A Comparison of Methods for Creating Multiple Imputations of Nominal Variables

Many variables that are analyzed by social scientists are nominal in nature. When missing data occur on these variables, optimal recovery of the analysis model's parameters is a challenging endeavor. One of the most popular methods to deal with missing nominal data is multiple imputation (MI). This study evaluated the capabilities of five MI methods that can be used to treat incomplete nominal variables: multiple imputation with chained equations (MICE) using polytomous regression as the elementary imputation method; MICE based on classification and regression trees (CART); MICE based on nested logistic regressions; the ranking procedure described by Allison (2002 Allison, P. D. (2002). Missing data. Thousand Oaks, CA: Sage Publications. https://doi.org/10.4135/9780857020994.n4[Crossref] , [Google Scholar]); and a joint modeling approach based on the general location model. We first motivate our inquiry with an applied example and then present the results of a Monte Carlo simulation study that compared the performance of the five imputation methods under conditions of varying sample size, percentage of missing data, and number of nominal response categories. We found that MICE with polytomous regression was the strongest performer while the Allison (2002 Allison, P. D. (2002). Missing data. Thousand Oaks, CA: Sage Publications. https://doi.org/10.4135/9780857020994.n4[Crossref] , [Google Scholar]) ranking procedure and MICE with CART performed poorly in most conditions.     

Police Attitudes in England to Return Interviews,in Repeat Missing Person Cases

The aim of this article is to examine the attitudes of English police officers to return interviews of people who are reported missing repeatedly (e.g. three times or more). In addition to a brief police ‘Safe & Well Check’ a return interview is also carried out by a police officer and seeks to find out where people went and why, in order to identify potential risks to their safety and whether they experienced harm whilst they were missing. A mixed‐methods survey of 50 constables from one police force in England ran in March 2014, using quantitative and open qualitative questions. Key themes that emerged were individual frustration at repetition, negativity around usefulness of the interviews, a challenge to involve third sector partners, and development areas in training. Statistical significance was found in variables relating to officer experience and gender, against views on interviewing missing people. The article looks at the limited existing literature and makes recommendations about best practice with return interviews, advocating a multi‐agency approach to improve interventions, and better training to improve positivity towards missing people. Copyright © 2016 John Wiley & Sons, Ltd.     

Does matching up features mess up job choice? Boundary conditions on attribute‐salience effects

Research in decision making has suggested that the degree to which features of an option are shared versus unique influences preferences in a way that violates normative rules. The generalizability of these findings to job choice was investigated. Senior‐level, undergraduate job seekers (N = 216) were presented with three jobs from which they were asked to choose one. Attributes for two of the jobs (A and B) remained invariant across conditions, and attributes for a third job (C) were manipulated such that it shared unfavorable features with one of the invariant jobs (A or B) and favorable attributes with the other job (B or A). Results suggested that jobs with unique positive features and shared negative features were preferred over those with unique negative features and shared positive features only when information was presented in a simple (versus complex) format and when participants did not rate the importance of attributes prior to the choice task. We suggest that inferences from feature‐matching research should be qualified by these boundary conditions. Copyright © 2002 John Wiley & Sons, Ltd.     

Gender, Discrimination, and Capability: Insights from Amartya Sen

This essay critically examines economist and philosopher Amartya Sen's writings as a potential resource in religious ethicists' efforts to analyze discrimination against girls and women and to address their well-being and agency. Delineating how Sen's discussions of "missing women" and "gender and cooperative conflict" fit within his "capability approach" to economic and human development, the article explores how Sen's methodology employs empirical analysis toward normative ends. Those ends expand the capability of girls and women to function in all aspects of their society. It concludes with a discussion of ways to engage Sen's work within religious ethics.     

The Performance of Multivariate Methods for Two-Group Comparisons with Small Samples and Incomplete Data

Abstract

In intervention studies having multiple outcomes, researchers often use a series of univariate tests (e.g., ANOVAs) to assess group mean differences. Previous research found that this approach properly controls Type I error and generally provides greater power compared to MANOVA, especially under realistic effect size and correlation combinations. However, when group differences are assessed for a specific outcome, these procedures are strictly univariate and do not consider the outcome correlations, which may be problematic with missing outcome data. Linear mixed or multivariate multilevel models (MVMMs), implemented with maximum likelihood estimation, present an alternative analysis option where outcome correlations are taken into account when specific group mean differences are estimated. In this study, we use simulation methods to compare the performance of separate independent samples t tests estimated with ordinary least squares and analogous t tests from MVMMs to assess two-group mean differences with multiple outcomes under small sample and missingness conditions. Study results indicated that a MVMM implemented with restricted maximum likelihood estimation combined with the Kenward–Roger correction had the best performance. Therefore, for intervention studies with small N and normally distributed multivariate outcomes, the Kenward–Roger procedure is recommended over traditional methods and conventional MVMM analyses, particularly with incomplete data.     

Using Response Times for Joint Modeling of Response and Omission Behavior

Abstract

For adequate modeling of missing responses, a thorough understanding of the nonresponse mechanisms is vital. As a large number of major testing programs are in the process or already have been moving to computer-based assessment, a rich body of additional data on examinee behavior becomes easily accessible. These additional data may contain valuable information on the processes associated with nonresponse. Bringing together research on item omissions with approaches for modeling response time data, we propose a framework for simultaneously modeling response behavior and omission behavior utilizing timing information for both. As such, the proposed model allows (a) to gain a deeper understanding of response and nonresponse behavior in general and, in particular, of the processes underlying item omissions in LSAs, (b) to model the processes determining the time examinees require to generate a response or to omit an item, and (c) to account for nonignorable item omissions. Parameter recovery of the proposed model is studied within a simulation study. An illustration of the model by means of an application to real data is provided.     

Analysis of Interactions and Nonlinear Effects with Missing Data: A Factored Regression Modeling Approach Using Maximum Likelihood Estimation

Abstract

When estimating multiple regression models with incomplete predictor variables, it is necessary to specify a joint distribution for the predictor variables. A convenient assumption is that this distribution is a multivariate normal distribution, which is also the default in many statistical software packages. This distribution will in general be misspecified if predictors with missing data have nonlinear effects (e.g., x²) or are included in interaction terms (e.g., x·z). In the present article, we introduce a factored regression modeling approach for estimating regression models with missing data that is based on maximum likelihood estimation. In this approach, the model likelihood is factorized into a part that is due to the model of interest and a part that is due to the model for the incomplete predictors. In three simulation studies, we showed that the factored regression modeling approach produced valid estimates of interaction and nonlinear effects in regression models with missing values on categorical or continuous predictor variables under a broad range of conditions. We developed the R package mdmb, which facilitates a user-friendly application of the factored regression modeling approach, and present a real-data example that illustrates the flexibility of the software.