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BUACC5931 - Research and Statistical Methods for Business

Investigate the relationship between job characteristics and job satisfaction in the banking industry in Belgium through quantitative research.

12 Pages2686 Words316 Views
   

Federation University of Australia

   

Research and Statistical Methods for Business (BUACC 5931)

   

Added on  2020-03-04

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BUACC5931 - Job Satisfaction in the Banking Industry. The study of job satisfaction among Bank employees is important because there are various aspects of the job that are highly attractive and lead to satisfaction and aspects of the job that lead to dissatisfaction. Positive aspects include the opportunity to work in Bank and employees to accomplish common goals, developing a banking background, and the ability to work with coworkers. The research aimed to carry out quantitative research to investigate the relationship between job characteristics and job satisfaction at all Belgian banks at individual levels.

BUACC5931 - Research and Statistical Methods for Business

Investigate the relationship between job characteristics and job satisfaction in the banking industry in Belgium through quantitative research.

   

Federation University of Australia

   

Research and Statistical Methods for Business (BUACC 5931)

   Added on 2020-03-04

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Running head: STATISTICS AND BUSINESS RESEARCHStatistics and Business ResearchName of the StudentName of the UniversityAuthor note
BUACC5931 - Research and Statistical Methods for Business_1
1STATISTICS AND BUSINESS RESEARCHTable of ContentsAnswer 1....................................................................................................................................2Sample size.............................................................................................................................2Answer 2....................................................................................................................................3Sampling method...................................................................................................................3Answer 3....................................................................................................................................5Research design......................................................................................................................5Answer 4....................................................................................................................................6Procedure of data collection...................................................................................................6Answer 5....................................................................................................................................8Secondary data.......................................................................................................................8References................................................................................................................................10
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2STATISTICS AND BUSINESS RESEARCHAnswer 1Sample size. Samples are drawn in order to make some precision about the population. It is notpossible to collect information from all the sixty nine thousand bank employees. Keeping thetime and budget constraint in mind only a small percentage (21%) of total population is takenfor consideration. The advantage of a small sample is the ease of calculation. Smaller thesample is lesser will be the complexity in calculation. It is costly to arrange a primary surveyover a large number of samples. Setting of questionnaire and sending them to the respondentsis subject to high cost (Marshall et al., 2013). Apart from cost factor, small samples help theresearchers to complete the research within small time framework.Besides the advantages, a sample of small size has some shortcomings. With a smallsample size the variability of the estimated statistics increases. The variability is reflected inthe measure of variance. Smaller the sample greater is the variance. A large variance alsoincreases the possibility of obtaining a biased estimate. One type of bias arises from smallsample is variable response bias (Malterud, Siersma & Guassora, 2016). When a considerablysmall sample is selected then there is high chance of getting similar kind of responses. Theresponse 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 shouldclarity about the expectation of sample. The objective of sampling estimation helps todetermine sampling size. A probability statement is needed to connect the populationprecision (Johnson & Wichern, 2014). with the sample size. Depending on that statement, thesample size is selected. Estimation about the sampling cost is of greater importance indetermining sample size. In times of very crucial decision, making less variability is desirable
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3STATISTICS AND BUSINESS RESEARCHand hence a large sample is selected for valuable decision-making. The selected sample sizeshould pass the test of practical applicability. In order to obtain a close estimate to populationparameter a large sample size is always desirable. However, given the limited budget this isnot always possible to select a large sample. The way out for conducting a precise estimationwith a small sample is estimation and quantification of associated risk with the selectedsample.Answer 2Sampling methodThe current method of sampling is called simple random sampling. In simple randomsampling, all observations in the population have an equal chance of being selected in thesample. Here in order to find the relationship between job characteristics and job satisfactionrandom samples are taken from each of the participating banks. Therefore, all the membersworking in these banks have an equal chance of being included in the sample. Hence, themethod of sampling is similar to that with simple random sampling.AdvantagesSimple random sampling requires minimum prior information about the intendedpopulation. This works as the biggest advantage random sampling method. As the sample is selected from the whole population, randomly no classification isneeded 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 thepopulation. It gives bias free estimates of population parameters. Hence, random
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