Structural Equation Modeling or Popularly known as SEM is a second generation statistical techniques developed for analyzing the inter-relationships among multiple variables in a model. The inter-relationships among variables could be expressed in a series of single ad multiple regression equations. The Structural Equation Modeling technique employs the combination of quantitative data with the correlational and casual assumptions into the model.
SEM is a more powerful statistical technique to solve the following requirements:
- Running the Confirmatory Factor Analysis (CFA)
- Analyzing multiple regression models simultaneously.
- Analyzing regressions with multi-collinearity problem.
- Analyzing the path analysis with multiple dependents.
- Estimating the correlation and co-variance in a model.
- Modeling the inter-relationships in a model simultaneously.
1.1 The Concept of SEM and How it Works
SEM begins with a theory where the researcher intends to test the relationship among constructs of interest in the study, The relationships are modeled into a theoretical framework represented a schematic diagram. The schematic diagram presents the hypotheses of interest to be tested in the study. The constructs of interest involved are measured using a set of items in a questionnaire. The measurement scale for each item should be either interval or ratio. The ideal measurement scale is an interval ranging from 1 to 10 so the the data measure is more independence and thus meet the requirement for parametric analysis.
Throughout the chapter, the readers would find the term variable and construct are used interchangeably. A variable is meant for the directly measured score such as age, ecam score, income etc, while the construct is meant for an indirectly measured score such as Job Satisfaction, Perceived Usefulness, and Loyalty Intensions. In fact the construct is a hypothetical concept of something, or the respondents’ perception concerning certain issue. A construct is measured through a set of items in a questionnaire.
1.2 The Advantages of SEM Compared to OLS
Structural Equation Modeling or SEM is capable of estimate a series of inter-relationship among talent constructs simultaneously in a model. SEM is the most efficient method for Confirmatory Factors Analysis (CFA) to validate latent constructs and analyze the casual paths among these constructs in a structural model. SEM could also estimate the variance and covariance between constructs; and more importantly SEM could be employed to test the hypotheses for mediators and moderators in a model.
As has been said earlier, latent costructs could not be measured directly since it is only a hypothetical concepts of something. Thus, the researcher could not model them using the Ordinary Least Squares (OLS) regression. The examples of latent constructs measured through a set of items are in a questionnaire are:
- Service Quality
- Customer Satisfaction
- Job Satisfaction
- Corporate Image
- Product Image
- Customer Loyalty
- Purchase Intention
- Consumer Behavior
- Employee Soft Skills
- Perceived usefulness
- Relational Bond
- Financial Bond
- Structural Bond
- Relationship Quality
- Attitudinal Loyalty
- Behavioural Loyalty
Those constructs cannot be measured directly like counting the number of kids in a family, total income of a household, monthly phone bills, daily production, weekly price of chicken, etc. The variable which could be measured directly is called the observed variable, while the variable which could not be measured directly is called latent construct. These latent constructs could only be measured indirectly using a set of items in a questionnaire.
Source : SEM Made Simple