Sampling Design Process

The sampling design process followed in a research study is determined by its research population; i.e. every possible existing member (e.g. person, household, etc.), which share characteristics defined by the research objectives (Webb, 1992). Hence, the sampling design process and its associated decisions must be closely integrated with the research objectives and its associated decisions, as in the five step sampling design process depicted by Maholtra (2007):

1. Define the target population 2. Determine the sampling frame 3. Select a sampling technique(s) 4. Determine the sample size 5. Execute the sampling process

The sampling design process in this research will follow the five steps within Malhotra’s (2007) approach, as discussed in the following sub-sections.

4.10.1 Defining Target Population

A research (target) population is a collection of members of relevance to the research objectives and the researcher, and which needs to be targeted for selection in the research sample (Dillon et al. 1994; Brace, 2004). The research population needs to be precisely defined to ensure the research is not ineffective or misleading (Maholtra, 2007). Its definition must cover the research population in terms of its: elements, i.e.

the members about which or from which the information will be obtained (Webb, 1992); sampling units, i.e. an element, or a unit containing the element, which is available for selection at some stage in the sampling process (Crask et al. 1995);

extent, i.e. the geographic boundaries of the research population (Schuman and Kalton, 1985); and time.

Online sampling has limitations that need to be taken into consideration (Howard, Rainie and Jones, 2001; Andrews et al. 2003). Sample size of online research samples inevitably decrease or increase due to: duplication of e-mail addresses; multiple responses from respondents; malfunctioning URLS; and ‘bouncing’ of unrecognised e-mail addresses (Sheehan and McMillan, 1999; Couper, 2000; Andrews et al. 2003).

some may not (Stanton, 1998; Thompson et al. 2003) and responses may then also be imprecise as respondents may alter their responses to seem more socially desirable (Wright, 2005). However, these problems are counteracted to an extent by the advantage of being able to compare knowledge of the respondents against the characteristics of non-respondent characteristics in order to validate the online questionnaire results (Ray and Tabor, 2003).

4.10.2 Determining the Sample Frame

The sampling frame is a list of the selected members that will be used in the research activities as a representation of the research population (Schuman and Kalton, 1985;

Crask et al. 1995) and is identified once the research population has been defined.

There are five key characteristics of a sampling frame; each element should be included only once; no elements should be excluded; the frame should cover the entire population; the information, which is used to design the frame, should be up-to-date;

and the frame should be easy to use (Webb, 1992).

4.10.3 Selecting a Sampling Technique

Whether to use a Bayesian or traditional approach, to sample with or without replacement, and to use non-probability or probability sampling, must be determined in order to select an appropriate research sampling technique (Brace, 2008). The traditional sampling approach is the most commonly utilised for academic research and selects the entire sample prior to data collection (Schuman and Kalton. 1985), i.e.

without replacement, in which case an element cannot be included in the sample more than once (Oppenheim, 1992). The Bayesian approach alternatively selects the sample sequently with replacement throughout data collection (Malhotra, 2007) and explicitly incorporates prior information about the research population characteristics as well as the associated costs and probabilities associated with doing things incorrectly (Webb, 1992).

4.10.4 Determining Sample Size

Sample size is the number of members to be included in the data collection, the determination of which comprises of a complex balance of qualitative and quantitative considerations (Dillon et al. 1994). Quantitatve considerations of sample size include:

importance of the decision; the nature of the analysis; sample sizes used in similar

research; incidence rates; completion rates; and resource constraints. Once all respondents have completed questionnaire, the completed sample is determined (Dillman et al. 2009).

4.10.5 Executing the Sampling Process

The sampling process is executed through the decision-making and specification of the sampling design and the sampling frame, sampling unit, sampling technique, and sample size integration and implementation (Malhotra, 2007); with the intent to maximise responses and minimise potential errors.

4.10.6 Potential Sources of Error

Sampling error is a result of just a subset of the entire population being surveyed (Dillman et al. 2009). Random sampling error occurs when the research sample is an imperfect representation of the research population, and is a measure of the variation between the true mean value of the research population and the true mean value for the original research sample (Churchill and Brown, 2007).

Non-sampling errors are a result of non-response errors (Schuman and Kalton, 1985) and may be random and non-random (Dillon et al. 1994). Non-sampling error can be a result of controllable errors by the researcher in areas such as problem definition, approach, scales, questionnaire design, interviewing methods, and data preparation and analysis (Crask et al. 1995; Maholtra and Birks, 2000). Uncontrollable non-sampling errors can also occur, due to non-deliberate inaccurate responses by the respondents, because of unfamiliarity, fatigue, boredom, faulty recall, question format, question content and other factors (Webb, 1992; Dillon et al. 1994).

Deliberate inaccurate responses by the respondents an also incur uncontrollable non-sampling errors, because of respondent’s willingness to provide accurate information (Schuman and Kalton, 1985) and intentionally misreported responses because of the desire to provide socially acceptable answers, avoid embarrassment, or please the interviewer (Malhotra, 2007). Sampling error is the least problematic source of potential error because it is measurable and relatively small in magnitude, whilst non-sampling error cannot be calculated or predicted and is a major contributor to the total

4.10.7 Sampling Method Adopted

In this research, the research population was plus size fashion online consumers in 2009, the element and sampling units of which are the online consumers of the plus size fashion online shopping retailer. Hence, although the online research conducted in this research utilised a non-probability sampling approach that results in inevitable sampling bias (Van Selm and Jankowski, 2006), the sampling frame is controlled, as the plus size fashion online retailer’s database contains specific plus size fashion online consumers (Evans and Mathur, 2005; Wright, 2005). The e-mail lists of the plus size fashion online consumer are tested for accuracy and updated when necessary (Tingling, Parent and Wade, 2003).

The sample was judged to be valid due to its accessibility, large size of 100,000 members, and its strong alignment to the characteristics of the research population (Evans and Mathur, 2005). Although sampling errors are inevitable as the sample is only a representation of the entire population of plus size fashion online consumers (Van Selm and Jankowski, 2006), it does incorporate an entire representative population of the selected retailer (Sills and Song, 2002) which reduces the level of the resulting sampling bias on non-probability sampling.

The online consumers of the plus size fashion online shopping retailer will serve as the sample frame for this research: each online consumer in the retailer’s database will only be listed once; no online consumers that have not opted-out to e-mail communication from the retailer will be excluded; the frame will only include relatively recent online shoppers of the retailer; and the frame will be a list of e-mail addresses that can be accessed at any time at the discretion of the retailer.

This research adopted a traditional replacement sampling technique using non-probability Quota Sampling, to judge and control the sampling frame and subsequent quality of data collected. The research category/quota was defined as plus size fashion online consumers, and a judged sample of the online consumers of a plus size online shopping retailer was selected. The sample size of this research is 100,000; the number of the online consumers of the plus size fashion online shopping retailer.

This research investigates plus size fashion online shopping motivations in the United Kingdom in 2009, by evaluating 502 responses from the members of the sampling frame of online consumers of the plus size fashion online shopping retailer who completed the online questionnaire. Hence, the completed sample had 502 respondents; an excellent response level and over five times the amount of the research variables (Comrey and Lee, 1992; Hair, Anderson, Black and Tatham, 2010).