Descriptive statistics

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Descriptive statistics provide a snapshot of variables. They describe quantitative data by presenting the average or typical case. These types of descriptive statistics are called measures of central tendency. You can also describe data by showing how much the cases are spread out or clustered together. These types of statistics are called measures of dispersion. Measures of central tendency and measures of dispersions can be useful descriptors on their own, or they can be used as “building blocks” for more advanced statistics.

Neither approach (measures of central tendency or measures of dispersion) is superior to the other. They are often used in combination with each other to provide a fuller description of variables. For this week’s Discussion, you will consider which type of descriptive statistics (measures of central tendency or measures of dispersion) would be useful in describing the information you need to evaluate the program, problem, or policy you selected for your Final Project.

  • Review Chapter 12 in your course text, Research Methods for Public Administrators, paying particular attention to the section on “Characteristics of a Distribution.”
  • Review the article, “Introduction to Descriptive Statistics,” paying particular attention to examples of descriptive statistics.
  • Think of a specific purpose(s) for using descriptive statistics in your selected organization.
  • Consider why descriptive statistics would be used for this purpose(s).
  • Consider the type(s) of descriptive statistics you might use, and whether the use of other descriptive statistics, might be valuable for this purpose.

Review the Learning Resources for this week. Consider the types of descriptive statistics that would help answer your research question.

Post  a description of the descriptive statistics that might work well for the Evaluation Design in your Final Project. Explain how these statistics could be used, and justify why they are appropriate.


Data Analysis for Qualitative Information

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Qualitative data is the descriptive and conceptual findings collected through questionnaires, interviews or observations, (Silverman, D. 2015). Amazon is known for its disruption of established industries through technological innovation and mass scale. It is the world’s largest known marketplace, AI assistant provider, live-streaming platform as measured by revenue and market capitalization. Amazon offers 24hour delivery and uses economics of scale, allowing the company to attract many customers. Amazon has focused on profit and customer service, leaving the employees in a vulnerable position of over-exploitation. Workers at Amazon warehouses across the nation have long complained about grueling working conditions. Employees say they have limited time to visit the bathroom and an unsafe working environment. Amazon is exploiting its employees by paying them low wages, pushing them to work at fast speeds, and giving them no job security. Another study revealed that Amazon sets high production goals for its warehouse workers, creating constant stress that puts them at risk of mental and physical stress. Workers also say that Amazon has set unrealistic goals that are hard to achieve.

An interview with anonymous workers shows that Amazon has placed too much focus on the customers and hence forgetting the workers that have made the company’s success a reality so far (Kurdi et al., 2020). The study shows that due to these manipulations to the workers in Amazon, the company, in the long run, might face a problem in service quality unless it makes these worker-related conditions favorable. After interviewing a random number of workers, I found that workers were not happy. Visiting the organization to observe my findings, I saw that workers only take a 3-5 minute break to head to the bathroom and back. If an employee takes a longer time than the set time, close monitoring is done to ensure that the employee is not taking little breaks and evading duties. I also found that there is no free lunch for the workers in Amazon, which should be. An open-ended questionnaire showed that workers would love to have free lunch due to the tight schedule that revolves around the company. The questionnaire worked so well because it didn’t disclose the worker’s name, thus enabling the employees to write all their feelings and how they would like the administration of amazon to consider.

Interviews gave the worker a platform to air their grievances without fear of confrontation to their jobs. They came out bravely and suggested that Amazon is a good company and would only get better if the workers were treated more generously. The interview also revealed that an increase in the salary would motivate the workers to do better, knowing that the pocket will fill at the end of the month. An interview is also favorable since one gets to tell whether the worker under interview is telling the truth or lying. The questionnaire gave the workers a private and silent place, unlike the ones conducted through emails which would have had a completely different response. It enabled me to control the order of the respondent as well. A questionnaire proved economical both to the worker and me in time, effort, and cost. The desires of the workers met without any disclosure of who said what. The questionnaire proved to be better since it places less pressure on the respondent. Also can be used for future purposes. Observation helped me understand the behavior of the one under observation. Another advantage is that we can correct information at the time it occurs. Observation enables one to understand an ongoing process. Observations are considered strong invalidity because the researcher can collect a depth of information about a particular behavior.


Kurdi, B., Alshurideh, M., & Alnaser, A. (2020). The impact of employee satisfaction on customer satisfaction: Theoretical and empirical underpinning. Management Science Letters10(15), 3561-3570.

Silverman, D. (2015). Interpreting qualitative data. Sage.

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