The Use of Structural Equation Models in Interpreting Regression Equations Including Suppressor and Enhancer Variables

Applied Psychological Measurement - Tập 3 Số 1 - Trang 123-135 - 1979
Robert M. McFatter1
1University of Denver;

Tóm tắt

It is shown that the usual interpretation of "sup pressor" effects in a multiple regression equation assumes that the correlations among variables have been generated by a particular structural (causal) model, namely, Conger's (1974) two-factor model. A distinction is drawn between the technical definition of "suppression," which is more fittingly labelled enhancement, and suppression as the appropriate interpretation of a regression equation exhibiting enhancement when that equation has been gen erated by the two-factor model. It is demonstrated that a number of models can generate enhancement but cannot sensibly be interpreted in terms of the measuring, removing, or suppressing of irrelevant or invalid variance. How a regression equation is interpreted thus depends critically on the structural model deemed appropriate.

Từ khóa


Tài liệu tham khảo

Cohen, J., 1975, Applied multiple regression/correlation analysis for the behavioral sciences

10.1177/001316447403400105

10.1037/h0025471

Duncan, O.D., 1975, Introduction to structural equation models

Heise, D.R., 1975, Causal analysis

Johnston, J.J., 1972, Econometric methods, 2

Lord, F.M., 1968, Statistical theories of mental test scores

Thông tin
Thông tin xuất bản

Applied Psychological Measurement

Tập 3 Số 1

123-135

Thông tin tác giả