Pub Date : 2024-05-01Epub Date: 2024-02-13DOI: 10.1080/00273171.2023.2289058
Jonathan J Park, Sy-Miin Chow, Sacha Epskamp, Peter C M Molenaar
Recent years have seen the emergence of an "idio-thetic" class of methods to bridge the gap between nomothetic and idiographic inference. These methods describe nomothetic trends in idiographic processes by pooling intraindividual information across individuals to inform group-level inference or vice versa. The current work introduces a novel "idio-thetic" model: the subgrouped chain graphical vector autoregression (scGVAR). The scGVAR is unique in its ability to identify subgroups of individuals who share common dynamic network structures in both lag(1) and contemporaneous effects. Results from Monte Carlo simulations indicate that the scGVAR shows promise over similar approaches when clusters of individuals differ in their contemporaneous dynamics and in showing increased sensitivity in detecting nuanced group differences while keeping Type-I error rates low. In contrast, a competing approach-the Alternating Least Squares VAR (ALS VAR) performs well when groups were separated by larger distances. Further considerations are provided regarding applications of the ALS VAR and scGVAR on real data and the strengths and limitations of both methods.
近年来,出现了一类 "特异推理 "方法,以弥补提名推理和特异推理之间的差距。这些方法通过汇集跨个体的个体内信息来为群体层面的推断提供信息,反之亦然,从而描述特异过程中的提名趋势。目前的工作引入了一种新颖的 "特异性 "模型:分组链图向量自回归(scGVAR)。scGVAR 的独特之处在于它能够识别在滞后效应(1)和同期效应中具有共同动态网络结构的个体子群。蒙特卡洛模拟结果表明,当个体集群的同期动态存在差异时,scGVAR 有望超越类似方法,并在检测细微群体差异方面显示出更高的灵敏度,同时保持较低的类型一误差率。相比之下,一种与之竞争的方法--交替最小二乘法 VAR(ALS VAR)--在组间距离较大的情况下表现良好。本文还就 ALS VAR 和 scGVAR 在实际数据中的应用以及这两种方法的优势和局限性做了进一步的探讨。
{"title":"Subgrouping with Chain Graphical VAR Models.","authors":"Jonathan J Park, Sy-Miin Chow, Sacha Epskamp, Peter C M Molenaar","doi":"10.1080/00273171.2023.2289058","DOIUrl":"10.1080/00273171.2023.2289058","url":null,"abstract":"<p><p>Recent years have seen the emergence of an \"idio-thetic\" class of methods to bridge the gap between nomothetic and idiographic inference. These methods describe nomothetic trends in idiographic processes by pooling intraindividual information across individuals to inform group-level inference or vice versa. The current work introduces a novel \"idio-thetic\" model: the subgrouped chain graphical vector autoregression (scGVAR). The scGVAR is unique in its ability to identify subgroups of individuals who share common dynamic network structures in both lag(1) and contemporaneous effects. Results from Monte Carlo simulations indicate that the scGVAR shows promise over similar approaches when clusters of individuals differ in their contemporaneous dynamics and in showing increased sensitivity in detecting nuanced group differences while keeping Type-I error rates low. In contrast, a competing approach-the Alternating Least Squares VAR (ALS VAR) performs well when groups were separated by larger distances. Further considerations are provided regarding applications of the ALS VAR and scGVAR on real data and the strengths and limitations of both methods.</p>","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"543-565"},"PeriodicalIF":5.3,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11187704/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139731017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-01Epub Date: 2024-02-23DOI: 10.1080/00273171.2024.2310418
Sophia J Lamp, David P MacKinnon
{"title":"Correcting Regression Coefficients for Collider Bias in Psychological Research.","authors":"Sophia J Lamp, David P MacKinnon","doi":"10.1080/00273171.2024.2310418","DOIUrl":"10.1080/00273171.2024.2310418","url":null,"abstract":"","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"647-648"},"PeriodicalIF":5.3,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11187666/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139934145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-01Epub Date: 2024-02-13DOI: 10.1080/00273171.2023.2288589
Sijia Huang
Student evaluation of teaching (SET) questionnaires are ubiquitously applied in higher education institutions in North America for both formative and summative purposes. Data collected from SET questionnaires are usually item-level data with cross-classified structure, which are characterized by multivariate categorical outcomes (i.e., multiple Likert-type items in the questionnaires) and cross-classified structure (i.e., non-nested students and instructors). Recently, a new approach, namely the cross-classified IRT model, was proposed for appropriately handling SET data. To inform researchers in higher education, in this article, the cross-classified IRT model, along with three existing approaches applied in SET studies, including the cross-classified random effects model (CCREM), the multilevel item response theory (MLIRT) model, and a two-step integrated strategy, was reviewed. The strengths and weaknesses of each of the four approaches were also discussed. Additionally, the new and existing approaches were compared through an empirical data analysis and a preliminary simulation study. This article concluded by providing general suggestions to researchers for analyzing SET data and discussing limitations and future research directions.
在北美的高等教育机构中,学生教学评价(SET)问卷被广泛应用于形成性和总结性教学评价。从 SET 问卷中收集的数据通常是具有交叉分类结构的项目级数据,其特点是多变量分类结果(即问卷中有多个李克特类型的项目)和交叉分类结构(即非嵌套的学生和教师)。最近,有人提出了一种新方法,即交叉分类 IRT 模型,用于适当处理 SET 数据。为了给高等教育研究人员提供参考,本文回顾了交叉分类 IRT 模型以及应用于 SET 研究的三种现有方法,包括交叉分类随机效应模型 (CCREM)、多层次项目反应理论 (MLIRT) 模型和两步综合策略。还讨论了这四种方法各自的优缺点。此外,还通过实证数据分析和初步模拟研究对新方法和现有方法进行了比较。文章最后为研究人员提供了分析 SET 数据的一般建议,并讨论了局限性和未来研究方向。
{"title":"Approaches to Item-Level Data with Cross-Classified Structure: An Illustration with Student Evaluation of Teaching.","authors":"Sijia Huang","doi":"10.1080/00273171.2023.2288589","DOIUrl":"10.1080/00273171.2023.2288589","url":null,"abstract":"<p><p>Student evaluation of teaching (SET) questionnaires are ubiquitously applied in higher education institutions in North America for both formative and summative purposes. Data collected from SET questionnaires are usually item-level data with cross-classified structure, which are characterized by multivariate categorical outcomes (i.e., multiple Likert-type items in the questionnaires) and cross-classified structure (i.e., non-nested students and instructors). Recently, a new approach, namely the cross-classified IRT model, was proposed for appropriately handling SET data. To inform researchers in higher education, in this article, the cross-classified IRT model, along with three existing approaches applied in SET studies, including the cross-classified random effects model (CCREM), the multilevel item response theory (MLIRT) model, and a two-step integrated strategy, was reviewed. The strengths and weaknesses of each of the four approaches were also discussed. Additionally, the new and existing approaches were compared through an empirical data analysis and a preliminary simulation study. This article concluded by providing general suggestions to researchers for analyzing SET data and discussing limitations and future research directions.</p>","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"523-542"},"PeriodicalIF":3.8,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139731016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-01Epub Date: 2024-02-13DOI: 10.1080/00273171.2024.2307034
Mark H C Lai, Yichi Zhang, Feng Ji
With clustered data, such as where students are nested within schools or employees are nested within organizations, it is often of interest to estimate and compare associations among variables separately for each level. While researchers routinely estimate between-cluster effects using the sample cluster means of a predictor, previous research has shown that such practice leads to biased estimates of coefficients at the between level, and recent research has recommended the use of latent cluster means with the multilevel structural equation modeling framework. However, the latent cluster mean approach may not always be the best choice as it (a) relies on the assumption that the population cluster sizes are close to infinite, (b) requires a relatively large number of clusters, and (c) is currently only implemented in specialized software such as Mplus. In this paper, we show how using empirical Bayes estimates of the cluster means can also lead to consistent estimates of between-level coefficients, and illustrate how the empirical Bayes estimate can incorporate finite population corrections when information on population cluster sizes is available. Through a series of Monte Carlo simulation studies, we show that the empirical Bayes cluster-mean approach performs similarly to the latent cluster mean approach for estimating the between-cluster coefficients in most conditions when the infinite-population assumption holds, and applying the finite population correction provides reasonable point and interval estimates when the population is finite. The performance of EBM can be further improved with restricted maximum likelihood estimation and likelihood-based confidence intervals. We also provide an R function that implements the empirical Bayes cluster-mean approach, and illustrate it using data from the classic High School and Beyond Study.
对于聚类数据,如学生嵌套在学校内或员工嵌套在组织内,通常需要分别估计和比较各层次变量之间的关联。虽然研究人员通常使用预测因子的样本聚类均值来估计聚类间效应,但以往的研究表明,这种做法会导致对聚类间系数的估计出现偏差,因此最近的研究建议在多层次结构方程建模框架下使用潜在聚类均值。然而,潜在聚类平均值方法并不总是最佳选择,因为它(a)依赖于群体聚类大小接近无限的假设,(b)需要相对较多的聚类,(c)目前只能在 Mplus 等专业软件中实现。在本文中,我们展示了如何利用对聚类均值的经验贝叶斯估计也能得出水平间系数的一致估计值,并说明了经验贝叶斯估计如何在有聚类规模信息的情况下纳入有限聚类校正。通过一系列蒙特卡罗模拟研究,我们表明,当无限人口假设成立时,经验贝叶斯聚类均值法在大多数条件下估计聚类间系数的表现与潜在聚类均值法相似,而当人口有限时,应用有限人口校正可提供合理的点和区间估计值。限制最大似然估计和基于似然的置信区间可以进一步提高 EBM 的性能。我们还提供了一个实现经验贝叶斯聚类均值方法的 R 函数,并使用经典的 "高中及高中以上研究 "中的数据进行了说明。
{"title":"Correcting for Sampling Error in between-Cluster Effects: An Empirical Bayes Cluster-Mean Approach with Finite Population Corrections.","authors":"Mark H C Lai, Yichi Zhang, Feng Ji","doi":"10.1080/00273171.2024.2307034","DOIUrl":"10.1080/00273171.2024.2307034","url":null,"abstract":"<p><p>With clustered data, such as where students are nested within schools or employees are nested within organizations, it is often of interest to estimate and compare associations among variables separately for each level. While researchers routinely estimate between-cluster effects using the sample cluster means of a predictor, previous research has shown that such practice leads to biased estimates of coefficients at the between level, and recent research has recommended the use of latent cluster means with the multilevel structural equation modeling framework. However, the latent cluster mean approach may not always be the best choice as it (a) relies on the assumption that the population cluster sizes are close to infinite, (b) requires a relatively large number of clusters, and (c) is currently only implemented in specialized software such as Mplus. In this paper, we show how using empirical Bayes estimates of the cluster means can also lead to consistent estimates of between-level coefficients, and illustrate how the empirical Bayes estimate can incorporate finite population corrections when information on population cluster sizes is available. Through a series of Monte Carlo simulation studies, we show that the empirical Bayes cluster-mean approach performs similarly to the latent cluster mean approach for estimating the between-cluster coefficients in most conditions when the infinite-population assumption holds, and applying the finite population correction provides reasonable point and interval estimates when the population is finite. The performance of EBM can be further improved with restricted maximum likelihood estimation and likelihood-based confidence intervals. We also provide an R function that implements the empirical Bayes cluster-mean approach, and illustrate it using data from the classic High School and Beyond Study.</p>","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"584-598"},"PeriodicalIF":3.8,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139724964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-01Epub Date: 2024-02-15DOI: 10.1080/00273171.2024.2310429
Kayla M Garner
{"title":"The Forgotten Trade-off between Internal Consistency and Validity.","authors":"Kayla M Garner","doi":"10.1080/00273171.2024.2310429","DOIUrl":"10.1080/00273171.2024.2310429","url":null,"abstract":"","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"656-657"},"PeriodicalIF":3.8,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139742636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-01Epub Date: 2024-02-20DOI: 10.1080/00273171.2023.2283865
Simran K Johal, Emilio Ferrer
Accelerated longitudinal designs allow researchers to efficiently collect longitudinal data covering a time span much longer than the study duration. One important assumption of these designs is that each cohort (a group defined by their age of entry into the study) shares the same longitudinal trajectory. Although previous research has examined the impact of violating this assumption when each cohort is defined by a single age of entry, it is possible that each cohort is instead defined by a range of ages, such as groups that experience a particular historical event. In this paper we examined how including cohort membership in linear and quadratic multilevel models performed in detecting and controlling for cohort effects in this scenario. Using a Monte Carlo simulation study, we assessed the performance of this approach under conditions related to the number of cohorts, the overlap between cohorts, the strength of the cohort effect, the number of affected parameters, and the sample size. Our results indicate that models including a proxy variable for cohort membership based on age at study entry performed comparably to using true cohort membership in detecting cohort effects accurately and returning unbiased parameter estimates. This indicates that researchers can control for cohort effects even when true cohort membership is unknown.
{"title":"Detecting Cohort Effects in Accelerated Longitudinal Designs Using Multilevel Models.","authors":"Simran K Johal, Emilio Ferrer","doi":"10.1080/00273171.2023.2283865","DOIUrl":"10.1080/00273171.2023.2283865","url":null,"abstract":"<p><p>Accelerated longitudinal designs allow researchers to efficiently collect longitudinal data covering a time span much longer than the study duration. One important assumption of these designs is that each cohort (a group defined by their age of entry into the study) shares the same longitudinal trajectory. Although previous research has examined the impact of violating this assumption when each cohort is defined by a single age of entry, it is possible that each cohort is instead defined by a range of ages, such as groups that experience a particular historical event. In this paper we examined how including cohort membership in linear and quadratic multilevel models performed in detecting and controlling for cohort effects in this scenario. Using a Monte Carlo simulation study, we assessed the performance of this approach under conditions related to the number of cohorts, the overlap between cohorts, the strength of the cohort effect, the number of affected parameters, and the sample size. Our results indicate that models including a proxy variable for cohort membership based on age at study entry performed comparably to using true cohort membership in detecting cohort effects accurately and returning unbiased parameter estimates. This indicates that researchers can control for cohort effects even when true cohort membership is unknown.</p>","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"482-501"},"PeriodicalIF":3.8,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139914050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-01Epub Date: 2024-02-27DOI: 10.1080/00273171.2023.2292598
Kenneth McClure, Brooke A Ammerman, Ross Jacobucci
Recent shifts to prioritize prediction, rather than explanation, in psychological science have increased applications of predictive modeling methods. However, composite predictors, such as sum scores, are still commonly used in practice. The motivations behind composite test scores are largely intertwined with reducing the influence of measurement error in answering explanatory questions. But this may be detrimental for predictive aims. The present paper examines the impact of utilizing composite or item-level predictors in linear regression. A mathematical examination of the bias-variance decomposition of prediction error in the presence of measurement error is provided. It is shown that prediction bias, which may be exacerbated by composite scoring, drives prediction error for linear regression. This may be particularly salient when composite scores are comprised of heterogeneous items such as in clinical scales where items correspond to symptoms. With sufficiently large training samples, the increased prediction variance associated with item scores becomes negligible even when composite scores are sufficient. Practical implications of predictor scoring are examined in an empirical example predicting suicidal ideation from various depression scales. Results show that item scores can markedly improve prediction particularly for symptom-based scales. Cross-validation methods can be used to empirically justify predictor scoring decisions.
{"title":"On the Selection of Item Scores or Composite Scores for Clinical Prediction.","authors":"Kenneth McClure, Brooke A Ammerman, Ross Jacobucci","doi":"10.1080/00273171.2023.2292598","DOIUrl":"10.1080/00273171.2023.2292598","url":null,"abstract":"<p><p>Recent shifts to prioritize prediction, rather than explanation, in psychological science have increased applications of predictive modeling methods. However, composite predictors, such as sum scores, are still commonly used in practice. The motivations behind composite test scores are largely intertwined with reducing the influence of measurement error in answering explanatory questions. But this may be detrimental for predictive aims. The present paper examines the impact of utilizing composite or item-level predictors in linear regression. A mathematical examination of the bias-variance decomposition of prediction error in the presence of measurement error is provided. It is shown that prediction bias, which may be exacerbated by composite scoring, drives prediction error for linear regression. This may be particularly salient when composite scores are comprised of heterogeneous items such as in clinical scales where items correspond to symptoms. With sufficiently large training samples, the increased prediction variance associated with item scores becomes negligible even when composite scores are sufficient. Practical implications of predictor scoring are examined in an empirical example predicting suicidal ideation from various depression scales. Results show that item scores can markedly improve prediction particularly for symptom-based scales. Cross-validation methods can be used to empirically justify predictor scoring decisions.</p>","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"566-583"},"PeriodicalIF":3.8,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139984528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-01Epub Date: 2024-01-21DOI: 10.1080/00273171.2023.2283638
Kai Jannik Nehler, Martin Schultze
Network analysis has gained popularity as an approach to investigate psychological constructs. However, there are currently no guidelines for applied researchers when encountering missing values. In this simulation study, we compared the performance of a two-step EM algorithm with separated steps for missing handling and regularization, a combined direct EM algorithm, and pairwise deletion. We investigated conditions with varying network sizes, numbers of observations, missing data mechanisms, and percentages of missing values. These approaches are evaluated with regard to recovering population networks in terms of loss in the precision matrix, edge set identification and network statistics. The simulation showed adequate performance only in conditions with large samples () or small networks (p = 10). Comparing the missing data approaches, the direct EM appears to be more sensitive and superior in nearly all chosen conditions. The two-step EM yields better results when the ratio of n/p is very large - being less sensitive but more specific. Pairwise deletion failed to converge across numerous conditions and yielded inferior results overall. Overall, direct EM is recommended in most cases, as it is able to mitigate the impact of missing data quite well, while modifications to two-step EM could improve its performance.
网络分析作为一种研究心理结构的方法,已经广受欢迎。然而,目前还没有针对应用研究人员在遇到缺失值时的指导原则。在这项模拟研究中,我们比较了分两步处理缺失和正则化的 EM 算法、组合式直接 EM 算法和成对删除算法的性能。我们研究了不同网络规模、观测数据数量、缺失数据机制和缺失值百分比的条件。我们从精确度矩阵损失、边缘集识别和网络统计等方面对这些方法恢复群体网络的效果进行了评估。模拟结果表明,只有在样本较大(n≥500)或网络较小(p = 10)的情况下,才有足够的性能。比较缺失数据方法,在几乎所有选择条件下,直接 EM 似乎更灵敏、更优越。当 n/p 的比率非常大时,两步电磁法会产生更好的结果--灵敏度较低,但特异性更高。成对删除法在许多条件下都无法收敛,总体结果较差。总的来说,在大多数情况下,建议使用直接 EM,因为它能够很好地减轻缺失数据的影响,而对两步 EM 的修改则可以提高其性能。
{"title":"Simulation-Based Performance Evaluation of Missing Data Handling in Network Analysis.","authors":"Kai Jannik Nehler, Martin Schultze","doi":"10.1080/00273171.2023.2283638","DOIUrl":"10.1080/00273171.2023.2283638","url":null,"abstract":"<p><p>Network analysis has gained popularity as an approach to investigate psychological constructs. However, there are currently no guidelines for applied researchers when encountering missing values. In this simulation study, we compared the performance of a two-step EM algorithm with separated steps for missing handling and regularization, a combined direct EM algorithm, and pairwise deletion. We investigated conditions with varying network sizes, numbers of observations, missing data mechanisms, and percentages of missing values. These approaches are evaluated with regard to recovering population networks in terms of loss in the precision matrix, edge set identification and network statistics. The simulation showed adequate performance only in conditions with large samples (<math><mrow><mi>n</mi><mo>≥</mo><mn>500</mn></mrow></math>) or small networks (<i>p</i> = 10). Comparing the missing data approaches, the direct EM appears to be more sensitive and superior in nearly all chosen conditions. The two-step EM yields better results when the ratio of n/p is very large - being less sensitive but more specific. Pairwise deletion failed to converge across numerous conditions and yielded inferior results overall. Overall, direct EM is recommended in most cases, as it is able to mitigate the impact of missing data quite well, while modifications to two-step EM could improve its performance.</p>","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"461-481"},"PeriodicalIF":3.8,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139513919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-01Epub Date: 2023-08-31DOI: 10.1080/00273171.2023.2235682
Bo Zhang, Jing Luo, Jian Li
The graded forced-choice (FC) format has recently emerged as an alternative that may preserve the advantages and overcome the issues of the dichotomous FC measures. The current study presented the first large-scale evaluation of the performance of three types of FC measures (FC2, FC4 and FC5 with 2, 4 and 5 response options, respectively) and compared their performance to their Likert (LK) counterparts (LK2, LK4, and LK5) on (1) psychometric properties, (2) respondent reactions, and (3) susceptibility to response styles. Results showed that, compared to LK measures with the same number of response options, the three FC scales provided better support for the hypothesized factor structure, were perceived as more faking-resistant and cognitive demanding, and were less susceptible to response styles. FC4/5 and LK4/5 demonstrated similarly good reliability, while LK2 provided more reliable scores than FC2. When compared across the three FC measures, FC4 and FC5 displayed comparable psychometric performance and respondent reactions. FC4 exhibited a moderate presence of extreme response style, while FC5 had a weak presence of both extreme and middle response styles. Based on these findings, the study recommends the use of graded FC over dichotomous FC and LK, particularly FC5 when extreme response style is a concern.
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Pub Date : 2024-04-12DOI: 10.1080/00273171.2024.2325210
Published in Multivariate Behavioral Research (Vol. 59, No. 2, 2024)
发表于《多元行为研究》(第 59 卷第 2 期,2024 年)
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