Organizational Research Methods最新文献

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Call for Papers for a Feature Topic: Having A Way with Words: Innovations and Improvements in Text Analysis Methods 征文专题:用文字表达:文本分析方法的创新与改进

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Organizational Research Methods

Pub Date : 2023-09-11 DOI: 10.1177/10944281231195704

Jason Kiley, Aaron McKenny, Jeremy Short, Anne Smith

引用次数: 0

A Constrained Factor Mixture Model for Detecting Careless Responses that is Simple to Implement 一种易于实现的检测粗心响应的约束因子混合模型

IF 9.5 2区管理学 Q1 MANAGEMENT

Organizational Research Methods

Pub Date : 2023-08-30 DOI: 10.1177/10944281231195298

C. Kam, S. Cheung

Using constrained factor mixture models (FMM) for careless response identification is still in its infancy. Existing models have overly restrictive statistical assumptions that do not identify all types of careless respondents. The current paper presents a novel constrained FMM model with more reasonable assumptions that capture both longstring and random careless respondents. We provide a comprehensive comparison of the statistical assumptions between the proposed model and two previous constrained models. The proposed model was evaluated using both real data ( N = 1,455) and statistical simulation. The results showed that the model had a superior fit, stronger convergent validity with other indicators of careless responding, more accurate parameter recovery and more accurate identification of careless respondents when compared to its predecessors. The proposed model does not require additional data collection effort, and thus researchers can routinely use it to control careless responses. We provide user-friendly syntax with detailed explanations online to facilitate its use.

使用约束因子混合模型（FMM）进行粗心反应识别仍处于起步阶段。现有模型的统计假设过于严格，无法识别出所有类型的粗心受访者。本文提出了一种新的约束FMM模型，该模型具有更合理的假设，既能捕捉到长期和随机粗心的受访者。我们对所提出的模型和之前的两个约束模型之间的统计假设进行了全面的比较。使用两个真实数据（N = 1455）和统计模拟。结果表明，与前人相比，该模型具有更好的拟合性，与其他粗心回答指标的收敛有效性更强，参数恢复更准确，对粗心回答者的识别更准确。所提出的模型不需要额外的数据收集工作，因此研究人员可以经常使用它来控制粗心的反应。我们在线提供用户友好的语法和详细的解释，以方便使用。

引用次数: 0

From Ties to Events in the Analysis of Interorganizational Exchange Relations. 从纽带到事件:组织间交流关系分析。

IF 9.5 2区管理学 Q1 MANAGEMENT

Organizational Research Methods

Pub Date : 2023-07-01 DOI: 10.1177/10944281211058469

Federica Bianchi, Alessandro Lomi

Relational event models expand the analytical possibilities of existing statistical models for interorganizational networks by: (i) making efficient use of information contained in the sequential ordering of observed events connecting sending and receiving units; (ii) accounting for the intensity of the relation between exchange partners, and (iii) distinguishing between short- and long-term network effects. We introduce a recently developed relational event model (REM) for the analysis of continuously observed interorganizational exchange relations. The combination of efficient sampling algorithms and sender-based stratification makes the models that we present particularly useful for the analysis of very large samples of relational event data generated by interaction among heterogeneous actors. We demonstrate the empirical value of event-oriented network models in two different settings for interorganizational exchange relations-that is, high-frequency overnight transactions among European banks and patient-sharing relations within a community of Italian hospitals. We focus on patterns of direct and generalized reciprocity while accounting for more complex forms of dependence present in the data. Empirical results suggest that distinguishing between degree- and intensity-based network effects, and between short- and long-term effects is crucial to our understanding of the dynamics of interorganizational dependence and exchange relations. We discuss the general implications of these results for the analysis of social interaction data routinely collected in organizational research to examine the evolutionary dynamics of social networks within and between organizations.

关系事件模型通过以下方式扩大现有组织间网络统计模型的分析可能性:(i)有效利用连接发送单位和接收单位的观察到的事件的顺序所包含的信息;(ii)考虑交换伙伴之间关系的强度，以及(iii)区分短期和长期网络效应。本文介绍了一种最新开发的关系事件模型(REM)，用于分析连续观察的组织间交换关系。有效的抽样算法和基于发送者的分层相结合，使得我们提出的模型对于分析由异质参与者之间的相互作用产生的关系事件数据的非常大的样本特别有用。我们在组织间交换关系的两种不同设置中展示了面向事件的网络模型的经验价值，即欧洲银行之间的高频隔夜交易和意大利医院社区内的患者共享关系。我们专注于直接和广义互惠的模式，同时考虑到数据中存在的更复杂的依赖形式。实证结果表明，区分基于程度和强度的网络效应，以及区分短期和长期效应，对于我们理解组织间依赖和交换关系的动态至关重要。我们讨论了这些结果对分析组织研究中常规收集的社会互动数据的一般含义，以检查组织内部和组织之间的社会网络的进化动态。

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引用次数: 7

A Mixture Model for Random Responding Behavior in Forced-Choice Noncognitive Assessment: Implication and Application in Organizational Research 强迫选择非认知评估中随机反应行为的混合模型及其在组织研究中的应用

IF 9.5 2区管理学 Q1 MANAGEMENT

Organizational Research Methods

Pub Date : 2023-06-27 DOI: 10.1177/10944281231181642

Siwei Peng, K. Man, B. Veldkamp, Yan Cai, Dongbo Tu

For various reasons, respondents to forced-choice assessments (typically used for noncognitive psychological constructs) may respond randomly to individual items due to indecision or globally due to disengagement. Thus, random responding is a complex source of measurement bias and threatens the reliability of forced-choice assessments, which are essential in high-stakes organizational testing scenarios, such as hiring decisions. The traditional measurement models rely heavily on nonrandom, construct-relevant responses to yield accurate parameter estimates. When survey data contain many random responses, fitting traditional models may deliver biased results, which could attenuate measurement reliability. This study presents a new forced-choice measure-based mixture item response theory model (called M-TCIR) for simultaneously modeling normal and random responses (distinguishing completely and incompletely random). The feasibility of the M-TCIR was investigated via two Monte Carlo simulation studies. In addition, one empirical dataset was analyzed to illustrate the applicability of the M-TCIR in practice. The results revealed that most model parameters were adequately recovered, and the M-TCIR was a viable alternative to model both aberrant and normal responses with high efficiency.

由于各种原因，被迫选择评估（通常用于非认知心理结构）的受访者可能会因犹豫不决而对个别项目做出随机反应，或因脱离而对全局做出反应。因此，随机回答是衡量偏差的复杂来源，并威胁到强制选择评估的可靠性，而强制选择评估在高风险的组织测试场景中至关重要，例如招聘决策。传统的测量模型在很大程度上依赖于非随机的、构造相关的响应来产生准确的参数估计。当调查数据包含许多随机响应时，拟合传统模型可能会产生有偏差的结果，这可能会削弱测量的可靠性。本研究提出了一种新的基于强迫选择测度的混合项目反应理论模型（称为M-TCIR），用于同时建模正常和随机反应（区分完全随机和不完全随机）。通过两次蒙特卡罗模拟研究，研究了M-TCIR的可行性。此外，还分析了一个经验数据集，以说明M-TCIR在实践中的适用性。结果表明，大多数模型参数都得到了充分恢复，M-TCIR是高效模拟异常和正常反应的可行替代方案。

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引用次数: 1

Demographic Inference in the Digital Age: Using Neural Networks to Assess Gender and Ethnicity at Scale 数字时代的人口推断:使用神经网络大规模评估性别和种族

IF 9.5 2区管理学 Q1 MANAGEMENT

Organizational Research Methods

Pub Date : 2023-06-14 DOI: 10.1177/10944281231175904

Amal Chekili, Ivan Hernandez

Gender and ethnicity are increasingly studied topics within I-O psychology, helpful for understanding the composition of collectives, experiences of marginalized group members, and differences in outcomes between demographics and capturing diversity at higher levels. However, the absence of explicit, structured, demographic information online makes applying these research questions to Big Data sources challenging. We highlight how deep neural networks can be used to infer demographics based on people's names, which are commonly found online (e.g., social media profiles, employee pages, and membership rosters), using broad international data to train and evaluate the effectiveness of these models and find that validity coefficients meet minimum reliability thresholds at the individual level ( rgender = .91, rethnicity = .80) highlighting their ability to contextualize and facilitate Big Data research. Using empirical data extracted from databases, websites, and mobile apps, we highlight how these models can be applied to large organizational data sets by presenting illustrative demonstrations of research questions that incorporate the information provided by the model. To promote broader usage, we offer an online application to infer demographics from names without requiring advanced programming knowledge.

性别和种族是io心理学中越来越多的研究主题，有助于理解集体的组成，边缘化群体成员的经历，以及人口统计学结果的差异，并在更高层次上捕捉多样性。然而，由于缺乏明确的、结构化的、在线的人口统计信息，使得将这些研究问题应用于大数据源具有挑战性。我们强调如何使用深度神经网络来根据人们的姓名推断人口统计数据，这些数据通常在网上发现(例如，社交媒体简介，员工页面和会员名单)，使用广泛的国际数据来训练和评估这些模型的有效性，并发现有效性系数满足个人层面的最小可靠性阈值(rgender =)。91，种族= .80)，突出了他们在背景化和促进大数据研究方面的能力。利用从数据库、网站和移动应用程序中提取的经验数据，我们通过展示包含模型提供的信息的研究问题的说明性演示，强调了这些模型如何应用于大型组织数据集。为了促进更广泛的使用，我们提供了一个在线应用程序，可以从名字中推断人口统计数据，而不需要高级编程知识。

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引用次数: 0

Heterogeneity in Meta-Analytic Effect Sizes: An Assessment of the Current State of the Literature 元分析效应大小的异质性：对文献现状的评估

IF 9.5 2区管理学 Q1 MANAGEMENT

Organizational Research Methods

Pub Date : 2023-05-19 DOI: 10.1177/10944281231169942

S. Kepes, Wenhao Wang, J. Cortina

Heterogeneity refers to the variability in effect sizes across different samples and is one of the major criteria to judge the importance and advancement of a scientific area. To determine how studies in the organizational sciences address heterogeneity, we conduct two studies. In study 1, we examine how meta-analytic studies conduct heterogeneity assessments and report and interpret the obtained results. To do so, we coded heterogeneity-related information from meta-analytic studies published in five leading journals. We found that most meta-analytic studies report several heterogeneity statistics. At the same time, however, there tends to be a lack of detail and thoroughness in the interpretation of these statistics. In study 2, we review how primary studies report heterogeneity-related results and conclusions from meta-analyses. We found that the quality of the reporting of heterogeneity-related information in primary studies tends to be poor and unrelated to the detail and thoroughness with which meta-analytic studies report and interpret the statistics. Based on our findings, we discuss implications for practice and provide recommendations for how heterogeneity assessments should be conducted and communicated in future research.

异质性是指不同样本间效应大小的可变性，是判断一个科学领域重要性和先进性的主要标准之一。为了确定组织科学研究如何处理异质性，我们进行了两项研究。在研究1中，我们研究了元分析研究如何进行异质性评估，并报告和解释所获得的结果。为此，我们对发表在五种主要期刊上的荟萃分析研究中的异质性相关信息进行了编码。我们发现大多数荟萃分析研究报告了一些异质性统计数据。然而，与此同时，对这些统计数字的解释往往缺乏细节和彻底性。在研究2中，我们回顾了原始研究如何报告异质性相关的结果和荟萃分析的结论。我们发现，在初级研究中，报告异质性相关信息的质量往往较差，与元分析研究报告和解释统计数据的细节和彻底性无关。基于我们的研究结果，我们讨论了对实践的影响，并就异质性评估应如何在未来的研究中进行和交流提供了建议。

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引用次数: 1

Assessing Common-Metric Effect Sizes to Refine Mediation Models 评估常用度量效应大小以改进中介模型

IF 9.5 2区管理学 Q1 MANAGEMENT

Organizational Research Methods

Pub Date : 2023-05-08 DOI: 10.1177/10944281231169943

Juan I. Sanchez, Chen Wang, A. Ponnapalli, Hock-Peng Sin, Le Xu, M. Lapeira, Mohan Song

Mediation analysis tests X → M → Y processes in which an independent variable ( X) exerts an indirect effect on a dependent variable ( Y) through its influence on an intervening or mediator variable ( M). A preponderance of mediation studies, however, focuses on determining solely whether mediation effects are statistically significant, instead of focusing on what the results tell us about potential theoretical refinements in the mediation model. We argue in favor of employing a set of three standardized effect sizes based on variance proportions that allow researchers to compare their results with those of other mediation studies employing similar combinations of X, M, and Y variables. These standardized effect sizes constitute a set of common metrics signaling potential gaps in a mediation model, and as such provide useful insights for the theoretical refinement of mediation models in organizational research. We illustrate the utility of comparing these common-metric effect sizes using the examples of abusive and transformational leadership effects on employee outcomes as transmitted by social exchange quality.

中介分析测试X→ M→ Y过程，其中自变量（X）通过对干预变量或中介变量（M）的影响对因变量（Y）施加间接影响。然而，大多数中介研究只关注于确定中介效果是否具有统计学意义，而不是关注结果告诉我们中介模型中潜在的理论改进。我们主张使用一组基于方差比例的三种标准化效应大小，使研究人员能够将他们的结果与使用X、M和Y变量类似组合的其他中介研究的结果进行比较。这些标准化的效应大小构成了一组共同的指标，表明中介模型中存在潜在的差距，因此为组织研究中中介模型的理论完善提供了有用的见解。我们通过社会交换质量传递的滥用和转型领导对员工结果的影响的例子，说明了比较这些常见度量效应大小的效用。

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引用次数: 0

Out of Shape: The Implications of (Extremely) Nonnormal Dependent Variables 变形:(极度)非正常因变量的含义

IF 9.5 2区管理学 Q1 MANAGEMENT

Organizational Research Methods

Pub Date : 2023-05-07 DOI: 10.1177/10944281231167839

S. Trevis Certo, Kristen Raney, Latifa Albader, John R. Busenbark

Organizational researchers have increasingly noted the problems associated with nonnormal dependent variable distributions. Most of this scholarship focuses on variables with positive values and lo...

组织研究人员越来越注意到与非正态因变量分布相关的问题。这些学术研究大多集中在具有正值和低值的变量上。

引用次数: 0

Team Composition Revisited: Expanding the Team Member Attribute Alignment Approach to Consider Patterns of More Than Two Attributes 重新审视团队组成:扩展团队成员属性对齐方法，以考虑两个以上属性的模式

IF 9.5 2区管理学 Q1 MANAGEMENT

Organizational Research Methods

Pub Date : 2023-05-03 DOI: 10.1177/10944281231166656

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

The attribute alignment approach to team composition allows researchers to assess variation in team member attributes, which occurs simultaneously within and across individual team members. This approach facilitates the development of theory testing the proposition that individual members are themselves complex systems comprised of multiple attributes and that the configuration of those attributes affects team-level processes and outcomes. Here, we expand this approach, originally developed for two attributes, by describing three ways researchers may capture the alignment of three or more team member attributes: (a) a geometric approach, (b) a physical approach accentuating ideal alignment, and (c) an algebraic approach accentuating the direction (as opposed to magnitude) of alignment. We also provide examples of the research questions each could answer and compare the methods empirically using a synthetic dataset assessing 100 teams of three to seven members across four attributes. Then, we provide a practical guide to selecting an appropriate method when considering team-member attribute patterns by answering several common questions regarding applying attribute alignment. Finally, we provide code ( https://github.com/kjem514/Attribute-Alignment-Code ) and apply this approach to a field data set in our appendices.

团队组成的属性比对方法使研究人员能够评估团队成员属性的变化，这种变化同时发生在单个团队成员内部和之间。这种方法有助于理论的发展，测试个人成员本身就是由多个属性组成的复杂系统，这些属性的配置会影响团队级别的过程和结果。在这里，我们扩展了这种最初针对两个属性开发的方法，通过描述研究人员可以捕捉三种或更多团队成员属性对齐的三种方式：（a）几何方法，（b）强调理想对齐的物理方法，以及（c）强调对齐方向（而不是大小）的代数方法。我们还提供了每个人都可以回答的研究问题的例子，并使用合成数据集对四个属性的100个由三到七名成员组成的团队进行了实证比较。然后，我们通过回答关于应用属性对齐的几个常见问题，提供了一个在考虑团队成员属性模式时选择适当方法的实用指南。最后，我们提供代码（https://github.com/kjem514/Attribute-Alignment-Code），并将此方法应用于我们附录中的现场数据集。

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引用次数: 0

Macro-iterativity: A Qualitative Multi-arc Design for Studying Complex Issues and Big Questions 宏观迭代性：研究复杂问题和大问题的定性多弧设计

IF 9.5 2区管理学 Q1 MANAGEMENT

Organizational Research Methods

Pub Date : 2023-04-17 DOI: 10.1177/10944281231166649

Christina Hoon, Alina M. Baluch

The impact and relevance of our discipline's research is determined by its ability to engage the big questions of the grand challenges we face today. Our central argument is that we need innovative methods that engage large-scope phenomena, not least because these phenomena benefit from going beyond individual study design. We introduce the concept of macro-iterativity which involves multiple iterations that move between, and link across, a set of research cycles. We offer a multi-arc research design that comprises the discovery arc and extension arc and three extension logics through which scholars can combine these arcs of inquiry in a coherent way. Based on this research design, we develop a roadmap that guides scholars through the four steps of how to engage in multi-arc research along with the main techniques and outputs. We argue that a multi-arc design supports the move toward more generative theorizing that is required for researching problems dealing with the complex issues and big questions of our time.

我们学科研究的影响力和相关性取决于它处理我们今天面临的重大挑战中的重大问题的能力。我们的核心论点是，我们需要创新的方法来处理大范围的现象，尤其是因为这些现象受益于超越个人学习设计。我们引入了宏观迭代性的概念，它涉及在一组研究周期之间移动和链接的多次迭代。我们提供了一个多弧研究设计，包括发现弧和扩展弧，以及三个扩展逻辑，通过这些逻辑，学者可以以连贯的方式将这些研究弧结合起来。基于这一研究设计，我们制定了一个路线图，指导学者完成如何进行多弧研究的四个步骤以及主要技术和产出。我们认为，多弧设计支持向更具生成性的理论化迈进，这是研究处理我们这个时代的复杂问题和重大问题所必需的。

引用次数: 1

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