Associations Between Quantitative And Qualitative Job Insecurity And Well-being

Research questions

The sample size of 21% of the total population was not necessary. It was a large proportion considering the type of research that was being carried out. In determining the sample size, the total population does not matter but how varied the population is ( Nicholas, 2008). Let us use an example to justify that the sample was large. For example, let us assume that the population of Spain was exactly the same. What sample size will be needed to conduct a research? Only one person will be needed because he or she can be used to characterize the whole population with 100% precision since there is no variability in the population (Gojanovic, 2007). For this reason, since the research only focused on one industry and asked questions that only affected the banking employees, there was no need to take such a large sample. Secondly, given the data collection tool that was to be used to collect the data, it was only proper for a smaller sample to be used. Travelling to every point to dispatch the questionnaires and collecting them back was time consuming and costly taking into account they were 15,000 questionnaires to be dispatched and collected from the 63 banks across Belgium.

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 Simple random sampling or probability sampling was used to select the employees from each of the banks. This is because the employees were selected without using any trick. There are advantages as much as disadvantages that come along with using simple random sampling as a method of selecting samples. The following are advantages and disadvantages of using simple random sampling.

Advantages of simple random sampling

  • Each member of the population has an equal chance of being selected into the sample since random sampling is a probability sampling method. It is therefore very easy since randomness is the only requirement in picking the sample. This makes it a shorter and cheaper method because there are no sub-divisions of the population.
  • Simple random sampling help in having a sample that has less sampling error since it minimizes cases of biasness.
  • In this method, other methods of randomness can be applied to further minimize cases of biasness and hence sampling error. The first method is using the lottery where members of the population pick numbers to determine who will be included in the sample. The other method involves assigning numbers to members of the population then randomly selecting the numbers to be included in the sample.
  • It requires less knowledge to execute the method. The method is not complicated and as such can be done by a researcher with basic knowledge of data collection.

Disadvantages of simple random sampling

  • It can encourage personal bias. A researcher can decide to take a sample from a region where he or she thinks has the characteristics that he wants. So the process facilitates intentional bias. The results that will therefore be obtained from data collected around such scenario will not be a representative of the whole population. So much caution should be exercised by a researcher who is employing simple random sample as a method of sampling. He or she should allow the principles of this method to override his or her own desires to allow the results obtained from the sample characterize the whole population.
  • A large sample is a must when it comes to simple random sampling to ensure the results are a representative of the whole population. It is not practical to apply simple random sampling from a small population since the resultant sample arrived at will not be a sufficient sample to be used to represent the whole population. To add on, when the sample is large, it ensures applicability of various statistical tests which are usually sensitive to normality of the data. Parametric tests such as analysis of variance, chi-square association, Pearson correlation and t-tests will only be used when the data is normally distributed. Normality of the data can only be achieved when the sample is large.
  • The data quality is dependent on the quality of the individual or researcher collecting the data. An experienced researcher will be able to collect more data while an inexperienced interviewer might get distracted by follow-up questions thereby ending up with poor and shallow information. Therefore proper training must be given to inexperienced data collectors especially if they are to use simple random sampling as a method of data collection. However, the experienced data collectors must also exercise due diligence to ensure that they are not swayed away from the principles of simple random sampling such as equality of chance to every member of the population to be included in the sample.
  • It is always costly and time consuming as it requires one-on-one interviews. This sometime means employment of many research interviewers to be able to cover the whole target population. If the target populations are so much spread geographically, it might pose a big challenge to the research team logistically and financially since the data collectors must travel to the various areas to conduct the interviews. To add on, since the interviewer must have one-on-one interview with the respondent, it takes a lot of time interviewing the respondents thus making the research to require a lot of time to conduct the research and present it to the research committee or relevant stakeholders for decision making.

Cronbach’s alpha measures reliability or internal consistency of a set of scaled variables. Scaled variables are those variables that are normally weighed on a likert scale. For example, a research might want to know the extent to which the respondents agree or disagree with a certain situation. An example of a question is, ‘half of the college students are drunkards’. The responses can therefore be rated as, ‘1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree and 5 = strongly agree’. Chronbach’s alpha measures the strength of the consistency. The reliability coefficient ranges from 0 to 1 (Dovich & Robert, 2010). The higher the value of the chronbach coefficient, the higher the chances that the items are measuring the same concept. A cronbach coefficient of 0.5 and below is considered unacceptable while a coefficient that is above 0.65 is considered appropriate. The cronbach alpha values obtained for the items in the research were all above 0.8. This is an indication that the items have high internal consistency. This test helps in qualitative research where likert scales are used. The cronbach alpha value will help the research identify the items that should be removed from the questionnaire and the items that should be retained. The items with low internal consistency are normally removed from the list items while the items with higher internal consistency are retained.

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Data collection and respondents

Although the research was about establishing the associations between quantitative and qualitative job insecurity and well-being, other variables such as demographic variables help the research to further establish whether the presence of demographics played a role in influencing some of the associations found (McCaslin, 2014). For example perceptions of females about job insecurity and well-being might be different from the males’ perception about job insecurity and well-being. On the same note, people with different employees with different educational backgrounds might be treated differently in places of work thus having divergent opinions about certain ideas. So establishing associations with these segmentations might help the research have a more informative outcome which will help in characterizing the whole population. In some research such as longitudinal research, demographic information of the participants is important as it helps the research keep track of all the participants so as to monitor and evaluate their progress.

This is a survey type of research design. This is because the research was based on wanting to collect information about a well-defined situation using a questionnaire. To add on, a written questioning has been involved in the collection of data. It is also considered a survey design since the research is focused on establishing the measures of attitudes, behavior and habits of the respondents concerning a particular situation which in this case was bank employees’ opinion about their satisfaction at their workplaces. Surveys therefore require information so that they can describe a particular situation, analyze the situation, find problem to the situation and identify the solution to the problem.

Advantages of surveys

The advantages of survey as a research design are numerous. It is easier to execute compared to other research designs. It is also not complicated thus very simple to develop compared to other research designs. To add on, if the right mode is applied, this design can lead to a reduction of the cost of conducting research (Petty, 2015). It is also a convenient method since the surveys can be conducted in various platforms such as on mobile phones, on telephone, through mails and on online platforms. The method also enables collection of data from a large number of respondents. The method also allows more than one question to be asked concerning a particular subject. This enables the research to have exhaustive information about a particular subject. Lastly, survey designs are normally free of errors especially when they are standardized. It can also be added that with surveys, there is always a lot of flexibility when it comes to questioning of the respondents. On the same note, in surveys, the interviewer will always have a larger control of the interview process. For example he or she is the one who decides who to ask questions and when to ask them. He or she also decides when to engage the whole sample. This ensures that there is order in the whole process of data collection. This method also ensures that there is high rate of response since the interviewer can always revisit the questions and make them clearer to the respondent thus ensuring high rate of feedback. Survey method also enables the researcher to be able to collect supplement data that might help in explaining some situations which need further explanation. This is not possible with other methods of data collection. In social research, surveys always come in handy as it involves collection of peoples opinions and views about certain aspects of their lives. This ensures that first-hand information is obtained from the sample. This in turn makes the results after data analysis to be valid and with minimal errors. This type of primary data can be used to accurately characterize the whole population.

Disadvantages of surveys

In surveys, respondents might give inaccurate or responses which are not honest. This might lead to wrong results when the data is eventually analyzed. Some respondents may also conceal very important information in fear that it will portray them in bad picture. Closed-ended questions in this type of design may restrict the respondents thus leading to lower validity levels for those questions. Some survey questions that already have option answers might be interpreted wrongly by the respondents thus leading to wrong information being collected. For example answers on a likert scale such as ‘agree’ and ‘somehow agree’ may be interpreted differently by different people. Errors may also arise in this research design due to cases of non-responses. Some questions might appear so sensitive to the respondent thus making him or her shy away from answering the question in the presence of the interviewer. For example a respondent might find it hard to disclose his or her monthly income to the interviewer due to the privacy nature of the question and for security purposes. However, in other methods of data collection, the respondent is able to disclose even the private information since he or she is alone.

References

Nicholas, L. (2008). Zero Acceptance Number Sampling Plans (5 ed.). Milwaukee: Quality Press.

Dovich, F., & Robert, A. (2010). Reliability Statistics . Milwaukee:: Quality Press.

Gojanovic, T. (2007). “Zero Defect Sampling”, Quality Progress (2 ed., Vol. 4).

McCaslin, M. J. (2014). The need for cognition. Handbook of individual differences in social behaviour. New York: Guilford Press.

Petty, R. E. (2015). The need for cognition. Journal of Personality and Social Psychology(46), 116–131.

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