User Guide

Chapter
22
GLM Univaria
te Analysis
The GLM Univariate procedure provides regression analysis and analysis of
variance fo
r one dependent variable by one or more factors and/or variables. The
factor variables divide the population into groups. Using this General Linear Model
procedure, you can test null hypotheses about the effects of other variables on the
means of va
rious groupings of a single dependent variable. You can investigate
interactions between factors as well as the effects of individual factors, some of which
may be random. In addition, the effects of covariates and covariate interactions
with fact
ors can be included. For regression analysis, the independent (predictor)
variables are specified as covariates.
Both balanced and unbalanced models can be tested. A design is balanced if
each cell
in the model contains the same number of cases. In addition to testing
hypotheses, GLM Univariate produces estimates of parameters.
Commonly used a priori contrasts are available to perform hypothesis testing.
Additio
nally, after an overall F test has shown significance, you can use post hoc
tests to evaluate differences among specific means. Estimated marginal means
give estimates of predicted mean values for the cells in the model, and profile
plots (i
nteraction plots) of these means allow you to easily visualize some of the
relationships.
Residuals, predicted values, Cook’s distance, and leverage values can be saved as
new var
iables in your data file for checking assumptions.
WLS Weight allows you to specify a variable used to give observations different
weights for a weighted least-squares (WLS) analysis, perhaps to compensate for
adiffe
rent precision of measurement.
Example. Data are gathered for individual runners in the Chicago marathon for
several years. The time in which each runner finishes is the dependent variable.
Other f
actors include weather (cold, pleasant, or hot), number of months of training,
number of previous marathons, and gender. Age is considered a covariate. You
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