{"title":"分解对发散思维--评估创造力的任务的评分反应中的真实分数差异:多特征多方法分析》。","authors":"David Jendryczko","doi":"10.3390/jintelligence12100095","DOIUrl":null,"url":null,"abstract":"<p><p>It is shown how the Correlated Traits Correlated Methods Minus One (CTC(M - 1)) Multitrait-Multimethod model for cross-classified data can be modified and applied to divergent thinking (DT)-task responses scored for miscellaneous aspects of creative quality by several raters. In contrast to previous Confirmatory Factor Analysis approaches to analyzing DT-tasks, this model explicitly takes the cross-classified data structure resulting from the employment of raters into account and decomposes the true score variance into target-specific, DT-task object-specific, rater-specific, and rater-target interaction-specific components. This enables the computation of meaningful measurement error-free relative variance-parameters such as trait-consistency, object-method specificity, rater specificity, rater-target interaction specificity, and model-implied intra-class correlations. In the empirical application with alternate uses tasks as DT-measures, the model is estimated using Bayesian statistics. The results are compared to the results yielded with a simplified version of the model, once estimated with Bayesian statistics and once estimated with the maximum likelihood method. The results show high trait-correlations and low consistency across DT-measures which indicates more heterogeneity across the DT-measurement instruments than across different creativity aspects. Substantive deliberations and further modifications, extensions, useful applications, and limitations of the model are discussed.</p>","PeriodicalId":52279,"journal":{"name":"Journal of Intelligence","volume":"12 10","pages":""},"PeriodicalIF":2.8000,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11508196/pdf/","citationCount":"0","resultStr":"{\"title\":\"Decomposing the True Score Variance in Rated Responses to Divergent Thinking-Tasks for Assessing Creativity: A Multitrait-Multimethod Analysis.\",\"authors\":\"David Jendryczko\",\"doi\":\"10.3390/jintelligence12100095\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>It is shown how the Correlated Traits Correlated Methods Minus One (CTC(M - 1)) Multitrait-Multimethod model for cross-classified data can be modified and applied to divergent thinking (DT)-task responses scored for miscellaneous aspects of creative quality by several raters. In contrast to previous Confirmatory Factor Analysis approaches to analyzing DT-tasks, this model explicitly takes the cross-classified data structure resulting from the employment of raters into account and decomposes the true score variance into target-specific, DT-task object-specific, rater-specific, and rater-target interaction-specific components. This enables the computation of meaningful measurement error-free relative variance-parameters such as trait-consistency, object-method specificity, rater specificity, rater-target interaction specificity, and model-implied intra-class correlations. In the empirical application with alternate uses tasks as DT-measures, the model is estimated using Bayesian statistics. The results are compared to the results yielded with a simplified version of the model, once estimated with Bayesian statistics and once estimated with the maximum likelihood method. The results show high trait-correlations and low consistency across DT-measures which indicates more heterogeneity across the DT-measurement instruments than across different creativity aspects. Substantive deliberations and further modifications, extensions, useful applications, and limitations of the model are discussed.</p>\",\"PeriodicalId\":52279,\"journal\":{\"name\":\"Journal of Intelligence\",\"volume\":\"12 10\",\"pages\":\"\"},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2024-09-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11508196/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Intelligence\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://doi.org/10.3390/jintelligence12100095\",\"RegionNum\":3,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PSYCHOLOGY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Intelligence","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.3390/jintelligence12100095","RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, MULTIDISCIPLINARY","Score":null,"Total":0}
Decomposing the True Score Variance in Rated Responses to Divergent Thinking-Tasks for Assessing Creativity: A Multitrait-Multimethod Analysis.
It is shown how the Correlated Traits Correlated Methods Minus One (CTC(M - 1)) Multitrait-Multimethod model for cross-classified data can be modified and applied to divergent thinking (DT)-task responses scored for miscellaneous aspects of creative quality by several raters. In contrast to previous Confirmatory Factor Analysis approaches to analyzing DT-tasks, this model explicitly takes the cross-classified data structure resulting from the employment of raters into account and decomposes the true score variance into target-specific, DT-task object-specific, rater-specific, and rater-target interaction-specific components. This enables the computation of meaningful measurement error-free relative variance-parameters such as trait-consistency, object-method specificity, rater specificity, rater-target interaction specificity, and model-implied intra-class correlations. In the empirical application with alternate uses tasks as DT-measures, the model is estimated using Bayesian statistics. The results are compared to the results yielded with a simplified version of the model, once estimated with Bayesian statistics and once estimated with the maximum likelihood method. The results show high trait-correlations and low consistency across DT-measures which indicates more heterogeneity across the DT-measurement instruments than across different creativity aspects. Substantive deliberations and further modifications, extensions, useful applications, and limitations of the model are discussed.