Making Sense of Advanced Statistical Procedures in Research Articles

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
  R2 with 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
• Cronbachs alpha (a)
• 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

• Dont 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!