Quantitative data analysis can be both fun and frustrating. You can minimize frustration by having a clear plan for your analysis and doing preliminary work such as cleaning your data and re-coding variables. Your preliminary work should also include revisiting your research question(s) and hypotheses, reviewing your methodology and taking note of your procedures (how you coded the data, developed scales etc.) as well as your research question. These background tasks will help you interpret your data correctly, clarify confusions and remove ambiguities. Think of a scenario where one of your variables is arranged in descending order and the other is arranged in ascending order. The asymmetrical order will make interpretation more tricky than if both variables were in the same direction. While this is not a problem in itself, having multiple variables coded asymmetrically can increase the risk of making errors in your interpretation. Hence, the background task of re-coding to a more consistent standard can make your analysis less frustrating. Finally, your preparatory activity should include a determination of the key variables of interest, planned statistical techniques and getting practice with the analytic program that you will be using (e.g., SPSS, STATA, R, or Jamovi).
In this chapter, we make two bold assumptions: (a) that you know how to conduct quantitative procedures in the statistical program of choice and (b) that you know how to interpret the output from statistical results (if not, we will provide resources to help you with both throughout and at the end of the chapter). Accordingly, we will focus on two things: (1) how to present your findings; and (2) how to interpret the findings and draw conclusions. The chapter begins with an overview of secondary data analysis, followed by a discussion on types of quantitative analysis and presentation formats. Next, we discuss and , and hypothesis testing. We also recap how to identify the appropriate analysis tool for evaluating the significance of your statistics (Chi Square, Pearson’s R, t-test, regression etc.). We finish with a brief commentary about big data along with a testimony on the use of data visualizations in social research.
Description or summary of the characteristics of a sample.
Statistical procedures that allow researchers to draw inferences from the data, i.e., make predictions or deductions about the population from which the sample is drawn.