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Objective: An overwhelming body of research based on cross-sectional data has identified a positive relationship between perceptions of police procedural justice and legitimacy. Following Tyler’s theoretical framework these studies have often interpreted the observed relationship as evidence of an unequivocal causal connection from procedural justice to legitimacy. Here we re-examined the validity of this conclusion by considering the temporal order of that association and the potential biasing effect of time-invariant third common causes. Hypotheses: 1) Past perceptions of police procedural justice predict future perceptions of legitimacy; 2) Past perceptions of police legitimacy predict future perceptions of procedural justice; and 3) Perceptions of police procedural justice and legitimacy are associated as a result of third common causes. Method: We fitted random intercepts cross-lagged panel models to seven waves of a longitudinal sample of 1,354 young offenders in the ‘Pathways to Desistance’ study. This allowed us to explore the directional paths between perceptions of police procedural justice and legitimacy, while controlling for time-invariant subject heterogeneity. Results: We did not find evidence of the assumed temporal association; lagged within subject perceptions of procedural justice rarely predicted within subject perceptions of legitimacy. We did not find evidence of a reciprocal relationship either. Instead, we detected substantial time-invariant subject heterogeneity, and perceptions of legitimacy being self-reproduced. Conclusions: Our findings challenge the internal validity of the commonly reported positive associations between procedural justice and legitimacy found in studies using cross-sectional data.

In this thesis I carry out an encompassing analysis of the problem of measurement error in retrospectively collected work histories using data from the “Longitudinal Study of the Unemployed”. This dataset has the unique feature of linking survey responses to a retrospective question on work status to administrative data from the Swedish Register of Unemployment. Under the assumption that the register data is a gold standard I explore three research questions: i) what is the prevalence of and the reasons for measurement error in retrospective reports of unemployment; ii) what are the consequences of using such survey data subject to measurement error in event history analysis; and iii) what are the most effective statistical methods to adjust for such measurement error.Regarding the first question I find substantial measurement error in retrospective reports of unemployment, e.g. only 54% of the subjects studied managed to report the correct number of spells of unemployment experienced in the year prior to the interview. Some reasons behind this problem are clear, e.g. the longer the recall period the higher the prevalence of measurement error. However, some others depend on how measurement error is defined, e.g. women were associated with a higher probability of misclassifying spells of unemployment but not with misdating them.To answer the second question I compare different event history models using duration data from the survey and the register as their response variable. Here I find that the impact of measurement error is very large, attenuating regression estimates by about 90% of their true value, and this impact is fairly consistent regardless of the type of event history model used. In the third part of the analysis I implement different adjustment methods and compare their effectiveness. Here I note how standard methods based on strong assumptions such as SIMEX or Regression Calibration are incapable of dealing with the complexity of the measurement process under analysis. More positive results are obtained through the implementation of ad hoc Bayesian adjustments capable of accounting for the different patterns of measurement error using a mixture model.