Purpose and applicability
Can prediction-oriented segmentation identify a stable two-segment structure in the organizational-identification model?
No completed-result prerequisite is required for the first run.
Do not use it merely because it is available
Use PLS-POS 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
- organizational-identification-model-comparison.csv
- Observations
- 305
- Study type
- Deterministic synthetic teaching study
- Calculate route
- Calculate → PLS-POS in the shared MultiMod workspace
Open the data dictionary and variable-role reference
Model
Organizational Prestige (8 indicators), Organizational Identification (6), Affective Commitment (Joy) (4), and Affective Commitment (Love) (3), all reflective
Organizational Prestige → Organizational Identification; Organizational Identification → Affective Commitment (Joy) and Affective Commitment (Love)
Exact workflow
Download organizational-identification-model-comparison.csv and the prepared organizational-identification-multimod.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 305 observations. Return to the model or calculation workspace.
Confirm the saved model specification: Organizational Prestige (8 indicators), Organizational Identification (6), Affective Commitment (Joy) (4), and Affective Commitment (Love) (3), all reflective. Structural specification: Organizational Prestige → Organizational Identification; Organizational Identification → Affective Commitment (Joy) and Affective Commitment (Love).
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-POS in the shared MultiMod workspace.
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
- Evaluate the K = 2 PLS-POS candidate with 20 deterministic starts
- Review convergence and reproduced-optimum counts before interpreting a solution
- Do not lock the candidate because only one start reproduced the optimum and the saved stability requirement is two
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
- Candidate segmentation diagnostics
- Converged and stable-start counts
- Candidate information criteria
- Explicit stability blocker
Interpretation guidance
For PLS-POS, 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 qualified K = 2 teaching run completed but is intentionally not locked: 10 of 20 starts converged and only one reproduced the optimum, below the required two. QuickPLS correctly withholds locked-segment estimates.
Reporting guidance
Report the QuickPLS version, PLS-POS 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.