Data sampling

Whatever the research question(s) and objectives, a researcher needs to consider whether they need to utilise sampling (Saunders, 2011), and if so, the researcher needs to choose a sample. This is similarly significant whether the researcher is aiming to use interviews, a questionnaire, observations or some other data collection technique (Saunders, 2011). Sampling is the technique by which units from a population are chosen to contribute to the data gathering phase of the research (Saunders, 2011). Two sampling techniques exist when conducting research, and these are:

1. Probability or representative sampling 2. Non-probability or judgmental sampling

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4.8.6.1 Probability Sampling

Probability sampling infers that the units from the population were selected with some level of randomness (Trochim and Donnelly, 2001). The probability or chance of selecting any unit from the population is the most equivalent to using this technique (Saunders, 2011). Probability sampling techniques are frequently utilised in survey-based research strategies when statistical implications are needed to analyse data, and therefore, the outcomes may be considered representative of the general population (Saunders, 2011). The procedure of probability sampling can be categorised into four phases:

1. Detect a suitable sampling frame based on the research question(s) and objectives. Population target for this research is UK construction clients. It is extremely difficult to detect sample frame for client’s organisations inside UK for several reasons such as there is no official source can provide this information and any organisation can re- enact client role when they decide to build something. Therefore, this research will target all organisations how used BIM from client perspective without any specific sample frame.

2. Adopt a suitable sample size.

As it was mentioned above, there is no definite sample frame for this research. Therefore, it is impossible to choose any sample size. However, this research will seek to invite all organisation the preformed client roles to increase the chance of generalization.

3. Select the most suitable technique and choose the sample.

There are four main techniques to choose research sample. Firstly, simple random sampling, which is defined as a subset of a statistical population in which each member of the subset, has an equal probability of being chosen. Secondly, systematic sampling, which is defined as a type of probability sampling method in which sample members from a larger population are selected according to a random starting point and a fixed periodic interval. This interval, called the sampling interval, is calculated by dividing the population size by the desired sample size. Thirdly, stratified sampling, which is defined as a probability sampling technique wherein the researcher divides the entire population into different subgroups or strata, then randomly selects the final subjects proportionally from the different strata. Finally, cluster sampling, a sampling technique used when "natural" but relatively heterogeneous groupings are evident in a statistical

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population. It is often used in marketing research. In this technique, the total population is divided into these groups (or clusters) and a simple random sample of the groups is selected. Due to the limitation in detecting sample frame, the first two method cannot be implement. In addition, divided clients in-group depend on geographical factors will not meet research aim. Therefore, the stratified method will be implemented where the selected clients must have been using BIM so they will be able to answer the survey questions.

4. Check that the sample is representative of the population.

Without sample frame, it will be impossible to check if the sample is representative of the population, which will be considered as one of this research limitation.

As this research will use a questionnaire, probability sampling will be employed in this study. Sampling properties, will be discussed in detail in Chapter 7 (questionnaire data analysis).

4.8.6.2 Non-probability sampling

Non-probability sampling techniques are used when the research aim and objectives require another form of sample selection (Saunders et al., 2009). Saunders et al. (2009) indicates that there are four main methods for sample selections . Firstly, purposive sampling is utilised when the sample is selected with a particular purpose in mind. Secondly, convenience samples (haphazard) where sample is selected from elements of a population that are easily accessible. Thirdly, volunteer which contain two main categories, Snowball sampling (friend of friend….etc.) and self-selection. Finally, Quota sample which where sample will be divided into groups based on certain factors such as sex .In the context of this research, Multiple methods have been implemented, start with purposive sampling was used to select a client organisation in the first phase of the research then snowball sampling. For this research, the views of BIM experts inside client organisations will be used in the creation of a framework for the relationship between BIM maturity and BIM benefits. Hence, BIM experts will be chosen based on their availability and willingness to participate. Data from this stage will be used in the development of the conceptual framework to explain the relationship between BIM maturity and benefits. This technique has been further classified as ‘expert sampling’ by Trochim and Donnelly (2001) and as ‘homogenous sampling’ by Saunders et al. (2009). This technique is recommended by Saunders et al. (2009) when the intention of the data gathering is to establish an in-depth understanding of issues. Fifteen BIM experts from six different types of client organisation will be contacted to participate in the interviews; this number was based

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on the study conducted by Guest et al. (2016) who found that data saturation occurred after twelve interviews in qualitative studies.