Pub Date : 2024-03-13DOI: 10.1177/01466216241238743
Franz Classe, Christoph Kern
Differential item functioning (DIF) is a common challenge when examining latent traits in large scale surveys. In recent work, methods from the field of machine learning such as model-based recursive partitioning have been proposed to identify subgroups with DIF when little theoretical guidance and many potential subgroups are available. On this basis, we propose and compare recursive partitioning techniques for detecting DIF with a focus on measurement models with multiple latent variables and ordinal response data. We implement tree-based approaches for identifying subgroups that contribute to DIF in multidimensional latent variable modeling and propose a robust, yet scalable extension, inspired by random forests. The proposed techniques are applied and compared with simulations. We show that the proposed methods are able to efficiently detect DIF and allow to extract decision rules that lead to subgroups with well fitting models.
{"title":"Detecting Differential Item Functioning in Multidimensional Graded Response Models With Recursive Partitioning","authors":"Franz Classe, Christoph Kern","doi":"10.1177/01466216241238743","DOIUrl":"https://doi.org/10.1177/01466216241238743","url":null,"abstract":"Differential item functioning (DIF) is a common challenge when examining latent traits in large scale surveys. In recent work, methods from the field of machine learning such as model-based recursive partitioning have been proposed to identify subgroups with DIF when little theoretical guidance and many potential subgroups are available. On this basis, we propose and compare recursive partitioning techniques for detecting DIF with a focus on measurement models with multiple latent variables and ordinal response data. We implement tree-based approaches for identifying subgroups that contribute to DIF in multidimensional latent variable modeling and propose a robust, yet scalable extension, inspired by random forests. The proposed techniques are applied and compared with simulations. We show that the proposed methods are able to efficiently detect DIF and allow to extract decision rules that lead to subgroups with well fitting models.","PeriodicalId":48300,"journal":{"name":"Applied Psychological Measurement","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140247720","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}
Pub Date : 2024-03-11DOI: 10.1177/01466216241238741
Naidan Tu, Lavanya S. Kumar, Sean Joo, Stephen Stark
Applications of multidimensional forced choice (MFC) testing have increased considerably over the last 20 years. Yet there has been little, if any, research on methods for linking the parameter estimates from different samples. This research addressed that important need by extending four widely used methods for unidimensional linking and comparing the efficacy of new estimation algorithms for MFC linking coefficients based on the Multi-Unidimensional Pairwise Preference model (MUPP). More specifically, we compared the efficacy of multidimensional test characteristic curve (TCC), item characteristic curve (ICC; Haebara, 1980), mean/mean (M/M), and mean/sigma (M/S) methods in a Monte Carlo study that also manipulated test length, test dimensionality, sample size, percentage of anchor items, and linking scenarios. Results indicated that the ICC method outperformed the M/M method, which was better than the M/S method, with the TCC method being the least effective. However, as the number of items “per dimension” and the percentage of anchor items increased, the differences between the ICC, M/M, and M/S methods decreased. Study implications and practical recommendations for MUPP linking, as well as limitations, are discussed.
{"title":"Linking Methods for Multidimensional Forced Choice Tests Using the Multi-Unidimensional Pairwise Preference Model","authors":"Naidan Tu, Lavanya S. Kumar, Sean Joo, Stephen Stark","doi":"10.1177/01466216241238741","DOIUrl":"https://doi.org/10.1177/01466216241238741","url":null,"abstract":"Applications of multidimensional forced choice (MFC) testing have increased considerably over the last 20 years. Yet there has been little, if any, research on methods for linking the parameter estimates from different samples. This research addressed that important need by extending four widely used methods for unidimensional linking and comparing the efficacy of new estimation algorithms for MFC linking coefficients based on the Multi-Unidimensional Pairwise Preference model (MUPP). More specifically, we compared the efficacy of multidimensional test characteristic curve (TCC), item characteristic curve (ICC; Haebara, 1980), mean/mean (M/M), and mean/sigma (M/S) methods in a Monte Carlo study that also manipulated test length, test dimensionality, sample size, percentage of anchor items, and linking scenarios. Results indicated that the ICC method outperformed the M/M method, which was better than the M/S method, with the TCC method being the least effective. However, as the number of items “per dimension” and the percentage of anchor items increased, the differences between the ICC, M/M, and M/S methods decreased. Study implications and practical recommendations for MUPP linking, as well as limitations, are discussed.","PeriodicalId":48300,"journal":{"name":"Applied Psychological Measurement","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140253400","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}
Pub Date : 2024-03-11DOI: 10.1177/01466216241238744
E. Kraus, Johannes Wild, Sven Hilbert
This study presents a novel method to investigate test fairness and differential item functioning combining psychometrics and machine learning. Test unfairness manifests itself in systematic and demographically imbalanced influences of confounding constructs on residual variances in psychometric modeling. Our method aims to account for resulting complex relationships between response patterns and demographic attributes. Specifically, it measures the importance of individual test items, and latent ability scores in comparison to a random baseline variable when predicting demographic characteristics. We conducted a simulation study to examine the functionality of our method under various conditions such as linear and complex impact, unfairness and varying number of factors, unfair items, and varying test length. We found that our method detects unfair items as reliably as Mantel–Haenszel statistics or logistic regression analyses but generalizes to multidimensional scales in a straight forward manner. To apply the method, we used random forests to predict migration backgrounds from ability scores and single items of an elementary school reading comprehension test. One item was found to be unfair according to all proposed decision criteria. Further analysis of the item’s content provided plausible explanations for this finding. Analysis code is available at: https://osf.io/s57rw/?view_only=47a3564028d64758982730c6d9c6c547 .
{"title":"Using Interpretable Machine Learning for Differential Item Functioning Detection in Psychometric Tests","authors":"E. Kraus, Johannes Wild, Sven Hilbert","doi":"10.1177/01466216241238744","DOIUrl":"https://doi.org/10.1177/01466216241238744","url":null,"abstract":"This study presents a novel method to investigate test fairness and differential item functioning combining psychometrics and machine learning. Test unfairness manifests itself in systematic and demographically imbalanced influences of confounding constructs on residual variances in psychometric modeling. Our method aims to account for resulting complex relationships between response patterns and demographic attributes. Specifically, it measures the importance of individual test items, and latent ability scores in comparison to a random baseline variable when predicting demographic characteristics. We conducted a simulation study to examine the functionality of our method under various conditions such as linear and complex impact, unfairness and varying number of factors, unfair items, and varying test length. We found that our method detects unfair items as reliably as Mantel–Haenszel statistics or logistic regression analyses but generalizes to multidimensional scales in a straight forward manner. To apply the method, we used random forests to predict migration backgrounds from ability scores and single items of an elementary school reading comprehension test. One item was found to be unfair according to all proposed decision criteria. Further analysis of the item’s content provided plausible explanations for this finding. Analysis code is available at: https://osf.io/s57rw/?view_only=47a3564028d64758982730c6d9c6c547 .","PeriodicalId":48300,"journal":{"name":"Applied Psychological Measurement","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140253015","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}
Pub Date : 2024-01-20DOI: 10.1177/01466216241227547
Christoph König, Rainer W. Alexandrowicz
When using Bayesian hierarchical modeling, a popular approach for Item Response Theory (IRT) models, researchers typically face a tradeoff between the precision and accuracy of the item parameter estimates. Given the pooling principle and variance-dependent shrinkage, the expected behavior of Bayesian hierarchical IRT models is to deliver more precise but biased item parameter estimates, compared to those obtained in nonhierarchical models. Previous research, however, points out the possibility that, in the context of the two-parameter logistic IRT model, the aforementioned tradeoff has not to be made. With a comprehensive simulation study, we provide an in-depth investigation into this possibility. The results show a superior performance, in terms of bias, RMSE and precision, of the hierarchical specifications compared to the nonhierarchical counterpart. Under certain conditions, the bias in the item parameter estimates is independent of the bias in the variance components. Moreover, we provide a bias correction procedure for item discrimination parameter estimates. In sum, we show that IRT models create a unique situation where the Bayesian hierarchical approach indeed yields parameter estimates that are not only more precise, but also more accurate, compared to nonhierarchical approaches. We discuss this beneficial behavior from both theoretical and applied point of views.
{"title":"Benefits of the Curious Behavior of Bayesian Hierarchical Item Response Theory Models—An in-Depth Investigation and Bias Correction","authors":"Christoph König, Rainer W. Alexandrowicz","doi":"10.1177/01466216241227547","DOIUrl":"https://doi.org/10.1177/01466216241227547","url":null,"abstract":"When using Bayesian hierarchical modeling, a popular approach for Item Response Theory (IRT) models, researchers typically face a tradeoff between the precision and accuracy of the item parameter estimates. Given the pooling principle and variance-dependent shrinkage, the expected behavior of Bayesian hierarchical IRT models is to deliver more precise but biased item parameter estimates, compared to those obtained in nonhierarchical models. Previous research, however, points out the possibility that, in the context of the two-parameter logistic IRT model, the aforementioned tradeoff has not to be made. With a comprehensive simulation study, we provide an in-depth investigation into this possibility. The results show a superior performance, in terms of bias, RMSE and precision, of the hierarchical specifications compared to the nonhierarchical counterpart. Under certain conditions, the bias in the item parameter estimates is independent of the bias in the variance components. Moreover, we provide a bias correction procedure for item discrimination parameter estimates. In sum, we show that IRT models create a unique situation where the Bayesian hierarchical approach indeed yields parameter estimates that are not only more precise, but also more accurate, compared to nonhierarchical approaches. We discuss this beneficial behavior from both theoretical and applied point of views.","PeriodicalId":48300,"journal":{"name":"Applied Psychological Measurement","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139523601","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}
Pub Date : 2024-01-18DOI: 10.1177/01466216231223043
{"title":"Corrigendum to “irtplay: An R Package for Online Item Calibration, Scoring, Evaluation of Model Fit, and Useful Functions for Unidimensional IRT”","authors":"","doi":"10.1177/01466216231223043","DOIUrl":"https://doi.org/10.1177/01466216231223043","url":null,"abstract":"","PeriodicalId":48300,"journal":{"name":"Applied Psychological Measurement","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139614768","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}
Pub Date : 2024-01-17DOI: 10.1177/01466216241227544
Chun Wang, Ruoyi Zhu
Evaluating items for potential differential item functioning (DIF) is an essential step to ensuring measurement fairness. In this article, we focus on a specific scenario, namely, the continuous response, severely sparse, computerized adaptive testing (CAT). Continuous responses items are growingly used in performance-based tasks because they tend to generate more information than traditional dichotomous items. Severe sparsity arises when many items are automatically generated via machine learning algorithms. We propose two uniform DIF detection methods in this scenario. The first is a modified version of the CAT-SIBTEST, a non-parametric method that does not depend on any specific item response theory model assumptions. The second is a regularization method, a parametric, model-based approach. Simulation studies show that both methods are effective in correctly identifying items with uniform DIF. A real data analysis is provided in the end to illustrate the utility and potential caveats of the two methods.
{"title":"Detecting uniform differential item functioning for continuous response computerized adaptive testing","authors":"Chun Wang, Ruoyi Zhu","doi":"10.1177/01466216241227544","DOIUrl":"https://doi.org/10.1177/01466216241227544","url":null,"abstract":"Evaluating items for potential differential item functioning (DIF) is an essential step to ensuring measurement fairness. In this article, we focus on a specific scenario, namely, the continuous response, severely sparse, computerized adaptive testing (CAT). Continuous responses items are growingly used in performance-based tasks because they tend to generate more information than traditional dichotomous items. Severe sparsity arises when many items are automatically generated via machine learning algorithms. We propose two uniform DIF detection methods in this scenario. The first is a modified version of the CAT-SIBTEST, a non-parametric method that does not depend on any specific item response theory model assumptions. The second is a regularization method, a parametric, model-based approach. Simulation studies show that both methods are effective in correctly identifying items with uniform DIF. A real data analysis is provided in the end to illustrate the utility and potential caveats of the two methods.","PeriodicalId":48300,"journal":{"name":"Applied Psychological Measurement","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139616965","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}
Pub Date : 2024-01-16DOI: 10.1177/01466216241227548
Hyeon-Ah Kang, Gregory Arbet, Joe Betts, William Muntean
The article presents adaptive testing strategies for polytomously scored technology-enhanced innovative items. We investigate item selection methods that match examinee’s ability levels in location and explore ways to leverage test-taking speeds during item selection. Existing approaches to selecting polytomous items are mostly based on information measures and tend to experience an item pool usage problem. In this study, we introduce location indices for polytomous items and show that location-matched item selection significantly improves the usage problem and achieves more diverse item sampling. We also contemplate matching items’ time intensities so that testing times can be regulated across the examinees. Numerical experiment from Monte Carlo simulation suggests that location-matched item selection achieves significantly better and more balanced item pool usage. Leveraging working speed in item selection distinctly reduced the average testing times as well as variation across the examinees. Both the procedures incurred marginal measurement cost (e.g., precision and efficiency) and yet showed significant improvement in the administrative outcomes. The experiment in two test settings also suggested that the procedures can lead to different administrative gains depending on the test design.
{"title":"Location-Matching Adaptive Testing for Polytomous Technology-Enhanced Items","authors":"Hyeon-Ah Kang, Gregory Arbet, Joe Betts, William Muntean","doi":"10.1177/01466216241227548","DOIUrl":"https://doi.org/10.1177/01466216241227548","url":null,"abstract":"The article presents adaptive testing strategies for polytomously scored technology-enhanced innovative items. We investigate item selection methods that match examinee’s ability levels in location and explore ways to leverage test-taking speeds during item selection. Existing approaches to selecting polytomous items are mostly based on information measures and tend to experience an item pool usage problem. In this study, we introduce location indices for polytomous items and show that location-matched item selection significantly improves the usage problem and achieves more diverse item sampling. We also contemplate matching items’ time intensities so that testing times can be regulated across the examinees. Numerical experiment from Monte Carlo simulation suggests that location-matched item selection achieves significantly better and more balanced item pool usage. Leveraging working speed in item selection distinctly reduced the average testing times as well as variation across the examinees. Both the procedures incurred marginal measurement cost (e.g., precision and efficiency) and yet showed significant improvement in the administrative outcomes. The experiment in two test settings also suggested that the procedures can lead to different administrative gains depending on the test design.","PeriodicalId":48300,"journal":{"name":"Applied Psychological Measurement","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139619277","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}
Pub Date : 2024-01-13DOI: 10.1177/01466216241227535
Sebastian Weirich, Karoline A. Sachse, Sofie Henschel, Carola Schnitzler
The article compares the trajectories of students’ self-reported test-taking effort during a 120 minutes low-stakes large-scale assessment of English comprehension between a paper-and-pencil (PPA) and a computer-based assessment (CBA). Test-taking effort was measured four times during the test. Using a within-subject design, each of the N = 2,676 German ninth-grade students completed half of the test in PPA and half in CBA mode, where the sequence of modes was balanced between students. Overall, students’ test-taking effort decreased considerably during the course of the test. On average, effort was lower in CBA than in PPA. While on average, effort was lower in CBA than in PPA, the decline did not vary between both modes during the test. That is, students’ self-reported effort was higher if the items were easier (compared to students’ abilities). The consequences of these results concerning the further development of CBA tests and large-scale assessments in general are discussed.
{"title":"Comparing Test-Taking Effort Between Paper-Based and Computer-Based Tests","authors":"Sebastian Weirich, Karoline A. Sachse, Sofie Henschel, Carola Schnitzler","doi":"10.1177/01466216241227535","DOIUrl":"https://doi.org/10.1177/01466216241227535","url":null,"abstract":"The article compares the trajectories of students’ self-reported test-taking effort during a 120 minutes low-stakes large-scale assessment of English comprehension between a paper-and-pencil (PPA) and a computer-based assessment (CBA). Test-taking effort was measured four times during the test. Using a within-subject design, each of the N = 2,676 German ninth-grade students completed half of the test in PPA and half in CBA mode, where the sequence of modes was balanced between students. Overall, students’ test-taking effort decreased considerably during the course of the test. On average, effort was lower in CBA than in PPA. While on average, effort was lower in CBA than in PPA, the decline did not vary between both modes during the test. That is, students’ self-reported effort was higher if the items were easier (compared to students’ abilities). The consequences of these results concerning the further development of CBA tests and large-scale assessments in general are discussed.","PeriodicalId":48300,"journal":{"name":"Applied Psychological Measurement","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139531162","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}
Pub Date : 2023-11-03DOI: 10.1177/01466216231209758
Matthew Naveiras, Sun-Joo Cho
Marginal maximum likelihood estimation (MMLE) is commonly used for item response theory item parameter estimation. However, sufficiently large sample sizes are not always possible when studying rare populations. In this paper, empirical Bayes and hierarchical Bayes are presented as alternatives to MMLE in small sample sizes, using auxiliary item information to estimate the item parameters of a graded response model with higher accuracy. Empirical Bayes and hierarchical Bayes methods are compared with MMLE to determine under what conditions these Bayes methods can outperform MMLE, and to determine if hierarchical Bayes can act as an acceptable alternative to MMLE in conditions where MMLE is unable to converge. In addition, empirical Bayes and hierarchical Bayes methods are compared to show how hierarchical Bayes can result in estimates of posterior variance with greater accuracy than empirical Bayes by acknowledging the uncertainty of item parameter estimates. The proposed methods were evaluated via a simulation study. Simulation results showed that hierarchical Bayes methods can be acceptable alternatives to MMLE under various testing conditions, and we provide a guideline to indicate which methods would be recommended in different research situations. R functions are provided to implement these proposed methods.
{"title":"Using Auxiliary Item Information in the Item Parameter Estimation of a Graded Response Model for a Small to Medium Sample Size: Empirical Versus Hierarchical Bayes Estimation","authors":"Matthew Naveiras, Sun-Joo Cho","doi":"10.1177/01466216231209758","DOIUrl":"https://doi.org/10.1177/01466216231209758","url":null,"abstract":"Marginal maximum likelihood estimation (MMLE) is commonly used for item response theory item parameter estimation. However, sufficiently large sample sizes are not always possible when studying rare populations. In this paper, empirical Bayes and hierarchical Bayes are presented as alternatives to MMLE in small sample sizes, using auxiliary item information to estimate the item parameters of a graded response model with higher accuracy. Empirical Bayes and hierarchical Bayes methods are compared with MMLE to determine under what conditions these Bayes methods can outperform MMLE, and to determine if hierarchical Bayes can act as an acceptable alternative to MMLE in conditions where MMLE is unable to converge. In addition, empirical Bayes and hierarchical Bayes methods are compared to show how hierarchical Bayes can result in estimates of posterior variance with greater accuracy than empirical Bayes by acknowledging the uncertainty of item parameter estimates. The proposed methods were evaluated via a simulation study. Simulation results showed that hierarchical Bayes methods can be acceptable alternatives to MMLE under various testing conditions, and we provide a guideline to indicate which methods would be recommended in different research situations. R functions are provided to implement these proposed methods.","PeriodicalId":48300,"journal":{"name":"Applied Psychological Measurement","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135819514","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}
Pub Date : 2023-11-01DOI: 10.1177/01466216231209752
José H. Lozano, Javier Revuelta
The present paper introduces a random weights linear logistic test model for the measurement of individual differences in operation-specific practice effects within a single administration of a test. The proposed model is an extension of the linear logistic test model of learning developed by Spada (1977) in which the practice effects are considered random effects varying across examinees. A Bayesian framework was used for model estimation and evaluation. A simulation study was conducted to examine the behavior of the model in combination with the Bayesian procedures. The results demonstrated the good performance of the estimation and evaluation methods. Additionally, an empirical study was conducted to illustrate the applicability of the model to real data. The model was applied to a sample of responses from a logical ability test providing evidence of individual differences in operation-specific practice effects.
{"title":"A Bayesian Random Weights Linear Logistic Test Model for Within-Test Practice Effects","authors":"José H. Lozano, Javier Revuelta","doi":"10.1177/01466216231209752","DOIUrl":"https://doi.org/10.1177/01466216231209752","url":null,"abstract":"The present paper introduces a random weights linear logistic test model for the measurement of individual differences in operation-specific practice effects within a single administration of a test. The proposed model is an extension of the linear logistic test model of learning developed by Spada (1977) in which the practice effects are considered random effects varying across examinees. A Bayesian framework was used for model estimation and evaluation. A simulation study was conducted to examine the behavior of the model in combination with the Bayesian procedures. The results demonstrated the good performance of the estimation and evaluation methods. Additionally, an empirical study was conducted to illustrate the applicability of the model to real data. The model was applied to a sample of responses from a logical ability test providing evidence of individual differences in operation-specific practice effects.","PeriodicalId":48300,"journal":{"name":"Applied Psychological Measurement","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135371926","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}
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