Study Questions for Quiz #1

(Parenthetical info will not appear on quiz – ya gotta know to include those parts!)

	Survey of Correlation Models
1	Briefly describe the members of each of the major kinds of correlation/regression questions and their interrelationships. (be sure to include the bases for their inter-relationships)
2	Briefly describe the members of each of the major kinds of correlation/regression questions and the significance tests used for each.
3
	Correlation & RH: Testing
4	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.
5	Describe the consequences of "weak" vs. "strong" nonlinear relationships upon the meaningfulness of r values.
6	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.
7	Describe the things one must be careful/specific about when comparing correlations.
	Bivariate Regression
8	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"?
9	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
10	Describe the statistical analyses that should precede your multiple regression analyses 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	What is “under specification” and what difficulties might it cause when using multiple regression? What are the solutions to these problems?
13	Describe “proxy variables” and tell what difficulties they might cause. What is the relationship between “under specification” and “proxy variables”? What are the solutions to these difficulties?
14	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?
15	What is the risk when you decide not to further consider a predictor in your multivariate analyses? Tell the things you should consider before making such a decision (opdef, simple, full mreg, various mreg, curvilinear, interactions, indirect).
	Some Details About Multiple Regression
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”.
18	What are suppressor variables (remember to distinguish the two kinds), how are they identified, interpreted and “dealt with” in the present and future studies?
19	Describe collinearity, 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 “regressions to remember” (multivariate power ,null flooding, extreme collinearity, collinearity patterns). What is the source of the surprise in each. If we get surprised, which should we believe, the bivariate or the multivariate result and why?
	Statistical Control
22	Distinguish between experimental (be sure to mention randomization, matching, holding constant, and balancing and how they relate to each other) and statistical control and tell the correlation, regression and ANOVA models used to apply the latter. What are the advantages and disadvantages of statistical control?
23	Describe the use of "residualization" to apply statistical control. Include a specific example with the necessary formulas to describe and carry out the procedure for each of the six statistical control models you know (partial, multiple partial, semi-partial, multiple semi-partial, ANCOVA & nested models).
	Multiple Regression Research Hypothesis Testing
24	Describe the alternative ways of asking if a particular predictor has a relationship with a criterion that is unique from a set of other predictors.
25	Discriminate full and reduced models and tell the various approaches to generating the latter, commenting carefully upon the relative quality of each.
26	Describe substitution analyses and tell how they differ from the construction and comparison of "nested models.” Also, describe the different reasons for proposing a substitution, and how these might relate to the intent of the analysis (descriptive vs. predictive).
27	Describe the theoretical and applied reasons for comparing regression models across populations. Discriminate between the comparison of “fit”, “substitution” and “structure” when comparing models in this way.
28	Describe the theoretical and applied reasons for comparing regression models across criterion variables.
29	Describe the known problems of each of the forward, backward, and forward stepwise statistical modeling procedures? What does it mean to say that the decisions at each step in these procedures are both statistical and numeric and it is the numeric part that causes the problems?
	Path Analysis
30	What is path analysis? What is the “starting point” of a path analysis and what kinds of research hypotheses can we and can we not test? How can a knowledge of path analysis help when designing research and analyzing regression models (temporal/causal, indirect & suppressors)?
31	Path analysis is also often called “causal analysis”. Can path analysis be used to study causal relationships among variables? What do we get out of a path analysis that we don’t get out of a multiple regression using the same variables?
32	How does a “mediation analysis” differ from a “path analysis”? Of what must we be careful in any path analysis, but especially so when doing a mediation analysis?