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Making Sense of Advanced Statistical Procedures in Research Articles

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Title: 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!
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