Categorical Predictor Variables in Process using SmartPLS4

TL;DR

Use SmartPLS4’s PROCESS macro to build mediation models with multiple independent and dependent variables, including categorical predictors.

Briefing Cornell Notes

Briefing

SmartPLS4’s PROCESS macro can handle categorical predictor variables directly in a mediation-style model, using bootstrapped path significance to test whether group differences translate into statistically meaningful effects. In the example, gender (coded 0 = male, 1 = female) is tested as a predictor of organizational learning, with organizational learning also linked to organizational performance as a mediator. After running bias-corrected and accelerated (BCa) bootstrapping with 5,000 resamples and two-tailed tests, the gender → organizational learning path is not significant, meaning the observed difference between males and females does not reach statistical significance.

The output also uses the sign of the path coefficient to indicate direction for categorical contrasts. A negative sign on the gender-related coefficient corresponds to the lower-coded category (male) showing higher organizational learning than the higher-coded category (female). Even with that directional pattern, the difference remains statistically non-significant. The same logic applies to the gender → organizational performance comparison: the coefficient is positive (suggesting females rate organizational performance higher than males), but the difference is still not significant.

Mediation is then evaluated by checking whether organizational learning carries the effect of gender onto organizational performance. The mediation result is also non-significant, so organizational learning does not mediate the gender–organizational performance relationship in this setup.

The session then expands the approach to other categorical predictors and highlights when recoding is or isn’t needed. For job rank, treated as ordinal, there is no requirement to convert it into dummy variables; the model assumes that increases in job rank correspond to a monotonic change in organizational learning and organizational performance. For a nominal categorical variable with three or more unordered categories—type of bank (public vs private, with the example also referencing additional categories)—the workflow requires dummy coding. In the demonstration, type of bank is added to the PROCESS model and bootstrapped again.

Results for type of bank show an insignificant effect on organizational learning and on organizational performance. The coefficient sign is interpreted through the coding scheme: public banks are assigned a value of zero, so a negative sign indicates that employees in public sector banks perceive organizational learning (and organizational performance) as higher than employees in private sector banks. Yet, as with gender, the differences are not statistically significant. Overall, the example shows how to run a PROCESS model with categorical predictors in SmartPLS4, interpret coefficient signs as category contrasts, and rely on bootstrapped significance tests to determine whether group-based differences and mediation effects are supported.

Cornell Notes

SmartPLS4’s PROCESS macro can test mediation models with categorical predictors by bootstrapping path coefficients for significance. In the example, gender (0 = male, 1 = female) predicts organizational learning and organizational performance, with organizational learning positioned as a mediator. Bootstrapping (5,000 BCa resamples, two-tailed) finds no significant gender effects on organizational learning or organizational performance, and organizational learning does not mediate the gender–performance link. The sign of categorical coefficients indicates which coded group scores higher (negative aligns with the lower-coded category). The workflow also distinguishes ordinal predictors (no dummy coding needed) from nominal multi-category predictors (dummy coding required), then applies the same bootstrapped significance testing to type of bank.

How does SmartPLS4 interpret categorical predictor variables like gender in a PROCESS model?

Categorical predictors are interpreted through contrasts between coded categories. With gender coded 0 (male) and 1 (female), the sign of the path coefficient indicates direction: a negative sign corresponds to the lower-coded category (male) showing higher values on the outcome (e.g., organizational learning) than the higher-coded category (female). A positive sign would indicate the higher-coded category (female) scores higher. In the example, both the gender → organizational learning and gender → organizational performance paths are not significant, so direction alone cannot be treated as evidence of a real group difference.

What bootstrapping settings are used to test whether paths are statistically significant?

The model uses bootstrapping with 5,000 resamples, applying bias-corrected and accelerated (BCa) bootstrap and two-tailed testing. The goal is to determine whether the proposed paths (e.g., gender → organizational learning, organizational learning → organizational performance, and direct effects) are significant based on the bootstrapped results.

How is mediation assessed in this categorical predictor setup?

Mediation is evaluated by testing whether organizational learning carries the effect of gender onto organizational performance. After bootstrapping, the mediation effect is checked and found to be non-significant. That means organizational learning does not mediate the relationship between gender and organizational performance in this model.

Why doesn’t job rank require dummy coding in the example?

Job rank is treated as ordinal, so it can be modeled as an ordered predictor without converting it into dummy variables. The assumption is that higher job rank corresponds to a corresponding change in organizational learning and organizational performance, rather than separate, unordered group contrasts.

When does the example call for dummy variables for categorical predictors?

Dummy coding is required for nominal categorical variables with three or more unordered categories, because there is no inherent order to treat as a gradient. The session notes that type of bank is such a case (public/private and potentially additional unordered categories), so dummy variables are needed to represent category membership before running the PROCESS model and bootstrapping path significance.

How should the sign of coefficients for type of bank be interpreted given the coding scheme?

The example assigns public banks a value of zero. When a coefficient is negative, that sign is attributed to the public sector category (the lower-coded group). In practice, the negative sign indicates employees in public sector banks perceive organizational learning and organizational performance as higher than employees in private sector banks—but the differences are still statistically insignificant in the bootstrapped results.

Review Questions

In a binary categorical predictor coded 0 and 1, what does a negative path coefficient typically indicate about which group scores higher?
What conditions in the example lead to dummy coding versus leaving a predictor as-is?
Why can mediation fail even when a predictor shows a directional (positive or negative) coefficient?

Key Points

1
Use SmartPLS4’s PROCESS macro to build mediation models with multiple independent and dependent variables, including categorical predictors.
2
Run BCa bootstrapping (5,000 resamples in the example) with two-tailed tests to judge whether proposed paths are statistically significant.
3
Interpret the sign of categorical predictor coefficients as a category contrast: negative aligns with the lower-coded category and positive aligns with the higher-coded category.
4
Non-significant direct paths imply the predictor’s group difference on the outcome is not supported statistically, even if the coefficient direction suggests a pattern.
5
Mediation requires a significant indirect effect; organizational learning can fail to mediate even when it is theoretically positioned as the mediator.
6
Ordinal predictors like job rank can be modeled without dummy coding, while nominal multi-category predictors require dummy variables.
7
For nominal predictors, coding choices (e.g., public = 0) determine how coefficient signs map onto which group appears higher, but significance still depends on bootstrapped results.

Highlights

Gender (0 = male, 1 = female) shows no significant effect on organizational learning or organizational performance after BCa bootstrapping (5,000 resamples).

Organizational learning does not mediate the gender–organizational performance relationship in the tested model.

Coefficient signs for categorical variables indicate which coded group scores higher, but statistical significance—not direction—determines whether conclusions hold.

Ordinal job rank is handled without dummy coding, while nominal multi-category variables like type of bank require dummy variables.

Type of bank differences (public vs private coding with public = 0) are directionally interpretable via coefficient signs but remain statistically insignificant. 

Topics

Mentioned

SmartPLS4
BCa