Job Satisfaction in the Banking Industry-Free-Samples for Students

Questions: 1.Discuss the advantages and disadvantages of having a sample of this size. What factors should be considered in decision on sample size? 2.What are the advantages and disadvantages of the current sampling method? What are your suggestions to improve the sampling methods? 3.What are the advantages and disadvantages of the current research design compared with a longitudinal research design. 4.Discuss some of the problems in the process of data collection and how to address them in future study 5.What secondary dataset can be used to check the representativeness of the sample and how can it be used? Answers: 1.Sample size Samples are drawn in order to make some precision about the population. It is not possible to collect information from all the sixty nine thousand bank employees. Keeping the time and budget constraint in mind only a small percentage (21%) of total population is taken for consideration. The advantage of a small sample is the ease of calculation. Smaller the sample is lesser will be the complexity in calculation. It is costly to arrange a primary survey over a large number of samples. Setting of questionnaire and sending them to the respondents is subject to high cost (Marshall et al., 2013). Apart from cost factor, small samples help the researchers to complete the research within small time framework. Besides the advantages, a sample of small size has some shortcomings. With a small sample size the variability of the estimated statistics increases. The variability is reflected in the measure of variance. Smaller the sample greater is the variance. A large variance also increases the possibility of obtaining a biased estimate. One type of bias arises from small sample is variable response bias (Malterud, Siersma Guassora, 2016). When a considerably small sample is selected then there is high chance of getting similar kind of responses. The response may come from a group that strongly in support or oppose of something. In times of selecting sample size, certain things need to be keep in mind. There should clarity about the expectation of sample. The objective of sampling estimation helps to determine sampling size. A probability statement is needed to connect the population precision (Johnson Wichern, 2014). with the sample size. Depending on that statement, the sample size is selected. Estimation about the sampling cost is of greater importance in determining sample size. In times of very crucial decision, making less variability is desirable and hence a large sample is selected for valuable decision-making. The selected sample size should pass the test of practical applicability. In order to obtain a close estimate to population parameter a large sample size is always desirable. However, given the limited budget this is not always possible to select a large sample. The way out for conducting a precise estimation with a small sample is estimation and quantification of associated risk with the sel ected sample. 2.Sampling method The current method of sampling is called simple random sampling. In simple random sampling, all observations in the population have an equal chance of being selected in the sample. Here in order to find the relationship between job characteristics and job satisfaction random samples are taken from each of the participating banks. Therefore, all the members working in these banks have an equal chance of being included in the sample. Hence, the method of sampling is similar to that with simple random sampling. Advantages Simple random sampling requires minimum prior information about the intended population. This works as the biggest advantage random sampling method. As the sample is selected from the whole population, randomly no classification is needed at all. Therefore, there is no chance for occurrence of classification errors (Koyuncu Kadilar, 2016). It is the most suitable form of sampling method for drawing any inference from the population. It gives bias free estimates of population parameters. Hence, random sampling is considered as the best representative of population. In addition, assessment of sampling error is also easy in this method. It is extremely easy to conduct simple random sampling. It does not make any discrimination at times of selecting samples. Disadvantages In times when there are widely varied subpopulation then there larger possibility of contain sampling errors with simple random sampling. Here, stratified sampling is more appropriate. In case population units are highly dispersed, it becomes extremely difficult to collect sample using simple random sampling (Levy Lemeshow, 2013). Simple random sampling cannot be used in case where population observations are not homogenous. The sampling method has least scope of using prior knowledge of population concerned. Suggestion Before going for random sampling technique, information about the population is needed. If it is observed that there are specific subgroups in the population then stratified sampling, techniques should be used. In the presence of clusters in population, clustering samples is more appropriate (Lewis, 2015). In times of collecting samples, any types of biasness should be avoided. Neither too large nor too big samples are desirable. A good sampling method includes selection of samples of appropriate size. 3.Research design In a cross, sectional research design analysis is over a specific context. A group of observation or social phenomenon are studied here in terms of selecting a particular sample. This is a widely used method of research design. It allows the researcher to conduct a comprehensive analysis within a particular research framework. Longitudinal research design involves a continuous analysis of the population. The research here continued for a much longer period as compared to cross sectional research. In longitudinal research method, at every phase of analysis same sample is used (Campbell Stanley, 2015) The most significant advantage of cross section research design over the longitudinal research is, it is less time consuming in nature. It does not require overtime analysis of same sample., Instead, the complete study is made only once. On the other hand, research method under longitudinal design is subject to a long time. Cross sectional study is able to analyse different variables at a same point of time. Therefore, it is particularly suitable for research projects that have time bound. Here, the sample is selected only for once and decision has been taken based on analysis of the samples. There are least amount of risk with cross sectional arrangement. It is able to estimate the prevalence of interested outcome more accurately as the sample is drawn considering the entire population (Palinkas et al., 2015). Research design under cross sectional studies is able to generate hypotheses. Another advantage of cross sectional research design is its relative cost advantage over longitud inal research methodology. It is inexpensive to study the sample once as compared to studied them overtime. Hence, there is no loss as such to follow up. Cross sectional research unable to capture cause and affect relationship. Cross sectional studies provides the picture of a single point of time. Scenario before or after the study is not considered. Hence, it fails to give specific information regarding the relation among the variables. This can be done by designing research with a longitudinal study (Lessler et al., 2015). There are circumstances where overtime behaviour needs to be recorded. For this purpose, use of longitudinal research is suitable as cross sectional research is not able state overtime behaviour. 4.Procedure of data collection There are several problems associated with the collection of data. In times of gatheri8ng responses the following problems can be encountered. Possibility of erroneous data collection In times of primary data collection, there is high possibility that participants do not give proper response to the designed questionnaire. This leads to misinterpretation of the situation and make the research objectives futile (Miles, Huberman Saldana, 2013). In the above case if the respondent in the selected sample gives wrong information, then the true level of job satisfaction can never be revealed. Therefore, in future the researchers need to ensure that evaluation is made only on correct responses. A cross verification of the responses can be done to judge the truthfulness of the responses. Lack of willingness among the respondent The selected respondent may be unwilling to disclose information. In this situation, even the questionnaires have been sent to the entire selected sample but only few answers. Research cannot be conducted with too few responses. This is equally problematic as the case for wrong data collection. In order to address this issue assurance should be given about maintenance of privacy the response. The respondents should be well aware about the research objectives. Researchers should inform them in advance about the research questionnaires and ensure that all of them revert as soon as they receive the questionnaires. Obstacles in terms of language Language of the prepared questionnaire is the foremost important aspects. There is no use of making the questionnaire if the respondents unable to understand the language. In the given survey, language appears as a serious problem in gathering data. There are two dominants language in Belgium- French and Flemish. Thus, questioner needs to be prepared in both the regional language. In times of sending questionnaire, any mismatch of location and language creates serious problem. Before sending questionnaire, researchers should conduct a detailed study on native languages of chosen samples. This can be time consuming, but this will help to make a successful research and fulfil the true objectives of research. Insufficient time In the surveying method where researchers send questionnaire there are considerable lag between the time of questionnaire send and responses received. Sometimes the respondents delay without any reasons. This is problematic for cross sectional research design because it is conducted at a point of time. Too late responses are of no use for the researchers. It is not likely to be case in times of direct or one to one response collection. In times of mailing or sending questionnaire, a certain time can be mentioned. Collection of proper responses within time increases the accuracy of the research. 5.Secondary data Secondary data are those collected from any reliable source rather that gathering the data by direct investigation. For examining representativeness of selected sample, information collected with primary survey is compared with already recorded information. After checking compatibility of the selected sample decisions are taken on regarding weighting of selected data set, usage of it and in the extreme case, the data are discarded. In order to check representativeness of the sample, three types o0f secondary data can be used. Secondary data, that are free standing Data collected as for the purpose of government measure or official statistics. Data collected for research purposes like professional data and others. Data classified as freestanding qualitative data are not collected either by government official or by professional researchers. There are different organizations that provide information related to financial industry, food industry, leisure industry or for other general purposes (Sekaran Bougie, 2016). The data here is available at free of cost. These data often plays an important role in research methodology by integrating the information with qualitative aspect. Freestanding data depicts a complete picture of features for social organisation structure and thus comple3ments the qualitative research capturing individual behaviour. However, the cr5edibilioty aspects of these kinds of data are always questioned because free access of the data. Therefore, for crucial research purpose use of these data is least. Government data or official statistics are one vital form of secondary data. Government collects data on important social trends. In the official site, data are available on labour market, transport, crime rate, education, household and others. Statistics are also available in the form of census data, registration of birth rate, death rate, marriage and other important events (Clark, G. (2013). This information is more reliable than those of free quantitative data. Vital information is often of limited access and thus is free from any manipulation. The third category of secondary data is that published on professional journals. The data are usually kept on data archive. These data along with government data and free quantitative data are considered as secondary data. These can be used to link with primary data for checking representativeness. The professional research data is appropriate for projects undertaken at small scale. For research conducted in large scale, despite being reliable cannot be used. 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