PhD Researchers & Doctoral Candidates
A defensible measurement and structural model for a thesis—built and explained so you can present and defend every fit index.
Research Methods
Structural Equation Modeling & Psychometrics
A path model is only as trustworthy as the measures beneath it. We establish that your constructs are measured well—reliable, valid, and comparable across groups—before we estimate the relationships between them, so the structural results you report are ones a reviewer will accept.
CB-SEM · PLS-SEM
Measurement validated first
R (lavaan) · Mplus · SmartPLS
Reproducible, journal-ready
Sample output
latent construct
outcome
Overview
Measurement first, then the model
The most common reason an SEM paper is rejected is not the structural model—it is the measurement underneath it. Constructs with poor reliability, indicators that cross-load, or a discriminant-validity failure between two "distinct" factors mean the path coefficients are estimating relationships between things that were never cleanly measured. No amount of good fit at the structural stage repairs that.
So we work in the right order. First the measurement model: confirmatory factor analysis, reliability, convergent and discriminant validity, and—where groups or time points are compared—measurement invariance. Only once the constructs hold do we estimate the structural relationships, choosing CB-SEM or PLS-SEM to match your goal, testing mediation and moderation with bootstrap inference rather than outdated shortcuts.
The deliverable is a model a reviewer can trust: full measurement evidence, fit indices reported against current thresholds, the structural results with appropriate inference, and reproducible syntax you keep. Whether the work is a single mediation model, a scale-development study, or a longitudinal RI-CLPM, the discipline is the same.
Who We Work With
For research built on latent constructs
If your theory is about things you measure indirectly—attitudes, capabilities, perceptions—this is the right desk to write to.
Management & Marketing Researchers
CB-SEM and PLS-SEM for theory testing in organizational behavior, marketing, IS, and strategy, done to the field's current standards.
Psychology & Behavioral Scientists
Factor analysis, IRT, and latent variable modeling for constructs where measurement rigor is the whole game.
Scale Developers
End-to-end instrument development—item generation, EFA, confirmatory validation, reliability, and invariance.
Survey & Applied Researchers
Teams with questionnaire data who need mediation, moderation, and multi-group models done correctly.
Longitudinal Research Groups
Latent growth, growth mixture, and cross-lagged panel models—including RI-CLPM—for change measured over time.
Capabilities
The full latent-variable toolkit
Organized from measurement through structural and longitudinal models. If your study needs a method not listed here, ask—this is the core, not the boundary.
SEM, factor analysis & effects
The core of latent-variable modeling—measurement established first, then structural paths, mediation, and moderation.
- Structural equation modeling
- CB-SEM
- PLS-SEM
- Confirmatory factor analysis
- Exploratory factor analysis
- Mediation
- Moderation
- Moderated mediation
- Latent variables
- Measurement invariance
- Multi-group analysis
Change, nesting & dynamics
For constructs measured over time or nested within groups—including the modern cross-lagged models journals now expect.
- Multilevel SEM
- Latent growth models
- Growth mixture models
- Cross-lagged models
- RI-CLPM
Scales, items & classes
For developing and validating instruments and for examining how individual items and latent classes behave.
- Psychometric analysis
- Scale development
- Item response theory
- Rasch models
- Latent class analysis
- Latent profile analysis
How the Analysis Works
Six steps from constructs to structural model
A transparent sequence that respects the order SEM requires—measurement established before structure, every fit index documented. Nothing is a black box.
Steps are adapted to your study: reflective vs. formative constructs, CB-SEM vs. PLS-SEM, single time point vs. longitudinal. We confirm the model with you before estimation begins.
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1
Specify
Translate your theory into a measurement and structural model—constructs, indicators, and the hypothesized paths between them.
Inputs: theory · constructs · indicators · hypotheses
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2
Measure
Estimate and validate the measurement model before anything structural—the step that determines whether the rest is interpretable.
Methods: CFA · EFA · reliability · convergent validity
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3
Validate
Establish discriminant validity and, where groups or time are compared, measurement invariance—so comparisons are meaningful.
Tests: HTMT · Fornell–Larcker · configural/metric/scalar
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4
Estimate
Fit the structural model with the appropriate estimator, and test mediation and moderation with bias-corrected bootstrap inference.
Models: CB-SEM · PLS-SEM · bootstrap indirect effects
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5
Assess fit
Evaluate model fit and robustness against current thresholds, and probe alternative and competing specifications.
Indices: CFI · TLI · RMSEA · SRMR · alternative models
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6
Report
Deliver path diagrams, measurement and structural tables, fit statistics, methodology, and reproducible model syntax you keep.
Output: path diagram · fit tables · methods · lavaan/Mplus syntax
Rigor by default
The checks that decide whether a model is interpretable
A structural path is only meaningful if the constructs beneath it are sound. The measurement and fit evidence that proves it is standard on every engagement.
Included on every project
- Reliability and convergent validity (CR, AVE)
- Discriminant validity (HTMT, Fornell–Larcker)
- Measurement invariance for group and time comparisons
- Model fit reported against current thresholds
- Reproducible model syntax you keep
What You Receive
Every engagement, delivered in full
Not a black-box result and a number, but a complete, documented package you can submit, defend, and reproduce.
- Clean, documented dataset and analysis files
- Validated measurement model with reliability and validity evidence
- Measurement invariance results, where relevant
- Structural model with path estimates and inference
- Model fit indices and path diagram
- Mediation and moderation results with bootstrap CIs
- Interpretation of effects and their practical meaning
- Reproducible lavaan, Mplus, or SmartPLS syntax
- Journal-ready methodology and results sections
Where this fits
Part of a larger arc
SEM is strongest when the survey design ahead of it is sound and the reporting after it is precise—each handled with the same care.
Research Design & Planning
Identification strategy, power, and specification decided before estimation begins.
Explore methodsStatistical & Methodological Audit
An independent check of assumptions, specification, and reproducibility before submission.
Explore auditPublication & Research Support
Methods and results reporting, journal selection, and reviewer-response support.
Explore support
FAQ
Common questions
Answers to what most researchers and project leads ask before we begin an SEM or psychometrics engagement.
It depends on your goal and your model. Covariance-based SEM is the standard for theory testing and confirmatory work with well-established measures and adequate sample size; PLS-SEM suits prediction-oriented studies, complex models, formative constructs, or smaller samples. We recommend the approach your research question and data support, and explain the trade-off rather than defaulting to whichever is fashionable in your field.
Always. A structural model built on measures that fail reliability, convergent validity, or discriminant validity is not interpretable. We establish the measurement model first—factor loadings, composite reliability, AVE, and discriminant-validity criteria such as HTMT—before any path coefficient is reported.
Yes. We estimate indirect effects with bias-corrected bootstrap confidence intervals rather than the outdated causal-steps approach, test moderation and moderated mediation with the correct interaction specification, and report conditional indirect effects clearly.
Yes—that is measurement invariance testing, and it is essential before comparing latent means across groups or over time. We test configural, metric, and scalar invariance (and partial invariance where needed) so that any group comparison you report is actually valid.
Yes. Scale development is a core capability—from item generation and exploratory factor analysis through confirmatory validation, reliability, and, where appropriate, item response theory or Rasch analysis to examine item-level functioning.
Yes. We fit latent growth models, growth mixture models, and cross-lagged panel models—including the random-intercept cross-lagged panel model (RI-CLPM), which separates within-person change from stable between-person differences and is now expected in many journals for cross-lagged claims.
R (lavaan, and packages for PLS-SEM and IRT), Mplus, and SmartPLS, matched to the model and your environment. You receive the model syntax, fit output, and a methods section written to journal standards.
Yes, and it is the ideal time. The measurement structure, the number of indicators per construct, and the sample size all need to be settled before data collection—designing them in advance prevents the underidentified or underpowered models that force a study to be redone.
Related Expertise
Often paired with econometrics
Building a latent-variable model?
Tell us your constructs and how you measured them—we'll tell you the right SEM approach, what the measurement needs, and how to make the model hold.