Pub Date : 2023-10-02DOI: 10.1080/15366367.2021.2000830
Yawei Shen, Shiyu Wang
ABSTRACTThis study explores various approaches to investigate participants’ testing performance and learning behaviors in a computer-based spatial rotation learning program. Using multivariate learning and assessment data, including responses, response times, learning times and selected covariates, a comprehensive data analytic framework is developed that not only utilizes the test level information but also the item level information. This top-down and multivariate data analytic framework can shed light on conducting exploratory analysis with high-dimensional and mixed-type multivariate data, especially on how to aggregate information from the test-level and item-level. The findings about participants’ testing performance and learning behaviors are valuable in guiding the design of an adaptive learning platform in the future and can also provide some support in developing confirmatory statistical methods to model testing and learning behaviors.KEYWORDS: Clustering analysismulticategory logit modelsmixted-type dataresponse timeslearning timeslearning behaviors Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1. The normality assumption of the above two paired t-test is violated, and thus, a generalized Yuen s robust test using trimmed mean is used in R package WRS2 (Mair & Wilcox, Citation2018). Similar for the two paired t-test at TC2.
{"title":"Explore Testing Performance and Learning Behaviors","authors":"Yawei Shen, Shiyu Wang","doi":"10.1080/15366367.2021.2000830","DOIUrl":"https://doi.org/10.1080/15366367.2021.2000830","url":null,"abstract":"ABSTRACTThis study explores various approaches to investigate participants’ testing performance and learning behaviors in a computer-based spatial rotation learning program. Using multivariate learning and assessment data, including responses, response times, learning times and selected covariates, a comprehensive data analytic framework is developed that not only utilizes the test level information but also the item level information. This top-down and multivariate data analytic framework can shed light on conducting exploratory analysis with high-dimensional and mixed-type multivariate data, especially on how to aggregate information from the test-level and item-level. The findings about participants’ testing performance and learning behaviors are valuable in guiding the design of an adaptive learning platform in the future and can also provide some support in developing confirmatory statistical methods to model testing and learning behaviors.KEYWORDS: Clustering analysismulticategory logit modelsmixted-type dataresponse timeslearning timeslearning behaviors Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1. The normality assumption of the above two paired t-test is violated, and thus, a generalized Yuen s robust test using trimmed mean is used in R package WRS2 (Mair & Wilcox, Citation2018). Similar for the two paired t-test at TC2.","PeriodicalId":476852,"journal":{"name":"Measurement: Interdisciplinary Research & Perspective","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135902048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-02DOI: 10.1080/15366367.2023.2165891
Jennifer Reimers, Ronna C. Turner, Jorge N. Tendeiro, Wen-Juo Lo, Elizabeth Keiffer
ABSTRACTPerson-fit analyses are commonly used to detect aberrant responding in self-report data. Nonparametric person fit statistics do not require fitting a parametric test theory model and have performed well compared to other person-fit statistics. However, detection of aberrant responding has primarily focused on dominance response data, thus the effectiveness of person-fit statistics in detecting different aberrant behaviors in ideal point data is unclear. This study compares the performance of nonparametric person-fit statistics in unfolding and dominance model contexts. Results for dominance data indicate that increases in detection rates depend, among other factors, on type of aberrant responding and person-fit statistic used. The detection of aberrant responses in ideal point data was ineffective using four nonparametric person-fit statistics, with slightly higher type I error and power less than 0.25. Additional research is needed to identify or develop nonparametric or parametric person-fit statistics effective for aberrant behavior exhibited in ideal point data.KEYWORDS: Nonparametricperson-fit statisticsaberrantideal-pointdominanceresponse models Disclosure statementNo potential conflict of interest was reported by the authors.
{"title":"Performance of Nonparametric Person-Fit Statistics with Unfolding versus Dominance Response Models","authors":"Jennifer Reimers, Ronna C. Turner, Jorge N. Tendeiro, Wen-Juo Lo, Elizabeth Keiffer","doi":"10.1080/15366367.2023.2165891","DOIUrl":"https://doi.org/10.1080/15366367.2023.2165891","url":null,"abstract":"ABSTRACTPerson-fit analyses are commonly used to detect aberrant responding in self-report data. Nonparametric person fit statistics do not require fitting a parametric test theory model and have performed well compared to other person-fit statistics. However, detection of aberrant responding has primarily focused on dominance response data, thus the effectiveness of person-fit statistics in detecting different aberrant behaviors in ideal point data is unclear. This study compares the performance of nonparametric person-fit statistics in unfolding and dominance model contexts. Results for dominance data indicate that increases in detection rates depend, among other factors, on type of aberrant responding and person-fit statistic used. The detection of aberrant responses in ideal point data was ineffective using four nonparametric person-fit statistics, with slightly higher type I error and power less than 0.25. Additional research is needed to identify or develop nonparametric or parametric person-fit statistics effective for aberrant behavior exhibited in ideal point data.KEYWORDS: Nonparametricperson-fit statisticsaberrantideal-pointdominanceresponse models Disclosure statementNo potential conflict of interest was reported by the authors.","PeriodicalId":476852,"journal":{"name":"Measurement: Interdisciplinary Research & Perspective","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135902049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-02DOI: 10.1080/15366367.2022.2159686
Yu Bao, Nicolas Emundo Mireles
{"title":"Handbook of Diagnostic Classification Models: Models and Model Extensions, Applications, Software Packages <b>Handbook of Diagnostic Classification Models: Models and Model Extensions, Applications, Software Packages</b> , by Matthias von Davier, Young-Sun Lee, New York, United States, Springer, 2019, 656 pp., ISBN: 978-3-030-05583-7","authors":"Yu Bao, Nicolas Emundo Mireles","doi":"10.1080/15366367.2022.2159686","DOIUrl":"https://doi.org/10.1080/15366367.2022.2159686","url":null,"abstract":"","PeriodicalId":476852,"journal":{"name":"Measurement: Interdisciplinary Research & Perspective","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135902051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-02DOI: 10.1080/15366367.2022.2158017
Mahmut Sami Yigiter, Nuri Dogan
ABSTRACTIn recent years, Computerized Multistage Testing (MST), with their versatile benefits, have found themselves a wide application in large scale assessments and have increased their popularity. The fact that forms can be made ready before the exam application, such as a linear test, and that they can be adapted according to the test taker's ability level, such as computerized adaptive tests, has brought MST to the forefront. It is observed that simulation studies are often used in research on MST. The R programming language used in the conduct of simulation studies is widely used for statistical calculation, data visualization, and Monte Carlo research. Researchers can perform their analysis according to their own research questions, both by writing their own code in R and by using the packages in the library. This study aims to demonstrate the design and implementation of MST simulation examples using the R programming language. In this context, first of all, the basic components of MST were discussed, then R packages written on MST were examined in terms of advantages, disadvantages and analysis facility. Then, three different MST simulation examples were designed with the R programming language. It is considered that this study will be useful to those who are interested in MST.KEYWORDS: Computerized multistage testingcomputerized adaptive testingRmonte carlosimulation Disclosure statementNo potential conflict of interest was reported by the authors.
{"title":"Computerized Multistage Testing: Principles, Designs and Practices with R","authors":"Mahmut Sami Yigiter, Nuri Dogan","doi":"10.1080/15366367.2022.2158017","DOIUrl":"https://doi.org/10.1080/15366367.2022.2158017","url":null,"abstract":"ABSTRACTIn recent years, Computerized Multistage Testing (MST), with their versatile benefits, have found themselves a wide application in large scale assessments and have increased their popularity. The fact that forms can be made ready before the exam application, such as a linear test, and that they can be adapted according to the test taker's ability level, such as computerized adaptive tests, has brought MST to the forefront. It is observed that simulation studies are often used in research on MST. The R programming language used in the conduct of simulation studies is widely used for statistical calculation, data visualization, and Monte Carlo research. Researchers can perform their analysis according to their own research questions, both by writing their own code in R and by using the packages in the library. This study aims to demonstrate the design and implementation of MST simulation examples using the R programming language. In this context, first of all, the basic components of MST were discussed, then R packages written on MST were examined in terms of advantages, disadvantages and analysis facility. Then, three different MST simulation examples were designed with the R programming language. It is considered that this study will be useful to those who are interested in MST.KEYWORDS: Computerized multistage testingcomputerized adaptive testingRmonte carlosimulation Disclosure statementNo potential conflict of interest was reported by the authors.","PeriodicalId":476852,"journal":{"name":"Measurement: Interdisciplinary Research & Perspective","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135790217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-02DOI: 10.1080/15366367.2022.2061250
Yun-Ruei Ku
{"title":"Measuring and Modeling Persons and Situations <b>Measuring and Modeling Persons and Situations</b> . Wood, D., Read, S. J., Harms, P. D., & Slaughter, A., Cambridge: Academic Press, 2021, US$105.00, (paperback), ISBN 9780128192009. 732 pp","authors":"Yun-Ruei Ku","doi":"10.1080/15366367.2022.2061250","DOIUrl":"https://doi.org/10.1080/15366367.2022.2061250","url":null,"abstract":"","PeriodicalId":476852,"journal":{"name":"Measurement: Interdisciplinary Research & Perspective","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135902050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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