scholarly journals The Importance of Isomorphism for Conclusions about Homology: A Bayesian Multilevel Structural Equation Modeling Approach with Ordinal Indicators

2016 ◽  
Vol 7 ◽  
Author(s):  
Nigel Guenole
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Multilevel Structural Equation Modeling ◽  
Equation Modeling ◽  
Modeling Approach ◽  
Multilevel Structural Equation

Meta-Accuracy and Perceived Reciprocity From the Perception-Meta-Perception Social Relations Model

2019 ◽  
Vol 50 (1) ◽  
pp. 24-37
Author(s):  
Ben Porter ◽  
Camilla S. Øverup ◽  
Julie A. Brunson ◽  
Paras D. Mehta
Keyword(s):  
Structural Equation Modeling ◽  
Social Relations ◽  
Latent Variables ◽  
Structural Equation ◽  
Multilevel Structural Equation Modeling ◽  
Equation Modeling ◽  
Complete Understanding ◽  
Social Relations Model ◽  
Flexible Framework ◽  
Multilevel Structural Equation

Abstract. Meta-accuracy and perceptions of reciprocity can be measured by covariances between latent variables in two social relations models examining perception and meta-perception. We propose a single unified model called the Perception-Meta-Perception Social Relations Model (PM-SRM). This model simultaneously estimates all possible parameters to provide a more complete understanding of the relationships between perception and meta-perception. We describe the components of the PM-SRM and present two pedagogical examples with code, openly available on https://osf.io/4ag5m . Using a new package in R (xxM), we estimated the model using multilevel structural equation modeling which provides an approachable and flexible framework for evaluating the PM-SRM. Further, we discuss possible expansions to the PM-SRM which can explore novel and exciting hypotheses.

scholarly journals Analyzing Organizational Growth in Repeated Cross-Sectional Designs Using Multilevel Structural Equation Modeling

Methodology ◽  
2017 ◽  
Vol 13 (3) ◽  
pp. 83-97 ◽  
Author(s):  
Jan Hochweber ◽  
Johannes Hartig
Keyword(s):  
Structural Equation Modeling ◽  
Growth Curve ◽  
Cross Sections ◽  
Structural Equation ◽  
Organizational Level ◽  
Multilevel Structural Equation Modeling ◽  
Equation Modeling ◽  
Organizational Growth ◽  
Multilevel Structural Equation ◽  
Level Variable

Abstract. In repeated cross-sections of organizations, different individuals are sampled from the same set of organizations at each time point of measurement. As a result, common longitudinal data analysis methods (e.g., latent growth curve models) cannot be applied in the usual way. In this contribution, a multilevel structural equation modeling approach to analyze data from repeated cross-sections is presented. Results from a simulation study are reported which aimed at obtaining guidelines on appropriate sample sizes. We focused on a situation where linear growth occurs at the organizational level, and organizational growth is predicted by a single organizational level variable. The power to identify an effect of this organizational level variable was moderately to strongly positively related to number of measurement occasions, number of groups, group size, intraclass correlation, effect size, and growth curve reliability. The Type I error rate was close to the nominal alpha level under all conditions.

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