Using restricted factor analysis with latent moderated structures to detect uniform and nonuniform measurement bias; a simulation study

By M. Barendse, F. Oort, G. Garst. 2010

53 citations recorded by the source corpus.

Factor analysis is an established technique for the detection of measurement bias. Multigroup factor analysis (MGFA) can detect both uniform and nonuniform bias. Restricted factor analysis (RFA) can also be used to detect measurement bias, albeit only uniform measurement bias. Latent moderated structural equations (LMS) enable the estimation of nonlinear interaction effects in structural equation modelling. By extending the RFA method with LMS, the RFA method should be suited to detect nonuniform bias as well as uniform bias. In a simulation study, the RFA/LMS method and the MGFA method are compared in detecting uniform and nonuniform measurement bias under various conditions, varying the size of uniform bias, the size of nonuniform bias, the sample size, and the ability distribution. For each condition, 100 sets of data were generated and analysed through both detection methods. The RFA/LMS and MGFA methods turned out to perform equally well. Percentages of correctly identified items as biased (true positives) generally varied between 92% and 100%, except in small sample size conditions in which the bias was nonuniform and small. For both methods, the percentages of false positives were generally higher than the nominal levels of significance.

Subfields: Computer Networks and Communications, Food Science, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/4ea8b99fca620105ffa6a3c9e8c62b19ed95eb55

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In Computer Networks and Communications

In Food Science

In Management Science and Operations Research

Citations in the corpus

Testing Measurement Invariance Using MIMIC (2012, methodology): “RFA is equivalent to MIMIC except that correlation between a factor and a covariate (hi and Kim et al. 475 Xi, respectively, in Equation 6) is specified in RFA instead of a causal effect (Barendse et al., 2010).”
Testing for Nonuniform Differential Item Functioning With Multiple Indicator Multiple Cause Models (2011, methodology): “The approach of Barendse et al. (2010) differs from the present methodology because it uses continuous indicators, represents group as a latent variable,2 and treats all other items as anchors for model comparisons.”