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Leifeng Xiao; Kit-Tai Hau; Melissa Dan Wang – Educational Measurement: Issues and Practice, 2024
Short scales are time-efficient for participants and cost-effective in research. However, researchers often mistakenly expect short scales to have the same reliability as long ones without considering the effect of scale length. We argue that applying a universal benchmark for alpha is problematic as the impact of low-quality items is greater on…
Descriptors: Measurement, Benchmarking, Item Sampling, Sample Size
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Winton, Bradley G.; Sabol, Misty A. – International Journal of Social Research Methodology, 2022
Convenience sampling dominates social science research. But there is a paucity of studies comparing the impact of sample source type based on composite-based theoretical model relationships. This study empirically tests four different sample sources (e.g. student, crowdsourced, professional panel, and respondent driven social network) to assess…
Descriptors: Sampling, Sample Size, Social Science Research, Measurement
Bramley, Tom – Research Matters, 2020
The aim of this study was to compare, by simulation, the accuracy of mapping a cut-score from one test to another by expert judgement (using the Angoff method) versus the accuracy with a small-sample equating method (chained linear equating). As expected, the standard-setting method resulted in more accurate equating when we assumed a higher level…
Descriptors: Cutting Scores, Standard Setting (Scoring), Equated Scores, Accuracy
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Michaelides, Michalis P.; Haertel, Edward H. – Applied Measurement in Education, 2014
The standard error of equating quantifies the variability in the estimation of an equating function. Because common items for deriving equated scores are treated as fixed, the only source of variability typically considered arises from the estimation of common-item parameters from responses of samples of examinees. Use of alternative, equally…
Descriptors: Equated Scores, Test Items, Sampling, Statistical Inference
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Jia, Yue; Stokes, Lynne; Harris, Ian; Wang, Yan – Journal of Educational and Behavioral Statistics, 2011
In this article, we consider estimation of parameters of random effects models from samples collected via complex multistage designs. Incorporation of sampling weights is one way to reduce estimation bias due to unequal probabilities of selection. Several weighting methods have been proposed in the literature for estimating the parameters of…
Descriptors: Sampling, Computation, Statistical Bias, Statistical Analysis
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Tabor, Josh – Journal of Statistics Education, 2010
On the 2009 AP[c] Statistics Exam, students were asked to create a statistic to measure skewness in a distribution. This paper explores several of the most popular student responses and evaluates which statistic performs best when sampling from various skewed populations. (Contains 8 figures, 3 tables, and 4 footnotes.)
Descriptors: Advanced Placement, Statistics, Tests, High School Students
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von Davier, Matthias; Sinharay, Sandip – Journal of Educational and Behavioral Statistics, 2007
Reporting methods used in large-scale assessments such as the National Assessment of Educational Progress (NAEP) rely on latent regression models. To fit the latent regression model using the maximum likelihood estimation technique, multivariate integrals must be evaluated. In the computer program MGROUP used by the Educational Testing Service for…
Descriptors: Simulation, Computer Software, Sampling, Data Analysis
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Smyth, Bruce – Family Matters, 2002
This article examines methodological issues confronting the study of qualitative and quantitative differences in ways that separated parents share time with their children. Measurement issues explored include disentangling dimensions of contact, identifying economic implications of contact, and measuring quality of contact. Sampling issues relate…
Descriptors: Children, Divorce, Measurement, Parent Child Relationship
Pollicino, Elizabeth B. – 1998
This paper outlines procedures used to derive variables from data in the National Survey of Postsecondary Faculty; these variables were then used to create measures not expressly included as items in that survey. The derived variables were used to examine faculty satisfaction in two contexts: first, the complexity of satisfaction, and second, the…
Descriptors: College Faculty, Factor Analysis, Faculty College Relationship, Higher Education