Job Satisfaction Statistical Research

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Dear Sir,

I hereby take this opportunity to write this informative e-mail to you.

As a team, we have managed to come up with a comprehensive database for this study. Our database captures key aspects of job satisfaction. It captures both qualitative and quantitative data from the field. We have categorized qualitative data into the following key demographic elements; Gender, Age, Department, Tenure, and Position, where position reflects employment terms as opposed to the normal job ranking. Quantitative data, on the other hand, are categorized into four categories, i.e. Job satisfaction, Intrinsic, Extrinsic, and Benefits. We believe that these data sets will form the basis of a comprehensive data analysis, and the results will reflect the true situation on the ground.

Sir, I would like to bring to your attention the significant role statistics play in an organization. Companies use statistics to measure their market share (Webster, 1992). Through statistics, companies are now able to compute their sales volumes and even forecast their market performance based on the prevailing market conditions (Webster, 1992). Besides, statistics play a very significant role in marketing strategic planning. This is achieved through marketing research to monitor consumer behavior (Webster, 1992).

The role played by statistics in determining the likelihood of an event is an asset that only the insurance industry knows best. Insurance companies use probability to compute life insurance premiums. Finally, statistics can help an organization to revise its internal policies and ensure that their employees remain motivated and satisfied in their job.

Having given you this overview, allow me to describe various aspects of our database.

Our research was gender-inclusive and collected information from both sides of the gender. However, our initial data analysis has shown that more females were picked for this study, despite the unbiased sampling methodology used. In a sample frame of 32 individuals, 18 were female, and only fourteen were male. Our data also shows some striking characteristics in company distribution by gender. Even though the majority of those interviewed reported tenure of fewer than two years, female employees dominated this category. On the other hand, male employees dominated company tenure of five years and above. It can thus lead to a preliminary conclusion that male employees prefer longer contract periods.

Looking at the quantitative aspects of our data, a lot can be said about gender distribution by extrinsic job satisfaction. The sample means value for females stands at 5.394444, and the sample means value for males stands at 5.457143, resulting in an overall sample mean value of 5.425794. On average, both male and female employees recorded a high job satisfaction derived from extrinsic factors.

Apart from gender and tenure, our data also reveal some striking features in its distribution by the department. Information technology is the leading department constituting 62.5% of the sampled individuals. The human resources department constitutes 31.23%, while Administration has the lowest number of individuals representing only 6.25% of the sample frame.

As I have already mentioned earlier in this e-mail, statistics play an important role not only in an organization but also in research in general. I would now want to shift your attention to more advanced features of our data and use probability as a statistical tool of evaluation. Probability is a very important statistical tool for any business. Probability is used to compute and forecast long-term business losses and gains (Webster, 1992). Using probability to predict the likelihood of events, companies can strategically plan their businesses and stay ahead of their competitors. This is very important to help companies make significant decisions that can lead to their long-term success.

This study targeted individuals aged between 16 and 65 years. The majority of the employees picked for the study were in the age category of 22-49 years. Employees aged between 16 and 21 years were only nine out of the total sample size of 32 individuals. This results in a probability of 0.3, meaning that there is a low likelihood of employees being in the age category of 16-21 years.

It is also possible to predict an individual’s level of job satisfaction using job satisfaction data set. For instance, from the data, the probability that an individual’s job satisfaction is 5.2 or less is 0.47676 almost close to half. This means that there is an approximately 50% likelihood that an individual’s satisfaction will be 5.2 or less. A comparison of data from the Department and Gender data sets reveals a similar situation with a probability of 0.7 for the likelihood of an employee being female in this department. This is a true reflection of our data since male employees were only three out of the ten individuals sampled from this department.

Comparing data from Position and Intrinsic satisfaction data sets, a likelihood of one is recorded for the chance that an individual will be a salaried employee with an intrinsic value of five and above. This reflects the fact that all individuals interviewed under this category reported an intrinsic satisfaction level of more than five.

I hope you found this email informative.

Reference

Webster, A. (1992). Applied Statistics for Business and Economics. Homewood , IL : Richard D. Irwin.

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Job Satisfaction Statistical Research

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