SmartPLS 4 Tutorial for Beginners: PLS-SEM Step by Step
Learn PLS-SEM with SmartPLS 4: build your model, assess the measurement model (reliability and validity) and the structural model, then interpret the results for your thesis.
Data Académique
10/13/20264 min read
Has your thesis supervisor mentioned "structural equation modeling" and SmartPLS? This software has become essential in management, marketing and business research. This guide explains, without unnecessary jargon, what the PLS-SEM method is, when to use it and which indicators to check to validate your model.
Prefer step-by-step guidance? Our SmartPLS course takes you from building the model to interpreting the results, with replays and a certificate.
In short: PLS-SEM is a structural equation method that tests several relationships between latent variables measured by items at the same time. In SmartPLS, you first evaluate the measurement model (reliability, convergent and discriminant validity), then the structural model (path coefficients, significance through bootstrapping, R²).
What is PLS-SEM?
Structural equation modeling (SEM) lets you test several relationships between latent variables at once, that is, concepts that cannot be measured directly: satisfaction, trust, purchase intention, engagement… Each latent variable is measured by several questions in your questionnaire (the indicators or items).
The PLS (Partial Least Squares) method is a prediction-oriented approach to SEM. SmartPLS is the most widely used software to apply it.
When should you choose PLS-SEM rather than regression or AMOS?
Your model contains several relationships (for example A → B → C, with mediating effects).
Your main goal is to explain and predict a target variable.
Your sample is modest (often 100 to 300 respondents in theses), or your data is not normally distributed.
Your model contains formative constructs, which PLS handles easily.
If your goal is to confirm a well-established theory with a large sample, a CB-SEM approach (AMOS, lavaan) may be more suitable. Discuss it with your supervisor.
The two parts of a model
The measurement model (outer model): the links between each latent variable and its items.
The structural model (inner model): the links between latent variables, which correspond to your hypotheses.
The measurement model is always evaluated before the structural model.
Step 1: evaluate the measurement model
For reflective constructs (the most common case), the most cited benchmarks (Hair et al., 2022) are:
Loadings: each item measures its construct well. Threshold: ≥ 0.708 (between 0.40 and 0.70, to be discussed).
Cronbach's alpha: internal consistency. Threshold: ≥ 0.70.
Composite reliability (CR, rho_c): construct reliability. Threshold: between 0.70 and 0.95.
AVE: convergent validity. Threshold: ≥ 0.50.
HTMT: discriminant validity. Threshold: < 0.85 (or < 0.90 for similar concepts).
If an item has a low loading and removing it improves AVE or CR, it can be removed, with justification. For alpha, see also our article on Cronbach's alpha.
Step 2: evaluate the structural model
Collinearity: VIF values should ideally stay below 3 (5 at most).
Path coefficients: their sign and size show the direction and strength of each relationship.
Significance: run bootstrapping (5,000 subsamples is the usual recommendation). A relationship is significant if t > 1.96 or p < 0.05 (two-tailed test).
R²: the share of the target variable explained by the model. Often-cited benchmarks: 0.75 substantial, 0.50 moderate, 0.25 weak (to be put in perspective depending on the field).
f²: the effect size of each variable (0.02 small, 0.15 medium, 0.35 large).
Q² or PLSpredict: the model's predictive power.
The SmartPLS workflow in brief
Prepare your data file in CSV (one column per item, no missing values or with a defined code).
Create a project and import the data.
Draw the model: one latent variable per concept, link the items, draw the arrows of your hypotheses.
Run the PLS-SEM algorithm for the measurement model, R², f², VIF.
Run bootstrapping for the significance of paths and indirect effects (mediation).
Export the results and build clean tables for the thesis.
Common beginner mistakes
Going straight to the hypotheses without validating the measurement model.
Confusing reflective and formative constructs.
Claiming mediation without testing the indirect effect with bootstrapping.
Pasting software screenshots instead of formatted tables.
Forgetting to cite the methodological reference (Hair et al. for the thresholds).
The complete data analysis path
For a questionnaire-based thesis, the steps follow this order:
cross categorical variables with the chi-square test;
test the effect of several variables with linear regression;
for models with latent variables: SmartPLS and PLS-SEM.
Downloading SmartPLS
SmartPLS is commercial software published by SmartPLS GmbH and available on its official website (smartpls.com), which offers a trial version. Download it only from the official website. To learn to use it with your own data, see our online SmartPLS 4 course.
Official website: SmartPLS (download and documentation).
Frequently asked questions
What sample size do I need for SmartPLS?
The "10 times rule" (10 times the maximum number of arrows pointing at a construct) is well known but considered too simplistic. Authors rather recommend a power analysis or the inverse square root method. See also our article on sample size.
Is SmartPLS free?
There is a trial version and a student license. Check the current conditions on the publisher's website.
Do I need to know statistics to use SmartPLS?
The basics are enough to start, but interpretation requires method. That is the goal of our course.
What is the difference between SmartPLS and a regression in SPSS?
Regression tests the effect of observed variables on a single dependent variable. PLS-SEM tests a complete model with several latent variables, mediations and dependent variables at the same time.
Master SmartPLS for your thesis or dissertation
Our online SmartPLS course teaches you to build, validate and interpret your PLS-SEM model with your own data. Prefer support with the analysis? Discover our thesis support or message us on WhatsApp.
Reference: Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage.
