19:24
Identification and Estimation of Confirmatory Factor Analysis Models
17:25
Introduction to Latent Class Analysis with Example in R
08:46
Testing metric versus scalar invariance with categorical indicators using Mplus
12:54
Automated Invariance Testing with Mplus
18:53
Sensitivity analysis for structural equation modeling using Mplus
26:15
Sensitivity analysis for structural equation modeling (SEM)
10:08
Analysis of heterogeneity of treatment effects with causal forests using the grf package in R
11:18
Path analysis with Mplus - Model Identification and Default Settings
13:45
Neural networks to estimate propensity scores
33:32
Overview of Propensity Score Estimation
24:45
Overview of Quasi-experimental designs
29:27
Overview of propensity score analysis
08:48
Estimation of average treatment effect of a continuous treatment with inverse probability weights
13:09
Evaluation of covariate balance of continuous treatment doses using inverse probability weighting
11:34
Inverse Probability Weighting for Continuous Treatment Doses
17:46
How to estimate the generalized propensity score and the dose response function
07:58
How to estimate generalized propensity scores for multiple treatment versions using R
23:57
How to estimate treatment effects with differences-in-differences and fixed effects models
18:56
Inverse Probability Weighting for Time-Varying Treatments
08:53
Optimal Full Matching on the Propensity Score using the MatchIt package in R
13:18
One-to-many matching with a genetic algorithm with the Matching package in R
11:54
Variable ratio greedy propensity score matching with Matching Package in R
11:25
One-to-one greedy propensity score matching with the MatchIt package in R
17:34
Marginal Mean Weighting Through Stratification to Estimate the Average Treatment Effect with R
16:08
Propensity Score Stratification in R with the MatchIt and survey packages
08:06
Doubly Robust Estimation with Propensity Score Weighting in R with the survey package
08:49
How to estimate a treatment effect with propensity score weights in R with the survey package
10:25
How to evaluate covariate balance with propensity score weights in R with the twang package
12:16
How to calculate propensity score weights in R
07:11
Generalized Boosted Modeling to Estimate Propensity Scores in R with the twang package
06:01
Classification trees and random forests to estimate propensity scores in R with the party package
12:43
Logistic regression to estimate propensity scores in R
11:43
Multiple Imputation of Missing Data Prior to Propensity Score Estimation in R with the Mice
14:46
How to Run a Regression Analysis Using R and RStudio
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About
This channel shows tutorials on how to perform propensity score analysis using the R Statistical Software, including propensity score matching, weighting and stratification. All aspects of propensity score analysis for quasi-experimental research are demonstrated, including estimation of propensity scores, overlap check, covariate balance evaluation, treatment effect estimation, and sensitivity analysis.
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