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Specialized analysis of research data across statistical, qualitative, multi-criteria, econometric and machine-learning methods — executed in the dedicated software of each method. Send us the details in a message and we will review them.

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Analysis Services

Every method, in its dedicated software

Eight service categories; a dedicated page for each method with data requirements, deliverables and software.

Quantitative Statistics

t-test & ANOVA · regression · factor analysis

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Structural Equation Modeling

PLS-SEM · CB-SEM · mediation & moderation

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Qualitative Analysis

thematic analysis · grounded theory · meta-synthesis

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MCDM & Fuzzy Methods

fuzzy DEMATEL · AHP & ANP · TOPSIS · Dynamic FCM

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Econometrics & Time Series

ARDL & NARDL · GARCH · panel data

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Machine Learning & Mining

neural networks · random forest · clustering

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Simulation & Efficiency

system dynamics · DEA · mathematical programming

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Scientometrics & Reviews

PRISMA · meta-analysis · VOSviewer

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Statistical methods (t-test, ANOVA, regression)Structural equation modeling (PLS-SEM, CB-SEM)Qualitative methods (thematic, grounded theory)Classic & modern MCDM (DEMATEL, AHP, TOPSIS, MICMAC)Multi-objective methods (goal programming, NSGA-II)Econometrics, classic & specialized (ARDL, NARDL, GARCH, panel)Dynamic Fuzzy Cognitive Mapping (FCM)Machine learning & mining (neural nets, SVM)Simulation & efficiency (system dynamics, DEA)Scientometrics & systematic reviews (PRISMA, VOSviewer)Statistical methods (t-test, ANOVA, regression)Structural equation modeling (PLS-SEM, CB-SEM)Qualitative methods (thematic, grounded theory)Classic & modern MCDM (DEMATEL, AHP, TOPSIS, MICMAC)Multi-objective methods (goal programming, NSGA-II)Econometrics, classic & specialized (ARDL, NARDL, GARCH, panel)Dynamic Fuzzy Cognitive Mapping (FCM)Machine learning & mining (neural nets, SVM)Simulation & efficiency (system dynamics, DEA)Scientometrics & systematic reviews (PRISMA, VOSviewer)

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METODA RESEARCH LAB · 5 STATIONS
Lab running

Interactive 3D Research Pipeline — 5 Stations

From data intake and assumption screening to model estimation (PLS-SEM / GARCH / MCDM, etc.), validation and reporting in APA, Vancouver & more.

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Buy a ready-made Excel file, enter your data, get the output. Automatic purchase via the Bale bot, or card payment in the site.

Fuzzy

Fuzzy Delphi Excel File

Defuzzification and expert-consensus threshold calculation.

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Fuzzy

Fuzzy DEMATEL Excel File

Relation matrix, causal diagram, influence and dependence totals.

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MCDM

TOPSIS Excel File

Weighting and ranking alternatives by distance from the ideal.

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The full list of ready-made Excel files on the shop page

20+ Dedicated Software & Simulator Ecosystem

Every method, its standard software

Your analysis runs on the tool that is standard for the method — you choose which one.

Full Script & SyntaxAPA, Vancouver & More
SEMSmartPLSPLS-SEM · BootstrappingSTATSSPSS & AMOSCB-SEM · ANOVA · CFAECONEViews & OxMetricsARDL · NARDL · GARCHPANELStataPanel Data · GMM · LogitQUALMAXQDA & NVivoThematic · Grounded TheoryMCDMSuper DecisionsANP · AHP · Fuzzy MCDMAI/MLPython (ML/SHAP)Scikit · XGBoost · PyTorchARCHgem5 & McPATComputer Arch · Cycle-AccurateSTAT/RR Studiolavaan · Tidyverse · MetaMATHMATLABFuzzy · DEA · SimulinkOPTGAMS & LINGOMathematical OptimizationSCIVOSviewerBibliometrics · PRISMAMetodaCORE ENGINE
SEMSmartPLSPLS-SEM · BootstrapAI/MLPython (ML/SHAP)XGBoost · PyTorchARCHgem5 & McPATComputer Arch SimSTATSSPSS & AMOSCB-SEM · ANOVAECONEViewsARDL · NARDL · GARCHPANELStataPanel Data · GMMSTAT/RR Studiolavaan · Meta-AnalysisMATHMATLABFuzzy · DEA · SimulinkQUALMAXQDAThematic · QualitativeMCDMSuper DecisionsANP · AHP · MCDMOPTGAMS & LINGOMath OptimizationSCIVOSviewerBibliometrics · PRISMAMetodaCORE ENGINE
01
Q1 Journal Compliance

Tool selection tailored to your sample size, distribution, and field conventions

02
Full Project & Syntax Delivery

Receive raw software project files alongside the complete narrative report

03
Supervisor-Preferred Software

Strict adherence to any specific software package requested by your advisor

Sample Outputs

Deliverables you actually receive

All samples are built with training data; real client files are never published.

SmartPLS · Bootstrapping (5,000 subsamples)PLS-SEM
MOT1MOT2MOT3LDR1LDR2MotivationAVE=0.68LeadershipAVE=0.71CommitmentR² = 0.642PerformanceR² = 0.784β=0.54***β=0.39***β=0.62***SRMR = 0.048 · NFI = 0.932 · HTMT < 0.85 · Q²_predict = 0.591 (p < 0.001)
Structural Equation Model (PLS-SEM)

Path coefficients, outer loadings, bootstrapping & R² — SmartPLS

TISM & MICMAC Analyzer · Cross-Impact MatrixISM / TISM
IV. Independent (Drivers)III. Linkage (Unstable)I. AutonomousII. DependentC1 (AI Gov)C4 (R&D)C2 (Agility)C5 (Data Ops)C8C6 (ROI)C9Dependence Power (∑Y) →↑ Driving
MICMAC Cross-Impact Matrix

Four driving–dependence quadrants — ISM / TISM structural analysis

Stata / R (ggplot2) · OLS & 95% CI BandEconometrics
OLS: Y = 1.42 + 0.684*XAdj. R² = 0.742 | F = 148.6t = 12.19 (p < 0.0001) ***Independent Variable (X) →
OLS Regression & 95% CI Band

Fitted line, confidence interval shading & APA hypothesis test table

MATLAB / Python · Fuzzy DEMATEL Causal MapFuzzy MCDM
CAUSE GROUP (D - R > 0)EFFECT GROUP (D - R < 0)F1+1.42F2+0.88F3-0.64F4-1.19Prominence (D + R) · Threshold θ = 0.245 →
Fuzzy DEMATEL Causal Digraph

Cause–effect mapping (D+R, D-R) & thresholded influence flows

EViews · Structural VAR Impulse ResponseSVAR / NARDL
Peak: +0.048 (t = 3)1357912Horizon (Quarters) · ±2 S.E. Bootstrap CI →
Impulse Response Function (IRF)

Dynamic shock response with 95% bootstrap confidence bands — SVAR

EViews / R (rmgarch) · DCC-EGARCH(1,1) VolatilityDCC-GARCH
Panel A: Conditional Volatility (σ_t) & 99% VaR Threshold2σ Crisis BandPanel B: Dynamic Conditional Correlation (ρ₁₂,t Spillover)α₁+β₁ = 0.982 · DCC(a+b) = 0.964 · Leverage γ = -0.14*** · Student-t (df=6.4)
Conditional Volatility (DCC-GARCH)

Volatility clustering, regime spikes & risk spillover — EViews / R

gem5 + McPAT · Out-of-Order RISC-V / ARM CoreComputer Arch
CPI Stack Breakdown (SPEC2017)BaseStrideBOPProposedEnergy-Delay Pareto (EDP)Optimal CoreIPC: 2.48 (+18.4%) · L2 MPKI: 3.12 · Power: 4.82W @ 7nm · EDP: -24.6%
Computer Architecture Sim (gem5 + McPAT)

Cycle-accurate CPI stack, L1/L2/L3 cache hit rates & energy-delay Pareto

Python · XGBoost + SHAP Beeswarm & ROCMachine Learning
SHAP Feature ImpactFeature_AFeature_BFeature_CFeature_DFeature_EAUC = 0.942F1-Score = 0.9185-Fold Stratified CV · RMSE = 0.084 · DeLong p < 0.001
Explainable AI (SHAP & ROC-AUC)

Feature attribution beeswarm plot & ROC curve (AUC = 0.942) — Python

GAMS / MATLAB · NSGA-III Multi-Objective ParetoOptimization
Knee Solution (Compromise)f1 = $14.2k · f2 = 96.4% Rel.Objective 1: Total Cost (Min) → | ↑ Obj 2: Service Risk
Multi-Objective Pareto Front (NSGA-III)

Cost–time–reliability trade-off surface & optimal knee solution — GAMS / MATLAB

R (brms / Stan) · Bayesian Posterior & DAGBayesian MCMC
Posterior Density P(θ|D)Mean = 0.4895% HDI [0.29, 0.67]Causal DAG StructureX1MY4 Chains × 4,000 Iter · R-hat = 1.001 · ESS = 3,840 · BF₁₀ = 42.8
Bayesian Network & MCMC Posterior

Directed acyclic graph (DAG) with posterior density & trace convergence

VOSviewer · Co-occurrence NetworkScientometrics
Deep LearningPLS-SEMSurveyMCDMGARCHRiskPRISMAScopus + WoS (N = 1,420 Papers) · 4 Thematic Clusters · TLS = 842
Bibliometric Co-word Network

Thematic keyword clusters & co-citation density map — VOSviewer

Fuzzy Delphi Engine · Triangular Fuzzy NumbersExpert Panel
Threshold S ≥ 0.70Consensus: 0.84Round 1Round 2Round 3 (Final)Defuzzified Score (Crisp) · Mean Diff < 0.20 →
Fuzzy Delphi Consensus Convergence

Multi-round expert convergence crossing the 0.70 acceptance threshold

Research Fields

We speak your discipline’s language

For every field we know the methods commonly used in its theses and papers.

ManagementHuman ResourcesAccountingFinanceEconomicsPsychologyMarketingIndustrial Eng.Computer Eng.Computer ArchitectureHealthOther fields

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