Job characteristics are believed to have an impact on stress and well being at w

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Job characteristics are believed to have an impact on stress and well being at work (Karasek & Theorell, 1990). The demands of the job on the one hand and the extent to which you have control over your own activities (decision latitude) on the other are two factors, which together define how stressful a job is. Those jobs, which are high demand, but offer limited control, are considered to be high-strain and carry an increased risk of job dissatisfaction, stress and burnout. Based on this theoretical framework, the Union of Belgian Banks sent out a research call to several institutions, with a bidding process based on criteria such as quality of the proposal, timing, and – above all – budget. The aim of the research was to carry out quantitative research to measure the relationship between job characteristics and job satisfaction in all Belgian banks (see Cambré et al., forthcoming). But in order to do this effectively, several methodological issues needed to be resolved during the research process. First of all, a research consortium was selected to conduct the research, or more precisely, the two highest ranked bidders were asked to jointly undertake the research. This was the outcome of a political decision by the banks, since the employers preferred one partner and the unions (employee representatives) preferred the other. The two competing research institutes, a private company specialising in stress at work and the Katholieke Universiteit Leuven (Belgium), were required to co-operate and develop a level of trust in order to conduct the research. For example, both research institutes had different ideas as to which scale should be used in the questionnaire. They could not just combine the scales or include both scales, simply because they are supposed to measure the same concept. Furthermore, this would also make the questionnaire too complex. Therefore, the research institutes had to combine their knowledge, look for compromises and jointly work on a shared vision, which is, to say the least, rather time consuming. A second obstacle that needed to be overcome was the sample. In total, 69,000 employees work for Belgian banks and it was decided that questioning all employees would be too complicated and too expensive. Therefore the research committee, consisting of representatives of the banks, the unions and the research consortium, opted for a cross-sectional design with a fixed sample of 15,000 employees. In this sample, the small banks were over-represented in order to be able to make conclusions at the level of each bank. Within each bank, the respondents were selected at random with no particular quota for gender, age or employee level. In the postal survey several steps were taken to improve the response rate. The survey was based on addresses which had been provided by the banks (name, language, address) and each employee randomly selected in the sample received a personalized envelope through regular mail, sent to him/her by the employer. The completed questionnaire needed to be returned (free of charge) through the internal post within each bank. This caused two problems: (1) a perceived lack of anonymity, because the employees received a personalized envelope; and (2) potential bias to the reliability and the response rate because the completed questionnaires were collected by the banks themselves. The researchers were able to overcome this by communicating clearly in a letter (1) that although the data collection was not completely anonymous (home address on envelope), the data analysis would be completely anonymous; and (2) that the completed questionnaires were collected by the bank but were transferred immediately to the researchers without being opened or read.

Sample size is n=15,000. Is this large sample really necessary? Discuss its relative and absolute size. What other options could have been taken?

Explanation / Answer

The sample size is the number of representatives included in a statistical sample. The sample size plays a critical role in research, as it helps the researcher make inferences of the a large population. The sample size is always a balance of expenses of data collection and need to have a good representation of the population. The level of diversity in a given population would also determine the size of the sample. In a population with a lot of starters then you would expect a larger sample size. However, it is important to remember there is a difference between sampling and a census.

A sample of 15,000 sample given that the banks are concerned on the time taken on the research. A large sample size will mean that there will be more data to analyze and the results theoretical will be more representatives. However, in the case the sample does not represent the sample well. This is evident in that the small banks have been over represented. The sample does not consider any strata’s such as the age, gender, or the employee level. This means that despite having such a large sample it does not serve it purpose and at the end of the study, they may end up getting unreliable results.

Relative and Absolute size

The aim of sampling is getting a good representation of the population. The size of the sample will determine how well the population will be represented. This is the reason why there is a confidence level; which is a range of values containing the true but unknown value of the population parameters of interest. The relative and absolute sizes are an attempt of establishing that size.

The absolute size is concerned with using a single value of a given sample statist to estimate population parameters. Example if a sample of 100 students from a class are picked at random to estimate the weight of the students in a school then it my happen that the absolute weight of the sample is 65 kg in absolute size, there is no margin for error. In this case the sample used is an absolute figure 15,000.

With the relative size a reasonable estimate is established around the absolute size. It refers to the probability that a particular random variable of the sample statistics drawn from the sample will be found within a certain range. In this case study, they may have given a range 14,000-16,000.

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