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Chapter 17

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.

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Chapter 17

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  1. Chapter 17 Making Sense of Advanced Statistical Procedures in Research Articles

  2. Brief Review of Multiple Regression • Predicting scores on a criterion variable from two or more predictor variables • Proportion of variance accounted for (R2)

  3. 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 R2with first predictor variable • Repeat until best predictor variable does not give significant improvement

  4. 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

  5. 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

  6. Reliability • Reliability • Test-retest reliability • Split-half reliability • Cronbach’s alpha (α) • Interrater reliability

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

  8. Causal Modeling • Measured large number of variables • Does the pattern of correlations match theory of which variables cause which? • Path analysis • Path • Path coefficient

  9. Causal Modeling • Path analysis

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

  11. Causal Modeling • Structural equation modeling

  12. Causal Modeling • Structural equation modeling

  13. Causal Modeling • Limitations • Other patterns of causality possible • Alternative theories • Correlation and causality • Linear relationships • Restriction in range

  14. Independent and Dependent Variables • Independent variable • Predictor variable • Dependent variable • Criterion variable

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

  16. 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

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

  18. Overview of Statistical Techniques

  19. Controversy: Should Statistics be Controversial? • Fisher • Neyman • Pearson

  20. 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!

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