Organizational Research Methods

The current study examined the Type I error rates and power of several item response theory (IRT) item fit indices used in conjunction with the graded response model (GRM). Specifically, S — χ², χ^2*, and adjusted χ ² degrees of freedom ratios (χ²/dfs) were examined. Model misfit was introduced by manipulating item parameters and by using a different IRT model to generate item data. Results indicated lower than expected Type I error rates for S — χ² and χ ^2*. Adjusted χ²/dfs resulted in large Type I error rates when used with cross validation and very low Type I error rates when used without cross validation. χ^2* and adjusted χ ²/dfs without cross validation were the most powerful overall.

In this overview, the authors use the seven studies included in the feature topic as a platform to delineate three areas that latent class procedures are particularly useful for in advancing the field of organizational research. The first topic area focuses on dealing with the need to identify and understand unobserved subpopulations in organizational research. The second topic area focuses on recognizing the unobserved heterogeneity in measurement functioning. The third topic area focuses on addressing the challenges surrounding the existence of multiple longitudinal change (both quantitative and qualitative) patterns in organizational research. The authors conclude this overview by highlighting further thoughts on the ways that latent class procedures should be utilized to advance organizational research.

Faultline theory proposes that when the distributions of individuals’ attributes in groups are aligned, they create homogeneous subgroups, characterized by within-group similarities and between-group differences. As homogeneity increases, these differences are increasingly likely to acquire meaning to subgroup members and thus to influence behavior. Although the face validity of faultlines is appealing, empirical methods have been difficult. The most commonly used, Fau and FLS, have several limitations, for instance difficulty with integrating nominal, categorical, and continuous variables. This article proposes latent class cluster analysis (LCCA) as an additional analytical tool. After reviewing the literature involving interdependent attributes, the most common faultline measures are described and compared with LCCA. A study of faultlines in a large organization is presented. LCCA induces a five-class model of organizational faultlines. A comparison of work-related communication contacts indicates that subjects have more within-subgroup than between-subgroup contacts, supporting the criterion-related validity of the faultline solution.

The current study aims to explore the usefulness of a person-centered perspective to the study of workplace affective commitment (WAC). Five distinct profiles of employees were hypothesized based on their levels of WAC directed toward seven foci (organization, workgroup, supervisor, customers, job, work, and career). This study applied latent profile analyses and factor mixture analyses to a sample of 404 Canadian workers. The construct validity of the extracted latent profiles was verified by their associations with multiple predictors (gender, age, tenure, social relationships at work, workplace satisfaction, and organizational justice perceptions) and outcomes (in-role performance, organizational citizenship behaviors, and intent to quit). The analyses confirmed that a model with five latent profiles adequately represented the data: (a) highly committed toward all foci; (b) weakly committed toward all foci; (c) committed to their supervisor and moderately committed to the other foci; and (d) committed to their career and moderately uncommitted to the other foci; (e) committed mostly to their proximal work environment. These latent profiles present theoretically coherent patterns of associations with the predictors and outcomes, which suggests their adequate construct validity.

The Psychometric Latent Agreement Model (PLAM) is proposed for estimating the subpopulation membership of individuals (e.g., satisfactory performers vs. unsatisfactory performers) at discrete levels of multiple latent trait variables. A binary latent Type variable is introduced to take account of the possibility that, for a given set of observed variables, the latent group memberships of some individuals are indeterminate. The latent Type variable allows for separating individuals who can reliably be assigned to satisfactory versus unsatisfactory performers classes from those individuals whose ratings do not contain the necessary information to make the class assignment possible for a particular set of rating items. Agreements among discrete latent trait variables are also estimated. The PLAM was illustrated with two examples using real data on behavioral rating measures. One example involved ratings of two behavioral constructs by a single rater type, whereas the other involved ratings of one construct by three rater types. Implications were presented for using behavioral ratings to determine the subpopulation membership, such as qualified versus unqualified groupings in hiring decisions and pass versus fail groupings in performance evaluations.

In this article, the authors illustrate the use of mixed-model item response theory (MM-IRT) and explain its usefulness for analyzing organizational surveys. The authors begin by giving an overview of MM-IRT, focusing on both technical aspects and previous organizational applications. Guidance is provided on how researchers can use MM-IRT to check scoring assumptions, identify the influence of systematic responding that is unrelated to item content (i.e., response sets), and evaluate individual and group difference variables as predictors of class membership. After summarizing the current body of research using MM-IRT to address problems relevant to organizational researchers, the authors present an illustration of the use of MM-IRT with the Job Descriptive Index (JDI), focusing on the use of the ‘‘?’’ response option. Three classes emerged, one most likely to respond in the positive direction, one most likely to respond in the negative direction, and another most likely to use the ‘‘?’’ response. Trust in management, job tenure, age, race, and sex were considered as correlates of class membership. Results are discussed in terms of the applicability of MM-IRT and future research endeavors.

Traditional item response theory (IRT) measurement invariance approaches examine measurement equivalence (ME) between observed groups (e.g., race, gender, culture). By contrast, mixed-measurement item response theory (MM-IRT) ascertains ME among unobserved groups (i.e., latent classes [LC] of respondents distinguished by differences in scale use). Both approaches can be integrated by using the Mixed-Measurement Item Response Theory with Covariates (MM-IRT-C) model, in which covariates (i.e., observed characteristics) are modeled in conjunction with LCs, thereby elucidating if ME is attributable to observed and/or unobserved groupings. We first show how this technique can be used to ascertain ME over multiple observed characteristics (categorical and/or continuous) concomitantly, thereby advancing a more general approach to observed ME. Next, we illustrate how the full MM-IRT-C can be used to: (a) infer underlying latent measurement classes (LCs), (b) determine associations of LC membership with observed characteristics, and (c) determine if observed measurement nonequivalence occurs predominantly within a particular latent measurement class. This method is demonstrated using a measure of union citizenship behavior, with years of work experience and gender as covariates. The proposed framework extends organizational ME research from considering a single question (i.e., Is there ME between categorical observed groups?) to addressing eight, separate questions about observed and unobserved ME. The substantive and methodological contributions of this model for rethinking ME and its use in organizational research are discussed.

Dimensional approaches assume that all individuals within hierarchical units (e.g., organizations or countries) share the same measurement model. However, such models are less applicable when researchers are interested in obtaining classes of individuals who share the same measurement model across hierarchical units and to obtain hierarchical latent classes (LCs). The authors present the multilevel mixed-measurement item response theory (MMM-IRT) model as an alternative. This model yields classes of individuals with a common measurement model that spans across hierarchical units. In addition, hierarchical units are classified together to the extent that they share similar proportions of individual-level classes. The authors illustrate the MMM-IRT model with data on self-reported emotions from 121,740 individuals across 116 countries where four individual classes and five country classes were found. Theoretical and methodological implications concerning cross-cultural, multilevel, and measurement equivalence research are discussed.

The success of open source software (OSS) projects depends heavily on the voluntary participation of a large number of developers. To remain sustainable, it is vital for an OSS project community to maintain a critical mass of core developers. Yet, only a small number of participants (identified here as ‘‘joiners’’) can successfully socialize themselves into the core developer group. Despite the importance of joiners’ socialization behavior, quantitative longitudinal research in this area is lacking. This exploratory study examines joiners’ temporal socialization trajectories and their impacts on joiners’ status progression. Guided by social resource theory and using the growth mixture modeling (GMM) approach to study 133 joiners in 40 OSS projects, the authors found that these joiners differed in both their initial levels and their growth trajectories of socialization and identified four distinct classes of joiner socialization behavior. They also found that these distinct latent classes of joiners varied in their status progression within their communities. The implications for research and practice are correspondingly discussed.