Most organizational researchers need to determine the sample size correctly any as inappropriate, inadequate, or excessive sample sizes will influence the quality and accuracy of the research. The research conducted by Bartlett, Kotrlik and Higgins (2001) described the procedures for determining sample size for continuous and categorical variables using Cochran’s (1977) formula. Although it is not unusual for researchers to have different opinions as to how sample size should be calculated but the process used in the research should always be reported. This will enable the reader to make his or her own judgments as to whether they accept the researcher’s assumptions and procedures. Generally a researcher could use the standard factors identified for the sample size determination process. More reliable, valid and generalizable results can be achieved by using an adequate sample together with high quality data collection. This could also result in other resource savings.
Research conducted by Rao, Yoonkyung and Jason (2010) on the calculation of sample size for a validation study to meet pre-specified sensitivity and specificity requirement state it is done in such way so as to avoid futility of pharmacogenomic development. Change of platforms is taken into account in the sample sizes calculation by statistical modelling. The proposed formulation for meeting minimal sensitivity and specificity requirements calls for estimation of both measures. Their confidence lower bounds can substitute the unknown true values in the sample size calculation procedure. However, the confidence level has to be calibrated for appropriate sample sizes to ensure that the probability of a successful validation experiment exceeds a desired level. The study by Rao, Yoonkyung and Jason (2010) shows the relationship between the underlying sensitivity and the required confidence level in a normal distribution setting. The results can be used as a practical guideline to set the level of confidence adaptively.
The study on practical guidelines on effective sample size conducted by Wan M., Wan Abdul A., Nor Azlida and Norizan M. (2012) is to determine the right sample size in observational study which is focusing on medical or health sciences field. Sample size calculation is actually depending on the type and how the research is designed. For example; different formulas are used to calculate the sample size in different type of research. In the research by Wan M., Wan Abdul A., Nor Azlida and Norizan M. (2012) the formula of single proportion and two proportions is discussed and an example from medical research is used which may contribute to the understanding of the problem. The application of the proposed formula for the sample size determination has been discussed by approaching the case of determination associated factors of HIV-infected Tuberculosis. Ahmad et al. (2011) pointed out that the larger the samples the more confident we can be that their answers truly reflect the population. However, there are few guidelines that have to be addressed in the particular area of health sciences.
The research conducted by Delice (2010) investigated 90 qualitative master’s thesis submitted for the primary and secondary school science and mathematics of 10 universities in Turkey between 1996 and 2007 in terms of population and sample using document analysis. Identification of sample size is essential for generalizability and repeatability (Delice, 2010 & Henn, 2006). The reason is to apply the relationship obtained among the variables to the general the population. That is why the selection of sample representative of the population is essential. Every research investigates simultaneously a number of variables with differing variability. A variable with a greater variability will require a larger sample to achieve a certain precision level than a variable with a smaller variability. When we use the largest sample, cost and time is a problem and we need to choose the sample size based on the variable for which the greatest precision is required.
3.6 Inclusion and Exclusion Criteria
Researcher has included the protocols of a thorough description of the population. The most important priority is that the respondent population must have the attribute that will make it possible to accomplish the purpose of this research. The researcher has specified inclusion and exclusion criteria for participation in this research. Defining inclusion and exclusion criteria increases the likelihood of getting reliable result. Inclusion and exclusion criteria help researchers to find the most suitable respondents to participate in any quantitative survey and establishing a baseline set of standards is very important when conducting a research. Inclusion criteria helps researchers to have a set of inclusive standards that will guide researcher to screen potential respondents that include general information such as age and gender, designation and type of respondent required. The researcher needs to establish and adhere to research protocol in order to yield the best result in his research. Salkind (2010) explained that exclusion criteria are a set of predefined definitions that is used to identify subjects who will not be included in the research. The inclusion criteria make up the eligibility criteria that rule in or out the respondents in a research. Similar to inclusion criteria, exclusion criteria is guided by the scientific objectives of the research and it has very important implication to the scientific rigor of the research as well as for the assurance of ethical principles (Salkind, 2010). Researcher has reviewed the inclusion and exclusion criteria and decided if any group of respondent is inappropriately excluded. If the justification for the exclusion of the respondent is not reasonable with regards to the risks, benefits, and the purpose of the research, then this group should be included as explained by Salkind (2010).
The methodological quality standards should be based on statistical conclusion validity, internal validity, construct validity, external validity and descriptive validity (Farrington, 2003). Inclusion and exclusion criteria in systematic reviews are inevitably contentious because they are seen as potentially threatening by some evaluation researchers. The following are the inclusion and exclusion criteria used to identify the relevant respondent in this research:
a) Exclusion Criteria
– The trucks drivers and lower category workers within the transportation company shall not be included as respondent
– Transportation companies with less than 10 trucks may not represent the population and have been excluded
– Transportation companies outside peninsular Malaysia.
b) Inclusion Criteria
– The respondents are the management staffs of transportation companies who are able to understand English
– Members of several transportation and haulage associations in peninsular Malaysia
– The targeted respondents are foreign based companies operating in Malaysia and also local transportation or trucking companies
– Transportation companies with more than 10 trucks
– Transportation companies operating within peninsular Malaysia
3.7 Interviewing Respondents
Collecting data is to interview respondents in order to obtain information on the research area. Interviews could be conducted in unstructured or structured method and also face to face, by telephone or conducted online. Interview is one of the methods used in qualitative research. In this research, structured interview is being used as complimentary to quantitative survey. Structured interviews are those conducted when it is known at the outset what information is required (Sekaran, 2003). A list of predetermined questions for the respondents has been determined and prepared before conducting the interview. The questions are focused on factors that are relevant to the research questions. As the respondents express their views, researcher noted them down. It is very important that the same question has been asked to all the respondents in the same manner in order to be consistent. By conducting interview there will be chances that new factors can be identified and respondents are able to express their view which will result in a deeper understanding of the issues. When there is sufficient structured interviews being conducted and adequate information have been obtained for the research then the researcher can stop the interviews. The information was tabulated and the data analyzed. This has helped the researcher to accomplish the task set out to be done, for example, to describe the research questions, quantify them, identify the specific problem and evolve a theory of the factors that influence the problem and find answers to the research questions. This will be a much qualitative research if it is done in this manner.
The information obtained during the interviews should be as free as possible of bias. Bias refers to errors or inaccuracies in the data collected. Biases could be introduced by the interviewer, the interviewee or the situation. The interviewer could bias the data if proper trust and rapport are not established with the interviewee, or when the responses are either misinterpreted or distorted, or when the interviewer unintentionally encourages or discourages certain types of responses through gestures and facial expressions. Listening attentively to the interviewee, evincing keen interest in what the respondent has to say, exercising tact in questioning, repeating and/or clarifying the questions posed, and paraphrasing some of the answers to ensure their thorough understanding, go a long way in keeping alive the interest of the respondents throughout the interview. Recording accurately the information revealed by the responses is equally important.
Baum (2002) and Patton (1990) clarify that there are no exact rules for sample size in qualitative research while Miles and Huberman (1994); Patton (1990) argued that sampling in qualitative research usually rely on small numbers with the aim of studying them in depth and detail. In exploring the amount of data about a particular phenomenon, the sample is derived purposefully rather than randomly (Reed et al., 1996; Mays & Pope, 1995; Ezzy, 2002). There are some drawbacks on the interview survey method as described by Proctor (2003) and he stresses the fact that the information obtained by interviewing is mainly based upon interviewee statements about their past experiences and their future plan. Denscombe (2008) accept the shortcomings of face-to-face interviews such as being expensive and time consuming. However the advantage of this method is that the information obtained is more detailed and rich. The advantage of the possibility of immediately validating the data far outweighs the disadvantages. The following are three conditions of valid research interviews as specified by Hutchinson (2007):
a. Interviewer should have an open mind when conducting the interview. Even if the interviewer does not agree with the interviewee, he should stay objective and should not display disagreement with the personal opinions of interviewees when answering the research questions.
b. Interviewers should ask questions effectively. Any questions should be avoided that could lead interviewees to specific answers.
c. The timing and environment for the interview should be effective. Interviews should be conducted in a relaxed environment and the interviewees should be free of any kind of pressure whatsoever.
Beiski (2007) warned that unstructured interview is best conducted by a very experience researcher and should not be carried out by a non-experienced researcher or interviewer. Connaway and Powell (2010) warned that while conducting the interview, the interviewer should attempt to create a friendly and non-threatening atmosphere during the interview. It is as much as the researcher does with the cover letter; the interviewer should also give a brief explanation, casual introduction to the research topic and stress the important of the respondent participation and assure anonymity and confidentiality. In addition, there is a possibility where the interviewee would be bias during the primary data collection process and argue that interviewee bias would seriously jeopardise the validity of the research findings. On the other hand some interviewer bias can be avoided by ensuring that the interviewer does not over react to responses of the interviewee. Other steps that can be taken to avoid or reduce interviewer bias include having the interviewer dress inconspicuously and appropriately for the interview session, holding the interview in a private setting and keeping the interview as informal as possible.