Structural equation modeling with longitudinal data: Strategies for examining group differences and reciprocal relationships.

1994 ◽  
Vol 62 (3) ◽  
pp. 477-487 ◽  
Author(s):  
Albert D. Farrell
Keyword(s):  
Structural Equation Modeling ◽  
Longitudinal Data ◽  
Structural Equation ◽  
Equation Modeling ◽  
Group Differences ◽  
Reciprocal Relationships

New developments in latent variable panel analyses of longitudinal data

2007 ◽  
Vol 31 (4) ◽  
pp. 357-365 ◽  
Author(s):  
Todd D. Little ◽  
Kristopher J. Preacher ◽  
James P. Selig ◽  
Noel A. Card
Keyword(s):  
Structural Equation Modeling ◽  
Longitudinal Data ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Null Model ◽  
Factorial Invariance ◽  
Equation Modeling ◽  
New Developments ◽  
Mediated Effects

We review fundamental issues in one traditional structural equation modeling (SEM) approach to analyzing longitudinal data — cross-lagged panel designs. We then discuss a number of new developments in SEM that are applicable to analyzing panel designs. These issues include setting appropriate scales for latent variables, specifying an appropriate null model, evaluating factorial invariance in an appropriate manner, and examining both direct and indirect (mediated), effects in ways better suited for panel designs. We supplement each topic with discussion intended to enhance conceptual and statistical understanding.

Does Happiness in the Cyberspace Promote That in the Real-World?

2015 ◽  
Vol 11 (2) ◽  
pp. 33-44 ◽  
Author(s):  
Sen-Chi Yu ◽  
Chien Chou
Keyword(s):  
Structural Equation Modeling ◽  
Real World ◽  
Virtual World ◽  
Structural Equation ◽  
College Freshmen ◽  
Equation Modeling ◽  
Psychological States ◽  
Reciprocal Relationships ◽  
The Real ◽  
Causal Priority

To examine reciprocal relationships between “virtual world”-context cyberspace positive-psychological states (CPSs) and “real world”-context positive-psychological states (PSs), this study conducted a two-wave panel design with about two-semester interval on 251 Taiwan college freshmen and analyzed the data using cross-lagged structural equation modeling. The analytical results show that CPSs have causal priority over PSs, but not vise versa. Therefore, the cyberspace PSs of the former stage influenced the real-world PSs during the latter stage. These results indicate that college students tended to incorporate their cyberspace positive-psychological states into their “real world.” The authors have concluded that cyberspace positive-psychological states do not substitute for and, indeed, contribute to real-world states.

scholarly journals DOES ENVIRONMENTAL, SOCIAL AND GOVERNANCE PERFORMANCE INFLUENCE ECONOMIC PERFORMANCE?

2020 ◽  
Vol 21 (4) ◽  
pp. 1165-1184 ◽  
Author(s):  
Kemal Cek ◽  
Serife Eyupoglu
Keyword(s):  
Structural Equation Modeling ◽  
Linear Regression ◽  
Longitudinal Data ◽  
Economic Performance ◽  
Significant Relationship ◽  
Environmental Performance ◽  
Regression Models ◽  
Structural Equation ◽  
Equation Modeling ◽  
Company Performance

The purpose of this paper is to evaluate the influence of environmental, social and governance performance on the economic performance of the Standard & Poor’s 500 companies. Structural equation modeling and linear regression have been utilized to measure the overall and individual influence of environmental, social and governance (ESG) performance on economic performance using longitudinal data comprising the years from 2010 to 2015. The overall ESG model had a significant relationship on economic performance. Furthermore, the findings of this study show that social and governance performance significantly affects economic performance in all regression models. However, environmental performance failed to show a significant relationship. The research contributes to the literature by providing insights for investors, managers and employees about the influence of ESG performance on company performance.

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