Purpose and applicability
How much of training quality's association with job performance operates through self-efficacy and training transfer?
Prerequisites
- Run PLS-SEM Algorithm first when starting from a newly authored or changed model
- Choose Create revision → Edit model before rerunning the supplied result-bearing project
Do not use it merely because it is available
Use Mediation only when its estimand, data roles and assumptions answer the stated research question. Choose a related estimator or diagnostic when the intended outcome is not among the documented outputs below.
Study preparation
- Teaching data
- digital-training-mediation.csv
- Observations
- 420
- Study type
- Deterministic synthetic teaching study
- Calculate route
- Calculate → PLS-SEM Bootstrapping
Open the data dictionary and variable-role reference
Model
Training Quality (training_quality_1–training_quality_4), Self-efficacy (self_efficacy_1–self_efficacy_4), Training Transfer (training_transfer_1–training_transfer_4), and Job Performance (job_performance_1–job_performance_4), all reflective
Training Quality → Self-efficacy → Training Transfer → Job Performance, plus Training Quality → Training Transfer, Training Quality → Job Performance, and Self-efficacy → Job Performance
Exact workflow
Download digital-training-mediation.csv and the prepared digital-training-mediation.qpls project. Keep both files in a writable study folder.
Start QuickPLS, choose Open Project, and open the prepared project. Its saved model is already arranged and fitted.
Open Data and confirm that the study contains 420 observations. Return to the model or calculation workspace.
Confirm the saved model specification: Training Quality (training_quality_1–training_quality_4), Self-efficacy (self_efficacy_1–self_efficacy_4), Training Transfer (training_transfer_1–training_transfer_4), and Job Performance (job_performance_1–job_performance_4), all reflective. Structural specification: Training Quality → Self-efficacy → Training Transfer → Job Performance, plus Training Quality → Training Transfer, Training Quality → Job Performance, and Self-efficacy → Job Performance.
Confirm that construct labels, indicators, and arrows are readable. The supplied project is saved after Arrange and Fit; do not rearrange it before the tutorial run.
Choose Calculate, then use Calculate → PLS-SEM Bootstrapping.
Review the essential settings listed below. Open Advanced settings only when the design requires a non-default option.
Resolve actionable blockers, review applicability warnings, and leave Open Results when finished selected.
Choose Start calculation. Wait for Completed, then confirm that Results opens for this method.
Inspect the named outputs below in order. Use Save Report, table Copy, or Export as required, then save and reopen the project to confirm the result remains available.
Essential and advanced settings
- Use 1,000 subsamples, a fixed seed, and two-tailed 95% intervals
Keep other advanced controls at their documented defaults unless the study design requires a justified change. Record every non-default value in the research log.
Results to inspect
- Specific indirect effects
- Aggregate indirect effects
- Direct effects
- Total effects
- Bootstrap confidence intervals
Interpretation guidance
For Mediation, interpret the listed outputs together with the prerequisite result, the selected settings, model assumptions and data quality. The supplied values are instructional; they are not validation against an external paper or another software package.
- The installed screenshot journey uses QuickPLS's valid 100-subsample minimum as bounded workflow evidence; use the 1,000-subsample tutorial setting or a larger design-appropriate value for substantive inference
Reporting guidance
Report the QuickPLS version, Mediation route, sample size, model or variable roles, preprocessing, essential settings, non-default advanced settings, and the named primary outputs. Retain the project, data checksum and exported table used in the manuscript.
Common mistakes and recovery
- Running the method before completing its prerequisite calculation.
- Changing the data, model or variable roles after the prerequisite result was saved.
- Treating an unavailable or not-applicable value as zero.
- Reporting an estimate without its method-appropriate uncertainty or diagnostic context.
Screen-by-screen evidence
Images shown here are mapped to this tutorial’s required installed-application evidence. Missing captures are labelled explicitly and are not replaced with generic screenshots.