Lost but not forgotten : attrition in the Étude longitudinale du développement des enfants du Québec (ÉLDEQ), Julien BÉRARD-CHAGNON and Simona BIGNAMI-VAN ASSCHE Département de démographie Département de démographie Project financed by the Fonds québécois de recherche sur la société et la culture (FQRSC) Background Longitudinal surveys are increasingly used in the social sciences to describe behaviour dynamics, to identify the influence of past on current behaviours, and to make stronger causal inferences than it is possible with cross-sectional surveys. Longitudinal surveys are increasingly used in the social sciences to describe behaviour dynamics, to identify the influence of past on current behaviours, and to make stronger causal inferences than it is possible with cross-sectional surveys. In demography, longitudinal data are particularly relevant for the study of family transitions and life course analysis. In demography, longitudinal data are particularly relevant for the study of family transitions and life course analysis. One of the main weaknesses of longitudinal surveys is that they are prone to attrition, that is, the loss of study subjects over time that is due to respondents leaving the study prematurely and permanently. One of the main weaknesses of longitudinal surveys is that they are prone to attrition, that is, the loss of study subjects over time that is due to respondents leaving the study prematurely and permanently. Why study survey attrition ? Reduces the sample size (thus reducing the power of statistical estimations). Reduces the sample size (thus reducing the power of statistical estimations). Makes the study of small sub-samples harder. Makes the study of small sub-samples harder. Affects sample representativeness. Affects sample representativeness. Might lead to selection bias. Might lead to selection bias.Data ÉLDEQ: Ongoing longitudinal survey aimed at studying the factors influencing child development in Québec for a cohort of 2120 children born in ÉLDEQ: Ongoing longitudinal survey aimed at studying the factors influencing child development in Québec for a cohort of 2120 children born in The ÉLDEQ survey team has made lots of efforts to minimize attrition and track down attritors. The ÉLDEQ survey team has made lots of efforts to minimize attrition and track down attritors. Selected sample for the analysis: waves 1-8. Selected sample for the analysis: waves 1-8. Attrition in the ÉLDEQ Attrition is measured by the participation to the main survey instrument (QIRI). Attrition is measured by the participation to the main survey instrument (QIRI). Attrition was very low in the first phase of the survey (waves 1-5, before the sampled children entered primary school), but high in the second phase (waves 6-8). Attrition was very low in the first phase of the survey (waves 1-5, before the sampled children entered primary school), but high in the second phase (waves 6-8). Many factors (uncertainty about the future of the survey, increasing length of interviews, etc.) explain this finding. Many factors (uncertainty about the future of the survey, increasing length of interviews, etc.) explain this finding. Attritors’ characteristics Attritors have different characteristics than the other respondents in the sample. Attritors have different characteristics than the other respondents in the sample. Overall, attritors distinguish themselves from non-attritors by their characteristics associated with instability, poverty, immigration and social exclusion. Overall, attritors distinguish themselves from non-attritors by their characteristics associated with instability, poverty, immigration and social exclusion. A : Wave 1 attritorsB : Wave 5 attritorsC : Non-attritors Study objectives 1.Compare the characteristics of attritors and non- attritors using chi-square tests and one-way ANOVA. 2.Identify the factors influencing the probability of attrition using multivariate probit regressions. 3.Measure attrition bias using BGLW tests for selected variables. Conclusions Probability of attrition The probability of attrition is modeled using a probit model with a set of background and other individual characteristics as independent variables. The probability of attrition is modeled using a probit model with a set of background and other individual characteristics as independent variables. Most variables do not predict significatively the probability of attrition. Most variables do not predict significatively the probability of attrition. Attrition bias Attrition bias is evaluated by means of BGLW tests (Becketti, Gould, Lillard and Welch, 1988) by regressing a selected variable of interest on a set of control variables plus a dichotomous variable representing attrition in the following waves. The presence and magnitude of attrition bias is inferred from the significance of the estimated coefficient for attrition in this equation. Attrition bias is evaluated by means of BGLW tests (Becketti, Gould, Lillard and Welch, 1988) by regressing a selected variable of interest on a set of control variables plus a dichotomous variable representing attrition in the following waves. The presence and magnitude of attrition bias is inferred from the significance of the estimated coefficient for attrition in this equation. Attrition does not exert a signficant bias on most variables of interest (e.g. delay in child’ growth) with the exception of mothers’ immigrant status and abortion. Attrition does not exert a signficant bias on most variables of interest (e.g. delay in child’ growth) with the exception of mothers’ immigrant status and abortion. *** ** * *** Legend: * p<0,10; ** p<0,05; *** p<0,01. Notes: The household’s characteristics included in the models are: household income, household income squared, number of siblings, whether home is owned). The individual characteristics of the mother included in the models are: age, highest diploma, occupation. All probabilities are calculated using the mean score for continuous variables and the mode for discrete variables. ** ** Legend: * p<0,10; ** p<0,05; *** p<0,01. Note: The background characteristics considered for the BGLW tests are the same used for the probit models. Respondents’ attitude towards surveys (i.e. level of education) and geographic mobility are the two most important factors associated with attrition. Respondents’ attitude towards surveys (i.e. level of education) and geographic mobility are the two most important factors associated with attrition. Although attrition exerts important biases for univariate analyses, it does not generally bias multivariate analyses. Although attrition exerts important biases for univariate analyses, it does not generally bias multivariate analyses. The main effect of attrition for analyses of the ÉLDEQ data is to decrease the sample size and thus reduce the power of statistical inferences. The main effect of attrition for analyses of the ÉLDEQ data is to decrease the sample size and thus reduce the power of statistical inferences. Continuing efforts are made by the ÉLDEQ survey team to track down respondents and thus limit attrition in future waves. Continuing efforts are made by the ÉLDEQ survey team to track down respondents and thus limit attrition in future waves. Future research should focus on the consequences of attrition for longitudinal analyses of the ÉLDEQ data (survival analysis and multi-level analysis). Future research should focus on the consequences of attrition for longitudinal analyses of the ÉLDEQ data (survival analysis and multi-level analysis). Researchers using longitudinal survey data should always check for attrition bias in their analyses. Researchers using longitudinal survey data should always check for attrition bias in their analyses. ***