A Practical Comparison of PLS-SEM and CB-SEM Advantages and Disadvantages

Directly to the table:

Dimension PLS-SEM (Variance/Prediction-Oriented) CB-SEM (Covariance/Validation-Oriented)
Core Objective Maximize R², explain and predict dependent variables Test overall model fit, validate theory
Sample Size Robust with small samples (30–100 is sufficient) Requires larger samples, generally ≥ 200
Data Distribution Non-parametric, does not assume multivariate normality Parametric method, requires approximate multivariate normality
Measurement Philosophy Composite model: indicators can be interchangeable Common-factor model: indicators should be highly correlated
Measurement Types Reflective, formative, and single-item are all acceptable Mainly reflective; formative requires special settings
Model Complexity Can handle 50+ constructs and a large number of indicators Fit may fail with many indicators, needs simplification
Fit Indices Traditional χ², CFI are not applicable; relies on SRMR, NFI, HTMT and other “approximate” indices, still evolving Provides mature fit indices: χ², CFI, TLI, RMSEA, SRMR, etc., can be tested globally
Structural Path Coefficients Usually slightly lower than CB; high statistical power, easy to achieve significance Absolute values of coefficients are larger, but power decreases with small samples
Predictive Power Assessment Built-in PLSpredict, CVPAT, can report out-of-sample prediction errors (RMSE/MAE) Traditional CB lacks built-in predictive indices, requires additional k-fold, etc.
Theoretical Stage Exploratory/Development Theory (early-stage) preferred Validation/Comparative Theory (mature-stage) preferred
Publishability of Results Recently accepted by mainstream journals, but must explain “why use PLS” Still regarded as the “gold standard”, especially for factor models
Common Misconceptions “Small sample + non-normality” is not the only reason; must emphasize prediction or theoretical development purpose Fit indices may be distorted with insufficient samples or severe non-normality

In summary:

If the research is in the theoretical development stage, has limited samples, or includes formative constructs, PLS-SEM should be prioritized; if the research aims to rigorously test the fit of mature theories, then CB-SEM is more appropriate.

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