SATURNO EN ASPECTO CON JÚPITER

Ideally, a researcher would like to obtain the data from all members of the population. However; it is almost impossible; therefore a small subset of the population which must be representative of all the members in that population is drawn. This subset which mirrors the characteristics of population is called sample (Zikmund, 2000; Hair et al., 2007, p. 170).

According to Hair et al. (2007, p. 171), a set of well defined procedures to obtain a representative sample is as follows:

1. Defining the population under investigation (The complete group of elements relevant to the research).

2. Determining the appropriate sample frame (a complete list of all the elements in that population).

3. Sampling method selection.

4. Calculation of the sample size.

5.Implementation of the sampling plan.

There are two broad categories of sampling methods available including probability and non-probability methods. Both of these methods are explained in details below.

4.7.1. Probability Sampling

According to Hair et al. (2007, p. 175) and Saunders et al. (2009, p. 214), drawing a probability sample is based on this premise that each element in the population has a known and nonzero probability of being selected. Also, in this method selected samples are usually large to be representative of the population; therefore, with a specified level of confidence, the findings can be generalized to the population under investigation. Probability sampling methods are classified as Simple random sampling, Systematic sampling, Stratified sampling, Cluster sampling, and Multi- stage sampling.

Simple random sampling

In the simple random sampling (sometimes called just random sampling), all the elements in the population have equal chances of being selected. In this method, the resulting sample is representative of the population if its calculated size is sufficiently large. It is also an appropriate method for a geographically dispersed area if the researcher uses an alternative data collection technique such as telephone interviewing or online questionnaires (Hair et al., 2007, pp. 175–176; Saunders et al., 2009, p. 222).

Systematic sampling

In systematic sampling, an initial starting point on a list is randomly selected and after that, the researcher selects every nth element in the sampling frame. Thus, to draw the sample, we should first calculate the sample size and the sampling interval. This method is useful for geographically dispersed cases only if face to face contact in not required. Also, using this sampling method, the researcher can obtain representative data (Hair et al., 2007, p. 177; Saunders et al., 2009, p. 226).

Stratified sampling

To use this method, the sampling frame must be divided into distinct and relatively homogeneous subgroups which are called strata. This is usually done by the researcher's past experience or specified by the client. Then the researcher determines the size of total sample and the size for each individual strata. The composite of the samples taken from the strata shapes the stratified sample and to select the elements of this sample, systematic or simple random samples of the strata of the population must

be drawn. The strength of this method is that it helps increasing the accuracy of the sample information (Hair et al., 2007, p. 178).

Cluster sampling

In this method, the researcher views the population as made up of heterogeneous groups, each of them is called a cluster. A cluster group can be for example geographic areas, households, firms, and so on. It should be noted that cluster sampling can produce representative data if it is done properly. It is important to say that in this method the sampling frame is the list of clusters rather than a list of individual elements in the population. In order to increase the representativeness of the sample, the researcher must increase the number of sub-areas (Hair et al., 2007, p. 180; Saunders et al., 2009, p. 230).

Multi-stage sampling

Multi-stage sampling which is also called Multi-stage cluster sampling is a development of cluster sampling method. This method is useful when the population is a large geographical region and face to face contact with the elements is needed or it is hard to construct a sampling frame for that region; however, it can be used for not geographically based discrete groups (Hair et al., 2007, p. 181; Saunders et al., 2009, p. 231).

4.7.2. Non-probability Sampling

Hair et al. (2007, pp. 181–182) explain that in this technique, the probability of each element in the population is not known and the selected sample is not necessarily representative of the population statistically. In this situation, to select the elements in the sample the researcher uses expert judgment, experience, and convenience. Therefore, unlike probability samples, the results can not be generalized to the population (Hair et al., 2007, pp. 181–182). These authors also discuss that the most common types of non-probability sampling techniques include Convenience sampling, Judgment sampling, Snowball sampling, Self selection sampling, and Quota sampling.

As it has been mentioned before, the population of this survey is all Iranian banks' managers (the top manager in each branch). However, because of the time limitation,

we limited this population to all the top banks' mangers in the province of Tehran which is the heart of financial activities. In addition, according to Bank and Insurance Manifest agency of Iran (2010), it has the highest rate of e-banking usage. In the next level, among all of the mentioned banks, we selected four of them including two governmental and two private banks. Among governmental banks, bank Refah and bank Maskan and among private banks, bank Pasargad and bank Saman have been chosen. We chose these banks because they are old and in comparison with other banks they are more pioneering in e-banking. In addition, they have the greatest number of branches in Iran and have been recognized as the most effective banks in e- banking context in Iran.

In the final level, we used simple random sampling to choose the branches of each bank to distribute the questionnaires to the respondents with equal chances of being selected. Therefore, according to the definitions of each sampling method in the previous section, we have used a probability stratified sampling method.