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Article

Structural Equation Modelling  

Wayne Crawford and Esther Lamarre Jean

Structural equation modelling (SEM) is a family of models where multivariate techniques are used to examine simultaneously complex relationships among variables. The goal of SEM is to evaluate the extent to which proposed relationships reflect the actual pattern of relationships present in the data. SEM users employ specialized software to develop a model, which then generates a model-implied covariance matrix. The model-implied covariance matrix is based on the user-defined theoretical model and represents the user’s beliefs about relationships among the variables. Guided by the user’s predefined constraints, SEM software employs a combination of factor analysis and regression to generate a set of parameters (often through maximum likelihood [ML] estimation) to create the model-implied covariance matrix, which represents the relationships between variables included in the model. Structural equation modelling capitalizes on the benefits of both factor analysis and path analytic techniques to address complex research questions. Structural equation modelling consists of six basic steps: model specification; identification; estimation; evaluation of model fit; model modification; and reporting of results. Conducting SEM analyses requires certain data considerations as data-related problems are often the reason for software failures. These considerations include sample size, data screening for multivariate normality, examining outliers and multicollinearity, and assessing missing data. Furthermore, three notable issues SEM users might encounter include common method variance, subjectivity and transparency, and alternative model testing. First, analyzing common method variance includes recognition of three types of variance: common variance (variance shared with the factor); specific variance (reliable variance not explained by common factors); and error variance (unreliable and inexplicable variation in the variable). Second, SEM still lacks clear guidelines for the modelling process which threatens replicability. Decisions are often subjective and based on the researcher’s preferences and knowledge of what is most appropriate for achieving the best overall model. Finally, reporting alternatives to the hypothesized model is another issue that SEM users should consider when analyzing structural equation models. When testing a hypothesized model, SEM users should consider alternative (nested) models derived from constraining or eliminating one or more paths in the hypothesized model. Alternative models offer several benefits; however, they should be driven and supported by existing theory. It is important for the researcher to clearly report and provide findings on the alternative model(s) tested. Common model-specific issues are often experienced by users of SEM. Heywood cases, nonidentification, and nonpositive definite matrices are among the most common issues. Heywood cases arise when negative variances or squared multiple correlations greater than 1.0 are found in the results. The researcher could resolve this by considering a small plausible value that could be used to constrain the residual. Non-positive definite matrices result from linear dependencies and/or correlations greater than 1.0. To address this, researchers can attempt to ensure all indicator variables are independent, inspect output manually for negative residual variances, evaluate if sample size is appropriate, or re-specify the proposed model. When used properly, structural equation modelling is a powerful tool that allows for the simultaneous testing of complex models.

Article

Intersectionality Theory and Practice  

Doyin Atewologun

Intersectionality is a critical framework that provides us with the mindset and language for examining interconnections and interdependencies between social categories and systems. Intersectionality is relevant for researchers and for practitioners because it enhances analytical sophistication and offers theoretical explanations of the ways in which heterogeneous members of specific groups (such as women) might experience the workplace differently depending on their ethnicity, sexual orientation, and/or class and other social locations. Sensitivity to such differences enhances insight into issues of social justice and inequality in organizations and other institutions, thus maximizing the chance of social change. The concept of intersectional locations emerged from the racialized experiences of minority ethnic women in the United States. Intersectional thinking has gained increased prominence in business and management studies, particularly in critical organization studies. A predominant focus in this field is on individual subjectivities at intersectional locations (such as examining the occupational identities of minority ethnic women). This emphasis on individuals’ experiences and within-group differences has been described variously as “content specialization” or an “intracategorical approach.” An alternate focus in business and management studies is on highlighting systematic dynamics of power. This encompasses a focus on “systemic intersectionality” and an “intercategorical approach.” Here, scholars examine multiple between-group differences, charting shifting configurations of inequality along various dimensions. As a critical theory, intersectionality conceptualizes knowledge as situated, contextual, relational, and reflective of political and economic power. Intersectionality tends to be associated with qualitative research methods due to the central role of giving voice, elicited through focus groups, narrative interviews, action research, and observations. Intersectionality is also utilized as a methodological tool for conducting qualitative research, such as by researchers adopting an intersectional reflexivity mindset. Intersectionality is also increasingly associated with quantitative and statistical methods, which contribute to intersectionality by helping us understand and interpret the individual, combined (additive or multiplicative) effects of various categories (privileged and disadvantaged) in a given context. Future considerations for intersectionality theory and practice include managing its broad applicability while attending to its sociopolitical and emancipatory aims, and theoretically advancing understanding of the simultaneous forces of privilege and penalty in the workplace.

Article

Experience Sampling Methodology  

Joel Koopman and Nikolaos Dimotakis

Experience sampling is a method aimed primarily at examining within-individual covariation of transient phenomena utilizing repeated measures. It can be applied to test nuanced predictions of extant theories and can provide insights that are otherwise difficult to obtain. It does so by examining the phenomena of interest close to where they occur and thus avoiding issues with recall and similar concerns. Data collected through the experience sampling method (ESM) can, alternatively, be utilized to collect highly reliable data to investigate between-individual phenomena. A number of decisions need to be made when designing an ESM study. Study duration and intensity (that is, total days of measurement and total assessments per day) represent a tradeoff between data richness and participant fatigue that needs to be carefully weighed. Other scheduling options need to be considered, such as triggered versus scheduled surveys. Researchers also need to be aware of the generally high potential cost of this approach, as well as the monetary and nonmonetary resources required. The intensity of this method also requires special consideration of the sample and the context. Proper screening is invaluable; ensuring that participants and their context is applicable and appropriate to the design is an important first step. The next step is ensuring that the surveys are planned in a compatible way to the sample, and that the surveys are designed to appropriately and rigorously collect data that can be used to accomplish the aims of the study at hand. Furthermore, ESM data typically requires proper consideration in regards to how the data will be analyzed and how results will be interpreted. Proper attention to analytic approaches (typically multilevel) is required. Finally, when interpreting results from ESM data, one must not forget that these effects typically represent processes that occur continuously across individuals’ working lives—effect sizes thus need to be considered with this in mind.

Article

Organizational Neuroscience  

Sebastiano Massaro and Dorotea Baljević

Organizational neuroscience—a novel scholarly domain using neuroscience to inform management and organizational research, and vice versa—is flourishing. Still missing, however, is a comprehensive coverage of organizational neuroscience as a self-standing scientific field. A foundational account of the potential that neuroscience holds to advance management and organizational research is currently a gap. The gap can be addressed with a review of the main methods, systematizing the existing scholarly literature in the field including entrepreneurship, strategic management, and organizational behavior, among others.

Article

Qualitative Designs and Methodologies for Business, Management, and Organizational Research  

Robert P. Gephart and Rohny Saylors

Qualitative research designs provide future-oriented plans for undertaking research. Designs should describe how to effectively address and answer a specific research question using qualitative data and qualitative analysis techniques. Designs connect research objectives to observations, data, methods, interpretations, and research outcomes. Qualitative research designs focus initially on collecting data to provide a naturalistic view of social phenomena and understand the meaning the social world holds from the point of view of social actors in real settings. The outcomes of qualitative research designs are situated narratives of peoples’ activities in real settings, reasoned explanations of behavior, discoveries of new phenomena, and creating and testing of theories. A three-level framework can be used to describe the layers of qualitative research design and conceptualize its multifaceted nature. Note, however, that qualitative research is a flexible and not fixed process, unlike conventional positivist research designs that are unchanged after data collection commences. Flexibility provides qualitative research with the capacity to alter foci during the research process and make new and emerging discoveries. The first or methods layer of the research design process uses social science methods to rigorously describe organizational phenomena and provide evidence that is useful for explaining phenomena and developing theory. Description is done using empirical research methods for data collection including case studies, interviews, participant observation, ethnography, and collection of texts, records, and documents. The second or methodological layer of research design offers three formal logical strategies to analyze data and address research questions: (a) induction to answer descriptive “what” questions; (b) deduction and hypothesis testing to address theory oriented “why” questions; and (c) abduction to understand questions about what, how, and why phenomena occur. The third or social science paradigm layer of research design is formed by broad social science traditions and approaches that reflect distinct theoretical epistemologies—theories of knowledge—and diverse empirical research practices. These perspectives include positivism, interpretive induction, and interpretive abduction (interpretive science). There are also scholarly research perspectives that reflect on and challenge or seek to change management thinking and practice, rather than producing rigorous empirical research or evidence based findings. These perspectives include critical research, postmodern research, and organization development. Three additional issues are important to future qualitative research designs. First, there is renewed interest in the value of covert research undertaken without the informed consent of participants. Second, there is an ongoing discussion of the best style to use for reporting qualitative research. Third, there are new ways to integrate qualitative and quantitative data. These are needed to better address the interplay of qualitative and quantitative phenomena that are both found in everyday discourse, a phenomenon that has been overlooked.