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Looking for help with a specific technique? Here's an expanded list from our toolkit.

Surveys and psychometrics

Survey and questionnaire design
Scale development: distilling multiple items into themes and quantifying each person's score
Testing for survey bias, including straightlining; preferences for consistent or extreme or middle responses; social desirability bias; or common method bias
Validation
Multitrait, multimethod analysis
Reliability analysis, including Cronbach’s alpha and Cohen’s kappa
Exploratory factor analysis (EFA) and principal component analysis (PCA)
Multidimensional scaling
Cluster analysis
Pricing analysis, including Van Westendorp, Gabor-Granger, monadic, vignette, conjoint, market basket, and behavioral-economics techniques

Univariate and bivariate statistical methods

Z-tests and T-tests of mean differences
Correlation; manipulation of correlation coefficients using Fisher’s Z; adjustments for attenuation
Non-parametric measures of association (e.g., phi, Cramer’s V, Somers’ D, Hoeffding’s D)
Effect sizes (e.g., Cohen’s d, R-squared, eta-squared, f, pseudo-R-squared alternatives, concordance, odds ratios, risk ratios)
Chi-square testing
Analysis of proportions and the differences between them
Analysis of distributions (e.g., normal, bimodal, poisson, lognormal)

Types of regression and related predictive or explanatory multivariate models

Predictive modeling, predictive analytics, risk scoring, machine learning
Multiple linear regression
Analysis of variance (ANOVA)
Analysis of covariance (ANCOVA) and analysis of gain scores (change scores)
Multiple logistic regression (including binary and multinomial)
Least Absolute Shrinkage and Selection Operator (LASSO) regression
Survival analysis and regression incorporating censoring
Instrumental variables and Two-Stage Least Squares (2SLS) analysis
Time series analysis, including autocorrelation and Autoregressive Integrated Moving Average (ARIMA) models
Random forest models

Approaches that can enhance modeling

Power analysis
Variable selection techniques, both automated and intentional, including sequential and stepwise modeling
Data transformations
Modeling of nonlinear effects
Modeling of interaction effects, including Chi-Square-Based Automatic Interaction Detection (CHAID)
Model diagnostics, including analysis of outliers, leverage, residuals, linearity, and multicollinearity
Partial and semipartial correlations
Imputation of missing data (including Multiple Imputation using Chained Equations, or MICE)
Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC)
Cross-validation (e.g., k-fold)
Sensitivity analysis
Monte Carlo simulation
A wide variety of methods for creating data graphics -- for exploring data, illuminating findings, and instructing or persuading an audience