SaNaSoft fsQCA
Free · v1.2Guided fuzzy-set QCA: calibration, necessary conditions, truth table, complex, parsimonious and intermediate solutions, robustness and predictive checks, and an APA-style Word report. Benchmarked against the R package QCA.
SaNaPLS takes you from an uploaded survey file to latent variable scores, bootstrapped path coefficients, fsQCA configurations, neural-network importance rankings and cIPMA — with every cut-off traced to a published source. Nothing you upload ever leaves your browser.
Quantitative Analysis — the SaNaPLS dashboard: survey data, PLS-SEM, fsQCA, ANN and cIPMA.
Thematic Analysis — SaNaQual, for interviews and open-ended answers, including follow-up interviews that explain your PLS-SEM results. Version 1.0.0: coding, themes, intercoder agreement, checklists and a Word report.
No account. No install. No data leaves your device.
PLS-SEM path model, reliability and validity
Configurations for high and low outcomes
What matters most, and what's necessary
Why not just use what you have
Runs in any modern browser — Windows, Mac or Linux, no setup.
Every calculation runs on your machine. Nothing is uploaded to a server.
After the first visit, keep running analyses without a connection.
Upload once; every tool below reads the same file and the same constructs.
The workflow
Bring in a CSV, Excel or SPSS file, confirm what each column is, and screen the data once.
Summarise your sample, cross-tabulate demographics, check every item and score for normality, and explore which questions belong together with a factor analysis.
Calculate latent variable scores with PLS-SEM, then check reliability, validity and path significance with bootstrapping, and look for hidden groups of respondents with FIMIX-PLS and PLS-POS. Run correlations, linear regression and logistic regression on your uploaded data or on the PLS-SEM scores.
Go beyond the path model: fsQCA configurations, neural-network importance rankings, and cIPMA — built on your estimated model or uploaded scores.
Stand-alone apps
If you already have latent variable scores from your PLS-SEM software, you can use our classic single-purpose apps. They run in your browser and need no sign-in.
Guided fuzzy-set QCA: calibration, necessary conditions, truth table, complex, parsimonious and intermediate solutions, robustness and predictive checks, and an APA-style Word report. Benchmarked against the R package QCA.
Guided artificial neural network analysis for the PLS-SEM + ANN hybrid approach (Leong et al., 2025): ten networks, RMSE tables, sensitivity analysis, the PLS-SEM vs ANN comparison and a publication-ready report.
No guessing at thresholds
Developed by
Professor in Management and IT
Department of Management and Marketing