Chapter 17 Making Sense of Advanced Statistical Procedures in Research Articles

Brief Review of Multiple Regression  Predicting scores on a criterion variable from two or more predictor variables  Proportion of variance accounted for ( R 2 )

Hierarchical and Stepwise Multiple Regression  Hierarchical multiple regression –Examine contribution to the prediction of each variable added in a sequential fashion  Stepwise Multiple regression –Controversial exploratory procedure –Predictor variable with best prediction located –Find next predictor variable that gives highest R 2 with first predictor variable –Repeat until best predictor variable does not give significant improvement

Hierarchical and Stepwise Multiple Regression  Both involve adding variables a stage at a time and checking for significant improvement of prediction  Theory/plan determines order of variables in hierarchical regression  No initial plan in stepwise regression –Useful in exploratory and applied research

Partial Correlation  Association between two variables, over and above influence of one or more other variables  Holding constant, partialing out, controlling for, adjusting for  Partial correlation coefficient

Reliability  Reliability –Test-retest reliability –Split-half reliability –Cronbach’s alpha (α) –Interrater reliability

Factor Analysis  Measured large number of variables  Identifies variables that clump together  Factor  Factor loading  Several approaches to factor analysis  Naming the factors

Causal Modeling  Measured large number of variables  Does the pattern of correlations match theory of which variables cause which?  Path analysis –Path –Path coefficient

Causal Modeling  Path analysis

Causal Modeling  Structural equation modeling –Elaboration of path analysis –Fit index e.g., RMSEA –Latent variable –Measured variable

Causal Modeling  Structural equation modeling

Causal Modeling  Structural equation modeling

Causal Modeling  Limitations –Other patterns of causality possible –Alternative theories –Correlation and causality –Linear relationships –Restriction in range

Independent and Dependent Variables  Independent variable –Predictor variable  Dependent variable –Criterion variable

Analysis of Covariance (ANCOVA)  ANOVA adjusting the dependent variable for effect of additional variables  Analogous to partial correlation  Covariate  Adjusted means

Multivariate Analysis of Variance (MANOVA) and Covariance (MANCOVA)  Multivariate statistics –More than one dependent variable  Multivariate analysis of variance (MANOVA) –ANOVA with more than one dependent variable –Univariate ANOVA

Multivariate Analysis of Variance (MANOVA) and Covariance (MANCOVA)  Multivariate analysis of covariance (MANCOVA) –ANCOVA with more than one dependent variable –MANOVA with covariates

Overview of Statistical Techniques

Controversy: Should Statistics be Controversial?  Fisher  Neyman  Pearson

Reading Results Using Unfamiliar Techniques  Don’t panic!  Look for a p level  Look for pattern of results that is considered significant  Look for degree of association or size of the difference  Look up in statistics book  Take more statistics courses!