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Myoung-jae Lee; Goeun Lee; Jin-young Choi – Sociological Methods & Research, 2025
A linear model is often used to find the effect of a binary treatment D on a noncontinuous outcome Y with covariates X. Particularly, a binary Y gives the popular "linear probability model (LPM)," but the linear model is untenable if X contains a continuous regressor. This raises the question: what kind of treatment effect does the…
Descriptors: Probability, Least Squares Statistics, Regression (Statistics), Causal Models
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Ole J. Kemi – Advances in Physiology Education, 2025
Students are assessed by coursework and/or exams, all of which are marked by assessors (markers). Student and marker performances are then subject to end-of-session board of examiner handling and analysis. This occurs annually and is the basis for evaluating students but also the wider learning and teaching efficiency of an academic institution.…
Descriptors: Undergraduate Students, Evaluation Methods, Evaluation Criteria, Academic Standards
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Kilic, Abdullah Faruk; Dogan, Nuri – International Journal of Assessment Tools in Education, 2021
Weighted least squares (WLS), weighted least squares mean-and-variance-adjusted (WLSMV), unweighted least squares mean-and-variance-adjusted (ULSMV), maximum likelihood (ML), robust maximum likelihood (MLR) and Bayesian estimation methods were compared in mixed item response type data via Monte Carlo simulation. The percentage of polytomous items,…
Descriptors: Factor Analysis, Computation, Least Squares Statistics, Maximum Likelihood Statistics
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Williams, Matt N.; Gomez Grajales, Carlos Alberto; Kurkiewicz, Dason – Practical Assessment, Research & Evaluation, 2013
In 2002, an article entitled "Four assumptions of multiple regression that researchers should always test" by Osborne and Waters was published in "PARE." This article has gone on to be viewed more than 275,000 times (as of August 2013), and it is one of the first results displayed in a Google search for "regression…
Descriptors: Multiple Regression Analysis, Misconceptions, Reader Response, Predictor Variables
Allen, Nancy L.; Dunbar, Stephen B. – 1988
A recurring problem in educational research is how to account for non-random selection that has restricted the range of the variables of interest in correlational analyses. Several expressions due to H. Pearson (1903) and presented in matrix notation by D. N. Lawley (1943-44) are commonly used in selection settings to adjust for samples chosen on…
Descriptors: Computer Simulation, Correlation, Error of Measurement, Matrices
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Gross, Alan L.; Kagen, Edward – Educational and Psychological Measurement, 1983
This paper compares an uncorrected with a corrected correlation between a selection test and a test-criterion in terms of expected mean square error (EMSE). It presents evidence that although the uncorrected may be more biased than the corrected correlation, it may have a smaller EMSE value, especially in small samples. (Author/PN)
Descriptors: Competitive Selection, Correlation, Error of Measurement, Research Methodology
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Bannister, Brendan D.; And Others – Educational and Psychological Measurement, 1987
To control for response bias in student ratings of college teachers, an index of rater error was used that was theoretically independent of actual performance. Partialing out the effects of this extraneous response bias enhanced validity, but partialing out overall effectiveness resulted in reduced convergent and discriminant validities.…
Descriptors: Error of Measurement, Higher Education, Interrater Reliability, Response Style (Tests)