Process of data collection

Potential participants were selected from the client database of the commercial partner in the following manner. A subset of the client database was taken according to age, those between the ages of 40 and 70 years. This subset was then categorised into state and gender. This was done to generate as reflective a sample as possible of the Australian population. The commercial partner’s head office and major source of clients was in Victoria. It followed that a random sample of the 40-70 year old subset had the potential to be biased in terms of the proportion of Victorian participants. To create a more representative sample the database of customers was stratified to reflect that of the Australian population. The percentage of the Australian population in each state can be seen in Table 3-5, as can the number of initial letters which were sent to potential participants in each state.

Table 3-5: The Australian population and state of residence (Source: Australian Bureau of Statistics, 2004b)

Proportions Population Pop Prop Letters

Australia 19,872,646 100% 10000

New South Wales 6,682,053 34% 3362

Victoria 4,911,425 25% 2471

Queensland 3,801,039 19% 1913

Western Australia 1,949,948 10% 981

South Australia 1,526,301 8% 768

Tasmania 477,305 2% 240

Australian Capital Territory 323,363 2% 163

Northern Territory 198,544 1% 100

Randomly allocated unique numbers were allocated to each potential participant in the 40 -70 subset to remove any order bias. This removed the possibility of sampling all those with a surname beginning with A or sampling all the clients from one particular area. Then the potential participants were further categorised into state and gender to create a more representative sample. The numbers of letters as per the above table were then selected from the lowest unique identifier. Care was taken that the sample from within each state contained an equal number of men and women. This allowed the possibility of a more reflective sample than simple random sampling of the commercial partner’s database.

After participants were randomly selected from the stratified commercial partner’s client database, the questionnaire was delivered to the potential participants via the following process:

Table 3-6: Participant Acquisition Process

A letter from the commercial partner was sent to selected clients offering them the opportunity to participate in the research.

o 10,000 letters were sent using a stratified random sample of 40 - 70 year olds as discussed in the previous section.

If clients were willing to contribute, they acknowledged this by signing the consent form and returning it to an independent mail-house (via reply paid envelope). A survey was then dispatched to the participant from this mail-house.

o Approximately 10% (1,000) of the potential participants consented to receiving a questionnaire.

Having received and completed the questionnaire participants returned it, via reply paid envelope, to the independent mail-house for response handling and data collation.

o 78% of responses (776) were received.

o Those participants who wished to receive a summary of the results indicated so at this stage and a summary of the findings was dispatched to them

A ‘thank you letter’, from all the parties involved, was then dispatched by the mail-house to the 1,000 consenting participants, as an expression of gratitude for contributing to the research.

Copies of this material can be seen in Appendix 10.2 (p.285).

This four stage process ensured that all participants were recruited in accordance with all applicable privacy laws and ethical considerations. The use of the independent

mail-house was to ensure that respondents were completely unidentifiable to staff of both RMIT and the commercial partner. This process of an initial letter an explanation and a survey an offer of a summary of the results and a follow up is common practice in academic research (Fink, 2009).

To further guarantee the anonymity of the participants an external mail house was used to facilitate the data collection process. Whilst participant details and responses were required at several stages no single member of the commercial partner or the university was involved in every step of the process. This decision guaranteed the anonymity of participants.

3.4.2 Sampling

For representative reasons an appropriate sample size needed to be chosen. A sample is a selection of a population’s elements (Blaikie, 2000; Jupp, 2006; Sekaran, 2003). The perfect sample is one that represents the proportions within the population in equal measures (Blaikie, 2000; Overton & van Dierman, 2003; Wimmer & Dominick, 2006).

Given the limited resources inherent in PhD studies sampling is an effective method to reduce the costs of reaching a representative proportion of the population (Blaikie, 2000; Overton & van Dierman, 2003).

In this study stratified random sampling was undertaken for the main data collection which is discussed in the following paragraphs.

3.4.2.1 Sampling in main data collection

Sampling in the main data collection phase required generalisability. For a sample to be representative and the results generalisable to the population of more than 1 million (at a 95% confidence interval and a margin of error of 5%) the study would require 384 participants (Krejcie & Morgan, 1970). Based on sample size calculations widely in use Krejcie and Morgan (1970) tabulated the diminished returns relationship between

required representative sample size and population generalisation for a given confidence interval and margin of error. This table has been widely reproduced (Sekaran, 2003, p.294) and shows that for populations over 1 million there is little need for samples of over 384 participants.

For quantitative studies there are four larger concerns with regard to statistical generalisability which researchers need to address. These were carefully considered when framing the sample size and will be discussed in the following section.

3.4.2.2 Generalisability of the results

The four larger concerns with regard to statistical generalisability are the level of statistical significance, statistical power, effect size and data analysis procedures.

Firstly the level of statistical significance used in this study abided by the convention in marketing research and the social sciences which is to use the .05 level of significance (Hair, 2006; Tabachnick & Fidell, 2007). The higher the level of statistical significance implies the lower the likelihood of a null hypothesis being incorrectly rejected. This study requires a .05 level of significance to adhere to standard academic practice. It is suggested by Cohen (1988) that large sample sizes can produce spurious associations which are statistically significant but of no practical significance. Prudence when interpreting the results is required.

Secondly statistical power is required to correctly identify differences between groups and is dependent on the size of the sample. A sample size above 100 is considered large enough for the power to be acceptable (Stevens, 2009), dependent on the effect sought.

This study aims to be generalisable for the mature consumer population. A sample size of approximately 500 was deemed sufficient to provide ample power for the results.

The third component that needs consideration when designing sample sizes is the statistical effect of the results. Care must be taken by the researcher when interpreting results with regard to sample sizes with over 150 participants (Tabachnick and Fidel, 2007). Findings regarding the effect of sample size are discussed further in the results section (see chapter five).

Finally care must also be given to the methods of analysis expected in this study. They are the comparison between groups and analysis of variance or non parametric equivalents. A sample size of 30 or greater for each group is adequate to establish the differences between groups to a 95% confidence interval (Tabachnick and Fidel, 2007).

However, sample sizes of 10 to 30 are considered sufficient for exploratory studies where such sizes are sufficiently large for testing the hypotheses (Isaac & Michael, 1995). Generally accepted norms allow for a minimum of 30 participants in sub-groups for comparison (Sekaran, 2003). This is an important benchmark as the study is expected to provide comparative analyses within the sample.

To summarise, the consideration of these four aspects allows the following conclusions to be drawn for this study. The sample size selected for this study was 500. This was large enough to detect any differences between groups and to be able to produce statistically significant results to the level of 0.05 with a confidence level of 95 percent.

A sample size of 500 also allowed generalisability to the subset of the population as well as being large enough to produce subgroups of sufficient size for comparison. This sample size is large enough to detect any differences should they exist and large enough to allow sufficient useful responses in the case of non-responses occurring