Develop a data analysis plan in 7 steps
Create a roadmap for analyzing data to meet your research objectives.
Survey results are in; it’s time to develop a data analysis project plan. Not sure how to do this? Don’t worry. This article shares a data analysis plan example, a step-by-step guide to creating this plan, and several best practices to follow.
What is a data analysis plan?
A data analysis plan is a blueprint outlining the strategies, methods, and steps for organizing data from a survey or market research.
A data analysis plan is essential for research success, guiding data processing and interpretation to minimize errors and enhance reliability. It keeps data organized, supports better decision-making, and ensures alignment with research objectives.
How to create a data analysis plan: 7 steps
Creating a data analysis plan can be broken down into 7 key steps. Follow these steps to organize your data analysis for the best results.
1. Define your objectives
A data analysis plan should align with your initial survey goals. Revisit your survey objectives before you create a data analysis plan.
Let’s take a look at how you would align a plan and objectives in a data analysis plan example:
You are surveying university students on their campus dining options. Your goal is to gather feedback on the current options and see what other dining options students want.
To reach this goal, your research questions may have looked something like this:
- On a scale of 1 to 5, how satisfied are you with the variety of dining options available in the student union?
- Which of the following restaurants do you visit most frequently?
- What do you like most about the restaurant you visit most frequently? Select all that apply.
- If you could add another dining option, which restaurant or food chain would you choose?
You should adapt your data analysis method based on the survey questions and gathered data. In this case, you should create a data analysis plan for quantitative research.
2. Inspect the data
Next, clean your data to ensure you have accurate results that represent your target population before drawing conclusions. Cleaning data helps eliminate bias, reduce noise, and improve the quality of results.
To clean the data, filter out the following:
- Respondents who only answer a portion of your questions
- Respondents who don’t meet your target criteria
- Respondents who “straightline”/choose the same answer
- Respondents who provide unrealistic answers
- Respondents who give inconsistent responses
- Respondents who offer nonsensical feedback in your open-ended questions
With SurveyMonkey, you can use our Question Bank to encourage candid results, survey logic to vet respondents, and easily filter responses by completeness.
3. Prepare data for analysis
After cleaning the data, it is time to prepare it for statistical analysis. This involves structuring your dataset to ensure the appropriate analytical methods are applied to address your research questions.
To prepare data for analysis, organize your questions methodically by aligning them with each of your central research questions. Organizing these in a table format can be helpful for easy viewing.
For instance, in the previous data analysis example about university dining options, the table may look something like this:
| Research Question | Survey Questions(s) |
| Do students want more dining options in the student union? | - On a scale of 1 to 5, how satisfied are you with the variety of dining options available in the student union? - If you could add another dining option, which restaurant or food chain would you choose? |
| Which dining options are most popular and why? | - Which of the following restaurants do you visit most frequently? - What do you like most about the restaurant you visit most frequently? Select all that apply. |
| What type of students prefer each dining option? | - How old are you? - What gender do you identify as? - Are you enrolled in an undergraduate or graduate program? |
4. Choose the right analysis method
Next, select the most suitable analysis method for your research, ensuring it aligns with the relationships you aim to explore in the data.
Some common data analysis methods include:
- Descriptive analysis
- This data analysis summarizes the features of the dataset.
- Methods: Mean, median, mode, frequency distribution, percentage, and standard deviation.
- Example: What is the average satisfaction rating of student survey participants?
- Comparative analysis
- This analysis compares groups and the related data to determine differences.
- Methods: T-tests, ANOVA (Analysis of Variance), and Chi-square test.
- Example: Is there a significant difference in the satisfaction level of undergraduate vs. graduate students?
- Correlation analysis
- Correlation analysis evaluates the relationship between at least two variables.
- Methods: Pearson correlation, Spearman's rank correlation.
- Example: Is there a relationship between the age of a student and their satisfaction with the dining options?
- Qualitative data analysis
- This involves analyzing open-ended responses for textual patterns. This type of data analysis plan is best for qualitative research.
- Methods: Thematic analysis, content analysis, coding.
- Example: Is there a theme among what additional dining options respondents want to see on campus?
5. Establish timeline and resources
Finally, establish a project timeline for your data analysis plan and allocate resources.
Break down tasks into manageable steps to create a project timeline. Set realistic deadlines for each task to maintain progress toward your goal. Identify both small and large milestones to stay motivated throughout the process.
Evaluating resources requires assigning tasks to team members according to their skills and expertise. It also involves identifying the appropriate software or technology, like SPSS, SAS, or Tableau.
Additionally, regular check-ins should be established to monitor progress and ensure tasks are completed on time.
This approach promotes accountability, optimizes resource use, and helps maintain momentum toward project goals.
6. Interpret and report findings
Once you’ve analyzed the data, your next step is to interpret and report on the findings. This involves linking your findings to your original research objectives and preparing a survey analysis report. Such a report highlights patterns, trends, and key insights in a clear format for stakeholders.
Use visual aids such as infographics, charts, and graphs to display data. When writing the report, be sure to address detailed findings and limitations and provide recommendations (if applicable).
Related reading: How to analyze survey data
7. Review and refine the data analysis plan
After you’ve interpreted the data and created a survey report, it’s important to review the effectiveness of your data analysis plan.
This helps you improve your analysis process and ensure future analyses are effective. Incorporate stakeholder and team feedback to refine your next data analysis plan. And consider creating a data analysis plan template if you regularly conduct market research.
Best practices for data analysis plan
Before you go, here are a few last best practices for creating your data analysis plan.
- Align the plan with research objectives. Be sure that you keep the focus of your data analysis plan on your original research objectives. This keeps your findings aligned with the intended purpose of the survey.
- Plan for data cleaning. It’s good to plan early for data cleaning to ensure that the data you analyze is relevant to your project. Establish protocols for handling missing data and inconsistencies before analyzing the data.
- Choose the best-fit analytical technique. You will choose the right analytical technique based on the type of data (quantitative, qualitative, categorical, etc.) and the relationships you are investigating. Using the correct method boosts the validity and relevance of the results.
- Use a data analysis plan template. If you regularly conduct market research, you may want to create a data analysis plan template or use a third-party template.
SurveyMonkey supports market research data analysis
A data analysis plan serves as a roadmap for organizing survey data. Creating a data analysis plan is critical to the market research process and leads to more efficient time management and detailed analysis.
The SurveyMonkey Market Research Solution empowers you to get AI-powered insights to expedite each stage of market research. This intuitive platform is designed to help you get quick insights that drive better decisions. It even offers custom reporting and exports to make presenting your findings simple.