Data Analysis

Methodologists have emphasized the integration of qualitative and quantitative data as the centrepiece of mixed methods (O'Cathain et al., 2007) and it therefore requires more than presenting qualitative and quantitative results separately.

Bazeley (2012) defined integration in mixed methods research:

‘Integration can be said to occur to the extent that different data elements and various strategies for analysis of those elements are combined throughout a study in such a way as to become

interdependent in reaching a common theoretical or research goal, thereby producing findings that are greater than the sum of the parts.’

One of the challenges of using the convergent design is how to merge two sets of very different data and their results in a meaningful way (Creswell and Clark, 2011, pg 66). Creswell and Clark (2011, pg 67) suggest that the

different data sets need to address the same concepts to facilitate the merging process. Bazeley (2012) described five different strategies of integration, one being integrating results from analyses of separate data components, whereby data from each component is analysed using

traditional methods and then integrated. Weiss et al., (2005) described how

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this approach could enhance validity, delineate overlapping but distinct aspects of a phenomenon, elaborate one set of findings with data from

another, expand potential findings and uncover paradoxes and contradictions between results based on different methods.

Once the data sets are analysed Creswell (2014a, pg 222) described several ways to integrate or merge them:

 Side-by-side comparison – whereby the researcher will first report the quantitative statistical results and then discuss the qualitative findings that either confirm or disconfirm the statistical results.

 Data transformation – whereby the researcher changes the qualitative codes into quantitative variables and then combines the two

databases.

 Joint display of data – the researcher merges the two forms of data into a table or graph, effectively merges them into a single visual display.

In order to achieve a meaningful merger of the data the researcher collected and analysed the data using traditional quantitative and qualitative methods (as detailed in Chapter 4). The results from each data set were then written up separately. The researcher was then able to compare the separate and integrated results before interpreting to what extent, and in what ways, the two data sets of results converged or diverged from each other; related to each other, and/or combined to create a better understanding in response to the study objectives, as suggested by Creswell and Clark (2011, pg 136) (Figure 3.3).

In relation to this study Figure 3.4 shows the procedures that were applied to follow the convergent design. Following combined data interpretation the results naturally fit into two distinct areas; caring at home and care home placement.

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Figure 3.4 Steps in a Convergent Mixed Method Design

(Adapted from Creswell and Plano Clark, 2011, pg 79) Quantitative Strand:

Study objectives agreed Questionnaires agreed

Collect Quantitative Data:

Gain consent Sample identified

Collect closed-ended data with instruments

Qualitative Strand:

Study objectives agreed In-depth semi-structured interviews

Collect Qualitative Data

Gain consent Sample identified

Collect open-ended data with protocols

Analyse Quantitative Data

Quantitative data analysed using descriptive statistics, inferential statistics and regression model building approaches

Analyse Qualitative Data

Qualitative data analysed using thematic analysis

Strategies Used to Merge the Two Sets of Data

Content areas identified in both data sets which were compared, contrasted and results synthesized into a discussion or a table.

Interpreting the Merged Results

Summarised and interpreted the results.

Discussed to what extent and in what ways results from the two types of data converged, diverged, related to each other, and/or produced a more

complete understanding.

1.

2.

3.

4.

Steps

and

and

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As a consequence the results section has been divided into two chapters:

1. Chapter 5 - The role of the carer and understanding what causes the greatest levels of carer strain whilst the PwP is still at home

(incorporating objectives 1 and 2).

2. Chapter 6 - Understanding the triggers for care home placement, the role of carer strain within this decision, and identifying if it is possible to predict who is most likely to go into a care home (incorporating study objectives 3, 4 and 5).

Within chapter 5 the quantitative data provides information on the carer’s role and tasks undertaken. The semi-structured in-depth interviews explored the carer’s role in greater detail, and provide a better understanding of the impact of their role. The quantitative data was used to test an adapted stress-appraisal model based on the work by Goldsworthy and Knowles (2008), that predicts the interplay between primary stressors, secondary stressors, primary appraisal, protective factors and tertiary appraisal to produce the outcome of carer strain of PwP. The qualitative data was compared against the quantitative data to see if the data sets converge, enabling us to understand the reasons why carers find those symptoms most troublesome. Following baseline analysis of both quantitative and qualitative data, it should be possible to not only identify which factors had caused the greatest influence on carer strain but also understand why these factors caused the greatest carer strain.

In chapter 6 the issues around triggers to care home placement are explored and the interviews examine carer strain within the decision making process around care home placement. Quantitative data was used to quantify any issues raised, such as number of PwP with carers who had gone into care and route into a care home. At the end of the study period the same

quantitative data were split into those carers who had continued to care for the PwP within their own home and those carers of PwP who went into a care home placement. The data were then re-analysed and by comparing data sets the researcher was able to identify differences between the two groups that could predict those more likely to go into a care home placement.

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The quantitative data were compared to the qualitative data to see if they converged or diverged, and by combining the data sets a better

understanding of the reasons for care home placement for PwP was achieved. The researcher was also able to understand what impact the outcome had on carer strain and how their life had changed since care home placement.

Following data integration the quantitative and qualitative results were found to converge with the qualitative findings significantly relating to the

quantitative results. The researcher felt that the quantitative data provided a clear picture of the issues surrounding carer strain and care home placement, whereas the qualitative data provided the rich detail within the picture to enhance the detail and level of understanding of the phenomenon. The researcher felt that by using side by-side comparisons, where quantitative or qualitative data is first presented and then supported by the other data set, and joint displays of data, where both qualitative and quantitative data are presented in the same table, the analysis would provide added value, as already identified by Weiss et al., (2005) with the ability to elaborate, expand and/or uncover results using the different methods employed.