Planned Missingness With Purposefully Selected Samples

• In Event: Methods for Causal Inference and Estimation of Treatment Effects

Abstract

Two-method measurement planned missing research designs are an increasingly popular tool for applied researchers to design cost-efficient research. Previous TMM research has typically focused on data that are missing completely at random, leaving the question of TMM under other missingness mechanisms unaddressed. This study fills the gap by conducting a Monte Carlo simulation study varying the conditions of missing mechanisms and autoregressive path size. Results suggest a) higher correlation between the auxiliary variable and observed variable is associated with greater estimation biases in factor loadings and autoregressive paths; b) the inclusion of the auxiliary variable to the analysis model well recovers estimation accuracy; c) statistical power of detecting the treatment effect is affected by autoregressive path size.

Authors

• Menglin Xu, The Ohio State University

• Jessica Logan, The Ohio State University - Columbus