- Award ID(s):
- Publication Date:
- NSF-PAR ID:
- Journal Name:
- Journal of Survey Statistics and Methodology
- Sponsoring Org:
- National Science Foundation
More Like this
Assessing several individuals intensively over time yields intensive longitudinal data (ILD). Even though ILD provide rich information, they also bring other data analytic challenges. One of these is the increased occurrence of missingness with increased study length, possibly under non-ignorable missingness scenarios. Multiple imputation (MI) handles missing data by creating several imputed data sets, and pooling the estimation results across imputed data sets to yield final estimates for inferential purposes. In this article, we introduce dynr.mi(), a function in the R package, Dynamic Modeling in R (dynr). The package dynr provides a suite of fast and accessible functions for estimating and visualizing the results from fitting linear and nonlinear dynamic systems models in discrete as well as continuous time. By integrating the estimation functions in dynr and the MI procedures available from the R package, Multivariate Imputation by Chained Equations (MICE), the dynr.mi() routine is designed to handle possibly non-ignorable missingness in the dependent variables and/or covariates in a user-specified dynamic systems model via MI, with convergence diagnostic check. We utilized dynr.mi() to examine, in the context of a vector autoregressive model, the relationships among individuals’ ambulatory physiological measures, and self-report affect valence and arousal. The results from MI weremore »
Social science approaches to missing values predict avoided, unrequested, or lost information from dense data sets, typically surveys. The authors propose a matrix factorization approach to missing data imputation that (1) identifies underlying factors to model similarities across respondents and responses and (2) regularizes across factors to reduce their overinfluence for optimal data reconstruction. This approach may enable social scientists to draw new conclusions from sparse data sets with a large number of features, for example, historical or archival sources, online surveys with high attrition rates, or data sets created from Web scraping, which confound traditional imputation techniques. The authors introduce matrix factorization techniques and detail their probabilistic interpretation, and they demonstrate these techniques’ consistency with Rubin’s multiple imputation framework. The authors show via simulations using artificial data and data from real-world subsets of the General Social Survey and National Longitudinal Study of Youth cases for which matrix factorization techniques may be preferred. These findings recommend the use of matrix factorization for data reconstruction in several settings, particularly when data are Boolean and categorical and when large proportions of the data are missing.
Effects on Panel Attrition and Fieldwork Outcomes from Selection for a Supplemental Study: Evidence from the Panel Study of Income DynamicsA key issue for panel surveys is the relationship between changes in respondent burden and resistance or attrition in future waves. In this chapter, the authors use data from multiple waves of the Panel Study of Income Dynamics (PSID) from 1997 to 2015 to examine the effects on attrition and on various other measures of respondent cooperation of being invited to take part in a major supplemental study to PSID, namely the 1997 PSID Child Development Supplement (CDS). They describe their conceptual framework and previous research. The authors also describe the data and methods. The PSID is the world’s longest-running household panel survey. PSID has a number of supplemental studies, which began in 1997 with the original CDS. To describe and analyse the effects of CDS on sample attrition in PSID, the authors also use survival curves and univariate and multivariate discrete time hazard models.
Abstract In recent years, household surveys have expended significant effort to counter well-documented increases in direct refusals and greater difficulty contacting survey respondents. A substantial amount of fieldwork effort in panel surveys using telephone interviewing is devoted to the task of contacting the respondent to schedule the day and time of the interview. Higher fieldwork effort leads to greater costs and is associated with lower response rates. A new approach was experimentally evaluated in the 2017 wave of the Panel Study of Income Dynamics (PSID) Transition into Adulthood Supplement (TAS) that allowed a randomly selected subset of respondents to choose their own day and time of their telephone interview through the use of an online appointment scheduler. TAS is a nationally representative study of US young adults aged 18–28 years embedded within the worlds’ longest running panel study, the PSID. This paper experimentally evaluates the effect of offering the online appointment scheduler on fieldwork outcomes, including number of interviewer contact attempts and interview sessions, number of days to complete the interview, and response rates. We describe panel study members’ characteristics associated with uptake of the online scheduler and examine differences in the effectiveness of the treatment across subgroups. Finally, potential cost-savingsmore »
A Hybrid Approach for The Stratified Mark-Specific Proportional Hazards Model with Missing Covariates and Missing Marks, with Application to Vaccine Efficacy Trials
Deployment of the recently licensed tetravalent dengue vaccine based on a chimeric yellow fever virus, CYD-TDV, requires understanding of how the risk of dengue disease in vaccine recipients depends jointly on a host biomarker measured after vaccination (neutralization titre—neutralizing antibodies) and on a ‘mark’ feature of the dengue disease failure event (the amino acid sequence distance of the dengue virus to the dengue sequence represented in the vaccine). The CYD14 phase 3 trial of CYD-TDV measured neutralizing antibodies via case–cohort sampling and the mark in dengue disease failure events, with about a third missing marks. We addressed the question of interest by developing inferential procedures for the stratified mark-specific proportional hazards model with missing covariates and missing marks. Two hybrid approaches are investigated that leverage both augmented inverse probability weighting and nearest neighbourhood hot deck multiple imputation. The two approaches differ in how the imputed marks are pooled in estimation. Our investigation shows that nearest neighbourhood hot deck imputation can lead to biased estimation without properly selected neighbourhoods. Simulations show that the hybrid methods developed perform well with unbiased nearest neighbourhood hot deck imputations from proper neighbourhood selection. The new methods applied to CYD14 show that neutralizing antibody levelmore »