Journal of Educational and Behavioral Statistics最新文献

{"title":"Handling Missing Data in Cross-Classified Multilevel Analyses: An Evaluation of Different Multiple Imputation Approaches","authors":"S. Grund, O. Lüdtke, A. Robitzsch","doi":"10.3102/10769986231151224","DOIUrl":"https://doi.org/10.3102/10769986231151224","url":null,"abstract":"Multiple imputation (MI) is a popular method for handling missing data. In education research, it can be challenging to use MI because the data often have a clustered structure that need to be accommodated during MI. Although much research has considered applications of MI in hierarchical data, little is known about its use in cross-classified data, in which observations are clustered in multiple higher-level units simultaneously (e.g., schools and neighborhoods, transitions from primary to secondary schools). In this article, we consider several approaches to MI for cross-classified data (CC-MI), including a novel fully conditional specification approach, a joint modeling approach, and other approaches that are based on single- and two-level MI. In this context, we clarify the conditions that CC-MI methods need to fulfill to provide a suitable treatment of missing data, and we compare the approaches both from a theoretical perspective and in a simulation study. Finally, we illustrate the use of CC-MI in real data and discuss the implications of our findings for research practice.","PeriodicalId":48001,"journal":{"name":"Journal of Educational and Behavioral Statistics","volume":"48 1","pages":"454 - 489"},"PeriodicalIF":2.4,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41948412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing Longitudinal Social Relations Model Data Using the Social Relations Structural Equation Model","authors":"S. Nestler, O. Lüdtke, A. Robitzsch","doi":"10.3102/10769986211056541","DOIUrl":"https://doi.org/10.3102/10769986211056541","url":null,"abstract":"The social relations model (SRM) is very often used in psychology to examine the components, determinants, and consequences of interpersonal judgments and behaviors that arise in social groups. The standard SRM was developed to analyze cross-sectional data. Based on a recently suggested integration of the SRM with structural equation models (SEM) framework, we show here how longitudinal SRM data can be analyzed using the SR-SEM. Two examples are presented to illustrate the model, and we also present the results of a small simulation study comparing the SR-SEM approach to a two-step approach. Altogether, the SR-SEM has a number of advantages compared to earlier suggestions for analyzing longitudinal SRM data, making it extremely useful for applied research.","PeriodicalId":48001,"journal":{"name":"Journal of Educational and Behavioral Statistics","volume":"47 1","pages":"231 - 260"},"PeriodicalIF":2.4,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47561898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Item Pool Quality Control in Educational Testing: Change Point Model, Compound Risk, and Sequential Detection","authors":"Yunxiao Chen, Yi-Hsuan Lee, Xiaoou Li","doi":"10.3102/10769986211059085","DOIUrl":"https://doi.org/10.3102/10769986211059085","url":null,"abstract":"In standardized educational testing, test items are reused in multiple test administrations. To ensure the validity of test scores, the psychometric properties of items should remain unchanged over time. In this article, we consider the sequential monitoring of test items, in particular, the detection of abrupt changes to their psychometric properties, where a change can be caused by, for example, leakage of the item or change of the corresponding curriculum. We propose a statistical framework for the detection of abrupt changes in individual items. This framework consists of (1) a multistream Bayesian change point model describing sequential changes in items, (2) a compound risk function quantifying the risk in sequential decisions, and (3) sequential decision rules that control the compound risk. Throughout the sequential decision process, the proposed decision rule balances the trade-off between two sources of errors, the false detection of prechange items, and the nondetection of postchange items. An item-specific monitoring statistic is proposed based on an item response theory model that eliminates the confounding from the examinee population which changes over time. Sequential decision rules and their theoretical properties are developed under two settings: the oracle setting where the Bayesian change point model is completely known and a more realistic setting where some parameters of the model are unknown. Simulation studies are conducted under settings that mimic real operational tests.","PeriodicalId":48001,"journal":{"name":"Journal of Educational and Behavioral Statistics","volume":"47 1","pages":"322 - 352"},"PeriodicalIF":2.4,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43301337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A New Multiprocess IRT Model With Ideal Points for Likert-Type Items","authors":"K. Jin, Yi-Jhen Wu, Hui-Fang Chen","doi":"10.3102/10769986211057160","DOIUrl":"https://doi.org/10.3102/10769986211057160","url":null,"abstract":"For surveys of complex issues that entail multiple steps, multiple reference points, and nongradient attributes (e.g., social inequality), this study proposes a new multiprocess model that integrates ideal-point and dominance approaches into a treelike structure (IDtree). In the IDtree, an ideal-point approach describes an individual’s attitude and then a dominance approach describes their tendency for using extreme response categories. Evaluation of IDtree performance via two empirical data sets showed that the IDtree fit these data better than other models. Furthermore, simulation studies showed a satisfactory parameter recovery of the IDtree. Thus, the IDtree model sheds light on the response processes of a multistage structure.","PeriodicalId":48001,"journal":{"name":"Journal of Educational and Behavioral Statistics","volume":"47 1","pages":"297 - 321"},"PeriodicalIF":2.4,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48319208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Acknowledgments","authors":"Stephen R. Aichele, Michael Gottfried","doi":"10.3102/10769986211056337","DOIUrl":"https://doi.org/10.3102/10769986211056337","url":null,"abstract":"Stephen Aichele, Colorado State University Usama Ali, Educational Testing Service Marı́a Álvarez Hernández, Centro Universitario de la Defensa Eva Baker, University of California, Los Angeles Michela Battauz, University of Udine Daniel Bauer, University of North Carolina Jorge Bazán, Universidade de São Paulo William Belzak, University of North Carolina at Chapel Hill Yoav Bergner, New York University Howard Bloom, Manpower Demonstration Research Corporation (MDRC) Ulf Bockenholt, Northwestern University Maria Bolsinova, Tilburg University Daniel Bolt, University of Wisconsin Zach Branson, Carnegie Mellon University Robert Brennan, University of Iowa Tiago Calico, American Institutes for Research Jodi Casabianca, Educational Testing Service Katherine Castellano, Educational Testing Service Mei-Hsiu Chen, State University of New York at Binghamton Ping Chen, Beijing Normal University Yinghan Chen, University of Nevada Reno Yunxiao Chen, London School of Economics and Political Science Michael Cheung, National University of Singapore Chia-Yi Chiu, Rutgers, The State University of New Jersey Kilchan Choi, University of California, Los Angeles Karl Bang Christensen, University of Copenhagen Brian Clauser, National Board of Medical Examiners (NBME) Paul De Boeck, Ohio State University Dries Debeer, University of Leuven (KU Leuven) Ben Domingue, Stanford University Nianbo Dong, University of North Carolina at Chapel Hill Jeffrey Douglas, University of Illinois Urbana-Champaign Han Du, University of California, Los Angeles Georgios Fellouris, University of Illinois Urbana-Champaign Leah Feuerstahler, Fordham University William Finch, Ball State University Jean-Paul Fox, University of Twente Ken Fujimoto, Loyola University Chicago Johann Gagnon-Bartsch, University of Michigan Michael Garet, American Institutes for Research Andrew Gelman, Columbia University Flavio Gonçalves, Universidade Federal de Minas Gerais Jorge Gonzaléz, Pontificia Universidad Católica de Chile Maithreyi Gopalan, Penn State College of Education Journal of Educational and Behavioral Statistics 2021, Vol. 46, No. 6, pp. 776–778 DOI: 10.3102/10769986211056337 Article reuse guidelines: sagepub.com/journals-permissions © 2021 AERA. https://journals.sagepub.com/home/jeb","PeriodicalId":48001,"journal":{"name":"Journal of Educational and Behavioral Statistics","volume":"46 1","pages":"776 - 778"},"PeriodicalIF":2.4,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44572876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the Generalized S − X 2 –Test of Item Fit: Some Variants, Residuals, and a Graphical Visualization","authors":"Jochen Ranger, Kay Brauer","doi":"10.3102/10769986211050304","DOIUrl":"https://doi.org/10.3102/10769986211050304","url":null,"abstract":"The generalized S − X 2 –test is a test of item fit for items with polytomous responses format. The test is based on a comparison of the observed and expected number of responses in strata defined by the test score. In this article, we make four contributions. We demonstrate that the performance of the generalized S − X 2 –test depends on how sparse cells are pooled. We propose alternative implementations of the test within the framework of limited information testing. We derive the distribution of the S − X 2 –residuals that can be used for post hoc analyses. We suggest a diagnostic plot that visualizes the form of the misfit. The performance of the alternative implementations is investigated in a simulation study. The simulation study suggests that the alternative implementations are capable of controlling the Type-I error rate well and have high power. An empirical application concludes this article.","PeriodicalId":48001,"journal":{"name":"Journal of Educational and Behavioral Statistics","volume":"47 1","pages":"202 - 230"},"PeriodicalIF":2.4,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41455826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reporting Proficiency Levels for Examinees With Incomplete Data","authors":"S. Sinharay","doi":"10.3102/10769986211051379","DOIUrl":"https://doi.org/10.3102/10769986211051379","url":null,"abstract":"Takers of educational tests often receive proficiency levels instead of or in addition to scaled scores. For example, proficiency levels are reported for the Advanced Placement (AP®) and U.S. Medical Licensing examinations. Technical difficulties and other unforeseen events occasionally lead to missing item scores and hence to incomplete data on these tests. The reporting of proficiency levels to the examinees with incomplete data requires estimation of the performance of the examinees on the missing part and essentially involves imputation of missing data. In this article, six approaches from the literature on missing data analysis are brought to bear on the problem of reporting of proficiency levels to the examinees with incomplete data. Data from several large-scale educational tests are used to compare the performances of the six approaches to the approach that is operationally used for reporting proficiency levels for these tests. A multiple imputation approach based on chained equations is shown to lead to the most accurate reporting of proficiency levels for data that were missing at random or completely at random, while the model-based approach of Holman and Glas performed the best for data that are missing not at random. Several recommendations are made on the reporting of proficiency levels to the examinees with incomplete data.","PeriodicalId":48001,"journal":{"name":"Journal of Educational and Behavioral Statistics","volume":"47 1","pages":"263 - 296"},"PeriodicalIF":2.4,"publicationDate":"2021-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43010884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of Within- and Between-Series Effect Estimates in the Meta-Analysis of Multiple Baseline Studies","authors":"Seang-Hwane Joo, Yan Wang, J. Ferron, S. N. Beretvas, Mariola Moeyaert, W. Van den Noortgate","doi":"10.3102/10769986211035507","DOIUrl":"https://doi.org/10.3102/10769986211035507","url":null,"abstract":"Multiple baseline (MB) designs are becoming more prevalent in educational and behavioral research, and as they do, there is growing interest in combining effect size estimates across studies. To further refine the meta-analytic methods of estimating the effect, this study developed and compared eight alternative methods of estimating intervention effects from a set of MB studies. The methods differed in the assumptions made and varied in whether they relied on within- or between-series comparisons, modeled raw data or effect sizes, and did or did not standardize. Small sample functioning was examined through two simulation studies, which showed that when data were consistent with assumptions the bias was consistently less than 5% of the effect size for each method, whereas root mean squared error varied substantially across methods. When assumptions were violated, substantial biases were found. Implications and limitations are discussed.","PeriodicalId":48001,"journal":{"name":"Journal of Educational and Behavioral Statistics","volume":"47 1","pages":"131 - 166"},"PeriodicalIF":2.4,"publicationDate":"2021-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48923433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing Cross-Sectionally Clustered Data Using Generalized Estimating Equations","authors":"Francis L. Huang","doi":"10.3102/10769986211017480","DOIUrl":"https://doi.org/10.3102/10769986211017480","url":null,"abstract":"The presence of clustered data is common in the sociobehavioral sciences. One approach that specifically deals with clustered data but has seen little use in education is the generalized estimating equations (GEEs) approach. We provide a background on GEEs, discuss why it is appropriate for the analysis of clustered data, and provide worked examples using both continuous and binary outcomes. Comparisons are made between GEEs, multilevel models, and ordinary least squares results to highlight similarities and differences between the approaches. Detailed walkthroughs are provided using both R and SPSS Version 26.","PeriodicalId":48001,"journal":{"name":"Journal of Educational and Behavioral Statistics","volume":"47 1","pages":"101 - 125"},"PeriodicalIF":2.4,"publicationDate":"2021-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43238549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Sequence Mining Techniques for Understanding Incorrect Behavioral Patterns on Interactive Tasks","authors":"Esther Ulitzsch, Qiwei He, S. Pohl","doi":"10.3102/10769986211010467","DOIUrl":"https://doi.org/10.3102/10769986211010467","url":null,"abstract":"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.","PeriodicalId":48001,"journal":{"name":"Journal of Educational and Behavioral Statistics","volume":"47 1","pages":"3 - 35"},"PeriodicalIF":2.4,"publicationDate":"2021-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41989802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}