The Sample and Research Representativeness

Sample is a proportion of people being analysed (May, 2011). Survey sampling includes the process of selecting a sample of people from a target population in order to perform a survey with the chances for perfect representable samples being relatively small (De Vaus, 2002). The objective of the sample is properly mirror the population for which is intended to represent.

To assure that from the population the chosen sample is representative it is essential that several types of people from the population are included and all of them have an equal chance (May, 2011). The samples are of two broad types, the probability and non-probability samples.

The probability sample is considered of random selection of individuals and has frequently identified chance to be selected. Probability samples are the most certain way to acquire representative samples from the population (De Vaus, 2013). Still, it is unlikely to achieve perfect representation of the sample as differences will occur among the sample and the population partly due to “sampling error”.

What is the important here is the features of randomly selected samples to be close to that of the population. Through probability theory it can be estimated how close the actual population figure is with the selected one from the sample.

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The term “standard error” is used for this purpose. Probability samples can create representative samples and allow improved sample accuracy. There are four types of it: the Simple Random sampling (SRS), the Systematic sampling, the Stratified sampling and the Multi-stage cluster sampling (De Vaus, 2013). In Random sampling (SRS) the key thing is that each unit of the population has an equal probability to be chosen in the sample. A random sample is taken through allocating a number to each unit of the population and through the use of a random number table it creates the sample list (Altinay and Paraskevas, 2008). By selecting random sampling bias can be avoided and therefore it becomes more representative. Its major problem is that its cost is prohibitive as it would involve interviews that needed to travel huge distances (De Vaus, 2013).

The Systematic sample most of the times is used in occasions where collection of data takes place during a process operation and that is being accompanied with a methodical rule, i.e. every fifth unit, the first 10 units every hour etc. It is a simpler version of the SRS and apart from the cost problem here an additional one would be the “periodicity” of the sampling frame. That means that certain type of person may reoccur at regular intervals within the sampling frame excluding others systematically. Here one risk that is entailed is that this systematic rule possibly matches some primary structure ending in sample bias. The Stratified sample is a modification of SRS designed for more representative and accurate samples. Its main focus is on dividing the population in homogeneous groups with specific characteristics such as gender, age or even market segment (Altinay and Paraskevas, 2008). The problem here which also occurs to the two previous techniques is that they are of limited use on their own when there is attempts to sample disperse geographical population. There is also no assist in drawing a sample in which no sampling frame is available, something which exists when conducting large area surveys. One solution according to De Vaus (2013) is the Multi-stage cluster whereas in this technique there is involvement of several different samples through division of the area into clusters in such a way to minimise cost as much as possible. Through cluster sampling the primary sampling unit (which is the first stage of the sampling process) is not population units for sampling but groupings of those units (Bryman and Bell, 2015). This process involves aggregation of population units which are known as clusters. The cluster sampling necessitates a large population which has geographical diversity (Altinay and Paraskevas, 2008). Through this technique there is division e.g. of a city into areas (clusters) and within these areas there is selection of smaller areas (blocks) where from each block there is selection of people to participate in the questionnaire survey.

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According to Malhotra and Birks (2006) there can be an aggregation of 32 minimum dissimilar probability practices such as stratified and un-stratified selection, systematic and random or multistage clustering and single stage clustering methods.

The non-probability sampling on the other hand has one distinction from the probability sampling as it does not involve random selection. The difference here is that there is human interference and therefore accidental selections are unidentified or even zero for some elements (Bradley, 1999). Does this being interpreted as lack of representativeness with the non-probability sampling? Not particularly as in the tourism industry research field quite regularly many non-probability samples take place mostly with the form of a convenience on- line sample (Bojanic and Warnick, 2012).

There are certain situations of why we use non-probability sampling. The one which is the most common and that what the reason for the current researcher to follow that path was because the non-probability sampling is less expensive in comparison with the probability one (Battaglia, 2011). The second reason is because it can be implemented quicker in comparison with the probability sampling (Battaglia, 2011).

The non-probability sample can be distinguished into three types, the Quota sampling, the Purposive sampling and the Convenience sampling. To illustrate inferences from a non-probability sample requires different actions than from a probability one but the latest advances in technology (i.e. Internet) created new approaches and favoured higher usage of the non-probability sampling (Battaglia, 2011).This is due to the fact that the respondents can use the Web to complete questionnaires and that means surveys can be carried out much quicker and much cheaper in relation to probability samples.

Quota sampling is quite similar to the Stratified sample. Here the basic idea is to complete a certain amount of interviews with specific subgroups of the population of interest i.e to create 50% of the interviews with males and 50% with females in a random-digit interview survey through the telephone (Battaglia, 2011). The main issue with Quota sampling is that an unknown number of sampling biases has been inserted into the survey estimations (Battaglia, 2011).

Purposive sampling’s target is to create a sample that can be treated as “representative” with regards to the population. Usually it is chosen when selecting small samples from a limited geographic area but the knowledge and experience of the person making the selections is a key aspect of the success of the sample (Battaglia, 2011). It would also be problematic to quantify the sample characteristics (Battaglia, 2011).

119 Convenience sampling differentiates from purposive sampling in the fact that skilful judgement is not applied to select a representative sample of elements. Instead the main selection principle relate to the comfort of getting a sample (Battaglia, 2011). Obtaining the sample with comfort relates with the cost applied in locating elements of the population, what geographical distribution is being involved and acquiring the questionnaire data from selected elements (Battaglia, 2011).

In the current study there is usage of a convenience sample which is a non- probability one and that condition has been accomplished through researcher’s interference into the selection of several types of people which were included in order to cover a variety of age, trip purpose, domestic/international flights, frequency of flying, nationality, airline brand name, travel class from a variety of countries. Similar situation achieved through researcher’s data collection. The demographic profile of the sample in this research has included the areas of gender and age. With regards to gender there were 209 male and 157 female travellers. 33 didn’t reveal their gender (which makes all together 400 in total). With regards to age this research has included all the six different age groups (18-24: 189 participants, 25-34: 129 participants, 35-44: 30 participants, 45-54: 9 participants, 55-64: 2 participants, 65 and over: 1 participant). 40 didn’t reveal their age group (which makes all together 400 in total).

Additionally there has been included other related information such as the “Purpose of trip” – [(i)Business, (ii)Leisure/Holiday and (iii)Other (please write)], “Nationality background” – [country issued the passport], “Current job occupation”, “Airline carrier of the trip”, “Travel class” – [(i)First class, (ii)Business class, (iii)Economy class], “Domestic or international flight” – [(i)Domestic within the UK, (ii)International in Europe, (iii)International outside Europe] and “Flight frequency of the traveller with the same airline” – [(i)First time, (ii)Once before, (iii)Twice before, (iv)3-5 times, (v)6-10 times, (vi)More than 10 times].

The major objective was people from all ages that had recently flown domestic or internationally. The Manchester airport comprised of a major data collection point to that.

Despite being only a single place the variety and variation of its air travellers with regard to their background provided representativeness to the sample as the researcher tried to include all possible combinations of air traveller characteristics (age, sex, domestic and international travellers). Due to the fact that the city of Manchester is famous for its residents’ international background (e.g. college and university students that come from around the globe) the airport gathers huge amount of diversity of air travellers.Additionally was the Piccadilly train station as many people were using the train from the airport to reach city centre.

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Further on this convenience sample there was university students of Salford University involved in both undergraduate and post-graduate level with an effort to include additionally significant number of mature students (aged 25 and above) to balance and have greater variety among them.

One major reason for that was apart from the fact that it was less costly and time consuming in comparison with the rest of the sample (go the airport, Piccadilly train station, Piccadilly garden square, several other areas) was the fact that Salford university comprises of a vast amount of international students that became air travellers in order to reach their destination for study and also visiting their home countries for several times (e.g. Christmas/Easter/Summer breaks) during their time of study in the university.

Even though a convenience sample includes a good response rate it does not represent a general view of country’s air travellers. Nevertheless the students’ international background and their recent experience of flying (only the last 2-3 years) represented a very good opportunity not to be missed.

One thing that has to be mentioned here is the fact that population have dissimilar characteristics with regards to their accessibility. Particularly in the airport or in the train station, and during peak / busy times they tend to develop insignificant levels of cooperation for surveys (O’Neill and Charters, 2000). Therefore it is crucial for the researcher to take under consideration the accessibility factor towards the air travellers and have also included a contingency plan (Daniel, 2012). Also here when air travellers are about to board into the plane might not have the mind frame to complete a questionnaire.