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Abstract
Emerging adulthood is recognized as a recent and developmentally distinct phase in the life-course, characterized as a period of identity exploration, of instability, being self-focused, feeling in-between, and an age of possibilities. To measure the subjective experience of emerging adulthood Reifman, Arnett, and Colwell developed the Inventory of the Dimensions of Emerging Adulthood (IDEA). While a series of studies demonstrated the applicability of the five dimensions of emerging adulthood and effectiveness of IDEA for measuring these in US samples, results from non-US samples revealed important cultural differences in how emerging adulthood is experienced. The current study tests the extent to which emerging adulthood is experienced by Dutch young people, and the relevance and validity of the IDEA for a general urban population sample from the Netherlands. We examined differences between socioeconomic and ethnic groups within this population. The results revealed that certain aspects of the Dutch emerging adulthood experience are different from the USA. In addition, there are small but significant differences between Dutch socioeconomic and ethnic groups in how this period of life is experienced. Economic and cultural explanations for these differences are discussed.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes
1. The overarching objective of this study is to examine (desistance from) criminal behavior in young adults, specifically focusing on people of Moroccan and Antillean descent as these are the ethnic groups most over-represented in the crime figures in the Netherlands: Hence the oversampling of young people with police contact, and those with Moroccan and Antillean ethnicities.
2. It is possible that young people who have police contact during their adolescence experience emerging adulthood differently to those who do not. We intend by weighting the data to ensure that our results are not biased by having an overly criminal sample.
3. Principal axis factoring (PAF) treats each item as providing information about the same small set of latent variables, called factors, as other items, yet influenced by unique sources of error. Whereas principal components analysis sets out to represent all of the variance in the items through a small set of components, PAF tries to understand only the shared variance in a set of item measurements through a small set of latent variables (Warner Citation2003).
4. PAF was carried out on 50 random data-sets. The 95th percentile of the eigenvalues generated was then compared to the eigenvalues from the actual data. Eigenvalues larger than those randomly generated were then retained.