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Reference:
Garson, G. D. (2014). Path Analysis. Asheboro, NC: Statistical Associates Publishers.
 

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Table of Contents
 
ASIN number (e-book counterpart to ISBN): ASIN: B0093GKTKS
 
@c 2014 by G. David Garson and Statistical Associates Publishers. worldwide rights reserved in all languages and on all media. Permission is not granted to copy, distribute, or post e-books or passwords.
 


PATH ANALYSIS (2014 EDITION)

An illustrated tutorial and introduction to path analysis using SPSS, AMOS, SAS, or Stata. Suitable for introductory graduate-level study.

The 2014 edition is a major update to the 2012 edition. Among the new features are these:

The full content is now available from Statistical Associates Publishers. Click here.

Below is the unformatted table of contents.

PATH ANALYSIS
Overview	6
Data used in this volume	7
Key Concepts and Terms	7
Estimation	7
Path model	8
Exogenous and endogenous variables	8
Disturbance terms	8
Causal paths	9
Path Coefficients	10
Path coefficents/weights	10
Path multiplication rule	11
Effect decomposition	12
Path analysis example in OLS regression	13
Overview	13
SPSS regression method	14
SAS regression method	16
Stata regression method	17
Path analysis example in SEM	18
SEM example data	18
SEM in SPSS Amos, SAS, and Stata	19
Path analysis in SPSS AMOS	19
AMOS Inputs	19
AMOS outputs: Path estimates	25
AMOS outputs: Partition of total effects	27
AMOS outputs: Goodness of Fit measures	29
AMOS outputs: Correlations	34
AMOS outputs: Modification indexes	35
Path Analysis in SAS PROC CALIS	36
Overview	36
SAS inputs	37
SAS outputs: Path estimates	39
SAS outputs: Partition of total effects	40
SAS outputs: Goodness of fit measures	41
SAS outputs: Correlations	45
SAS outputs: Modification indices	46
Path Analysis in Stata	47
Overview	47
Stata inputs	48
Stata outputs: Path estimates	49
Stata outputs: Partition of total effects	51
Stata outputs: Goodness of fit measures	53
Stata outputs: Correlations	55
Stata outputs: Modification indices	55
Assumptions	57
Linearity	57
Interval level variables	57
Uncorrelated error	58
Proper specification	59
Normally distributed error	59
Low multicollinearity	60
Identification of the model	60
Recursivity	60
Adequate sample size	60
Frequently Asked Questions	61
Does path analysis confirm causation in a model?	61
Can path analysis be used for exploratory rather than confirmatory purposes?	61
How does the significance of a path coefficient compare with the significance of the corresponding regression coefficient?	62
How is the path multiplication rule used in computation of indirect effects?	62
Partition Model A: Simple mediation	63
Partition Model B: Anteceding effect on endogenous variables	64
Partition Model C: Anteceding effect on former exogenous and endogenous variables	66
Partition Model D:  Correlated exogenous variables	67
Why might the direct effect be zero?	69
What is Simpson's paradox?	69
What is Berkson's paradox (case-control bias)?	70
How are path coefficients related to a covariance/correlation matrix for purposes of testing a model?	71
How, exactly, can I compute path coefficients in the regression method?	72
How do I compute the value of the path from an error term to an endogenous variable?	73
How can multiple group path analysis determine if the path model differs across groups in my sample?	73
Could I substitute logistic regression when doing effect decomposition?	73
Can path analysis handle hierarchical/multilevel data?	73
What is partial least squares path analysis?	74
What is the historical origin of path analysis?	74
Bibliography	76

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