Positivistic data analysis

Positivistic Data Analysis John Couper 1. Recall that the central goal of positivistic research is to a) gradually improve theory by b) testing c) predicted relationship(s) d) among variables e) against actuality 2. Theory explains what (variable interactions) to learn how (cause-effect) and how much 3. Social science cannot “prove” anything, but can confirm interactions of change and/or comparison to show cause-and-effect beyond accepted levels of probability 4. Goal: answer RQs, hypotheses methodically and fully to address the “problem” (issue) 5. Analysis seeks results that are as simple and clear as possible: a. How/how much independent and mediating variables affect dependent variables b. What this means about the strengths and limitations of the theory c. Even disconfirmation is valuable because it is also part of theory improvement 6. Analysis is rarely considered as early or as carefully as it should be a. it is rarely explained because it is harder to standardize than data collection 7. Good research plans for analysis from the beginning of the process, for example by “letting the variables vary” and making the results as reliable as possible a. plan the whole study from the analysis back to the selection of variables 8. Look for the “main effect”: the factor that (through location or significance) explains most of the variation, with secondary variables/effects that play a role a. “fishing”: using stats to find barely-predicted relationships (wrong but common) 9. Tip: create a model of the process before analysis, then gradually refining it 10. Even better: make two models to compare predicted process against discovered process 11. Refine the model by a. working up from variables/effects to sections of the process, and b. down to subvariables c. grouping variables and d. breaking them down into sub-factors e. finding patterns (e.g., sets or sequences of variables) that help explain the effects 12. One useful statistical tool is “weighting”: giving extra prominence to certain variables 13. Apply common sense at each step to a. check conclusions and b. identify mistakes 14. Return to the RQs/hypotheses and see how the results answer them 15. Once you have the results, identify the abstract principle that they represent a. i.e., what happened that could be applied to another similar but distinct process b. from detail to category, and from category to concepts 16. Start from the original problem and theory but don’t be limited by them 17. Decide: a. what is the difference between the prediction and the findings? b. what does this say about the application of the theory in this case? c. what does it say about the theory in general (for use in future studies)? d. what issues are raised but not answered?

18. Use the discussion section to project and extend the value of these conclusions in terms of theory refinement (or contradiction!), future research, variants on the study, etc.