Study Questions for Quiz #1
Simple Correlation
1. Compare and contrast "correlation research" and "prediction research", telling both intent and process differences.
2. Be prepared to describe the patterns of data that result in various combinations of size and direction of r for both
quantitative and binary X variables.
3. Describe the consequences of "weak" vs. "strong" nonlinear relationships upon the meaningfulness of r values.
4. Distinguish between “comparing our results to our RH:” and “inferential accuracy of our results” and tell the likely
reasons for the different outcomes of each.
Simple Regression
5. Describe the graphical process of prediction and using that description, elucidate the relationship between the
strength of a linear relationship and the accuracy of subsequent linear prediction.
6. Describe how to interpret regression weights and constants for raw and standardized models involving quantitative
and binary predictors. Be sure to differentiate the interpretation of these values for dummy-coded (0-1) and unitcoded (1-2) binary predictors. Be sure to explain why there is no regression constant included in standardized
models.
7. Describe linear regression as a "linear transformation" and describe how this transformation functions. How does
this function highlight that the data analyst must correctly decide "which regression model to use"?
8. Distinguish “univariate prediction” from “bivariate prediction” and tell when to use each. Describe the advantage of
a significant binary predictor over univariate prediction.
Multiple Regression & Prediction
9. Describe the pragmatic and theoretical advantages of multiple regression over simple regression.
10. Describe the statistical analyses that should precede the multiple regression analysis and what you would be
looking for when doing each.
11. Carefully distinguish between the proper interpretations of regression weights for simple and multiple regression
models. Describe how to interpret the multiple regression weights of continuous vs. binary predictors.
12. Describe the process of inspecting, interpreting and describing the results from a multiple regression, including the
reason for the order of steps, the significance tests involved (and H0:s), and their interpretation.
13. What is “under specification” and what difficulties might it cause when using multiple regression? What are the
solutions to these problems?
14. Describe “proxy variables” and tell what difficulties they might cause. What is the relationship between
“underspecification” and “proxy variables”? What are the solutions to these difficulties?
15. Describe the three attributes of “the multiple regression model” for a criterion variable and explain why the third
attribute is critical. What usually prevents the discovery of “the model” and what should we do for a particular
analysis and for our programmatic research?
Multivariate Regression Details
16. Tell the various things that influence the calculated values and significance tests of simple correlations and simple
and multiple regression weights. Discuss the use of standardized weights to evaluate the relative contribution of
the predictors to the model.
17. Why do we conduct both bivariate and multivariate analyses of the relationships between a set of predictors and a
criterion? What are the possible results of this “dual analysis”.for a given predictor-criterion variable pair?
18. What are suppressor variables (remember to distinguish the two kinds), how are they identified, interpreted and
“dealt with” in the present and in future studies?
19. Describe colinearity, tell how to distinguish among different levels of it, and tell the problems related to each level.
What are the ways of dealing with the problems caused?
20. What is "range restriction" and for what reasons does it happen? What problems does it cause for multiple
regression analyses? What are the suggested solutions to it, and what problems might they cause?
21. Briefly describe the three “possibly surprising results” when comparing correlation and multiple regression results.
What is the source of the surprise in each case. If we get surprised, which should we believe, the Bivariate or the
multivariate result and why?
22. What are “missing values” how do they come about and what are our options for handling them in our analyses?
Power Analysis
23. What is the “subjects-to-variables” ratio, and how has it been applied to determining the sample size needed for a
multiple regression analysis? Describe the 2-part approach to selecting the sample size for a study that is
preferred by your instructor?
24. Why is a priori power analysis important? What are the kinds of information that are necessary to perform such an
analysis and where might the researcher obtain this information? When are post hoc power analyses important?
25. Distinguish among the different types of power analyses one might perform to prepare for a study that will use
correlational and multiple regression analyses. How does one decide upon the sample size one will use for their
study?