What are the advantages and disadvantages of using PLS-SEM over CB-SEM in complex models?

One thing I've found helpful in deciding between CB-SEM and PLS-SEM is identifying whether the scholar aims to confirm or develop the theory. In the case of exploratory studies, we generally advocate PLS, and in the case of testing theories CB-SEM.

When testing for model fit, CB-SEM is the better because it generates fit indices such as CFI, TLI, RMSEA, SRMR among others. CB-SEM has comprehensive assumptions such as normality of the data whereas PLS-SEM doesnt and is focused on variance maximization which makes it prone to overfitting.

1. Covariance-Based (CB) Methods: - Assumptions: - Latent variables are continuous and normally distributed. - Measurement errors are uncorrelated. - Technique: Structural Equation Modeling (SEM) is a prominent CB method. - Use Cases: - Testing theories and hypotheses. - Assessing causal relationships. - Validating model fit. - Strengths: - Well-suited for theory-driven research. - Provides detailed insights into relationships. - Utilizes maximum likelihood estimation. - Limitations: - Assumes specific distributional properties. - May not handle non-normal data well.

Exploring the trade-offs between covariance-based (CB) and component-based (CB) methods enhances the strategic approach to multivariate analysis. CB methods, offering rigorous theoretical testing, are ideal when the research aim is to confirm hypotheses within a well-specified model under assumptions of normality and linearity. On the other hand, component-based methods like PLS adapt flexibly to complex data structures and are particularly useful in early stages of research or when predictive accuracy is prioritized over model fit. This flexibility makes PLS advantageous in applied research contexts where theoretical models are under development or the data deviates from normal distribution assumptions.

One of the biggest advantages of PLS over SEM is that pls works component wise. It is a great method for theory building while sem works great for theory testing.

Covariance-Based (CB) SEM: - Assumes continuous, normally distributed latent variables. - Requires uncorrelated measurement errors. - Well-suited for testing theories, assessing causal relationships, and validating model fit. - Utilizes maximum likelihood estimation. - May struggle with non-normal data and small sample sizes. Partial Least Squares (PLS) SEM: - Flexible regarding latent variable distributions and measurement errors. - Robust even with small sample sizes. - Handles non-normal, categorical, and ordinal data. - Accommodates higher-order constructs (both reflective and formative). - Supports formative measurement models. - Analyzes mediation and moderation using product indicators or interaction terms.

Emphasizing the predictive orientation of PLS-SEM offers a distinct advantage over CB-SEM, especially in applied research contexts where the goal is to forecast outcomes or understand the predictive power of variables within a model. PLS-SEM's ability to focus on prediction while handling complex models and data types makes it exceptionally useful for scenarios where practical applicability of the findings is paramount. This predictive focus, combined with its flexibility in model construction and data requirements, positions PLS-SEM as a powerful tool for researchers aiming to derive actionable insights from their analyses, bridging the gap between theoretical constructs and real-world applications.

A crucial limitation of PLS-SEM relative to CB-SEM is its focus on maximizing the explained variance of dependent constructs, which might lead to overfitting, particularly in exploratory stages or with complex models. This focus can overshadow the theoretical underpinning of relationships, potentially compromising the theoretical contribution of research. While PLS-SEM excels in prediction and handling complex data structures, its methodological approach may not rigorously validate the theoretical framework as CB-SEM does through its comprehensive fit indices and error measurement capabilities.

Considering a study seeking to predict students' performance as an example, by analyzing the interplay between learning styles, attitudes, motivation, socio-economic background, and teacher-student interactions is venturing into new territory. In this case, PLS-SEM would be the optimal choice since its predictive capabilities can unravel the intricate relationships between these variables. Conversely, if the objective is to assess the validity of an existing model of student performance, built upon established factors like IQ, discipline, and teaching quality, against a more diverse population and stringent distribution requirements, CB-SEM would be the better option. The choice ultimately hinges on the specific goals of the research.

Consider a market research study where PLS-SEM suits a small dataset with non-normal responses, ensuring accurate predictions. In contrast, CB-SEM becomes preferable in extensive surveys with normal distribution assumptions for detailed latent variable insights. Selecting between them hinges on dataset characteristics and research goals.

The choice between PLS-SEM and CB-SEM also benefits from considering the stage of the research field. In emerging fields or studies where the theoretical background is still developing, PLS-SEM's flexibility and exploratory nature can be particularly valuable for building and refining theoretical models. Conversely, in well-established fields where theories have been rigorously tested, CB-SEM's emphasis on model fit and hypothesis testing provides a robust framework for confirming or refuting existing theories. This nuanced approach ensures that the selection of the analytical method aligns with both the empirical data and the broader research context, facilitating more meaningful and impactful findings.