Estimation en présence de données incohérentes : étude de l'impact des biais de déclaration d'âge à l'initiation de la consommation de substances psychoactives à partir de données longitudinales
Survey data play an essential role in scientific production in the social sciences. However, the presence of bias in the data at the time of collection, including response and non-response errors, could affect the reliability of the results obtained. Although the problem of response and nonresponse errors is widely discussed, little attention has been paid in the existing literature to the mechanisms by which these errors influence the estimated measures. Therefore, the main objective of this thesis is to make a methodological contribution to the understanding of survey estimates in the presence of erroneous and missing data, by determining their explanatory part in the estimated models. Using two repeated measures of age at first substance use collected by the NLSCY (1994-2009) from Canadian youth, when they were 12-13 years old and again at 14-15 years old, this article-based dissertation aimed to investigate: (1) the types of inconsistencies or biases imputed in the second measure relative to the first (baseline) measure and thus infer the characteristics of respondents whose reports are inconsistent (Paper 1); (2) the impact of these biases in determining predictors of early use, i.e., at age 13 and younger (Paper 2); and (3) their impact on predicting use at age 16-17 as a function of age of initiation (Paper 3). The impact of bias is determined by correcting for (1) selection bias due to censoring of respondents with inconsistent reports from the sample, when age of initiation is the dependent variable in the estimated model (Paper 2) and (2) endogeneity bias due to the presence of erroneous and missing values in age of initiation, when age of initiation has an explanatory variable in the estimated model (Paper 3).  The first article reveals that at the second survey round, 14–15 years old do not necessarily provide ages of initiation consistent with those provided when they were 12-13 years old. The proportion of inconsistency recorded is not negligible; it is 43% for alcohol, 33% for tobacco, and 32% for drugs. These youth are likely to report later ages of initiation or omit previous experimentation (telescopic forward bias: BTA), earlier ages of initiation (telescopic backward bias: BTR) and may also fail to report initiation on at least one of the two survey rounds (unreported bias: BND). Multinomial regression results show that the risk of detecting these biases is not random; it varies with respondents' socio-demographic and personal characteristics, including gender, family structure, and region of residence. Because of the bias in the second report on age of initiation, the second article demonstrates that the identification of groups at risk for early use is affected by the potential for bias within these groups. Using Heckman's approach, it was concluded that earlier (later) reported initiation ages generate an overestimation (underestimation) of the risks of early use (at age 13 and younger) in the groups most likely to provide backward-biased (forward-biased) ages. However, this risk is underestimated in groups that did not report their age of initiation at the first survey round (BND). This indicates that for these groups, the age of initiation that was not reported in the first round is likely to be an early age (around 12-13 years). The third paper concludes that biases attributed to age of initiation affect the estimate of the relationship between age of initiation and frequency of use at age 16-17. Whether this relationship is overestimated or underestimated depends specifically on the type of bias and its correlation with later use. Finally, this thesis attempts to provide empirical evidence highlighting response and nonresponse bias as an additional source of information that characterizes the study sample and has its own explanatory part in the estimated models. The validity of survey data is thus of great benefit to the validity of studies results.
Sous/surestimation Inconsistent and missing longitudinal data Telescopic forward bias Telescopic backward bias Age at substance initiation Early use Impact of reporting bias Under/overestimation Selection/endogeneity bias