Phase 2b: Pilot study

An imperative phase for the progress and usefulness of the survey was to conduct a pilot study running it with a similar sample of respondents, under the same circumstances as expected in the actual implementation of the survey (Shaughnessy et al. 2012). This was an effective way to recognize problems and improve the research survey design (Waters 2011). The validity, reliability and feasibility of the survey were enhanced through a pilot study of the survey (Cohen, Manion & Morrison 2005). It ensured that the research guidelines can be comprehended by the respondents and that the procedures set in place and the questions were clear (Cozby & Bates 2012). Executing a pilot study for this research was important to enhance the precision of the survey instrument.

The objective of the pilot survey was to refine the scales for the concluding stage of the research, which was conducted in Karachi. Being one of the cosmopolitan cities of Pakistan with a multicultural population, Karachi offered the perfect setting for this research (Pakistan Bureau of Statistics 2017). The surveys were conducted at participants’ homes, workplaces and universities.

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When the respondents expressed their readiness to participate in the survey, the researcher passed on the participation information sheet, consent form and survey questionnaire. (See appendix D for study’s survey questionnaire). Once the respondents read the information sheet and signed the consent form, they continued on to complete the survey. The researcher remained with the respondents while they filled in the questionnaires entirely to reply to any questions that arose during the process.

Pilot study sample selection

Researchers are still unable to find any precise answer about an ideal sample size. According to Cohen, Manion and Morrison (2005) the exact sample size needed relies on the environment of the population under scrutiny and the intention of the study. A pilot study sample of 12 to 30 respondents has been suggested (Hunt et al. 1982). The pilot study sample in this research had the demographic characteristics of those to be involved in the final sample, namely, housewives, professionals and university students. A sample size of 375 was deemed appropriate since there were 75 scale items and exploratory factor analysis requires a minimum 5:1 ratio of respondents to items (Hair et al. 2017). To run the factor analysis, a sample size smaller than 100 is considered to be “dangerous” and sample sizes larger than 200 are considered safe for adequate conclusions (Winter, Dodou & Wieringa 2009).

The purpose of an exploratory factor analysis (EFA) is to cleanse the scales used in the survey instrument. See the relevant section below for further details of the EFA conducted on the pilot study results.

It is imperative to note that in EFA, the sample size is important (Williams et al. 2010). A number of perspectives and requirements regarding the sample size in EFA are reported in the literature review (Comrey 1973; Hair et al. 1995; Hogarty et al. 2005; Tabachnick & Fidell 2001). Hair et al. (1995) recommended that the sample size for an EFA should be greater than 100 cases while some other analysts propose a minimum of 300 cases (Hogarty et al. 2005; Tabachnick & Fidell 2007). Research conducted by Comrey (1973) suggested a range of degrees of excellence for EFA sample sizes: 100 is poor, 200 is fair, 300 is good, 500 is very good, and 1000 or more is excellent.

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To run the pilot test study, the sample of respondents was selected by visiting three universities, randomly asking housewives outside supermarkets once they had finished the shopping and visiting selected offices to tap the professionals’ responses. The sample tended to consist of keen volunteer respondents. The benchmark taken into account in choosing the sampling unit was that the individuals had to be over the age of 18 and needed to be a buyer of western imported food. As this condition was fairly general, choosing the right respondents was not a burdensome process.

Data groundwork

The principal phase in formulating the data for further analysis was to check the questionnaires for completeness. The questionnaires were screened to recognize unqualified, half-finished and vague responses. This stage was followed by a coding procedure, which comprised allocating a particular number to the different levels which appeared in each variable. The coding was carried out following the procedures of Cohen, West and Aiken (2003): each code entered was numeric, the same variable for each respondent occupied the same column in the SPSS Version 23 data editor and the codes assigned for each variable were equally exclusive, and this was done constantly for all the variables. The data records were checked comprehensively to confirm that they were reliable and no items were omitted.

Exploratory factor analysis (EFA)

EFA was then executed on the data gathered for the pilot study in order to improve the scales’ relationship to the factors affecting consumers’ purchase behavior in the context of western imported food. EFA is known to be an effective and generally used method for the cleansing of scales of a survey instrument (Hair, Money, Samouel & Page 2007). The investigation employed principal component analysis as the extraction method. A Promax oblique rotation was used since it was appropriate under the supposition that the factors may be correlated. The scale cleansing process was adopted as per the techniques suggested by Hair, Money, Samouel and Page (2007).

3.18 Validity and reliability of the quantitative stage

One of the main objectives of this research was the gathering of data from a range of diverse sources. The researchers checked the validity and reliability of the data

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because they constitute important foundations of the scientific technique to measure the quality of data (Cozby & Bates 2012). The processes used are outlined in detail below.

Internal validity

Prior to the pre-test, several steps were taken to ensure the internal validity of the data. The internal validity determines the structure of an experimental design and explains every stage of the methodology to evaluate the cause and effect associations of empirical evidence (Cohen, Manion & Morrison 2005). Validation is the mechanism to establish what should be measured in the research method (Bhattacherjee 2012). There is no common scale of validity, as a result of which the content validity arising from the measures adapted from different academics and from the feedback of participants in the pre-test guaranteed the validity of this research.

A researcher should focus on the input of professionals such as marketing experts and academics to measure content validity because they have complete command of the subject about which the research is conducted (Cavana et al. 2001; Hair et al. 2006). In this study, some items were adopted from previous studies and others were taken from the qualitative phase. Details of this have been given in the preceding section.

The content validity of the information represented by the research tool was conducted through a comprehensive review of the available literature, by a thorough process of article selection and item modification during the survey development stage. Tools of measurement for evaluating the constructs were deduced by strictly defining variables used in previous studies into measurable factors, and were moderately refined to match the requirements of the research as well as being assessed by a pre-test. An academic who was an expert in marketing was consulted in the preparing the pre-test of the survey instrument. Prior to delivering the pre-test, the survey was given to 15 people, of whom ten were academics, including this research project’s supervision team and five persons who were marketing professionals. Of these, eight people sent back their information and comments.

External validity | Reliability

Reliability is focused on the consistency of responses by the respondents and any specific outcomes must be intrinsically repeatable (Field 2009). For reliability, the

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results should be accurate and consistent. If a compiled result is accurate for the total number of participants it can be considered reliable (Golafshani 2003). The research instrument is understood to be reliable if the research results are replicated after repetition with same methodology and consistency of results is evident (Golafshani 2003).

In this research, this step was taken for data from the pilot study. All items were analyzed in detail and checked thoroughly. A test of reliability to estimate the measurement’s internal consistencies was carried out in this stage. For every construct, the internal consistency of the items in the survey instrument dealing with it was assessed via Cronbach’s alpha. The use of IBM SPSS Statistics Version 23 for the calculation of Cronbach’s alpha is the evidence of internal consistency reliability (Cozby & Bates 2012). Many researchers have articulated that 0.7 is an accepted value (Field 2009; Stafford & Turan 2011). In order to increase the alpha coefficient some of the items were removed as a result. (See Chapter 5 in Section 5.13 for further details).