Conduct an a priori power analysis for or using the tables in the Appendix E of R2 sr2 CCWA. Tell what additional information is needed to determine the critical sample size for a given value of sr2. Discuss the concept of and use the formula. Tell how the R2 “shrinkage” “shrunken” differs from the and when each is important. R2, Define the following suppressor variable, multicollinearity, cross-validation, unit weights, tolerance. Identify, label, and interpret each part of the standard output from the SPSS regression program. Understand the statistical assumptions underlying least squares regression analysis, and the consequences of violating these assumptions. Be able to use graphical methods to assess their viability in your sample. Reading Assignments and Objectives Unit 8: Multiple Regression Models Readings: CCWA, Ch. 5 Hoyt, Imel, & Chan (2008, pp. 321-325) Objectives: this unit’s class discussion and readings you should be able to: Extend principles of MRC discussed in Ch. 2 and 3 to research problems representing the factors (IVs) of interest by sets of variables rather than individual measures. State three structural reasons why sets of variables may be needed to represent an IV in a multiple regression framework. Distinguish between simultaneous and hierarchical regression; discuss rationales for determining order of entry of predictor variables (or sets of variables) in hierarchical regression. Discuss why approaches are rarely useful techniques for “stepwise” evaluating the relative importance of predictor variables. Discuss verbally and diagrammatically (using Venn diagrams) the different approaches to estimating the proportion of variance in the DV accounted for by individual (and sets of) IVs.
Conduct an a priori power analysis for R2 or sr2 using the tables in the Appendix E of CCWA. Tell what additional information is needed to determine the critical sample size for a given value of sr2. Discuss the concept of “shrinkage” and use the formula. Tell how the “shrunken” R2 differs from the R2, and when each is important. Define the following suppressor variable, multicollinearity, cross-validation, unit weights, tolerance. Identify, label, and interpret each part of the standard output from the SPSS regression program. Understand the statistical assumptions underlying least squares regression analysis, and the consequences of violating these assumptions. Be able to use graphical methods to assess their viability in your sample. Reading Assignments and Objectives Unit 8: Multiple Regression Models Readings: CCWA, Ch. 5 Hoyt, Imel, & Chan (2008, pp. 321-325) Objectives: this unit’s class discussion and readings you should be able to: Extend principles of MRC discussed in Ch. 2 and 3 to research problems representing the factors (IVs) of interest by sets of variables rather than individual measures. State three structural reasons why sets of variables may be needed to represent an IV in a multiple regression framework. Distinguish between simultaneous and hierarchical regression; discuss rationales for determining order of entry of predictor variables (or sets of variables) in hierarchical regression. Discuss why “stepwise” approaches are rarely useful techniques for evaluating the relative importance of predictor variables. Discuss verbally and diagrammatically (using Venn diagrams) the different approaches to estimating the proportion of variance in the DV accounted for by individual (and sets of) IVs.
CP%20960%20syllabus%20(S%2015)-1.docx