and remote consultations.

WHAT THIS STUDY ADDS

Rare safety incidents (involving death or serious harm) in remote encounters can be traced back to various clinical, communicative, technical and logistical causes. Telephone and video encounters in general practice are occurring in a high-risk (extremely busy and sometimes understaffed) context in which remote workflows may not be optimised. Front-line staff use creativity and judgement to help make care safer.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

As remote modalities become mainstreamed in primary care, staff should be trained in the upstream causes of safety incidents and how they can be mitigated. The subtle and creative ways in which front-line staff already contribute to safety culture should be recognised and supported.

Introduction

In early 2020, remote triage and remote consultations (together, ‘remote encounters’), in which the patient is in a different physical location from the clinician or support staff member, were rapidly expanded as a safety measure in many countries because they eliminated the risk of transmitting COVID-19. 1–4 But by mid-2021, remote encounters had begun to be depicted as potentially unsafe because they had come to be associated with stories of patient harm, including avoidable deaths and missed cancers. 5–8

Providing triage and clinical care remotely is sometimes depicted as a partial solution to the system pressures facing primary healthcare in many countries, 9–11 including rising levels of need or demand, the ongoing impact of the COVID-19 pandemic and workforce challenges (especially short-term or longer-term understaffing). In this context, remote encounters may be an important component of a mixed-modality health service when used appropriately alongside in-person contacts. 12 13 But this begs the question of what ‘appropriate’ and ‘safe’ use of remote modalities in a primary care context is. Safety incidents (defined as ‘any unintended or unexpected incident which could have, or did, lead to harm for one or more patients receiving healthcare 14 ’) are extremely rare in primary healthcare consultations generally, 15 16 in-hours general practice telephone triage 17 and out-of-hours primary care. 18 But the recent widespread expansion of remote triage and remote consulting in primary care means that a wider range of patients and conditions are managed remotely, making it imperative to re-examine where the risks lie.

Theoretical approaches to safety in healthcare fall broadly into two traditions. 19 ‘Safety I’ studies focus on what went wrong. Incident reports are analysed to identify ‘root causes’ and ‘safety gaps’, and recommendations are made to reduce the chance that further similar incidents will happen in the future. 20 Such studies, undertaken in isolation, tend to lead to a tightening of rules, procedures and protocols. ‘Safety II’ studies focus on why, most of the time, things do not go wrong. Ethnography and other qualitative methods are employed to study how humans respond creatively to unique and unforeseen situations, thereby preventing safety incidents most of the time. 19 Such studies tend to show that actions which achieve safety are highly context specific, may entail judiciously breaking the rules and require human qualities such as courage, initiative and adaptability. 21 Few previous studies have combined both approaches.

In this study, we aimed to use Safety I methods to learn why safety incidents occur (although rarely) in remote primary care encounters and also apply Safety II methods to examine the kinds of creative actions taken by front-line staff that contribute to a safety culture and thereby prevent such incidents.

Study design and origins

Multimethod qualitative study across UK, including incident analysis, longitudinal ethnography and national stakeholder interviews.

The idea for this safety study began during a longitudinal ethnographic study of 12 general practices across England, Scotland and Wales as they introduced (and, in some cases, subsequently withdrew) various remote and digital modalities. Practices were selected for maximum diversity in geographical location, population served and digital maturity and followed from mid-2021 to end 2023 using staff and patient interviews and in-person ethnographic visits. The study protocol, 22 baseline findings 23 and a training needs analysis 24 have been published. To provide context for our ethnography, we interviewed a sample of national stakeholders in remote and digital primary care, including out-of-hours providers running telephone-led services, and held four online multistakeholder workshops, one of which was on the theme of safety, for policymakers, clinicians, patients and other parties. Early data from this detailed qualitative work revealed staff and patient concerns about the safety of remote encounters but no actual examples of harm.

To explore the safety theme further, we decided to take a dual approach. First, following Safety I methodology for the study of rare harms, 20 we set out to identify and analyse a sample of safety incidents involving remote encounters. These were sourced from arm’s-length bodies (NHS England, NHS Resolution, Healthcare Safety Investigation Branch) and providers of healthcare at scale (health boards, integrated care systems and telephone advice services), since our own small sample had not identified any of these rare occurrences. Second, we extended our longitudinal ethnographic design to more explicitly incorporate Safety II methodology, 19 allowing us to examine safety culture and safety practices in our 12 participating general practices, especially the adaptive work done by staff to avert potential safety incidents.

Data sources and management

Table 1 summarises the data sources.

• View inline

Summary of data sources

The Safety I dataset (rows 2-5) consisted of 95 specific incident reports, including complaints submitted to the main arm’s-length NHS body in England, NHS England, between 2020 and 2023 (n=69), closed indemnity claims that had been submitted to a national indemnity body, NHS Resolution, between 2015 and 2023 (n=16), reports from an urgent care telephone service in Wales (NHS 111 Wales) between 2020 and 2023 (n=6) and a report on an investigation of telephone advice during the COVID-19 crisis between 2020 and 2022 7 (n=4). These 95 incidents were organised using Microsoft Excel spreadsheets.

The Safety II dataset (rows 6-10) consisted of extracts from fieldnotes, workshop transcripts and interviews collected over 2 years, stored and coded on NVivo qualitative software. These were identified by searching for text words and codes (e.g. ‘risk’, ‘safety’, ‘incident’) and by asking researchers-in-residence, who were closely familiar with practices, to highlight safety incidents involving harm and examples of safety-conscious work practices. This dataset included over 100 formal interviews and numerous on-the-job interviews with practice staff, plus interviews with a sample of 10 GP (general practitioner) trainers and 10 GP trainees (penultimate row of table 1 ) and with six clinical safety experts identified through purposive sampling from government, arm’s-length bodies and health boards (bottom row of table 1 ).

Data analysis

We analysed incident reports, interview data and ethnographic fieldnotes using thematic analysis as described by Braun and Clarke. 25 These authors define a theme as an important, broad pattern in a set of qualitative data, which can (where necessary) be further refined using coding.

Themes in the incident dataset were identified by five steps. First, two researchers (both medically qualified) read each source repeatedly to gain familiarity. Second, those researchers worked independently using Braun and Clarke’s criterion (‘whether it captures something important in relation to the overall research question’—p 82 25 ) to identify themes. Third, they discussed their initial interpretations with each other and resolved differences through discussion. Fourth, they extracted evidence from the data sources to illustrate and refine each theme. Finally, they presented their list of themes along with illustrative examples to the wider team. Cases used to illustrate themes were systematically fictionalised by changing age, randomly allocating gender and altering clinical details. 26 For example, an acute appendicitis could be changed to acute diverticulitis if the issue was a missed acute abdomen.

These safety themes were then used to sensitise us to seek relevant (confirming and disconfirming) material from our ethnographic and interview datasets. For example, the theme ‘poor communication’ (and subthemes such as ‘failure to seek further clarification’ within this) promoted us to look for examples in our stakeholder interviews of poor communication offered as a cause of safety incidents and examples in our ethnographic notes of good communication (including someone seeking clarification). We used these wider data to add nuance to the initial list of themes.

As a final sense-checking step, the draft findings from this study were shown to each of the six safety experts in our sample and refined in the light of their comments (in some cases, for example, they considered the case to have been overfictionalised, thereby losing key clinical messages; they also gave additional examples to illustrate some of the themes we had identified, which underlined the importance of those themes).

Overview of dataset

The dataset ( table 1 ) consisted of 95 incident reports (see fictionalised examples in box 1 ), plus approximately 400 pages of extracts from interviews, ethnographic fieldnotes and workshop discussions, including situated safety practices (see examples in box 2 ), plus strategic insights relating to policy, organisation and planning of services. Notably, almost all incidents related to telephone calls.

Examples of safety incidents involving death or serious harm in remote encounters

All these cases have been systematically fictionalised as explained in the text.

Case 1 (death)

A woman in her 70s experiencing sudden breathlessness called her GP (general practitioner) surgery. The receptionist answered the phone and informed her that she would place her on the doctor’s list for an emergency call-back. The receptionist was distracted by a patient in the waiting room and did not do so. The patient deteriorated and died at home that afternoon.—NHS Resolution case, pre-2020

Case 2 (death)

An elderly woman contacted her GP after a telephone contact with the out-of-hours service, where constipation had been diagnosed. The GP prescribed laxatives without seeing the patient. The patient self-presented to the emergency department (ED) the following day in obstruction secondary to an incarcerated hernia and died in the operating theatre.—NHS Resolution case, pre-2020

Case 3 (risk to vulnerable patients)

A daughter complained that her elderly father was unable to access his GP surgery as he could not navigate the online triage system. When he phoned the surgery directly, he was directed back to the online system and told to get a relative to complete the form for him.—Complaint to NHS England, 2021

Case 4 (harm)

A woman in her first pregnancy at 28 weeks’ gestation experiencing urinary incontinence called NHS 111. She was taken down by a ‘urinary problems’ algorithm. Both the call handler and the subsequent clinician failed to recognise that she had experienced premature rupture of membranes. She later presented to the maternity department in active labour, and the opportunity to give early steroids to the premature infant was missed.—NHS Resolution case, pre-2020

Case 5 (death)

A doctor called about a 16-year-old girl with lethargy, shaking, fever and poor oral intake who had been unwell for 5 days. The doctor spoke to her older sister and advised that the child had likely glandular fever and should rest. When the parents arrived home, they called an ambulance but the child died of sepsis in the ED.—NHS Resolution case, pre-2020

Case 6 (death)

A 40-year-old woman, 6 weeks after caesarean section, contacted her GP due to shortness of breath, increased heart rate and dry cough. She was advised to get a COVID test and to dial 111 if she developed a productive cough, fever or pain. The following day she collapsed and died at home. The postmortem revealed a large pulmonary embolus. On reviewing the case, her GP surgery felt that had she been seen face to face, her oxygen saturations would have been measured and may have led to suspicion of the diagnosis.—NHS Resolution case, 2020

Case 7 (death)

A son complained that his father with diabetes and chronic kidney disease did not receive any in-person appointments over a period of 1 year. His father went on to die following a leg amputation arising from a complication of his diabetes.—Complaint to NHS England, 2021

Case 8 (death)

A 73-year-old diabetic woman with throat pain and fatigue called the surgery. She was diagnosed with a viral illness and given self-care advice. Over the next few days, she developed worsening breathlessness and was advised to do a COVID test and was given a pulse oximeter. She was found dead at home 4 days later. Postmortem found a blocked coronary artery and a large amount of pulmonary oedema. The cause of death was myocardial infarction and heart failure.—NHS Resolution case, pre-2020

Case 9 (harm)

A patient with a history of successfully treated cervical cancer developed vaginal bleeding. A diagnosis of fibroids was made and the patient received routine care by telephone over the next few months until a scan revealed a local recurrence of the original cancer.—Complaint to NHS England, 2020

Case 10 (death)

A 65-year-old female smoker with chronic cough and breathlessness presented to her GP. She was diagnosed with chronic obstructive pulmonary disease (COPD) and monitored via telephone. She did not respond to inhalers or antibiotics but continued to receive telephone monitoring without further investigation. Her symptoms continued to worsen and she called an ambulance. In the ED, she was diagnosed with heart failure and died soon after.—Complaint to NHS England, 2021

Case 11 (harm)

A 30-year-old woman presented with intermittent episodes of severe dysuria over a period of 2 years. She was given repeated courses of antibiotics but no urine was sent for culture and she was not examined. After 4 months of symptoms, she saw a private GP and was diagnosed with genital herpes.—Complaint to NHS England, 2021

Case 12 (harm)

There were repeated telephone consultations about a baby whose parents were concerned that the child was having a funny colour when feeding or crying. The 6-week check was done by telephone and at no stage was the child seen in person. Photos were sent in, but the child’s dark skin colour meant that cyanosis was not easily apparent to the reviewing clinician. The child was subsequently admitted by emergency ambulance where a significant congenital cardiac abnormality was found.—Complaint to NHS England, 2020 1

Case 13 (harm)

A 35-year-old woman in her third trimester of pregnancy had a telephone appointment with her GP about a breast lump. She was informed that this was likely due to antenatal breast changes and was not offered an in-person appointment. She attended after delivery and was referred to a breast clinic where a cancer was diagnosed.—Complaint to NHS England, 2020

Case 14 (harm)

A 63-year-old woman with a variety of physical symptoms including diarrhoea, hip girdle pain, palpitations, light-headedness and insomnia called her surgery on multiple occasions. She was told her symptoms were likely due to anxiety, but was diagnosed with stage 4 ovarian cancer and died soon after.—Complaint to NHS England, 2021

Case 15 (death)

A man with COPD with worsening shortness of breath called his GP surgery. The staff asked him if it was an emergency, and when the patient said no, scheduled him for 2 weeks later. The patient died before the appointment.—Complaint to NHS England, 2021

Examples of safety practices

Case 16 (safety incident averted by switching to video call for a sick child)

‘I’ve remembered one father that called up. Really didn’t seem to be too concerned. And was very much under-playing it and then when I did a video call, you know this child… had intercostal recession… looked really, really poorly. And it was quite scary actually that, you know, you’d had the conversation and if you’d just listened to what Dad was saying, actually, you probably wouldn’t be concerned.’—GP (general practitioner) interview 2022

Case 17 (‘red flag’ spotted by support staff member)

A receptionist was processing routine ‘administrative’ encounters sent in by patients using AccuRx (text messaging software). She became concerned about a sick note renewal request from a patient with a mental health condition. The free text included a reference to feeling suicidal, so the receptionist moved the request to the ‘red’ (urgent call-back) list. In interviews with staff, it became apparent that there had recently been heated discussion in the practice about whether support staff were adding ‘too many’ patients to the red list. After discussing cases, the doctors concluded that it should be them, not the support staff, who should absorb the risk in uncertain cases. The receptionist said that they had been told: ‘if in doubt, put it down as urgent and then the duty doctor can make a decision.’—Ethnographic fieldnotes from general practice 2023

Case 18 (‘check-in’ phone call added on busy day)

A duty doctor was working through a very busy Monday morning ‘urgent’ list. One patient had acute abdominal pain, which would normally have triggered an in-person appointment, but there were no slots and hard decisions were being made. This patient had had the pain already for a week, so the doctor judged that the general rule of in-person examination could probably be over-ridden. But instead of simply allocating to a call-back, the doctor asked a support staff member to phone the patient, ask ‘are you OK to wait until tomorrow?’ and offer basic safety-netting advice.—Ethnographic fieldnotes from general practice 2023

Case 19 (receptionist advocating on behalf of ‘angry’ walk-in patient)

A young Afghan man with limited English walked into a GP surgery on a very busy day, ignoring the prevailing policy of ‘total triage’ (make contact by phone or online in the first instance). He indicated that he wanted a same-day in-person appointment for a problem he perceived as urgent. A heated exchange occurred with the first receptionist, and the patient accused her of ‘racism’. A second receptionist of non-white ethnicity herself noted the man’s distress and suspected that there may indeed be an urgent problem. She asked the first receptionist to leave the scene, saying she wanted to ‘have a chat’ with the patient (‘the colour of my skin probably calmed him down more than anything’). Through talking to the patient and looking through his record, she ascertained that he had an acute infection that likely needed prompt attention. She tried to ‘bend the rules’ and persuade the duty doctor to see the patient, conveying the clinical information but deliberately omitting the altercation. But the first receptionist complained to the doctor (‘he called us racists’) and the doctor decided that the patient would not therefore be offered a same-day appointment. The second receptionist challenged the doctor (‘that’s not a reason to block him from getting care’). At this point, the patient cried and the second receptionist also became upset (‘this must be serious, you know’). On this occasion, despite her advocacy the patient was not given an immediate appointment.—Ethnographic fieldnotes from general practice 2022

Case 20 (long-term condition nurse visits ‘unengaged’ patients at home)

An advanced nurse practitioner talks of two older patients, each with a long-term condition, who are ‘unengaged’ and lacking a telephone. In this practice, all long-term condition reviews are routinely done by phone. She reflects that some people ‘choose not to have avenues of communication’ (ie, are deliberately not contactable), and that there may be reasons for this (‘maybe health anxiety or just old’). She has, on occasion, ‘turned up’ unannounced at the patient’s home and asked to come in and do the review, including bloods and other tests. She reflects that while most patients engage well with the service, ‘half my job is these patients who don’t engage very well.’—Ethnographic fieldnotes from digitally advanced general practice 2022

Case 21 (doctor over-riding patient’s request for telephone prescribing)

A GP trainee described a case of a 53-year-old first-generation immigrant from Pakistan, a known smoker with hypertension and diabetes. He had booked a telephone call for vomiting and sinus pain. There was no interpreter available but the man spoke some English. He said he had awoken in the night with pain in his sinuses and vomiting. All he wanted was painkillers for his sinuses. The story did not quite make sense, and the man ‘sounded unwell’. The GP told him he needed to come in and be examined. The patient initially resisted but was persuaded to come in. When the GP went to call him in, the man was visibly unwell and lying down in the waiting room. When seen in person, he admitted to shoulder pain. The GP sent him to accident and emergency (A&E) where a myocardial infarction was diagnosed.—Trainee interview 2023

Below, we describe the main themes that were evident in the safety incidents: a challenging organisational and system context, poor communication compounded by remote modalities, limited clinical information, patient and carer burden and inadequate training. Many safety incidents illustrated multiple themes—for example, poor communication and failures of clinical assessment or judgement and patient complexity and system pressures. In the detailed findings below, we illustrate why safety incidents occasionally occur and why they are usually avoided.

The context for remote consultations: system and operational challenges

Introduction of remote triage and expansion of remote consultations in UK primary care occurred at a time of unprecedented system stress (an understaffed and chronically under-resourced primary care sector, attempting to cope with a pandemic). 23 Many organisations had insufficient telephone lines or call handlers, so patients struggled to access services (eg, half of all calls to the emergency COVID-19 telephone service in March 2020 were never answered 7 ). Most remote consultations were by telephone. 27

Our safety incident dataset included examples of technically complex access routes which patients found difficult or impossible to navigate (case 3 in box 1 ) and which required non-clinical staff to make clinical or clinically related judgements (cases 4 and 15). Our ethnographic dataset contained examples of inflexible application of triage rules (eg, no face-to-face consultation unless the patient had already had a telephone call), though in other practices these rules could be over-ridden by staff using their judgement or asking colleagues. Some practices had a high rate of failed telephone call-backs (patient unobtainable).

High demand, staff shortages and high turnover of clinical and support staff made the context for remote encounters inherently risky. Several incidents were linked to a busy staff member becoming distracted (case 1). Telephone consultations, which tend to be shorter, were sometimes used in the hope of improving efficiency. Some safety incidents suggested perfunctory and transactional telephone consultations, with flawed decisions made on the basis of incomplete information (eg, case 2).

Many practices had shifted—at least to some extent—from a demand-driven system (in which every request for an appointment was met) to a capacity-driven one (in which, if a set capacity was exceeded, patients were advised to seek care elsewhere), though the latter was often used flexibly rather than rigidly with an expectation that some patients would be ‘squeezed in’. In some practices, capacity limits had been introduced to respond to escalation of demand linked to overuse of triage templates (eg, to inquire about minor symptoms).

As a result of task redistribution and new staff roles, a single episode of care for one problem often involved multiple encounters or tasks distributed among clinical and non-clinical staff (often in different locations and sometimes also across in-hours and out-of-hours providers). Capacity constraints in onward services placed pressure on primary care to manage risk in the community, leading in some cases to failure to escalate care appropriately (case 6).

Some safety incidents were linked to organisational routines that had not adapted sufficiently to remote—for example, a prescription might be issued but (for various reasons) it could not be transmitted electronically to the pharmacy. Certain urgent referrals were delayed if the consultation occurred remotely (a referral for suspected colon cancer, for example, would not be accepted without a faecal immunochemical test).

Training, supervising and inducting staff was more difficult when many were working remotely. If teams saw each other less frequently, relationship-building encounters and ‘corridor’ conversations were reduced, with knock-on impacts for individual and team learning and patient care. Those supervising trainees or allied professionals reported loss of non-verbal cues (eg, more difficult to assess how confident or distressed the trainee was).

Clinical and support staff regularly used initiative and situated judgement to compensate for an overall lack of system resilience ( box 1 ). Many practices had introduced additional safety measures such as lists of patients who, while not obviously urgent, needed timely review by a clinician. Case 17 illustrates how a rule of thumb ‘if in doubt, put it down as urgent’ was introduced and then applied to avert a potentially serious mental health outcome. Case 18 illustrates how, in the context of insufficient in-person slots to accommodate all high-risk cases, a unique safety-netting measure was customised for a patient.

Poor communication is compounded by remote modalities

Because sense data (eg, sight, touch, smell) are missing, 28 remote consultations rely heavily on the history. Many safety incidents were characterised by insufficient or inaccurate information for various reasons. Sometimes (cases 2, 5, 6, 8, 9, 10 and 11), the telephone consultation was too short to do justice to the problem; the clinician asked few or no questions to build rapport, obtain a full history, probe the patient’s answers for additional detail, confirm or exclude associated symptoms and inquire about comorbidities and medication. Video provided some visual cues but these were often limited to head and shoulders, and photographs were sometimes of poor quality.

Cases 2, 4, 5 and 9 illustrate the dangers of relying on information provided by a third party (another staff member or a relative). A key omission (eg, in case 5) was failing to ask why the patient was unable to come to the phone or answer questions directly.

Some remote triage conversations were conducted using an inappropriate algorithm. In case 4, for example, the call handler accepted a pregnant patient’s assumption that leaking fluid was urine when the problem was actually ruptured membranes. The wrong pathway was selected; vital questions remained unasked; and a skewed history was passed to (and accepted by) the clinician. In case 8, the patient’s complaint of ‘throat’ pain was taken literally and led to ‘viral illness’ advice, overlooking a myocardial infarction.

The cases in box 2 illustrate how staff compensated for communication challenges. In case 16, a GP plays a hunch that a father’s account of his child’s asthma may be inaccurate and converts a phone encounter to video, revealing the child’s respiratory distress. In case 19 (an in-person encounter but relevant because the altercation occurs partly because remote triage is the default modality), one receptionist correctly surmises that the patient’s angry demeanour may indicate urgency and uses her initiative and interpersonal skills to obtain additional clinical information. In case 20, a long-term condition nurse develops a labour-intensive workaround to overcome her elderly patients’ ‘lack of engagement’. More generally, we observed numerous examples of staff using both formal tools (eg, see ‘red list’ in case 17) and informal measures (eg, corridor chats) to pass on what they believed to be crucial information.

Remote consulting can provide limited clinical information

Cases 2 and 4–14 all describe serious conditions including congenital cyanotic heart disease, pulmonary oedema, sepsis, cancer and diabetic foot which would likely have been readily diagnosed with an in-person examination. While patients often uploaded still images of skin lesions, these were not always of sufficient quality to make a confident diagnosis.

Several safety incidents involved clinicians assuming that a diagnosis made on a remote consultation was definitive rather than provisional. Especially when subsequent consultations were remote, such errors could become ingrained, leading to diagnostic overshadowing and missed or delayed diagnosis (cases 2, 8, 9, 10, 11 and 13). Patients with pre-existing conditions (especially if multiple or progressive), the very young and the elderly were particularly difficult to assess by telephone (cases 1, 2, 8, 10, 12 and 16). Clinical conditions difficult to assess remotely included possible cardiac pain (case 8), acute abdomen (case 2), breathing difficulties (cases 1, 6 and 10), vague and generalised symptoms (cases 5 and 14) and symptoms which progressed despite treatment (cases 9, 10 and 11). All these categories came up repeatedly in interviews and workshops as clinically risky.

Subtle aspects of the consultation which may have contributed to safety incidents in a telephone consultation included the inability to fully appraise the patient’s overall health and well-being (including indicators relevant to mental health such as affect, eye contact, personal hygiene and evidence of self-harm), general demeanour, level of agitation and concern, and clues such as walking speed and gait (cases 2, 5, 6, 7, 8, 10, 12 and 14). Our interviews included stories of missed cases of new-onset frailty and dementia in elderly patients assessed by telephone.

In most practices we studied, most long-term condition management was undertaken by telephone. This may be appropriate (and indeed welcome) when the patient is well and confident and a physical examination is not needed. But diabetes reviews, for example, require foot examination. Case 7 describes the deterioration and death of a patient with diabetes whose routine check-ups had been entirely by telephone. We also heard stories of delayed diagnosis of new diabetes in children when an initial telephone assessment failed to pick up lethargy, weight loss and smell of ketones, and point-of-care tests of blood or urine were not possible.

Nurses observed that remote consultations limit opportunities for demonstrating or checking the patient’s technique in using a device for monitoring or treating their condition such as an inhaler, oximeter or blood pressure machine.

Safety netting was inadequate in many remote safety incidents, even when provided by a clinician (cases 2, 5, 6, 8, 10, 12 and 13) but especially when conveyed by a non-clinician (case 15). Expert interviewees identified that making life-changing diagnoses remotely and starting patients on long-term medication without an in-person appointment was also risky.

Our ethnographic data showed that various measures were used to compensate for limited clinical information, including converting a phone consultation to video (case 16), asking the patient if they felt they could wait until an in-person slot was available (case 18), visiting the patient at home (case 20) and enacting a ‘if the history doesn’t make sense, bring the patient in for an in-person assessment’ rule of thumb (case 21). Out-of-hours providers added examples of rules of thumb that their services had developed over years of providing remote services, including ‘see a child face-to-face if the parent rings back’, ‘be cautious about third-party histories’, ‘visit a palliative care patient before starting a syringe driver’ and ‘do not assess abdominal pain remotely’.

Remote modalities place additional burdens on patients and carers

Given the greater importance of the history in remote consultations, patients who lacked the ability to communicate and respond in line with clinicians’ expectations were at a significant disadvantage. Several safety incidents were linked to patients’ limited fluency in the language and culture of the clinician or to specific vulnerabilities such as learning disability, cognitive impairment, hearing impairment or neurodiversity. Those with complex medical histories and comorbidities, and those with inadequate technical set-up and skills (case 3), faced additional challenges.

In many practices, in-person appointments were strictly limited according to more or less rigid triage criteria. Some patients were unable to answer the question ‘is this an emergency?’ correctly, leading to their condition being deprioritised (case 15). Some had learnt to ‘game’ the triage system (eg, online templates 29 ) by adapting their story to obtain the in-person appointment they felt they needed. This could create distrust and lead to inaccurate information on the patient record.

Our ethnographic dataset contained many examples of clinical and support staff using initiative to compensate for vulnerable patients’ inability or unwillingness to take on the additional burden of remote modalities (cases 19 and 20 in Box 2 30 31 ).

Training for remote encounters is often inadequate

Safety incidents highlighted various training needs for support staff members (eg, customer care skills, risks of making clinical judgements) and clinicians (eg, limitations of different modalities, risks of diagnostic overshadowing). Whereas out-of-hours providers gave thorough training to novice GPs (covering such things as attentiveness, rapport building, history taking, probing, attending to contextual cues and safety netting) in telephone consultations, 32–34 many in-hours clinicians had never been formally taught to consult by telephone. Case 17 illustrates how on-the-job training based on acknowledgement of contextual pressures and judicious use of rules of thumb may be very effective in averting safety incidents.

Statement of principal findings

An important overall finding from this study is that examples of deaths or serious harms associated with remote encounters in primary care were extremely rare, amounting to fewer than 100 despite an extensive search going back several years.

Analysis of these 95 safety incidents, drawn from multiple complementary sources, along with rich qualitative data from ethnography, interviews and workshops has clarified where the key risks lie in remote primary care. Remote triage and consultations expanded rapidly in the context of the COVID-19 crisis; they were occurring in the context of resource constraints, understaffing and high demand. Triage and care pathways were complex, multilayered and hard to navigate; some involved distributed work among multiple clinical and non-clinical staff. In some cases, multiple remote encounters preceded (and delayed) a needed in-person assessment.

In this high-risk context, safety incidents involving death or serious harm were rare, but those that occurred were characterised by a combination of inappropriate choice of modality, poor rapport building, inadequate information gathering, limited clinical assessment, inappropriate clinical pathway (eg, wrong algorithm) and failure to take account of social circumstances. These led to missed, inaccurate or delayed diagnoses, underestimation of severity or urgency, delayed referral, incorrect or delayed treatment, poor safety netting and inadequate follow-up. Patients with complex or multiple pre-existing conditions, cardiac or abdominal emergencies, vague or generalised symptoms, safeguarding issues and failure to respond to previous treatment, and those who (for any reason) had difficulty communicating, seemed particularly at risk.

Strengths and limitations of the study

The main strength of this study was that it combined the largest Safety I study undertaken to date of safety incidents in remote primary care (using datasets which have not previously been tapped for research), with a large, UK-wide ethnographic Safety II analysis of general practice as well as stakeholder interviews and workshops. Limitations of the safety incident sample (see final column in table 1 ) include that it was skewed towards very rare cases of death and serious harm, with relatively few opportunities for learning that did not result in serious harm. Most sources were retrospective and may have suffered from biases in documentation and recall. We also failed to obtain examples of safeguarding incidents (which would likely turn up in social care audits). While all cases involved a remote modality (or a patient who would not or could not use one), it is impossible to definitively attribute the harm to that modality.

Comparison with existing literature

This study has affirmed previous findings that processes, workflows and training in in-hours general practice have not adapted adequately to the booking, delivery and follow-up of remote consultations. 24 35 36 Safety issues can arise, for example, from how the remote consultation interfaces with other key practice routines (eg, for making urgent referrals for possible cancer). The sheer complexity and fragmentation of much remote and digital work underscores the findings from a systematic review of the importance of relational coordination (defined as ‘a mutually reinforcing process of communicating and relating for the purpose of task integration ’ (p 3) 37 ) and psychological safety (defined as ‘people’s perceptions of the consequences of taking interpersonal risks in a particular context such as a workplace ’ (p 23) 38 ) in building organisational resilience and assuring safety.

The additional workload and complexity associated with running remote appointments alongside in-person ones is cognitively demanding for staff and requires additional skills for which not all are adequately trained. 24 39 40 We have written separately about the loss of traditional continuity of care as primary care services become digitised, 41–43 and about the unmet training needs of both clinical and support staff for managing remote and digital encounters. 24

Our findings also resonate with research showing that remote modalities can interfere with communicative tasks such as rapport building, establishing a therapeutic relationship and identifying non-verbal cues such as tearfulness 35 36 44 ; that remote consultations tend to be shorter and feature less discussion, information gathering and safety netting 45–48 ; and that clinical assessment in remote encounters may be challenging, 27 49 50 especially when physical examination is needed. 35 36 51 These factors may rarely contribute to incorrect or delayed diagnoses, underestimation of the seriousness or urgency of a case, and failure to identify a deteriorating trajectory. 35 36 52–54

Even when systems seem adequate, patients may struggle to navigate them. 23 30 31 This finding aligns with an important recent review of cognitive load theory in the context of remote and digital health services: because such services are more cognitively demanding for patients, they may widen inequities of access. 55 Some patients lack navigating and negotiating skills, access to key technologies 13 36 or confidence in using them. 30 35 The remote encounter may require the patient to have a sophisticated understanding of access and cross-referral pathways, interpret their own symptoms (including making judgements about severity and urgency), obtain and use self-monitoring technologies (such as a blood pressure machine or oximeter) and convey these data in medically meaningful ways (eg, by completing algorithmic triage forms or via a telephone conversation). 30 56 Furthermore, the remote environment may afford fewer opportunities for holistically evaluating, supporting or safeguarding the vulnerable patient, leading to widening inequities. 13 35 57 Previous work has also shown that patients with pre-existing illness, complex comorbidities or high-risk states, 58 59 language non-concordance, 13 35 inability to describe their symptoms (eg, due to autism 60 ), extremes of age 61 and those with low health or system literacy 30 are more difficult to assess remotely.

Lessons for safer care

Many of the contributory factors to safety incidents in remote encounters have been suggested previously, 35 36 and align broadly with factors that explain safety incidents more generally. 53 62 63 This new study has systematically traced how upstream factors may, very rarely, combine to contribute to avoidable human tragedies—and also how primary care teams develop local safety practices and cultures to help avoid them. Our study provides some important messages for practices and policymakers.

First, remote encounters in general practice are mostly occurring in a system designed for in-person encounters, so processes and workflows may work less well.

Second, because the remote encounter depends more on history taking and dialogue, verbal communication is even more mission critical. Working remotely under system pressures and optimising verbal communication should both be priorities for staff training.

Third, the remote environment may increase existing inequities as patients’ various vulnerabilities (eg, extremes of age, poverty, language and literacy barriers, comorbidities) make remote communication and assessment more difficult. Our study has revealed impressive efforts from staff to overcome these inequities on an individual basis; some of these workarounds may become normalised and increase efficiency, but others are labour intensive and not scalable.

A final message from this study is that clinical assessment provides less information when a physical examination (and even a basic visual overview) is not possible. Hence, the remote consultation has a higher degree of inherent uncertainty. Even when processes have been optimised (eg, using high-quality triage to allocate modality), but especially when they have not, diagnoses and assessments of severity or urgency should be treated as more provisional and revisited accordingly. We have given examples in the Results section of how local adaptation and rule breaking bring flexibility into the system and may become normalised over time, leading to the creation of locally understood ‘rules of thumb’ which increase safety.

Overall, these findings underscore the need to share learning and develop guidance about the drivers of risk, how these play out in different kinds of remote encounters and how to develop and strengthen Safety II approaches to mitigate those risks. Table 2 shows proposed mitigations at staff, process and system levels, as well as a preliminary list of suggestions for patients, which could be refined with patient input using codesign methods. 64

Reducing safety incidents in remote primary care

Unanswered questions and future research

This study has helped explain where the key risks lie in remote primary care encounters, which in our dataset were almost all by telephone. It has revealed examples of how front-line staff create and maintain a safety culture, thereby helping to prevent such incidents. We suggest four key avenues for further research. First, additional ethnographic studies in general practice might extend these findings and focus on specific subquestions (eg, how practices identify, capture and learn from near-miss incidents). Second, ethnographic studies of out-of-hours services, which are mostly telephone by default, may reveal additional elements of safety culture from which in-hours general practice could learn. Third, the rise in asynchronous e-consultations (in which patients complete an online template and receive a response by email) raises questions about the safety of this new modality which could be explored in mixed-methods studies including quantitative analysis of what kinds of conditions these consultations cover and qualitative analysis of the content and dynamics of the interaction. Finally, our findings suggest that the safety of new clinically related ‘assistant’ roles in general practice should be urgently evaluated, especially when such staff are undertaking remote assessment or remote triage.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

Ethical approval was granted by the East Midlands—Leicester South Research Ethics Committee and UK Health Research Authority (September 2021, 21/EM/0170 and subsequent amendments). Access to the NHS Resolution dataset was obtained by secondment of the RP via honorary employment contract, where she worked with staff to de-identify and fictionalise relevant cases. The Remote by Default 2 study (referenced in main text) was co-designed by patients and lay people; it includes a diverse patient panel. Oversight was provided by an independent external advisory group with a lay chair and patient representation. A person with lived experience of a healthcare safety incident (NS) is a co-author on this paper and provided input to data analysis and writing up, especially the recommendations for patients in table 2 .

Acknowledgments

We thank the participating organisations for cooperating with this study and giving permission to use fictionalised safety incidents. We thank the participants in the ethnographic study (patients, practice staff, policymakers, other informants) who gave generously of their time and members of the study advisory group.

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X @dakinfrancesca, @trishgreenhalgh

Contributors RP led the Safety I analysis with support from AC. The Safety II analysis was part of a wider ethnographic study led by TG and SS, on which all other authors undertook fieldwork and contributed data. TG and RP wrote the paper, with all other authors contributing refinements. All authors checked and approved the final manuscript. RP is guarantor.

Funding Funding was from NIHR HS&DR (grant number 132807) (Remote by Default 2 study) and NIHR School for Primary Care Research (grant number 594) (ModCons study), plus an NIHR In-Practice Fellowship for RP.

Competing interests RP was National Professional Advisor, Care Quality Commission 2017–2022, where her role included investigation of safety issues.

Provenance and peer review Not commissioned; externally peer reviewed.

Linked Articles

• Editorial Examining telehealth through the Institute of Medicine quality domains: unanswered questions and research agenda Timothy C Guetterman Lorraine R Buis BMJ Quality & Safety 2024; 33 552-555 Published Online First: 09 May 2024. doi: 10.1136/bmjqs-2023-016872

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