Organizational Research Methods最新文献_第7页

Interaction Effects in Cross-Lagged Panel Models: SEM with Latent Interactions Applied to Work-Family Conflict, Job Satisfaction, and Gender 交叉滞后面板模型的交互作用:潜在交互作用的SEM在工作-家庭冲突、工作满意度和性别中的应用

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2021-11-29 DOI: 10.1177/10944281211043733

Ozlem Ozkok, Manuel J Vaulont, M. Zyphur, Zhen Zhang, Kristopher J Preacher, Peter Koval, Yixia Zheng

{"title":"Interaction Effects in Cross-Lagged Panel Models: SEM with Latent Interactions Applied to Work-Family Conflict, Job Satisfaction, and Gender","authors":"Ozlem Ozkok, Manuel J Vaulont, M. Zyphur, Zhen Zhang, Kristopher J Preacher, Peter Koval, Yixia Zheng","doi":"10.1177/10944281211043733","DOIUrl":"https://doi.org/10.1177/10944281211043733","url":null,"abstract":"Researchers often combine longitudinal panel data analysis with tests of interactions (i.e., moderation). A popular example is the cross-lagged panel model (CLPM). However, interaction tests in CLPMs and related models require caution because stable (i.e., between-level, B) and dynamic (i.e., within-level, W) sources of variation are present in longitudinal data, which can conflate estimates of interaction effects. We address this by integrating literature on CLPMs, multilevel moderation, and latent interactions. Distinguishing stable B and dynamic W parts, we describe three types of interactions that are of interest to researchers: 1) purely dynamic or WxW; 2) cross-level or BxW; and 3) purely stable or BxB. We demonstrate estimating latent interaction effects in a CLPM using a Bayesian SEM in Mplus to apply relationships among work-family conflict and job satisfaction, using gender as a stable B variable. We support our approach via simulations, demonstrating that our proposed CLPM approach is superior to a traditional CLPMs that conflate B and W sources of variation. We describe higher-order nonlinearities as a possible extension, and we discuss limitations and future research directions.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"25 1","pages":"673 - 715"},"PeriodicalIF":9.5,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48057256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Inaugural Editorial 就职社论

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2021-11-13 DOI: 10.1177/10944281211058903

T. Köhler, L. Lambert

引用次数: 0

Team Composition Revisited: A Team Member Attribute Alignment Approach 重新审视团队组成:团队成员属性对齐方法

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2021-10-18 DOI: 10.1177/10944281211042388

Kyle J. Emich, Li Lu, Amanda J. Ferguson, R. Peterson, Michael McCourt

{"title":"Team Composition Revisited: A Team Member Attribute Alignment Approach","authors":"Kyle J. Emich, Li Lu, Amanda J. Ferguson, R. Peterson, Michael McCourt","doi":"10.1177/10944281211042388","DOIUrl":"https://doi.org/10.1177/10944281211042388","url":null,"abstract":"Research methods for studying team composition tend to employ either a variable-centered or person-centered approach. The variable-centered approach allows scholars to consider how patterns of attributes between team members influence teams, while the person-centered approach allows scholars to consider how variation in multiple attributes within team members influences subgroup formation and its effects. Team composition theory, however, is becoming increasingly sophisticated, assuming variation on multiple attributes both within and between team members—for example, in predicting how a team functions differently when its most assertive members are also optimistic rather than pessimistic. To support this new theory, we propose an attribute alignment approach, which complements the variable-centered and person-centered approaches by modeling teams as matrices of their members and their members’ attributes. We first demonstrate how to calculate attribute alignment by determining the vector norm and vector angle between team members’ attributes. Then, we demonstrate how the alignment of team member personality attributes (neuroticism and agreeableness) affects team relationship conflict. Finally, we discuss the potential of using the attribute alignment approach to enrich broader team research.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"25 1","pages":"642 - 672"},"PeriodicalIF":9.5,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42555071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Assessing Dimensionality of the Ideal Point Item Response Theory Model Using Posterior Predictive Model Checking 用后验预测模型检验评估理想点项目反应理论模型的维度

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2021-10-18 DOI: 10.1177/10944281211050609

Seang-Hwane Joo, Philseok Lee, Jung Yeon Park, Stephen E. Stark

{"title":"Assessing Dimensionality of the Ideal Point Item Response Theory Model Using Posterior Predictive Model Checking","authors":"Seang-Hwane Joo, Philseok Lee, Jung Yeon Park, Stephen E. Stark","doi":"10.1177/10944281211050609","DOIUrl":"https://doi.org/10.1177/10944281211050609","url":null,"abstract":"Although the use of ideal point item response theory (IRT) models for organizational research has increased over the last decade, the assessment of construct dimensionality of ideal point scales has been overlooked in previous research. In this study, we developed and evaluated dimensionality assessment methods for an ideal point IRT model under the Bayesian framework. We applied the posterior predictive model checking (PPMC) approach to the most widely used ideal point IRT model, the generalized graded unfolding model (GGUM). We conducted a Monte Carlo simulation to compare the performance of item pair discrepancy statistics and to evaluate the Type I error and power rates of the methods. The simulation results indicated that the Bayesian dimensionality detection method controlled Type I errors reasonably well across the conditions. In addition, the proposed method showed better performance than existing methods, yielding acceptable power when 20% of the items were generated from the secondary dimension. Organizational implications and limitations of the study are further discussed.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"26 1","pages":"353 - 382"},"PeriodicalIF":9.5,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45044899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

ORM-CARMA Virtual Feature Topics for Advanced Reviewer Development 用于高级评审员开发的ORM-CARMA虚拟功能主题

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2021-10-01 DOI: 10.1177/10944281211030648

L. J. Williams, G. Banks, R. Vandenberg

{"title":"ORM-CARMA Virtual Feature Topics for Advanced Reviewer Development","authors":"L. J. Williams, G. Banks, R. Vandenberg","doi":"10.1177/10944281211030648","DOIUrl":"https://doi.org/10.1177/10944281211030648","url":null,"abstract":"Providing developmental peer reviewers is one of the most critical services performed by researchers in the organizational sciences (Bedeian, 2003). Yet, completing helpful and constructive reviews is not easy (Epstein, 1995; Feldman, 2005). This challenge may be due, in part, to the fact that our field provides only limited formal reviewer training in graduate programs and through professional development workshops (PDWs). Much of what new reviewers learn happens through informal training with mentors (Carpenter, 2009). Without effective training, reviewers may be prone to biases in their methodological evaluations of manuscripts (Banks et al., 2016; Bedeian, Taylor, & Miller, 2010; Emerson et al., 2010) or may simply lack the expertise needed to evaluate manuscripts due to the large variety of content areas and methodological techniques being employed in research. Many editorials have been written to provide guidance for basic reviewer development (e.g., Lee, 1995). Recently, the Society for Industrial and Organizational Psychology (SIOP) and the Consortium for the Advancement of Research Methods and Analysis (CARMA) started an initiative around basic reviewer development (http://carmarmep.org/siop-carma-reviewer-series/). This ongoing training serves to introduce basic reviewer competencies (Koehler et al., 2020), recommend readings, and training videos that are freely available to help new and even experienced reviewers improve the quality of their reviews. While basic reviewer development is laudable, there is also a need for more formal training on advanced methodological topics. Hence, Organizational Research Methods along with CARMA are now introducing a new Virtual Feature Topic targeted at advanced reviewer development.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"24 1","pages":"675 - 677"},"PeriodicalIF":9.5,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49065474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

The Power, Accuracy, and Precision of the Relational Event Model 关系事件模型的功能、准确性和精确性

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2021-10-01 DOI: 10.1177/1094428120963830

Aaron Schecter, E. Quintane

{"title":"The Power, Accuracy, and Precision of the Relational Event Model","authors":"Aaron Schecter, E. Quintane","doi":"10.1177/1094428120963830","DOIUrl":"https://doi.org/10.1177/1094428120963830","url":null,"abstract":"The relational event model (REM) solves a problem for organizational researchers who have access to sequences of time-stamped interactions. It enables them to estimate statistical models without collapsing the data into cross-sectional panels, which removes timing and sequence information. However, there is little guidance in the extant literature regarding issues that may affect REM’s power, precision, and accuracy: How many events or actors are needed? How large should the risk set be? How should statistics be scaled? To gain insights into these issues, we conduct a series of experiments using simulated sequences of relational events under different conditions and using different sampling and scaling strategies. We also provide an empirical example using email communications in a real-life context. Our results indicate that, in most cases, the power and precision levels of REMs are good, making it a strong explanatory model. However, REM suffers from issues of accuracy that can be severe in certain cases, making it a poor predictive model. We provide a set of practical recommendations to guide researchers’ use of REMs in organizational research.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"24 1","pages":"802 - 829"},"PeriodicalIF":9.5,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1094428120963830","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46952812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Recommendations for Reviewing Meta-Analyses in Organizational Research 组织研究中回顾元分析的建议

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2021-10-01 DOI: 10.1177/1094428120967089

J. DeSimone, M. Brannick, Ernest H. O’Boyle, J. Ryu

引用次数: 18

Applying Neuroscience to Emergent Processes in Teams 将神经科学应用于团队中的紧急过程

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2021-07-01 DOI: 10.1177/1094428120915516

Danni Wang, D. Waldman, Pierre A. Balthazard, Maja Stikic, Nicola M. Pless, Thomas Maak, C. Berka, Travis Richardson

引用次数: 2

Scoring Dimension-Level Job Performance From Narrative Comments: Validity and Generalizability When Using Natural Language Processing 从叙述性评论中评分维度水平的工作表现:使用自然语言处理时的有效性和概括性

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2021-07-01 DOI: 10.1177/1094428120930815

Andrew B. Speer

{"title":"Scoring Dimension-Level Job Performance From Narrative Comments: Validity and Generalizability When Using Natural Language Processing","authors":"Andrew B. Speer","doi":"10.1177/1094428120930815","DOIUrl":"https://doi.org/10.1177/1094428120930815","url":null,"abstract":"Performance appraisal narratives are qualitative descriptions of employee job performance. This data source has seen increased research attention due to the ability to efficiently derive insights using natural language processing (NLP). The current study details the development of NLP scoring for performance dimensions from narrative text and then investigates validity and generalizability evidence for those scores. Specifically, narrative valence scores were created to measure a priori performance dimensions. These scores were derived using bag of words and word embedding features and then modeled using modern prediction algorithms. Construct validity evidence was investigated across three samples, revealing that the scores converged with independent human ratings of the text, aligned numerical performance ratings made during the appraisal, and demonstrated some degree of discriminant validity. However, construct validity evidence differed based on which NLP algorithm was used to derive scores. In addition, valence scores generalized to both downward and upward rating contexts. Finally, the performance valence algorithms generalized better in contexts where the same qualitative survey design was used compared with contexts where different instructions were given to elicit narrative text.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"24 1","pages":"572 - 594"},"PeriodicalIF":9.5,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1094428120930815","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42014110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Corrigendum to From Nuisance to Novel Research Questions: Using Multilevel Models to Predict Heterogeneous Variances 从麻烦到新颖的研究问题:使用多水平模型预测异质方差的勘误表

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2021-07-01 DOI: 10.1177/10944281211011778

引用次数: 0