Pub Date : 2023-12-01DOI: 10.1007/s41237-023-00216-z
Bilal Baris Alkan, Muhammet Kumartas
{"title":"Suggestions for combining psychometric-based and supervised classification methods to detect cheating in online exams","authors":"Bilal Baris Alkan, Muhammet Kumartas","doi":"10.1007/s41237-023-00216-z","DOIUrl":"https://doi.org/10.1007/s41237-023-00216-z","url":null,"abstract":"","PeriodicalId":39640,"journal":{"name":"Behaviormetrika","volume":"5 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138625442","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 : 2023-11-24DOI: 10.1007/s41237-023-00212-3
L. Feuerstahler, J. R. Ahn, Xing Chen, Daniel Lorenzi, Jay Plourde
{"title":"On the monotonicity of the residual heteroscedasticity item response model","authors":"L. Feuerstahler, J. R. Ahn, Xing Chen, Daniel Lorenzi, Jay Plourde","doi":"10.1007/s41237-023-00212-3","DOIUrl":"https://doi.org/10.1007/s41237-023-00212-3","url":null,"abstract":"","PeriodicalId":39640,"journal":{"name":"Behaviormetrika","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139242100","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 : 2023-11-13DOI: 10.1007/s41237-023-00211-4
Keiko Mizuno, Hiroshi Shimizu
Abstract This study proposes a method of measuring social value orientation using model-based scoring and a task suitable for such scoring. We evaluated this method by means of parameter recovery simulation (Study 1), and we examined its retest reliability (Study 2) and its predictive validity (Study 3). The results indicate that the proposed method has low bias and sufficient predictive validity. While the improvement in predictive validity of altruism was negligible and comparable to previous scoring methods in terms of confidence intervals, the measurement of equality using the proposed model and task combination produced a moderate correlation that was not observed with other methods. Although SVO is a concept used primarily in psychology, the model assumed in this study is mathematically equivalent to a well-known economics model. We, therefore, suggest that this method may lead to cross-disciplinary research.
{"title":"Measuring social value orientation by model-based scoring","authors":"Keiko Mizuno, Hiroshi Shimizu","doi":"10.1007/s41237-023-00211-4","DOIUrl":"https://doi.org/10.1007/s41237-023-00211-4","url":null,"abstract":"Abstract This study proposes a method of measuring social value orientation using model-based scoring and a task suitable for such scoring. We evaluated this method by means of parameter recovery simulation (Study 1), and we examined its retest reliability (Study 2) and its predictive validity (Study 3). The results indicate that the proposed method has low bias and sufficient predictive validity. While the improvement in predictive validity of altruism was negligible and comparable to previous scoring methods in terms of confidence intervals, the measurement of equality using the proposed model and task combination produced a moderate correlation that was not observed with other methods. Although SVO is a concept used primarily in psychology, the model assumed in this study is mathematically equivalent to a well-known economics model. We, therefore, suggest that this method may lead to cross-disciplinary research.","PeriodicalId":39640,"journal":{"name":"Behaviormetrika","volume":"64 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136347309","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 : 2023-10-12DOI: 10.1007/s41237-023-00209-y
Emanuela Ingusci, Mario Angelelli, Giovanna Alessia Sternativo, Alessia Anna Catalano, Elisa De Carlo, Claudio G. Cortese, Evangelia Demerouti, Enrico Ciavolino
Abstract In this study, we highlight Life Crafting Scale (LCS) factor structure and model specifications by using partial least squares structural equations modelling (PLS-SEM) and confirmatory composite analysis (CCA), with a sample of Italian students ( $$n=953$$ n=953 ). From the validation results obtained through PLS-CCA, we identify the emergence of both the reflective nature of the scores of the LCS subscale and an alternative measurement model of the LCS scores as a second-order reflective–reflective model.
摘要本研究采用偏最小二乘结构方程模型(PLS-SEM)和验证性复合分析(CCA)，以意大利学生($$n=953$$ n = 953)为样本，重点分析了生命制作量表(LCS)的因子结构和模型规格。从PLS-CCA获得的验证结果中，我们发现了LCS子量表得分的反射性质，以及LCS得分的另一种测量模型，即二阶反射-反射模型。
{"title":"A higher-order life crafting scale validation using PLS-CCA: the Italian version","authors":"Emanuela Ingusci, Mario Angelelli, Giovanna Alessia Sternativo, Alessia Anna Catalano, Elisa De Carlo, Claudio G. Cortese, Evangelia Demerouti, Enrico Ciavolino","doi":"10.1007/s41237-023-00209-y","DOIUrl":"https://doi.org/10.1007/s41237-023-00209-y","url":null,"abstract":"Abstract In this study, we highlight Life Crafting Scale (LCS) factor structure and model specifications by using partial least squares structural equations modelling (PLS-SEM) and confirmatory composite analysis (CCA), with a sample of Italian students ( $$n=953$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>=</mml:mo> <mml:mn>953</mml:mn> </mml:mrow> </mml:math> ). From the validation results obtained through PLS-CCA, we identify the emergence of both the reflective nature of the scores of the LCS subscale and an alternative measurement model of the LCS scores as a second-order reflective–reflective model.","PeriodicalId":39640,"journal":{"name":"Behaviormetrika","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136013381","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 : 2023-10-11DOI: 10.1007/s41237-023-00207-0
Axel Preis, Stefanie Schwaar
Abstract The analysis of text data using artificial intelligence and statistical methods has become increasingly important in recent years. One application is the automatic assignment of documents. For this purpose, a classification model is trained on the basis of historical data. If the structure of the texts to be classified changes over time, the quality of the classification will decrease. Change point detection algorithms can counteract this. Such algorithms automatically detect changes in the structure of the texts and indicate that the trained classification model has to be adapted. However, the undesired influence of the length of the document needs to be handled when modeling the text data. We present a multinomial change-point model detecting changes in text structures. The results are supported by simulation studies.
{"title":"Change point detection in text data","authors":"Axel Preis, Stefanie Schwaar","doi":"10.1007/s41237-023-00207-0","DOIUrl":"https://doi.org/10.1007/s41237-023-00207-0","url":null,"abstract":"Abstract The analysis of text data using artificial intelligence and statistical methods has become increasingly important in recent years. One application is the automatic assignment of documents. For this purpose, a classification model is trained on the basis of historical data. If the structure of the texts to be classified changes over time, the quality of the classification will decrease. Change point detection algorithms can counteract this. Such algorithms automatically detect changes in the structure of the texts and indicate that the trained classification model has to be adapted. However, the undesired influence of the length of the document needs to be handled when modeling the text data. We present a multinomial change-point model detecting changes in text structures. The results are supported by simulation studies.","PeriodicalId":39640,"journal":{"name":"Behaviormetrika","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136209726","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 : 2023-09-29DOI: 10.1007/s41237-023-00210-5
Nobuyuki Uto
{"title":"Monetary incentives and eye movements: an eye-tracking investigation in a risky choice experiment with real and hypothetical incentives","authors":"Nobuyuki Uto","doi":"10.1007/s41237-023-00210-5","DOIUrl":"https://doi.org/10.1007/s41237-023-00210-5","url":null,"abstract":"","PeriodicalId":39640,"journal":{"name":"Behaviormetrika","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135194000","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 : 2023-09-11DOI: 10.1007/s41237-023-00208-z
Golnoosh Babaeic, Paolo Giudicid
Abstract The growth of artificial intelligence applications requires to find out which explanatory variables mostly contribute to the predictions. Model-agnostic methods, such as SHapley Additive exPlanations (SHAP) can solve this problem: they can determine the contribution of each variable to the predictions of any machine learning model. The SHAP approach requires a background dataset, which usually consists of random instances sampled from the train data. In this paper, we aim to understand the insofar unexplored effect of the background dataset on SHAP and, to this end, we propose a variant of SHAP, InstanceSHAP, that uses instance-based learning to produce a more effective background dataset for binary classification. We exemplify our proposed methods on an application that concerns peer-to-peer lending credit risk assessment. Our experimental results reveal that the proposed model can effectively improve the ordinary SHAP method, leading to Shapley values for the variables that are more concentrated on fewer variables, leading to simpler explanations.
{"title":"InstanceSHAP: an instance-based estimation approach for Shapley values","authors":"Golnoosh Babaeic, Paolo Giudicid","doi":"10.1007/s41237-023-00208-z","DOIUrl":"https://doi.org/10.1007/s41237-023-00208-z","url":null,"abstract":"Abstract The growth of artificial intelligence applications requires to find out which explanatory variables mostly contribute to the predictions. Model-agnostic methods, such as SHapley Additive exPlanations (SHAP) can solve this problem: they can determine the contribution of each variable to the predictions of any machine learning model. The SHAP approach requires a background dataset, which usually consists of random instances sampled from the train data. In this paper, we aim to understand the insofar unexplored effect of the background dataset on SHAP and, to this end, we propose a variant of SHAP, InstanceSHAP, that uses instance-based learning to produce a more effective background dataset for binary classification. We exemplify our proposed methods on an application that concerns peer-to-peer lending credit risk assessment. Our experimental results reveal that the proposed model can effectively improve the ordinary SHAP method, leading to Shapley values for the variables that are more concentrated on fewer variables, leading to simpler explanations.","PeriodicalId":39640,"journal":{"name":"Behaviormetrika","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135982360","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 : 2023-07-29DOI: 10.1007/s41237-023-00205-2
G. Szepannek, Björn-Hergen von Holt
{"title":"Can’t see the forest for the trees","authors":"G. Szepannek, Björn-Hergen von Holt","doi":"10.1007/s41237-023-00205-2","DOIUrl":"https://doi.org/10.1007/s41237-023-00205-2","url":null,"abstract":"","PeriodicalId":39640,"journal":{"name":"Behaviormetrika","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"53133280","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 : 2023-07-12DOI: 10.1007/s41237-023-00204-3
M. de Rooij
{"title":"A new algorithm and a discussion about visualization for logistic reduced rank regression","authors":"M. de Rooij","doi":"10.1007/s41237-023-00204-3","DOIUrl":"https://doi.org/10.1007/s41237-023-00204-3","url":null,"abstract":"","PeriodicalId":39640,"journal":{"name":"Behaviormetrika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45507423","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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