QoL, motivation, stress and academic achievement

Marcus Henning and Craig Webster

Quality of life, motivation to learn, stress and academic achievement


 

Researchers

Marcus Henning, Chris Krägeloh, Roger Booth, Erin Hill, Julie Chen and Craig Webster

 

Aims/Objectives of the Project

Aims: To determine the quality life, motivation, academic attainment, and stress levels of students studying in the Biomedical/Health Science Overlapping Year 1 programme.

Research Questions

  • What are the quality of life, motivation and stress experiences of students studying in the Biomedical/Health Science Overlapping Year 1 programme?
  • Are there any links between quality of life, motivation, stress, competition, and academic achievement?

 

Project Summary

The University of Auckland has made a pledge to promote the notion of wellness amongst students and staff: “We [the University] deliver high quality wellbeing services to students and staff, supporting them in their learning and teaching activities”. It is, therefore, reasonable that students associated with this university need to be researched in terms of their quality of life and levels of stress, and how this may impact on their motivations to learn, competiveness, and academic achievement to confirm that appropriate levels of well-being are being maintained.

Issues linked with the notion of quality of life amongst medical students in New Zealand have been well documented (e.g. Dyrbye et al., 2006).  Fourth- and fifth-year medical students report sleep deprivation, anxiety, and stress, as well as lower quality of life overall compared to the general population (Henning et al., 2010). However, much of the research in New Zealand has centred on the clinical training years in medicine. There is some literature suggesting that pre-medical students are at-risk of experiencing high levels of stress which may be connected with alcohol /drug abuse, interpersonal relationship difficulties, depression, and anxiety (Shapiro et al., 1998). The pre-medical student group are potentially at risk of lowered satisfaction measures and stress-induced difficulties due to the uncertainty of being accepted into medicine (Samaranayake et al., 2011).

Consequently, there is a dearth of studies, considering the impact of quality of life, motivation, stress and competitiveness on academic achievement at the Biomedical/Health Science Overlapping Year 1 stage of education. 

 

Study Design

The study design is correlational and reliant on survey methods (via SurveyGizmo) for collecting data.

All students in the Biomedical Common and Overlapping Year 1 programme will be emailed

and invited to take part in the survey. The resulting sample size will likely be in excess of 400  Biomedical Common Year and Overlapping Year 1 students (out of an approximate total of 1000; a sample of 400 will yield a margin of error = 3.8%). Participation will be self-selected and voluntary.

Students will be asked to participate by responding to an online survey generated by Survey Gizmo (see Appendix 1 for full details of the questionnaire), and students will thus be able to respond to the survey after scheduled class time. Students who elect to be part of the draw will have the chance to win one of five $100 vouchers. Participation in this draw will be voluntary and cannot be traced back to questionnaire responses.

The variables of interest will be measures of quality of life, stress, motivation and competitiveness. The measures include: Motivated Strategies for Learning Questionnaire, World Health Organisation Quality Of Life questionnaire (New Zealand Version), the Revised Competitiveness Index, and the Perceived Stress Scale.

Data analysis encompasses a wide range of multivariate analyses. In-depth analyses will take the form of structural equation modelling (or path analyses) which is primarily a cross-sectional statistical modelling technique with a strong focus on confirmatory analysis. It allows for investigations into latent constructs. It is useful in terms of constructing a model that likely explains the interrelationships between continuous variables.