Online Article

Comparison between Multiple Regression and SEM

13th January 2023

Related topic: Quantitative research

Author: Ady Hameme N. A.

Multiple regression and structural equation modeling (SEM) are two popular statistical methods used in social sciences, psychology, and business research to analyze the relationships between multiple variables. Both methods are widely used to examine the relationships between independent variables and a dependent variable, but they have some key differences.

Multiple regression, as defined by Bollen (1989), is a statistical method used to examine the relationship between a dependent variable and one or more independent variables. The method estimates the relationship between the independent variables and the dependent variable by fitting a line of best fit through the data. Multiple regression is a powerful tool for analyzing relationships between variables, but it has some limitations. For example, it is not suitable for analyzing complex relationships between multiple variables.

SEM, on the other hand, is a more advanced method for analyzing relationships between multiple variables. According to Hair et al. (2010), SEM is a multivariate statistical method used to examine the relationships between multiple variables by testing a set of hypothesized relationships among the variables. SEM allows researchers to test multiple hypotheses simultaneously and to examine the relationships between latent variables, which are not directly observable. SEM is useful in situations where multiple regression is not sufficient, such as when analyzing complex relationships between multiple variables.

Kline (2015) points out that, SEM is particularly useful in testing complex models, such as those involving latent variables and measurement errors. SEM also allows researchers to test the measurement invariance of a measurement instrument across groups. Long and Ervin (2000) also found that SEM is particularly useful in situations where the researcher is interested in testing measurement models, such as confirmatory factor analysis (CFA) and exploratory factor analysis (EFA).

Tabachnick and Fidell (2013) note that multiple regression does not test causal relationships between variables, whereas SEM does. SEM allows researchers to test for causality by testing for the direction and strength of relationships between variables. Thelwall and Wilkinson (2018) found that SEM provides a more complete picture of relationships between variables than multiple regression, because it allows researchers to examine the relationships between latent variables and measurement errors.

In summary, multiple regression and SEM are two popular statistical methods used in social sciences, psychology, and business research to analyze the relationships between multiple variables. Both methods are widely used to examine the relationships between independent variables and a dependent variable, but they have some key differences. Multiple regression is a powerful tool for analyzing relationships between variables, but it has some limitations, particularly when analyzing complex relationships between multiple variables. SEM is a more advanced method that allows researchers to test multiple hypotheses simultaneously and to examine the relationships between latent variables and measurement errors. It is also useful in testing complex models and testing for causality.

Cite this article: Ady Hameme, N. A. (2023, January 13). Comparison between Multiple Regression and SEM. Retrieved <insert month> <insert date>, <insert year>, from https://www.myadvrc.com/publications/article-12

< previous next >