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Data from a three-wave complete longitudinal design survey on career calling and related constructs (N = 6368)
This dataset provides de-identified raw responses to a non-anonymous three-wave online survey with a 12-month time lag. Data collection was part of a larger project on the development of career calling in Italian college students. The first wave was collected during the fall of 2014. Participants we...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6702381/ https://www.ncbi.nlm.nih.gov/pubmed/31453291 http://dx.doi.org/10.1016/j.dib.2019.104310 |
Sumario: | This dataset provides de-identified raw responses to a non-anonymous three-wave online survey with a 12-month time lag. Data collection was part of a larger project on the development of career calling in Italian college students. The first wave was collected during the fall of 2014. Participants were bachelor's or master's students enrolled in 24 different study domains and 4 different universities. Sample sizes for each wave are N(T)(1) = 5,886, N(T)(2) = 1,700 and N(T)(3) = 881, 434 participants provided valid responses at all the three waves. Consent form was electronic. Dataset and codebook can be found here: https://osf.io/v56du/. The sample is mainly composed of women (63.8%, at Time 1). Participants' mean age at Time 1 was 23.37 years (SD = 5.39). The survey was in Italian and included multiple-item measures of career calling, intrinsic and extrinsic motivation, social support, engaged learning, clarity of professional identity, and quality of mentorship. Socio-demographic information and academic performance indicators are provided. The dataset is necessary to reproduce previously published results (Vianello et al., 2018) and can be useful to 1) investigate cross-cultural differences between college students from Italy and other countries; 2) investigate person-level variability in predictors and consequences of change in the variables collected over time; 3) develop and/or validate new statistical models for longitudinal data; and 4) develop and/or test original theoretical hypotheses. |
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