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  Subjects -> STATISTICS (Total: 130 journals)
Showing 1 - 151 of 151 Journals sorted alphabetically
Advances in Complex Systems     Hybrid Journal   (Followers: 11)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 61)
Annals of Applied Statistics     Full-text available via subscription   (Followers: 39)
Applied Categorical Structures     Hybrid Journal   (Followers: 4)
Argumentation et analyse du discours     Open Access   (Followers: 11)
Asian Journal of Mathematics & Statistics     Open Access   (Followers: 8)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 4)
Australian & New Zealand Journal of Statistics     Hybrid Journal   (Followers: 13)
Bernoulli     Full-text available via subscription   (Followers: 9)
Biometrical Journal     Hybrid Journal   (Followers: 11)
Biometrics     Hybrid Journal   (Followers: 52)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 18)
Building Simulation     Hybrid Journal   (Followers: 2)
Bulletin of Statistics     Full-text available via subscription   (Followers: 4)
CHANCE     Hybrid Journal   (Followers: 5)
Communications in Statistics - Simulation and Computation     Hybrid Journal   (Followers: 9)
Communications in Statistics - Theory and Methods     Hybrid Journal   (Followers: 11)
Computational Statistics     Hybrid Journal   (Followers: 14)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 37)
Current Research in Biostatistics     Open Access   (Followers: 8)
Decisions in Economics and Finance     Hybrid Journal   (Followers: 11)
Demographic Research     Open Access   (Followers: 15)
Electronic Journal of Statistics     Open Access   (Followers: 8)
Engineering With Computers     Hybrid Journal   (Followers: 5)
Environmental and Ecological Statistics     Hybrid Journal   (Followers: 7)
ESAIM: Probability and Statistics     Full-text available via subscription   (Followers: 5)
Extremes     Hybrid Journal   (Followers: 2)
Fuzzy Optimization and Decision Making     Hybrid Journal   (Followers: 9)
Geneva Papers on Risk and Insurance - Issues and Practice     Hybrid Journal   (Followers: 13)
Handbook of Numerical Analysis     Full-text available via subscription   (Followers: 5)
Handbook of Statistics     Full-text available via subscription   (Followers: 7)
IEA World Energy Statistics and Balances -     Full-text available via subscription   (Followers: 2)
International Journal of Computational Economics and Econometrics     Hybrid Journal   (Followers: 6)
International Journal of Quality, Statistics, and Reliability     Open Access   (Followers: 17)
International Journal of Stochastic Analysis     Open Access   (Followers: 3)
International Statistical Review     Hybrid Journal   (Followers: 13)
International Trade by Commodity Statistics - Statistiques du commerce international par produit     Full-text available via subscription  
Journal of Algebraic Combinatorics     Hybrid Journal   (Followers: 4)
Journal of Applied Statistics     Hybrid Journal   (Followers: 21)
Journal of Biopharmaceutical Statistics     Hybrid Journal   (Followers: 21)
Journal of Business & Economic Statistics     Full-text available via subscription   (Followers: 39, SJR: 3.664, CiteScore: 2)
Journal of Combinatorial Optimization     Hybrid Journal   (Followers: 7)
Journal of Computational & Graphical Statistics     Full-text available via subscription   (Followers: 20)
Journal of Econometrics     Hybrid Journal   (Followers: 84)
Journal of Educational and Behavioral Statistics     Hybrid Journal   (Followers: 6)
Journal of Forecasting     Hybrid Journal   (Followers: 17)
Journal of Global Optimization     Hybrid Journal   (Followers: 7)
Journal of Interactive Marketing     Hybrid Journal   (Followers: 10)
Journal of Mathematics and Statistics     Open Access   (Followers: 8)
Journal of Nonparametric Statistics     Hybrid Journal   (Followers: 6)
Journal of Probability and Statistics     Open Access   (Followers: 10)
Journal of Risk and Uncertainty     Hybrid Journal   (Followers: 33)
Journal of Statistical and Econometric Methods     Open Access   (Followers: 5)
Journal of Statistical Physics     Hybrid Journal   (Followers: 13)
Journal of Statistical Planning and Inference     Hybrid Journal   (Followers: 8)
Journal of Statistical Software     Open Access   (Followers: 21, SJR: 13.802, CiteScore: 16)
Journal of the American Statistical Association     Full-text available via subscription   (Followers: 72, SJR: 3.746, CiteScore: 2)
Journal of the Korean Statistical Society     Hybrid Journal   (Followers: 1)
Journal of the Royal Statistical Society Series C (Applied Statistics)     Hybrid Journal   (Followers: 33)
Journal of the Royal Statistical Society, Series A (Statistics in Society)     Hybrid Journal   (Followers: 27)
Journal of the Royal Statistical Society, Series B (Statistical Methodology)     Hybrid Journal   (Followers: 43)
Journal of Theoretical Probability     Hybrid Journal   (Followers: 3)
Journal of Time Series Analysis     Hybrid Journal   (Followers: 16)
Journal of Urbanism: International Research on Placemaking and Urban Sustainability     Hybrid Journal   (Followers: 30)
Law, Probability and Risk     Hybrid Journal   (Followers: 8)
Lifetime Data Analysis     Hybrid Journal   (Followers: 7)
Mathematical Methods of Statistics     Hybrid Journal   (Followers: 4)
Measurement Interdisciplinary Research and Perspectives     Hybrid Journal   (Followers: 1)
Metrika     Hybrid Journal   (Followers: 4)
Modelling of Mechanical Systems     Full-text available via subscription   (Followers: 1)
Monte Carlo Methods and Applications     Hybrid Journal   (Followers: 6)
Monthly Statistics of International Trade - Statistiques mensuelles du commerce international     Full-text available via subscription   (Followers: 2)
Multivariate Behavioral Research     Hybrid Journal   (Followers: 5)
Optimization Letters     Hybrid Journal   (Followers: 2)
Optimization Methods and Software     Hybrid Journal   (Followers: 8)
Oxford Bulletin of Economics and Statistics     Hybrid Journal   (Followers: 34)
Pharmaceutical Statistics     Hybrid Journal   (Followers: 17)
Probability Surveys     Open Access   (Followers: 4)
Queueing Systems     Hybrid Journal   (Followers: 7)
Research Synthesis Methods     Hybrid Journal   (Followers: 8)
Review of Economics and Statistics     Hybrid Journal   (Followers: 128)
Review of Socionetwork Strategies     Hybrid Journal  
Risk Management     Hybrid Journal   (Followers: 15)
Sankhya A     Hybrid Journal   (Followers: 2)
Scandinavian Journal of Statistics     Hybrid Journal   (Followers: 9)
Sequential Analysis: Design Methods and Applications     Hybrid Journal  
Significance     Hybrid Journal   (Followers: 7)
Sociological Methods & Research     Hybrid Journal   (Followers: 38)
SourceOCDE Comptes nationaux et Statistiques retrospectives     Full-text available via subscription  
SourceOCDE Statistiques : Sources et methodes     Full-text available via subscription  
SourceOECD Bank Profitability Statistics - SourceOCDE Rentabilite des banques     Full-text available via subscription   (Followers: 1)
SourceOECD Insurance Statistics - SourceOCDE Statistiques d'assurance     Full-text available via subscription   (Followers: 2)
SourceOECD Main Economic Indicators - SourceOCDE Principaux indicateurs economiques     Full-text available via subscription   (Followers: 1)
SourceOECD Measuring Globalisation Statistics - SourceOCDE Mesurer la mondialisation - Base de donnees statistiques     Full-text available via subscription  
SourceOECD Monthly Statistics of International Trade     Full-text available via subscription   (Followers: 1)
SourceOECD National Accounts & Historical Statistics     Full-text available via subscription  
SourceOECD OECD Economic Outlook Database - SourceOCDE Statistiques des Perspectives economiques de l'OCDE     Full-text available via subscription   (Followers: 2)
SourceOECD Science and Technology Statistics - SourceOCDE Base de donnees des sciences et de la technologie     Full-text available via subscription  
SourceOECD Statistics Sources & Methods     Full-text available via subscription   (Followers: 1)
SourceOECD Taxing Wages Statistics - SourceOCDE Statistiques des impots sur les salaires     Full-text available via subscription  
Stata Journal     Full-text available via subscription   (Followers: 9)
Statistica Neerlandica     Hybrid Journal   (Followers: 1)
Statistical Applications in Genetics and Molecular Biology     Hybrid Journal   (Followers: 5)
Statistical Communications in Infectious Diseases     Hybrid Journal  
Statistical Inference for Stochastic Processes     Hybrid Journal   (Followers: 3)
Statistical Methodology     Hybrid Journal   (Followers: 7)
Statistical Methods and Applications     Hybrid Journal   (Followers: 6)
Statistical Methods in Medical Research     Hybrid Journal   (Followers: 27)
Statistical Modelling     Hybrid Journal   (Followers: 19)
Statistical Papers     Hybrid Journal   (Followers: 4)
Statistical Science     Full-text available via subscription   (Followers: 13)
Statistics & Probability Letters     Hybrid Journal   (Followers: 13)
Statistics & Risk Modeling     Hybrid Journal   (Followers: 3)
Statistics and Computing     Hybrid Journal   (Followers: 13)
Statistics and Economics     Open Access   (Followers: 1)
Statistics in Medicine     Hybrid Journal   (Followers: 198)
Statistics, Politics and Policy     Hybrid Journal   (Followers: 6)
Statistics: A Journal of Theoretical and Applied Statistics     Hybrid Journal   (Followers: 15)
Stochastic Models     Hybrid Journal   (Followers: 3)
Stochastics An International Journal of Probability and Stochastic Processes: formerly Stochastics and Stochastics Reports     Hybrid Journal   (Followers: 2)
Structural and Multidisciplinary Optimization     Hybrid Journal   (Followers: 12)
Teaching Statistics     Hybrid Journal   (Followers: 7)
Technology Innovations in Statistics Education (TISE)     Open Access   (Followers: 2)
TEST     Hybrid Journal   (Followers: 3)
The American Statistician     Full-text available via subscription   (Followers: 23)
The Annals of Applied Probability     Full-text available via subscription   (Followers: 8)
The Annals of Probability     Full-text available via subscription   (Followers: 10)
The Annals of Statistics     Full-text available via subscription   (Followers: 34)
The Canadian Journal of Statistics / La Revue Canadienne de Statistique     Hybrid Journal   (Followers: 11)
Wiley Interdisciplinary Reviews - Computational Statistics     Hybrid Journal   (Followers: 1)

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Similar Journals
Journal Cover
Journal of Educational and Behavioral Statistics
Journal Prestige (SJR): 1.952
Citation Impact (citeScore): 2
Number of Followers: 6  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1076-9986 - ISSN (Online) 1935-1054
Published by Sage Publications Homepage  [1151 journals]
  • Introduction to JEBS Special Issue on NAEP Linked Aggregate Scores
    • Authors: Daniel F. McCaffrey, Steven A. Culpepper
      Pages: 135 - 137
      Abstract: Journal of Educational and Behavioral Statistics, Volume 46, Issue 2, Page 135-137, April 2021.

      Citation: Journal of Educational and Behavioral Statistics
      PubDate: 2021-03-16T05:25:37Z
      DOI: 10.3102/10769986211001480
      Issue No: Vol. 46, No. 2 (2021)
       
  • Validation Methods for Aggregate-Level Test Scale Linking: A Rejoinder
    • Authors: Andrew D. Ho, Sean F. Reardon, Demetra Kalogrides
      Pages: 209 - 218
      Abstract: Journal of Educational and Behavioral Statistics, Volume 46, Issue 2, Page 209-218, April 2021.

      Citation: Journal of Educational and Behavioral Statistics
      PubDate: 2021-03-16T05:25:40Z
      DOI: 10.3102/1076998621994540
      Issue No: Vol. 46, No. 2 (2021)
       
  • Using Sequence Mining Techniques for Understanding Incorrect Behavioral
           Patterns on Interactive Tasks
    • Authors: Esther Ulitzsch, Qiwei He, Steffi Pohl
      Abstract: Journal of Educational and Behavioral Statistics, Ahead of Print.
      Interactive tasks designed to elicit real-life problem-solving behavior are rapidly becoming more widely used in educational assessment. Incorrect responses to such tasks can occur for a variety of different reasons such as low proficiency levels, low metacognitive strategies, or motivational issues. We demonstrate how behavioral patterns associated with incorrect responses can, in part, be understood, supporting insights into the different sources of failure on a task. To this end, we make use of sequence mining techniques that leverage the information contained in time-stamped action sequences commonly logged in assessments with interactive tasks for (a) investigating what distinguishes incorrect behavioral patterns from correct ones and (b) identifying subgroups of examinees with similar incorrect behavioral patterns. Analyzing a task from the Programme for the International Assessment of Adult Competencies 2012 assessment, we find incorrect behavioral patterns to be more heterogeneous than correct ones. We identify multiple subgroups of incorrect behavioral patterns, which point toward different levels of effort and lack of different subskills needed for solving the task. Albeit focusing on a single task, meaningful patterns of major differences in how examinees approach a given task that generalize across multiple tasks are uncovered. Implications for the construction and analysis of interactive tasks as well as the design of interventions for complex problem-solving skills are derived.
      Citation: Journal of Educational and Behavioral Statistics
      PubDate: 2021-05-03T09:06:31Z
      DOI: 10.3102/10769986211010467
       
  • A Rating Scale Mixture Model to Account for the Tendency to Middle and
           Extreme Categories
    • Authors: Roberto Colombi, Sabrina Giordano, Gerhard Tutz
      Abstract: Journal of Educational and Behavioral Statistics, Ahead of Print.
      A mixture of logit models is proposed that discriminates between responses to rating questions that are affected by a tendency to prefer middle or extremes of the scale regardless of the content of the item (response styles) and purely content-driven preferences. Explanatory variables are used to characterize the content-driven way of answering as well as the tendency to middle or extreme categories. The proposed model is extended to account for the presence of response styles in the case of several items, and the association among responses is described, both when they are content driven or dictated by response styles. In addition, stochastic orderings, related to the tendency to select middle or extreme categories, are introduced and investigated. A simulation study describes the effectiveness of the proposed model, and an application to a questionnaire on attitudes toward ethnic minorities illustrates the applicability of the modeling approach.
      Citation: Journal of Educational and Behavioral Statistics
      PubDate: 2021-03-31T08:58:11Z
      DOI: 10.3102/1076998621992554
       
  • Item Characteristic Curve Asymmetry: A Better Way to Accommodate Slips and
           Guesses Than a Four-Parameter Model'
    • Authors: Xiangyi Liao, Daniel M. Bolt
      Abstract: Journal of Educational and Behavioral Statistics, Ahead of Print.
      Four-parameter models have received increasing psychometric attention in recent years, as a reduced upper asymptote for item characteristic curves can be appealing for measurement applications such as adaptive testing and person-fit assessment. However, applications can be challenging due to the large number of parameters in the model. In this article, we demonstrate in the context of mathematics assessments how the slip and guess parameters of a four-parameter model may often be empirically related. This observation also has a psychological explanation to the extent that both asymptote parameters may be manifestations of a single item complexity characteristic. The relationship between lower and upper asymptotes motivates the consideration of an asymmetric item response theory model as a three-parameter alternative to the four-parameter model. Using actual response data from mathematics multiple-choice tests, we demonstrate the empirical superiority of a three-parameter asymmetric model in several standardized tests of mathematics. To the extent that a model of asymmetry ultimately portrays slips and guesses not as purely random but rather as proficiency-related phenomena, we argue that the asymmetric approach may also have greater psychological plausibility.
      Citation: Journal of Educational and Behavioral Statistics
      PubDate: 2021-03-29T04:19:13Z
      DOI: 10.3102/10769986211003283
       
  • Detecting Noneffortful Responses Based on a Residual Method Using an
           Iterative Purification Process
    • Authors: Yue Liu, Hongyun Liu
      Abstract: Journal of Educational and Behavioral Statistics, Ahead of Print.
      The prevalence and serious consequences of noneffortful responses from unmotivated examinees are well-known in educational measurement. In this study, we propose to apply an iterative purification process based on a response time residual method with fixed item parameter estimates to detect noneffortful responses. The proposed method is compared with the traditional residual method and noniterative method with fixed item parameters in two simulation studies in terms of noneffort detection accuracy and parameter recovery. The results show that when severity of noneffort is high, the proposed method leads to a much higher true positive rate with a small increase of false discovery rate. In addition, parameter estimation is significantly improved by the strategies of fixing item parameters and iteratively cleansing. These results suggest that the proposed method is a potential solution to reduce the impact of data contamination due to severe low test-taking effort and to obtain more accurate parameter estimates. An empirical study is also conducted to show the differences in the detection rate and parameter estimates among different approaches.
      Citation: Journal of Educational and Behavioral Statistics
      PubDate: 2021-03-29T03:38:58Z
      DOI: 10.3102/1076998621994366
       
  • Monitoring Item Performance With CUSUM Statistics in Continuous Testing
    • Authors: Yi-Hsuan Lee, Charles Lewis
      Abstract: Journal of Educational and Behavioral Statistics, Ahead of Print.
      In many educational assessments, items are reused in different administrations throughout the life of the assessments. Ideally, a reused item should perform relatively similarly over time. In reality, an item may become easier with exposure, especially when item preknowledge has occurred. This article presents a novel cumulative sum procedure for detecting item preknowledge in continuous testing where data for each reused item may be obtained from small and varying sample sizes across administrations. Its performance is evaluated with simulations and analytical work. The approach is effective in detecting item preknowledge quickly with group size at least 10 and is easy to implement with varying item parameters. In addition, it is robust to the ability estimation error introduced in the simulations.
      Citation: Journal of Educational and Behavioral Statistics
      PubDate: 2021-03-08T09:22:21Z
      DOI: 10.3102/1076998621994563
       
  • Estimating Difference-Score Reliability in Pretest–Posttest Settings
    • Authors: Zhengguo Gu, Wilco H. M. Emons, Klaas Sijtsma
      Abstract: Journal of Educational and Behavioral Statistics, Ahead of Print.
      Clinical, medical, and health psychologists use difference scores obtained from pretest–posttest designs employing the same test to assess intraindividual change possibly caused by an intervention addressing, for example, anxiety, depression, eating disorder, or addiction. Reliability of difference scores is important for interpreting observed change. This article compares the well-documented traditional method and the unfamiliar, rarely used item-level method for estimating difference-score reliability. We simulated data under various conditions that are typical of change assessment in pretest–posttest designs. The item-level method had smaller bias and greater precision than the traditional method and may be recommended for practical use.
      Citation: Journal of Educational and Behavioral Statistics
      PubDate: 2021-02-15T09:39:06Z
      DOI: 10.3102/1076998620986948
       
  • Cross-Classified Random Effects Modeling for Moderated Item Calibration
    • Authors: Seungwon Chung, Li Cai
      Abstract: Journal of Educational and Behavioral Statistics, Ahead of Print.
      In the research reported here, we propose a new method for scale alignment and test scoring in the context of supporting students with disabilities. In educational assessment, students from these special populations take modified tests because of a demonstrated disability that requires more assistance than standard testing accommodation. Updated federal education legislation and guidance require that these students be assessed and included in state education accountability systems, and their achievement reported with respect to the same rigorous content and achievement standards that the state adopted. Routine item calibration and linking methods are not feasible because the size of these special populations tends to be small. We develop a unified cross-classified random effects model that utilizes item response data from the general population as well as judge-provided data from subject matter experts in order to obtain revised item parameter estimates for use in scoring modified tests. We extend the Metropolis–Hastings Robbins–Monro algorithm to estimate the parameters of this model. The proposed method is applied to Braille test forms in a large operational multistate English language proficiency assessment program. Our work not only allows a broader range of modifications that is routinely considered in large-scale educational assessments but also directly incorporates the input from subject matter experts who work directly with the students needing support. Their structured and informed feedback deserves more attention from the psychometric community.
      Citation: Journal of Educational and Behavioral Statistics
      PubDate: 2021-01-12T02:18:43Z
      DOI: 10.3102/1076998620983908
       
  • Commentary on Reardon, Kalogrides, and Ho’s “Validation Methods for
           Aggregate-Level Test Scale Linking: A Case Study Mapping School District
           Test Score Distributions to a Common Scale”
    • Authors: Daniel Bolt
      Pages: 168 - 172
      Abstract: Journal of Educational and Behavioral Statistics, Volume 46, Issue 2, Page 168-172, April 2021.
      The studies presented by Reardon, Kalogrides, and Ho provide preliminary support for a National Assessment of Educational Progress–based aggregate linking of state assessments when used for research purposes. In this commentary, I suggest future efforts to explore possible sources of district-level bias, evaluation of predictive accuracy at the state level, and a better understanding of the performance of the linking when applied to the inevitable nonrepresentative district samples that will be encountered in research studies.
      Citation: Journal of Educational and Behavioral Statistics
      PubDate: 2020-08-13T08:09:28Z
      DOI: 10.3102/1076998620948267
      Issue No: Vol. 46, No. 2 (2020)
       
  • Commentary on “Validation Methods for Aggregate-Level Test Scale
           Linking: A Case Study Mapping School District Test Score Distributions to
           a Common Scale”
    • Authors: Mark L. Davison
      Pages: 173 - 186
      Abstract: Journal of Educational and Behavioral Statistics, Volume 46, Issue 2, Page 173-186, April 2021.
      This paper begins by setting the linking methods of Reardon, Kalogrides, and Ho in the broader literature on linking. Trends in the validity data suggest that there may be a conditional bias in the estimates of district means, but the data in the article are not conclusive on this point. Further, the data used in their case study might support the validity of the methods only over a limited range of the ability continuum. Applications of the method are then discussed. Contrary to the title, the application of the linking results is not limited to aggregate-level data. Because the potential application is so broad, further research is needed on issues such as the possibility of conditional bias and the validity of estimates over the full range of possible values. Validity is not a dichotomous concept where validity exists or it does not. The evidence reported by Reardon et al. provides substantial, but incomplete, support for the validity of the linked measures in this case study.
      Citation: Journal of Educational and Behavioral Statistics
      PubDate: 2020-08-25T10:34:19Z
      DOI: 10.3102/1076998620949172
      Issue No: Vol. 46, No. 2 (2020)
       
  • Aggregate-Level Test-Scale Linking: A New Solution for an Old Problem'
    • Authors: Tim Moses, Neil J. Dorans
      Pages: 187 - 202
      Abstract: Journal of Educational and Behavioral Statistics, Volume 46, Issue 2, Page 187-202, April 2021.
      The Reardon, Kalogrides, and Ho article on validation methods for aggregate-level test scale linking is an attempt to validate a district-level scale aligning procedure that appears to be a new solution to an old problem. Their aligning procedure uses the National Assessment of Educational Progress (NAEP) scale to piece together a patchwork of data structures from different tests of different constructs obtained under different administration conditions and used in different ways by different states. In this article, we critique their linking and validation efforts. Our critique has three components. First, we review the recommendations for linking state assessments to NAEP from several studies and commentaries to provide background from which to interpret Reardon et al.’s validation attempts. Second, we provide a replication of the Reardon et al. empirical validations of its proposed linking procedure to demonstrate that correlations between district means on two test scores can be high even when (1) the constructs being measured by the tests are different and (2) the district-level means estimated using the Reardon et al. linking approach can differ substantially from actual district-level means. Then, we suggest additional checks for construct similarity and subpopulation invariance from other concordance studies that could be used to assess whether the inferences made by Reardon et al. are warranted. Finally, until such checks are made, we urge cautious use of the results of the Reardon et al. results.
      Citation: Journal of Educational and Behavioral Statistics
      PubDate: 2020-10-01T04:25:19Z
      DOI: 10.3102/1076998620960089
      Issue No: Vol. 46, No. 2 (2020)
       
  • Commentary on “Validation Methods for Aggregate-Level Test Scale
           Linking: A Case Study Mapping School District Test Score Distributions to
           a Common Scale”
    • Authors: Alina A. von Davier
      Pages: 203 - 208
      Abstract: Journal of Educational and Behavioral Statistics, Volume 46, Issue 2, Page 203-208, April 2021.
      In this commentary, I share my perspective on the goals of assessments in general, on linking assessments that were developed according to different specifications and for different purposes, and I propose several considerations for the authors and the readers. This brief commentary is structured around three perspectives (1) the context of this research, (2) the methodology proposed here, and (3) the consequences for applied research.
      Citation: Journal of Educational and Behavioral Statistics
      PubDate: 2020-09-21T09:16:18Z
      DOI: 10.3102/1076998620956668
      Issue No: Vol. 46, No. 2 (2020)
       
  • The Bayesian Covariance Structure Model for Testlets
    • Authors: Jean-Paul Fox, Jeremias Wenzel, Konrad Klotzke
      Pages: 219 - 243
      Abstract: Journal of Educational and Behavioral Statistics, Volume 46, Issue 2, Page 219-243, April 2021.
      Standard item response theory (IRT) models have been extended with testlet effects to account for the nesting of items; these are well known as (Bayesian) testlet models or random effect models for testlets. The testlet modeling framework has several disadvantages. A sufficient number of testlet items are needed to estimate testlet effects, and a sufficient number of individuals are needed to estimate testlet variance. The prior for the testlet variance parameter can only represent a positive association among testlet items. The inclusion of testlet parameters significantly increases the number of model parameters, which can lead to computational problems. To avoid these problems, a Bayesian covariance structure model (BCSM) for testlets is proposed, where standard IRT models are extended with a covariance structure model to account for dependences among testlet items. In the BCSM, the dependence among testlet items is modeled without using testlet effects. This approach does not imply any sample size restrictions and is very efficient in terms of the number of parameters needed to describe testlet dependences. The BCSM is compared to the well-known Bayesian random effects model for testlets using a simulation study. Specifically for testlets with a few items, a small number of test takers, or weak associations among testlet items, the BCSM shows more accurate estimation results than the random effects model.
      Citation: Journal of Educational and Behavioral Statistics
      PubDate: 2020-07-23T04:23:01Z
      DOI: 10.3102/1076998620941204
      Issue No: Vol. 46, No. 2 (2020)
       
  • Estimation of Latent Regression Item Response Theory Models Using a
           Second-Order Laplace Approximation
    • Authors: Björn Andersson, Tao Xin
      Pages: 244 - 265
      Abstract: Journal of Educational and Behavioral Statistics, Volume 46, Issue 2, Page 244-265, April 2021.
      The estimation of high-dimensional latent regression item response theory (IRT) models is difficult because of the need to approximate integrals in the likelihood function. Proposed solutions in the literature include using stochastic approximations, adaptive quadrature, and Laplace approximations. We propose using a second-order Laplace approximation of the likelihood to estimate IRT latent regression models with categorical observed variables and fixed covariates where all parameters are estimated simultaneously. The method applies when the IRT model has a simple structure, meaning that each observed variable loads on only one latent variable. Through simulations using a latent regression model with binary and ordinal observed variables, we show that the proposed method is a substantial improvement over the first-order Laplace approximation with respect to the bias. In addition, the approach is equally or more precise to alternative methods for estimation of multidimensional IRT models when the number of items per dimension is moderately high. Simultaneously, the method is highly computationally efficient in the high-dimensional settings investigated. The results imply that estimation of simple-structure IRT models with very high dimensions is feasible in practice and that the direct estimation of high-dimensional latent regression IRT models is tractable even with large sample sizes and large numbers of items.
      Citation: Journal of Educational and Behavioral Statistics
      PubDate: 2020-08-13T11:24:36Z
      DOI: 10.3102/1076998620945199
      Issue No: Vol. 46, No. 2 (2020)
       
  • A Practical Guide for Analyzing Large-Scale Assessment Data Using Mplus: A
           Case Demonstration Using the Program for International Assessment of Adult
           Competencies Data
    • Authors: Takashi Yamashita, Thomas J. Smith, Phyllis A. Cummins
      Abstract: Journal of Educational and Behavioral Statistics, Ahead of Print.
      In order to promote the use of increasingly available large-scale assessment data in education and expand the scope of analytic capabilities among applied researchers, this study provides step-by-step guidance, and practical examples of syntax and data analysis using Mplus. Concise overview and key unique aspects of large-scale assessment data from the 2012/2014 Program for International Assessment of Adult Competencies (PIAAC) are described. Using commonly-used statistical software including SAS and R, a simple macro program and syntax are developed to streamline the data preparation process. Then, two examples of structural equation models are demonstrated using Mplus. The suggested data preparation and analytic approaches can be immediately applicable to existing large-scale assessment data.
      Citation: Journal of Educational and Behavioral Statistics
      PubDate: 2020-12-16T09:32:11Z
      DOI: 10.3102/1076998620978554
       
  • A Review of Handbook of Item Response Theory: Vol. 1
    • Authors: Peter F. Halpin
      Abstract: Journal of Educational and Behavioral Statistics, Ahead of Print.

      Citation: Journal of Educational and Behavioral Statistics
      PubDate: 2020-12-16T09:31:11Z
      DOI: 10.3102/1076998620978551
       
  • Adaptive Weight Estimation of Latent Ability: Application to Computerized
           Adaptive Testing With Response Revision
    • Authors: Shiyu Wang, Houping Xiao, Allan Cohen
      Abstract: Journal of Educational and Behavioral Statistics, Ahead of Print.
      An adaptive weight estimation approach is proposed to provide robust latent ability estimation in computerized adaptive testing (CAT) with response revision. This approach assigns different weights to each distinct response to the same item when response revision is allowed in CAT. Two types of weight estimation procedures, nonfunctional and functional weight, are proposed to determine the weight adaptively based on the compatibility of each revised response with the assumed statistical model in relation to remaining observations. The application of this estimation approach to a data set collected from a large-scale multistage adaptive testing demonstrates the capability of this method to reveal more information regarding the test taker’s latent ability by using the valid response path compared with only using the very last response. Limited simulation studies were concluded to evaluate the proposed ability estimation method and to compare it with several other estimation procedures in literature. Results indicate that the proposed ability estimation approach is able to provide robust estimation results in two test-taking scenarios.
      Citation: Journal of Educational and Behavioral Statistics
      PubDate: 2020-11-24T09:52:53Z
      DOI: 10.3102/1076998620972800
       
  • Ordinal Approaches to Decomposing Between-Group Test Score Disparities
    • Authors: David M. Quinn, Andrew D. Ho
      Abstract: Journal of Educational and Behavioral Statistics, Ahead of Print.
      The estimation of test score “gaps” and gap trends plays an important role in monitoring educational inequality. Researchers decompose gaps and gap changes into within- and between-school portions to generate evidence on the role schools play in shaping these inequalities. However, existing decomposition methods assume an equal-interval test scale and are a poor fit to coarsened data such as proficiency categories. This leaves many potential data sources ill-suited for decomposition applications. We develop two decomposition approaches that overcome these limitations: an extension of V, an ordinal gap statistic, and an extension of ordered probit models. Simulations show V decompositions have negligible bias with small within-school samples. Ordered probit decompositions have negligible bias with large within-school samples but more serious bias with small within-school samples. More broadly, our methods enable analysts to (1) decompose the difference between two groups on any ordinal outcome into portions within- and between some third categorical variable and (2) estimate scale-invariant between-group differences that adjust for a categorical covariate.
      Citation: Journal of Educational and Behavioral Statistics
      PubDate: 2020-11-11T04:30:51Z
      DOI: 10.3102/1076998620967726
       
  • On the Treatment of Missing Data in Background Questionnaires in
           Educational Large-Scale Assessments: An Evaluation of Different Procedures
           
    • Authors: Simon Grund, Oliver Lüdtke, Alexander Robitzsch
      Abstract: Journal of Educational and Behavioral Statistics, Ahead of Print.
      Large-scale assessments (LSAs) use Mislevy’s “plausible value” (PV) approach to relate student proficiency to noncognitive variables administered in a background questionnaire. This method requires background variables to be completely observed, a requirement that is seldom fulfilled. In this article, we evaluate and compare the properties of methods used in current practice for dealing with missing data in background variables in educational LSAs, which rely on the missing indicator method (MIM), with other methods based on multiple imputation. In this context, we present a fully conditional specification (FCS) approach that allows for a joint treatment of PVs and missing data. Using theoretical arguments and two simulation studies, we illustrate under what conditions the MIM provides biased or unbiased estimates of population parameters and provide evidence that methods such as FCS can provide an effective alternative to the MIM. We discuss the strengths and weaknesses of the approaches and outline potential consequences for operational practice in educational LSAs. An illustration is provided using data from the PISA 2015 study.
      Citation: Journal of Educational and Behavioral Statistics
      PubDate: 2020-10-27T09:45:21Z
      DOI: 10.3102/1076998620959058
       
  • Design Considerations in Multisite Randomized Trials Probing Moderated
           Treatment Effects
    • Authors: Nianbo Dong, Benjamin Kelcey, Jessaca Spybrook
      Abstract: Journal of Educational and Behavioral Statistics, Ahead of Print.
      Past research has demonstrated that treatment effects frequently vary across sites (e.g., schools) and that such variation can be explained by site-level or individual-level variables (e.g., school size or gender). The purpose of this study is to develop a statistical framework and tools for the effective and efficient design of multisite randomized trials (MRTs) probing moderated treatment effects. The framework considers three core facets of such designs: (a) Level 1 and Level 2 moderators, (b) random and nonrandomly varying slopes (coefficients) of the treatment variable and its interaction terms with the moderators, and (c) binary and continuous moderators. We validate the formulas for calculating statistical power and the minimum detectable effect size difference with simulations, probe its sensitivity to model assumptions, execute the formulas in accessible software, demonstrate an application, and provide suggestions in designing MRTs probing moderated treatment effects.
      Citation: Journal of Educational and Behavioral Statistics
      PubDate: 2020-10-14T04:46:47Z
      DOI: 10.3102/1076998620961492
       
  • Testing the Within-State Distribution in Mixture Models for Responses and
           Response Times
    • Authors: Renske E. Kuijpers, Ingmar Visser, Dylan Molenaar
      Abstract: Journal of Educational and Behavioral Statistics, Ahead of Print.
      Mixture models have been developed to enable detection of within-subject differences in responses and response times to psychometric test items. To enable mixture modeling of both responses and response times, a distributional assumption is needed for the within-state response time distribution. Since violations of the assumed response time distribution may bias the modeling results, choosing an appropriate within-state distribution is important. However, testing this distributional assumption is challenging as the latent within-state response time distribution is by definition different from the observed distribution. Therefore, existing tests on the observed distribution cannot be used. In this article, we propose statistical tests on the within-state response time distribution in a mixture modeling framework for responses and response times. We investigate the viability of the newly proposed tests in a simulation study, and we apply the test to a real data set.
      Citation: Journal of Educational and Behavioral Statistics
      PubDate: 2020-09-28T04:31:50Z
      DOI: 10.3102/1076998620957240
       
  • The Use of the Posterior Probability in Score Differencing
    • Authors: Sandip Sinharay, Matthew S. Johnson
      Abstract: Journal of Educational and Behavioral Statistics, Ahead of Print.
      Score differencing is one of the six categories of statistical methods used to detect test fraud (Wollack & Schoenig, 2018) and involves the testing of the null hypothesis that the performance of an examinee is similar over two item sets versus the alternative hypothesis that the performance is better on one of the item sets. We suggest, to perform score differencing, the use of the posterior probability of better performance on one item set compared to another. In a simulation study, the suggested approach performs satisfactory compared to several existing approaches for score differencing. A real data example demonstrates how the suggested approach may be effective in detecting fraudulent examinees. The results in this article call for more attention to the use of posterior probabilities, and Bayesian approaches in general, in investigations of test fraud.
      Citation: Journal of Educational and Behavioral Statistics
      PubDate: 2020-09-22T04:44:32Z
      DOI: 10.3102/1076998620957423
       
  • A Class of Cognitive Diagnosis Models for Polytomous Data
    • Authors: Xuliang Gao, Wenchao Ma, Daxun Wang, Yan Cai, Dongbo Tu
      Abstract: Journal of Educational and Behavioral Statistics, Ahead of Print.
      This article proposes a class of cognitive diagnosis models (CDMs) for polytomously scored items with different link functions. Many existing polytomous CDMs can be considered as special cases of the proposed class of polytomous CDMs. Simulation studies were carried out to investigate the feasibility of the proposed CDMs and the performance of several information criteria (Akaike’s information criterion [AIC], consistent Akaike’s information criterion [CAIC], and Bayesian information criterion [BIC]) in model selection. The results showed that the parameters of the proposed CDMs could be recovered adequately under varied conditions. In addition, CAIC and BIC had better performance in selecting the most appropriate model than AIC. Finally, a set of real data was analyzed to illustrate the application of the proposed CDMs.
      Citation: Journal of Educational and Behavioral Statistics
      PubDate: 2020-09-15T10:09:18Z
      DOI: 10.3102/1076998620951986
       
  • Testing Latent Variable Distribution Fit in IRT Using Posterior Residuals
    • Authors: Scott Monroe
      Abstract: Journal of Educational and Behavioral Statistics, Ahead of Print.
      This research proposes a new statistic for testing latent variable distribution fit for unidimensional item response theory (IRT) models. If the typical assumption of normality is violated, then item parameter estimates will be biased, and dependent quantities such as IRT score estimates will be adversely affected. The proposed statistic compares the specified latent variable distribution to the sample average of latent variable posterior distributions commonly used in IRT scoring. Formally, the statistic is an instantiation of a generalized residual and is thus asymptotically distributed as standard normal. Also, the statistic naturally complements residual-based item-fit statistics, as both are conditional on the latent trait, and can be presented with graphical plots. In addition, a corresponding unconditional statistic, which controls for multiple comparisons, is proposed. The statistics are evaluated using a simulation study, and empirical analyses are provided.
      Citation: Journal of Educational and Behavioral Statistics
      PubDate: 2020-09-14T09:24:15Z
      DOI: 10.3102/1076998620953764
       
  • Hybridizing Machine Learning Methods and Finite Mixture Models for
           Estimating Heterogeneous Treatment Effects in Latent Classes
    • Authors: Youmi Suk, Jee-Seon Kim, Hyunseung Kang
      Abstract: Journal of Educational and Behavioral Statistics, Ahead of Print.
      There has been increasing interest in exploring heterogeneous treatment effects using machine learning (ML) methods such as causal forests, Bayesian additive regression trees, and targeted maximum likelihood estimation. However, there is little work on applying these methods to estimate treatment effects in latent classes defined by well-established finite mixture/latent class models. This article proposes a hybrid method, a combination of finite mixture modeling and ML methods from causal inference to discover effect heterogeneity in latent classes. Our simulation study reveals that hybrid ML methods produced more precise and accurate estimates of treatment effects in latent classes. We also use hybrid ML methods to estimate the differential effects of private lessons across latent classes from Trends in International Mathematics and Science Study data.
      Citation: Journal of Educational and Behavioral Statistics
      PubDate: 2020-09-11T05:45:21Z
      DOI: 10.3102/1076998620951983
       
  • Insights on Variance Estimation for Blocked and Matched Pairs Designs
    • Authors: Nicole E. Pashley, Luke W. Miratrix
      Abstract: Journal of Educational and Behavioral Statistics, Ahead of Print.
      Evaluating blocked randomized experiments from a potential outcomes perspective has two primary branches of work. The first focuses on larger blocks, with multiple treatment and control units in each block. The second focuses on matched pairs, with a single treatment and control unit in each block. These literatures not only provide different estimators for the standard errors of the estimated average impact, but they are also built on different sets of assumptions. Neither literature handles cases with blocks of varying size that contain singleton treatment or control units, a case which can occur in a variety of contexts, such as with different forms of matching or poststratification. In this article, we reconcile the literatures by carefully examining the performance of variance estimators under several different frameworks. We then use these insights to derive novel variance estimators for experiments containing blocks of different sizes.
      Citation: Journal of Educational and Behavioral Statistics
      PubDate: 2020-08-13T11:24:37Z
      DOI: 10.3102/1076998620946272
       
 
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