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Longitudinal data is almost always burdened with missing data. However, in educational and psychological research, there is a large discrepancy between methodological suggestions and research practice. The former suggests applying sensitivity analysis in order to the robustness of the results in terms of varying assumptions regarding the mechanism generating the missing data. However, in research practice, participants with missing data are usually discarded by relying on listwise deletion. To help bridge the gap between methodological recommendations and applied research in the educational and psychological domain, this study provides a tutorial example of sensitivity analysis for latent growth analysis. The example data concern students’ changes in learning strategies during higher education. One cohort of students in a Belgian university college was asked to complete the Inventory of Learning Styles–Short Version, in three measurement waves. A substantial number of students did not participate on each occasion. Change over time in student learning strategies was assessed using eight missing data techniques, which assume different mechanisms for missingness. The results indicated that, for some learning strategy subscales, growth estimates differed between the models. Guidelines in terms of reporting the results from sensitivity analysis are synthesised and applied to the results from the tutorial example. [ABSTRACT FROM AUTHOR]

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Workplace learning is becoming a central tenet for a large proportion of today's employees. This seems especially true for so-called knowledge workers. Today, it remains unclear how differences in the quality of workplace learning are affected by differences in perception of the workplace environment and the motivation of knowledge workers to learn. Moreover, the possible role of motivation as a mediating factor between workplace climate factors and learning is underexplored. This paper therefore investigates direct and indirect links between perceptions of the workplace climate, motivation to learn and approaches to learning in the workplace. Knowledge workers ( N = 202) in one knowledge intensive organisation were questioned using existing and adapted questionnaires to measure learning approaches, motivation and workplace climate. Correlations and multivariate regression analyses were carried out to assess direct relationships amongst variables. Path analysis was carried out to assess the mediating role of motivation. Results show that both workplace climate factors and motivation directly influence employees' approaches to learning. Some direct relationships between workplace climate factors and motivation were also uncovered. Results regarding the mediating role of motivation showed that the effect of good supervision on deep learning is completely mediated by autonomous motivation. The effect of choice independence on deep learning approach is partially mediated by the same motivational drive. A-motivation was found to partially mediate the link between good supervision and a surface disorganised approach. Implications for research and practice are discussed. [ABSTRACT FROM AUTHOR]

The change in learning strategies during higher education is an important topic of research in the Student Approaches to Learning field. Although the studies on this topic are increasingly longitudinal, analyses have continued to rely primarily on traditional statistical methods. The present research is innovative in the way it uses a multi-indicator latent growth analysis in order to more accurately estimate the general and differential development in learning strategy scales. Moreover, the predictive strength of the latent growth models are estimated. The sample consists of one cohort of Flemish University College students, 245 of whom participated in the three measurement waves by filling out the processing and regulation strategies scales of the Inventory of Learning Styles – Short Versions. Independent-samples t-tests revealed that the longitudinal group is a non-random subset of students starting University College. For each scale, a multi-indicator latent growth model is estimated using Mplus 6.1. Results suggest that, on average, during higher education, students persisting in their studies in a non-delayed manner seem to shift towards high-quality learning and away from undirected and surface-oriented learning. Moreover, students from the longitudinal group are found to vary in their initial levels, while, unexpectedly, not in their change over time. Although the growth models fit the data well, significant residual variances in the latent factors remain. [ABSTRACT FROM AUTHOR]