Statistics — Page 38
Explore statistical methods, their assumptions, interpretation and limitations.
Research field
Method family
Multi-group discriminant validity
Multi-group discriminant validity assessment tests whether constructs measured by a scale are empirically distinct not just in one sample but consistently across two or more groups (e.g., cultures, genders, age cohorts). It extends standard...
Multi-group item analysis
Multi-group item analysis computes classical item statistics — difficulty, discrimination, and corrected item-total correlations — separately for each subgroup in a sample and then compares those statistics across groups. It is a standard d...
Multi-group item response theory
Multi-group item response theory fits IRT models simultaneously across two or more defined groups — such as males and females, or different cultural samples — to determine whether item parameters are invariant across those groups. It is the...
Multi-group measurement invariance
Multi-group measurement invariance testing examines whether a latent construct is measured in the same way across two or more distinct groups — such as cultures, genders, or age cohorts. It is a prerequisite for meaningful group comparisons...
Multi-group scale development
Multi-group scale development constructs and validates a measurement scale simultaneously across two or more distinct populations or groups. The approach integrates standard item generation and factor-analytic procedures with a systematic h...
Multi-group test-retest reliability
Multi-group test-retest reliability evaluates whether a measure produces stable scores across time separately for two or more defined groups — such as different genders, age cohorts, or clinical populations — and determines whether the degr...
Multi-period Causal Impact Analysis
Multi-period Causal Impact Analysis extends the Bayesian structural time-series framework of Brodersen et al. (2015) to settings where an intervention occurs across multiple distinct periods, is applied at staggered times to different units...
Multi-period Coarsened Exact Matching
Multi-period Coarsened Exact Matching (multi-period CEM) extends the CEM framework of Iacus, King, and Porro to longitudinal data with multiple pre- and post-treatment periods. It bins continuous covariates into coarsened categories, matche...
Multi-period Counterfactual Impact Evaluation
Multi-period Counterfactual Impact Evaluation (CIE) estimates the causal effect of a policy or program by constructing what would have happened to treated units across multiple time periods had they not been treated. Unlike single-period ev...
Multi-period Difference-in-differences
Multi-period Difference-in-Differences extends the classic two-period DiD framework to settings where units adopt treatment at different points in time. Formalised by Callaway and Sant'Anna (2021) and Goodman-Bacon (2021), it decomposes the...
Multi-period Doubly Robust Estimation
Multi-period doubly robust (DR) estimation extends the classic doubly robust approach to longitudinal settings with multiple treatment periods and time points. It combines an outcome regression model and a propensity score model for each pe...
Multi-period Event Study Design
The multi-period event study design estimates causal treatment effects at each point in time relative to the treatment onset, using panel data with multiple pre- and post-treatment periods. By plotting the full path of treatment coefficient...
Multi-period Fuzzy Regression Discontinuity
Multi-period fuzzy regression discontinuity design estimates a local average treatment effect when a cutoff rule only partially determines treatment — that is, crossing the threshold raises the probability of treatment but does not guarante...
Multi-period Interrupted Time Series
Multi-period Interrupted Time Series (MITS) extends the classic ITS framework to settings where two or more interventions occur at known time points within the same series. By fitting a segmented regression with multiple breakpoints, MITS e...
Multi-period Inverse Probability Weighting
Multi-period Inverse Probability Weighting (IPW) estimates the causal effect of a treatment that varies across multiple time periods by reweighting observations according to the probability of receiving each period's treatment given past tr...
Multi-period Matching Estimator
The multi-period matching estimator extends the standard matching framework to settings with multiple time periods, pairing each treated unit to similar untreated units based on pre-treatment covariates or propensity scores, then using with...
Multi-period Propensity Score Weighting
Multi-period propensity score weighting extends the standard propensity score weighting framework to settings with repeated measurements and time-varying treatments. It constructs stabilised inverse probability weights (IPW) at each time po...
Multi-period Regression Discontinuity Design
Multi-period Regression Discontinuity Design extends the classic RDD to settings where a cutoff-based treatment is applied in multiple waves, across repeated time periods, or with varying thresholds. By pooling or comparing period-specific...
Multi-period Synthetic Control Method
The multi-period synthetic control method extends the classic synthetic control framework to settings where treatment occurs across several distinct periods or where the researcher needs to track causal effects over a prolonged post-treatme...
Multidimensional Item Response Theory
Multidimensional item response theory (MIRT) generalizes IRT to tests that measure more than one latent ability at once. Instead of a single ability θ, each examinee is characterized by a vector of abilities, and each item by a vector of di...
Multidimensional Loneliness Assessment
Multidimensional loneliness assessment treats loneliness not as simple aloneness but as the distressing perceived gap between the social relationships a person wants and the ones they actually have. This cognitive-discrepancy view, rooted i...
Multidimensional Scaling
Multidimensional scaling maps objects described only by pairwise similarities or dissimilarities into a low-dimensional geometric space so that distances in that space reflect the original proximity structure as faithfully as possible. It i...
Multidimensional Unfolding
Multidimensional unfolding places both individuals and the stimuli they evaluate — candidates, parties, bills — in a single joint low-dimensional space, so that each person's preferences are explained by their proximity to the stimuli. In p...
Multifactor Leadership Questionnaire
The Multifactor Leadership Questionnaire (MLQ) is the standard instrument for measuring the full-range leadership model, which spans laissez-faire (non-) leadership, transactional leadership, and transformational leadership. Building on Jam...