Examples: Regression And Path Analysis
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CHAPTER 3
EXAMPLES: REGRESSION AND
PATH ANALYSIS
Regression analysis with univariate or multivariate dependent variables
is a standard procedure for modeling relationships among observed
variables. Path analysis allows the simultaneous modeling of several
related regression relationships. In path analysis, a variable can be a
dependent variable in one relationship and an independent variable in
another. These variables are referred to as mediating variables. For both
types of analyses, observed dependent variables can be continuous,
censored, binary, ordered categorical (ordinal), counts, or combinations
of these variable types. In addition, for regression analysis and path
analysis for non-mediating variables, observed dependent variables can
be unordered categorical (nominal).
For continuous dependent variables, linear regression models are used.
For censored dependent variables, censored-normal regression models
are used, with or without inflation at the censoring point. For binary and
ordered categorical dependent variables, probit or logistic regression
models are used. Logistic regression for ordered categorical dependent
variables uses the proportional odds specification. For unordered
categorical dependent variables, multinomial logistic regression models
are used. For count dependent variables, Poisson regression models are
used, with or without inflation at the zero point. Both maximum
likelihood and weighted least squares estimators are available.
All regression and path analysis models can be estimated using the
following special features:
Single or multiple group analysis
Missing data
Complex survey data
Random slopes
Linear and non-linear parameter constraints
Indirect effects including specific paths
Maximum likelihood estimation for all outcome types
Bootstrap standard errors and confidence intervals
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