Method skills run inside a workflow — ask for the analysis you need ("run a CFA on these 12 items", "score SUS and compare versions") and the workflow calls the method with its assumption checks, effect sizes and figures. Every method has a reference page; switch any of them off under Customize → Skills.
Psychometrics & stats
| Skill | Description |
|---|---|
| Descriptive statistics | Mean, SD, median, range, skew/kurtosis, and Shapiro–Wilk normality per variable. |
| Reliability (Cronbach α · McDonald ω) | Internal-consistency reliability: Cronbach's alpha (with CI), McDonald's omega, item-rest correlations. |
| Correlation analysis | Pairwise correlations (Pearson/Spearman) with p-values. |
| t-test (with assumptions) | One- or two-sample t-test with normality & equal-variance checks, Cohen's d, and 95% CI. |
| One-way ANOVA | One-way ANOVA with Levene homogeneity test and η² effect size. |
| Exploratory Factor Analysis | EFA with KMO, Bartlett's test, rotated loadings, and variance explained. |
| Confirmatory Factor Analysis | CFA of a measurement model with fit indices (CFI, TLI, RMSEA), loadings, and a path diagram. |
| Structural Equation Modeling | Fit an SEM (lavaan-style) with fit indices, path estimates, and a rendered path diagram. |
| Item Response Theory | IRT item parameters (difficulty, discrimination) for dichotomous items — 2PL or Rasch. |
| Mixed-effects models | Linear mixed-effects (random intercept) via formula — for nested/repeated-measures designs. |
| Power analysis | Statistical power / required sample size for t-tests and one-way ANOVA. |
| Regression (linear · logistic) | Linear (OLS) or logistic regression from a formula, with coefficients and fit. |
| Bayesian estimation (Bayes factor) | Bayes factors (BF10) for t-tests and correlations, with evidence interpretation. |
| Assumption checks & test selection | Check normality/homogeneity/outliers/sphericity for an analysis, and recommend the statistical test (with alternatives) from the design and the data. |
| Effect size estimation | Cohen's d / Hedges' g / η² / r with 95% CI and interpretation — from data or from summary statistics (means, SDs, ns). |
| Multiple-comparison correction | Adjust a family of p-values (Bonferroni, Holm, Benjamini–Hochberg FDR) and report which results survive. |
| Test selection & execution (parametric ↔ non-parametric) | Design- and assumption-driven test choice with execution: compare_groups (auto Student/Welch/Mann-Whitney, paired t/Wilcoxon, ANOVA/Welch ANOVA/Kruskal, RM-ANOVA/Friedman with effect sizes, CIs, post hoc, table + figure), explicit non-parametric tests, chi-square family, and factorial/repeated/mixed/ANCOVA designs. |
Multimodal biosignals
| Skill | Description |
|---|---|
| ECG / HRV (heart-rate variability) | Heart-rate variability from RR/IBI intervals — SDNN, RMSSD, pNN50, mean HR (+LF/HF). |
| EDA / GSR (skin conductance) | Electrodermal activity — tonic level (SCL) and phasic responses (SCR count & amplitude). |
| Eye tracking · Gaze heatmap | Eye-tracking analysis — fixations (I-DT) and a rendered gaze heatmap image. |
| EEG (band power) | EEG spectral band powers (delta/theta/alpha/beta/gamma) via Welch PSD. |
| Facial expression / AU | Summarize facial Action Unit intensities and infer likely emotions (EMFACS heuristic). |
| Voice emotion (acoustic features) | Acoustic-prosodic features from a voice clip — pitch, intensity, voice quality, pitch contour. |
| Respiration (RSP) | Respiration rate and variability from a raw respiratory signal (belt / thermistor / derived). |
| Power spectral density | Welch PSD of any periodic physiological signal with peak frequency and custom band powers. |
| EEG analysis (preprocess · band power · topomap · ERP · ERD/ERS · SSVEP) | Multichannel EEG from a table (channels = numeric columns in µV, time_s, optional marker): artifact/line-noise removal, band power with peak-alpha and asymmetry, scalp topography, event-related potentials, event-related (de)synchronization and SSVEP frequency detection — deterministic, with tables and figures. |
| Speech-to-text (local whisper) | Transcribe an uploaded audio file or a video's sound track to text with timestamps (faster-whisper, offline). |
| Facial expression (image · video, measured) | Measured facial Action-Unit approximation from an image or video via mediapipe blendshapes (52) → FACS AUs, EMFACS emotion heuristic, and head pose (yaw/pitch/roll). Per-frame CSV and AU time-series figure for video. |
| Body pose (image · video, measured) | Measured body pose from an image or video via mediapipe PoseLandmarker (33 landmarks): joint angles, posture class (standing/sitting/bent), arm position, torso tilt, facing, movement per frame; overlay image and CSV. |
| Remote PPG from face video (rPPG) | Heart rate from a face video without contact — face-ROI mean RGB → POS / CHROM / GREEN (published open algorithms) → 0.7–4 Hz band → HR (bpm), SNR, peak-based IBI/RMSSD; pulse + spectrum figure and trace CSV. |
| Acoustic features (formants · MFCC) | Formant frequencies (F1–F3), MFCC statistics, and spectral descriptors from a voice clip. |
HCI & usability
| Skill | Description |
|---|---|
| SUS (System Usability Scale) | Standard SUS scoring (0–100) with grade and acceptability interpretation. |
| NASA-TLX (workload) | Raw NASA-TLX workload index from 6 subscales, plus per-subscale means. |
| A/B testing | Compare a metric across two groups — proportion (conversion) or continuous, with effect size. |
| UEQ (User Experience Questionnaire) | Standard UEQ scoring — 6 UX scales (−3..+3) plus pragmatic/hedonic quality. |
Emotion engineering
| Skill | Description |
|---|---|
| Semantic Differential (SD) | SD profile — mean rating per bipolar adjective scale, optionally by stimulus/product. |
| Conjoint analysis | Ratings-based conjoint: part-worth utilities and attribute importance via OLS. |
| Quantification Theory I | Hayashi Quantification Type I — predict a quantitative criterion from categorical items; category scores, item importance (partial correlation), and multiple R. |
Qualitative & NLP
| Skill | Description |
|---|---|
| Text overview (open-ended responses) | Response counts, length distribution, and top TF-IDF terms + bigrams for a free-text column. |
| Topic modeling (LDA · NMF) | Discover latent topics in open-ended responses (LDA or NMF) with top words per topic. |
| Thematic clustering (assisted coding) | Cluster open-ended responses (TF-IDF + KMeans) into candidate themes with representative quotes — support for inductive thematic analysis / open coding. |
| Sentiment analysis (VADER) | Lexicon-based sentiment (positive/neutral/negative) for open-ended responses; English-optimized. |
Data preparation
| Skill | Description |
|---|---|
| Data inspection & validation | Missing-value audit, variable-type detection, and rule-based dataset validation before analysis. |
| Dataset transformation | Reverse-score items, build composite scores, z-score, recode, filter, rename, drop — saved as a new dataset with the recipe recorded for reproducibility. |
Domain guides
- Neutropic for psychometrics — reliability, EFA/CFA, SEM, IRT
- Neutropic for psychophysiology — ECG/HRV, EDA, EEG, face and voice from video
- Neutropic for HCI & usability — SUS, NASA-TLX, UEQ, A/B tests
- Neutropic for emotion engineering — semantic differential, conjoint, Quantification Theory I