Abstract
Structural Equation Model (SEM) is a multivariate statistical technique that has been explored to test relationships between variables. The use of SEM to analyze relationship between variables is premised on the weak assumption of path analysis, regression analysis and so on; that variables are measured without error. This review thus sheds light on the meaning of SEM, its assumptions, steps and some of the terms used in SEM. The importance of item parcelling to SEM and its methods were briefly examined. It also dealt on the stages involved in SEM, similarities and differences between SEM and conventional statistical methods, software packages that can be used for SEM. This article employed systematic literature review method because it critically synthesized research studies and findings on structural equation modeling (SEM). It could be concluded that SEM is useful in analyzing a set of relationships between variables using diagrams. SEM can also be useful in minimizing measurement errors and in enhancing reliability of constructs. Based on this, it is recommended that SEM should be employed to test relationship between variables since it can explore complex relationships among variables such as direct, indirect, spurious, hierarchical and non-hierarchical.
Highlights
Structural equation modeling (SEM) is a collection of statistical techniques that allow a set of relationships between one or more independent variables and one or more dependent variables to be examined
Structural equation model (SEM) is a set of statistical techniques used for examining relationships between variables
The results indicated negative teacher feedback and effort feedback were both related to students’ relationships with their teachers, while ability feedback was associated with perceptions of the classroom environment
Summary
Structural equation modeling (SEM) is a collection of statistical techniques that allow a set of relationships between one or more independent variables and one or more dependent variables to be examined Such statistical techniques include path analysis, regression analysis, factor (confirmatory) analysis, analysis of variance, etc. Structural equation modeling is a general term that has been used to describe a large number of statistical methods employed to evaluate the validity of substantive theories with empirical data (Pui-Wa & Quiong, 2007) It represents an extension of general linear modeling (GLM) procedures such as the ANOVA and multiple regression analysis. These include: (i) SEM allows the measure of overall fit between the theory (as described in the path model) and the correlations among the scores in the sample through a fit index, (ii) SEM uses or models latent variables in its analysis and such latent variables (constructs) are measured through some fallible indicators that can be observed, (iii) In SEM, a variable (latent or manifest) can serve both as a dependent or an independent variable in a chain of causal hypotheses, (iv) SEM includes measurement model in its analysis which removes the biases due to errors of measurement, (v) Current development in SEM, which path analysis has not addressed, include the modeling of changes over time (growth models), modeling of latent classes or profiles, modeling of data having nested structures (such as multilevel model, multi-sample model, multitrait-multimethod model, etc.) as well as nonhierarchical model
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