Tourism Research Methods (Thm 208)

BUSINESS RESEARCH METHODS (EMBA710) THE RESEARCH METHODOLOGY 1. INTRODUCTION 1.1 Definition of Research Methodology

“The research methodology is an operational framework or a set of procedures and methods that are used to carry out research”

1.2 Types of research

Jennings (2001) pointed out that, various cascading classification systems exist within the social sciences to describe the types of research that tourism researchers can undertake as summarised in table 1.

Table 1 Core Function Information Needs Type of data collected

Exploratory Qualitative Descriptive Pure Explanatory Causal Quantitative

Comparative Applied Evaluative Mixed Predictive

(a) Types of research designs by core function In terms of its core function, a research can either be pure or applied. A pure research is basic research. Its purpose is to explain how the world works. It advances our knowledge of the world and hence data from this research can be used to construct theories, frameworks and models. Smith et. al. (2002) point out that, academics are the main users of this type of research. On the other hand, applied research is generally designed to gather information relating to a problem, an issue or a planning need (Jennings 2001). Therefore, a research can be applied or basic, depending on whether the results are expected to contribute directly to a managerial decisions (applied) or are intended to provide answers to questions of a theoretical nature (Walliman, 2001).

(b) Types of research designs by functional objective (c) Types of research design by time dimensions  Snap shot/cross sectional research designs. These are carried out once and for all, at a particular point and time e.g. the impacts of the 2002 solar eclipse on tourist arrivals to Southern Africa.  Repeated/Longitudinal research designs As suggested by the name, these are studies which are repeated again and again with the same study units/subjects/samples. Such studies are carried out to test for reliability and validity of the data. The longitudinal research designs can take a one-group pretest-post test form, in which measurements are taken before and after an occurrence to assess the influence of the event/occurrence. It can also take the form of a static group comparison, where two groups of research subjects are surveyed. In this case, for the first group, measurements are taken before the event, they experience it and are then questioned again after the experience. Group B subjects are questioned before and after the event, in parallel with group A, but do not experience the event. Such a study is usually carried out to determine the impact of a change of something e.g. changes in the quality of services at a particular tourist resort. (Robson, 2000).

Finally, there are other complex longitudinal research designs, which are commonly used in the physical and biological sciences such as medicine, pharmacy e.t.c. These designs are rarely used in tourism research and therefore are beyond the scope of this book. (d) Other types of research designs Research designs can also be classified by method of data collection, nature of data collected. Case studies and triangulation are also other research designs which one can adopt. Please read research methods textbooks for you to gain a deeper understanding of these methods.

1.3 Factors determining the research design you choose The type of research design you choose must depend on;  the resources available for the study both in terms of money and time  the type of data to be collected  the equipment required for the research and  expertise required by the data collection method

2. DETERMINATION OF STUDY POPULATIONS/UNITS/SUBJECTS 2.1 Steps in selecting the study subjects

Step 1: Define the study population The population consists of all the elements that exhibit the characteristics of interest to you as the researcher. It is determined in terms of time, geographical location, number of elements and the degree of homogeneity or heterogeneity of the study subjects. An incomplete definition of the population destroys the validity and reliability of the survey results. As an example, in a study of hotel customer loyalty programs, one student can study only 3 star hotels in one region of a country with ten regions. Why might this definition of the population be incomplete? Under what conditions might this study population be deemed complete? Step 2: Specify the sampling frame This sampling frame is intended to represent all the members of the population. Unfortunately, such a listing is rarely available and the sample frame actually used is likely to differ somewhat from the theoretical target population. This means that, frame errors are particularly unavoidable but as researchers we should attempt to recognize potential sources of such error and minimize them whenever possible. As an example, in the example on customer loyalty programs highlighted in step 1, some of the hotels might not be registered and hence would be deemed non-existent. Step 3: Specify the sampling units or study subjects These are the basic units containing the elements of the population to be surveyed or measured. The units maybe the elements themselves or they maybe containing the study subjects e.g. hotel marketing managers. The design of the project also influences the selection of the study subjects e.g. mail surveys require a sampling frame with postal addresses, telephone interviews require units with telephone numbers and so on. Step 4: Select the sampling method. Types of sampling There are two main types of sampling i.e. probability and non-probability sampling. The following write up will shed more light on these types of sampling. a) Probability sampling This type of sampling is used when the sampling frame is known. Probability samples imply the use of a random selection mechanism. Random selection eliminates bias in the selection process and underlies the theories used to infer the sample results to the population. Random here does not mean haphazard or arbitrary. Random selection is a very careful and specific procedure that ensures that, each item in the sample is independent of the selection of any other item. Randomness translates to the independence of each selection, that is, the selection of any population member does not affect the likelihood of any other population member being selected.

Types of probability sampling techniques (i) Simple random sampling This is by far the most familiar type of probability sampling. The simple random samples are selected in such a way that each member of the study population has an equal chance of being selected. In this case, all the members of the study population are either physically present or listed and the members are selected at random until a previously determined number of members or units has been selected. To select these members or units, the lottery method can be used.

Disadvantages of random sampling  The greatest disadvantage of this sampling technique is the necessity of having a complete listing of the members of the population. This process is generally difficult and is not feasible in most study situations.  Gathering the research information can also be expensive if the elements randomly chosen for study are widely spaced.  The chosen elements can produce one-sided conclusions. As an example, in a study of tourists frequenting a particular destination, if a random sampling technique is used to choose the study subjects, there may not be a fair representation of the sexes hence the resulting conclusions from the research will be gender biased. Despite these disadvantages, a random sample may be quite useful whenever we sample a population that is small, for which a suitable listing is available and where the geographical dispersion of the sample elements is not a problem. However, to cope with the above problems, other probability techniques have been developed to alleviate some of the problems. These will be discussed next.

ii Systematic sampling A systematic sample is often referred to as a “pseudo random sample” because it has properties similar to simple random samples. This type of sampling works on the basis of applying a fixed sampling interval to a list of population elements (Henry 1990). As an example, if you have a population of 1000 tourists and require a sample of a 100, you could use a sampling interval of 10. After randomly selecting the first element, you would then select every tenth tourist.

On the surface, systematic sampling is expected to provide every element with an equal chance of selection. However, this is not usually the case, as the selection of any one element affects the probability of selection of any other element. This is because, without using a random start, some members of the study population have a zero probability of selection and the sample cannot be considered as a probability sample. A problem with this sampling method can occur if the population is arranged in a cyclical way/fashion and the cycle coincides with the selection interval. As an example, if the interval is 7 and the data are listed by days of the week, the data from the same day may be chosen. Therefore, it is always better to re-arrange the data when using systematic sampling. iii) Stratified sampling In this sampling technique, each member of the study population is assigned to a group or stratum and then a simple random sample is conducted for each stratum. This technique lessens the probability of producing one-sided conclusions. The following steps are to be taken to produce a stratified sample;  Decide on the sample frame e.g. all the registered hotels in a country.  Divide the sample frame into sub-samples e.g. 0 star, 2 star, 3 star, four star and 5 star hotels.  If you want to study only 100 tourists, then you must select a representative sample from each of the hotel categories for study.  Combine the conclusions from all of the hotel categories to get conclusions for all the hotels in the country.  iv) Cluster sampling In this technique, the population members are divided into unique, non- overlapping groups prior to sampling. The groups are referred to as clusters. The clusters are then randomly selected and each member of the cluster is included in the sample. The cluster is a sampling unit in this case, not the individual member of the population. In contrast to stratified sampling, a cluster sample involves the selection of a few groups and data are collected from all group members in each chosen cluster (Henry, 1990). As an example, to sample hotels in a country, the hotels could be divided into clusters by grade as before and then a sample of the grades would be used as the study units e.g. only 2, 4, and 5 star hotels.

Cluster samples are also used because they typically make sampling easier especially where we don’t have listings of population elements. In such cases, the cluster sampling technique makes the actual task of conducting a survey more manageable and thus cheaper. As an example, if one wants to study tourists interested in beach volleyball at several overpopulated beaches along a coastline, it would be quite difficult to do a listing of all such tourists. In this case, therefore, the tourists are divided into clusters by beaches, then the clusters will be chosen at random. The researcher will then visit each cluster and pick out the tourists interested in beach volleyball for study. Notice here that, where the number of elements sampled from a cluster is small and the degree of homogeneity within a cluster is low, the conclusions drawn from such clusters will be less accurate for the population. The obvious sampling strategy is to maximize the number of clusters and reduce the number of sampled elements per cluster. This, however, works against the reasons for clustering, so a careful balance must be considered (Devaus 2002). v. Multi-stage sampling The concept underlying cluster sampling is used and extended in this type of sampling. In the first place, clusters are selected as in the cluster sample, and then sample members are selected from cluster members by simple random sampling (Saunders, 2003). This technique was purely developed to save money and time. As an example, in a study of all the hotels in a country, it is possible to choose the individual hotels for study from a listing of all the registered hotels in the country using the purely random sampling technique. However, the chosen hotels will certainly be scattered throughout the country and it will be difficult and expensive to travel to each of the chosen hotels to acquire data. In this case, therefore, one would use the multi- stage sampling technique in which the following steps can be taken:  Divide the country into provinces e.g. 10 provinces  Select certain provinces at random for study e.g. 4 provinces  Allocate hotels to provinces  Choose the hotels in the four selected provinces at random and these will be your study units. If possible, you could first stratify the hotels by grade before choosing, in order to come up with conclusions which are representative of all the hotels in the chosen provinces. b) Non-probability sampling Non-probability sampling samples are actually a collection of sampling approaches that have the distinguishing characteristic that subjective judgments play role in the selection of the sample. This selection method contrasts with that of probability samples that are selected by a randomized mechanism that ensures selection which is independent of subjective judgments. These non- probability samples cannot be rigorously analyzed to determine possible bias and likely error. Circumstances under which non-probability sampling can be used; a) Where the sample frame cannot be derived. As an example, in studies of tourists interested in sex tourism, it would be impossible to put up a list needed to draw a probability sample. In this case, a snowball sampling technique can be used. This will be discussed later. b) Where the researcher is truly interested in particular members of the population that comprise the sample e.g. all hotels with employee share ownership schemes. c) Where resources are limited and one cannot study the intended sample units.

Types of non-probability sampling (i) Convenience/haphazard sampling In this case, one just chooses the study units on the bases of their availability for the study. As an example, if the study units are tourists entering a game park, then one will just choose and ask those who are willing. Convenience samples are mostly used in exploratory research studies and where cost minimization is important. However, we cannot infer conclusions from such samples to a wider population. Convenience sampling is therefore not very recommended in academic research.

(ii) Snowball sampling This type of sampling is commonly used where it is difficult to identify members of the study population e.g. tourists interested in sex tourism. In this case, the research makes contact with one or two cases in the population and then the already selected members will identify additional members to be included in the sample (Saunders 2003).

(iii) Purposive/judgmental sampling This refers to a judgmental type of sampling in which the researcher purposefully chooses study units depending their relevance to the issue being studied e.g. The most common purposive sampling methods include the following;  Typical case sampling This is done when there are extreme limitations of time and resources, which prohibit probability samples. In this case, the researcher thus selects a few cases that are felt to be normal or usual e.g. selecting 3 star hotels meeting only the basic requirements for a 3 star hotel.  Critical Case In this case, cases that are essential for overall assessment or acceptance of conclusions are chosen. As an example, in an evaluation of hotel marketing strategies, the researcher can select only hotel marketing managers. These are the sources of data that are critical to the research problem i.e. hotel marketing strategies.  Extreme case/Deviant sampling This type of sampling focuses on extreme, unusual or special cases, on the grounds that the extreme conclusions drawn from these cases will be relevant to the rest of the more typical cases of the population. As an example, in a study of five star hotels in a country, one can choose the least and the best hotel in terms of meeting the minimum standards for a five star hotel.

Step 5: Determine the necessary sample size. In non-probability sampling, the sample size is subjectively determined by the researcher and therefore can be any figure, which he/she deems suitable. For probability sampling, a rule of the thumb would be to take 1/3 of the population

In determining the sample, the following points should also be noted;  The sample size must be representative of the population; otherwise the conclusions drawn will lack validity and reliability.  It should be increased where members are relatively varied and reduced when members for the study are relatively similar.  Beyond a certain point, an increase in the sample size will only slightly increase the accuracy of the research results. Under such circumstances, do a cost benefit analysis to see whether the increased accuracy is justified by the cost.