Longitudinal data are often collected in different research areas such as medicine, biology, education, and psychology. We can build a transitional model using longitudinal binary data, which aims...Show moreLongitudinal data are often collected in different research areas such as medicine, biology, education, and psychology. We can build a transitional model using longitudinal binary data, which aims to model the probability of transition between the response categories. In this type of data is common to find missing values due to dropouts, costs, and organizational problems. The missing-indicator model is often used as a method to handle missing values in this type of data. This method consists in creating a new category for the missing values. Therefore, the binary logistic model changes to a baseline-category logit model. This study aims to evaluate the bias of the estimated coefficients when the missing-indicator method is used in the response of a binary transitional model. Based on an empirical example, a Monte Carlo simulation with three factors is carried out: (1) type of missingness, (2) sample size, and (3) proportion of missing data. The coefficients bias from the baseline-category logit model is evaluated using boxplots and a three-way MANOVA analysis. The results suggest that sample size, the proportion of missing data, type of missingness and the interaction between sample size and proportion affect the bias of the estimated coefficients; nonetheless, the effect size is small. When each dependent variable is analysed separately using ANOVA, the effects of the proportion of missing and the interaction between sample size and proportion were statistically significant for only one coefficient. However, the effect size is still small. Therefore, the conclusion is that the estimated coefficients' bias for all the missingness types is low.Show less
Research master thesis | Developmental Psychopathology in Education and Child Studies (research) (MSc)
open access
2014-07-31T00:00:00Z
The current study elaborates on a former study by Veen and colleagues (2011), using extended data and a two-year follow up period. Criminal careers of 288 male adolescent offenders of native Dutch...Show moreThe current study elaborates on a former study by Veen and colleagues (2011), using extended data and a two-year follow up period. Criminal careers of 288 male adolescent offenders of native Dutch and Moroccan origin were compared using criminal record data. Using latent class analysis, a five-class model was found of offender types; violent offenders, property offenders, property/minor violent offender, violent offenders/arsonists, and sexual offenders. Category of first registered offence held predictive value of future offender type membership. Native Dutch and Moroccan male adolescent offenders differed significantly from each other based on categories of offences committed, number of offences committed and offender type membership. Moroccan male adolescent offenders showed a higher number of registered offences and a strong overrepresentation in violent and nonviolent property offences compared to native Dutch offenders. Overall, it can be concluded that male adolescent offenders of native Dutch and Moroccan origin represent significantly different offender types. These differences could only partially be accounted for by SES.Show less
