Using restricted factor analysis with latent moderated structures to detect uniform and nonuniform measurement bias; a simulation study Barendse, M.T.; Oort, F.J.; Garst, G.J.A. Published in:AStA-Advances in Statistical Analysis DOI:10.1007/s10182-010-0126-1 A. (2010). Using restricted factor analysis with latent moderated structures to detect uniform and nonuniform measurement bias; a simulation study. AStA-Advances in Statistical Analysis, 94(2), 117-127. DOI: 10.1007/s10182-010-0126-1 Link to publication Citation for published version (APA): General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. Download date: 11 May 2018AStA Abstract 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.Adv Stat Anal (2010) 94: 117-127 DOI 10.1007/s10182-010-0126-

We propose a three step procedure to investigate measurement bias and response shift, a special case of measurement bias in longitudinal data. Structural equation modelling is used in each of the three steps, which can be described as (1) establishing a measurement model using confirmatory factor analysis, (2) detecting measurement bias by testing the equivalence of model parameters across measurement occasions, (3) detecting measurement bias with respect to additional exogenous variables by testing their direct effects on the indicator variables. The resulting model can be used to investigate true change in the attributes of interest, by testing changes in common factor means. Solutions for the issue of constraint interaction and for chance capitalisation in model specification searches are discussed as part of the procedure. The procedure is illustrated by applying it to longitudinal health-related quality-of-life data of HIV/AIDS patients, collected at four semi-annual measurement occasions.

Reframing cognitions is assumed to play an important role in treatment for obsessive-compulsive disorder (OCD). However, there hardly is any empirical support for this assumption, especially for children. The aim of this study was to examine if changing dysfunctional beliefs is a mediating mechanism of cognitive behavioral therapy (CBT) for childhood OCD. Fifty-eight children (8-18 years) with OCD received CBT. Dysfunctional beliefs (OBQ-CV) and OCD severity (CY-BOCS) were measured pre-treatment, mid-treatment, post-treatment, and at 16-week follow-up. Results showed that OCD severity and dysfunctional beliefs decreased during CBT. Changes in severity predicted changes in beliefs within the same time interval. Our results did not support the hypothesis that changing dysfunctional beliefs mediates treatment effect. Future studies are needed to replicate these findings and shed more light on the role of explicit and implicit cognitions in treatment for childhood OCD.