This simulation study was designed to assess the impact of three ad hoc procedures for handling missing level two (here, school) identifiers in multilevel modeling. A multiple membership data structure was generated and both conventional hierarchical linear modeling (HLM) and multiple membership random effects modeling (MMREM) were employed. HLM models purely hierarchical data structures while MMREM appropriately models multiple membership data structures. Two of the ad hoc procedures investigated involved removing different subsamples of students from the analysis (HLM-Delete and MMREM-Delete) while the other procedure retained all subjects and involved creating a pseudo-identifier for the missing level two identifier (MMREM-Unique). Relative parameter and standard error (SE) bias were calculated for each parameter estimated to assess parameter recovery. Across the conditions and parameters investigated, each procedure had some level of substantial bias. MMREM-Unique and MMREM-Delete resulted in the least amount of relative parameter bias while HLM-Delete resulted in the least amount of relative SE bias. Results and implications for applied researchers are discussed. / text

Missing values in sample survey can lead to biased estimation if not treated. Imputation was posted asa popular way to deal with missing values. In this paper, based on Särndal (1994, 2005)’s research, aMonte-Carlo simulation is conducted to study how the estimators work in different situations and howdifferent imputation methods work for different response distributions.

People who go missing are often perceived to have done so voluntarily, and yet, many missing persons in Canada are Aboriginal, visible minorities, homeless, and are fleeing from violence, abuse, and neglect. Integrating the concept of social exclusion and an intersectional perspective with a sample of 724 missing persons cases drawn from one Canadian police service, this dissertation examines the systemic issues underlying peoples’ disappearances. This dissertation also explores the role of social and economic disadvantage in the risk of a long term disappearance. A combination of univariate (descriptions), bivariate (cross-tabulations), and multivariate (logistic regression) analyses identify correlates and causes of going missing and correlates and causes of long term disappearances. The concept of social exclusion explains how structural processes prevent particular groups and individuals from gaining access to valued social relationships and economic opportunities in a particular society, resulting in considerable hardship and disadvantage. This dissertation argues that people who are marginalized and excluded have few resources to rely on to cope with stress and strain and may resort to going missing if confronted with adversity. Groups who are overrepresented among missing persons compared to the general population are identified by cross-tabulations and chi-square tests. Multivariate analysis (partial tables and logistic regression) is used to control for possible sources of spuriousness, in order to have more confidence in imputing causal relationships between membership in disadvantaged groups and going missing. Moreover, if disadvantaged groups go missing, they further sever ties with families, the labour market, and other mainstream institutions. As a result of extreme disadvantage, they may find it difficult to (re)connect with conventional social relationships and mainstream society. For example, youth who are escaping violence and abuse at home often end up on the streets and sever ties with schools, families, and other conventional support networks and become engaged in street culture. As a result of extreme disadvantage these young people are at risk of a long term disappearance. In other words, social exclusion is expected to be a risk and causal factor in long term disappearances. Groups who are overrepresented among long term disappearances compared to short term disappearances are identified by cross-tabulations and chi-square tests. Logistic regression analysis is used to draw conclusions about causal factors in long term disappearances. This research finds that excluded groups such as disadvantaged youth, Aboriginal people, women and other visible minorities, victims of violence, and youth in care are at disproportionate risk of going missing. Consistent with an intersectional perspective, this dissertation shows that certain groups who are multiply marginalized such as Aboriginal women and young women face an especially high risk of going missing. Aboriginal identity, labour force status, and homelessness are also implicated as causal factors in peoples’ disappearances. Moreover, this research finds that social exclusion is a risk and causal factor in long term disappearances as Aboriginal people, homeless people, minorities and other excluded groups face a high risk of a long term disappearance. Linking missing persons with the concept of social exclusion highlights the role of structural issues in peoples’ disappearances and refutes the common misperception that going missing is a choice. In terms of policy, the findings from this research indicate that prevention and intervention depend on targeting poverty, discrimination, gender inequality, violence, and other structural issues associated with social exclusion.

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Hierarchical models are applicable to modeling data from complex surveys or longitudinal data when a clustered or multistage sample design is employed. The focus of this thesis is to investigate inference for discrete hierarchical models in the presence of missing data. This thesis is divided into two parts: in the first part, methods are developed to analyze the discrete and ordinal response data from hierarchical longitudinal studies. Several approximation methods have been developed to estimate the parameters for the fixed and random effects in the context of generalized linear models. The thesis focuses on two likelihood-based estimation procedures, the pseudo likelihood (PL) method and the adaptive Gaussian quadrature (AGQ) method. The simulation results suggest that AGQ is preferable to PL when the goal is to estimate the variance of the random intercept in a complex hierarchical model. AGQ provides smaller biases for the estimate of the variance of the random intercept. Furthermore, it permits greater flexibility in accommodating user-defined likelihood functions. In the second part, simulated data are used to develop a method for modeling longitudinal binary data when non-response depends on unobserved responses. This simulation study modeled three-level discrete hierarchical data with 30% and 40% missing data using a missing not at random (MNAR) missing-data mechanism. It focused on a monotone missing data-pattern. The imputation methods used in this thesis are: complete case analysis (CCA), last observation carried forward (LOCF), available case missing value (ACMVPM) restriction, complete case missing value (CCMVPM) restriction, neighboring case missing value (NCMVPM) restriction, selection model with predictive mean matching method (SMPM), and Bayesian pattern mixture model. All three restriction methods and the selection model used the predictive mean matching method to impute missing data. Multiple imputation is used to impute the missing values. These m imputed values for each missing data produce m complete datasets. Each dataset is analyzed and the parameters are estimated. The results from the m analyses are then combined using the method of Rubin(1987), and inferences are made from these results. Our results suggest that restriction methods provide results that are superior to those of other methods. The selection model provides smaller biases than the LOCF methods but as the proportion of missing data increases the selection model is not better than LOCF. Among the three restriction methods the ACMVPM method performs best. The proposed method provides an alternative to standard selection and pattern-mixture modeling frameworks when data are not missing at random. This method is applied to data from the third Waterloo Smoking Project, a seven-year smoking prevention study having substantial non-response due to loss-to-follow-up.

This article examines methods to efficiently estimate the mean response in a linear model with an unknown error distribution under the assumption that the responses are missing at random. We show how the asymptotic variance is affected by the estimator of the regression parameter and by the imputation method. To estimate the regression parameter the Ordinary Least Squares method is efficient only if the error distribution happens to be normal. If the errors are not normal, then we propose a One Step Improvement estimator or a Maximum Empirical Likelihood estimator to estimate the parameter efficiently. In order to investigate the impact that imputation has on estimation of the mean response, we compare the Listwise Deletion method and the Propensity Score method (which do not use imputation at all), and two imputation methods. We show that Listwise Deletion and the Propensity Score method are inefficient. Partial Imputation, where only the missing responses are imputed, is compared to Full Imputation, where both missing and non-missing responses are imputed. Our results show that in general Full Imputation is better than Partial Imputation. However, when the regression parameter is estimated very poorly, then Partial Imputation will outperform Full Imputation. The efficient estimator for the mean response is the Full Imputation estimator that uses an efficient estimator of the parameter.

Thesis (Ph. D.)--Oregon State University, 2008. / Printout. Includes bibliographical references (leaves 64-66). Also available on the World Wide Web.

Thesis (Ph.D.) -- University of Adelaide, Dept. of Physics and Mathematical Physics, 2001. / Bibliography: leaves 100-106.

Thesis (M. Phil.)--University of Hong Kong, 2007. / Title proper from title frame. Also available in printed format.

Thesis (Ph. D.)--Duke University, 2007.