data analysis and interpretation in research example

A Guide To The Methods, Benefits & Problems of The Interpretation of Data

Table of Contents

1) What Is Data Interpretation?

2) How To Interpret Data?

3) Why Data Interpretation Is Important?

4) Data Interpretation Skills

5) Data Analysis & Interpretation Problems

6) Data Interpretation Techniques & Methods

7) The Use of Dashboards For Data Interpretation

8) Business Data Interpretation Examples

Data analysis and interpretation have now taken center stage with the advent of the digital age… and the sheer amount of data can be frightening. In fact, a Digital Universe study found that the total data supply in 2012 was 2.8 trillion gigabytes! Based on that amount of data alone, it is clear the calling card of any successful enterprise in today’s global world will be the ability to analyze complex data, produce actionable insights, and adapt to new market needs… all at the speed of thought.

Business dashboards are the digital age tools for big data. Capable of displaying key performance indicators (KPIs) for both quantitative and qualitative data analyses, they are ideal for making the fast-paced and data-driven market decisions that push today’s industry leaders to sustainable success. Through the art of streamlined visual communication, data dashboards permit businesses to engage in real-time and informed decision-making and are key instruments in data interpretation. First of all, let’s find a definition to understand what lies behind this practice.

What Is Data Interpretation?

Data interpretation refers to the process of using diverse analytical methods to review data and arrive at relevant conclusions. The interpretation of data helps researchers to categorize, manipulate, and summarize the information in order to answer critical questions.

The importance of data interpretation is evident, and this is why it needs to be done properly. Data is very likely to arrive from multiple sources and has a tendency to enter the analysis process with haphazard ordering. Data analysis tends to be extremely subjective. That is to say, the nature and goal of interpretation will vary from business to business, likely correlating to the type of data being analyzed. While there are several types of processes that are implemented based on the nature of individual data, the two broadest and most common categories are “quantitative and qualitative analysis.”

Yet, before any serious data interpretation inquiry can begin, it should be understood that visual presentations of data findings are irrelevant unless a sound decision is made regarding measurement scales. Before any serious data analysis can begin, the measurement scale must be decided for the data as this will have a long-term impact on data interpretation ROI. The varying scales include:

• Nominal Scale: non-numeric categories that cannot be ranked or compared quantitatively. Variables are exclusive and exhaustive.
• Ordinal Scale: exclusive categories that are exclusive and exhaustive but with a logical order. Quality ratings and agreement ratings are examples of ordinal scales (i.e., good, very good, fair, etc., OR agree, strongly agree, disagree, etc.).
• Interval: a measurement scale where data is grouped into categories with orderly and equal distances between the categories. There is always an arbitrary zero point.
• Ratio: contains features of all three.

For a more in-depth review of scales of measurement, read our article on data analysis questions . Once measurement scales have been selected, it is time to select which of the two broad interpretation processes will best suit your data needs. Let’s take a closer look at those specific methods and possible data interpretation problems.

How To Interpret Data? Top Methods & Techniques

When interpreting data, an analyst must try to discern the differences between correlation, causation, and coincidences, as well as many other biases – but he also has to consider all the factors involved that may have led to a result. There are various data interpretation types and methods one can use to achieve this.

The interpretation of data is designed to help people make sense of numerical data that has been collected, analyzed, and presented. Having a baseline method for interpreting data will provide your analyst teams with a structure and consistent foundation. Indeed, if several departments have different approaches to interpreting the same data while sharing the same goals, some mismatched objectives can result. Disparate methods will lead to duplicated efforts, inconsistent solutions, wasted energy, and inevitably – time and money. In this part, we will look at the two main methods of interpretation of data: qualitative and quantitative analysis.

Qualitative Data Interpretation

Qualitative data analysis can be summed up in one word – categorical. With this type of analysis, data is not described through numerical values or patterns but through the use of descriptive context (i.e., text). Typically, narrative data is gathered by employing a wide variety of person-to-person techniques. These techniques include:

• Observations: detailing behavioral patterns that occur within an observation group. These patterns could be the amount of time spent in an activity, the type of activity, and the method of communication employed.
• Focus groups: Group people and ask them relevant questions to generate a collaborative discussion about a research topic.
• Secondary Research: much like how patterns of behavior can be observed, various types of documentation resources can be coded and divided based on the type of material they contain.
• Interviews: one of the best collection methods for narrative data. Inquiry responses can be grouped by theme, topic, or category. The interview approach allows for highly focused data segmentation.

A key difference between qualitative and quantitative analysis is clearly noticeable in the interpretation stage. The first one is widely open to interpretation and must be “coded” so as to facilitate the grouping and labeling of data into identifiable themes. As person-to-person data collection techniques can often result in disputes pertaining to proper analysis, qualitative data analysis is often summarized through three basic principles: notice things, collect things, and think about things.

After qualitative data has been collected through transcripts, questionnaires, audio and video recordings, or the researcher’s notes, it is time to interpret it. For that purpose, there are some common methods used by researchers and analysts.

• Content analysis : As its name suggests, this is a research method used to identify frequencies and recurring words, subjects, and concepts in image, video, or audio content. It transforms qualitative information into quantitative data to help discover trends and conclusions that will later support important research or business decisions. This method is often used by marketers to understand brand sentiment from the mouths of customers themselves. Through that, they can extract valuable information to improve their products and services. It is recommended to use content analytics tools for this method as manually performing it is very time-consuming and can lead to human error or subjectivity issues. Having a clear goal in mind before diving into it is another great practice for avoiding getting lost in the fog.  
• Thematic analysis: This method focuses on analyzing qualitative data, such as interview transcripts, survey questions, and others, to identify common patterns and separate the data into different groups according to found similarities or themes. For example, imagine you want to analyze what customers think about your restaurant. For this purpose, you do a thematic analysis on 1000 reviews and find common themes such as “fresh food”, “cold food”, “small portions”, “friendly staff”, etc. With those recurring themes in hand, you can extract conclusions about what could be improved or enhanced based on your customer’s experiences. Since this technique is more exploratory, be open to changing your research questions or goals as you go. 
• Narrative analysis: A bit more specific and complicated than the two previous methods, it is used to analyze stories and discover their meaning. These stories can be extracted from testimonials, case studies, and interviews, as these formats give people more space to tell their experiences. Given that collecting this kind of data is harder and more time-consuming, sample sizes for narrative analysis are usually smaller, which makes it harder to reproduce its findings. However, it is still a valuable technique for understanding customers' preferences and mindsets.  
• Discourse analysis : This method is used to draw the meaning of any type of visual, written, or symbolic language in relation to a social, political, cultural, or historical context. It is used to understand how context can affect how language is carried out and understood. For example, if you are doing research on power dynamics, using discourse analysis to analyze a conversation between a janitor and a CEO and draw conclusions about their responses based on the context and your research questions is a great use case for this technique. That said, like all methods in this section, discourse analytics is time-consuming as the data needs to be analyzed until no new insights emerge.  
• Grounded theory analysis : The grounded theory approach aims to create or discover a new theory by carefully testing and evaluating the data available. Unlike all other qualitative approaches on this list, grounded theory helps extract conclusions and hypotheses from the data instead of going into the analysis with a defined hypothesis. This method is very popular amongst researchers, analysts, and marketers as the results are completely data-backed, providing a factual explanation of any scenario. It is often used when researching a completely new topic or with little knowledge as this space to start from the ground up. 

Quantitative Data Interpretation

If quantitative data interpretation could be summed up in one word (and it really can’t), that word would be “numerical.” There are few certainties when it comes to data analysis, but you can be sure that if the research you are engaging in has no numbers involved, it is not quantitative research, as this analysis refers to a set of processes by which numerical data is analyzed. More often than not, it involves the use of statistical modeling such as standard deviation, mean, and median. Let’s quickly review the most common statistical terms:

• Mean: A mean represents a numerical average for a set of responses. When dealing with a data set (or multiple data sets), a mean will represent the central value of a specific set of numbers. It is the sum of the values divided by the number of values within the data set. Other terms that can be used to describe the concept are arithmetic mean, average, and mathematical expectation.
• Standard deviation: This is another statistical term commonly used in quantitative analysis. Standard deviation reveals the distribution of the responses around the mean. It describes the degree of consistency within the responses; together with the mean, it provides insight into data sets.
• Frequency distribution: This is a measurement gauging the rate of a response appearance within a data set. When using a survey, for example, frequency distribution, it can determine the number of times a specific ordinal scale response appears (i.e., agree, strongly agree, disagree, etc.). Frequency distribution is extremely keen in determining the degree of consensus among data points.

Typically, quantitative data is measured by visually presenting correlation tests between two or more variables of significance. Different processes can be used together or separately, and comparisons can be made to ultimately arrive at a conclusion. Other signature interpretation processes of quantitative data include:

• Regression analysis: Essentially, it uses historical data to understand the relationship between a dependent variable and one or more independent variables. Knowing which variables are related and how they developed in the past allows you to anticipate possible outcomes and make better decisions going forward. For example, if you want to predict your sales for next month, you can use regression to understand what factors will affect them, such as products on sale and the launch of a new campaign, among many others. 
• Cohort analysis: This method identifies groups of users who share common characteristics during a particular time period. In a business scenario, cohort analysis is commonly used to understand customer behaviors. For example, a cohort could be all users who have signed up for a free trial on a given day. An analysis would be carried out to see how these users behave, what actions they carry out, and how their behavior differs from other user groups.
• Predictive analysis: As its name suggests, the predictive method aims to predict future developments by analyzing historical and current data. Powered by technologies such as artificial intelligence and machine learning, predictive analytics practices enable businesses to identify patterns or potential issues and plan informed strategies in advance.
• Prescriptive analysis: Also powered by predictions, the prescriptive method uses techniques such as graph analysis, complex event processing, and neural networks, among others, to try to unravel the effect that future decisions will have in order to adjust them before they are actually made. This helps businesses to develop responsive, practical business strategies.
• Conjoint analysis: Typically applied to survey analysis, the conjoint approach is used to analyze how individuals value different attributes of a product or service. This helps researchers and businesses to define pricing, product features, packaging, and many other attributes. A common use is menu-based conjoint analysis, in which individuals are given a “menu” of options from which they can build their ideal concept or product. Through this, analysts can understand which attributes they would pick above others and drive conclusions.
• Cluster analysis: Last but not least, the cluster is a method used to group objects into categories. Since there is no target variable when using cluster analysis, it is a useful method to find hidden trends and patterns in the data. In a business context, clustering is used for audience segmentation to create targeted experiences. In market research, it is often used to identify age groups, geographical information, and earnings, among others.

Now that we have seen how to interpret data, let's move on and ask ourselves some questions: What are some of the benefits of data interpretation? Why do all industries engage in data research and analysis? These are basic questions, but they often don’t receive adequate attention.

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Why Data Interpretation Is Important

The purpose of collection and interpretation is to acquire useful and usable information and to make the most informed decisions possible. From businesses to newlyweds researching their first home, data collection and interpretation provide limitless benefits for a wide range of institutions and individuals.

Data analysis and interpretation, regardless of the method and qualitative/quantitative status, may include the following characteristics:

• Data identification and explanation
• Comparing and contrasting data
• Identification of data outliers
• Future predictions

Data analysis and interpretation, in the end, help improve processes and identify problems. It is difficult to grow and make dependable improvements without, at the very least, minimal data collection and interpretation. What is the keyword? Dependable. Vague ideas regarding performance enhancement exist within all institutions and industries. Yet, without proper research and analysis, an idea is likely to remain in a stagnant state forever (i.e., minimal growth). So… what are a few of the business benefits of digital age data analysis and interpretation? Let’s take a look!

1) Informed decision-making: A decision is only as good as the knowledge that formed it. Informed data decision-making can potentially set industry leaders apart from the rest of the market pack. Studies have shown that companies in the top third of their industries are, on average, 5% more productive and 6% more profitable when implementing informed data decision-making processes. Most decisive actions will arise only after a problem has been identified or a goal defined. Data analysis should include identification, thesis development, and data collection, followed by data communication.

If institutions only follow that simple order, one that we should all be familiar with from grade school science fairs, then they will be able to solve issues as they emerge in real-time. Informed decision-making has a tendency to be cyclical. This means there is really no end, and eventually, new questions and conditions arise within the process that need to be studied further. The monitoring of data results will inevitably return the process to the start with new data and sights.

2) Anticipating needs with trends identification: data insights provide knowledge, and knowledge is power. The insights obtained from market and consumer data analyses have the ability to set trends for peers within similar market segments. A perfect example of how data analytics can impact trend prediction is evidenced in the music identification application Shazam . The application allows users to upload an audio clip of a song they like but can’t seem to identify. Users make 15 million song identifications a day. With this data, Shazam has been instrumental in predicting future popular artists.

When industry trends are identified, they can then serve a greater industry purpose. For example, the insights from Shazam’s monitoring benefits not only Shazam in understanding how to meet consumer needs but also grant music executives and record label companies an insight into the pop-culture scene of the day. Data gathering and interpretation processes can allow for industry-wide climate prediction and result in greater revenue streams across the market. For this reason, all institutions should follow the basic data cycle of collection, interpretation, decision-making, and monitoring.

3) Cost efficiency: Proper implementation of analytics processes can provide businesses with profound cost advantages within their industries. A recent data study performed by Deloitte vividly demonstrates this in finding that data analysis ROI is driven by efficient cost reductions. Often, this benefit is overlooked because making money is typically viewed as “sexier” than saving money. Yet, sound data analyses have the ability to alert management to cost-reduction opportunities without any significant exertion of effort on the part of human capital.

A great example of the potential for cost efficiency through data analysis is Intel. Prior to 2012, Intel would conduct over 19,000 manufacturing function tests on their chips before they could be deemed acceptable for release. To cut costs and reduce test time, Intel implemented predictive data analyses. By using historical and current data, Intel now avoids testing each chip 19,000 times by focusing on specific and individual chip tests. After its implementation in 2012, Intel saved over $3 million in manufacturing costs. Cost reduction may not be as “sexy” as data profit, but as Intel proves, it is a benefit of data analysis that should not be neglected.

4) Clear foresight: companies that collect and analyze their data gain better knowledge about themselves, their processes, and their performance. They can identify performance challenges when they arise and take action to overcome them. Data interpretation through visual representations lets them process their findings faster and make better-informed decisions on the company's future.

Key Data Interpretation Skills You Should Have

Just like any other process, data interpretation and analysis require researchers or analysts to have some key skills to be able to perform successfully. It is not enough just to apply some methods and tools to the data; the person who is managing it needs to be objective and have a data-driven mind, among other skills. 

It is a common misconception to think that the required skills are mostly number-related. While data interpretation is heavily analytically driven, it also requires communication and narrative skills, as the results of the analysis need to be presented in a way that is easy to understand for all types of audiences. 

Luckily, with the rise of self-service tools and AI-driven technologies, data interpretation is no longer segregated for analysts only. However, the topic still remains a big challenge for businesses that make big investments in data and tools to support it, as the interpretation skills required are still lacking. It is worthless to put massive amounts of money into extracting information if you are not going to be able to interpret what that information is telling you. For that reason, below we list the top 5 data interpretation skills your employees or researchers should have to extract the maximum potential from the data. 

• Data Literacy: The first and most important skill to have is data literacy. This means having the ability to understand, work, and communicate with data. It involves knowing the types of data sources, methods, and ethical implications of using them. In research, this skill is often a given. However, in a business context, there might be many employees who are not comfortable with data. The issue is the interpretation of data can not be solely responsible for the data team, as it is not sustainable in the long run. Experts advise business leaders to carefully assess the literacy level across their workforce and implement training instances to ensure everyone can interpret their data. 
• Data Tools: The data interpretation and analysis process involves using various tools to collect, clean, store, and analyze the data. The complexity of the tools varies depending on the type of data and the analysis goals. Going from simple ones like Excel to more complex ones like databases, such as SQL, or programming languages, such as R or Python. It also involves visual analytics tools to bring the data to life through the use of graphs and charts. Managing these tools is a fundamental skill as they make the process faster and more efficient. As mentioned before, most modern solutions are now self-service, enabling less technical users to use them without problem.
• Critical Thinking: Another very important skill is to have critical thinking. Data hides a range of conclusions, trends, and patterns that must be discovered. It is not just about comparing numbers; it is about putting a story together based on multiple factors that will lead to a conclusion. Therefore, having the ability to look further from what is right in front of you is an invaluable skill for data interpretation. 
• Data Ethics: In the information age, being aware of the legal and ethical responsibilities that come with the use of data is of utmost importance. In short, data ethics involves respecting the privacy and confidentiality of data subjects, as well as ensuring accuracy and transparency for data usage. It requires the analyzer or researcher to be completely objective with its interpretation to avoid any biases or discrimination. Many countries have already implemented regulations regarding the use of data, including the GDPR or the ACM Code Of Ethics. Awareness of these regulations and responsibilities is a fundamental skill that anyone working in data interpretation should have. 
• Domain Knowledge: Another skill that is considered important when interpreting data is to have domain knowledge. As mentioned before, data hides valuable insights that need to be uncovered. To do so, the analyst needs to know about the industry or domain from which the information is coming and use that knowledge to explore it and put it into a broader context. This is especially valuable in a business context, where most departments are now analyzing data independently with the help of a live dashboard instead of relying on the IT department, which can often overlook some aspects due to a lack of expertise in the topic. 

Common Data Analysis And Interpretation Problems

The oft-repeated mantra of those who fear data advancements in the digital age is “big data equals big trouble.” While that statement is not accurate, it is safe to say that certain data interpretation problems or “pitfalls” exist and can occur when analyzing data, especially at the speed of thought. Let’s identify some of the most common data misinterpretation risks and shed some light on how they can be avoided:

1) Correlation mistaken for causation: our first misinterpretation of data refers to the tendency of data analysts to mix the cause of a phenomenon with correlation. It is the assumption that because two actions occurred together, one caused the other. This is inaccurate, as actions can occur together, absent a cause-and-effect relationship.

• Digital age example: assuming that increased revenue results from increased social media followers… there might be a definitive correlation between the two, especially with today’s multi-channel purchasing experiences. But that does not mean an increase in followers is the direct cause of increased revenue. There could be both a common cause and an indirect causality.
• Remedy: attempt to eliminate the variable you believe to be causing the phenomenon.

2) Confirmation bias: our second problem is data interpretation bias. It occurs when you have a theory or hypothesis in mind but are intent on only discovering data patterns that support it while rejecting those that do not.

• Digital age example: your boss asks you to analyze the success of a recent multi-platform social media marketing campaign. While analyzing the potential data variables from the campaign (one that you ran and believe performed well), you see that the share rate for Facebook posts was great, while the share rate for Twitter Tweets was not. Using only Facebook posts to prove your hypothesis that the campaign was successful would be a perfect manifestation of confirmation bias.
• Remedy: as this pitfall is often based on subjective desires, one remedy would be to analyze data with a team of objective individuals. If this is not possible, another solution is to resist the urge to make a conclusion before data exploration has been completed. Remember to always try to disprove a hypothesis, not prove it.

3) Irrelevant data: the third data misinterpretation pitfall is especially important in the digital age. As large data is no longer centrally stored and as it continues to be analyzed at the speed of thought, it is inevitable that analysts will focus on data that is irrelevant to the problem they are trying to correct.

• Digital age example: in attempting to gauge the success of an email lead generation campaign, you notice that the number of homepage views directly resulting from the campaign increased, but the number of monthly newsletter subscribers did not. Based on the number of homepage views, you decide the campaign was a success when really it generated zero leads.
• Remedy: proactively and clearly frame any data analysis variables and KPIs prior to engaging in a data review. If the metric you use to measure the success of a lead generation campaign is newsletter subscribers, there is no need to review the number of homepage visits. Be sure to focus on the data variable that answers your question or solves your problem and not on irrelevant data.

4) Truncating an Axes: When creating a graph to start interpreting the results of your analysis, it is important to keep the axes truthful and avoid generating misleading visualizations. Starting the axes in a value that doesn’t portray the actual truth about the data can lead to false conclusions. 

• Digital age example: In the image below, we can see a graph from Fox News in which the Y-axes start at 34%, making it seem that the difference between 35% and 39.6% is way higher than it actually is. This could lead to a misinterpretation of the tax rate changes. 

* Source : www.venngage.com *

• Remedy: Be careful with how your data is visualized. Be respectful and realistic with axes to avoid misinterpretation of your data. See below how the Fox News chart looks when using the correct axis values. This chart was created with datapine's modern online data visualization tool.

5) (Small) sample size: Another common problem is using a small sample size. Logically, the bigger the sample size, the more accurate and reliable the results. However, this also depends on the size of the effect of the study. For example, the sample size in a survey about the quality of education will not be the same as for one about people doing outdoor sports in a specific area. 

• Digital age example: Imagine you ask 30 people a question, and 29 answer “yes,” resulting in 95% of the total. Now imagine you ask the same question to 1000, and 950 of them answer “yes,” which is again 95%. While these percentages might look the same, they certainly do not mean the same thing, as a 30-person sample size is not a significant number to establish a truthful conclusion. 
• Remedy: Researchers say that in order to determine the correct sample size to get truthful and meaningful results, it is necessary to define a margin of error that will represent the maximum amount they want the results to deviate from the statistical mean. Paired with this, they need to define a confidence level that should be between 90 and 99%. With these two values in hand, researchers can calculate an accurate sample size for their studies.

6) Reliability, subjectivity, and generalizability : When performing qualitative analysis, researchers must consider practical and theoretical limitations when interpreting the data. In some cases, this type of research can be considered unreliable because of uncontrolled factors that might or might not affect the results. This is paired with the fact that the researcher has a primary role in the interpretation process, meaning he or she decides what is relevant and what is not, and as we know, interpretations can be very subjective.

Generalizability is also an issue that researchers face when dealing with qualitative analysis. As mentioned in the point about having a small sample size, it is difficult to draw conclusions that are 100% representative because the results might be biased or unrepresentative of a wider population. 

While these factors are mostly present in qualitative research, they can also affect the quantitative analysis. For example, when choosing which KPIs to portray and how to portray them, analysts can also be biased and represent them in a way that benefits their analysis.

• Digital age example: Biased questions in a survey are a great example of reliability and subjectivity issues. Imagine you are sending a survey to your clients to see how satisfied they are with your customer service with this question: “How amazing was your experience with our customer service team?”. Here, we can see that this question clearly influences the response of the individual by putting the word “amazing” on it. 
• Remedy: A solution to avoid these issues is to keep your research honest and neutral. Keep the wording of the questions as objective as possible. For example: “On a scale of 1-10, how satisfied were you with our customer service team?”. This does not lead the respondent to any specific answer, meaning the results of your survey will be reliable. 

Data Interpretation Best Practices & Tips

Data analysis and interpretation are critical to developing sound conclusions and making better-informed decisions. As we have seen with this article, there is an art and science to the interpretation of data. To help you with this purpose, we will list a few relevant techniques, methods, and tricks you can implement for a successful data management process. 

As mentioned at the beginning of this post, the first step to interpreting data in a successful way is to identify the type of analysis you will perform and apply the methods respectively. Clearly differentiate between qualitative (observe, document, and interview notice, collect and think about things) and quantitative analysis (you lead research with a lot of numerical data to be analyzed through various statistical methods). 

1) Ask the right data interpretation questions

The first data interpretation technique is to define a clear baseline for your work. This can be done by answering some critical questions that will serve as a useful guideline to start. Some of them include: what are the goals and objectives of my analysis? What type of data interpretation method will I use? Who will use this data in the future? And most importantly, what general question am I trying to answer?

Once all this information has been defined, you will be ready for the next step: collecting your data. 

2) Collect and assimilate your data

Now that a clear baseline has been established, it is time to collect the information you will use. Always remember that your methods for data collection will vary depending on what type of analysis method you use, which can be qualitative or quantitative. Based on that, relying on professional online data analysis tools to facilitate the process is a great practice in this regard, as manually collecting and assessing raw data is not only very time-consuming and expensive but is also at risk of errors and subjectivity. 

Once your data is collected, you need to carefully assess it to understand if the quality is appropriate to be used during a study. This means, is the sample size big enough? Were the procedures used to collect the data implemented correctly? Is the date range from the data correct? If coming from an external source, is it a trusted and objective one? 

With all the needed information in hand, you are ready to start the interpretation process, but first, you need to visualize your data. 

3) Use the right data visualization type 

Data visualizations such as business graphs , charts, and tables are fundamental to successfully interpreting data. This is because data visualization via interactive charts and graphs makes the information more understandable and accessible. As you might be aware, there are different types of visualizations you can use, but not all of them are suitable for any analysis purpose. Using the wrong graph can lead to misinterpretation of your data, so it’s very important to carefully pick the right visual for it. Let’s look at some use cases of common data visualizations. 

• Bar chart: One of the most used chart types, the bar chart uses rectangular bars to show the relationship between 2 or more variables. There are different types of bar charts for different interpretations, including the horizontal bar chart, column bar chart, and stacked bar chart. 
• Line chart: Most commonly used to show trends, acceleration or decelerations, and volatility, the line chart aims to show how data changes over a period of time, for example, sales over a year. A few tips to keep this chart ready for interpretation are not using many variables that can overcrowd the graph and keeping your axis scale close to the highest data point to avoid making the information hard to read. 
• Pie chart: Although it doesn’t do a lot in terms of analysis due to its uncomplex nature, pie charts are widely used to show the proportional composition of a variable. Visually speaking, showing a percentage in a bar chart is way more complicated than showing it in a pie chart. However, this also depends on the number of variables you are comparing. If your pie chart needs to be divided into 10 portions, then it is better to use a bar chart instead. 
• Tables: While they are not a specific type of chart, tables are widely used when interpreting data. Tables are especially useful when you want to portray data in its raw format. They give you the freedom to easily look up or compare individual values while also displaying grand totals. 

With the use of data visualizations becoming more and more critical for businesses’ analytical success, many tools have emerged to help users visualize their data in a cohesive and interactive way. One of the most popular ones is the use of BI dashboards . These visual tools provide a centralized view of various graphs and charts that paint a bigger picture of a topic. We will discuss the power of dashboards for an efficient data interpretation practice in the next portion of this post. If you want to learn more about different types of graphs and charts , take a look at our complete guide on the topic. 

4) Start interpreting 

After the tedious preparation part, you can start extracting conclusions from your data. As mentioned many times throughout the post, the way you decide to interpret the data will solely depend on the methods you initially decided to use. If you had initial research questions or hypotheses, then you should look for ways to prove their validity. If you are going into the data with no defined hypothesis, then start looking for relationships and patterns that will allow you to extract valuable conclusions from the information. 

During the process of interpretation, stay curious and creative, dig into the data, and determine if there are any other critical questions that should be asked. If any new questions arise, you need to assess if you have the necessary information to answer them. Being able to identify if you need to dedicate more time and resources to the research is a very important step. No matter if you are studying customer behaviors or a new cancer treatment, the findings from your analysis may dictate important decisions in the future. Therefore, taking the time to really assess the information is key. For that purpose, data interpretation software proves to be very useful.

5) Keep your interpretation objective

As mentioned above, objectivity is one of the most important data interpretation skills but also one of the hardest. Being the person closest to the investigation, it is easy to become subjective when looking for answers in the data. A good way to stay objective is to show the information related to the study to other people, for example, research partners or even the people who will use your findings once they are done. This can help avoid confirmation bias and any reliability issues with your interpretation. 

Remember, using a visualization tool such as a modern dashboard will make the interpretation process way easier and more efficient as the data can be navigated and manipulated in an easy and organized way. And not just that, using a dashboard tool to present your findings to a specific audience will make the information easier to understand and the presentation way more engaging thanks to the visual nature of these tools. 

6) Mark your findings and draw conclusions

Findings are the observations you extracted from your data. They are the facts that will help you drive deeper conclusions about your research. For example, findings can be trends and patterns you found during your interpretation process. To put your findings into perspective, you can compare them with other resources that use similar methods and use them as benchmarks.

Reflect on your own thinking and reasoning and be aware of the many pitfalls data analysis and interpretation carry—correlation versus causation, subjective bias, false information, inaccurate data, etc. Once you are comfortable with interpreting the data, you will be ready to develop conclusions, see if your initial questions were answered, and suggest recommendations based on them.

Interpretation of Data: The Use of Dashboards Bridging The Gap

As we have seen, quantitative and qualitative methods are distinct types of data interpretation and analysis. Both offer a varying degree of return on investment (ROI) regarding data investigation, testing, and decision-making. But how do you mix the two and prevent a data disconnect? The answer is professional data dashboards. 

For a few years now, dashboards have become invaluable tools to visualize and interpret data. These tools offer a centralized and interactive view of data and provide the perfect environment for exploration and extracting valuable conclusions. They bridge the quantitative and qualitative information gap by unifying all the data in one place with the help of stunning visuals. 

Not only that, but these powerful tools offer a large list of benefits, and we will discuss some of them below. 

1) Connecting and blending data. With today’s pace of innovation, it is no longer feasible (nor desirable) to have bulk data centrally located. As businesses continue to globalize and borders continue to dissolve, it will become increasingly important for businesses to possess the capability to run diverse data analyses absent the limitations of location. Data dashboards decentralize data without compromising on the necessary speed of thought while blending both quantitative and qualitative data. Whether you want to measure customer trends or organizational performance, you now have the capability to do both without the need for a singular selection.

2) Mobile Data. Related to the notion of “connected and blended data” is that of mobile data. In today’s digital world, employees are spending less time at their desks and simultaneously increasing production. This is made possible because mobile solutions for analytical tools are no longer standalone. Today, mobile analysis applications seamlessly integrate with everyday business tools. In turn, both quantitative and qualitative data are now available on-demand where they’re needed, when they’re needed, and how they’re needed via interactive online dashboards .

3) Visualization. Data dashboards merge the data gap between qualitative and quantitative data interpretation methods through the science of visualization. Dashboard solutions come “out of the box” and are well-equipped to create easy-to-understand data demonstrations. Modern online data visualization tools provide a variety of color and filter patterns, encourage user interaction, and are engineered to help enhance future trend predictability. All of these visual characteristics make for an easy transition among data methods – you only need to find the right types of data visualization to tell your data story the best way possible.

4) Collaboration. Whether in a business environment or a research project, collaboration is key in data interpretation and analysis. Dashboards are online tools that can be easily shared through a password-protected URL or automated email. Through them, users can collaborate and communicate through the data in an efficient way. Eliminating the need for infinite files with lost updates. Tools such as datapine offer real-time updates, meaning your dashboards will update on their own as soon as new information is available.  

Examples Of Data Interpretation In Business

To give you an idea of how a dashboard can fulfill the need to bridge quantitative and qualitative analysis and help in understanding how to interpret data in research thanks to visualization, below, we will discuss three valuable examples to put their value into perspective.

1. Customer Satisfaction Dashboard 

This market research dashboard brings together both qualitative and quantitative data that are knowledgeably analyzed and visualized in a meaningful way that everyone can understand, thus empowering any viewer to interpret it. Let’s explore it below. 

**click to enlarge**

The value of this template lies in its highly visual nature. As mentioned earlier, visuals make the interpretation process way easier and more efficient. Having critical pieces of data represented with colorful and interactive icons and graphs makes it possible to uncover insights at a glance. For example, the colors green, yellow, and red on the charts for the NPS and the customer effort score allow us to conclude that most respondents are satisfied with this brand with a short glance. A further dive into the line chart below can help us dive deeper into this conclusion, as we can see both metrics developed positively in the past 6 months. 

The bottom part of the template provides visually stunning representations of different satisfaction scores for quality, pricing, design, and service. By looking at these, we can conclude that, overall, customers are satisfied with this company in most areas. 

2. Brand Analysis Dashboard

Next, in our list of data interpretation examples, we have a template that shows the answers to a survey on awareness for Brand D. The sample size is listed on top to get a perspective of the data, which is represented using interactive charts and graphs. 

When interpreting information, context is key to understanding it correctly. For that reason, the dashboard starts by offering insights into the demographics of the surveyed audience. In general, we can see ages and gender are diverse. Therefore, we can conclude these brands are not targeting customers from a specified demographic, an important aspect to put the surveyed answers into perspective. 

Looking at the awareness portion, we can see that brand B is the most popular one, with brand D coming second on both questions. This means brand D is not doing wrong, but there is still room for improvement compared to brand B. To see where brand D could improve, the researcher could go into the bottom part of the dashboard and consult the answers for branding themes and celebrity analysis. These are important as they give clear insight into what people and messages the audience associates with brand D. This is an opportunity to exploit these topics in different ways and achieve growth and success. 

3. Product Innovation Dashboard 

Our third and last dashboard example shows the answers to a survey on product innovation for a technology company. Just like the previous templates, the interactive and visual nature of the dashboard makes it the perfect tool to interpret data efficiently and effectively. 

Starting from right to left, we first get a list of the top 5 products by purchase intention. This information lets us understand if the product being evaluated resembles what the audience already intends to purchase. It is a great starting point to see how customers would respond to the new product. This information can be complemented with other key metrics displayed in the dashboard. For example, the usage and purchase intention track how the market would receive the product and if they would purchase it, respectively. Interpreting these values as positive or negative will depend on the company and its expectations regarding the survey. 

Complementing these metrics, we have the willingness to pay. Arguably, one of the most important metrics to define pricing strategies. Here, we can see that most respondents think the suggested price is a good value for money. Therefore, we can interpret that the product would sell for that price. 

To see more data analysis and interpretation examples for different industries and functions, visit our library of business dashboards .

To Conclude…

As we reach the end of this insightful post about data interpretation and analysis, we hope you have a clear understanding of the topic. We've covered the definition and given some examples and methods to perform a successful interpretation process.

The importance of data interpretation is undeniable. Dashboards not only bridge the information gap between traditional data interpretation methods and technology, but they can help remedy and prevent the major pitfalls of the process. As a digital age solution, they combine the best of the past and the present to allow for informed decision-making with maximum data interpretation ROI.

To start visualizing your insights in a meaningful and actionable way, test our online reporting software for free with our 14-day trial !

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Home » Data Interpretation – Process, Methods and Questions

Data Interpretation – Process, Methods and Questions

Table of Contents

Data Interpretation

Definition :

Data interpretation refers to the process of making sense of data by analyzing and drawing conclusions from it. It involves examining data in order to identify patterns, relationships, and trends that can help explain the underlying phenomena being studied. Data interpretation can be used to make informed decisions and solve problems across a wide range of fields, including business, science, and social sciences.

Data Interpretation Process

Here are the steps involved in the data interpretation process:

• Define the research question : The first step in data interpretation is to clearly define the research question. This will help you to focus your analysis and ensure that you are interpreting the data in a way that is relevant to your research objectives.
• Collect the data: The next step is to collect the data. This can be done through a variety of methods such as surveys, interviews, observation, or secondary data sources.
• Clean and organize the data : Once the data has been collected, it is important to clean and organize it. This involves checking for errors, inconsistencies, and missing data. Data cleaning can be a time-consuming process, but it is essential to ensure that the data is accurate and reliable.
• Analyze the data: The next step is to analyze the data. This can involve using statistical software or other tools to calculate summary statistics, create graphs and charts, and identify patterns in the data.
• Interpret the results: Once the data has been analyzed, it is important to interpret the results. This involves looking for patterns, trends, and relationships in the data. It also involves drawing conclusions based on the results of the analysis.
• Communicate the findings : The final step is to communicate the findings. This can involve creating reports, presentations, or visualizations that summarize the key findings of the analysis. It is important to communicate the findings in a way that is clear and concise, and that is tailored to the audience’s needs.

Types of Data Interpretation

There are various types of data interpretation techniques used for analyzing and making sense of data. Here are some of the most common types:

Descriptive Interpretation

This type of interpretation involves summarizing and describing the key features of the data. This can involve calculating measures of central tendency (such as mean, median, and mode), measures of dispersion (such as range, variance, and standard deviation), and creating visualizations such as histograms, box plots, and scatterplots.

Inferential Interpretation

This type of interpretation involves making inferences about a larger population based on a sample of the data. This can involve hypothesis testing, where you test a hypothesis about a population parameter using sample data, or confidence interval estimation, where you estimate a range of values for a population parameter based on sample data.

Predictive Interpretation

This type of interpretation involves using data to make predictions about future outcomes. This can involve building predictive models using statistical techniques such as regression analysis, time-series analysis, or machine learning algorithms.

Exploratory Interpretation

This type of interpretation involves exploring the data to identify patterns and relationships that were not previously known. This can involve data mining techniques such as clustering analysis, principal component analysis, or association rule mining.

Causal Interpretation

This type of interpretation involves identifying causal relationships between variables in the data. This can involve experimental designs, such as randomized controlled trials, or observational studies, such as regression analysis or propensity score matching.

Data Interpretation Methods

There are various methods for data interpretation that can be used to analyze and make sense of data. Here are some of the most common methods:

Statistical Analysis

This method involves using statistical techniques to analyze the data. Statistical analysis can involve descriptive statistics (such as measures of central tendency and dispersion), inferential statistics (such as hypothesis testing and confidence interval estimation), and predictive modeling (such as regression analysis and time-series analysis).

Data Visualization

This method involves using visual representations of the data to identify patterns and trends. Data visualization can involve creating charts, graphs, and other visualizations, such as heat maps or scatterplots.

Text Analysis

This method involves analyzing text data, such as survey responses or social media posts, to identify patterns and themes. Text analysis can involve techniques such as sentiment analysis, topic modeling, and natural language processing.

Machine Learning

This method involves using algorithms to identify patterns in the data and make predictions or classifications. Machine learning can involve techniques such as decision trees, neural networks, and random forests.

Qualitative Analysis

This method involves analyzing non-numeric data, such as interviews or focus group discussions, to identify themes and patterns. Qualitative analysis can involve techniques such as content analysis, grounded theory, and narrative analysis.

Geospatial Analysis

This method involves analyzing spatial data, such as maps or GPS coordinates, to identify patterns and relationships. Geospatial analysis can involve techniques such as spatial autocorrelation, hot spot analysis, and clustering.

Applications of Data Interpretation

Data interpretation has a wide range of applications across different fields, including business, healthcare, education, social sciences, and more. Here are some examples of how data interpretation is used in different applications:

• Business : Data interpretation is widely used in business to inform decision-making, identify market trends, and optimize operations. For example, businesses may analyze sales data to identify the most popular products or customer demographics, or use predictive modeling to forecast demand and adjust pricing accordingly.
• Healthcare : Data interpretation is critical in healthcare for identifying disease patterns, evaluating treatment effectiveness, and improving patient outcomes. For example, healthcare providers may use electronic health records to analyze patient data and identify risk factors for certain diseases or conditions.
• Education : Data interpretation is used in education to assess student performance, identify areas for improvement, and evaluate the effectiveness of instructional methods. For example, schools may analyze test scores to identify students who are struggling and provide targeted interventions to improve their performance.
• Social sciences : Data interpretation is used in social sciences to understand human behavior, attitudes, and perceptions. For example, researchers may analyze survey data to identify patterns in public opinion or use qualitative analysis to understand the experiences of marginalized communities.
• Sports : Data interpretation is increasingly used in sports to inform strategy and improve performance. For example, coaches may analyze performance data to identify areas for improvement or use predictive modeling to assess the likelihood of injuries or other risks.

When to use Data Interpretation

Data interpretation is used to make sense of complex data and to draw conclusions from it. It is particularly useful when working with large datasets or when trying to identify patterns or trends in the data. Data interpretation can be used in a variety of settings, including scientific research, business analysis, and public policy.

In scientific research, data interpretation is often used to draw conclusions from experiments or studies. Researchers use statistical analysis and data visualization techniques to interpret their data and to identify patterns or relationships between variables. This can help them to understand the underlying mechanisms of their research and to develop new hypotheses.

In business analysis, data interpretation is used to analyze market trends and consumer behavior. Companies can use data interpretation to identify patterns in customer buying habits, to understand market trends, and to develop marketing strategies that target specific customer segments.

In public policy, data interpretation is used to inform decision-making and to evaluate the effectiveness of policies and programs. Governments and other organizations use data interpretation to track the impact of policies and programs over time, to identify areas where improvements are needed, and to develop evidence-based policy recommendations.

In general, data interpretation is useful whenever large amounts of data need to be analyzed and understood in order to make informed decisions.

Data Interpretation Examples

Here are some real-time examples of data interpretation:

• Social media analytics : Social media platforms generate vast amounts of data every second, and businesses can use this data to analyze customer behavior, track sentiment, and identify trends. Data interpretation in social media analytics involves analyzing data in real-time to identify patterns and trends that can help businesses make informed decisions about marketing strategies and customer engagement.
• Healthcare analytics: Healthcare organizations use data interpretation to analyze patient data, track outcomes, and identify areas where improvements are needed. Real-time data interpretation can help healthcare providers make quick decisions about patient care, such as identifying patients who are at risk of developing complications or adverse events.
• Financial analysis: Real-time data interpretation is essential for financial analysis, where traders and analysts need to make quick decisions based on changing market conditions. Financial analysts use data interpretation to track market trends, identify opportunities for investment, and develop trading strategies.
• Environmental monitoring : Real-time data interpretation is important for environmental monitoring, where data is collected from various sources such as satellites, sensors, and weather stations. Data interpretation helps to identify patterns and trends that can help predict natural disasters, track changes in the environment, and inform decision-making about environmental policies.
• Traffic management: Real-time data interpretation is used for traffic management, where traffic sensors collect data on traffic flow, congestion, and accidents. Data interpretation helps to identify areas where traffic congestion is high, and helps traffic management authorities make decisions about road maintenance, traffic signal timing, and other strategies to improve traffic flow.

Data Interpretation Questions

Data Interpretation Questions samples:

• Medical : What is the correlation between a patient’s age and their risk of developing a certain disease?
• Environmental Science: What is the trend in the concentration of a certain pollutant in a particular body of water over the past 10 years?
• Finance : What is the correlation between a company’s stock price and its quarterly revenue?
• Education : What is the trend in graduation rates for a particular high school over the past 5 years?
• Marketing : What is the correlation between a company’s advertising budget and its sales revenue?
• Sports : What is the trend in the number of home runs hit by a particular baseball player over the past 3 seasons?
• Social Science: What is the correlation between a person’s level of education and their income level?

In order to answer these questions, you would need to analyze and interpret the data using statistical methods, graphs, and other visualization tools.

Purpose of Data Interpretation

The purpose of data interpretation is to make sense of complex data by analyzing and drawing insights from it. The process of data interpretation involves identifying patterns and trends, making comparisons, and drawing conclusions based on the data. The ultimate goal of data interpretation is to use the insights gained from the analysis to inform decision-making.

Data interpretation is important because it allows individuals and organizations to:

• Understand complex data : Data interpretation helps individuals and organizations to make sense of complex data sets that would otherwise be difficult to understand.
• Identify patterns and trends : Data interpretation helps to identify patterns and trends in data, which can reveal important insights about the underlying processes and relationships.
• Make informed decisions: Data interpretation provides individuals and organizations with the information they need to make informed decisions based on the insights gained from the data analysis.
• Evaluate performance : Data interpretation helps individuals and organizations to evaluate their performance over time and to identify areas where improvements can be made.
• Communicate findings: Data interpretation allows individuals and organizations to communicate their findings to others in a clear and concise manner, which is essential for informing stakeholders and making changes based on the insights gained from the analysis.

Characteristics of Data Interpretation

Here are some characteristics of data interpretation:

• Contextual : Data interpretation is always contextual, meaning that the interpretation of data is dependent on the context in which it is analyzed. The same data may have different meanings depending on the context in which it is analyzed.
• Iterative : Data interpretation is an iterative process, meaning that it often involves multiple rounds of analysis and refinement as more data becomes available or as new insights are gained from the analysis.
• Subjective : Data interpretation is often subjective, as it involves the interpretation of data by individuals who may have different perspectives and biases. It is important to acknowledge and address these biases when interpreting data.
• Analytical : Data interpretation involves the use of analytical tools and techniques to analyze and draw insights from data. These may include statistical analysis, data visualization, and other data analysis methods.
• Evidence-based : Data interpretation is evidence-based, meaning that it is based on the data and the insights gained from the analysis. It is important to ensure that the data used in the analysis is accurate, relevant, and reliable.
• Actionable : Data interpretation is actionable, meaning that it provides insights that can be used to inform decision-making and to drive action. The ultimate goal of data interpretation is to use the insights gained from the analysis to improve performance or to achieve specific goals.

Advantages of Data Interpretation

Data interpretation has several advantages, including:

• Improved decision-making: Data interpretation provides insights that can be used to inform decision-making. By analyzing data and drawing insights from it, individuals and organizations can make informed decisions based on evidence rather than intuition.
• Identification of patterns and trends: Data interpretation helps to identify patterns and trends in data, which can reveal important insights about the underlying processes and relationships. This information can be used to improve performance or to achieve specific goals.
• Evaluation of performance: Data interpretation helps individuals and organizations to evaluate their performance over time and to identify areas where improvements can be made. By analyzing data, organizations can identify strengths and weaknesses and make changes to improve their performance.
• Communication of findings: Data interpretation allows individuals and organizations to communicate their findings to others in a clear and concise manner, which is essential for informing stakeholders and making changes based on the insights gained from the analysis.
• Better resource allocation: Data interpretation can help organizations allocate resources more efficiently by identifying areas where resources are needed most. By analyzing data, organizations can identify areas where resources are being underutilized or where additional resources are needed to improve performance.
• Improved competitiveness : Data interpretation can give organizations a competitive advantage by providing insights that help to improve performance, reduce costs, or identify new opportunities for growth.

Limitations of Data Interpretation

Data interpretation has some limitations, including:

• Limited by the quality of data: The quality of data used in data interpretation can greatly impact the accuracy of the insights gained from the analysis. Poor quality data can lead to incorrect conclusions and decisions.
• Subjectivity: Data interpretation can be subjective, as it involves the interpretation of data by individuals who may have different perspectives and biases. This can lead to different interpretations of the same data.
• Limited by analytical tools: The analytical tools and techniques used in data interpretation can also limit the accuracy of the insights gained from the analysis. Different analytical tools may yield different results, and some tools may not be suitable for certain types of data.
• Time-consuming: Data interpretation can be a time-consuming process, particularly for large and complex data sets. This can make it difficult to quickly make decisions based on the insights gained from the analysis.
• Incomplete data: Data interpretation can be limited by incomplete data sets, which may not provide a complete picture of the situation being analyzed. Incomplete data can lead to incorrect conclusions and decisions.
• Limited by context: Data interpretation is always contextual, meaning that the interpretation of data is dependent on the context in which it is analyzed. The same data may have different meanings depending on the context in which it is analyzed.

Difference between Data Interpretation and Data Analysis

Data interpretation and data analysis are two different but closely related processes in data-driven decision-making.

Data analysis refers to the process of examining and examining data using statistical and computational methods to derive insights and conclusions from it. It involves cleaning, transforming, and modeling the data to uncover patterns, relationships, and trends that can help in understanding the underlying phenomena.

Data interpretation, on the other hand, refers to the process of making sense of the findings from the data analysis by contextualizing them within the larger problem domain. It involves identifying the key takeaways from the data analysis, assessing their relevance and significance to the problem at hand, and communicating the insights in a clear and actionable manner.

In short, data analysis is about uncovering insights from the data, while data interpretation is about making sense of those insights and translating them into actionable recommendations.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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What Is Data Analysis? (With Examples)

Data analysis is the practice of working with data to glean useful information, which can then be used to make informed decisions.

"It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts," Sherlock Holme's proclaims in Sir Arthur Conan Doyle's A Scandal in Bohemia.

This idea lies at the root of data analysis. When we can extract meaning from data, it empowers us to make better decisions. And we’re living in a time when we have more data than ever at our fingertips.

Companies are wisening up to the benefits of leveraging data. Data analysis can help a bank to personalize customer interactions, a health care system to predict future health needs, or an entertainment company to create the next big streaming hit.

The World Economic Forum Future of Jobs Report 2023 listed data analysts and scientists as one of the most in-demand jobs, alongside AI and machine learning specialists and big data specialists [ 1 ]. In this article, you'll learn more about the data analysis process, different types of data analysis, and recommended courses to help you get started in this exciting field.

Read more: How to Become a Data Analyst (with or Without a Degree)

Beginner-friendly data analysis courses

Interested in building your knowledge of data analysis today? Consider enrolling in one of these popular courses on Coursera:

In Google's Foundations: Data, Data, Everywhere course, you'll explore key data analysis concepts, tools, and jobs.

In Duke University's Data Analysis and Visualization course, you'll learn how to identify key components for data analytics projects, explore data visualization, and find out how to create a compelling data story.

Data analysis process

As the data available to companies continues to grow both in amount and complexity, so too does the need for an effective and efficient process by which to harness the value of that data. The data analysis process typically moves through several iterative phases. Let’s take a closer look at each.

Identify the business question you’d like to answer. What problem is the company trying to solve? What do you need to measure, and how will you measure it? 

Collect the raw data sets you’ll need to help you answer the identified question. Data collection might come from internal sources, like a company’s client relationship management (CRM) software, or from secondary sources, like government records or social media application programming interfaces (APIs). 

Clean the data to prepare it for analysis. This often involves purging duplicate and anomalous data, reconciling inconsistencies, standardizing data structure and format, and dealing with white spaces and other syntax errors.

Analyze the data. By manipulating the data using various data analysis techniques and tools, you can begin to find trends, correlations, outliers, and variations that tell a story. During this stage, you might use data mining to discover patterns within databases or data visualization software to help transform data into an easy-to-understand graphical format.

Interpret the results of your analysis to see how well the data answered your original question. What recommendations can you make based on the data? What are the limitations to your conclusions? 

You can complete hands-on projects for your portfolio while practicing statistical analysis, data management, and programming with Meta's beginner-friendly Data Analyst Professional Certificate . Designed to prepare you for an entry-level role, this self-paced program can be completed in just 5 months.

Or, L earn more about data analysis in this lecture by Kevin, Director of Data Analytics at Google, from Google's Data Analytics Professional Certificate :

Read more: What Does a Data Analyst Do? A Career Guide

Types of data analysis (with examples)

Data can be used to answer questions and support decisions in many different ways. To identify the best way to analyze your date, it can help to familiarize yourself with the four types of data analysis commonly used in the field.

In this section, we’ll take a look at each of these data analysis methods, along with an example of how each might be applied in the real world.

Descriptive analysis

Descriptive analysis tells us what happened. This type of analysis helps describe or summarize quantitative data by presenting statistics. For example, descriptive statistical analysis could show the distribution of sales across a group of employees and the average sales figure per employee. 

Descriptive analysis answers the question, “what happened?”

Diagnostic analysis

If the descriptive analysis determines the “what,” diagnostic analysis determines the “why.” Let’s say a descriptive analysis shows an unusual influx of patients in a hospital. Drilling into the data further might reveal that many of these patients shared symptoms of a particular virus. This diagnostic analysis can help you determine that an infectious agent—the “why”—led to the influx of patients.

Diagnostic analysis answers the question, “why did it happen?”

Predictive analysis

So far, we’ve looked at types of analysis that examine and draw conclusions about the past. Predictive analytics uses data to form projections about the future. Using predictive analysis, you might notice that a given product has had its best sales during the months of September and October each year, leading you to predict a similar high point during the upcoming year.

Predictive analysis answers the question, “what might happen in the future?”

Prescriptive analysis

Prescriptive analysis takes all the insights gathered from the first three types of analysis and uses them to form recommendations for how a company should act. Using our previous example, this type of analysis might suggest a market plan to build on the success of the high sales months and harness new growth opportunities in the slower months. 

Prescriptive analysis answers the question, “what should we do about it?”

This last type is where the concept of data-driven decision-making comes into play.

Read more : Advanced Analytics: Definition, Benefits, and Use Cases

What is data-driven decision-making (DDDM)?

Data-driven decision-making, sometimes abbreviated to DDDM), can be defined as the process of making strategic business decisions based on facts, data, and metrics instead of intuition, emotion, or observation.

This might sound obvious, but in practice, not all organizations are as data-driven as they could be. According to global management consulting firm McKinsey Global Institute, data-driven companies are better at acquiring new customers, maintaining customer loyalty, and achieving above-average profitability [ 2 ].

Get started with Coursera

If you’re interested in a career in the high-growth field of data analytics, consider these top-rated courses on Coursera:

Begin building job-ready skills with the Google Data Analytics Professional Certificate . Prepare for an entry-level job as you learn from Google employees—no experience or degree required.

Practice working with data with Macquarie University's Excel Skills for Business Specialization . Learn how to use Microsoft Excel to analyze data and make data-informed business decisions.

Deepen your skill set with Google's Advanced Data Analytics Professional Certificate . In this advanced program, you'll continue exploring the concepts introduced in the beginner-level courses, plus learn Python, statistics, and Machine Learning concepts.

Frequently asked questions (FAQ)

Where is data analytics used ‎.

Just about any business or organization can use data analytics to help inform their decisions and boost their performance. Some of the most successful companies across a range of industries — from Amazon and Netflix to Starbucks and General Electric — integrate data into their business plans to improve their overall business performance. ‎

What are the top skills for a data analyst? ‎

Data analysis makes use of a range of analysis tools and technologies. Some of the top skills for data analysts include SQL, data visualization, statistical programming languages (like R and Python),  machine learning, and spreadsheets.

Read : 7 In-Demand Data Analyst Skills to Get Hired in 2022 ‎

What is a data analyst job salary? ‎

Data from Glassdoor indicates that the average base salary for a data analyst in the United States is $75,349 as of March 2024 [ 3 ]. How much you make will depend on factors like your qualifications, experience, and location. ‎

Do data analysts need to be good at math? ‎

Data analytics tends to be less math-intensive than data science. While you probably won’t need to master any advanced mathematics, a foundation in basic math and statistical analysis can help set you up for success.

Learn more: Data Analyst vs. Data Scientist: What’s the Difference? ‎

Article sources

World Economic Forum. " The Future of Jobs Report 2023 , https://www3.weforum.org/docs/WEF_Future_of_Jobs_2023.pdf." Accessed March 19, 2024.

McKinsey & Company. " Five facts: How customer analytics boosts corporate performance , https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/five-facts-how-customer-analytics-boosts-corporate-performance." Accessed March 19, 2024.

Glassdoor. " Data Analyst Salaries , https://www.glassdoor.com/Salaries/data-analyst-salary-SRCH_KO0,12.htm" Accessed March 19, 2024.

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Coursera’s editorial team is comprised of highly experienced professional editors, writers, and fact...

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

Data Interpretation: Definition, Method, Benefits & Examples

In today's digital world, any business owner understands the importance of collecting, analyzing, and interpreting data. Some statistical methods are always employed in this process. Continue reading to learn how to make the most of your data.

Apr 20 2021 ● 7 min read

Table of Contents

What is data interpretation, data interpretation examples, steps of data interpretation, what should users question during data interpretation, data interpretation methods, qualitative data interpretation method, quantitative data interpretation method, benefits of data interpretation.

Syracuse University defined data interpretation as the process of assigning meaning to the collected information and determining the conclusions, significance, and implications of the findings. In other words, normalizing data, aka giving meaning to the collected 'cleaned' raw data .

Data interpretation is the final step of data analysis . This is where you turn results into actionable items. To better understand it, here are 2 instances of interpreting data:

Let's say you've got four age groups of the user base. So a company can notice which age group is most engaged with their content or product. Based on bar charts or pie charts, they can either: develop a marketing strategy to make their product more appealing to non-involved groups or develop an outreach strategy that expands on their core user base.

Another case of data interpretation is how companies use recruitment CRM . They use it to source, track, and manage their entire hiring pipeline to see how they can automate their workflow better. This helps companies save time and improve productivity.

Interpreting data: Performance by gender

Data interpretation is conducted in 4 steps:

• Assembling the information you need (like bar graphs and pie charts);
• Developing findings or isolating the most relevant inputs;
• Developing conclusions;
• Coming up with recommendations or actionable solutions.

Considering how these findings dictate the course of action, data analysts must be accurate with their conclusions and examine the raw data from multiple angles. Different variables may allude to various problems, so having the ability to backtrack data and repeat the analysis using different templates is an integral part of a successful business strategy.

To interpret data accurately, users should be aware of potential pitfalls present within this process. You need to ask yourself if you are mistaking correlation for causation. If two things occur together, it does not indicate that one caused the other.

The 2nd thing you need to be aware of is your own confirmation bias . This occurs when you try to prove a point or a theory and focus only on the patterns or findings that support that theory while discarding those that do not.

The 3rd problem is irrelevant data. To be specific, you need to make sure that the data you have collected and analyzed is relevant to the problem you are trying to solve.

Data analysts or data analytics tools help people make sense of the numerical data that has been aggregated, transformed, and displayed. There are two main methods for data interpretation: quantitative and qualitative.

This is a method for breaking down or analyzing so-called qualitative data, also known as categorical data. It is important to note that no bar graphs or line charts are used in this method. Instead, they rely on text. Because qualitative data is collected through person-to-person techniques, it isn't easy to present using a numerical approach.

Surveys are used to collect data because they allow you to assign numerical values to answers, making them easier to analyze. If we rely solely on the text, it would be a time-consuming and error-prone process. This is why it must be transformed .

This data interpretation is applied when we are dealing with quantitative or numerical data. Since we are dealing with numbers, the values can be displayed in a bar chart or pie chart. There are two main types: Discrete and Continuous. Moreover, numbers are easier to analyze since they involve statistical modeling techniques like mean and standard deviation.

Mean is an average value of a particular data set obtained or calculated by dividing the sum of the values within that data set by the number of values within that same set.

Standard Deviation is a technique is used to ascertain how responses align with or deviate from the average value or mean. It relies on the meaning to describe the consistency of the replies within a particular data set. You can use this when calculating the average pay for a certain profession and then displaying the upper and lower values in the data set.

As stated, some tools can do this automatically, especially when it comes to quantitative data. Whatagraph is one such tool as it can aggregate data from multiple sources using different system integrations. It will also automatically organize and analyze that which will later be displayed in pie charts, line charts, or bar charts, however you wish.

Multiple data interpretation benefits explain its significance within the corporate world, medical industry, and financial industry:

Anticipating needs and identifying trends . Data analysis provides users with relevant insights that they can use to forecast trends. It would be based on customer concerns and expectations .

For example, a large number of people are concerned about privacy and the leakage of personal information . Products that provide greater protection and anonymity are more likely to become popular.

Clear foresight. Companies that analyze and aggregate data better understand their own performance and how consumers perceive them. This provides them with a better understanding of their shortcomings, allowing them to work on solutions that will significantly improve their performance.

Published on Apr 20 2021

Indrė is a copywriter at Whatagraph with extensive experience in search engine optimization and public relations. She holds a degree in International Relations, while her professional background includes different marketing and advertising niches. She manages to merge marketing strategy and public speaking while educating readers on how to automate their businesses.

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Research Methods

• Getting Started
• What is Research Design?
• Research Approach
• Research Methodology
• Data Collection
• Data Analysis & Interpretation
• Population & Sampling
• Theories, Theoretical Perspective & Theoretical Framework
• Useful Resources

Further Resources

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 Interpretation: Revealing and explaining trends

by Anne E. Egger, Ph.D., Anthony Carpi, Ph.D.

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Did you know that scientists don't always agree on what data mean? Different scientists can look at the same set of data and come up with different explanations for it, and disagreement among scientists doesn't point to bad science.

Data collection is the systematic recording of information; data analysis involves working to uncover patterns and trends in datasets; data interpretation involves explaining those patterns and trends.

Scientists interpret data based on their background knowledge and experience; thus, different scientists can interpret the same data in different ways.

By publishing their data and the techniques they used to analyze and interpret those data, scientists give the community the opportunity to both review the data and use them in future research.

Before you decide what to wear in the morning, you collect a variety of data: the season of the year, what the forecast says the weather is going to be like, which clothes are clean and which are dirty, and what you will be doing during the day. You then analyze those data . Perhaps you think, "It's summer, so it's usually warm." That analysis helps you determine the best course of action, and you base your apparel decision on your interpretation of the information. You might choose a t-shirt and shorts on a summer day when you know you'll be outside, but bring a sweater with you if you know you'll be in an air-conditioned building.

Though this example may seem simplistic, it reflects the way scientists pursue data collection, analysis , and interpretation . Data (the plural form of the word datum) are scientific observations and measurements that, once analyzed and interpreted, can be developed into evidence to address a question. Data lie at the heart of all scientific investigations, and all scientists collect data in one form or another. The weather forecast that helped you decide what to wear, for example, was an interpretation made by a meteorologist who analyzed data collected by satellites. Data may take the form of the number of bacteria colonies growing in soup broth (see our Experimentation in Science module), a series of drawings or photographs of the different layers of rock that form a mountain range (see our Description in Science module), a tally of lung cancer victims in populations of cigarette smokers and non-smokers (see our Comparison in Science module), or the changes in average annual temperature predicted by a model of global climate (see our Modeling in Science module).

Scientific data collection involves more care than you might use in a casual glance at the thermometer to see what you should wear. Because scientists build on their own work and the work of others, it is important that they are systematic and consistent in their data collection methods and make detailed records so that others can see and use the data they collect.

But collecting data is only one step in a scientific investigation, and scientific knowledge is much more than a simple compilation of data points. The world is full of observations that can be made, but not every observation constitutes a useful piece of data. For example, your meteorologist could record the outside air temperature every second of the day, but would that make the forecast any more accurate than recording it once an hour? Probably not. All scientists make choices about which data are most relevant to their research and what to do with those data: how to turn a collection of measurements into a useful dataset through processing and analysis , and how to interpret those analyzed data in the context of what they already know. The thoughtful and systematic collection, analysis, and interpretation of data allow them to be developed into evidence that supports scientific ideas, arguments, and hypotheses .

Data collection, analysis , and interpretation: Weather and climate

The weather has long been a subject of widespread data collection, analysis , and interpretation . Accurate measurements of air temperature became possible in the mid-1700s when Daniel Gabriel Fahrenheit invented the first standardized mercury thermometer in 1714 (see our Temperature module). Air temperature, wind speed, and wind direction are all critical navigational information for sailors on the ocean, but in the late 1700s and early 1800s, as sailing expeditions became common, this information was not easy to come by. The lack of reliable data was of great concern to Matthew Fontaine Maury, the superintendent of the Depot of Charts and Instruments of the US Navy. As a result, Maury organized the first international Maritime Conference , held in Brussels, Belgium, in 1853. At this meeting, international standards for taking weather measurements on ships were established and a system for sharing this information between countries was founded.

Defining uniform data collection standards was an important step in producing a truly global dataset of meteorological information, allowing data collected by many different people in different parts of the world to be gathered together into a single database. Maury's compilation of sailors' standardized data on wind and currents is shown in Figure 1. The early international cooperation and investment in weather-related data collection has produced a valuable long-term record of air temperature that goes back to the 1850s.

Figure 1: Plate XV from Maury, Matthew F. 1858. The Winds. Chapter in Explanations and Sailing Directions. Washington: Hon. Isaac Toucey.

This vast store of information is considered "raw" data: tables of numbers (dates and temperatures), descriptions (cloud cover), location, etc. Raw data can be useful in and of itself – for example, if you wanted to know the air temperature in London on June 5, 1801. But the data alone cannot tell you anything about how temperature has changed in London over the past two hundred years, or how that information is related to global-scale climate change. In order for patterns and trends to be seen, data must be analyzed and interpreted first. The analyzed and interpreted data may then be used as evidence in scientific arguments, to support a hypothesis or a theory .

Good data are a potential treasure trove – they can be mined by scientists at any time – and thus an important part of any scientific investigation is accurate and consistent recording of data and the methods used to collect those data. The weather data collected since the 1850s have been just such a treasure trove, based in part upon the standards established by Matthew Maury . These standards provided guidelines for data collections and recording that assured consistency within the dataset . At the time, ship captains were able to utilize the data to determine the most reliable routes to sail across the oceans. Many modern scientists studying climate change have taken advantage of this same dataset to understand how global air temperatures have changed over the recent past. In neither case can one simply look at the table of numbers and observations and answer the question – which route to take, or how global climate has changed. Instead, both questions require analysis and interpretation of the data.

Comprehension Checkpoint

• Data analysis: A complex and challenging process

Though it may sound straightforward to take 150 years of air temperature data and describe how global climate has changed, the process of analyzing and interpreting those data is actually quite complex. Consider the range of temperatures around the world on any given day in January (see Figure 2): In Johannesburg, South Africa, where it is summer, the air temperature can reach 35° C (95° F), and in Fairbanks, Alaska at that same time of year, it is the middle of winter and air temperatures might be -35° C (-31° F). Now consider that over huge expanses of the ocean, where no consistent measurements are available. One could simply take an average of all of the available measurements for a single day to get a global air temperature average for that day, but that number would not take into account the natural variability within and uneven distribution of those measurements.

Figure 2: Satellite image composite of average air temperatures (in degrees Celsius) across the globe on January 2, 2008 (http://www.ssec.wisc.edu/data/).

Defining a single global average temperature requires scientists to make several decisions about how to process all of those data into a meaningful set of numbers. In 1986, climatologists Phil Jones, Tom Wigley, and Peter Wright published one of the first attempts to assess changes in global mean surface air temperature from 1861 to 1984 (Jones, Wigley, & Wright, 1986). The majority of their paper – three out of five pages – describes the processing techniques they used to correct for the problems and inconsistencies in the historical data that would not be related to climate. For example, the authors note:

Early SSTs [sea surface temperatures] were measured using water collected in uninsulated, canvas buckets, while more recent data come either from insulated bucket or cooling water intake measurements, with the latter considered to be 0.3-0.7° C warmer than uninsulated bucket measurements.

Correcting for this bias may seem simple, just adding ~0.5° C to early canvas bucket measurements, but it becomes more complicated than that because, the authors continue, the majority of SST data do not include a description of what kind of bucket or system was used.

Similar problems were encountered with marine air temperature data . Historical air temperature measurements over the ocean were taken aboard ships, but the type and size of ship could affect the measurement because size "determines the height at which observations were taken." Air temperature can change rapidly with height above the ocean. The authors therefore applied a correction for ship size in their data. Once Jones, Wigley, and Wright had made several of these kinds of corrections, they analyzed their data using a spatial averaging technique that placed measurements within grid cells on the Earth's surface in order to account for the fact that there were many more measurements taken on land than over the oceans.

Developing this grid required many decisions based on their experience and judgment, such as how large each grid cell needed to be and how to distribute the cells over the Earth. They then calculated the mean temperature within each grid cell, and combined all of these means to calculate a global average air temperature for each year. Statistical techniques such as averaging are commonly used in the research process and can help identify trends and relationships within and between datasets (see our Statistics in Science module). Once these spatially averaged global mean temperatures were calculated, the authors compared the means over time from 1861 to 1984.

A common method for analyzing data that occur in a series, such as temperature measurements over time, is to look at anomalies, or differences from a pre-defined reference value . In this case, the authors compared their temperature values to the mean of the years 1970-1979 (see Figure 3). This reference mean is subtracted from each annual mean to produce the jagged lines in Figure 3, which display positive or negative anomalies (values greater or less than zero). Though this may seem to be a circular or complex way to display these data, it is useful because the goal is to show change in mean temperatures rather than absolute values.

Figure 3: The black line shows global temperature anomalies, or differences between averaged yearly temperature measurements and the reference value for the entire globe. The smooth, red line is a filtered 10-year average. (Based on Figure 5 in Jones et al., 1986).

Putting data into a visual format can facilitate additional analysis (see our Using Graphs and Visual Data module). Figure 3 shows a lot of variability in the data: There are a number of spikes and dips in global temperature throughout the period examined. It can be challenging to see trends in data that have so much variability; our eyes are drawn to the extreme values in the jagged lines like the large spike in temperature around 1876 or the significant dip around 1918. However, these extremes do not necessarily reflect long-term trends in the data.

In order to more clearly see long-term patterns and trends, Jones and his co-authors used another processing technique and applied a filter to the data by calculating a 10-year running average to smooth the data. The smooth lines in the graph represent the filtered data. The smooth line follows the data closely, but it does not reach the extreme values .

Data processing and analysis are sometimes misinterpreted as manipulating data to achieve the desired results, but in reality, the goal of these methods is to make the data clearer, not to change it fundamentally. As described above, in addition to reporting data, scientists report the data processing and analysis methods they use when they publish their work (see our Understanding Scientific Journals and Articles module), allowing their peers the opportunity to assess both the raw data and the techniques used to analyze them.

• Data interpretation: Uncovering and explaining trends in the data

The analyzed data can then be interpreted and explained. In general, when scientists interpret data, they attempt to explain the patterns and trends uncovered through analysis , bringing all of their background knowledge, experience, and skills to bear on the question and relating their data to existing scientific ideas. Given the personal nature of the knowledge they draw upon, this step can be subjective, but that subjectivity is scrutinized through the peer review process (see our Peer Review in Science module). Based on the smoothed curves, Jones, Wigley, and Wright interpreted their data to show a long-term warming trend. They note that the three warmest years in the entire dataset are 1980, 1981, and 1983. They do not go further in their interpretation to suggest possible causes for the temperature increase, however, but merely state that the results are "extremely interesting when viewed in the light of recent ideas of the causes of climate change."

• Making data available

The process of data collection, analysis , and interpretation happens on multiple scales. It occurs over the course of a day, a year, or many years, and may involve one or many scientists whose priorities change over time. One of the fundamentally important components of the practice of science is therefore the publication of data in the scientific literature (see our Utilizing the Scientific Literature module). Properly collected and archived data continues to be useful as new research questions emerge. In fact, some research involves re-analysis of data with new techniques, different ways of looking at the data, or combining the results of several studies.

For example, in 1997, the Collaborative Group on Hormonal Factors in Breast Cancer published a widely-publicized study in the prestigious medical journal The Lancet entitled, "Breast cancer and hormone replacement therapy: collaborative reanalysis of data from 51 epidemiological studies of 52,705 women with breast cancer and 108,411 women without breast cancer" (Collaborative Group on Hormonal Factors in Breast Cancer, 1997). The possible link between breast cancer and hormone replacement therapy (HRT) had been studied for years, with mixed results: Some scientists suggested a small increase of cancer risk associated with HRT as early as 1981 (Brinton et al., 1981), but later research suggested no increased risk (Kaufman et al., 1984). By bringing together results from numerous studies and reanalyzing the data together, the researchers concluded that women who were treated with hormone replacement therapy were more like to develop breast cancer. In describing why the reanalysis was used, the authors write:

The increase in the relative risk of breast cancer associated with each year of [HRT] use in current and recent users is small, so inevitably some studies would, by chance alone, show significant associations and others would not. Combination of the results across many studies has the obvious advantage of reducing such random fluctuations.

In many cases, data collected for other purposes can be used to address new questions. The initial reason for collecting weather data, for example, was to better predict winds and storms to help assure safe travel for trading ships. It is only more recently that interest shifted to long-term changes in the weather, but the same data easily contribute to answering both of those questions.

• Technology for sharing data advances science

One of the most exciting advances in science today is the development of public databases of scientific information that can be accessed and used by anyone. For example, climatic and oceanographic data , which are generally very expensive to obtain because they require large-scale operations like drilling ice cores or establishing a network of buoys across the Pacific Ocean, are shared online through several web sites run by agencies responsible for maintaining and distributing those data, such as the Carbon Dioxide Information Analysis Center run by the US Department of Energy (see Research under the Resources tab). Anyone can download those data to conduct their own analyses and make interpretations . Likewise, the Human Genome Project has a searchable database of the human genome, where researchers can both upload and download their data (see Research under the Resources tab).

The number of these widely available datasets has grown to the point where the National Institute of Standards and Technology actually maintains a database of databases. Some organizations require their participants to make their data publicly available, such as the Incorporated Research Institutions for Seismology (IRIS): The instrumentation branch of IRIS provides support for researchers by offering seismic instrumentation, equipment maintenance and training, and logistical field support for experiments . Anyone can apply to use the instruments as long as they provide IRIS with the data they collect during their seismic experiments. IRIS then makes these data available to the public.

Making data available to other scientists is not a new idea, but having those data available on the Internet in a searchable format has revolutionized the way that scientists can interact with the data, allowing for research efforts that would have been impossible before. This collective pooling of data also allows for new kinds of analysis and interpretation on global scales and over long periods of time. In addition, making data easily accessible helps promote interdisciplinary research by opening the doors to exploration by diverse scientists in many fields.

Table of Contents

• Data collection, analysis, and interpretation: Weather and climate
• Different interpretations in the scientific community
• Debate over data interpretation spurs further research

Activate glossary term highlighting to easily identify key terms within the module. Once highlighted, you can click on these terms to view their definitions.

Activate NGSS annotations to easily identify NGSS standards within the module. Once highlighted, you can click on them to view these standards.

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Data Analysis in Research: Types & Methods

Content Index

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.

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• 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 Collection, Analysis, and Interpretation

• First Online: 03 January 2022

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• Mark F. McEntee 5  

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Often it has been said that proper prior preparation prevents performance. Many of the mistakes made in research have their origins back at the point of data collection. Perhaps it is natural human instinct not to plan; we learn from our experiences. However, it is crucial when it comes to the endeavours of science that we do plan our data collection with analysis and interpretation in mind. In this section on data collection, we will review some fundamental concepts of experimental design, sample size estimation, the assumptions that underlie most statistical processes, and ethical principles.

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McEntee, M.F. (2021). Data Collection, Analysis, and Interpretation. In: Seeram, E., Davidson, R., England, A., McEntee, M.F. (eds) Research for Medical Imaging and Radiation Sciences. Springer, Cham. https://doi.org/10.1007/978-3-030-79956-4_6

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• 13 min read

What is Data Interpretation? Methods, Examples & Tools

What is Data Interpretation?

• Importance of Data Interpretation in Today's World

Types of Data Interpretation

Quantitative data interpretation, qualitative data interpretation, mixed methods data interpretation, methods of data interpretation, descriptive statistics, inferential statistics, visualization techniques, benefits of data interpretation, data interpretation process, data interpretation use cases, data interpretation tools, data interpretation challenges and solutions, overcoming bias in data, dealing with missing data, addressing data privacy concerns, data interpretation examples, sales trend analysis, customer segmentation, predictive maintenance, fraud detection, data interpretation best practices, maintaining data quality, choosing the right tools, effective communication of results, ongoing learning and development, data interpretation tips.

Data interpretation is the process of making sense of data and turning it into actionable insights. With the rise of big data and advanced technologies, it has become more important than ever to be able to effectively interpret and understand data.

In today's fast-paced business environment, companies rely on data to make informed decisions and drive growth. However, with the sheer volume of data available, it can be challenging to know where to start and how to make the most of it.

This guide provides a comprehensive overview of data interpretation, covering everything from the basics of what it is to the benefits and best practices.

Data interpretation refers to the process of taking raw data and transforming it into useful information. This involves analyzing the data to identify patterns, trends, and relationships, and then presenting the results in a meaningful way. Data interpretation is an essential part of data analysis, and it is used in a wide range of fields, including business, marketing, healthcare, and many more.

Importance of Data Interpretation in Today's World

Data interpretation is critical to making informed decisions and driving growth in today's data-driven world. With the increasing availability of data, companies can now gain valuable insights into their operations, customer behavior, and market trends. Data interpretation allows businesses to make informed decisions, identify new opportunities, and improve overall efficiency.

There are three main types of data interpretation: quantitative, qualitative, and mixed methods.

Quantitative data interpretation refers to the process of analyzing numerical data. This type of data is often used to measure and quantify specific characteristics, such as sales figures, customer satisfaction ratings, and employee productivity.

Qualitative data interpretation refers to the process of analyzing non-numerical data, such as text, images, and audio. This data type is often used to gain a deeper understanding of customer attitudes and opinions and to identify patterns and trends.

Mixed methods data interpretation combines both quantitative and qualitative data to provide a more comprehensive understanding of a particular subject. This approach is particularly useful when analyzing data that has both numerical and non-numerical components, such as customer feedback data.

There are several data interpretation methods, including descriptive statistics, inferential statistics, and visualization techniques.

Descriptive statistics involve summarizing and presenting data in a way that makes it easy to understand. This can include calculating measures such as mean, median, mode, and standard deviation.

Inferential statistics involves making inferences and predictions about a population based on a sample of data. This type of data interpretation involves the use of statistical models and algorithms to identify patterns and relationships in the data.

Visualization techniques involve creating visual representations of data, such as graphs, charts, and maps. These techniques are particularly useful for communicating complex data in an easy-to-understand manner and identifying data patterns and trends.

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Data interpretation plays a crucial role in decision-making and helps organizations make informed choices. There are numerous benefits of data interpretation, including:

• Improved decision-making: Data interpretation provides organizations with the information they need to make informed decisions. By analyzing data, organizations can identify trends, patterns, and relationships that they may not have been able to see otherwise.
• Increased efficiency: By automating the data interpretation process, organizations can save time and improve their overall efficiency. With the right tools and methods, data interpretation can be completed quickly and accurately, providing organizations with the information they need to make decisions more efficiently.
• Better collaboration: Data interpretation can help organizations work more effectively with others, such as stakeholders, partners, and clients. By providing a common understanding of the data and its implications, organizations can collaborate more effectively and make better decisions.
• Increased accuracy: Data interpretation helps to ensure that data is accurate and consistent, reducing the risk of errors and miscommunication. By using data interpretation techniques, organizations can identify errors and inconsistencies in their data, making it possible to correct them and ensure the accuracy of their information.
• Enhanced transparency: Data interpretation can also increase transparency, helping organizations demonstrate their commitment to ethical and responsible data management. By providing clear and concise information, organizations can build trust and credibility with their stakeholders.
• Better resource allocation: Data interpretation can help organizations make better decisions about resource allocation. By analyzing data, organizations can identify areas where they are spending too much time or money and make adjustments to optimize their resources.
• Improved planning and forecasting: Data interpretation can also help organizations plan for the future. By analyzing historical data, organizations can identify trends and patterns that inform their forecasting and planning efforts.

Data interpretation is a process that involves several steps, including:

• Data collection: The first step in data interpretation is to collect data from various sources, such as surveys, databases, and websites. This data should be relevant to the issue or problem the organization is trying to solve.
• Data preparation: Once data is collected, it needs to be prepared for analysis. This may involve cleaning the data to remove errors, missing values, or outliers. It may also include transforming the data into a more suitable format for analysis.
• Data analysis: The next step is to analyze the data using various techniques, such as statistical analysis, visualization, and modeling. This analysis should be focused on uncovering trends, patterns, and relationships in the data.
• Data interpretation: Once the data has been analyzed, it needs to be interpreted to determine what the results mean. This may involve identifying key insights, drawing conclusions, and making recommendations.
• Data communication: The final step in the data interpretation process is to communicate the results and insights to others. This may involve creating visualizations, reports, or presentations to share the results with stakeholders.

Data interpretation can be applied in a variety of settings and industries. Here are a few examples of how data interpretation can be used:

• Marketing: Marketers use data interpretation to analyze customer behavior, preferences, and trends to inform marketing strategies and campaigns.
• Healthcare: Healthcare professionals use data interpretation to analyze patient data, including medical histories and test results, to diagnose and treat illnesses.
• Financial Services: Financial services companies use data interpretation to analyze financial data, such as investment performance, to inform investment decisions and strategies.
• Retail: Retail companies use data interpretation to analyze sales data, customer behavior, and market trends to inform merchandising and pricing strategies.
• Manufacturing: Manufacturers use data interpretation to analyze production data, such as machine performance and inventory levels, to inform production and inventory management decisions.

These are just a few examples of how data interpretation can be applied in various settings. The possibilities are endless, and data interpretation can provide valuable insights in any industry where data is collected and analyzed.

Data interpretation is a crucial step in the data analysis process, and the right tools can make a significant difference in accuracy and efficiency. Here are a few tools that can help you with data interpretation:

• Share parts of your spreadsheet, including sheets or even cell ranges, with different collaborators or stakeholders.
• Review and approve edits by collaborators to their respective sheets before merging them back with your master spreadsheet.
• Integrate popular tools and connect your tech stack to sync data from different sources, giving you a timely, holistic view of your data.
• Google Sheets: Google Sheets is a free, web-based spreadsheet application that allows users to create, edit, and format spreadsheets. It provides a range of features for data interpretation, including functions, charts, and pivot tables.
• Microsoft Excel: Microsoft Excel is a spreadsheet software widely used for data interpretation. It provides various functions and features to help you analyze and interpret data, including sorting, filtering, pivot tables, and charts.
• Tableau: Tableau is a data visualization tool that helps you see and understand your data. It allows you to connect to various data sources and create interactive dashboards and visualizations to communicate insights.
• Power BI: Power BI is a business analytics service that provides interactive visualizations and business intelligence capabilities with an easy interface for end users to create their own reports and dashboards.
• R: R is a programming language and software environment for statistical computing and graphics. It is widely used by statisticians, data scientists, and researchers to analyze and interpret data.

Each of these tools has its strengths and weaknesses, and the right tool for you will depend on your specific needs and requirements. Consider the size and complexity of your data, the analysis methods you need to use, and the level of customization you require, before making a decision.

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Data interpretation can be a complex and challenging process, but there are several solutions that can help overcome some of the most common difficulties.

Data interpretation can often be biased based on the data sources and the people who interpret it. It is important to eliminate these biases to get a clear and accurate understanding of the data. This can be achieved by diversifying the data sources, involving multiple stakeholders in the data interpretation process, and regularly reviewing the data interpretation methodology.

Missing data can often result in inaccuracies in the data interpretation process. To overcome this challenge, data scientists can use imputation methods to fill in missing data or use statistical models that can account for missing data.

Data privacy is a crucial concern in today's data-driven world. To address this, organizations should ensure that their data interpretation processes align with data privacy regulations and that the data being analyzed is adequately secured.

Data interpretation is used in a variety of industries and for a range of purposes. Here are a few examples:

Sales trend analysis is a common use of data interpretation in the business world. This type of analysis involves looking at sales data over time to identify trends and patterns, which can then be used to make informed business decisions.

Customer segmentation is a data interpretation technique that categorizes customers into segments based on common characteristics. This can be used to create more targeted marketing campaigns and to improve customer engagement.

Predictive maintenance is a data interpretation technique that uses machine learning algorithms to predict when equipment is likely to fail. This can help organizations proactively address potential issues and reduce downtime.

Fraud detection is a use case for data interpretation involving data and machine learning algorithms to identify patterns and anomalies that may indicate fraudulent activity.

To ensure that data interpretation processes are as effective and accurate as possible, it is recommended to follow some best practices.

Data quality is critical to the accuracy of data interpretation. To maintain data quality, organizations should regularly review and validate their data, eliminate data biases, and address missing data.

Choosing the right data interpretation tools is crucial to the success of the data interpretation process. Organizations should consider factors such as cost, compatibility with existing tools and processes, and the complexity of the data to be analyzed when choosing the right data interpretation tool. Layer, an add-on that equips teams with the tools to increase efficiency and data quality in their processes on top of Google Sheets, is an excellent choice for organizations looking to optimize their data interpretation process.

Data interpretation results need to be communicated effectively to stakeholders in a way they can understand. This can be achieved by using visual aids such as charts and graphs and presenting the results clearly and concisely.

The world of data interpretation is constantly evolving, and organizations must stay up to date with the latest developments and best practices. Ongoing learning and development initiatives, such as attending workshops and conferences, can help organizations stay ahead of the curve.

Regardless of the data interpretation method used, following best practices can help ensure accurate and reliable results. These best practices include:

• Validate data sources: It is essential to validate the data sources used to ensure they are accurate, up-to-date, and relevant. This helps to minimize the potential for errors in the data interpretation process.
• Use appropriate statistical techniques: The choice of statistical methods used for data interpretation should be suitable for the type of data being analyzed. For example, regression analysis is often used for analyzing trends in large data sets, while chi-square tests are used for categorical data.
• Graph and visualize data: Graphical representations of data can help to quickly identify patterns and trends. Visualization tools like histograms, scatter plots, and bar graphs can make the data more understandable and easier to interpret.
• Document and explain results: Results from data interpretation should be documented and presented in a clear and concise manner. This includes providing context for the results and explaining how they were obtained.
• Use a robust data interpretation tool: Data interpretation tools can help to automate the process and minimize the risk of errors. However, choosing a reliable, user-friendly tool that provides the features and functionalities needed to support the data interpretation process is vital.

Data interpretation is a crucial aspect of data analysis and enables organizations to turn large amounts of data into actionable insights. The guide covered the definition, importance, types, methods, benefits, process, analysis, tools, use cases, and best practices of data interpretation.

As technology continues to advance, the methods and tools used in data interpretation will also evolve. Predictive analytics and artificial intelligence will play an increasingly important role in data interpretation as organizations strive to automate and streamline their data analysis processes. In addition, big data and the Internet of Things (IoT) will lead to the generation of vast amounts of data that will need to be analyzed and interpreted effectively.

Data interpretation is a critical skill that enables organizations to make informed decisions based on data. It is essential that organizations invest in data interpretation and the development of their in-house data interpretation skills, whether through training programs or the use of specialized tools like Layer. By staying up-to-date with the latest trends and best practices in data interpretation, organizations can maximize the value of their data and drive growth and success.

Hady has a passion for tech, marketing, and spreadsheets. Besides his Computer Science degree, he has vast experience in developing, launching, and scaling content marketing processes at SaaS startups.

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Qualitative Data Analysis Methods 101:

The “big 6” methods + examples.

By: Kerryn Warren (PhD) | Reviewed By: Eunice Rautenbach (D.Tech) | May 2020 (Updated April 2023)

Qualitative data analysis methods. Wow, that’s a mouthful. 

If you’re new to the world of research, qualitative data analysis can look rather intimidating. So much bulky terminology and so many abstract, fluffy concepts. It certainly can be a minefield!

Don’t worry – in this post, we’ll unpack the most popular analysis methods , one at a time, so that you can approach your analysis with confidence and competence – whether that’s for a dissertation, thesis or really any kind of research project.

What (exactly) is qualitative data analysis?

To understand qualitative data analysis, we need to first understand qualitative data – so let’s step back and ask the question, “what exactly is qualitative data?”.

Qualitative data refers to pretty much any data that’s “not numbers” . In other words, it’s not the stuff you measure using a fixed scale or complex equipment, nor do you analyse it using complex statistics or mathematics.

So, if it’s not numbers, what is it?

Words, you guessed? Well… sometimes , yes. Qualitative data can, and often does, take the form of interview transcripts, documents and open-ended survey responses – but it can also involve the interpretation of images and videos. In other words, qualitative isn’t just limited to text-based data.

So, how’s that different from quantitative data, you ask?

Simply put, qualitative research focuses on words, descriptions, concepts or ideas – while quantitative research focuses on numbers and statistics . Qualitative research investigates the “softer side” of things to explore and describe , while quantitative research focuses on the “hard numbers”, to measure differences between variables and the relationships between them. If you’re keen to learn more about the differences between qual and quant, we’ve got a detailed post over here .

So, qualitative analysis is easier than quantitative, right?

Not quite. In many ways, qualitative data can be challenging and time-consuming to analyse and interpret. At the end of your data collection phase (which itself takes a lot of time), you’ll likely have many pages of text-based data or hours upon hours of audio to work through. You might also have subtle nuances of interactions or discussions that have danced around in your mind, or that you scribbled down in messy field notes. All of this needs to work its way into your analysis.

Making sense of all of this is no small task and you shouldn’t underestimate it. Long story short – qualitative analysis can be a lot of work! Of course, quantitative analysis is no piece of cake either, but it’s important to recognise that qualitative analysis still requires a significant investment in terms of time and effort.

Need a helping hand?

In this post, we’ll explore qualitative data analysis by looking at some of the most common analysis methods we encounter. We’re not going to cover every possible qualitative method and we’re not going to go into heavy detail – we’re just going to give you the big picture. That said, we will of course includes links to loads of extra resources so that you can learn more about whichever analysis method interests you.

Without further delay, let’s get into it.

The “Big 6” Qualitative Analysis Methods 

There are many different types of qualitative data analysis, all of which serve different purposes and have unique strengths and weaknesses . We’ll start by outlining the analysis methods and then we’ll dive into the details for each.

The 6 most popular methods (or at least the ones we see at Grad Coach) are:

• Content analysis
• Narrative analysis
• Discourse analysis
• Thematic analysis
• Grounded theory (GT)
• Interpretive phenomenological analysis (IPA)

Let’s take a look at each of them…

QDA Method #1: Qualitative Content Analysis

Content analysis is possibly the most common and straightforward QDA method. At the simplest level, content analysis is used to evaluate patterns within a piece of content (for example, words, phrases or images) or across multiple pieces of content or sources of communication. For example, a collection of newspaper articles or political speeches.

With content analysis, you could, for instance, identify the frequency with which an idea is shared or spoken about – like the number of times a Kardashian is mentioned on Twitter. Or you could identify patterns of deeper underlying interpretations – for instance, by identifying phrases or words in tourist pamphlets that highlight India as an ancient country.

Because content analysis can be used in such a wide variety of ways, it’s important to go into your analysis with a very specific question and goal, or you’ll get lost in the fog. With content analysis, you’ll group large amounts of text into codes , summarise these into categories, and possibly even tabulate the data to calculate the frequency of certain concepts or variables. Because of this, content analysis provides a small splash of quantitative thinking within a qualitative method.

Naturally, while content analysis is widely useful, it’s not without its drawbacks . One of the main issues with content analysis is that it can be very time-consuming , as it requires lots of reading and re-reading of the texts. Also, because of its multidimensional focus on both qualitative and quantitative aspects, it is sometimes accused of losing important nuances in communication.

Content analysis also tends to concentrate on a very specific timeline and doesn’t take into account what happened before or after that timeline. This isn’t necessarily a bad thing though – just something to be aware of. So, keep these factors in mind if you’re considering content analysis. Every analysis method has its limitations , so don’t be put off by these – just be aware of them ! If you’re interested in learning more about content analysis, the video below provides a good starting point.

QDA Method #2: Narrative Analysis 

As the name suggests, narrative analysis is all about listening to people telling stories and analysing what that means . Since stories serve a functional purpose of helping us make sense of the world, we can gain insights into the ways that people deal with and make sense of reality by analysing their stories and the ways they’re told.

You could, for example, use narrative analysis to explore whether how something is being said is important. For instance, the narrative of a prisoner trying to justify their crime could provide insight into their view of the world and the justice system. Similarly, analysing the ways entrepreneurs talk about the struggles in their careers or cancer patients telling stories of hope could provide powerful insights into their mindsets and perspectives . Simply put, narrative analysis is about paying attention to the stories that people tell – and more importantly, the way they tell them.

Of course, the narrative approach has its weaknesses , too. Sample sizes are generally quite small due to the time-consuming process of capturing narratives. Because of this, along with the multitude of social and lifestyle factors which can influence a subject, narrative analysis can be quite difficult to reproduce in subsequent research. This means that it’s difficult to test the findings of some of this research.

Similarly, researcher bias can have a strong influence on the results here, so you need to be particularly careful about the potential biases you can bring into your analysis when using this method. Nevertheless, narrative analysis is still a very useful qualitative analysis method – just keep these limitations in mind and be careful not to draw broad conclusions . If you’re keen to learn more about narrative analysis, the video below provides a great introduction to this qualitative analysis method.

QDA Method #3: Discourse Analysis 

Discourse is simply a fancy word for written or spoken language or debate . So, discourse analysis is all about analysing language within its social context. In other words, analysing language – such as a conversation, a speech, etc – within the culture and society it takes place. For example, you could analyse how a janitor speaks to a CEO, or how politicians speak about terrorism.

To truly understand these conversations or speeches, the culture and history of those involved in the communication are important factors to consider. For example, a janitor might speak more casually with a CEO in a company that emphasises equality among workers. Similarly, a politician might speak more about terrorism if there was a recent terrorist incident in the country.

So, as you can see, by using discourse analysis, you can identify how culture , history or power dynamics (to name a few) have an effect on the way concepts are spoken about. So, if your research aims and objectives involve understanding culture or power dynamics, discourse analysis can be a powerful method.

Because there are many social influences in terms of how we speak to each other, the potential use of discourse analysis is vast . Of course, this also means it’s important to have a very specific research question (or questions) in mind when analysing your data and looking for patterns and themes, or you might land up going down a winding rabbit hole.

Discourse analysis can also be very time-consuming  as you need to sample the data to the point of saturation – in other words, until no new information and insights emerge. But this is, of course, part of what makes discourse analysis such a powerful technique. So, keep these factors in mind when considering this QDA method. Again, if you’re keen to learn more, the video below presents a good starting point.

QDA Method #4: Thematic Analysis

Thematic analysis looks at patterns of meaning in a data set – for example, a set of interviews or focus group transcripts. But what exactly does that… mean? Well, a thematic analysis takes bodies of data (which are often quite large) and groups them according to similarities – in other words, themes . These themes help us make sense of the content and derive meaning from it.

Let’s take a look at an example.

With thematic analysis, you could analyse 100 online reviews of a popular sushi restaurant to find out what patrons think about the place. By reviewing the data, you would then identify the themes that crop up repeatedly within the data – for example, “fresh ingredients” or “friendly wait staff”.

So, as you can see, thematic analysis can be pretty useful for finding out about people’s experiences , views, and opinions . Therefore, if your research aims and objectives involve understanding people’s experience or view of something, thematic analysis can be a great choice.

Since thematic analysis is a bit of an exploratory process, it’s not unusual for your research questions to develop , or even change as you progress through the analysis. While this is somewhat natural in exploratory research, it can also be seen as a disadvantage as it means that data needs to be re-reviewed each time a research question is adjusted. In other words, thematic analysis can be quite time-consuming – but for a good reason. So, keep this in mind if you choose to use thematic analysis for your project and budget extra time for unexpected adjustments.

QDA Method #5: Grounded theory (GT) 

Grounded theory is a powerful qualitative analysis method where the intention is to create a new theory (or theories) using the data at hand, through a series of “ tests ” and “ revisions ”. Strictly speaking, GT is more a research design type than an analysis method, but we’ve included it here as it’s often referred to as a method.

What’s most important with grounded theory is that you go into the analysis with an open mind and let the data speak for itself – rather than dragging existing hypotheses or theories into your analysis. In other words, your analysis must develop from the ground up (hence the name). 

Let’s look at an example of GT in action.

Assume you’re interested in developing a theory about what factors influence students to watch a YouTube video about qualitative analysis. Using Grounded theory , you’d start with this general overarching question about the given population (i.e., graduate students). First, you’d approach a small sample – for example, five graduate students in a department at a university. Ideally, this sample would be reasonably representative of the broader population. You’d interview these students to identify what factors lead them to watch the video.

After analysing the interview data, a general pattern could emerge. For example, you might notice that graduate students are more likely to read a post about qualitative methods if they are just starting on their dissertation journey, or if they have an upcoming test about research methods.

From here, you’ll look for another small sample – for example, five more graduate students in a different department – and see whether this pattern holds true for them. If not, you’ll look for commonalities and adapt your theory accordingly. As this process continues, the theory would develop . As we mentioned earlier, what’s important with grounded theory is that the theory develops from the data – not from some preconceived idea.

So, what are the drawbacks of grounded theory? Well, some argue that there’s a tricky circularity to grounded theory. For it to work, in principle, you should know as little as possible regarding the research question and population, so that you reduce the bias in your interpretation. However, in many circumstances, it’s also thought to be unwise to approach a research question without knowledge of the current literature . In other words, it’s a bit of a “chicken or the egg” situation.

Regardless, grounded theory remains a popular (and powerful) option. Naturally, it’s a very useful method when you’re researching a topic that is completely new or has very little existing research about it, as it allows you to start from scratch and work your way from the ground up .

QDA Method #6:   Interpretive Phenomenological Analysis (IPA)

Interpretive. Phenomenological. Analysis. IPA . Try saying that three times fast…

Let’s just stick with IPA, okay?

IPA is designed to help you understand the personal experiences of a subject (for example, a person or group of people) concerning a major life event, an experience or a situation . This event or experience is the “phenomenon” that makes up the “P” in IPA. Such phenomena may range from relatively common events – such as motherhood, or being involved in a car accident – to those which are extremely rare – for example, someone’s personal experience in a refugee camp. So, IPA is a great choice if your research involves analysing people’s personal experiences of something that happened to them.

It’s important to remember that IPA is subject – centred . In other words, it’s focused on the experiencer . This means that, while you’ll likely use a coding system to identify commonalities, it’s important not to lose the depth of experience or meaning by trying to reduce everything to codes. Also, keep in mind that since your sample size will generally be very small with IPA, you often won’t be able to draw broad conclusions about the generalisability of your findings. But that’s okay as long as it aligns with your research aims and objectives.

Another thing to be aware of with IPA is personal bias . While researcher bias can creep into all forms of research, self-awareness is critically important with IPA, as it can have a major impact on the results. For example, a researcher who was a victim of a crime himself could insert his own feelings of frustration and anger into the way he interprets the experience of someone who was kidnapped. So, if you’re going to undertake IPA, you need to be very self-aware or you could muddy the analysis.

How to choose the right analysis method

In light of all of the qualitative analysis methods we’ve covered so far, you’re probably asking yourself the question, “ How do I choose the right one? ”

Much like all the other methodological decisions you’ll need to make, selecting the right qualitative analysis method largely depends on your research aims, objectives and questions . In other words, the best tool for the job depends on what you’re trying to build. For example:

• Perhaps your research aims to analyse the use of words and what they reveal about the intention of the storyteller and the cultural context of the time.
• Perhaps your research aims to develop an understanding of the unique personal experiences of people that have experienced a certain event, or
• Perhaps your research aims to develop insight regarding the influence of a certain culture on its members.

As you can probably see, each of these research aims are distinctly different , and therefore different analysis methods would be suitable for each one. For example, narrative analysis would likely be a good option for the first aim, while grounded theory wouldn’t be as relevant. 

It’s also important to remember that each method has its own set of strengths, weaknesses and general limitations. No single analysis method is perfect . So, depending on the nature of your research, it may make sense to adopt more than one method (this is called triangulation ). Keep in mind though that this will of course be quite time-consuming.

As we’ve seen, all of the qualitative analysis methods we’ve discussed make use of coding and theme-generating techniques, but the intent and approach of each analysis method differ quite substantially. So, it’s very important to come into your research with a clear intention before you decide which analysis method (or methods) to use.

Start by reviewing your research aims , objectives and research questions to assess what exactly you’re trying to find out – then select a qualitative analysis method that fits. Never pick a method just because you like it or have experience using it – your analysis method (or methods) must align with your broader research aims and objectives.

Let’s recap on QDA methods…

In this post, we looked at six popular qualitative data analysis methods:

• First, we looked at content analysis , a straightforward method that blends a little bit of quant into a primarily qualitative analysis.
• Then we looked at narrative analysis , which is about analysing how stories are told.
• Next up was discourse analysis – which is about analysing conversations and interactions.
• Then we moved on to thematic analysis – which is about identifying themes and patterns.
• From there, we went south with grounded theory – which is about starting from scratch with a specific question and using the data alone to build a theory in response to that question.
• And finally, we looked at IPA – which is about understanding people’s unique experiences of a phenomenon.

Of course, these aren’t the only options when it comes to qualitative data analysis, but they’re a great starting point if you’re dipping your toes into qualitative research for the first time.

If you’re still feeling a bit confused, consider our private coaching service , where we hold your hand through the research process to help you develop your best work.

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 ...

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84 Comments

This has been very helpful. Thank you.

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Great to hear that. Good luck with your qualitative data analysis, Pramod!

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Precise explanation of method.

Hi, may we use 2 data analysis methods in our qualitative research?

Thanks for your comment. Most commonly, one would use one type of analysis method, but it depends on your research aims and objectives.

You explained it in very simple language, everyone can understand it. Thanks so much.

Thank you very much, this is very helpful. It has been explained in a very simple manner that even a layman understands

Thank nicely explained can I ask is Qualitative content analysis the same as thematic analysis?

Thanks for your comment. No, QCA and thematic are two different types of analysis. This article might help clarify – https://onlinelibrary.wiley.com/doi/10.1111/nhs.12048

This is my first time to come across a well explained data analysis. so helpful.

I have thoroughly enjoyed your explanation of the six qualitative analysis methods. This is very helpful. Thank you!

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i need a citation of your book.

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Hi Derek, What other theories/methods would you recommend when the data is a whole speech?

Keep writing useful artikel.

It is important concept about QDA and also the way to express is easily understandable, so thanks for all.

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Very helpful .Thanks.

Hi there! Very well explained. Simple but very useful style of writing. Please provide the citation of the text. warm regards

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Very insightful. Please, which of this approach could be used for a research that one is trying to elicit students’ misconceptions in a particular concept ?

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great overview

What do we call a research data analysis method that one use to advise or determining the best accounting tool or techniques that should be adopted in a company.

Informative video, explained in a clear and simple way. Kudos

Waoo! I have chosen method wrong for my data analysis. But I can revise my work according to this guide. Thank you so much for this helpful lecture.

This has been very helpful. It gave me a good view of my research objectives and how to choose the best method. Thematic analysis it is.

Very helpful indeed. Thanku so much for the insight.

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very educative

Nicely written especially for novice academic researchers like me! Thank you.

choosing a right method for a paper is always a hard job for a student, this is a useful information, but it would be more useful personally for me, if the author provide me with a little bit more information about the data analysis techniques in type of explanatory research. Can we use qualitative content analysis technique for explanatory research ? or what is the suitable data analysis method for explanatory research in social studies?

that was very helpful for me. because these details are so important to my research. thank you very much

I learnt a lot. Thank you

Relevant and Informative, thanks !

Well-planned and organized, thanks much! 🙂

I have reviewed qualitative data analysis in a simplest way possible. The content will highly be useful for developing my book on qualitative data analysis methods. Cheers!

Clear explanation on qualitative and how about Case study

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Wow, Thanks for making my life easy

This was helpful thanks .

Very helpful…. clear and written in an easily understandable manner. Thank you.

This was so helpful as it was easy to understand. I’m a new to research thank you so much.

so educative…. but Ijust want to know which method is coding of the qualitative or tallying done?

Thank you for the great content, I have learnt a lot. So helpful

precise and clear presentation with simple language and thank you for that.

very informative content, thank you.

You guys are amazing on YouTube on this platform. Your teachings are great, educative, and informative. kudos!

Brilliant Delivery. You made a complex subject seem so easy. Well done.

Beautifully explained.

Thanks a lot

Is there a video the captures the practical process of coding using automated applications?

Thanks for the comment. We don’t recommend using automated applications for coding, as they are not sufficiently accurate in our experience.

content analysis can be qualitative research?

THANK YOU VERY MUCH.

Thank you very much for such a wonderful content

do you have any material on Data collection

What a powerful explanation of the QDA methods. Thank you.

Great explanation both written and Video. i have been using of it on a day to day working of my thesis project in accounting and finance. Thank you very much for your support.

very helpful, thank you so much

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• Volume 17, Issue 1
• Qualitative data analysis: a practical example
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• Helen Noble 1 ,
• Joanna Smith 2
• 1 School of Nursing and Midwifery, Queens's University Belfast , Belfast , UK
• 2 Department of Health Sciences , University of Huddersfield , Huddersfield , UK
• Correspondence to : Dr Helen Noble School of Nursing and Midwifery, Queen's University Belfast, Medical Biology Centre, 97 Lisburn Road, Belfast BT9 7BL, UK; helen.noble{at}qub.ac.uk

https://doi.org/10.1136/eb-2013-101603

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The aim of this paper is to equip readers with an understanding of the principles of qualitative data analysis and offer a practical example of how analysis might be undertaken in an interview-based study.

What is qualitative data analysis?

What are the approaches in undertaking qualitative data analysis.

Although qualitative data analysis is inductive and focuses on meaning, approaches in analysing data are diverse with different purposes and ontological (concerned with the nature of being) and epistemological (knowledge and understanding) underpinnings. 2 Identifying an appropriate approach in analysing qualitative data analysis to meet the aim of a study can be challenging. One way to understand qualitative data analysis is to consider the processes involved. 3 Approaches can be divided into four broad groups: quasistatistical approaches such as content analysis; the use of frameworks or matrices such as a framework approach and thematic analysis; interpretative approaches that include interpretative phenomenological analysis and grounded theory; and sociolinguistic approaches such as discourse analysis and conversation analysis. However, there are commonalities across approaches. Data analysis is an interactive process, where data are systematically searched and analysed in order to provide an illuminating description of phenomena; for example, the experience of carers supporting dying patients with renal disease 4 or student nurses’ experiences following assignment referral. 5 Data analysis is an iterative or recurring process, essential to the creativity of the analysis, development of ideas, clarifying meaning and the reworking of concepts as new insights ‘emerge’ or are identified in the data.

Do you need data software packages when analysing qualitative data?

Qualitative data software packages are not a prerequisite for undertaking qualitative analysis but a range of programmes are available that can assist the qualitative researcher. Software programmes vary in design and application but can be divided into text retrievers, code and retrieve packages and theory builders. 6 NVivo and NUD*IST are widely used because they have sophisticated code and retrieve functions and modelling capabilities, which speed up the process of managing large data sets and data retrieval. Repetitions within data can be quantified and memos and hyperlinks attached to data. Analytical processes can be mapped and tracked and linkages across data visualised leading to theory development. 6 Disadvantages of using qualitative data software packages include the complexity of the software and some programmes are not compatible with standard text format. Extensive coding and categorising can result in data becoming unmanageable and researchers may find visualising data on screen inhibits conceptualisation of the data.

How do you begin analysing qualitative data?

Despite the diversity of qualitative methods, the subsequent analysis is based on a common set of principles and for interview data includes: transcribing the interviews; immersing oneself within the data to gain detailed insights into the phenomena being explored; developing a data coding system; and linking codes or units of data to form overarching themes/concepts, which may lead to the development of theory. 2 Identifying recurring and significant themes, whereby data are methodically searched to identify patterns in order to provide an illuminating description of a phenomenon, is a central skill in undertaking qualitative data analysis. Table 1 contains an extract of data taken from a research study which included interviews with carers of people with end-stage renal disease managed without dialysis. The extract is taken from a carer who is trying to understand why her mother was not offered dialysis. The first stage of data analysis involves the process of initial coding, whereby each line of the data is considered to identify keywords or phrases; these are sometimes known as in vivo codes (highlighted) because they retain participants’ words.

• View inline

Data extract containing units of data and line-by-line coding

When transcripts have been broken down into manageable sections, the researcher sorts and sifts them, searching for types, classes, sequences, processes, patterns or wholes. The next stage of data analysis involves bringing similar categories together into broader themes. Table 2 provides an example of the early development of codes and categories and how these link to form broad initial themes.

Development of initial themes from descriptive codes

Table 3 presents an example of further category development leading to final themes which link to an overarching concept.

Development of final themes and overarching concept

How do qualitative researchers ensure data analysis procedures are transparent and robust?

In congruence with quantitative researchers, ensuring qualitative studies are methodologically robust is essential. Qualitative researchers need to be explicit in describing how and why they undertook the research. However, qualitative research is criticised for lacking transparency in relation to the analytical processes employed, which hinders the ability of the reader to critically appraise study findings. 7 In the three tables presented the progress from units of data to coding to theme development is illustrated. ‘Not involved in treatment decisions’ appears in each table and informs one of the final themes. Documenting the movement from units of data to final themes allows for transparency of data analysis. Although other researchers may interpret the data differently, appreciating and understanding how the themes were developed is an essential part of demonstrating the robustness of the findings. Qualitative researchers must demonstrate rigour, associated with openness, relevance to practice and congruence of the methodological approch. 2 In summary qualitative research is complex in that it produces large amounts of data and analysis is time consuming and complex. High-quality data analysis requires a researcher with expertise, vision and veracity.

• Cheater F ,
• Robshaw M ,
• McLafferty E ,
• Maggs-Rapport F

Competing interests None.

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Presenting and Evaluating Qualitative Research

The purpose of this paper is to help authors to think about ways to present qualitative research papers in the American Journal of Pharmaceutical Education . It also discusses methods for reviewers to assess the rigour, quality, and usefulness of qualitative research. Examples of different ways to present data from interviews, observations, and focus groups are included. The paper concludes with guidance for publishing qualitative research and a checklist for authors and reviewers.

INTRODUCTION

Policy and practice decisions, including those in education, increasingly are informed by findings from qualitative as well as quantitative research. Qualitative research is useful to policymakers because it often describes the settings in which policies will be implemented. Qualitative research is also useful to both pharmacy practitioners and pharmacy academics who are involved in researching educational issues in both universities and practice and in developing teaching and learning.

Qualitative research involves the collection, analysis, and interpretation of data that are not easily reduced to numbers. These data relate to the social world and the concepts and behaviors of people within it. Qualitative research can be found in all social sciences and in the applied fields that derive from them, for example, research in health services, nursing, and pharmacy. 1 It looks at X in terms of how X varies in different circumstances rather than how big is X or how many Xs are there? 2 Textbooks often subdivide research into qualitative and quantitative approaches, furthering the common assumption that there are fundamental differences between the 2 approaches. With pharmacy educators who have been trained in the natural and clinical sciences, there is often a tendency to embrace quantitative research, perhaps due to familiarity. A growing consensus is emerging that sees both qualitative and quantitative approaches as useful to answering research questions and understanding the world. Increasingly mixed methods research is being carried out where the researcher explicitly combines the quantitative and qualitative aspects of the study. 3 , 4

Like healthcare, education involves complex human interactions that can rarely be studied or explained in simple terms. Complex educational situations demand complex understanding; thus, the scope of educational research can be extended by the use of qualitative methods. Qualitative research can sometimes provide a better understanding of the nature of educational problems and thus add to insights into teaching and learning in a number of contexts. For example, at the University of Nottingham, we conducted in-depth interviews with pharmacists to determine their perceptions of continuing professional development and who had influenced their learning. We also have used a case study approach using observation of practice and in-depth interviews to explore physiotherapists' views of influences on their leaning in practice. We have conducted in-depth interviews with a variety of stakeholders in Malawi, Africa, to explore the issues surrounding pharmacy academic capacity building. A colleague has interviewed and conducted focus groups with students to explore cultural issues as part of a joint Nottingham-Malaysia pharmacy degree program. Another colleague has interviewed pharmacists and patients regarding their expectations before and after clinic appointments and then observed pharmacist-patient communication in clinics and assessed it using the Calgary Cambridge model in order to develop recommendations for communication skills training. 5 We have also performed documentary analysis on curriculum data to compare pharmacist and nurse supplementary prescribing courses in the United Kingdom.

It is important to choose the most appropriate methods for what is being investigated. Qualitative research is not appropriate to answer every research question and researchers need to think carefully about their objectives. Do they wish to study a particular phenomenon in depth (eg, students' perceptions of studying in a different culture)? Or are they more interested in making standardized comparisons and accounting for variance (eg, examining differences in examination grades after changing the way the content of a module is taught). Clearly a quantitative approach would be more appropriate in the last example. As with any research project, a clear research objective has to be identified to know which methods should be applied.

Types of qualitative data include:

• Audio recordings and transcripts from in-depth or semi-structured interviews
• Structured interview questionnaires containing substantial open comments including a substantial number of responses to open comment items.
• Audio recordings and transcripts from focus group sessions.
• Field notes (notes taken by the researcher while in the field [setting] being studied)
• Video recordings (eg, lecture delivery, class assignments, laboratory performance)
• Case study notes
• Documents (reports, meeting minutes, e-mails)
• Diaries, video diaries
• Observation notes
• Press clippings
• Photographs

RIGOUR IN QUALITATIVE RESEARCH

Qualitative research is often criticized as biased, small scale, anecdotal, and/or lacking rigor; however, when it is carried out properly it is unbiased, in depth, valid, reliable, credible and rigorous. In qualitative research, there needs to be a way of assessing the “extent to which claims are supported by convincing evidence.” 1 Although the terms reliability and validity traditionally have been associated with quantitative research, increasingly they are being seen as important concepts in qualitative research as well. Examining the data for reliability and validity assesses both the objectivity and credibility of the research. Validity relates to the honesty and genuineness of the research data, while reliability relates to the reproducibility and stability of the data.

The validity of research findings refers to the extent to which the findings are an accurate representation of the phenomena they are intended to represent. The reliability of a study refers to the reproducibility of the findings. Validity can be substantiated by a number of techniques including triangulation use of contradictory evidence, respondent validation, and constant comparison. Triangulation is using 2 or more methods to study the same phenomenon. Contradictory evidence, often known as deviant cases, must be sought out, examined, and accounted for in the analysis to ensure that researcher bias does not interfere with or alter their perception of the data and any insights offered. Respondent validation, which is allowing participants to read through the data and analyses and provide feedback on the researchers' interpretations of their responses, provides researchers with a method of checking for inconsistencies, challenges the researchers' assumptions, and provides them with an opportunity to re-analyze their data. The use of constant comparison means that one piece of data (for example, an interview) is compared with previous data and not considered on its own, enabling researchers to treat the data as a whole rather than fragmenting it. Constant comparison also enables the researcher to identify emerging/unanticipated themes within the research project.

STRENGTHS AND LIMITATIONS OF QUALITATIVE RESEARCH

Qualitative researchers have been criticized for overusing interviews and focus groups at the expense of other methods such as ethnography, observation, documentary analysis, case studies, and conversational analysis. Qualitative research has numerous strengths when properly conducted.

Strengths of Qualitative Research

• Issues can be examined in detail and in depth.
• Interviews are not restricted to specific questions and can be guided/redirected by the researcher in real time.
• The research framework and direction can be quickly revised as new information emerges.
• The data based on human experience that is obtained is powerful and sometimes more compelling than quantitative data.
• Subtleties and complexities about the research subjects and/or topic are discovered that are often missed by more positivistic enquiries.
• Data usually are collected from a few cases or individuals so findings cannot be generalized to a larger population. Findings can however be transferable to another setting.

Limitations of Qualitative Research

• Research quality is heavily dependent on the individual skills of the researcher and more easily influenced by the researcher's personal biases and idiosyncrasies.
• Rigor is more difficult to maintain, assess, and demonstrate.
• The volume of data makes analysis and interpretation time consuming.
• It is sometimes not as well understood and accepted as quantitative research within the scientific community
• The researcher's presence during data gathering, which is often unavoidable in qualitative research, can affect the subjects' responses.
• Issues of anonymity and confidentiality can present problems when presenting findings
• Findings can be more difficult and time consuming to characterize in a visual way.

PRESENTATION OF QUALITATIVE RESEARCH FINDINGS

The following extracts are examples of how qualitative data might be presented:

Data From an Interview.

The following is an example of how to present and discuss a quote from an interview.

The researcher should select quotes that are poignant and/or most representative of the research findings. Including large portions of an interview in a research paper is not necessary and often tedious for the reader. The setting and speakers should be established in the text at the end of the quote.

The student describes how he had used deep learning in a dispensing module. He was able to draw on learning from a previous module, “I found that while using the e learning programme I was able to apply the knowledge and skills that I had gained in last year's diseases and goals of treatment module.” (interviewee 22, male)

This is an excerpt from an article on curriculum reform that used interviews 5 :

The first question was, “Without the accreditation mandate, how much of this curriculum reform would have been attempted?” According to respondents, accreditation played a significant role in prompting the broad-based curricular change, and their comments revealed a nuanced view. Most indicated that the change would likely have occurred even without the mandate from the accreditation process: “It reflects where the profession wants to be … training a professional who wants to take on more responsibility.” However, they also commented that “if it were not mandated, it could have been a very difficult road.” Or it “would have happened, but much later.” The change would more likely have been incremental, “evolutionary,” or far more limited in its scope. “Accreditation tipped the balance” was the way one person phrased it. “Nobody got serious until the accrediting body said it would no longer accredit programs that did not change.”

Data From Observations

The following example is some data taken from observation of pharmacist patient consultations using the Calgary Cambridge guide. 6 , 7 The data are first presented and a discussion follows:

Pharmacist: We will soon be starting a stop smoking clinic. Patient: Is the interview over now? Pharmacist: No this is part of it. (Laughs) You can't tell me to bog off (sic) yet. (pause) We will be starting a stop smoking service here, Patient: Yes. Pharmacist: with one-to-one and we will be able to help you or try to help you. If you want it. In this example, the pharmacist has picked up from the patient's reaction to the stop smoking clinic that she is not receptive to advice about giving up smoking at this time; in fact she would rather end the consultation. The pharmacist draws on his prior relationship with the patient and makes use of a joke to lighten the tone. He feels his message is important enough to persevere but he presents the information in a succinct and non-pressurised way. His final comment of “If you want it” is important as this makes it clear that he is not putting any pressure on the patient to take up this offer. This extract shows that some patient cues were picked up, and appropriately dealt with, but this was not the case in all examples.

Data From Focus Groups

This excerpt from a study involving 11 focus groups illustrates how findings are presented using representative quotes from focus group participants. 8

Those pharmacists who were initially familiar with CPD endorsed the model for their peers, and suggested it had made a meaningful difference in the way they viewed their own practice. In virtually all focus groups sessions, pharmacists familiar with and supportive of the CPD paradigm had worked in collaborative practice environments such as hospital pharmacy practice. For these pharmacists, the major advantage of CPD was the linking of workplace learning with continuous education. One pharmacist stated, “It's amazing how much I have to learn every day, when I work as a pharmacist. With [the learning portfolio] it helps to show how much learning we all do, every day. It's kind of satisfying to look it over and see how much you accomplish.” Within many of the learning portfolio-sharing sessions, debates emerged regarding the true value of traditional continuing education and its outcome in changing an individual's practice. While participants appreciated the opportunity for social and professional networking inherent in some forms of traditional CE, most eventually conceded that the academic value of most CE programming was limited by the lack of a systematic process for following-up and implementing new learning in the workplace. “Well it's nice to go to these [continuing education] events, but really, I don't know how useful they are. You go, you sit, you listen, but then, well I at least forget.”

The following is an extract from a focus group (conducted by the author) with first-year pharmacy students about community placements. It illustrates how focus groups provide a chance for participants to discuss issues on which they might disagree.

Interviewer: So you are saying that you would prefer health related placements? Student 1: Not exactly so long as I could be developing my communication skill. Student 2: Yes but I still think the more health related the placement is the more I'll gain from it. Student 3: I disagree because other people related skills are useful and you may learn those from taking part in a community project like building a garden. Interviewer: So would you prefer a mixture of health and non health related community placements?

GUIDANCE FOR PUBLISHING QUALITATIVE RESEARCH

Qualitative research is becoming increasingly accepted and published in pharmacy and medical journals. Some journals and publishers have guidelines for presenting qualitative research, for example, the British Medical Journal 9 and Biomedcentral . 10 Medical Education published a useful series of articles on qualitative research. 11 Some of the important issues that should be considered by authors, reviewers and editors when publishing qualitative research are discussed below.

Introduction.

A good introduction provides a brief overview of the manuscript, including the research question and a statement justifying the research question and the reasons for using qualitative research methods. This section also should provide background information, including relevant literature from pharmacy, medicine, and other health professions, as well as literature from the field of education that addresses similar issues. Any specific educational or research terminology used in the manuscript should be defined in the introduction.

The methods section should clearly state and justify why the particular method, for example, face to face semistructured interviews, was chosen. The method should be outlined and illustrated with examples such as the interview questions, focusing exercises, observation criteria, etc. The criteria for selecting the study participants should then be explained and justified. The way in which the participants were recruited and by whom also must be stated. A brief explanation/description should be included of those who were invited to participate but chose not to. It is important to consider “fair dealing,” ie, whether the research design explicitly incorporates a wide range of different perspectives so that the viewpoint of 1 group is never presented as if it represents the sole truth about any situation. The process by which ethical and or research/institutional governance approval was obtained should be described and cited.

The study sample and the research setting should be described. Sampling differs between qualitative and quantitative studies. In quantitative survey studies, it is important to select probability samples so that statistics can be used to provide generalizations to the population from which the sample was drawn. Qualitative research necessitates having a small sample because of the detailed and intensive work required for the study. So sample sizes are not calculated using mathematical rules and probability statistics are not applied. Instead qualitative researchers should describe their sample in terms of characteristics and relevance to the wider population. Purposive sampling is common in qualitative research. Particular individuals are chosen with characteristics relevant to the study who are thought will be most informative. Purposive sampling also may be used to produce maximum variation within a sample. Participants being chosen based for example, on year of study, gender, place of work, etc. Representative samples also may be used, for example, 20 students from each of 6 schools of pharmacy. Convenience samples involve the researcher choosing those who are either most accessible or most willing to take part. This may be fine for exploratory studies; however, this form of sampling may be biased and unrepresentative of the population in question. Theoretical sampling uses insights gained from previous research to inform sample selection for a new study. The method for gaining informed consent from the participants should be described, as well as how anonymity and confidentiality of subjects were guaranteed. The method of recording, eg, audio or video recording, should be noted, along with procedures used for transcribing the data.

Data Analysis.

A description of how the data were analyzed also should be included. Was computer-aided qualitative data analysis software such as NVivo (QSR International, Cambridge, MA) used? Arrival at “data saturation” or the end of data collection should then be described and justified. A good rule when considering how much information to include is that readers should have been given enough information to be able to carry out similar research themselves.

One of the strengths of qualitative research is the recognition that data must always be understood in relation to the context of their production. 1 The analytical approach taken should be described in detail and theoretically justified in light of the research question. If the analysis was repeated by more than 1 researcher to ensure reliability or trustworthiness, this should be stated and methods of resolving any disagreements clearly described. Some researchers ask participants to check the data. If this was done, it should be fully discussed in the paper.

An adequate account of how the findings were produced should be included A description of how the themes and concepts were derived from the data also should be included. Was an inductive or deductive process used? The analysis should not be limited to just those issues that the researcher thinks are important, anticipated themes, but also consider issues that participants raised, ie, emergent themes. Qualitative researchers must be open regarding the data analysis and provide evidence of their thinking, for example, were alternative explanations for the data considered and dismissed, and if so, why were they dismissed? It also is important to present outlying or negative/deviant cases that did not fit with the central interpretation.

The interpretation should usually be grounded in interviewees or respondents' contributions and may be semi-quantified, if this is possible or appropriate, for example, “Half of the respondents said …” “The majority said …” “Three said…” Readers should be presented with data that enable them to “see what the researcher is talking about.” 1 Sufficient data should be presented to allow the reader to clearly see the relationship between the data and the interpretation of the data. Qualitative data conventionally are presented by using illustrative quotes. Quotes are “raw data” and should be compiled and analyzed, not just listed. There should be an explanation of how the quotes were chosen and how they are labeled. For example, have pseudonyms been given to each respondent or are the respondents identified using codes, and if so, how? It is important for the reader to be able to see that a range of participants have contributed to the data and that not all the quotes are drawn from 1 or 2 individuals. There is a tendency for authors to overuse quotes and for papers to be dominated by a series of long quotes with little analysis or discussion. This should be avoided.

Participants do not always state the truth and may say what they think the interviewer wishes to hear. A good qualitative researcher should not only examine what people say but also consider how they structured their responses and how they talked about the subject being discussed, for example, the person's emotions, tone, nonverbal communication, etc. If the research was triangulated with other qualitative or quantitative data, this should be discussed.

Discussion.

The findings should be presented in the context of any similar previous research and or theories. A discussion of the existing literature and how this present research contributes to the area should be included. A consideration must also be made about how transferrable the research would be to other settings. Any particular strengths and limitations of the research also should be discussed. It is common practice to include some discussion within the results section of qualitative research and follow with a concluding discussion.

The author also should reflect on their own influence on the data, including a consideration of how the researcher(s) may have introduced bias to the results. The researcher should critically examine their own influence on the design and development of the research, as well as on data collection and interpretation of the data, eg, were they an experienced teacher who researched teaching methods? If so, they should discuss how this might have influenced their interpretation of the results.

Conclusion.

The conclusion should summarize the main findings from the study and emphasize what the study adds to knowledge in the area being studied. Mays and Pope suggest the researcher ask the following 3 questions to determine whether the conclusions of a qualitative study are valid 12 : How well does this analysis explain why people behave in the way they do? How comprehensible would this explanation be to a thoughtful participant in the setting? How well does the explanation cohere with what we already know?

CHECKLIST FOR QUALITATIVE PAPERS

This paper establishes criteria for judging the quality of qualitative research. It provides guidance for authors and reviewers to prepare and review qualitative research papers for the American Journal of Pharmaceutical Education . A checklist is provided in Appendix 1 to assist both authors and reviewers of qualitative data.

ACKNOWLEDGEMENTS

Thank you to the 3 reviewers whose ideas helped me to shape this paper.

Appendix 1. Checklist for authors and reviewers of qualitative research.

Introduction

• □ Research question is clearly stated.
• □ Research question is justified and related to the existing knowledge base (empirical research, theory, policy).
• □ Any specific research or educational terminology used later in manuscript is defined.
• □ The process by which ethical and or research/institutional governance approval was obtained is described and cited.
• □ Reason for choosing particular research method is stated.
• □ Criteria for selecting study participants are explained and justified.
• □ Recruitment methods are explicitly stated.
• □ Details of who chose not to participate and why are given.
• □ Study sample and research setting used are described.
• □ Method for gaining informed consent from the participants is described.
• □ Maintenance/Preservation of subject anonymity and confidentiality is described.
• □ Method of recording data (eg, audio or video recording) and procedures for transcribing data are described.
• □ Methods are outlined and examples given (eg, interview guide).
• □ Decision to stop data collection is described and justified.
• □ Data analysis and verification are described, including by whom they were performed.
• □ Methods for identifying/extrapolating themes and concepts from the data are discussed.
• □ Sufficient data are presented to allow a reader to assess whether or not the interpretation is supported by the data.
• □ Outlying or negative/deviant cases that do not fit with the central interpretation are presented.
• □ Transferability of research findings to other settings is discussed.
• □ Findings are presented in the context of any similar previous research and social theories.
• □ Discussion often is incorporated into the results in qualitative papers.
• □ A discussion of the existing literature and how this present research contributes to the area is included.
• □ Any particular strengths and limitations of the research are discussed.
• □ Reflection of the influence of the researcher(s) on the data, including a consideration of how the researcher(s) may have introduced bias to the results is included.

Conclusions

• □ The conclusion states the main finings of the study and emphasizes what the study adds to knowledge in the subject area.

Analyzing and Interpreting Data

Richard C. Dicker

• Planning the Analysis
• Analyzing Data from a Field Investigation
• Summary Exposure Tables

Stratified Analysis

• Confounding
• Effect Modification
• Dose-Response
• Interpreting Data from a Field Investigation

Field investigations are usually conducted to identify the factors that increased a person’s risk for a disease or other health outcome. In certain field investigations, identifying the cause is sufficient; if the cause can be eliminated, the problem is solved. In other investigations, the goal is to quantify the association between exposure (or any population characteristic) and the health outcome to guide interventions or advance knowledge. Both types of field investigations require suitable, but not necessarily sophisticated, analytic methods. This chapter describes the strategy for planning an analysis, methods for conducting the analysis, and guidelines for interpreting the results.

A thoughtfully planned and carefully executed analysis is as crucial for a field investigation as it is for a protocol-based study. Planning is necessary to ensure that the appropriate hypotheses will be considered and that the relevant data will be collected, recorded, managed, analyzed, and interpreted to address those hypotheses. Therefore, the time to decide what data to collect and how to analyze those data is before you design your questionnaire, not after you have collected the data.

An analysis plan is a document that guides how you progress from raw data to the final report. It describes where you are starting (data sources and data sets), how you will look at and analyze the data, and where you need to finish (final report). It lays out the key components of the analysis in a logical sequence and provides a guide to follow during the actual analysis.

An analysis plan includes some or most of the content listed in Box 8.1 . Some of the listed elements are more likely to appear in an analysis plan for a protocol-based planned study, but even an outbreak investigation should include the key components in a more abbreviated analysis plan, or at least in a series of table shells.

• List of the research questions or hypotheses
• Source(s) of data
• Description of population or groups (inclusion or exclusion criteria)
• Source of data or data sets, particularly for secondary data analysis or population denominators
• Type of study
• How data will be manipulated
• Data sets to be used or merged
• New variables to be created
• Key variables (attach data dictionary of all variables)
• Demographic and exposure variables
• Outcome or endpoint variables
• Stratification variables (e.g., potential confounders or effect modifiers)
• How variables will be analyzed (e.g., as a continuous variable or grouped in categories)
• How to deal with missing values
• Order of analysis (e.g., frequency distributions, two-way tables, stratified analysis, dose-response, or group analysis)
• Measures of occurrence, association, tests of significance, or confidence intervals to be used
• Table shells to be used in analysis
• Tables shells to be included in final report
• Research question or hypotheses . The analysis plan usually begins with the research questions or hypotheses you plan to address. Well-reasoned research questions or hypotheses lead directly to the variables that need to be analyzed and the methods of analysis. For example, the question, “What caused the outbreak of gastroenteritis?” might be a suitable objective for a field investigation, but it is not a specific research question. A more specific question—for example, “Which foods were more likely to have been consumed by case-patients than by controls?”—indicates that key variables will be food items and case–control status and that the analysis method will be a two-by-two table for each food.
• Analytic strategies . Different types of studies (e.g., cohort, case–control, or cross-sectional) are analyzed with different measures and methods. Therefore, the analysis strategy must be consistent with how the data will be collected. For example, data from a simple retrospective cohort study should be analyzed by calculating and comparing attack rates among exposure groups. Data from a case–control study must be analyzed by comparing exposures among case-patients and controls, and the data must account for matching in the analysis if matching was used in the design. Data from a cross-sectional study or survey might need to incorporate weights or design effects in the analysis.The analysis plan should specify which variables are most important—exposures and outcomes of interest, other known risk factors, study design factors (e.g., matching variables), potential confounders, and potential effect modifiers.
• Data dictionary . A data dictionary is a document that provides key information about each variable. Typically, a data dictionary lists each variable’s name, a brief description, what type of variable it is (e.g., numeric, text, or date), allowable values, and an optional comment. Data dictionaries can be organized in different ways, but a tabular format with one row per variable, and columns for name, description, type, legal value, and comment is easy to organize (see example in Table 8.1 from an outbreak investigation of oropharyngeal tularemia [ 1 ]). A supplement to the data dictionary might include a copy of the questionnaire with the variable names written next to each question.
• Get to know your data . Plan to get to know your data by reviewing (1) the frequency of responses and descriptive statistics for each variable; (2) the minimum, maximum, and average values for each variable; (3) whether any variables have the same response for every record; and (4) whether any variables have many or all missing values. These patterns will influence how you analyze these variables or drop them from the analysis altogether.
• Table shells . The next step in developing the analysis plan is designing the table shells. A table shell, sometimes called a dummy table , is a table (e.g., frequency distribution or two-by-two table) that is titled and fully labeled but contains no data. The numbers will be filled in as the analysis progresses. Table shells provide a guide to the analysis, so their sequence should proceed in logical order from simple (e.g., descriptive epidemiology) to more complex (e.g., analytic epidemiology) ( Box 8.2 ). Each table shell should indicate which measures (e.g., attack rates, risk ratios [RR] or odds ratios [ORs], 95% confidence intervals [CIs]) and statistics (e.g., chi-square and p value) should accompany the table. See Handout 8.1 for an example of a table shell created for the field investigation of oropharyngeal tularemia ( 1 ).

The first two tables usually generated as part of the analysis of data from a field investigation are those that describe clinical features of the case-patients and present the descriptive epidemiology. Because descriptive epidemiology is addressed in Chapter 6 , the remainder of this chapter addresses the analytic epidemiology tools used most commonly in field investigations.

Handout 8.2 depicts output from the Classic Analysis module of Epi Info 7 (Centers for Disease Control and Prevention, Atlanta, GA) ( 2 ). It demonstrates the output from the TABLES command for data from a typical field investigation. Note the key elements of the output: (1) a cross-tabulated table summarizing the results, (2) point estimates of measures of association, (3) 95% CIs for each point estimate, and (4) statistical test results. Each of these elements is discussed in the following sections.

Source: Adapted from Reference 1 .

Handout 8.2 : Time, by date of illness onset (could be included in Table 1, but for outbreaks, better to display as an epidemic curve).

Table 1 . Clinical features (e.g., signs and symptoms, percentage of laboratory-confirmed cases, percentage of hospitalized patients, and percentage of patients who died).

Table 2 . Demographic (e.g., age and sex) and other key characteristics of study participants by case–control status if case–control study.

Place (geographic area of residence or occurrence in Table 2 or in a spot or shaded map).

Table 3 . Primary tables of exposure-outcome association.

Table 4 . Stratification (Table 3 with separate effects and assessment of confounding and effect modification).

Table 5 . Refinements (Table 3 with, for example, dose-response, latency, and use of more sensitive or more specific case definition).

Table 6 . Specific group analyses.

Two-by-Two Tables

A two-by-two table is so named because it is a cross-tabulation of two variables—exposure and health outcome—that each have two categories, usually “yes” and “no” ( Handout 8.3 ). The two-by-two table is the best way to summarize data that reflect the association between a particular exposure (e.g., consumption of a specific food) and the health outcome of interest (e.g., gastroenteritis). The association is usually quantified by calculating a measure of association (e.g., a risk ratio [RR] or OR) from the data in the two-by-two table (see the following section).

• In a typical two-by-two table used in field epidemiology, disease status (e.g., ill or well, case or control) is represented along the top of the table, and exposure status (e.g., exposed or unexposed) along the side.
• Depending on the exposure being studied, the rows can be labeled as shown in Table 8.3 , or for example, as exposed and unexposed or ever and never . By convention, the exposed group is placed on the top row.
• Depending on the disease or health outcome being studied, the columns can be labeled as shown in Handout 8.3, or for example, as ill and well, case and control , or dead and alive . By convention, the ill or case group is placed in the left column.
• The intersection of a row and a column in which a count is recorded is known as a cell . The letters a, b, c , and d within the four cells refer to the number of persons with the disease status indicated in the column heading at the top and the exposure status indicated in the row label to the left. For example, cell c contains the number of ill but unexposed persons. The row totals are labeled H 1 and H 0 (or H 2 [H for horizontal ]) and the columns are labeled V 1 and V 0 (or V 2 [V for vertical ]). The total number of persons included in the two-by-two table is written in the lower right corner and is represented by the letter T or N .
• If the data are from a cohort study, attack rates (i.e., the proportion of persons who become ill during the time period of interest) are sometimes provided to the right of the row totals. RRs or ORs, CIs, or p values are often provided to the right of or beneath the table.

The illustrative cross-tabulation of tap water consumption (exposure) and illness status (outcome) from the investigation of oropharyngeal tularemia is displayed in Table 8.2 ( 1 ).

Table Shell: Association Between Drinking Water From Different Sources And Oropharyngeal Tularemia (Sancaktepe Village, Bayburt Province, Turkey, July– August 2013)

Abbreviation: CI, confidence interval. Adapted from Reference 1 .

Typical Output From Classic Analysis Module, Epi Info Version 7, Using The Tables Command

Source: Reference 2 .

Table Shell: Association Between Drinking Water From Different Sources and Oropharyngeal Tularemia (Sancaktepe Village, Bayburt Province, Turkey, July– August 2013)

Abbreviation: CI, confidence interval.

Risk ratio = 26.59 / 10.59 = 2.5; 95% confidence interval = (1.3–4.9); chi-square (uncorrected) = 8.7 (p = 0.003). Source: Adapted from Reference 1.

Measures of Association

A measure of association quantifies the strength or magnitude of the statistical association between an exposure and outcome. Measures of association are sometimes called measures of effect because if the exposure is causally related to the health outcome, the measure quantifies the effect of exposure on the probability that the health outcome will occur.

The measures of association most commonly used in field epidemiology are all ratios—RRs, ORs, prevalence ratios (PRs), and prevalence ORs (PORs). These ratios can be thought of as comparing the observed with the expected—that is, the observed amount of disease among persons exposed versus the expected (or baseline) amount of disease among persons unexposed. The measures clearly demonstrate whether the amount of disease among the exposed group is similar to, higher than, or lower than (and by how much) the amount of disease in the baseline group.

• The value of each measure of association equals 1.0 when the amount of disease is the same among the exposed and unexposed groups.
• The measure has a value greater than 1.0 when the amount of disease is greater among the exposed group than among the unexposed group, consistent with a harmful effect.
• The measure has a value less than 1.0 when the amount of disease among the exposed group is less than it is among the unexposed group, as when the exposure protects against occurrence of disease (e.g., vaccination).

Different measures of association are used with different types of studies. The most commonly used measure in a typical outbreak investigation retrospective cohort study is the RR , which is simply the ratio of attack rates. For most case–control studies, because attack rates cannot be calculated, the measure of choice is the OR .

Cross-sectional studies or surveys typically measure prevalence (existing cases) rather than incidence (new cases) of a health condition. Prevalence measures of association analogous to the RR and OR—the PR and POR , respectively—are commonly used.

Risk Ratio (Relative Risk)

The RR, the preferred measure for cohort studies, is calculated as the attack rate (risk) among the exposed group divided by the attack rate (risk) among the unexposed group. Using the notations in Handout 8.3,

RR=risk exposed /risk unexposed = (a/H 1 ) / (c/H 0 )

From Table 8.2 , the attack rate (i.e., risk) for acquiring oropharyngeal tularemia among persons who had drunk tap water at the banquet was 26.6%. The attack rate (i.e., risk) for those who had not drunk tap water was 10.6%. Thus, the RR is calculated as 0.266/ 0.106 = 2.5. That is, persons who had drunk tap water were 2.5 times as likely to become ill as those who had not drunk tap water ( 1 ).

The OR is the preferred measure of association for case–control data. Conceptually, it is calculated as the odds of exposure among case-patients divided by the odds of exposure among controls. However, in practice, it is calculated as the cross-product ratio. Using the notations in Handout 8.3,

The illustrative data in Handout 8.4 are from a case–control study of acute renal failure in Panama in 2006 (3). Because the data are from a case–control study, neither attack rates (risks) nor an RR can be calculated. The OR—calculated as 37 × 110/ (29 × 4) = 35.1—is exceptionally high, indicating a strong association between ingesting liquid cough syrup and acute renal failure.

Confounding is the distortion of an exposure–outcome association by the effect of a third factor (a confounder ). A third factor might be a confounder if it is

• Associated with the outcome independent of the exposure—that is, it must be an independent risk factor; and,
• Associated with the exposure but is not a consequence of it.

Consider a hypothetical retrospective cohort study of mortality among manufacturing employees that determined that workers involved with the manufacturing process were substantially more likely to die during the follow-up period than office workers and salespersons in the same industry.

• The increase in mortality reflexively might be attributed to one or more exposures during the manufacturing process.
• If, however, the manufacturing workers’ average age was 15 years older than the other workers, mortality reasonably could be expected to be higher among the older workers.
• In that situation, age likely is a confounder that could account for at least some of the increased mortality. (Note that age satisfies the two criteria described previously: increasing age is associated with increased mortality, regardless of occupation; and, in that industry, age was associated with job—specifically, manufacturing employees were older than the office workers).

Unfortunately, confounding is common. The first step in dealing with confounding is to look for it. If confounding is identified, the second step is to control for or adjust for its distorting effect by using available statistical methods.

Looking for Confounding

The most common method for looking for confounding is to stratify the exposure–outcome association of interest by the third variable suspected to be a confounder.

• Because one of the two criteria for a confounding variable is that it should be associated with the outcome, the list of potential confounders should include the known risk factors for the disease. The list also should include matching variables. Because age frequently is a confounder, it should be considered a potential confounder in any data set.
• For each stratum, compute a stratum-specific measure of association. If the stratification variable is sex, only women will be in one stratum and only men in the other. The exposure–outcome association is calculated separately for women and for men. Sex can no longer be a confounder in these strata because women are compared with women and men are compared with men.

The OR is a useful measure of association because it provides an estimate of the association between exposure and disease from case–control data when an RR cannot be calculated. Additionally, when the outcome is relatively uncommon among the population (e.g., <5%), the OR from a case–control study approximates the RR that would have been derived from a cohort study, had one been performed. However, when the outcome is more common, the OR overestimates the RR.

Prevalence Ratio and Prevalence Odds Ratio

Cross-sectional studies or surveys usually measure the prevalence rather than incidence of a health status (e.g., vaccination status) or condition (e.g., hypertension) among a population. The prevalence measures of association analogous to the RR and OR are, respectively, the PR and POR .

The PR is calculated as the prevalence among the index group divided by the prevalence among the comparison group. Using the notations in Handout 8.3 ,

PR = prevalence index / prevalence comparison = (a/H 1 ) / (c/H 0 )

The POR is calculated like an OR.

POR = ad/bc

In a study of HIV seroprevalence among current users of crack cocaine versus never users, 165 of 780 current users were HIV-positive (prevalence = 21.2%), compared with 40 of 464 never users (prevalence = 8.6%) (4). The PR and POR were close (2.5 and 2.8, respectively), but the PR is easier to explain.

Odds ratio = 35.1; 95% confidence interval = (11.6–106.4); chi-square (uncorrected) = 65.6 (p<0.001). Source: Adapted from Reference 3 .

Measures of Public Health Impact

A measure of public health impact places the exposure–disease association in a public health perspective. The impact measure reflects the apparent contribution of the exposure to the health outcome among a population. For example, for an exposure associated with an increased risk for disease (e.g., smoking and lung cancer), the attributable risk percent represents the amount of lung cancer among smokers ascribed to smoking, which also can be regarded as the expected reduction in disease load if the exposure could be removed or had never existed.

For an exposure associated with a decreased risk for disease (e.g., vaccination), the prevented fraction represents the observed reduction in disease load attributable to the current level of exposure among the population. Note that the terms attributable and prevented convey more than mere statistical association. They imply a direct cause-and-effect relationship between exposure and disease. Therefore, these measures should be presented only after thoughtful inference of causality.

Attributable Risk Percent

The attributable risk percent (attributable fraction or proportion among the exposed, etiologic fraction) is the proportion of cases among the exposed group presumably attributable to the exposure. This measure assumes that the level of risk among the unexposed group (who are considered to have the baseline or background risk for disease) also applies to the exposed group, so that only the excess risk should be attributed to the exposure. The attributable risk percent can be calculated with either of the following algebraically equivalent formulas:

Attributable risk percent = (risk exposed / risk unexposed ) / risk exposed = (RR–1) / RR

In a case– control study, if the OR is a reasonable approximation of the RR, an attributable risk percent can be calculated from the OR.

Attributable risk percent = (OR–1) / OR

In the outbreak setting, attributable risk percent can be used to quantify how much of the disease burden can be ascribed to particular exposure.

Prevented Fraction Among the Exposed Group (Vaccine Efficacy)

The prevented fraction among the exposed group can be calculated when the RR or OR is less than 1.0. This measure is the proportion of potential cases prevented by a beneficial exposure (e.g., bed nets that prevent nighttime mosquito bites and, consequently, malaria). It can also be regarded as the proportion of new cases that would have occurred in the absence of the beneficial exposure. Algebraically, the prevented fraction among the exposed population is identical to vaccine efficacy.

Prevented fraction among the exposed group = vaccine efficacy = (risk exposed / risk unexposed ) /= risk unexposed = 1 RR

Handout 8.5 displays data from a varicella (chickenpox) outbreak at an elementary school in Nebraska in 2004 ( 5 ). The risk for varicella was 13.6% among vaccinated children and 66.7% among unvaccinated children. The vaccine efficacy based on these data was calculated as (0.667 – 0.130)/ 0.667 = 0.805, or 80.5%. This vaccine efficacy of 80.5% indicates that vaccination prevented approximately 80% of the cases that would have otherwise occurred among vaccinated children had they not been vaccinated.

Risk ratio = 13.0/ 66.7 = 0.195; vaccine efficacy = (66.7 − 13.0)/ 66.7 = 80.5%. Source: Adapted from Reference 5 .

Tests of Statistical Significance

Tests of statistical significance are used to determine how likely the observed results would have occurred by chance alone if exposure was unrelated to the health outcome. This section describes the key factors to consider when applying statistical tests to data from two-by-two tables.

• Statistical testing begins with the assumption that, among the source population, exposure is unrelated to disease. This assumption is known as the null hypothesis . The alternative hypothesis , which will be adopted if the null hypothesis proves to be implausible, is that exposure is associated with disease.
• Next, compute a measure of association (e.g., an RR or OR).
• A small p value means that you would be unlikely to observe such an association if the null hypothesis were true. In other words, a small p value indicates that the null hypothesis is implausible, given available data.
• If this p value is smaller than a predetermined cutoff, called alpha (usually 0.05 or 5%), you discard (reject) the null hypothesis in favor of the alternative hypothesis. The association is then said to be statistically significant .
• If the p value is larger than the cutoff (e.g., p value >0.06), do not reject the null hypothesis; the apparent association could be a chance finding.
• In a type I error (also called alpha error ), the null hypothesis is rejected when in fact it is true.
• In a type II error (also called beta error ), the null hypothesis is not rejected when in fact it is false.

Testing and Interpreting Data in a Two-by-Two Table

For data in a two-by-two table Epi Info reports the results from two different tests—chi-square test and Fisher exact test—each with variations ( Handout 8.2 ). These tests are not specific to any particular measure of association. The same test can be used regardless of whether you are interested in RR, OR, or attributable risk percent.

• If the expected value in any cell is less than 5. Fisher exact test is the commonly accepted standard when the expected value in any cell is less than 5. (Remember: The expected value for any cell can be determined by multiplying the row total by the column total and dividing by the table total.)
• If all expected values in the two-by-two table are 5 or greater. Choose one of the chi-square tests. Fortunately, for most analyses, the three chi-square formulas provide p values sufficiently similar to make the same decision regarding the null hypothesis based on all three. However, when the different formulas point to different decisions (usually when all three p values are approximately 0.05), epidemiologic judgment is required. Some field epidemiologists prefer the Yates-corrected formula because they are least likely to make a type I error (but most likely to make a type II error). Others acknowledge that the Yates correction often overcompensates; therefore, they prefer the uncorrected formula. Epidemiologists who frequently perform stratified analyses are accustomed to using the Mantel-Haenszel formula; therefore, they tend to use this formula even for simple two-by-two tables.
• Measure of association. The measures of association (e.g., RRs and ORs) reflect the strength of the association between an exposure and a disease. These measures are usually independent of the size of the study and can be regarded as the best guess of the true degree of association among the source population. However, the measure gives no indication of its reliability (i.e., how much faith to put in it).
• Test of significance. In contrast, a test of significance provides an indication of how likely it is that the observed association is the result of chance. Although the chi-square test statistic is influenced both by the magnitude of the association and the study size, it does not distinguish the contribution of each one. Thus, the measure of association and the test of significance (or a CI; see Confidence Intervals for Measures of Association) provide complementary information.
• Role of statistical significance. Statistical significance does not by itself indicate a cause-and-effect association. An observed association might indeed represent a causal connection, but it might also result from chance, selection bias, information bias, confounding, or other sources of error in the study’s design, execution, or analysis. Statistical testing relates only to the role of chance in explaining an observed association, and statistical significance indicates only that chance is an unlikely, although not impossible, explanation of the association. Epidemiologic judgment is required when considering these and other criteria for inferring causation (e.g., consistency of the findings with those from other studies, the temporal association between exposure and disease, or biologic plausibility).
• Public health implications of statistical significance. Finally, statistical significance does not necessarily mean public health significance. With a large study, a weak association with little public health or clinical relevance might nonetheless be statistically significant. More commonly, if a study is small, an association of public health or clinical importance might fail to reach statistically significance.

Confidence Intervals for Measures of Association

Many medical and public health journals now require that associations be described by measures of association and CIs rather than p values or other statistical tests. A measure of association such as an RR or OR provides a single value (point estimate) that best quantifies the association between an exposure and health outcome. A CI provides an interval estimate or range of values that acknowledge the uncertainty of the single number point estimate, particularly one that is based on a sample of the population.

The 95% Confidence Interval

Statisticians define a 95% CI as the interval that, given repeated sampling of the source population, will include, or cover, the true association value 95% of the time. The epidemiologic concept of a 95% CI is that it includes range of values consistent with the data in the study ( 6 ).

Relation Between Chi-Square Test and Confidence Interval

The chi-square test and the CI are closely related. The chi-square test uses the observed data to determine the probability ( p value) under the null hypothesis, and one rejects the null hypothesis if the probability is less than alpha (e.g., 0.05). The CI uses a preselected probability value, alpha (e.g., 0.05), to determine the limits of the interval (1 − alpha = 0.95), and one rejects the null hypothesis if the interval does not include the null association value. Both indicate the precision of the observed association; both are influenced by the magnitude of the association and the size of the study group. Although both measure precision, neither addresses validity (lack of bias).

Interpreting the Confidence Interval

• Meaning of a confidence interval . A CI can be regarded as the range of values consistent with the data in a study. Suppose a study conducted locally yields an RR of 4.0 for the association between intravenous drug use and disease X; the 95% CI ranges from 3.0 to 5.3. From that study, the best estimate of the association between intravenous drug use and disease X among the general population is 4.0, but the data are consistent with values anywhere from 3.0 to 5.3. A study of the same association conducted elsewhere that yielded an RR of 3.2 or 5.2 would be considered compatible, but a study that yielded an RR of 1.2 or 6.2 would not be considered compatible. Now consider a different study that yields an RR of 1.0, a CI from 0.9 to 1.1, and a p value = 0.9. Rather than interpreting these results as nonsignificant and uninformative, you can conclude that the exposure neither increases nor decreases the risk for disease. That message can be reassuring if the exposure had been of concern to a worried public. Thus, the values that are included in the CI and values that are excluded by the CI both provide important information.
• Width of the confidence interval. The width of a CI (i.e., the included values) reflects the precision with which a study can pinpoint an association. A wide CI reflects a large amount of variability or imprecision. A narrow CI reflects less variability and higher precision. Usually, the larger the number of subjects or observations in a study, the greater the precision and the narrower the CI.
• Relation of the confidence interval to the null hypothesis. Because a CI reflects the range of values consistent with the data in a study, the CI can be used as a substitute for statistical testing (i.e., to determine whether the data are consistent with the null hypothesis). Remember: the null hypothesis specifies that the RR or OR equals 1.0; therefore, a CI that includes 1.0 is compatible with the null hypothesis. This is equivalent to concluding that the null hypothesis cannot be rejected. In contrast, a CI that does not include 1.0 indicates that the null hypothesis should be rejected because it is inconsistent with the study results. Thus, the CI can be used as a surrogate test of statistical significance.

Confidence Intervals in the Foodborne Outbreak Setting

In the setting of a foodborne outbreak, the goal is to identify the food or other vehicle that caused illness. In this setting, a measure of the association (e.g., an RR or OR) is calculated to identify the food(s) or other consumable(s) with high values that might have caused the outbreak. The investigator does not usually care if the RR for a specific food item is 5.7 or 9.3, just that the RR is high and unlikely to be caused by chance and, therefore, that the item should be further evaluated. For that purpose, the point estimate (RR or OR) plus a p value is adequate and a CI is unnecessary.

For field investigations intended to identify one or more vehicles or risk factors for disease, consider constructing a single table that can summarize the associations for multiple exposures of interest. For foodborne outbreak investigations, the table typically includes one row for each food item and columns for the name of the food; numbers of ill and well persons, by food consumption history; food-specific attack rates (if a cohort study was conducted); RR or OR; chi-square or p value; and, sometimes, a 95% CI. The food most likely to have caused illness will usually have both of the following characteristics:

• An elevated RR, OR, or chi-square (small p value), reflecting a substantial difference in attack rates among those who consumed that food and those who did not.
• The majority of the ill persons had consumed that food; therefore, the exposure can explain or account for most if not all of the cases.

In illustrative summary Table 8.3 , tap water had the highest RR (and the only p value <0.05, based on the 95% CI excluding 1.0) and might account for 46 of 55 cases.

Abbreviation: CI, confidence interval. Source: Adapted from Reference 1 .

Stratification is the examination of an exposure–disease association in two or more categories (strata) of a third variable (e.g., age). It is a useful tool for assessing whether confounding is present and, if it is, controlling for it. Stratification is also the best method for identifying effect modification . Both confounding and effect modification are addressed in following sections.

Stratification is also an effective method for examining the effects of two different exposures on a disease. For example, in a foodborne outbreak, two foods might seem to be associated with illness on the basis of elevated RRs or ORs. Possibly both foods were contaminated or included the same contaminated ingredient. Alternatively, the two foods might have been eaten together (e.g., peanut butter and jelly or doughnuts and milk), with only one being contaminated and the other guilty by association. Stratification is one way to tease apart the effects of the two foods.

Creating Strata of Two-by-Two Tables

• To stratify by sex, create a two-by-two table for males and another table for females.
• To stratify by age, decide on age groupings, making certain not to have overlapping ages; then create a separate two-by-two table for each age group.
• For example, the data in Table 8.2 are stratified by sex in Handouts 8.6 and 8.7 . The RR for drinking tap water and experiencing oropharyngeal tularemia is 2.3 among females and 3.6 among males, but stratification also allows you to see that women have a higher risk than men, regardless of tap water consumption.

The Two-by-Four Table

Stratified tables (e.g., Handouts 8.6 and 8.7 ) are useful when the stratification variable is not of primary interest (i.e., is not being examined as a cause of the outbreak). However, when each of the two exposures might be the cause, a two-by-four table is better for disentangling the effects of the two variables. Consider a case–control study of a hypothetical hepatitis A outbreak that yielded elevated ORs both for doughnuts (OR = 6.0) and milk (OR = 3.9). The data organized in a two-by-four table ( Handout 8.8 ) disentangle the effects of the two foods—exposure to doughnuts alone is strongly associated with illness (OR = 6.0), but exposure to milk alone is not (OR = 1.0).

When two foods cause illness—for example when they are both contaminated or have a common ingredient—the two-by-four table is the best way to see their individual and joint effects.

Source: Adapted from Reference 1.

Crude odds ratio for doughnuts = 6.0; crude odds ratio for milk = 3.9.

• To look for confounding, first examine the smallest and largest values of the stratum-specific measures of association and compare them with the value of the combined table (called the crude value ). Confounding is present if the crude value is outside the range between the smallest and largest stratum-specific values.
• If the crude risk ratio or odds ratio is outside the range of the stratum-specific ones.
• If the crude risk ratio or odds ratio differs from the Mantel-Haenszel adjusted one by >10% or >20%.

Controlling for Confounding

• One method of controlling for confounding is by calculating a summary RR or OR based on a weighted average of the stratum-specific data. The Mantel-Haenszel technique ( 6 ) is a popular method for performing this task.
• A second method is by using a logistic regression model that includes the exposure of interest and one or more confounding variables. The model produces an estimate of the OR that controls for the effect of the confounding variable(s).

Effect modification or effect measure modification means that the degree of association between an exposure and an outcome differs among different population groups. For example, measles vaccine is usually highly effective in preventing disease if administered to children aged 12 months or older but is less effective if administered before age 12 months. Similarly, tetracycline can cause tooth mottling among children, but not adults. In both examples, the association (or effect) of the exposure (measles vaccine or tetracycline) is a function of, or is modified by, a third variable (age in both examples).

Because effect modification means different effects among different groups, the first step in looking for effect modification is to stratify the exposure–outcome association of interest by the third variable suspected to be the effect modifier. Next, calculate the measure of association (e.g., RR or OR) for each stratum. Finally, assess whether the stratum-specific measures of association are substantially different by using one of two methods.

• Examine the stratum-specific measures of association. Are they different enough to be of public health or scientific importance?
• Determine whether the variation in magnitude of the association is statistically significant by using the Breslow-Day Test for homogeneity of odds ratios or by testing the interaction term in logistic regression.

If effect modification is present, present each stratum-specific result separately.

In epidemiology, dose-response means increased risk for the health outcome with increasing (or, for a protective exposure, decreasing) amount of exposure. Amount of exposure reflects quantity of exposure (e.g., milligrams of folic acid or number of scoops of ice cream consumed), or duration of exposure (e.g., number of months or years of exposure), or both.

The presence of a dose-response effect is one of the well-recognized criteria for inferring causation. Therefore, when an association between an exposure and a health outcome has been identified based on an elevated RR or OR, consider assessing for a dose-response effect.

As always, the first step is to organize the data. One convenient format is a 2-by-H table, where H represents the categories or doses of exposure. An RR for a cohort study or an OR for a case–control study can be calculated for each dose relative to the lowest dose or the unexposed group ( Handout 8.9 ). CIs can be calculated for each dose. Reviewing the data and the measures of association in this format and displaying the measures graphically can provide a sense of whether a dose-response association is present. Additionally, statistical techniques can be used to assess such associations, even when confounders must be considered.

The basic data layout for a matched-pair analysis is a two-by-two table that seems to resemble the simple unmatched two-by-two tables presented earlier in this chapter, but it is different ( Handout 8.10 ). In the matched-pair two-by-two table, each cell represents the number of matched pairs that meet the row and column criteria. In the unmatched two-by-two table, each cell represents the number of persons who meet the criteria.

In Handout 8.10 , cell e contains the number of pairs in which the case-patient is exposed and the control is exposed; cell f contains the number of pairs with an exposed case-patient and an unexposed control, cell g contains the number of pairs with an unexposed case-patient and an exposed control, and cell h contains the number of pairs in which neither the case-patient nor the matched control is exposed. Cells e and h are called concordant pairs because the case-patient and control are in the same exposure category. Cells f and g are called discordant pairs .

Odds ratio = f/  g.

In a matched-pair analysis, only the discordant pairs are used to calculate the OR. The OR is computed as the ratio of the discordant pairs.

The test of significance for a matched-pair analysis is the McNemar chi-square test.

Handout 8.11 displays data from the classic pair-matched case–control study conducted in 1980 to assess the association between tampon use and toxic shock syndrome ( 7 ).

Odds ratio = 9/ 1 = 9.0; uncorrected McNemar chi-square test = 6.40 (p = 0.01). Source: Adapted from Reference 7 .

• Larger matched sets and variable matching. In certain studies, two, three, four, or a variable number of controls are matched with case-patients. The best way to analyze these larger or variable matched sets is to consider each set (e.g., triplet or quadruplet) as a unique stratum and then analyze the data by using the Mantel-Haenszel methods or logistic regression to summarize the strata (see Controlling for Confounding).
• Does a matched design require a matched analysis? Usually, yes. In a pair-matched study, if the pairs are unique (e.g., siblings or friends), pair-matched analysis is needed. If the pairs are based on a nonunique characteristic (e.g., sex or grade in school), all of the case-patients and all of the controls from the same stratum (sex or grade) can be grouped together, and a stratified analysis can be performed.

In practice, some epidemiologists perform the matched analysis but then perform an unmatched analysis on the same data. If the results are similar, they might opt to present the data in unmatched fashion. In most instances, the unmatched OR will be closer to 1.0 than the matched OR (bias toward the null). This bias, which is related to confounding, might be either trivial or substantial. The chi-square test result from unmatched data can be particularly misleading because it is usually larger than the McNemar test result from the matched data. The decision to use a matched analysis or unmatched analysis is analogous to the decision to present crude or adjusted results; epidemiologic judgment must be used to avoid presenting unmatched results that are misleading.

Logistic Regression

In recent years, logistic regression has become a standard tool in the field epidemiologist’s toolkit because user-friendly software has become widely available and its ability to assess effects of multiple variables has become appreciated. Logistic regression is a statistical modeling method analogous to linear regression but for a binary outcome (e.g., ill/well or case/control). As with other types of regression, the outcome (the dependent variable) is modeled as a function of one or more independent variables. The independent variables include the exposure(s) of interest and, often, confounders and interaction terms.

• The exponentiation of a given beta coefficient (e β ) equals the OR for that variable while controlling for the effects of all of the other variables in the model.
• If the model includes only the outcome variable and the primary exposure variable coded as (0,1), e β should equal the OR you can calculate from the two-by-two table. For example, a logistic regression model of the oropharyngeal tularemia data with tap water as the only independent variable yields an OR of 3.06, exactly the same value to the second decimal as the crude OR. Similarly, a model that includes both tap water and sex as independent variables yields an OR for tap water of 3.24, almost identical to the Mantel-Haenszel OR for tap water controlling for sex of 3.26. (Note that logistic regression provides ORs rather than RRs, which is not ideal for field epidemiology cohort studies.)
• Logistic regression also can be used to assess dose-response associations, effect modification, and more complex associations. A variant of logistic regression called conditional logistic regression is particularly appropriate for pair-matched data.

Sophisticated analytic techniques cannot atone for sloppy data ! Analytic techniques such as those described in this chapter are only as good as the data to which they are applied. Analytic techniques—whether simple, stratified, or modeling—use the information at hand. They do not know or assess whether the correct comparison group was selected, the response rate was adequate, exposure and outcome were accurately defined, or the data coding and entry were free of errors. Analytic techniques are merely tools; the analyst is responsible for knowing the quality of the data and interpreting the results appropriately.

A computer can crunch numbers more quickly and accurately than the investigator can by hand, but the computer cannot interpret the results. For a two-by-two table, Epi Info provides both an RR and an OR, but the investigator must choose which is best based on the type of study performed. For that table, the RR and the OR might be elevated; the p value might be less than 0.05; and the 95% CI might not include 1.0. However, do those statistical results guarantee that the exposure is a true cause of disease? Not necessarily. Although the association might be causal, flaws in study design, execution, and analysis can result in apparent associations that are actually artifacts. Chance, selection bias, information bias, confounding, and investigator error should all be evaluated as possible explanations for an observed association. The first step in evaluating whether an apparent association is real and causal is to review the list of factors that can cause a spurious association, as listed in Epidemiologic Interpretation Checklist 1 ( Box 8.4 ).

• Selection bias
• Information bias
• Investigator error
• True association

Epidemiologic Interpretation Checklist 1

Chance is one possible explanation for an observed association between exposure and outcome. Under the null hypothesis, you assume that your study population is a sample from a source population in which that exposure is not associated with disease; that is, the RR and OR equal 1. Could an elevated (or lowered) OR be attributable simply to variation caused by chance? The role of chance is assessed by using tests of significance (or, as noted earlier, by interpreting CIs). Chance is an unlikely explanation if

• The p value is less than alpha (usually set at 0.05), or
• The CI for the RR or OR excludes 1.0.

However, chance can never be ruled out entirely. Even if the p value is as small as 0.01, that study might be the one study in 100 in which the null hypothesis is true and chance is the explanation. Note that tests of significance evaluate only the role of chance—they do not address the presence of selection bias, information bias, confounding, or investigator error.

Selection bias is a systematic error in the designation of the study groups or in the enrollment of study participants that results in a mistaken estimate of an exposure’s effect on the risk for disease. Selection bias can be thought of as a problem resulting from who gets into the study or how. Selection bias can arise from the faulty design of a case– control study through, for example, use of an overly broad case definition (so that some persons in the case group do not actually have the disease being studied) or inappropriate control group, or when asymptomatic cases are undetected among the controls. In the execution phase, selection bias can result if eligible persons with certain exposure and disease characteristics choose not to participate or cannot be located. For example, if ill persons with the exposure of interest know the hypothesis of the study and are more willing to participate than other ill persons, cell a in the two-by-two table will be artificially inflated compared with cell c , and the OR also will be inflated. Evaluating the possible role of selection bias requires examining how case-patients and controls were specified and were enrolled.

Information bias is a systematic error in the data collection from or about the study participants that results in a mistaken estimate of an exposure’s effect on the risk for disease. Information bias might arise by including poor wording or understanding of a question on a questionnaire; poor recall; inconsistent interviewing technique; or if a person knowingly provides false information, either to hide the truth or, as is common among certain cultures, in an attempt to please the interviewer.

Confounding is the distortion of an exposure–disease association by the effect of a third factor, as discussed earlier in this chapter. To evaluate the role of confounding, ensure that potential confounders have been identified, evaluated, and controlled for as necessary.

Investigator error can occur at any step of a field investigation, including design, conduct, analysis, and interpretation. In the analysis, a misplaced semicolon in a computer program, an erroneous transcription of a value, use of the wrong formula, or misreading of results can all yield artifactual associations. Preventing this type of error requires rigorous checking of work and asking colleagues to carefully review the work and conclusions.

To reemphasize, before considering whether an association is causal, consider whether the association can be explained by chance, selection bias, information bias, confounding, or investigator error . Now suppose that an elevated RR or OR has a small p value and narrow CI that does not include 1.0; therefore, chance is an unlikely explanation. Specification of case-patients and controls was reasonable and participation was good; therefore, selection bias is an unlikely explanation. Information was collected by using a standard questionnaire by an experienced and well-trained interviewer. Confounding by other risk factors was assessed and determined not to be present or to have been controlled for. Data entry and calculations were verified. However, before concluding that the association is causal, the strength of the association, its biologic plausibility, consistency with results from other studies, temporal sequence, and dose-response association, if any, need to be considered ( Box 8.5 ).

• Strength of the association
• Biologic plausibility
• Consistency with other studies
• Exposure precedes disease
• Dose-response effect

Epidemiologic Interpretation Checklist 2

Strength of the association means that a stronger association has more causal credibility than a weak one. If the true RR is 1.0, subtle selection bias, information bias, or confounding can result in an RR of 1.5, but the bias would have to be dramatic and hopefully obvious to the investigator to account for an RR of 9.0.

Biological plausibility means an association has causal credibility if is consistent with the known pathophysiology, known vehicles, natural history of the health outcome, animal models, and other relevant biological factors. For an implicated food vehicle in an infectious disease outbreak, has the food been implicated in previous outbreaks, or—even better—has the agent been identified in the food? Although some outbreaks are caused by new or previously unrecognized pathogens, vehicles, or risk factors, most are caused by those that have been recognized previously.

Consider c onsistency with other studies . Are the results consistent with those from previous studies? A finding is more plausible if it has been replicated by different investigators using different methods for different populations.

Exposure precedes disease seems obvious, but in a retrospective cohort study, documenting that exposure precedes disease can be difficult. Suppose, for example, that persons with a particular type of leukemia are more likely than controls to have antibodies to a particular virus. It might be tempting to conclude that the virus caused the leukemia, but caution is required because viral infection might have occurred after the onset of leukemic changes.

Evidence of a dose-response effect adds weight to the evidence for causation. A dose-response effect is not a necessary feature for an association to be causal; some causal association might exhibit a threshold effect, for example. Nevertheless, it is usually thought to add credibility to the association.

In many field investigations, a likely culprit might not meet all the criteria discussed in this chapter. Perhaps the response rate was less than ideal, the etiologic agent could not be isolated from the implicated food, or no dose-response was identified. Nevertheless, if the public’s health is at risk, failure to meet every criterion should not be used as an excuse for inaction. As George Comstock stated, “The art of epidemiologic reasoning is to draw sensible conclusions from imperfect data” ( 8 ). After all, field epidemiology is a tool for public health action to promote and protect the public’s health on the basis of science (sound epidemiologic methods), causal reasoning, and a healthy dose of practical common sense.

All scientific work is incomplete—whether it be observational or experimental. All scientific work is liable to be upset or modified by advancing knowledge. That does not confer upon us a freedom to ignore the knowledge we already have, or to postpone the action it seems to demand at a given time ( 9 ).

— Sir Austin Bradford Hill (1897–1991), English Epidemiologist and Statistician

• Aktas D, Celebi B, Isik ME, et al. Oropharyngeal tularemia outbreak associated with drinking contaminated tap water, Turkey, July–September 2013. Emerg Infect Dis. 2015;21:2194–6.
• Centers for Disease Control and Prevention. Epi Info. https://www.cdc.gov/epiinfo/index.html
• Rentz ED, Lewis L, Mujica OJ, et al. Outbreak of acute renal failure in Panama in 2006: a case-–control study. Bull World Health Organ. 2008;86:749–56.
• Edlin BR, Irwin KL, Faruque S, et al. Intersecting epidemics—crack cocaine use and HIV infection among inner-city young adults. N Eng J Med. 1994;331:1422–7.
• Centers for Disease Control and Prevention. Varicella outbreak among vaccinated children—Nebraska, 2004. MMWR. 2006;55;749–52.
• Rothman KJ. Epidemiology: an introduction . New York: Oxford University Press; 2002: p . 113–29.
• Shands KN, Schmid GP, Dan BB, et al. Toxic-shock syndrome in menstruating women: association with tampon use and Staphylococcus aureus and clinical features in 52 women. N Engl J Med . 1980;303:1436–42.
• Comstock GW. Vaccine evaluation by case–control or prospective studies. Am J Epidemiol. 1990;131:205–7.
• Hill AB. The environment and disease: association or causation? Proc R Soc Med. 1965;58:295–300.

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