Statistical Summary Preparation Part II

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Statistical Summary Preparation Part II Statistical Summary Preparation Part II Statistical Reasoning in Psychology PSY/315 Latesha McCormick University of Phoenix November 13, 2023 1

Statistical Summary Preparation Part II XYZ Tech Company is considering implementing a 4–day workweek to increase employee motivation and potentially improve performance. XYZ plans to test this change in two departments before making major organizational changes. Employee satisfaction will be measured using an anonymous survey conducted by the Devine Company. There’s been a lot of talk about the potential benefits of working a four-day week instead of a five-day week. Making 32 hours the norm instead of 40 can lead to improved well-being for workers without a loss of productivity for businesses. Several studies have shown that at some point, productivity decreases as the number of hours worked increases. Forty-hour workweeks may be wearing people out needlessly. The idea behind a four-day workweek is to achieve the same results in fewer hours so people have more time to pursue other interests, spend time with loved ones, and manage their lives. Companies could benefit through increased sales, decreased worker burnout, and lower turnover, among other positives. A four-day workweek is, ideally, a 32-hour workweek with no loss in productivity, pay, or benefits. Depending on the company and the industry, everyone might work Monday through Thursday and have Fridays off. Other possibilities include allowing each employee to choose their extra day off or having a company-wide policy of a different third day off, such as Monday or Wednesday. Several companies worldwide have pulled off a four-day workweek for a year or more, and Japan’s government has recommended it as a national policy. 2

Statistical Summary Preparation Part II To analyze the impact of the 4-day workweek, you can use hypothesis testing, like the example. In this context, the hypotheses could be defined as follows: Null Hypothesis (H0) : Implementing a 4-day workweek does not affect employee performance. Understand the difference between a Null and an Alternate Hypothesis, the two conditions that we want to investigate. Null Hypothesis - generally the status quo, e.g. There is NO DIFFERENCE in the population means from which these two samples were drawn. Alternative Hypothesis - the complement of the Null, e.g. There IS A DIFFERENCE in the population means. Research Hypothesis (H1) : Implementing a 4-day workweek improves employee performance. The dependent variable in this case would be the change in employee performance, while the independent variable would be the implementation of the 4-day workweek. The Variables would be dependent Variable : Employee performance (measured through the Devine Company's anonymous survey independent Variable : Workweek structure (4-day workweek vs. traditional 5-day workweek). To test these hypotheses correctly, you could use a t-test or ANOVA, depending on the data and distribution. These tests will help determine if there is a significant difference in employee performance between the two workweek structures. Remember, the goal is to reject the null hypothesis in favor of the research hypothesis. However, if the data does not support this, the company may need to reconsider the implementation of the 4-day workweek or explore other methods to increase employee motivation and performance. This statistical summary and hypothesis testing will provide a data-driven basis for the company's decision on whether to implement a 4-day workweek. It's important to 3

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Statistical Summary Preparation Part II remember that while statistics can provide valuable insights, they should be considered alongside other factors, such as employee feedback and business needs. The statistical measure used to compare groups was the independent t-test. The significance level of the comparison was p < 0.05. The alpha level was identified as 0.05. How to solve the means and variances for each variable were as follows: Group 1: mean = 5.61, variance = 4.798172 Group 2: mean = 7.326667, variance = 1.855816 the test statistic was t = 2.25. The critical value for the one-tailed test was 0.000651 and the critical value for the two-tailed test was 0.000142. The test was one-tailed. The null hypothesis was rejected, meaning that there was a statistically significant difference between the two groups. In everyday language, this means that there is a difference in the average scores of the two groups. In context to the organization, this means that the two groups may be responding differently to a particular shift change. A recommendation based on the findings would be to continue the study with a larger sample size to further confirm the results. Further research would also be needed to explore the reasons for the difference between the two groups. 4

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