Several marginal effect size (ES) statistics suitable for quantifying the magnitude of differential item functioning (DIF) have been proposed in the area of item response theory; for instance, the Differential Functioning of Items and Tests (DFIT) statistics, signed and unsigned item difference in the sample statistics (SIDS, UIDS, NSIDS, and NUIDS), the standardized indices of impact, and the differential response functioning (DRF) statistics. However, the relationship between these proposed statistics has not been fully discussed, particularly with respect to population parameter definitions and recovery performance across independent samples. To address these issues, this article provides a unified presentation of competing DIF ES definitions and estimators, and evaluates the recovery efficacy of these competing estimators using a set of Monte Carlo simulation experiments. Statistical and inferential properties of the estimators are discussed, as well as future areas of research in this model-based area of bias quantification.
{"title":"A Unified Comparison of IRT-Based Effect Sizes for DIF Investigations","authors":"R. Philip Chalmers","doi":"10.1111/jedm.12347","DOIUrl":"10.1111/jedm.12347","url":null,"abstract":"<p>Several marginal effect size (ES) statistics suitable for quantifying the magnitude of differential item functioning (DIF) have been proposed in the area of item response theory; for instance, the Differential Functioning of Items and Tests (DFIT) statistics, signed and unsigned item difference in the sample statistics (SIDS, UIDS, NSIDS, and NUIDS), the standardized indices of impact, and the differential response functioning (DRF) statistics. However, the relationship between these proposed statistics has not been fully discussed, particularly with respect to population parameter definitions and recovery performance across independent samples. To address these issues, this article provides a unified presentation of competing DIF ES definitions and estimators, and evaluates the recovery efficacy of these competing estimators using a set of Monte Carlo simulation experiments. Statistical and inferential properties of the estimators are discussed, as well as future areas of research in this model-based area of bias quantification.</p>","PeriodicalId":47871,"journal":{"name":"Journal of Educational Measurement","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47360097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A test of item compromise is presented which combines the test takers' responses and response times (RTs) into a statistic defined as the number of correct responses on the item for test takers with RTs flagged as suspicious. The test has null and alternative distributions belonging to the well-known family of compound binomial distributions, is simple to calculate, and has results that are easy to interpret. It also demonstrated nearly perfect power for the detection of compromise with no more than 10 test takers with preknowledge of the more difficult and discriminating items in a set of empirical examples. For the easier and less discriminating items, the presence of some 20 test takers with preknowledge still sufficed. A test based on the reverse statistic of the total time by test takers with responses flagged as suspicious may seem a natural alternative but misses the property of a monotone likelihood ratio necessary to decide between a test that should be left or right sided.
{"title":"A Statistical Test for the Detection of Item Compromise Combining Responses and Response Times","authors":"Wim J. van der Linden, Dmitry I. Belov","doi":"10.1111/jedm.12346","DOIUrl":"10.1111/jedm.12346","url":null,"abstract":"<p>A test of item compromise is presented which combines the test takers' responses and response times (RTs) into a statistic defined as the number of correct responses on the item for test takers with RTs flagged as suspicious. The test has null and alternative distributions belonging to the well-known family of compound binomial distributions, is simple to calculate, and has results that are easy to interpret. It also demonstrated nearly perfect power for the detection of compromise with no more than 10 test takers with preknowledge of the more difficult and discriminating items in a set of empirical examples. For the easier and less discriminating items, the presence of some 20 test takers with preknowledge still sufficed. A test based on the reverse statistic of the total time by test takers with responses flagged as suspicious may seem a natural alternative but misses the property of a monotone likelihood ratio necessary to decide between a test that should be left or right sided.</p>","PeriodicalId":47871,"journal":{"name":"Journal of Educational Measurement","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jedm.12346","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47060232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study proposed Gibbs sampling algorithms for variable selection in a latent regression model under a unidimensional two-parameter logistic item response theory model. Three types of shrinkage priors were employed to obtain shrinkage estimates: double-exponential (i.e., Laplace), horseshoe, and horseshoe+ priors. These shrinkage priors were compared to a uniform prior case in both simulation and real data analysis. The simulation study revealed that two types of horseshoe priors had a smaller root mean square errors and shorter 95% credible interval lengths than double-exponential or uniform priors. In addition, the horseshoe+ prior was slightly more stable than the horseshoe prior. The real data example successfully proved the utility of horseshoe and horseshoe+ priors in selecting effective predictive covariates for math achievement.
{"title":"Fully Gibbs Sampling Algorithms for Bayesian Variable Selection in Latent Regression Models","authors":"Kazuhiro Yamaguchi, Jihong Zhang","doi":"10.1111/jedm.12348","DOIUrl":"https://doi.org/10.1111/jedm.12348","url":null,"abstract":"<p>This study proposed Gibbs sampling algorithms for variable selection in a latent regression model under a unidimensional two-parameter logistic item response theory model. Three types of shrinkage priors were employed to obtain shrinkage estimates: double-exponential (i.e., Laplace), horseshoe, and horseshoe+ priors. These shrinkage priors were compared to a uniform prior case in both simulation and real data analysis. The simulation study revealed that two types of horseshoe priors had a smaller root mean square errors and shorter 95% credible interval lengths than double-exponential or uniform priors. In addition, the horseshoe+ prior was slightly more stable than the horseshoe prior. The real data example successfully proved the utility of horseshoe and horseshoe+ priors in selecting effective predictive covariates for math achievement.</p>","PeriodicalId":47871,"journal":{"name":"Journal of Educational Measurement","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50154343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The certainty of response index (CRI) measures respondents' confidence level when answering an item. In conjunction with the answers to the items, previous studies have used descriptive statistics and arbitrary thresholds to identify student knowledge profiles with the CRIs. Whereas this approach overlooked the measurement error of the observed item responses and indices, we address this by proposing a factor mixture model that integrates a latent class model to detect student subgroups and a measurement model to control for student ability and confidence level. Applying the model to 773 seventh graders' responses to an algebra test, where some items were related to new material that had not been taught in class, we found two subgroups: (1) students who had high confidence in answering items involving the new material; and (2) students who had low confidence in answering items involving the new material but higher general self-confidence than the first group. We regressed the posterior probability of the group membership on gender, prior achievement, and preview behavior and found preview behavior a significant factor associated with the membership. Finally, we discussed the implications of the current study for teaching practices and future research.
{"title":"A Factor Mixture Model for Item Responses and Certainty of Response Indices to Identify Student Knowledge Profiles","authors":"Chia-Wen Chen, Björn Andersson, Jinxin Zhu","doi":"10.1111/jedm.12344","DOIUrl":"10.1111/jedm.12344","url":null,"abstract":"<p>The certainty of response index (CRI) measures respondents' confidence level when answering an item. In conjunction with the answers to the items, previous studies have used descriptive statistics and arbitrary thresholds to identify student knowledge profiles with the CRIs. Whereas this approach overlooked the measurement error of the observed item responses and indices, we address this by proposing a factor mixture model that integrates a latent class model to detect student subgroups and a measurement model to control for student ability and confidence level. Applying the model to 773 seventh graders' responses to an algebra test, where some items were related to new material that had not been taught in class, we found two subgroups: (1) students who had high confidence in answering items involving the new material; and (2) students who had low confidence in answering items involving the new material but higher general self-confidence than the first group. We regressed the posterior probability of the group membership on gender, prior achievement, and preview behavior and found preview behavior a significant factor associated with the membership. Finally, we discussed the implications of the current study for teaching practices and future research.</p>","PeriodicalId":47871,"journal":{"name":"Journal of Educational Measurement","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jedm.12344","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43460732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Betty Lanteigne, Christine Coombe, & James Dean Brown. 2021. Challenges in Language Testing around the World: Insights for language test users. Singapore: Springer, 2021, 129.99 € (hardcover), ISBN 978-981-33-4232-3 (eBook). xxiii + 553 pp. https://doi.org/10.1007/978-981-33-4232-3","authors":"Bahram Kazemian, Shafigeh Mohammadian","doi":"10.1111/jedm.12343","DOIUrl":"10.1111/jedm.12343","url":null,"abstract":"","PeriodicalId":47871,"journal":{"name":"Journal of Educational Measurement","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2022-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45401317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to detect a wide range of aberrant behaviors, it can be useful to incorporate information beyond the dichotomous item scores. In this paper, we extend the