Tips & Tools for Data Analysis
Analyzing data can feel daunting. But once you’ve arrived at the analysis stage, most of the heavy lifting is behind you. It’s actually much more difficult to define your outcomes and conceptualize, operationalize and collect data on your key indicators. If you’ve done this work well (see our previous blog posts in this series!), the analysis of the data is actually a fairly straightforward affair.
Data Analysis is the process of inspecting, cleansing, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making. There are methods for both Quantitative and Qualitative Analysis. The type of data analysis depends on the types of data collected:
- Quantitative Analysis – is based on quantitative data such as surveys, administrative records
- Qualitative Analysis – is based on qualitative data such as interviews, focus groups, and observations
- Mixed Methods Integration – where quantitative and qualitative data are used in complementary ways
Choosing which data analysis methods are best for your organization is complicated, and there are many factors to consider. To assess which of the methods are appropriate for your data, you’ll want to consider the purpose of your evaluation, the feasibility of your data analysis efforts, and the quality of your data analysis.
Evaluation Purpose. When assessing the purpose of your evaluation, consider the key questions you are trying to answer:
- Do you need to measure the average of a particular variable or the frequencies of responses at each level of that variable?
- Are you measuring the % of people who do something or the prevalence of a particular view, or trying to understand the “why” of something?
- What types of analyses are appropriate for your audience? (What are their expectations in terms or rigor and sophistication of analyses? What is their skill level in understanding technical analyses?)
Feasibility of the Methods. When assessing the feasibility of your evaluation, consider the resources and capacity you’ll need to carry out data analysis:
- Consider the time needed: How soon do you need the data analyzed? How often will you need to analyze the data?
- Consider the resources (materials, financial, human) needed: Do you have (or can you acquire) what you need to analyze the data? (Microsoft Excel, SPSS, R, GIS, qualitative software)? Do you have a budget? How much staff time can be allocated to data analysis? Do you have staff with the appropriate skills to collect data?
Quality of Analysis. When assessing the methods for your analysis, consider both validity and reliability:
- Validity is the degree to which the data analysis method accurately answers the questions you are interested in. Try to identify a comparison group where you can compare your results to another group.
- Reliability is the degree to which a data analysis method produces stable and consistent results. Maintain consistency and transparency in data analysis procedures.
Once you’ve considered these questions, below are more detailed guides for getting started with your analysis!