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Lai, Mark H. C. – Journal of Educational and Behavioral Statistics, 2019
Previous studies have detailed the consequence of ignoring a level of clustering in multilevel models with straightly hierarchical structures and have proposed methods to adjust for the fixed effect standard errors (SEs). However, in behavioral and social science research, there are usually two or more crossed clustering levels, such as when…
Descriptors: Error of Measurement, Hierarchical Linear Modeling, Least Squares Statistics, Statistical Bias
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Grund, Simon; Lüdtke, Oliver; Robitzsch, Alexander – Journal of Educational and Behavioral Statistics, 2021
Large-scale assessments (LSAs) use Mislevy's "plausible value" (PV) approach to relate student proficiency to noncognitive variables administered in a background questionnaire. This method requires background variables to be completely observed, a requirement that is seldom fulfilled. In this article, we evaluate and compare the…
Descriptors: Data Analysis, Error of Measurement, Research Problems, Statistical Inference
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Klausch, Thomas; Schouten, Barry; Hox, Joop J. – Sociological Methods & Research, 2017
This study evaluated three types of bias--total, measurement, and selection bias (SB)--in three sequential mixed-mode designs of the Dutch Crime Victimization Survey: telephone, mail, and web, where nonrespondents were followed up face-to-face (F2F). In the absence of true scores, all biases were estimated as mode effects against two different…
Descriptors: Evaluation Methods, Statistical Bias, Sequential Approach, Benchmarking
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Pantelis, Peter C.; Kennedy, Daniel P. – Autism: The International Journal of Research and Practice, 2016
Two-phase designs in epidemiological studies of autism prevalence introduce methodological complications that can severely limit the precision of resulting estimates. If the assumptions used to derive the prevalence estimate are invalid or if the uncertainty surrounding these assumptions is not properly accounted for in the statistical inference…
Descriptors: Foreign Countries, Pervasive Developmental Disorders, Autism, Incidence
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Pampaka, Maria; Hutcheson, Graeme; Williams, Julian – International Journal of Research & Method in Education, 2016
Missing data is endemic in much educational research. However, practices such as step-wise regression common in the educational research literature have been shown to be dangerous when significant data are missing, and multiple imputation (MI) is generally recommended by statisticians. In this paper, we provide a review of these advances and their…
Descriptors: Data Analysis, Statistical Inference, Error of Measurement, Computation
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Köhler, Carmen; Pohl, Steffi; Carstensen, Claus H. – Educational and Psychological Measurement, 2015
When competence tests are administered, subjects frequently omit items. These missing responses pose a threat to correctly estimating the proficiency level. Newer model-based approaches aim to take nonignorable missing data processes into account by incorporating a latent missing propensity into the measurement model. Two assumptions are typically…
Descriptors: Competence, Tests, Evaluation Methods, Adults
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Zhuang, Jie; Chen, Peijie; Wang, Chao; Jin, Jing; Zhu, Zheng; Zhang, Wenjie – Research Quarterly for Exercise and Sport, 2013
Purpose: The purpose of this study was to determine which method, individual information-centered (IIC) or group information-centered (GIC), is more efficient in recovering missing physical activity (PA) data. Method: A total of 2,758 Chinese children and youth aged 9 to 17 years old (1,438 boys and 1,320 girls) wore ActiGraph GT3X/GT3X+…
Descriptors: Foreign Countries, Physical Activities, Measurement Equipment, Data Analysis