let's know it!

What is Structural Equation Modeling?

A multivariate technique.

Combines factor analysis and multiple regression.

Examines the relationship between observed (manifest) variable and unobserved (latent) variable.

Examines the relationship between latent variables.

SEM seeks to explain the relationship among multiple variable

Some Examples of Analyses using SEM

Example 1

Example 2

Example 3

Source:

Nawanir, G., Lim, K. T., Othman, S. N., & Adeleke, A. Q. (2018). Developing and validating lean manufacturing constructs: an SEM approach. Benchmarking: An International Journal, 25(5), 1382-1405. doi:10.1108/BIJ-02-2017-0029. Click here.

1st & 2nd Generation Multivariate TECHNIQUES

First Generation Technique

Can only analyze one layer of linkages between independent and dependent variables at once.

Examples: Linear regression, LOGIT, ANOVA, and MANOVA, etc.

Second generation techniques

Can analyze the relationships among multiple variables simultaneously. This refers to Structural Equation Modeling (SEM).

Comparison

if you use SPSS, How many factor analyses & regressions NEEDED for the following model?

Analytical Trend

In line with this extensive use of multi-item measures, most papers (96%) reported at least some degree of measurement analysis, most typically using SEM techniques.

Confirmatory factor analysis (CFA) results were reported for 86.6% of the studies, and exploratory factor analysis (EFA) results for 22.8%.

For papers that do not focused on scale development, the use of both CFA and EFA is likely unnecessary. In general, the use of CFA is preferred.

Query String: ALL ("PLS-SEM") OR ("PLS-Path Modeling") OR ("partial least square structural equation modeling")

Google Sites

Report abuse