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Showing 1 to 15 of 597 results Save | Export
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Lubbe, Dirk; Schuster, Christof – Journal of Educational and Behavioral Statistics, 2020
Extreme response style is the tendency of individuals to prefer the extreme categories of a rating scale irrespective of item content. It has been shown repeatedly that individual response style differences affect the reliability and validity of item responses and should, therefore, be considered carefully. To account for extreme response style…
Descriptors: Response Style (Tests), Rating Scales, Item Response Theory, Models
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Schochet, Peter Z. – Journal of Educational and Behavioral Statistics, 2020
This article discusses estimation of average treatment effects for randomized controlled trials (RCTs) using grouped administrative data to help improve data access. The focus is on design-based estimators, derived using the building blocks of experiments, that are conducive to grouped data for a wide range of RCT designs, including clustered and…
Descriptors: Randomized Controlled Trials, Data Analysis, Research Design, Multivariate Analysis
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van der Linden, Wim J.; Ren, Hao – Journal of Educational and Behavioral Statistics, 2020
The Bayesian way of accounting for the effects of error in the ability and item parameters in adaptive testing is through the joint posterior distribution of all parameters. An optimized Markov chain Monte Carlo algorithm for adaptive testing is presented, which samples this distribution in real time to score the examinee's ability and optimally…
Descriptors: Bayesian Statistics, Adaptive Testing, Error of Measurement, Markov Processes
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Johnson, Matthew S.; Sinharay, Sandip – Journal of Educational and Behavioral Statistics, 2020
One common score reported from diagnostic classification assessments is the vector of posterior means of the skill mastery indicators. As with any assessment, it is important to derive and report estimates of the reliability of the reported scores. After reviewing a reliability measure suggested by Templin and Bradshaw, this article suggests three…
Descriptors: Reliability, Probability, Skill Development, Classification
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Keller, Bryan – Journal of Educational and Behavioral Statistics, 2020
Widespread availability of rich educational databases facilitates the use of conditioning strategies to estimate causal effects with nonexperimental data. With dozens, hundreds, or more potential predictors, variable selection can be useful for practical reasons related to communicating results and for statistical reasons related to improving the…
Descriptors: Nonparametric Statistics, Computation, Testing, Causal Models
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Pang, Bo; Nijkamp, Erik; Wu, Ying Nian – Journal of Educational and Behavioral Statistics, 2020
This review covers the core concepts and design decisions of TensorFlow. TensorFlow, originally created by researchers at Google, is the most popular one among the plethora of deep learning libraries. In the field of deep learning, neural networks have achieved tremendous success and gained wide popularity in various areas. This family of models…
Descriptors: Artificial Intelligence, Regression (Statistics), Models, Classification
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Kang, Hyeon-Ah; Zheng, Yi; Chang, Hua-Hua – Journal of Educational and Behavioral Statistics, 2020
With the widespread use of computers in modern assessment, online calibration has become increasingly popular as a way of replenishing an item pool. The present study discusses online calibration strategies for a joint model of responses and response times. The study proposes likelihood inference methods for item paramter estimation and evaluates…
Descriptors: Adaptive Testing, Computer Assisted Testing, Item Response Theory, Reaction Time
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Sakworawich, Arnond; Wainer, Howard – Journal of Educational and Behavioral Statistics, 2020
Test scoring models vary in their generality, some even adjust for examinees answering multiple-choice items correctly by accident (guessing), but no models, that we are aware of, automatically adjust an examinee's score when there is internal evidence of cheating. In this study, we use a combination of jackknife technology with an adaptive robust…
Descriptors: Scoring, Cheating, Test Items, Licensing Examinations (Professions)
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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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Matta, Tyler H.; Soland, James – Journal of Educational and Behavioral Statistics, 2019
The development of academic English proficiency and the time it takes to reclassify to fluent English proficient status are key issues in English learner (EL) policy. This article develops a shared random effects model (SREM) to estimate English proficiency development and time to reclassification simultaneously, treating student-specific random…
Descriptors: English Language Learners, Language Proficiency, Classification, Language Fluency
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Culpepper, Steven Andrew; Chen, Yinghan – Journal of Educational and Behavioral Statistics, 2019
Exploratory cognitive diagnosis models (CDMs) estimate the Q matrix, which is a binary matrix that indicates the attributes needed for affirmative responses to each item. Estimation of Q is an important next step for improving classifications and broadening application of CDMs. Prior research primarily focused on an exploratory version of the…
Descriptors: Cognitive Measurement, Models, Bayesian Statistics, Computation
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Ma, Wenchao; de la Torre, Jimmy – Journal of Educational and Behavioral Statistics, 2019
Solving a constructed-response item usually requires successfully performing a sequence of tasks. Each task could involve different attributes, and those required attributes may be "condensed" in various ways to produce the responses. The sequential generalized deterministic input noisy "and" gate model is a general cognitive…
Descriptors: Test Items, Cognitive Measurement, Models, Hypothesis Testing
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Kim, Minjung; Hsu, Hsien-Yuan – Journal of Educational and Behavioral Statistics, 2019
Given the natural hierarchical structure in school-setting data, multilevel modeling (MLM) has been widely employed in education research using a number of different statistical software packages. The purpose of this article is to review a recent feature of Stat-JR, the statistical analysis assistants (SAAs) embedded in Stat-JR (Version 1.0.5),…
Descriptors: Hierarchical Linear Modeling, Statistical Analysis, Computer Software, Computer Software Evaluation
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Hayes, Timothy – Journal of Educational and Behavioral Statistics, 2019
Multiple imputation is a popular method for addressing data that are presumed to be missing at random. To obtain accurate results, one's imputation model must be congenial to (appropriate for) one's intended analysis model. This article reviews and demonstrates two recent software packages, Blimp and jomo, to multiply impute data in a manner…
Descriptors: Computer Software Evaluation, Computer Software Reviews, Hierarchical Linear Modeling, Data Analysis
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Choi, Kilchan; Kim, Jinok – Journal of Educational and Behavioral Statistics, 2019
This article proposes a latent variable regression four-level hierarchical model (LVR-HM4) that uses a fully Bayesian approach. Using multisite multiple-cohort longitudinal data, for example, annual assessment scores over grades for students who are nested within cohorts within schools, the LVR-HM4 attempts to simultaneously model two types of…
Descriptors: Regression (Statistics), Hierarchical Linear Modeling, Longitudinal Studies, Cohort Analysis
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