Hallazgos principales

(Sources: Brooks, Gibson and De Matteo 2008; Burgess, Connell & Dockery 2013; Charlesworth et al. 2014; Green 2006; Matthias & Benjamin 2004; Muňoz de Bustillo et al. 2011; Pocock & Skinner 2012.)

Likert scale

I used a question using a Likert scale for three reasons. First, the scaled question directly related to the first research question: How do in-home support workers define a ‘good’ job under individualised funding models? The scale would indicate the characteristics

participants most valued. Second, scaled questions are commonly used when a construct cannot be measure directly (DeVellis 2012). ‘Importance’ was such a construct. Third, the inclusion of a quantitative question to supplement other data was in line with a critical realist approach that advocates using more than one data collection method to gain a deeper

understanding of a subject.

Typically when a Likert scale is used, the scale is preceded by a declarative statement (DeVellis, 2012) rather than a question, However, a question rather than a declarative statement was used in this study to maintain the flow and fluidity of the interview, a key ingredient of effective interviewing (Mason, 2002). Participants were shown the Likert scale on the paper interview schedule to help them rate the level of the factor’s importance. (See Figure 7) This was done to minimise any confusion between ‘somewhat important’ and ‘not very important’.

Interview process

The interview process was consistent across all participants. After people who had responded to the invitation were screened to ensure they met the selection criteria, the researcher

confirmed the aims of the study and timelines, size of the sample and length of the interview. Participants provided their informed consent before the interviews were conducted in line with national ethical research standards. Interviews were scheduled at a time and venue convenient to the participant. Four interviews were conducted in the participant’s home because this was their preference, seven in cafes, one in an office and six in public libraries.

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The aim was to find venues that were quiet and private as well as convenient to the participant. Interviews lasted between 45 minutes and 90 minutes, most were one hour. In line with the national code of conduct for ethical research, I gave a $30 voucher to

participants who were interviewed in their own time and outside their employer’s workplace.

Reflexivity

Researchers are part of the social world they study and their personal characteristics, beliefs, values and quality all influence how they conceptualise and engage in the study of their world (Hammersley & Atkinson, 2007). From a critical realist’s perspective each of the steps

negotiated by the researcher to acquire data and make sense of it are real phenomena that influence the data collection and conclusions drawn. The relationships the researcher

develops with the participants are real phenomena so could potentially influence participants’ responses. I was concerned that participants might give answers they thought might be expected of them or that the researcher wanted to hear, a phenomenon described as ‘the self- esteem effect’ by Green (2006). To reduce the likelihood that the $30 voucher provided in recognition of their time would influence their responses, I reiterated several times the importance of candid perspectives from the workers about their work and their experiences.

Qualitative analysis of interview

The qualitative data were subject to thematic analysis using Nvivo 10. This involved creating matrices containing dialogue by each respondent under each of the 16 job quality

characteristics. Responses were then colour coded to determine if institutional factors, such as employment status, funding model or government employment, or demographic factors, such as length of employment and qualifications, could explain any differences or themes that emerged. The outcome of this analysis is described in the next chapter.

To synthesise the key characteristics of a good job additional tables were developed. These tables described the elements of a good job put forward by each participant. The number of participants who included each element in their descriptions of a good job was recorded.

The quantitative data generated from the Likert scale were analysed using the descriptive statistics function in IBM SPSS Statistics 22. This analysis provided both the distribution of ratings and the means. The findings are presented in Chapter 5 and 6 and the SPSS 22 tables of frequencies and percentages showing the distributions is provided in Appendix Six.

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Exploring a Secondary Data source

When developing the methodology the value of using secondary data to understand how workers in the disability sector, or the broader community services sector, rated different job quality characteristics was considered. Secondary analysis is the further analysis of an existing dataset where the researcher aims to address research questions distinct from the original purpose that led to the creation of the dataset. It generates new interpretations and conclusions (Hewson, 2006).

During the methodology phase I explored undertaking secondary analysis using items from the Victorian Work and Life (VicWAL) Survey Dataset (2009). This dataset was used to construct the VicWAL Job Quality Index (JQI) (2009). This has local relevance because it was developed through a partnership between Workforce Victoria, Regional Development Victoria, RMIT University and the University of Sydney (Charlesworth et al. 2014). It was based on a telephone survey conducted in 2009, and using random digit telephoning it achieved a total sample of 3007 adults living in Victoria. This identified six dimensions of ‘job quality’. It is designed to measure ‘poor job quality’. The six job quality components in the VicWAL JQI are: Job security, Job control, Workload, Skill development, Access to work-life provisions and Working-time autonomy (Charlesworth et al. 2014.

This data could provide valuable insights on how the large group of ‘community and personal services workers’ rated each of the six job quality components, and how their responses were distributed between the different categories of ‘quality jobs. However, its value was limited for the purpose of this study as the codes used for the occupational groupings were at a broader level than was optimal for a study of in-home support workers. The taxonomy used to code respondents’ occupations in the VicWAL survey dataset was the Australian and New Zealand Standard Classifications of Occupations (ANZSCO). Occupations had been coded using the broadest level: Major Groups 4 level. This classification system divides occupations into eight groups of which ‘Community and Personal Service Workers’ is one. While

disability support workers are captured in two Unit Groups within ‘Community and Personal Services Workers’, the data could not be dis-aggregated to distinguish these workers from other occupations within this large group. The large number of occupational categories contained in the ‘Community and Personal Service Workers’ classification is presented in

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Figure 8. This figure reveals that less that 20 per cent of ‘Community & Personal Services Workers would fall into the two categories relevant to this research. Furthermore, these two categories would include workers supporting people in facilities as well as at home, so the relevant number would be smaller still. As a result, it was decided that the data did not provide sufficient illumination of perceptions of job quality for in-home disability support workers to justify inclusion in the thesis.

Figure 8: Group 4 Community and Personal Services Workers in Victoria (Source: ABS 2011 Census)

Concluding Comments

This chapter has outlined the study’s mixed methods research design and the two data collection methods used. Semi-structured interviews with 18 in-home support workers provided qualitative data which was subject to thematic analysis using Nvivo. It also provided quantitative data generated from the use of the Likert scale, which was analysed using SPSS descriptive statistics.

4. Community and