Chapter
9
Complex Samples General Linear Model
The Complex Samples General Linear Model (CSGLM) procedure performs linear regression analysis, as well as analysis of variance and covariance, for samples drawn by complex sampling methods. Optionally, you can request analyses for a subpopulation.
Example. A grocery store chain surveyed a set of customers concerning their purchasing habits, according to a complex design. Given the survey results and how much each customer spent in the previous month, the store wants to see if the frequency with which customers shop is related to the amount they spend in a month, controlling for the gender of the customer and incorporating the sampling design.
Statistics. The procedure produces estimates, standard errors, confidence intervals, t tests, design effects, and square roots of design effects for model parameters, as well as the correlations and covariances between parameter estimates. Measures of model fit and descriptive statistics for the dependent and independent variables are also available. Additionally, you can request estimated marginal means for levels of model factors and factor interactions.
Data. The dependent variable is quantitative. Factors are categorical. Covariates are quantitative variables that are related to the dependent variable. Subpopulation variables can be string or numeric but should be categorical.
Assumptions. The cases in the data file represent a sample from a complex design that should be analyzed according to the specifications in the file selected in the Complex Samples Plan dialog box.
Obtaining a Complex Samples General Linear Model
From the menus choose:
Analyze > Complex Samples > General Linear Model...
ESelect a plan file. Optionally, select a custom joint probabilities file.
EClick Continue.
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