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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-

In the past, several assessment reports on writing repeatedly showed that elementary school students do not develop the essential writing skills to be successful in school. In this respect, prior research has pointed to the fact that cognitive and motivational challenges are at the root of the rather basic level of elementary students' writing performance. Additionally, previous research has revealed gender and achievement-level differences in elementary students' writing. In view of providing effective writing instruction for all students to overcome writing difficulties, the present study provides more indepth insight into (a) how cognitive and motivational challenges mediate and correlate with students' writing performance and (b) whether and how these relations vary for boys and girls and for writers of different achievement levels. In the present study, 1,577 fifth-and sixth-grade students completed questionnaires regarding their writing self-efficacy, writing motivation, and writing strategies. In addition, half of the students completed two writing tests, respectively focusing on the informational or narrative text genre. Based on multiple group structural equation modeling (MG-SEM), we put forward two models: a MG-SEM model for boys and girls and a MG-SEM model for low, average, and high achievers. The results underline the importance of studying writing models for different groups of students in order to gain more refined insight into the complex interplay between motivational and cognitive challenges related to students' writing performance. I n contemporary society, effective communication skills, such as reading and writing, are essential to communicate in personal and professional contexts. Being able to compose understandable and structured texts is necessary for successfully expressing oneself and is highly valued in professional, educational, and private contexts. Based on the social cognitive model of writing, Graham, Gillespie, and McKeown (2013) defined writing as a goal directed and self-sustained cognitive activity requiring the skillful management of (a) the writing environment; (b) the constraints imposed by the writing topic; (c) the intentions of the writer(s); and (d) the processes, knowledge, and skills involved in composing. (p. 4)

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