The problem of determining test bias in prediction
using regression models is reexamined. Past approaches
have made use of separate regression analyses
in each subgroup, moderated multiple regression
analysis using subgroup coding, and hierarchical multiple
regression strategies. Although it is agreed that
hierarchical multiple regression analysis is preferable
to either of the former methods, the approach presented
here differs with respect to the hypothesis testing
procedure to be employed in such an analysis.
This paper describes the difficulties in testing hypotheses
about the existence of bias in prediction using
step-up methods of analysis. Some shortcomings
of previously recommended approaches for testing
these hypotheses are discussed. Finally, a step-down
hierarchical multiple regression procedure is recommended.
Analysis of real data illustrates the potential
usefulness of the step-down procedure.
Lautenschlager, Gary J & Mendoza, Jorge L. (1986). A step-down hierarchical multiple regression analysis for examining hypotheses about test bias in prediction. Applied Psychological Measurement, 10, 133-139. doi:10.1177/014662168601000202
Lautenschlager, Gary J.; Mendoza, Jorge L..
A step-down hierarchical multiple regression analysis for examining hypotheses about test bias in prediction.
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