Missing Data and Complex Samples: The Impact of Listwise Deletion vs. Subpopulation Analysis on Statistical Bias and Hypothesis Test Results When Data Are MCAR and MAR

نویسندگان

  • Bethany A. Bell
  • Jeffrey D. Kromrey
  • John M. Ferron
چکیده

Secondary data analysis of complex sample survey results is common among social scientists. Yet, the degree to which unbiased estimates and accurate inferences can be made from complex samples depends on the care researchers take when analyzing the data, including strategies for the treatment of missing data. Several studies have illustrated that the results of subpopulation analysis may diverge from those obtained through listwise deletion. However, given the paucity of simulation work in this area, it is not clear how frequently discernable discrepancies will arise. This Monte Carlo study focuses on the impact of listwise deletion versus a subpopulation analysis, when the data are MCAR and MAR, in the context of multiple regression analysis of complex sample data. Results are presented in terms of statistical bias in parameter estimates and both confidence interval width and coverage.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Ml Estimation of Mean and Covariance Structures with Missing Data Using Complete Data Routines

We consider maximum likelihood (ML) estimation of mean and covariance structure models when data are missing. Expectation maximization (EM), generalized expectation maximization (GEM), Fletcher-Powell, and Fisherscoring algorithms are described for parameter estimation. It is shown how the machinery within a software that handles the complete data problem can be utilized to implement each algor...

متن کامل

Multiple Imputation for Missing Data: Making the Most of What you Know

Missing data is a common problem in psychological research. Missing data can occur due to attrition in a longitudinal study or non-response to questionnaire items in a laboratory or field setting. Improper treatments of missing data (e.g., listwise deletion, mean imputation) can lead to biased statistical inference using complete case analysis statistical techniques. This paper presents a metho...

متن کامل

Comparison of Four Methods for Handing Missing Data in Longitudinal Data Analysis through a Simulation Study

Missing data can frequently occur in a longitudinal data analysis. In the literature, many methods have been proposed to handle such an issue. Complete case (CC), mean substitution (MS), last observation carried forward (LOCF), and multiple imputation (MI) are the four most frequently used methods in practice. In a real-world data analysis, the missing data can be MCAR, MAR, or MNAR depending o...

متن کامل

How to deal with missing longitudinal data in cost of illness analysis in Alzheimer’s disease—suggestions from the GERAS observational study

BACKGROUND Missing data are a common problem in prospective studies with a long follow-up, and the volume, pattern and reasons for missing data may be relevant when estimating the cost of illness. We aimed to evaluate the effects of different methods for dealing with missing longitudinal cost data and for costing caregiver time on total societal costs in Alzheimer's disease (AD). METHODS GERA...

متن کامل

Out of sight, not out of mind: strategies for handling missing data.

OBJECTIVE To describe and illustrate missing data mechanisms (MCAR, MAR, NMAR) and missing data techniques (MDTs) and offer recommended best practices for addressing missingness. METHOD We simulated data sets and employed ad hoc MDTs (deletion techniques, mean substitution) and sophisticated MDTs (full information maximum likelihood, Bayesian estimation, multiple imputation) in linear regress...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2009