<p> 1 Psychology 716 - Multivariate Statistics Review Sheet for Test 3 (Final Exam)</p><p>New material: Discriminant Function Analysis, Canonical correlation, Factor Analysis, Path Analysis, and Structural Equation Modeling</p><p>In addition, major points from across the semester (see below)</p><p>Be able to determine which form analysis is appropriate for a given research question</p><p>For significance tests, know the null hypotheses that are being evaluated. You don’t need to know statistical notation, but know, for example, that the sig test (Wald test) for a slope in a logistic regression reflects our willingness to reject the null hypothesis that the slope in the population is zero (what would the null hypothesis be, in terms of the odds ratio?).</p><p>Readings o DFA 7A, 7B o Exploratory Factor Analysis 12A, 12B o Canonical correlation 193-195 o Confirmatory Factor Analysis 13A, 13B (just to get a sense of results) o Path Analysis & SEM 14A</p><p>Discriminant Function Analysis o Purpose o Read and interpret key SPSS output (eg coefficients in a structure matrix, canonical discriminant function coefficients) o Read and interpret a description of results (eg, from a journal article)</p><p>Exploratory Factor Analysis o Purpose o What is the trade off in terms of parsimony and “completeness” o Key questions in EFA o Key decision points in conducting an EFA, and the relevant information o Types of extraction o What is the basic difference between PCA and Factor analysis, in terms of what is causing what (see the CFA chapter for more about this) o Scree plot o What exactly is plotted? o Interpreting the plot o Rotation o What is rotation used for? o Kinds of rotation – . what are the two basic forms of rotation and how do they differ? . Specific rotational strategies (varimax, promax, oblimin) - o Factor loadings o What do they represent? o What are they used for? o Roughly, what size factor loading suggests that a variable loads on a factor? o Structure vs pattern coefficients o What is a “clean” structure (ie, simple structure)? o Why is factor analysis often a back-and-forth process? o Interpret SPSS output 2 o Interpret results presented in an article o Be able to “eyeball” a correlation matrix and speculate about the factor structure (number of factors, which variables load on which factors, correlations among factors</p><p>Canonical Correlation o Purpose o Read and interpret key output o Canonical correlations – what are they? o Significance tests of canonical correlations o Structure coefficients – in relation to latent variables/factors o Draw a model, based on output o Interpret results with regard to a given hypothesis o Read and interpret a description of results (eg, from a journal article)</p><p>Confirmatory Factor Analysis o Purpose o What is hypothesized in a CFA analysis? What needs to be articulated in specifying a model? o What parameters are generally estimated? o CFA does at least 3 basic things: 1) Evaluate the overall quality of the Model: o What is “fit”? Where does an “implied corr/covariance” matrix come from? What are residuals? What does the size of a residual represent? How are residuals related to modification indices and fit (in general)? o Fit indices (three different kinds) o Interpret – chi square, NFI, RMSEA. – which values reflect good fit? Problem with X2? 2) Provide parameter estimates o what parameters are generally estimated? o Interpreting parameter estimates 3) Provide modification indices o What are these? how are they used? o Explain what a CFA model implies a. (eg, what does it imply about the association between LVs and MVs) b. What do the elements represent (circules, arrows, etc) o Draw a model, based on a verbal description of the model o Meaning of common and unique factors o What is the trade off in terms of parsimony and “completeness” o Steps/process of a CFA o Reading & interpreting results presented in an article</p><p>Path Analysis and Structural Equation Modeling o Purpose o Similarity to CFA (model testing, model fit, parameter estimation, etc) o Different modes of conducting path analysis (logic, differences, current use?) o Series of regression o Model testing (what do we get from this mode that we don’t get from regression mode?) o Differences/overlap among CFA, PA, and SEM o Measurement models versus structural models o Endogenous vs exogenous variables o Single-stage vs multi-stage “causal” models o Process (again, similar to CFA) o Interpreting reported results o Alternative models o “Causality” – be familiar with issues of making causal inferences from supposedly “causal” modeling 3</p><p>In addition, major points from across the semester (see below) For the various procedures we’ve discussed – o Purpose (eg design, type of data/variables/questions, etc) o Interpreting results from a report o Interpreting SPSS output o Process (eg steps in a MANOVA or mediational analysis) o NO EQUATIONS</p>