ACCEPTED VERSION Why start a higher degree by research? An exploratory factor analysis of motivations to undertake doctoral studies

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A handful of recent studies have focused on motivations to undertake HDRs in specific disciplines, with a particular emphasis in the areas of Education and Business (see • Entry to academia • Professional development • Professional advancement • Entry to new career • Self-enrichment Business (Professional Doctorate) USA Stiber (2000) • Personal satisfaction • Preparation for teaching • Marketability for consulting • Unlike the studies listed above, our research was conducted across all faculties in a comprehensive Australian research-intensive university. The results of our study are reflective of the HDR candidate population across all faculties, rather than being focussed mainly on Education or Business as in existing studies. We did not distinguish between types of HDR in the current study. The Survey Questionnaire Development To the best of our knowledge, there is no relevant questionnaire readily available aimed at identifying motivations behind students pursuing postgraduate research. Therefore, we used a deductive approach, sometimes described as logical partitioning Participants were invited to indicate the strength of the influence of each element on a 7-point Likert scale, ranging from 1-not at all to 7-a lot. Survey Administration and Participants After approval by the Human Research Ethics Committee at the University, all currently enrolled HDR students were sent an email invitation to participate in the online survey. We received 405 responses. The percentage of responses from each faculty roughly mirrors the percentage of HDR students enrolled in each of those faculties. At the time there were approximately 1800 HDRs in the University, so the response rate was around 23%. There were 67 respondents who were eliminated from further analysis as they had completed less than 75% of the questionnaire. For the data from 338 participants who completed more than 75% of the survey, missing scale items were imputed by determining each mean for the items on the scale. Because less than 2.5% of data were missing, mean imputation is considered an appropriate data replacement strategy Descriptive statistics of the remaining sample (n=328) are presented in Statistical Analysis We used Exploratory Factor Analysis to uncover the underlying structure and dimensions of what motivated current HDR students to embark on research degrees. As the name suggests, EFA is exploratory in nature and has no expectations of the number or the nature of the variables. EFA involves a series of sequential steps (e.g., selection of the number of factors, selection of the factor rotation method) that also involves evaluating multiple options. This procedure and the decisions taken are explained in detail below. Preliminary Analyses Statisticians have argued about the minimum sample size needed for exploratory factor analysis for decades, some looking at total sample size (e.g., 100, 200, 300), some at the ratio of subjects to items (e.g., 3:1, 4:1, 5:1). For the total sample size, there are various opinions and several guiding norms that are cited in the literature. General guides include Tabachnick's rule of thumb Although the ratio of subject-to-variable is an important factor to be considered before conducting an EFA, when total sample size increases, this ratio becomes less important The correlation matrix was inspected for correlations in excess of 0.3. The literature warns that, if no correlation exceeds the threshold, the applicability of factor analysis should be reconsidered Exploratory Factor Analysis For the 42 items used in the questionnaire, a Principal Component Analysis (PCA) was conducted, as this process is best suited to establish preliminary solutions in EFA The recommended method is firstly to conduct an oblique rotation in situations where there is a choice between oblique rotation or varimax rotation • Item 5: I was encouraged by my lecturer. • Item 29: I was encouraged to read cutting edge research for assignments. • Item 30: I enjoyed reading articles written by my lecturer. • Item 31: I enjoyed reading books written by my lecturer. • Item 32: I enjoyed reading extra materials recommended by my lecturer. As a result of following this procedure, seven items were finally eliminated. Five factors emerged from the analysis: 1) Family and Friends; 2) Intrinsic Motivation; 3) Lecturer Influence; 4) Research Experience; and 5) Career Progression