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About SEMGate

Advanced research analytics, within every researcher's reach.

Research should be limited by the quality of an idea, not by access to analytical expertise. SEMGate is a structural equation modeling studio: you bring the research question, the theory and the data, and SEMGate helps you through the analysis, with established statistical engines underneath and you in control of every methodological decision.

Start your analysisRead the statistical foundation
  • PLS-SEM
  • CB-SEM
  • Model enhancement
  • Discriminant validity
  • Multigroup analysis
  • fsQCA

42

analyses across 9 families, from PLS-SEM and CB-SEM to fsQCA

Explore the analysis atlas

What SEMGate does

One studio, from model to manuscript

Draw your model, choose the analysis, and SEMGate estimates it, checks the results against published assessment criteria and drafts the tables and Methods text for your paper. PLS-SEM, CB-SEM and the analyses around them share one visual model.

You do not have to solve every analytical problem by hand before you can do sophisticated research. SEMGate takes care of the mechanics; the research stays yours.

You bring

  • The research question
  • The theory
  • The data

SEMGate helps with

  • Estimation on established engines
  • Diagnostics against published criteria
  • Guided model enhancement
  • Tables and a Methods draft

The shift we are working toward

  • Dependency→Independence
  • Repeated consultancy fees→Affordable analytical access
  • Technical barriers→Guided workflows
  • Manual complexity→Scientific automation
  • Limited analytical capability→Greater research capability

Why we built SEMGate

Closing the analytical gap in research

Many students and researchers have valuable questions and meaningful data, but not the specialised statistical training that advanced analysis demands. Some work alongside experienced statisticians and well-funded methods support; others, with the same intellectual potential, have to pay consultants to analyse data they have already collected.

That is an often-overlooked inequality, and SEMGate was created to help narrow it. Statistical experts remain essential for research design, methodological judgement and specialised consultation. Our aim is to reduce unnecessary dependency, so researchers can carry out much of their advanced analysis themselves.

Mission, vision and values

What guides the way we build SEMGate

Our mission

Democratise advanced research analytics, so that sophisticated methods are usable regardless of statistical training, institution or budget.

Our vision

A world where every researcher can analyse with confidence, and research is driven by ideas, evidence and curiosity rather than analytical inequality.

  • Established statistics

    Every statistical result is computed by a statistical engine (SEMGate's own PLS engine, lavaan or modsem), never generated by AI.

  • Scientific methodology

    Procedures follow established SEM methodology, including the assessment guidelines of Hair and colleagues.

  • Automation with a purpose

    Automation runs the checks, evaluates predefined criteria and compares alternatives, so less of your time goes into mechanics.

  • Researcher control

    You make the methodological decisions. SEMGate recommends; the researcher decides.

Scientific-guided, researcher-controlled

SEMGate recommends. The researcher decides.

Complex models do not always meet every criterion on the first run. The Model enhancer works through the problem systematically, following Hair and colleagues' guidelines: indicator loadings, reliability and AVE, discriminant validity (HTMT and Fornell–Larcker) and formative VIF for PLS-SEM; loadings, AVE and CR and the fit criteria you select, using modification indices, for CB-SEM.

It never changes your model on its own authority. After an enhancement has been evaluated you can accept it, or decline it and get your original model back exactly as it was.

  1. 1RunEstimate the model as specified.
  2. 2DiagnoseCheck loadings, reliability, AVE, discriminant validity, VIF or fit.
  3. 3EvaluateScore candidate modifications against the selected criteria.
  4. 4RecommendPropose the best-scoring alternative.
  5. 5You decideAccept it, or decline and restore your original model.

Social responsibility

Making advanced analysis accessible and affordable

Research already costs money: data collection, participants, software, travel and publication. Paying a consultant every time an analysis becomes complex adds to that burden, and for some researchers it is the barrier that stops a study from being finished.

SEMGate starts with 30 days free; individual plans then start at $15 per month, and universities can give their researchers access through an institutional plan.

Designed with these researchers in mind

  • Students
  • Doctoral researchers
  • Early-career academics
  • Independent researchers
  • Researchers in developing countries
  • Researchers at resource-constrained institutions
  • Universities that need affordable analytical infrastructure

What we hope changes

  • More research questions investigated
  • More students completing rigorous research
  • Limited research budgets preserved
  • Researchers from resource-constrained and under-represented settings contributing to global knowledge
Geography should not determine analytical opportunity. Financial resources should not determine research potential.
A student in Africa, a researcher in Asia, an academic in Europe or an independent researcher without an institutional budget should all be able to run advanced SEM.

Our commitment

  1. 01

    We believe advanced research analytics should not be a privilege reserved for researchers with extensive statistical training, expensive software or the budget to hire analytical experts.

  2. 02

    We believe technology can make sophisticated research more accessible.

  3. 03

    We believe automation can remove unnecessary technical barriers.

  4. 04

    We believe established statistical methodology should stay at the heart of analytical computation.

  5. 05

    We believe the researcher, not the technology, should stay at the centre of the research process.

No researcher should be kept from answering an important question because advanced analytical expertise is out of reach. SEMGate exists to help change that.

Part two · Statistical foundation

Statistical software is part of your research method.

This part sets out which engines estimate SEMGate models, what each result has been checked against, on which data, and how closely it agreed.

Last updated: 8 October 2026

Engines

What estimates your model

CB-SEM estimates come from lavaan, an open-source, peer-reviewed R package. PLS-SEM estimates come from SEMGate's own implementation of the PLS path-modeling algorithm. Because that engine is our own code, we check it against SmartPLS 4 and R and publish what we compared.

  • Engine

    SEMGate's own PLS path-modeling engine (Python)

    Analysis

    PLS-SEM, PLSc, bootstrap, PLSpredict, MGA / MICOM, FIMIX, IPMA, fsQCA

    Cross-checked against SmartPLS 4 and R reference implementations (see Validation scope).

  • Engine

    lavaan 0.7-2 (R)

    Analysis

    CB-SEM and CFA

    Rosseel (2012). Version pinned in the estimation worker.

  • Engine

    modsem 1.0.22 (R, on lavaan)

    Analysis

    CB-SEM latent interactions (LMS / QML)

    Slupphaug, Mehmetoglu & Mittner (2025). Version pinned.

  • Engine

    SEMinR (R)

    Analysis

    Bootstrap or PLSpredict on a two-stage higher-order construct that has no indicators yet (before a first Estimate creates its first-order scores); re-estimates inside the PLS Measurement enhancer

    The only PLS cases estimated outside the SEMGate engine. For some estimation errors SEMinR hands off to the SEMGate engine; other failures are reported as errors.

How an analysis runs

  1. 1Model building
  2. 2Analysis settings
  3. 3Estimation (SEMGate PLS engine or lavaan)
  4. 4Diagnostics
  5. 5Results and reporting

Validation scope

Same model, same data, same numbers

We check SEMGate's numbers against the established programs researchers already trust, running the same model on the same data in both and comparing the results.

Coming soon

SEMGate vs SmartPLS 4.1.1.8

PLS-SEM paths, loadings, reliability and bootstrap results, side by side.

Coming soon

SEMGate vs IBM SPSS Amos 27

CB-SEM estimates, standard errors and fit indices, side by side.

What matches

PLS-SEM

vs SmartPLS 4.1.1.8

  • Path coefficients, loadings, weights, R², f², VIF, HTMT, total effects, PLSpredict and bootstrap means agree to 9 decimal places.
  • Reliability (α, ρA, ρc, AVE) and bootstrap p-values agree within 0.0001; the reliability gap is rounding in the SmartPLS export.
  • Percentile, BC and CoMe confidence-interval bounds agree within about 0.0015, because the programs pick interval end-points slightly differently.
  • FIMIX agrees within 0.01. MGA reproduces SmartPLS's fixed-seed random numbers exactly, and its permutation p-values on the reference model.

CB-SEM

vs IBM SPSS Amos 27

  • Fit indices (CMIN, BCC, HOELTER, FMIN, AIC) and unstandardized ML standard errors match at the 3 decimal places Amos displays.
  • The lower bound of the NCP 90% interval differs by about 0.015.
  • A manual comparison on one reference model.

CB-SEM

vs Mplus, lavaan and modsem in R

  • Mplus 9.1.1 latent interaction (LMS) on a small reference model: estimates within 0.002, standard errors within 0.015.
  • modsem run directly in R gives identical results; lavaan run directly in R matches at reported precision.
  • The R checks confirm SEMGate passes your model to lavaan and modsem correctly; they are not independent checks of those packages.

HTMT and fsQCA

vs R semTools and R QCA

  • HTMT and HTMT2 agree with semTools to 15 decimal places on a public dataset.
  • fsQCA necessity, truth table and solutions agree with R QCA within 0.001 across five reference sessions; solution expressions are identical.
  • fs/QCA 4.1 is a secondary manual check; its PRI can differ from the Dușa PRI used by R QCA.

Agreement on a reference model shows that the implementation reproduces that software on that model; it does not guarantee identical output for every model.

Reproducibility

What SEMGate records about each analysis

Inside the Studio, results are labelled by engine family (PLS or CB-SEM); package names and versions are not currently shown there. Use the versions on this page when you report your software.

  • Fixed seed by default

    Re-running the same model on the same data with the same settings reproduces the same bootstrap results.

  • Bootstrap settings stored with the results

    Requested and completed subsamples, interval method, one- or two-tailed testing, α, seed mode and seed.

  • Model versions

    Each result is linked to the saved model version that produced it, along with the sample size.

  • Methods and Results draft

    The report export describes the method, estimator, bootstrap settings and seed in prose you can edit.

  • R script for CB-SEM

    CB-SEM models can be exported as a runnable lavaan script, so you can re-estimate them in R. The SEMGate PLS engine has no equivalent R script export.

Software and versions

  • SEMGate PLS enginePLS-SEM estimationInternal (Python)
  • lavaanCB-SEM / CFA estimation0.7-2 (pinned)
  • modsemCB-SEM latent interactions1.0.22 (pinned)
  • SEMinRTwo-stage higher-order constructs before first Estimate (Bootstrap / PLSpredict); PLS Measurement enhancerCurrent CRAN release
  • SmartPLS 4Reference (PLS-SEM)4.1.1.8
  • semToolsReference (HTMT / HTMT2)0.5-9
  • QCA (R)Reference (fsQCA)3.25
  • fs/QCASecondary reference (fsQCA)4.1
  • MplusReference (LMS latent interaction)9.1.1
  • IBM SPSS AmosReference (CB-SEM fit, standard errors)27

Questions your examiner or reviewer may ask

Which method did you use?
The method you select in the Studio (PLS-SEM, PLSc, CB-SEM, CFA, or another supported procedure). Results state whether the PLS or CB-SEM engine produced them.
Which estimator did you use?
PLS-SEM uses the PLS path-modeling algorithm with the weighting scheme set in Run options. CB-SEM uses lavaan with ML by default; MLR, GLS, ULS, SLS and WLS (ADF) are also available.
Which software performed the estimation?
SEMGate. PLS-SEM uses SEMGate's own engine; CB-SEM uses lavaan; CB-SEM latent interactions use modsem.
Which version was used?
The versions are listed on this page. The Studio does not currently display package versions, so record your access date.
How did you validate your results?
Cite the validation scope below, and report the settings that affect the digits: bootstrap subsamples, interval method, and seed mode.

How to cite

Citing SEMGate and the methods behind it

Cite SEMGate together with your access date, then cite the engine and method references that apply to your analysis. Report the settings that affect the digits: estimator, bootstrap subsamples, interval method and seed mode.

SEMGate (APA 7)

Coming soon

Example Methods wording

“The PLS-SEM model was estimated in SEMGate. Inference used 5,000 bootstrap subsamples with percentile confidence intervals and a fixed seed.”
“The CB-SEM model was estimated in SEMGate using lavaan 0.7-2 (Rosseel, 2012) with maximum-likelihood estimation.”

References

  • Dușa, A. (2019). QCA with R: A comprehensive resource. Springer. https://doi.org/10.1007/978-3-319-75668-4
  • Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). SAGE.
  • Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
  • Jorgensen, T. D., Pornprasertmanit, S., Schoemann, A. M., & Rosseel, Y. (2026). semTools: Useful tools for structural equation modeling (R package version 0.5-9). https://CRAN.R-project.org/package=semTools
  • Roemer, E., Schuberth, F., & Henseler, J. (2021). HTMT2—an improved criterion for assessing discriminant validity in structural equation modeling. Industrial Management & Data Systems, 121(12), 2637–2650. https://doi.org/10.1108/IMDS-02-2021-0082
  • Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36. https://doi.org/10.18637/jss.v048.i02
  • Slupphaug, K. S., Mehmetoglu, M., & Mittner, M. (2025). modsem: An R package for estimating latent interactions and quadratic effects. Structural Equation Modeling: A Multidisciplinary Journal, 32(4), 717–729. https://doi.org/10.1080/10705511.2024.2417409

Trademarks and affiliation

Names used on this page

SmartPLS is a trademark of SmartPLS GmbH. IBM SPSS Amos is a trademark of International Business Machines Corporation. Mplus is a trademark of Muthén & Muthén. fs/QCA is software developed by Charles C. Ragin and Sean Davey. R, lavaan, modsem, semTools, SEMinR and QCA are open-source software distributed under their own licenses.

SEMGate is not affiliated with, sponsored by, or endorsed by any of these companies or authors. Their names are used only to identify the software we compare against.

Questions about these checks: info@semgate.net.

You bring the research question. SEMGate helps with the analysis.

Build your model, run it on established engines and keep every methodological decision.

Start your analysis