Why are results different in SmartPLS 4?


In some cases, SmartPLS 4 results may be different from those obtained by SmartPLS 3. This can have several reasons, which can be related to the data import, updated algorithms, or the settings used in SmartPLS 4 or a combination of these issues.

Data file not setup correctly

Make sure that your datafile is set up correctly in SmartPLS 4. If it is displayed in red color, please double-click to open its setup. Then check the settings for the delimiter, escape character and locale.

Data setup

Missing value not marked correctly

When you import a new data file or open a data file from an old SmartPLS 3 project, make sure that missing values are handled. In SmartPLS 3, a placeholder String (e.g. -99) was defined to indicate missing values in the dataset. In SmartPLS 4 you can use the data file setup dialog to apply such a marker. It will then replace all values in the dataset with an empty value.

Mark missing values

Missing value handled differently

You have missing data in your dataset but use no or a different missing data treatment option in SmartPLS 4. When running an algorithm, please make dure to use an identical missing value treatment procedure.

Missing value treatment

Indicator scales not set up correctly

SmartPLS 3 only considered metric data. But if you used a binary variable for two categories in SmartPLS 3 (e.g., gender), then the values 0 and 1 (or 1 and 2) were use as metric scale in SmartPLS 3. SmartPLS 4 allows you to assign the correct metric to the variable and corrects the computations accordingly.

Also, your model can become invalid, e.g. it is not possible to compute results for models that have binary and categorical in certain parts of the model (e.g. in reflective measurement models together with metric variables).

Scale setup
Mixed scales model

“Standardized” vs “Unstandardized” results

You do not select the option “Standardized” for “Type of results” in the dialog of the PLS-SEM algorithm but “Unstandardized” of “Mean centered”. Change this setting to “Standardized” as you used it automatically in SmartPLS 3 before.

Standardization options

Using binary predictors in your SmartPLS 4 model

In SmartPLS 3 every variable/construct was standardized - also for binary variables. However, the results of standardized binary predictors are hard to interpret and require manual unstandardization to make them interpretable. Thus, in SmartPLS 4 we keep binary variables/constructs undstandardized (e.g., in their original 0/1 form). Thus, the coefficients are now unstandardized coefficients that represent the effect of a category change (i.e., for the “1” category with “0” as a reference). If you want like to get the "old" SmartPLS 3 results you need to define your binary variable as "metric" in the data file setup.

Further details: Most wanted PLS-SEM guidelines: Becker, J.-M./ Cheah, J.H./ Gholamzade, R./ Ringle, C.M./ Sarstedt, M.: PLS-SEM’s Most Wanted Guidance, International Journal of Contemporary Hospitality Management, Volume 35 (2023), Issue 1, pp. 321-346.