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Published in: Journal of Business and Psychology 1/2022

10-03-2021 | Original Paper

Doubly Latent Multilevel Procedures for Organizational Assessment and Prediction

Authors: Alexandre J. S. Morin, Ann-Renée Blais, Léandre Alexis Chénard-Poirier

Published in: Journal of Business and Psychology | Issue 1/2022

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Abstract

Organizational research has a rich tradition of multilevel investigations anchored in a long-standing recognition that conclusions obtained at the individual level of analysis cannot be expected to generalize at other levels. Doubly latent multilevel methods, which provide the way to combine multilevel analyses and structural equation models into a single analytic framework, enrich this tradition. Yet, some critical technical considerations have yet to be systematically integrated in applied research. This article aims to introduce organizational researchers to the estimation of doubly latent multilevel models while taking into account: (a) The need to systematically assess the multilevel measurement structure of the constructs included in the study; (b) the various types of measurement errors that can be controlled for (rather than simply estimated) as part of these models; (c) the importance of relying on a clear understanding of contextual versus climate constructs given their role in centering decisions. These issues are illustrated by an investigation of the multilevel relations between psychological empowerment, psychological health, and turnover intentions among a large sample (N = 5875 employees nested within 49 work units) of Canadian Defence employees.
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Footnotes
1
This is exactly the case in a bifactor model where all items are directly used to define the global factor, and approximately the case in a higher-order model where the global factor reflects the variance shared among first-order factors, themselves reflecting the variance shared among subsets of indicators.
 
2
Following Morin et al. (2016), we also estimated alternative bifactor solutions including two correlated global factors representing participants’ global levels of psychological well-being (cognitive engagement, emotional engagement, physical engagement, morale) and distress (disengagement, emotional exhaustion, psychological distress). However, these alternative models resulted in a similar level of fit to the data than their counterparts including a single global factor, and in the estimation of two G-factors that were so highly correlated at L1 (r = −.838) and L2 (G-factor r = −.932) that they suggested conceptual overlap.
 
3
When testing the statistical significance of indirect effects, it is generally recommended to rely on bootstrap confidence intervals (MacKinnon, Lockwood, & Williams, 2004), which have not yet been implemented with DL-MLSEM models. Fortunately, an alternative approach relying on Monte Carlo confidence intervals has been proposed for DL-MLSEM models (Bauer, Preacher, & Gil, 2006; Preacher & Selig, 2012). Confidence intervals for all indirect effects were thus calculated with this approach (using 20,000 replications), using the online calculator proposed by Preacher and Selig (2012) and available at quantpsy.​org. Detailed instructions on the use of these calculator are provided by Preacher and Selig (2012) and on quantpsy.​org, and all of the required information is provided in the “MODEL RESULTS,” “TECH1,” and “TECH3” sections of the Mplus output.
 
4
Marsh et al. (2009) provided annotated syntax examples for the estimation of Manifest-Manifest, Manifest-Latent, Latent-Manifest, and Doubly Latent models which are easy to expand to the FS models.
 
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Metadata
Title
Doubly Latent Multilevel Procedures for Organizational Assessment and Prediction
Authors
Alexandre J. S. Morin
Ann-Renée Blais
Léandre Alexis Chénard-Poirier
Publication date
10-03-2021
Publisher
Springer US
Published in
Journal of Business and Psychology / Issue 1/2022
Print ISSN: 0889-3268
Electronic ISSN: 1573-353X
DOI
https://doi.org/10.1007/s10869-021-09736-5

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