Introduction

The study design involved the use of a cross-sectional survey and this provided a simplified design and expedited the collection and analysis of the data (Creswell 2009). Kraus (2005) defines a cross sectional study as one where the researcher identifies a subset of the whole population under study and collects data at a single point in time. This subset must be well representative of the whole population. The cross sectional survey questionnaires interviews were conducted on one day again with an emphasis on time and expedience. The data collection and analysis was quantitive in nature, this approach was favoured as it would eventually produce data that was systematically arranged and that was easy to logically analyse (Creswell, Trout and Barbuto, 2004). The data hence collected was easy to analyse through various methods and mediums and help the researcher come to a deductive conclusion.

Participants and Sampling

The population was a convenience sample drawn from 100 international paramedic students who were given an opportunity to participate and from this n=30 participants completed the survey. A clustering technique was used so as to offer a notification to participate in several meetings as it is impossible to ascertain a sample without a list (Creswell 2009). Since surveys were handed out to only those who were international paramedic students and were available to participate it was a convenience sample (Babbie 1990, in Creswell 2009). Convenience sampling involves picking respondents from among those who are easily available, this helps ease the rigours involved in searching for respondents from a large sample. The major shortcoming of this approach though is that the data collected might not be representative of the whole population (Creswell 2009). There was no stratification, only male participants were invited to participate (Creswell 2009). With 100 surveys and an estimated 10% response sample the calculated margin of error is 4.9% with a confidence rating of 90% (Relevant Insights 2012).

Data Collection

Data collection was done from the self-administered questionnaires (Appendix 1) that were designed using a five point Likert scales, multiple choice and yes/no questions this enables participants to rank responses (Huck 2008). The questions were closed and answers were from a limited population that enabled generalization of findings for comparison (Creswell 2009). I was able to make estimates of attributes from a small group and thereby, make assertions about a larger population (Fowler 1998; Babbie 1990, in Charema 2004). From this it was possible to make generalisations about behavioural characteristics of the population.

The survey was sectionalised first section addressed demographic information, the second discussed experience and the third directly as to stress predictors. The researcher looked at previous studies in the field of job related stress for paramedics and narrowed down the questions from these studies to suit his research question and hypotheses. The researcher was fortunate to have had the input of the course supervisor who has also written extensively on the topic of job stress previously. The quantitative data collected from the questionnaires was time efficient and enabled the researcher to rapidly turn around the data and evaluate the information to publish findings (Creswell 2009).

References

Blau, G, Chapman, S & Gibson, G 2011, ‘Exploring the Importance of Different Items as Reasons for Leaving Emergency Medical Services Between Fully Compensated, Partially Compensated, and Non-Compensated/Volunteer Samples’, Journal of Allied Health, vol. 40, no. 3, pp.e33-e37.

Blumenfield, M, & Byrne, D 1997, ‘Development of Posttraumatic stress disorder in urban Emergency medical Service workers’, Medscape Mental Health e-Journal, vol.2, no.9.

Boudreaux, E, Mandry, C & Brentley, P 1997, ‘Stress, job satisfaction, coping, and psychological distress among emergency medical technicians’, Prehospital and Disaster Medicine, vol.12, vol. 4, pp.242-249.

Bryman, A., and Cramer, D. (2008), Quantitative Data Analysis with SPSS 14, 15, 16, Routledge, London.