QuickPLS Software comparison
QuickPLS vs cSEM
Build your model visually in QuickPLS, or specify it in R with cSEM. Compare the research workflow and see how closely the calculations agree on the same PLS-SEM example.
Same PLS model · Same 240 responses
All 29 results agree to 12 decimal places.
The example asks how quality and trust relate to satisfaction, and how satisfaction relates to loyalty. It contains four reflective constructs, each measured by three indicators. We calculated the same model using QuickPLS calculation code and cSEM in R.
All three paths, two R² values, 12 loadings and 12 weights pass the comparison. The largest difference before rounding is about 0.00000000000000122. The table contains all 29 results.
- Data
- 240 complete synthetic responses; 12 indicators
- Method
- Ordinary PLS-SEM; reflective Mode A; path weighting
- Calculation settings
- Standardised data; stopping criterion 0.0000001; up to 3,000 iterations
- What is compared
- Point estimates: fitted results before bootstrap intervals
Scroll sideways to see all comparison columns.
| Result | QuickPLS | cSEM | Absolute difference |
|---|---|---|---|
| Path coefficients | |||
| Quality → Satisfaction | 0.444621966846 | 0.444621966846 | 3.33e-16 |
| Trust → Satisfaction | 0.431073545328 | 0.431073545328 | 0 |
| Satisfaction → Loyalty | 0.646987729294 | 0.646987729294 | 2.22e-16 |
| Explained variance (R²) | |||
| Loyalty | 0.418593121857 | 0.418593121857 | 2.78e-16 |
| Satisfaction | 0.491363645949 | 0.491363645949 | 5.55e-17 |
| Indicator loadings | |||
| Quality 1 | 0.928119779049 | 0.928119779049 | 1.11e-16 |
| Quality 2 | 0.924467469555 | 0.924467469555 | 0 |
| Quality 3 | 0.931147493659 | 0.931147493659 | 1.11e-16 |
| Trust 1 | 0.923405902987 | 0.923405902987 | 2.22e-16 |
| Trust 2 | 0.919422648022 | 0.919422648022 | 3.33e-16 |
| Trust 3 | 0.908456627600 | 0.908456627600 | 1.22e-15 |
| Satisfaction 1 | 0.925546055773 | 0.925546055773 | 2.22e-16 |
| Satisfaction 2 | 0.937055323700 | 0.937055323700 | 2.22e-16 |
| Satisfaction 3 | 0.922492030794 | 0.922492030794 | 2.22e-16 |
| Loyalty 1 | 0.932122906029 | 0.932122906029 | 9.99e-16 |
| Loyalty 2 | 0.934764254150 | 0.934764254150 | 1.11e-16 |
| Loyalty 3 | 0.936854504836 | 0.936854504836 | 6.66e-16 |
| Indicator weights | |||
| Quality 1 | 0.363111944841 | 0.363111944841 | 1.67e-16 |
| Quality 2 | 0.345072027368 | 0.345072027368 | 1.11e-16 |
| Quality 3 | 0.369415973701 | 0.369415973701 | 3.89e-16 |
| Trust 1 | 0.366507482367 | 0.366507482367 | 4.44e-16 |
| Trust 2 | 0.352479857745 | 0.352479857745 | 1.67e-16 |
| Trust 3 | 0.371494744887 | 0.371494744887 | 5.55e-16 |
| Satisfaction 1 | 0.363315395356 | 0.363315395356 | 0 |
| Satisfaction 2 | 0.371486546266 | 0.371486546266 | 1.11e-16 |
| Satisfaction 3 | 0.342150839714 | 0.342150839714 | 2.22e-16 |
| Loyalty 1 | 0.359872593317 | 0.359872593317 | 2.78e-16 |
| Loyalty 2 | 0.353168721573 | 0.353168721573 | 4.44e-16 |
| Loyalty 3 | 0.356965797976 | 0.356965797976 | 3.89e-16 |
“Absolute difference” uses the saved values before display rounding. For example, 1.22e−15 means 0.00000000000000122. The accepted difference for each result was at most 0.00000001; every result is far below it.
Download this table (CSV) · Download the data, settings and R calculation
How this comparison was calculated
The calculation was saved on 6 November 2026 using cSEM 0.7.1 and the unmodified calculation code from QuickPLS v1.03.11. The QuickPLS calculation ran outside the desktop application. The downloadable pack contains the input data, model settings, both sets of answers and the comparison script.
The test covers point estimates. Bootstrap standard errors and confidence intervals are a separate analysis and are not included in these 29 results.
Further checks across 72 PLS configurations
An earlier comparison varied indicator modes, weighting schemes and the use of covariance or correlation matrices. It covered Mode A, Mode B and mixed measurement models, with path and factor weighting. All 2,160 recorded comparisons passed; the largest numerical difference was approximately 0.0000000000000434.
Read the wider PLS comparison ↗ · Read the separate consistent-PLS comparison
These are separate saved tests against cSEM 0.6.1. Their detailed pages identify the outputs checked and the definitions used.
Visual modelling or an R script?
Both support composite-based modelling. QuickPLS brings the model, calculation settings and result tables into one desktop project. cSEM suits researchers who want to specify and repeat their analyses in R.
Scroll sideways to see both applications.
| Task | QuickPLS Pro | cSEM |
|---|---|---|
| Model setup | Draw constructs, assign indicators and connect paths in the model workspace. Guide ↗ | Write the measurement and structural model in R model syntax. |
| PLS and consistent PLS | Run PLS-SEM and consistent PLS from the calculation menu. Guide ↗ | Estimate PLS and PLSc using csem(). |
| GSCA | Run GSCA and related component-model analyses in the same application. Guide ↗ | Includes GSCA and GSCAm among its weighting approaches. |
| Measurement assessment | Review loadings, reliability and discriminant validity in result tables. Guide ↗ | Use assessment and summary functions to examine measurement quality. |
| Bootstrap inference | Set resampling options and inspect intervals alongside the model results. Guide ↗ | Use resampling and inference functions in the R workflow. |
| Prediction and groups | Run prediction, MICOM and multigroup analyses from guided settings. Guide ↗ | Includes prediction, CVPAT, MICOM and group-difference procedures. |
| fsQCA and NCA | Calibrate sets, examine configurations and assess necessary conditions in QuickPLS. Guide ↗ | Outside the documented cSEM workflow; use separate R packages for these analyses. |
| Saving and repeating work | Keep the model, settings and results in a saved project; open sample projects to practise. Guide ↗ | Keep the model syntax and analysis code in an R script, with saved result objects. |
Package features checked against official cSEM documentation on 15 November 2026. Numerical comparisons below identify the package versions actually used in those tests.
Repeat the four-construct example
- Download and import the data. Use customer-experience.csv. It contains 240 rows, a case ID and 12 indicator columns. Leave the case ID out of the model.
- Build four reflective constructs. Assign quality_1–quality_3 to Quality, trust_1–trust_3 to Trust, satisfaction_1–satisfaction_3 to Satisfaction, and loyalty_1–loyalty_3 to Loyalty.
- Connect the paths. Draw Quality → Satisfaction, Trust → Satisfaction and Satisfaction → Loyalty. Run PLS-SEM with the settings listed above.
- Compare named outputs. Open the path coefficients, R², loadings and weights. For example, Quality → Satisfaction should be about 0.444622. Use the downloaded R calculation to repeat the same model in cSEM.
Follow the illustrated PLS-SEM guide ↗ · cSEM calculation options