Advanced PLS analysis · Tutorial 28 of 41

Nonlinear relationships

Does attractiveness have a curved relationship with competence after accounting for the wider corporate-reputation model?

Supported in 2.62.8

Purpose and applicability

Does attractiveness have a curved relationship with competence after accounting for the wider corporate-reputation model?

No completed-result prerequisite is required for the first run.

Do not use it merely because it is available

Use Nonlinear relationships 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
corporate-reputation.csv
Observations
344
Study type
Deterministic synthetic teaching study
Calculate route
Calculate → Quadratic Nonlinear Effects

Open the data dictionary and variable-role reference

Model

Attractiveness, Competence, Corporate social responsibility, Customer satisfaction, Customer loyalty, Likeability, Performance, and Quality using the 31-indicator Corporate Reputation teaching model

The saved Corporate Reputation structural model plus the authored quadratic effect Attractiveness² → Competence

Exact workflow

  1. Download corporate-reputation.csv and the prepared corporate-reputation-nonlinear.qpls project. Keep both files in a writable study folder.

  2. Start QuickPLS, choose Open Project, and open the prepared project. Its saved model is already arranged and fitted.

  3. Open Data and confirm that the study contains 344 observations. Return to the model or calculation workspace.

  4. Confirm the saved model specification: Attractiveness, Competence, Corporate social responsibility, Customer satisfaction, Customer loyalty, Likeability, Performance, and Quality using the 31-indicator Corporate Reputation teaching model. Structural specification: The saved Corporate Reputation structural model plus the authored quadratic effect Attractiveness² → Competence.

  5. 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.

  6. Choose Calculate, then use Calculate → Quadratic Nonlinear Effects.

  7. Review the essential settings listed below. Open Advanced settings only when the design requires a non-default option.

  8. Resolve actionable blockers, review applicability warnings, and leave Open Results when finished selected.

  9. Choose Start calculation. Wait for Completed, then confirm that Results opens for this method.

  10. 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

  • Confirm Attractiveness → Competence is the focal authored quadratic relationship
  • Use percentile bootstrap inference with 999 refits and the documented fixed seed

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

  1. Linear and quadratic coefficients
  2. Incremental R-square
  3. Effect curve
  4. Slopes and turning point

Interpretation guidance

For Nonlinear relationships, 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.

Reporting guidance

Report the QuickPLS version, Nonlinear relationships 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.

Open calculation and Results troubleshooting

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.

Step 1: Imported teaching dataset is open
Step 3: The prepared project opens with its saved, neatly arranged model diagram
Step 6: Correct method and required settings are visible
Step 9: Primary Nonlinear relationships result is visible
Step 9: Second important Nonlinear relationships result is visible

Limitations and related methods

This tutorial verifies the documented workflow and outputs for its supplied teaching fixture. It does not establish numerical identity with another package, replace method literature, or guarantee that the method is suitable for a different study.

Related methods in this family