ffsshhttiikk What I do
- Design and analyze psychological measurements
- Build and validate questionnaires
- Measure latent traits
- Assess reliability and validity
- Create scoring systems
- Handle survey data analysis
When to use me
Use me when:
- Building assessment tools
- Measuring user attitudes/personality
- Validating questionnaires
- Creating scoring models
Key Concepts
Psychometric Models
import numpy as np
from scipy import stats
# Classical Test Theory
def cronbach_alpha(items):
"""Calculate Cronbach's alpha for reliability"""
item_scores = np.array(items)
n_items = item_scores.shape[1]
# Item variances
item_vars = item_scores.var(axis=0, ddof=1)
total_var = item_scores.sum(axis=1).var(ddof=1)
alpha = (n_items / (n_items - 1)) * (1 - item_vars.sum() / total_var)
return alpha
# Item Response Theory (Simplified)
# 1PL Model: P(correct) = 1 / (1 + exp(-(theta - b)))
def logistic_1pl(theta, difficulty):
return 1 / (1 + np.exp(-(theta - difficulty)))
Reliability Measures
- Cronbach's Alpha: Internal consistency
- Test-retest: Stability over time
- Inter-rater: Agreement between raters
- Split-half: Correlation between halves
Validity Types
- Content: Measures intended construct
- Criterion: Correlates with outcome
- Construct: Theoretical underpinning
- Face: Appears valid to examinees
Factor Analysis
from sklearn.decomposition import FactorAnalysis
# Exploratory factor analysis
fa = FactorAnalysis(n_components=3)
factor_scores = fa.fit_transform(X)
# Interpretation
loadings = fa.components_.T
# High loadings indicate strong factor relationships
Item Analysis
- Difficulty index (p-value)
- Discrimination index
- Item-total correlation
- Response distribution