假设不同的基本响应过程,偏离响应对模型拟合的影响。

IF 1 4区 心理学 Q4 PSYCHOLOGY, MATHEMATICAL
Applied Psychological Measurement Pub Date : 2023-09-01 Epub Date: 2023-09-19 DOI:10.1177/01466216231201987
Jennifer Reimers, Ronna C Turner, Jorge N Tendeiro, Wen-Juo Lo, Elizabeth Keiffer
{"title":"假设不同的基本响应过程,偏离响应对模型拟合的影响。","authors":"Jennifer Reimers, Ronna C Turner, Jorge N Tendeiro, Wen-Juo Lo, Elizabeth Keiffer","doi":"10.1177/01466216231201987","DOIUrl":null,"url":null,"abstract":"<p><p>Aberrant responding on tests and surveys has been shown to affect the psychometric properties of scales and the statistical analyses from the use of those scales in cumulative model contexts. This study extends prior research by comparing the effects of four types of aberrant responding on model fit in both cumulative and ideal point model contexts using graded partial credit (GPCM) and generalized graded unfolding (GGUM) models. When fitting models to data, model misfit can be both a function of misspecification and aberrant responding. Results demonstrate how varying levels of aberrant data can severely impact model fit for both cumulative and ideal point data. Specifically, longstring responses have a stronger impact on dimensionality for both ideal point and cumulative data, while random responding tends to have the most negative impact on data model fit according to information criteria (AIC, BIC). The results also indicate that ideal point data models such as GGUM may be able to fit cumulative data as well as the cumulative model itself (GPCM), whereas cumulative data models may not provide sufficient model fit for data simulated using an ideal point model.</p>","PeriodicalId":48300,"journal":{"name":"Applied Psychological Measurement","volume":"47 5-6","pages":"420-437"},"PeriodicalIF":1.0000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10552732/pdf/","citationCount":"0","resultStr":"{\"title\":\"The Effects of Aberrant Responding on Model-Fit Assuming Different Underlying Response Processes.\",\"authors\":\"Jennifer Reimers, Ronna C Turner, Jorge N Tendeiro, Wen-Juo Lo, Elizabeth Keiffer\",\"doi\":\"10.1177/01466216231201987\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Aberrant responding on tests and surveys has been shown to affect the psychometric properties of scales and the statistical analyses from the use of those scales in cumulative model contexts. This study extends prior research by comparing the effects of four types of aberrant responding on model fit in both cumulative and ideal point model contexts using graded partial credit (GPCM) and generalized graded unfolding (GGUM) models. When fitting models to data, model misfit can be both a function of misspecification and aberrant responding. Results demonstrate how varying levels of aberrant data can severely impact model fit for both cumulative and ideal point data. Specifically, longstring responses have a stronger impact on dimensionality for both ideal point and cumulative data, while random responding tends to have the most negative impact on data model fit according to information criteria (AIC, BIC). The results also indicate that ideal point data models such as GGUM may be able to fit cumulative data as well as the cumulative model itself (GPCM), whereas cumulative data models may not provide sufficient model fit for data simulated using an ideal point model.</p>\",\"PeriodicalId\":48300,\"journal\":{\"name\":\"Applied Psychological Measurement\",\"volume\":\"47 5-6\",\"pages\":\"420-437\"},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2023-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10552732/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Psychological Measurement\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://doi.org/10.1177/01466216231201987\",\"RegionNum\":4,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/9/19 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q4\",\"JCRName\":\"PSYCHOLOGY, MATHEMATICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Psychological Measurement","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1177/01466216231201987","RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/9/19 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"PSYCHOLOGY, MATHEMATICAL","Score":null,"Total":0}
引用次数: 0

摘要

测试和调查的异常反应已被证明会影响量表的心理测量特性,以及在累积模型环境中使用这些量表的统计分析。本研究扩展了先前的研究,使用分级部分信用(GPCM)和广义分级展开(GGUM)模型比较了四种类型的异常反应对累积点模型和理想点模型中模型拟合的影响。当将模型与数据拟合时,模型不匹配可能是错误指定和异常响应的函数。结果表明,不同水平的异常数据会严重影响累积点数据和理想点数据的模型拟合。具体而言,对于理想点和累积数据,长串响应对维度的影响更强,而根据信息标准(AIC、BIC),随机响应往往对数据模型拟合产生最负面的影响。结果还表明,诸如GGUM的理想点数据模型可能能够拟合累积数据以及累积模型本身(GPCM),而累积数据模型可能不能为使用理想点模型模拟的数据提供足够的模型拟合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Effects of Aberrant Responding on Model-Fit Assuming Different Underlying Response Processes.

Aberrant responding on tests and surveys has been shown to affect the psychometric properties of scales and the statistical analyses from the use of those scales in cumulative model contexts. This study extends prior research by comparing the effects of four types of aberrant responding on model fit in both cumulative and ideal point model contexts using graded partial credit (GPCM) and generalized graded unfolding (GGUM) models. When fitting models to data, model misfit can be both a function of misspecification and aberrant responding. Results demonstrate how varying levels of aberrant data can severely impact model fit for both cumulative and ideal point data. Specifically, longstring responses have a stronger impact on dimensionality for both ideal point and cumulative data, while random responding tends to have the most negative impact on data model fit according to information criteria (AIC, BIC). The results also indicate that ideal point data models such as GGUM may be able to fit cumulative data as well as the cumulative model itself (GPCM), whereas cumulative data models may not provide sufficient model fit for data simulated using an ideal point model.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
2.30
自引率
8.30%
发文量
50
期刊介绍: Applied Psychological Measurement publishes empirical research on the application of techniques of psychological measurement to substantive problems in all areas of psychology and related disciplines.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信