The Use of Multitrait–Multimethod Data for Detecting Nonlinear Relationships: The Case of Psychological Climate and Job Satisfaction

Abstract

Previous monomethod research has found mixed support for nonlinear effects between certain job characteristics (e.g., job autonomy, job complexity) and outcome variables (e.g., job performance). We hypothesized that these weak nonlinear findings may be due to the prevalence of monomethod research that can lead to a lack of complete measurement and/or introduce common methods variance, either of which may mask the "true" shape of relationships. Using hierarchical regression analyses and a multitrait–multimethod (MTMM) research design, we found strikingly different results between monomethod and MTMM data when considering the relationships between three psychological climate variables and job satisfaction. While the monomethod results mirrored earlier inconclusive findings, the MTMM data indicated that nonlinear equations explained significantly more of the relationship between all three climate dimensions and job satisfaction. These results suggest that the use of MTMM data allows one to more effectively test for nonlinear effects. Furthermore, these nonlinear results suggest that the format of employee questionnaires should probably change from asking how much an employee has of certain constructs to asking does the employee want more or less of these constructs.