7 Key Strategies for Doing Data Analysis in Research

Posted by Tyler Archer on September 20th, 2022

Data analysis is one of the most important steps in research. Data analysis in research is the process of analyzing and interpreting your data (or a dataset) so that you can draw conclusions about the topic you are studying. Your results from data analysis will inform how you write up your research findings in a dissertation or thesis, as well as how you plan future studies on similar topics. It's important to follow some strategies for doing data analysis because this sets the stage for what kinds of questions will drive your approach to collecting and organizing information into meaningful datasets. In this article, we will discuss the proven key strategies for doing data analysis in research.

7 Strategies to Do Data Analysis

1. State your research question

The first step in doing data analysis is to make sure that you have a good research question. Your research question will be the most important part of your data analysis plan, so it's important to get it right. In order for your analysis to be useful, you need to clearly state what you're trying to find out. So before you start collecting and analyzing data, write down what your research question is; this will allow you not only to focus on finding relevant information but also ensure that any conclusions drawn from those results are valid.

2. Describe what you want to know

The second step in doing data analysis in research is to describe what you want to know. This is the research question. It's not just another way of saying what your paper is about; rather, it's a statement that describes what you're trying to find out as a result of analyzing your data, such as:

• What proportion of people who were exposed to the product had increased happiness scores?
• Are men more likely than women to prefer certain types of music?
• Do urban residents have higher rates of hypertension than rural residents?

3. State the data you will use (and how) to answer your question

Data analysis in research is a process, not an event. A data analysis plan is a way you use your data to answer a question. It's the way you find patterns in the data and use those patterns to address your research questions.

Data analysis in research always begins with asking questions about your data set, whether it's one piece of information or millions of pieces. Data analysis should begin with some sense of what it means for something to "make sense" within a given dataset; this will help guide how we look for evidence within our datasets—and what kinds of evidence we ought to be looking for!

4. Describe what you plan to do with the data

This is a key part of the research plan. You should think about what you’re going to do with your data, and how it will be used.

The way that you describe this depends on what kind of project it is—for example, if it’s a dissertation or academic paper, you can include the details in your methodology section; if it’s an institutional review board (IRB) submission for research involving human subjects, then they will want to see this information as part of your ethical application.

5. Briefly discuss anything that might go wrong (and how you will handle this)

This is an important step because it will help you stay on task and focused as you work through your data analysis in research. After all, if anything does go wrong, you want to be prepared for it. It’s also helpful to think about what might go wrong in advance so that there are no surprises later on in the project when something goes awry.

Here are some common things that can happen during data analysis:

• You may forget to do something (e.g., include a variable in your analysis).
• You may misinterpret or misread some information that was included in your dataset but not considered important enough at the time of collection (e.g., a column header).

6. Describe how your results will be presented and interpreted

The purpose of this section is to describe what the results mean, not simply to present them. You should start with a preliminary interpretation of your findings and then consider other possible interpretations. How will you explain these results to others? How do they fit into existing knowledge about the research problem or topic area? To make sure you're on track, ask yourself questions like these:

• What are my major findings?
• Are there any unexpected or surprising results in my data? If so, why do I think that's true?
• How do these results compare with those from similar studies (e.g., other studies by other researchers)?

7. Summarize the key points of your data analysis plan

At this point, you're ready to summarize your data analysis plan. It's a good idea to leave yourself notes on the steps you will take in performing each analysis. For example, if you are doing statistical tests, here is a list of the most common tests used:

• t-test compares two means (e.g., average scores) and checks for statistical significance.
• ANOVA (analysis of variance) uses more than two groups and checks for statistical significance.
• Regression estimates and predicts values from one or more independent variables.
• ANCOVA (analysis of covariance): extends ANOVA by including an additional group (e.g., males/females) as an interaction term when testing for differences between groups

It's also important to note how many testing iterations will be done during each analysis phase — like multiple rounds of college admissions! In other words, it is important that all analyses have their own independent results because there could be errors or missing data points that might affect results if they were not accounted for independently at each step along the way. If you are unable to apply the above-mentioned analysis techniques in your doctorate research work, then get PhD dissertation help from experts.

Conclusion

We hope you've enjoyed this overview of some of the top strategies for doing data analysis in research. As we've seen, it's a complex subject, with many different approaches available to researchers. We've also seen that data analysis is more than just applying statistical tests to your data—it's also about asking questions and making sense of what you find, which means that you need skills in both statistics and reading comprehension.