Abstract

In this session, we'll look at three important longitudinal studies that have been undertaken around the world. The LSMS survey, the ONS Coronavirus COVID-19 Infection Survey, and the Longitudinal Social Data Development Program in Canada are all taken into consideration. In the Session, we'll go over many facets of this type of research.
The first presentation focuses on the case of panels with a rotating sample design. This case represents a powerful hybrid solution for facing the sample erosion for deaths and movers and the impact of lack of sample representativeness for new births, migration flows. Moreover, the sample fatigue introduces an increasing measurement error. As the length of the panel surveys increases, there is an increasing interest, but also increasing challenges in preserving the quality of the panel sample estimates. The effect is particularly evident in long run panels. A correct design, implementation, and use of a panel survey shall consider a set of methods to deal with these problems at different stages of the statistical process: The sampling design, the data collection, and the estimation.
The second presentation illustrates how a proxy labour force indicator has been developed for the work domain of the Longitudinal Social Data Development Program (LSDDP) using microdata integration. The purpose of this work is to derive a monthly proxy employment status for each individual in the LSDDP population universe from the resulting algorithm. The microdata linkage required was performed using anonymized linkage keys and that quality assessments have been conducted and compared to results from the LFS and the Census. It is anticipated that employment statistics could be improved for different subdomains of interest (e.g. age, sex/gender, census geography, economic region) in using small area estimation technique. We will discuss the results and limitations of this work.
The third presentation highlights several aspects of the ONS Coronavirus (COVID-19) Infection Survey (CIS), which has a longitudinal structure retesting participant to see how swab positivity and antibody positivity change over time. The CIS represents a very relevant and almost unique experience for official statistics as it provides regular estimates of relevant parameters (infection rate, antibody positivity) related to the spread of the pandemic. It also allows more ad-hoc analysis into aspects such as a more detailed look into re-infections.
The last presentation focuses on nonresponse and attrition that are among the most significant problems for panel surveys, as they result in loss of data, decreased estimation efficiency, and increased risk of bias in research findings. We analyse in depth and disentangle these two phenomena, and propose an operational definition of attrition that strives to characterize attritors as units with a persistent non-respondent state over time. We use longitudinal data from the Ethiopia’s High Frequency Phone Survey to model our definition of attrition through survival analysis methods. The ultimate aim of our research would be to exploit the fitted survival model, after careful out-of-sample validation, to predict attrition hazards in similar ongoing panels, and optimize accordingly survey management decisions during data collection.