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Showing 1 to 15 of 69 results Save | Export
Mohammad, Nagham; McGivern, Lucinda – Online Submission, 2020
In regression analysis courses, there are many settings in which the response variable under study is continuous, strictly positive, and right skew. This type of response variable does not adhere to the normality assumptions underlying the traditional linear regression model, and accordingly may be analyzed using a generalized linear model…
Descriptors: Regression (Statistics), Statistical Distributions, Simulation, Data Analysis
Sales, Adam C.; Hansen, Ben B. – Journal of Educational and Behavioral Statistics, 2020
Conventionally, regression discontinuity analysis contrasts a univariate regression's limits as its independent variable, "R," approaches a cut point, "c," from either side. Alternative methods target the average treatment effect in a small region around "c," at the cost of an assumption that treatment assignment,…
Descriptors: Regression (Statistics), Computation, Statistical Inference, Robustness (Statistics)
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Yamaguchi, Kazuo – Sociological Methods & Research, 2016
This article describes (1) the survey methodological and statistical characteristics of the nonrandomized method for surveying sensitive questions for both cross-sectional and panel survey data and (2) the way to use the incompletely observed variable obtained from this survey method in logistic regression and in loglinear and log-multiplicative…
Descriptors: Data Analysis, Surveys, Statistical Analysis, Regression (Statistics)
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Wright, Daniel B. – International Journal of Research & Method in Education, 2017
Many education policies require estimating whether students in different grades are on track for achieving certain educational standards. One approach for constructing these cut scores is to estimate the values on tests that predict reaching targets on subsequent tests. Whether a student is deemed on target can affect the student's course…
Descriptors: Cutting Scores, Regression (Statistics), Elementary Secondary Education, Evaluation Methods
Christ, Theodore J.; Desjardins, Christopher David – Journal of Psychoeducational Assessment, 2018
Curriculum-Based Measurement of Oral Reading (CBM-R) is often used to monitor student progress and guide educational decisions. Ordinary least squares regression (OLSR) is the most widely used method to estimate the slope, or rate of improvement (ROI), even though published research demonstrates OLSR's lack of validity and reliability, and…
Descriptors: Bayesian Statistics, Curriculum Based Assessment, Oral Reading, Least Squares Statistics
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Kern, Holger L.; Stuart, Elizabeth A.; Hill, Jennifer; Green, Donald P. – Journal of Research on Educational Effectiveness, 2016
Randomized experiments are considered the gold standard for causal inference because they can provide unbiased estimates of treatment effects for the experimental participants. However, researchers and policymakers are often interested in using a specific experiment to inform decisions about other target populations. In education research,…
Descriptors: Educational Research, Generalization, Sampling, Participant Characteristics
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Jin, Ying; Eason, Hershel – Journal of Educational Issues, 2016
The effects of mean ability difference (MAD) and short tests on the performance of various DIF methods have been studied extensively in previous simulation studies. Their effects, however, have not been studied under multilevel data structure. MAD was frequently observed in large-scale cross-country comparison studies where the primary sampling…
Descriptors: Test Bias, Simulation, Hierarchical Linear Modeling, Comparative Analysis
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Li, Zhushan – Journal of Educational Measurement, 2014
Logistic regression is a popular method for detecting uniform and nonuniform differential item functioning (DIF) effects. Theoretical formulas for the power and sample size calculations are derived for likelihood ratio tests and Wald tests based on the asymptotic distribution of the maximum likelihood estimators for the logistic regression model.…
Descriptors: Test Bias, Sample Size, Statistical Analysis, Regression (Statistics)
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Gharaibeh, Besher; Hweidi, Issa; Al-Smadi, Ahmed – Cogent Education, 2017
Background: Simulation can produce highly qualified professionals, however, it can also be perceived as stressful and frustrating by the nursing students. Purposes: This study was to identify the attitudes and perceptions of Jordanian nursing students toward simulation as an educational strategy, to investigate whether certain students'…
Descriptors: Student Attitudes, Nursing Students, Nursing Education, Undergraduate Students
Sweet, Tracy M. – Journal of Educational and Behavioral Statistics, 2015
Social networks in education commonly involve some form of grouping, such as friendship cliques or teacher departments, and blockmodels are a type of statistical social network model that accommodate these grouping or blocks by assuming different within-group tie probabilities than between-group tie probabilities. We describe a class of models,…
Descriptors: Social Networks, Statistical Analysis, Probability, Models
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Devlieger, Ines; Mayer, Axel; Rosseel, Yves – Educational and Psychological Measurement, 2016
In this article, an overview is given of four methods to perform factor score regression (FSR), namely regression FSR, Bartlett FSR, the bias avoiding method of Skrondal and Laake, and the bias correcting method of Croon. The bias correcting method is extended to include a reliable standard error. The four methods are compared with each other and…
Descriptors: Regression (Statistics), Comparative Analysis, Structural Equation Models, Monte Carlo Methods
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Sinharay, Sandip; Wan, Ping; Choi, Seung W.; Kim, Dong-In – Journal of Educational Measurement, 2015
With an increase in the number of online tests, the number of interruptions during testing due to unexpected technical issues seems to be on the rise. For example, interruptions occurred during several recent state tests. When interruptions occur, it is important to determine the extent of their impact on the examinees' scores. Researchers such as…
Descriptors: Computer Assisted Testing, Testing Problems, Scores, Statistical Analysis
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Smith, Hayden; Michelsen, Niall – Journal of Political Science Education, 2017
Utilizing a web-based simulation Statecraft, we explore the relative influence of ideology (realism and idealism) on student behavior and learning. By placing students into ideologically cohesive groups, we are able to demonstrate the effect of their ideology on the goals they pursue and identify the constraints imposed on the system by the…
Descriptors: Political Science, Ideology, Computer Simulation, Foreign Policy
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Sinharay, Sandip; Wan, Ping; Whitaker, Mike; Kim, Dong-In; Zhang, Litong; Choi, Seung W. – Journal of Educational Measurement, 2014
With an increase in the number of online tests, interruptions during testing due to unexpected technical issues seem unavoidable. For example, interruptions occurred during several recent state tests. When interruptions occur, it is important to determine the extent of their impact on the examinees' scores. There is a lack of research on this…
Descriptors: Computer Assisted Testing, Testing Problems, Scores, Regression (Statistics)
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Glazerman, Steven; Dotter, Dallas – Educational Evaluation and Policy Analysis, 2017
We estimate school-choice preferences revealed by the rank-ordered lists submitted by more than 22,000 applicants to a citywide lottery for more than 200 traditional and charter public schools in Washington, D.C. The results confirm previously reported findings that commuting distance, school demographics, and academic indicators play important…
Descriptors: School Choice, Evidence, Charter Schools, Public Schools
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