Journal of Educational and Behavioral Statistics

Papers
(The TQCC of Journal of Educational and Behavioral Statistics is 3. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2020-11-01 to 2024-11-01.)
ArticleCitations
Using Sequence Mining Techniques for Understanding Incorrect Behavioral Patterns on Interactive Tasks23
Analyzing Cross-Sectionally Clustered Data Using Generalized Estimating Equations20
Mean Comparisons of Many Groups in the Presence of DIF: An Evaluation of Linking and Concurrent Scaling Approaches15
Deep Reinforcement Learning for Adaptive Learning Systems13
Modeling Item-Level Heterogeneous Treatment Effects With the Explanatory Item Response Model: Leveraging Large-Scale Online Assessments to Pinpoint the Impact of Educational Interventions11
Block What You Can, Except When You Shouldn’t10
Item Characteristic Curve Asymmetry: A Better Way to Accommodate Slips and Guesses Than a Four-Parameter Model?9
Speed–Accuracy Trade-Off? Not So Fast: Marginal Changes in Speed Have Inconsistent Relationships With Accuracy in Real-World Settings9
Testing Differential Item Functioning Without Predefined Anchor Items Using Robust Regression9
Cognitive Diagnosis Modeling Incorporating Response Times and Fixation Counts: Providing Comprehensive Feedback and Accurate Diagnosis8
Obtaining Interpretable Parameters From Reparameterized Longitudinal Models: Transformation Matrices Between Growth Factors in Two Parameter Spaces8
Power Approximations for Overall Average Effects in Meta-Analysis With Dependent Effect Sizes8
A New Multiprocess IRT Model With Ideal Points for Likert-Type Items7
Statistical Power for Estimating Treatment Effects Using Difference-in-Differences and Comparative Interrupted Time Series Estimators With Variation in Treatment Timing7
Zero and One Inflated Item Response Theory Models for Bounded Continuous Data6
Cross-Classified Item Response Theory Modeling With an Application to Student Evaluation of Teaching6
Cross-Classified Random Effects Modeling for Moderated Item Calibration6
The Restricted DINA Model: A Comprehensive Cognitive Diagnostic Model for Classroom-Level Assessments6
Reporting Proficiency Levels for Examinees With Incomplete Data6
A Practical Guide for Analyzing Large-Scale Assessment Data Using Mplus: A Case Demonstration Using the Program for International Assessment of Adult Competencies Data6
Monitoring Item Performance With CUSUM Statistics in Continuous Testing6
Detecting Noneffortful Responses Based on a Residual Method Using an Iterative Purification Process6
Nonparametric Classification Method for Multiple-Choice Items in Cognitive Diagnosis5
Analyzing Longitudinal Social Relations Model Data Using the Social Relations Structural Equation Model5
Computational Strategies and Estimation Performance With Bayesian Semiparametric Item Response Theory Models5
Regression Discontinuity Designs With an Ordinal Running Variable: Evaluating the Effects of Extended Time Accommodations for English-Language Learners4
Latent Transition Cognitive Diagnosis Model With Covariates: A Three-Step Approach4
Forced-Choice Ranking Models for Raters’ Ranking Data4
Identifying Informative Predictor Variables With Random Forests4
Comparison of Within- and Between-Series Effect Estimates in the Meta-Analysis of Multiple Baseline Studies4
Estimating Heterogeneous Treatment Effects Within Latent Class Multilevel Models: A Bayesian Approach3
Estimating Difference-Score Reliability in Pretest–Posttest Settings3
Diagnosing Primary Students’ Reading Progression: Is Cognitive Diagnostic Computerized Adaptive Testing the Way Forward?3
Using Response Times for Joint Modeling of Careless Responding and Attentive Response Styles3
Item Pool Quality Control in Educational Testing: Change Point Model, Compound Risk, and Sequential Detection3
A Psychometric Framework for Evaluating Fairness in Algorithmic Decision Making: Differential Algorithmic Functioning3
Jenss–Bayley Latent Change Score Model With Individual Ratio of the Growth Acceleration in the Framework of Individual Measurement Occasions3
A Case Study of Nonresponse Bias Analysis in Educational Assessment Surveys3
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