Bias in the correlated uniqueness model for MTMM data

Bias in the correlated uniqueness model for MTMM data

This simulation investigates bias in trait factor loadings and intercorrelations when analyzing multitrait-multimethod (MTMM) data using the correlated uniqueness (CU) confirmatory factor analysis (CFA) model. A theoretical weakness of the CU model is the assumption of uncorrelated methods. However,...

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Bibliographic Details
Main Authors: CONWAY, James M., LIEVENS, Filip, SCULLEN, Steven E., LANCE, Charles E.
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2004
Subjects:
Computer simulation
Correlation methods
Mathematical models
Matrix algebra
Parameter estimation
Human Resources Management
Organizational Behavior and Theory
Online Access:https://ink.library.smu.edu.sg/lkcsb_research/5588
https://ink.library.smu.edu.sg/context/lkcsb_research/article/6587/viewcontent/cu.pdf
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Institution: Singapore Management University
Language: English
Description
Summary:This simulation investigates bias in trait factor loadings and intercorrelations when analyzing multitrait-multimethod (MTMM) data using the correlated uniqueness (CU) confirmatory factor analysis (CFA) model. A theoretical weakness of the CU model is the assumption of uncorrelated methods. However, previous simulation studies have shown little bias in trait estimates even when true method correlations are large. We hypothesized that there would be substantial bias when both method factor correlations and method factor loadings were large. We generated simulated sample data using population parameters based on our review of actual MTMM results. Results confirmed the prediction; substantial bias occurred in trait factor loadings and correlations when both method loadings and method correlations were large.

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