Considerable amounts of event history data are collected through longitudinal surveys. These surveys have many particularities or features that are the results of the dynamic nature of the population under study and of the fact that data collected through longitudinal surveys involve the use of complex survey designs, with clustering and stratification. These particularities include: attrition, seam-effect, censoring, left-truncation and complications in the variance estimation due to the use of complex survey designs. This thesis focuses on the last two points. Statistical methods based on the stratified Cox proportional hazards model that account for intra-cluster dependence, when the sampling design is uninformative, are proposed. This is achieved using the theory of estimating equations in conjunction with empirical process theory. Issues concerning analytic inference from survey data and the use of weighted versus unweighted procedures are also discussed. The proposed methodology is applied to data from the U. S. Survey of Income and Program Participation (SIPP) and data from the Canadian Survey of Labour and Income Dynamics (SLID). Finally, different statistical methods for handling left-truncated sojourns are explored and compared. These include the conditional partial likelihood and other methods, based on the Exponential or the Weibull distributions.

Considerable amounts of event history data are collected through longitudinal surveys. These surveys have many particularities or features that are the results of the dynamic nature of the population under study and of the fact that data collected through longitudinal surveys involve the use of complex survey designs, with clustering and stratification. These particularities include: attrition, seam-effect, censoring, left-truncation and complications in the variance estimation due to the use of complex survey designs. This thesis focuses on the last two points. Statistical methods based on the stratified Cox proportional hazards model that account for intra-cluster dependence, when the sampling design is uninformative, are proposed. This is achieved using the theory of estimating equations in conjunction with empirical process theory. Issues concerning analytic inference from survey data and the use of weighted versus unweighted procedures are also discussed. The proposed methodology is applied to data from the U. S. Survey of Income and Program Participation (SIPP) and data from the Canadian Survey of Labour and Income Dynamics (SLID). Finally, different statistical methods for handling left-truncated sojourns are explored and compared. These include the conditional partial likelihood and other methods, based on the Exponential or the Weibull distributions.

Data from longitudinal surveys give rise to many statistical challenges. They often come from a vast, heterogeneous population and from a complex sampling design. Further, they are usually collected retrospectively at intermittent interviews spaced over a long period of time, which gives rise to missing information and loss to follow-up. As a result, duration data from this kind of surveys are subject to dependent censoring, which needs to be taken into account to prevent biased analysis. Methods for point and variance estimation are developed using Inverse Probability of Censoring (IPC) weights. These methods account for the random nature of the IPC weights and can be applied in the analysis of duration data in survey and non-survey settings. The IPC estimation techniques are based on parametric estimating function theory and involve the estimation of dropout models. Survival distributions without covariates are estimated via a weighted Kaplan-Meier method and regression modeling through the Cox Proportional Hazards model and other models is based on weighted estimating functions. The observational frameworks from Statistics Canada's Survey of Labour and Income Dynamics (SLID) and the UK Millenium Cohort Study are used as motivation, and durations of jobless spells from SLID are analyzed as an illustration of the methodology. Issues regarding missing information from longitudinal surveys are also discussed.

Data from longitudinal surveys give rise to many statistical challenges. They often come from a vast, heterogeneous population and from a complex sampling design. Further, they are usually collected retrospectively at intermittent interviews spaced over a long period of time, which gives rise to missing information and loss to follow-up. As a result, duration data from this kind of surveys are subject to dependent censoring, which needs to be taken into account to prevent biased analysis. Methods for point and variance estimation are developed using Inverse Probability of Censoring (IPC) weights. These methods account for the random nature of the IPC weights and can be applied in the analysis of duration data in survey and non-survey settings. The IPC estimation techniques are based on parametric estimating function theory and involve the estimation of dropout models. Survival distributions without covariates are estimated via a weighted Kaplan-Meier method and regression modeling through the Cox Proportional Hazards model and other models is based on weighted estimating functions. The observational frameworks from Statistics Canada's Survey of Labour and Income Dynamics (SLID) and the UK Millenium Cohort Study are used as motivation, and durations of jobless spells from SLID are analyzed as an illustration of the methodology. Issues regarding missing information from longitudinal surveys are also discussed.

This research is a systematic comparison of youth migration experiences between two birth cohorts, using the first ten rounds of two national longitudinal surveys of youth, NLSY79 and NLSY97. Results show both changes and continuities in youth migration patterns across cohorts for ages16-25. Specifically, youth today have a delayed but stronger migration momentum than the late baby boom generation, the dividing point being at age 22. Women are more likely to migrate than men in the recent cohort, but not in the older cohort. Whites migrate considerably more than blacks and Hispanics consistently across cohorts. The likely life events in youth's transition to adulthood are important indicators of youth's migration propensity for both cohorts. Particularly, graduating with a bachelor's degree is the most powerful predictor of youth's migration propensity. Other life events such as getting married; becoming separated, divorced, or widowed; dropping out of college; and losing a job are also significantly associated with youth migration. In general, the effects of these life events on youth's migration propensity are weakened across cohorts, but the importance of having a college degree on migration propensity has been increasing.

Longitudinal surveys have emerged in recent years as an important data collection tool for population studies where the primary interest is to examine population changes over time at the individual level. The National Longitudinal Survey of Children and Youth (NLSCY), a large scale survey with a complex sampling design and conducted by Statistics Canada, follows a large group of children and youth over time and collects measurement on various indicators related to their educational, behavioral and psychological development. One of the major objectives of the study is to explore how such development is related to or affected by familial, environmental and economical factors. The generalized estimating equation approach, sometimes better known as the GEE method, is the most popular statistical inference tool for longitudinal studies. The vast majority of existing literature on the GEE method, however, uses the method for non-survey settings; and issues related to complex sampling designs are ignored. This thesis develops methods for the analysis of longitudinal surveys when the response variable contains missing values. Our methods are built within the GEE framework, with a major focus on using the GEE method when missing responses are handled through hot-deck imputation. We first argue why, and further show how, the survey weights can be incorporated into the so-called Pseudo GEE method under a joint randomization framework. The consistency of the resulting Pseudo GEE estimators with complete responses is established under the proposed framework. The main focus of this research is to extend the proposed pseudo GEE method to cover cases where the missing responses are imputed through the hot-deck method. Both weighted and unweighted hot-deck imputation procedures are considered. The consistency of the pseudo GEE estimators under imputation for missing responses is established for both procedures. Linearization variance estimators are developed for the pseudo GEE estimators under the assumption that the finite population sampling fraction is small or negligible, a scenario often held for large scale population surveys. Finite sample performances of the proposed estimators are investigated through an extensive simulation study. The results show that the pseudo GEE estimators and the linearization variance estimators perform well under several sampling designs and for both continuous response and binary response.

Longitudinal surveys have emerged in recent years as an important data collection tool for population studies where the primary interest is to examine population changes over time at the individual level. The National Longitudinal Survey of Children and Youth (NLSCY), a large scale survey with a complex sampling design and conducted by Statistics Canada, follows a large group of children and youth over time and collects measurement on various indicators related to their educational, behavioral and psychological development. One of the major objectives of the study is to explore how such development is related to or affected by familial, environmental and economical factors. The generalized estimating equation approach, sometimes better known as the GEE method, is the most popular statistical inference tool for longitudinal studies. The vast majority of existing literature on the GEE method, however, uses the method for non-survey settings; and issues related to complex sampling designs are ignored. This thesis develops methods for the analysis of longitudinal surveys when the response variable contains missing values. Our methods are built within the GEE framework, with a major focus on using the GEE method when missing responses are handled through hot-deck imputation. We first argue why, and further show how, the survey weights can be incorporated into the so-called Pseudo GEE method under a joint randomization framework. The consistency of the resulting Pseudo GEE estimators with complete responses is established under the proposed framework. The main focus of this research is to extend the proposed pseudo GEE method to cover cases where the missing responses are imputed through the hot-deck method. Both weighted and unweighted hot-deck imputation procedures are considered. The consistency of the pseudo GEE estimators under imputation for missing responses is established for both procedures. Linearization variance estimators are developed for the pseudo GEE estimators under the assumption that the finite population sampling fraction is small or negligible, a scenario often held for large scale population surveys. Finite sample performances of the proposed estimators are investigated through an extensive simulation study. The results show that the pseudo GEE estimators and the linearization variance estimators perform well under several sampling designs and for both continuous response and binary response.

This thesis describes the design, maintenance and statistical analysis involved in undertaking a Longitudinal Survey. A longitudinal survey (or study) obtains observations or responses from individuals over several times over a defined period. This enables the direct study of changes in an individual's response over time. In particular, it distinguishes an individual's change over time from the baseline differences among individuals within the initial panel (or cohort). This is not possible in a cross-sectional study. As such, longitudinal surveys give correlated responses within individuals. Longitudinal studies therefore require different considerations for sample design and selection and analysis from standard cross-sectional studies. This thesis looks at the methodology for analysing social surveys. Most social surveys comprise of variables described as categorical variables. This thesis outlines the process of sample design and selection, interviewing and analysis for a longitudinal study. Emphasis is given to categorical response data typical of a survey. Included in this thesis are examples relating to the Goodna Longitudinal Survey and the Longitudinal Survey of Immigrants to Australia (LSIA). Analysis in this thesis also utilises data collected from these surveys. The Goodna Longitudinal Survey was conducted by the Queensland Office of Economic and Statistical Research (a portfolio office within Queensland Treasury) and began in 2002. It ran for two years whereby two waves of responses were collected.