Sampling and recruitment 62

Sampling is involved in all research as a single study cannot include everyone and everything (Punch, 2005, 2014). Quantitative studies usually involve the researcher(s) sampling where a sample is taken from the population of interest. The sample is analysed and the findings can then be generalised back to the population from which the sample was taken. The degree to which findings can be generalised to the population depends on how representative a sample was of that population (Bryman, 2012). To achieve

representativeness, probability sampling is used and is usually some form of random selection, in which each individual has an equal chance of being included in the sample (Punch, 2005, 2014).

However, a sampling plan is also dependent on the research aim and the research questions. This means that "the sampling plan should have a logic that fits in with the research questions" (Punch, 2005; 2014, p. 244). When representativeness is the aim, there is a need for a form of representative sampling. However, in some instances, deliberate or purposive sampling may be more appropriate if the research questions are about

relationships between variables or making comparisons. Sampling in quantitative research is moving away from strict mathematical sampling strategies. Because of problems in accessing large appropriate samples, researchers are now more inclined to utilise what is available (Punch, 2005, 2014).

Punch (2005, 2014) emphasised that sampling in mixed methods research should ensure that the sampling selection logically fits the overall logic of the study. The logic of this is that the research questions drive decisions about methodology and thus decisions about sampling. In a case study using a mixed methods approach, the case is the major focus of the investigation and sampling from the case can reveal valuable information (Teddlie & Fu, 2007). Within the bounds of the case, the researcher makes a decision on which people and research sites can provide the appropriate information and a sample is determined according to what is needed to provide the appropriate data.

In the quantitative phase of this research, the aim was to examine the documentation of vital signs in an EHR of patients who had suffered cardiac arrest, and thus, the sample had to reflect this group. It would not be possible to study all patients in this population, therefore, the sample was the electronic records of all patients who had suffered a cardiac arrest in the study hospital and on whom resuscitation had been attempted between 2007 and 2011. This was a purposive, and at the same time, convenience sample and therefore not the kind of sampling that quantitative researchers traditionally advocate, i.e., it was not randomly selected. However, this type of sample reflects what is written in more recent literature - that "the researcher must take whatever sample is available, and the incidence of convenience samples (where the researcher takes advantage of an accessible situation that happens to fit the research context and purposes) is increasing" (Punch, 2005, 2014, p.243). This is also in accordance with the pragmatist view that the main concern is to get the study done effectively and validly (Kervin, 2000).

Sampling strategies in qualitative research are equally important to those of quantitative research. Case studies require sampling within the case and involve selecting a focus (Punch, 2005, 2014). In qualitative research, purposive sampling is used to select

individuals who are good sources of information (Creswell, 2007). Purposive sampling is sampling in a deliberate way with a particular focus in mind. Convenience samples are often used to take advantage of informants who are easily accessible (Punch, 2005, 2014). Snowball sampling is when informants know additional people who can be good sources of information and is appropriate in cases where it is difficult to reach an adequate number of suitable participants. Essentially, sampling should provide the data needed to answer the research questions (Mason, 2002).

In mixed methods explanatory sequential design, in which a quantitative study has been performed first, the sampling in the qualitative second phase is guided by the results of the first phase study. Information provided in the quantitative results is used to inform the requirements of the sample in the qualitative study (Teddlie & Fu, 2007). Specific issues that need further explanation are identified and qualitative data are collected from respondents who can give the best explanation of these results (Creswell & Plano Clark, 2007).

In the current study, the sample was required to generate meaningful data that would help explain the quantitative results. Clearly, the most appropriate participants for the sample were the people who used the EHR for documenting vital signs, i.e., health care

professionals. Thus, the sampling strategy was purposive in that the nurses and doctors who used the EHR and worked in the hospital settings were selected for interview. These health care professionals were considered good sources of information who could provide meaningful data in context and where data could be generated to advance understanding (Mason, 2002). The sample was a purposive, convenience sample in that it involved people working within the case study setting who were available to be observed and interviewed at the time the researcher was available to visit the units to undertake the study. A more detailed account of sampling and recruitment for phases one and two of this research are presented in Chapters 4 and 5 respectively.