As cited by Bryman (2007), a mixed methods approach requires particular consideration at the research design stage and then again during data analyses and interpretation. During data interpretation, ontological, epistemological and theoretical issues are
highlighted, therefore, linking the two datasets is often problematic. Creswell and Plano Clark (2007), in their definition of mixed methods research, discuss mixing the datasets,
which provides a better understanding of the phenomenon. They suggest that there are three ways of mixing the data: merging the data, by bringing the qualitative and
quantitative datasets together; connecting the datasets, by having one build on the other; and embedding the data, where one dataset plays a supportive role for the other.
Chapter 4 reports the outcomes of the quantitative data of this research and Chapter 5 connects the qualitative data to the findings reported in Chapter 4.
3.8.1 Descriptive Statistics, differences tests and correlations
In Chapter 4, descriptive statistics describe the population of students in this research, providing the reader with a clear impression of the participants. As alluded to in section 3.4, these students are not necessarily representative of all health and life sciences students studying on a master’s programme in the UK or globally. Therefore, detailed reporting of the participants’ demographics is crucial to contextualise the outcomes; another population of students may have yielded different results. Descriptive statistics are also reported for presenting each of the cultural clusters’ scores for all of the
learning conceptions. To compare gender in predicted academic performance and actual academic achievement, independent t-tests were implemented as this is a widely
accepted statistical difference test with small sample sizes, testing the difference between the samples when the variances of two normal distributions are not known. When comparing differences across cultural clusters an analysis of variance (ANOVA) test was used to reduce the probability of making a type I error. ANOVA tests were also applied to cultural clusters and academic achievement, and cultural cluster and forecasted performance. To explore the relationship between forecasted academic performance and actual academic achievement, the Pearson correlation coefficient was implemented as is the most widely used test to measure the strength of the linear relationship between normally distributed variables.
3.8.2 Factor Analysis
As previously reported, the COLI (Purdie & Hattie, 2002) was used to quantitatively measure the students’ learning conceptions. Although this is a well-defined and regularly used measure across different cultures, the participants’ demographics in this research were quite different from other studies using the COLI. The researcher wanted to ensure that the items loaded onto the same factors as in Purdie and Hattie’s (2002) research. This could have been tested by using confirmatory factor analyses, which
uses knowledge of the theory and empirical research to test that the relationship between the 32 items and their underlying latent constructs, the six conceptions of learning, exists. However, after much consideration, exploratory factor analysis was considered to be more appropriate as it allows all of the 32 items to load freely without constraints. This resulted in eight conceptions of learning which were slightly different from Purdie and Hattie’s (2002) conceptions. The eight conceptions of learning that derived from the factor analysis conducted in this research were labelled, compared to Purdie and Hattie’s, and used for further analyses.
3.8.3 Regression Analysis
To understand whether any of the newly identified learning conceptions could predict academic achievement or predict how students forecasted how they would perform, multiple regression analyses were applied. Typically, a regression analysis is used for modelling the relationship between two or more variables. The data met all the
assumptions required for multiple regression, therefore regression analysis was deemed the most appropriate for exploring relationships between cultural clusters, learning conceptions, academic achievement and predicted academic performance.
3.8.4 Focus Group Activity
Due to the small sample of participants who participated in the focus groups (see section 3.4), multivariate statistical analyses were not applied to evaluate the focus group activity. However, as numerical ratings were compared across participants, these results are included as basic descriptors in Chapter 4 (section 4.7).
3.8.5 Focus Group Discussions
Triangulation of the data in a mixed methods research design, referring to the
corroboration of the results, is, according to Hammersley (1996), only one method of combining results. One dataset, either quantitative or qualitative, can elaborate or expand the other, adding to our understanding of the phenomenon. However, both datasets can be treated independently, sometimes generating different outcomes which are contrasted, resulting in a wider viewpoint. Alternatively, researchers may find that the two analyses contradict one another, leading to further research or a critical
evaluation of one or both of the research methods. Finally, mixed methods may be chosen as the first method which could generate new research questions or hypotheses
which can only be pursued by an alternative method. All these forms of data
triangulation make the assumption that there is a reality to be captured. As noted in the procedure section (section 3.7) above, the qualitative data were collected after the questionnaire but prior to performing the quantitative analyses, with the quantitative data being dominant in a simultaneous design.
There is a vast literature on the variety of qualitative analyses used for scrutinizing focus groups, some of which was considered for this research. However, due to resource implications, predominantly the time constraints and scope of an EdD thesis, data from the focus group discussions in this research were not formally coded. This, therefore, did not require a full transcription of the discussions. This could be
considered a threat to the trustworthiness of the data analysis. However, the caution taken over recurrently listening to the recordings, ensuring that all points made by the participants were noted to avoid cherry picking statements, promoted rigour in the qualitative data analysis. The researcher listened to the discussions repeatedly over a three-month period, noting themes and issues highlighted frequently by different participants. It was easy to identify the students when listening to the recordings of the three focus groups discussions due to the cultural differences in their accents. The data were left for a month and then revisited, again noting dominant themes and pertinent points. Relevant points were extracted and are presented in Chapter 5, contributing to the discussion of the quantitative analyses presented in Chapter 4. Chapter 6 goes on to discuss the implication of the findings.