Use of Structural Equation Modeling in Social Science Research

Abstract

A researcher mostly needs some statistical technique for the interpretation of the data at hand. This choice depends on the nature of the data and the researcher's own understanding and preferences of the available techniques. Structural Equation Modeling (SEM) is one among those techniques. The purpose of the present study is to present some basic aspects this powerful interdependence technique with and analysis of the most common issues of SEM. This paper will present a case as to how SEM excels other statistical techniques. Literature reveals that SEM is one of the most favored statistical techniques among the social science researchers and has been found to be better than other multivariate techniques including multiple regression analysis in examining series of dependence relationships simultaneously. However, it has been felt that the use of SEM in social research is equal to naught. Side by side there hardly exists any published review that systematically describes and critique the use of SEM. The present research is an endeavor to fill that gap. The study contributes to literature on SEM specifically and provides more holistic view of SEM for researchers to use SEM more effectively.

Perceived Risk and Intentions to Practice Health Protective Behaviors in a Mining-Impacted Region

“…The variable groupings derived from the EFA were entered into a confirmatory factor analysis (CFA) to evaluate the measurement properties of the SEM [ 64 ]. The factors (variables) identified in the EFA were treated as latent variables in the CFA.…”

The current phase of social sciences research: A thematic overview of the literature

“…SEM is one of the most favored analytical techniques among SS researchers (Rahman et al, 2015). It is observed as better than multiple regression analyses and other multivariate methods in investigating simultaneously series of dependence relationships.…”

Perceived Risk and Intentions to Practice Health Protective Behaviors in a Mining-Impacted Region

“…The variable groupings derived from the EFA were entered into a confirmatory factor analysis (CFA) to evaluate the measurement properties of the SEM [64]. Each factor (variable) identified in the EFA was treated as its own latent variable in the CFA.…”