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Published byEdgar Percival Jacobs Modified over 4 years ago

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Chapter 17 Making Sense of Advanced Statistical Procedures in Research Articles

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

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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

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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

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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

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

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

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

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Causal Modeling Path analysis

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

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Causal Modeling Structural equation modeling

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Causal Modeling Structural equation modeling

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

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

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

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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

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

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Overview of Statistical Techniques

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Controversy: Should Statistics be Controversial? Fisher Neyman Pearson

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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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