ProjectQuantitative Data Analysis
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  • Project Home
  • Microsimulation
  • The Analysis of Transfers, Taxes, and Income Security (ATTIS) microsimulation model
  • The Medicare Policy Microsimulation Model (MCARE-SIM)
  • The Model of Income in the Near Term (MINT)
  • The Tax Policy Center Microsimulation Model
  • The Dynamic Simulation of Income Model (DYNASIM)
  • The Health Insurance Policy Simulation Model (HIPSM)
  • The Transfer Income Model (TRIM)
  • Descriptive Data Analysis
  • Inference
  • Impact Analysis
  • Bias
  • Experiments
  • Paired Testing
  • Quasi-experimental Methods
  • Difference-in-Difference and Panel Methods
  • Instrumental Variables
  • Propensity Score Matching
  • Regression Discontinuity
  • Regression Techniques
  • Generalized Linear Model
  • Linear Regression
  • Logit and Probit Regression
  • Segregation Measures
  • Inequality Measures
  • Decomposition Methods
  • Performance Measurement and Management
    • Quasi-experimental Methods

    Regression adjustment—using linear regression methods, generalized linear models, binary regression, and other regression models—adjusts for differences across units in measured characteristics that might be correlated with outcomes and with other factors that change outcomes. Propensity score matching and reweighting methods accomplish a similar adjustment for measured characteristics but can dispense with certain functional form assumptions.

    Various panel regression techniques can also adjust for differences in unmeasured characteristics (such as native ability or intrinsic perseverance or patience) by comparing outcomes and predictors only within units and not across units. Instrumental variables and regression discontinuity models can also adjust for differences in unmeasured characteristics both across and within units under certain assumptions, and they can offer impact estimates with internal validity close to that of experimental methods.