Grasping the Role of Personal Factors in Work-Related Learning: A Latent Growth Mixture Modeling Approach

• In Event: 58.082 - Roundtable Session 25
  In Roundtable Session: 58.082-9 - Methodological Advancements in Quantitative Research on Work-Related Learning and Professional Development

Abstract

This study will illustrate the use of latent growth mixture modeling and examines whether the development of self-efficacy and motivation during the transition from higher education to the labor market predicts work-related learning after graduation. The main advantage of a latent growth mixture modeling approach is that it models the individual trajectories (i.e., growth) without assuming that all individuals have the same growth pattern (Kreuter & Muthén, 2007). When conducting the latent growth mixture model, a first step comprises the specification of an unconditional latent class model (Jung & Wickrama, 2008). In a next step, the number of classes is determined. After determining the number of classes (i.e., mixture model), the growth pattern is specified (Jung & Wickrama, 2008). In that sense, a latent growth mixture modeling approach captures an unobserved heterogeneity by identifying classes and modeling the growth trend for each of these classes (Heck & Thomas, 2015). Therefore, it can be used to get a more profound insight in the relation between the individual differences in growth and various outcomes.

For this illustration, data are used from a longitudinal project on the transition from higher education to the labor market. Previous research has shown that both self-efficacy and motivation are strong predictors for work-related learning (Kyndt & Baert, 2013). When focusing on the transition, it is assumed that self-efficacy and motivation will develop during the transition from higher education to the labor market, as this period is characterized by extensive change (Kuijpers & Meijers, 2012). Therefore, it would be valuable to focus on the different development patterns of these personal factors to study the effect on work-related learning. Within this project, students were administered questionnaires four times (i.e., three times during their final year in higher education and one time beyond graduation). In a first step of the data analysis, the structure of the used scales was examined by means of confirmatory factor analysis. In addition, data on self-efficacy and motivation were checked for longitudinal measurement invariance. Afterwards, the mixture growth model was specified, in line with the steps described by Jung and Wickrama (2008). Subsequently an ANOVA was executed to test whether class membership based on growth patterns predicted work-related learning (Kreuter & Muthén, 2007). The results show different classes of individuals, based on the developmental trajectory of self-efficacy and motivation during their transition from higher education to work. The membership of the different classes was shown to be related to work-related learning. In sum, it can be concluded that latent growth mixture modeling is a useful technique to study work-related learning, as it gives the opportunity to capture the unobserved heterogeneity and to explore the relationship between the developmental trajectories and work-related learning in depth.

Authors

• Ilke Grosemans, KU Leuven

• Liesje Coertjens, Université Catholique de Louvain

• Eva Kyndt, KU Leuven