Survey sampling

Sampling is the process of selecting subset of the population (Singh and Masuku, 2014, Wolverton, 2009, Burton, 2000, Rossi et al., 2013). Sampling is used for collecting information from the population for further research analysis. Therefore, it is very important to select suitable sampling techniques for a research. In general, there are two main types of sampling: probability samples and non-probability samples (Wolverton, 2009, Campbell et al., 2016). Regarding probability samples, there are four main techniques, including simple random sampling, stratified random sampling, systematic random sampling and cluster sampling.

 Simple random sampling: In this sample, every unit of population has the same probability of inclusion in the sample (Wolverton, 2009, Burton, 2000).Every unit in the sample has the chance of replacement or no replacement (Wolverton, 2009, Singh and Masuku, 2014). With replacement, the probability of selecting every unit in the population is 1/n, where n is the total population size. With no replacement, the probability of selecting every unit increases when the unit is selected and number of units in the population is reduced (Wolverton, 2009).

 Stratified random sampling: Units of stratified random sampling are assigned into subgroups or strata based on one or more significant characteristics (Burton, 2000). The technique is to ensure stratifying characteristics of sampling to be identical for sample proportions. This technique is able to reduce sampling error leading to the improvement of accuracy of inferences (Wolverton, 2009).

 Systematic random sampling: This sampling technique uses sorted data to select the sample. This technique is able to show systematic pattern, which is related to the sort of data (Wolverton, 2009). The most significant disadvantage of this technique is making the bias in the sampling when the periodic patterns may be seen within the dataset.

 Cluster sampling: units of population are assigned to a cluster, which may include random sample (Fogelman and Comber, 2002). The clusters are selected at random and all selected clusters are included in the sample (Burton, 2000). For this sampling,

one of the requirements that need to be considered is that list of clusters need to be provided (Rossi et al., 2013).

For non-probability sample, there are three common techniques, including purposive sampling, snowball sampling and quota sampling (Kalton and Graham, 1983). Each of these sampling techniques is explained below:

 Purposive sampling: this sampling method is used to select units from population based on specific purposes (Singh and Masuku, 2014). It may cause bias during selection process and therefore, not statistically recognised. Thus, purposive sampling should be used when there are some certain requirements of units provided.

 Snowball sampling: This sampling technique is used to expand the link with potential participants based on respondents participating in the research (Burton, 2000). This technique is able to work in the condition of less visual participants for the research. In this technique, it is possible that the aim and objectives of the research are misinterpreted in participant recruitment. Therefore, it is very important that research aim and objectives need to be clear for explaining to participants.

 Quota sampling: it is a popular technique for market research. Quota sampling is used to divide the sample into specified sub-samples (Singh and Masuku, 2014, Burton, 2000). This division is based on the population characteristics represented by the sample, such as gender and age. This technique may be biased, as it doesn’t create a chance for every unit in the population equally.

In this research, based on the research aim of establishing a MPDM framework, the survey objectives of collecting data for implementing an AHP to assess the results associated with GFTs and then developing this framework is the process followed. This survey does not focus on the test of hypotheses or the formation of main research conclusions. Instead, the survey seeks to gather sustainability assessment of green features and technologies from decision makers and project stakeholders. Therefore, the survey sample is too difficult to be determined as a definite number. Due to the requirements of participants’ knowledge and experience on sustainability and office projects, the sampling methods selected are

PhD thesis: Establishing MPDM framework for supporting the selection of GFTs 107 participants. These sampling methods may cause some difficulties in sample size determination.

Purposive sampling assists the recruitment of participants, who should be suitable to meet the requirements of recruitment and survey targets. Potential participants can be selected from two sources:

1. Participants from the Australian building and construction industry. They should have prior experiences working on green office projects.

2. Participants from academic institutions. They should have their research, publications and projects related to green office projects so they are also knowledgeable in this area of study.

From the building and construction industry, potential participants may be developers, investors, architects, builders, ESD consultants, facility managers and researchers. In general, they are project stakeholders or decision-makers related to green office projects. For contacting potential participants, stakeholders’ information is collected from information of Green Star-rated projects on GBCA website. Additionally, participants can be identified by sources within organisations associated with sustainability and green office projects, such as governmental bodies, councils and Australian property institutions.

The next sampling used is snowball sampling, which is the development of participant numbers from the social network of initial participants (Crano et al., 2014). The initial participants are asked to invite additional members and other participants based on their professional network and social relationship. By doing this, a number of participants are expanded but the quality of participants needs to be checked in term of their backgrounds and experiences. Nevertheless, snowball sampling depends on the willingness of initial participants and their own relationship with further recruitment of participants. Therefore, initial participants should be decided carefully for increasing sample size and ensuring the success of snowball sampling implementation.