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Statistical and Analytical Methods
Up one levelLatent Class Analysis
Latent class analysis (LCA) is a multivariate technique which can be applied to cluster analysis, factor analysis, or regression analysis. Latent constructs are created from indicator variables, as in structural equation modeling, and then used to construct clusters, factors, or to predict dependents in regression mode.
Multilevel Modeling
Collection of hierarchical linear modeling (hlm) and multilevel modeling references
Propensity Matching
Propensity score matching is a method that estimates the probability of taking a treatment given a vector of observed variables. It is a method to draw causal inference on observational data which many social science studies use. Through matching and balancing samples, propensity scores help adjust for selection bias and estimate counterfactual effects, and therefore obtain average treatment effect, average treatment effect for the treated (ATT), and average treatment effect for the untreated.
Structural Equation Modeling
Structural Equation Modeling (SEM) is a method for determining the extent to which data on a set of variables are consistent with hypotheses about causal association among the variables. Reults are produced by creating a path diagram and letting the statistical package determine the covariances, regression coefficients, and factor loadings (for latent constructs) that apply to the arrows drawn in the diagram. Benefits to using SEM include:1) use of maximum likelihood to make use of cases with missing data, 2) confirmatory factor analysis for latent constructs,3) measures of model fit and diagnostics to determine the source of ill fit, and 4) ability to apply assumptions to the model (such as 0 correlation between two variables).
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