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The dual trajectory approach: detecting developmental behavioural overlaps in longitudinal and intergenerational research

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Abstract

Prospective longitudinal study designs are often referred to as the ideal way to explain causal relationships. Nevertheless, the evaluation of such data often leads to a reduction of empirical information. This is done by eliminating the factor time by forming sum indices or by analysing correlations over only two points in time. Newer methods of longitudinal research resort to group-based trajectory models which enables the analyses of inter- and intraindividual changes over time. However, less attention has been paid to a very useful extension of this method: the dual trajectory approach which provides the opportunity to combine two separate trajectory models in one model. In this way, the relationship between two sequences of behaviours can be estimated simultaneously and checked for overlaps. This approach is introduced, and an application based on prospective panel data from the German Crime in the modern City study (CrimoC) is carried out for the intergenerational relationship between physical violent parenting and juvenile delinquency.

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Notes

  1. The last option cannot be shown because Mplus does not output confidence intervals for the robust maximum likelihood estimator used here.

  2. The first four waves took place in the school context while the next waves of data collection were used for a stepwise change into a postal mode.

  3. The cross-sectional re-interviewing rates ranged between 85 and 92% (2002 n = 3411; 2003 n = 3392; 2004 n = 3339; 2005 n = 3243; 2006 n = 4548; 2007 n = 3336; 2008 n = 3086; 2009 n = 3090; 2011 n = 3050; 2013 n = 2850; 2015 n = 2754. The data collection in the year 2006 was the most challenging one. Due to the school leave of respondents in the lower educational level schools and the compulsory school attendance for all adolescents up to age 18, the attempt was made to retrieve these school-leavers in selected classes at vocational schools. A consequence was that the cross-sectional data includes additional cases of individuals who attended these classes but who had not participated before. These additional cases have no impact on the panel-dataset because they could not be matched to previous cases.

  4. Since the age of 22, respondents have also been asked whether they have children of their own (= G3) and how they raise their children.

  5. For this purpose, the participants fill out a personal code each year, which is requested in the questionnaire. This code includes time-invariant characteristics such as the first letter of the mothers’ name and the last letter of the natural hair colour.

  6. A cross-validation between the cumulative prevalence rates (that can be interpreted as a measurement of versatility) and much skewer incidence index (that can be interpreted as a measurement of frequency) comes to the result that persons of the high rate offender class have also the highest average incidence rates across all groups and also for all other trajectories there is an equivalent development of incidence rates. For this reason, the cumulative prevalence rates are used for the following analyses.

  7. A six class solution was described for the age-period 13–18 in Bentrup (2018).

  8. A five class solution was described for the age-period 13–18 in Bentrup (2018).

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Acknowledgements

I would like to thank Klaus Boers and Jost Reinecke, the initiators of the here reported longitudinal panel study Crime in the modern City (CrimoC) for their support, and the best project team of all time for the practical as well as content-related cooperation. This study was supported by the German Research Foundation (DFG).

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  1. Department of Criminology, University of Muenster, Bispinghof 24/25, 48143, Muenster, Germany

    Christina Bentrup

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Bentrup, C. The dual trajectory approach: detecting developmental behavioural overlaps in longitudinal and intergenerational research. Qual Quant 54, 43–65 (2020). https://doi.org/10.1007/s11135-019-00934-1

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