Uday Arun Bhale The software applications AMOS (Analysis of Moment Structures) and SMART-PLS (Structural Equation Modeling by Means of Partial Least Squares) are used for structural equation modeling (SEM) and latent variable modeling. The decision between the two would be influenced by the exact study issue and data to be studied.

AMOS is a popular SEM application that is especially good for evaluating huge, complicated data sets. It contains an easy-to-use interface and a variety of capabilities, including the option to define numerous groups, missing data, and measurement models. AMOS also supports the estimate of several models and can deal with non-normal data.

In contrast, SMART-PLS is a customized SEM application that use partial least squares (PLS) estimation. PLS is a statistical strategy for evaluating big data sets having a large number of predictor factors and a limited number of observations. SMART-PLS is very beneficial for evaluating data from PLS path modeling, which is a sort of SEM used to examine correlations between latent variables.

In summary, AMOS is helpful for big, complicated data sets and has many characteristics, whereas SMART-PLS is useful for assessing data from PLS path modeling, especially when dealing with a large number of predictor variables and a limited number of observations.

AMOS (Analysis of Moment Structures) and SMART-PLS (Simultaneous Multi-group Analysis and Response-based PLS) are both software programs used for structural equation modeling (SEM). The choice between AMOS and SMART-PLS depends on the specific needs and goals of the research project.

AMOS is a general purpose SEM software that is widely used for analyzing complex relationships among multiple variables. It is particularly useful for testing measurement models, confirmatory factor analysis, and path analysis.

SMART-PLS, on the other hand, is a specialized SEM software that is specifically designed for PLS (Partial Least Squares) based SEM. It is particularly useful for analyzing relationships among latent variables in large datasets and for analyzing data with a high degree of multicollinearity.

If you are unsure which software to use, it is always a good idea to consult the literature and see which software is most commonly used in your field of research.

Apart from what Fatehmeh and Dinesh said, I believe the decision of using AMOS or SmartPLS should be made based on "technique" or "research requirement". AMOST and SmartPLS are just tools. The discussion at the following two links will help you understand the differences between CB-SEM and VB/PLS-SEM that are run in AMOS and SmartPLS respectively:

https://www.researchgate.net/post/What-is-the-difference-between-CB-SEM-and-PLS-SEM

https://www.smartpls.com/documentation/choosing-pls-sem/pls-sem-compared-with-cbsem/

An excerpt from the second link is here for a ready reference:

"In line with this notion, Hair et al. (2011; p. 144) recommend:

• If the goal is predicting key target constructs or identifying key 'driver' constructs, select PLS-SEM.
• If the goal is theory testing, theory confirmation, or comparison of alternative theories, select CB-SEM.
• If the research is exploratory or an extension of an existing structural theory, select PLS-SEM.”

Apart from what Ali, Fatemeh, and Dinesh said, I think AMOS is used when we have large and normal data, but SmartPLS is not sensitive to data normality and sample size.

It depends on your aim: if you want to test a model and show a model fit, AMOS is the best option. If your aim is exploratory study and theory development, PLS SEM is the best option.

Please be careful, small sample size is a myth in PLS SEM and researchers always should justify the minimum required sample size.

Please see the book of Hair et al. 2017.

Here are some links and literature recommendations:

• https://www.smartpls.com/documentation/choosing-pls-sem/more-insights-on-when-to-use-pls-sem-and-when-not
• https://www.smartpls.com/documentation/choosing-pls-sem/pls-sem-compared-with-cbsem
• https://www.smartpls.com/documentation/choosing-pls-sem/choosing-between-pls-sem-and-cb-sem

Value added to previous answers:

They are statistical techniques used to analyze relationships between variables. Here's a comparison of when to use each tool and their pros and cons:

AMOS (Analysis of Moment Structures):

When to use AMOS:

1. Complex models: AMOS is well-suited for analyzing complex structural equation models with multiple latent variables and observed indicators.

2. Confirmatory factor analysis (CFA): AMOS offers robust capabilities for CFA, allowing researchers to test and confirm the validity of measurement models.

3. Large sample sizes: AMOS is generally recommended for larger sample sizes as it relies on maximum likelihood estimation, which performs well with large datasets.

Pros of AMOS:

1. Rich graphical interface: AMOS provides a user-friendly graphical interface that allows users to specify, estimate, and evaluate models using a visual path diagram.

2. Advanced statistical techniques: AMOS offers a wide range of statistical techniques, including mediation, moderation, and multiple group analysis.

3. Integration with other statistical software: AMOS can be integrated with the IBM SPSS statistical software, making it convenient for users already familiar with SPSS.

Cons of AMOS:

1. Steeper learning curve: Compared to SMART-PLS, AMOS generally requires a steeper learning curve due to its more extensive set of features and graphical interface.

2. Assumptions and limitations: AMOS assumes multivariate normality and requires a sufficient sample size for accurate estimation. Violations of these assumptions may affect the validity of the results.

3. Computationally intensive: AMOS can be computationally intensive, especially with larger models and datasets, which may result in longer processing times.

SMART-PLS (Partial Least Squares):

When to use SMART-PLS:

1. Small sample sizes: SMART-PLS is particularly useful when dealing with smaller sample sizes, as it employs the partial least squares estimation method, which is more robust in such situations.

2. Predictive modeling: SMART-PLS is well-suited for predictive modeling, focusing on the relationships between latent variables and their indicators.

3. Exploratory research: SMART-PLS is often used in exploratory research settings, as it allows for model specification and testing with minimal assumptions.

Pros of SMART-PLS:

1. Simplicity and ease of use: SMART-PLS offers a user-friendly interface and requires less statistical background compared to AMOS, making it accessible to researchers with limited statistical expertise.

2. Robustness with small sample sizes: SMART-PLS employs a non-parametric bootstrapping technique, which is more robust with smaller sample sizes and less sensitive to distributional assumptions.

3. Flexibility in model building: SMART-PLS allows for a flexible approach to model building, enabling researchers to modify and refine models iteratively.

Cons of SMART-PLS:

1. Limited advanced statistical techniques: SMART-PLS is more focused on exploratory research and predictive modeling, with limited support for advanced statistical techniques such as mediation and moderation analysis.

2. Simplified treatment of measurement models: SMART-PLS treats measurement models more simplistically, which may not fully capture the nuances of complex measurement structures.

3. Lower statistical power: Due to its reliance on bootstrapping, SMART-PLS generally has lower statistical power compared to AMOS, especially with larger sample sizes.

Hope it helps.