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Write a 700- to 1,050-word response to the former employee’s plan. For each of the items in the first column of the table: Identify the employee’s plan. Discuss whether you think this is the correct plan.

Describe any modifications you might make, if applicable. Item to Review Employee’s Proposal – What did the employee plan to do? Your Assessment – Is this plan correct and appropriate? What modifications might you make to the plan? Purpose of Analysis Visual Representation – Creating a Chart Examine Descriptive Statistics (Central Tendency and Variance) Understanding What Quantitates a t-test tests Choosing the type of t-test Determining statistical significance Inferences that will be made from data

Paper For Above instruction

The employee’s plan for analyzing the desirability survey data demonstrates a commendable effort to utilize basic statistical techniques to interpret the results and inform organizational decisions. However, while the proposed approach contains several correct elements, it also reveals some misunderstandings and areas where modifications would enhance the appropriateness and rigor of the analysis.

Purpose of Analysis

The employee correctly identifies that the primary goal of the analysis is to assess staff perceptions of professional development and to compare the attitudes between managers and employees. Understanding whether perceptions differ across hierarchical levels is essential for tailoring organizational interventions. Defining clear objectives at this stage aligns with best practices in data analysis because it focuses the subsequent steps and ensures that the analysis answers actionable questions.

Visual Representation – Creating a Chart

The employee suggests using a pie chart for visualizing the data, believing it will help identify differences between managers and employees. While pie charts are useful for displaying proportions or percentages, they are less effective for comparing groups across multiple variables or understanding variability within groups. Instead, bar graphs or box plots would provide clearer comparative visuals, enabling the viewer to discern differences in central tendency and spread more intuitively. For categorical data, stacked bar charts could also be considered, especially if multiple categories are involved.

Examine Descriptive Statistics (Central Tendency and Variance)

The intention to calculate mean, median, mode, and standard deviation is appropriate, as these statistics describe the distribution of responses within each group. These summaries help in understanding whether the groups differ in their overall perceptions of desirability and the variability of responses. For instance, a higher standard deviation indicates greater diversity in opinions, which could influence the choice of subsequent analyses.

Understanding What Quantifies a t-test

The employee mentions conducting a t-test to compare the groups but expresses uncertainty about what the test measures. Clarifying that a t-test assesses whether there is a statistically significant difference in the means of two groups is essential. It is particularly useful when comparing responses from managers versus employees on a Likert-scale survey.

Choosing the Type of t-test

The employee plans to use a non-directional (two-tailed) t-test due to uncertainty about which group might perceive professional development as more desirable. This selection is appropriate given the preliminary nature of the analysis and the lack of a hypothesis about the direction of difference. However, before conducting the test, assumptions such as equal variances and normality should be checked. If sample sizes are small or variances unequal, adjustments like Welch’s t-test could be necessary.

Determining Statistical Significance

Identifying the p-value as the criterion for significance aligns with standard statistical practice. The employee plans to use an alpha level of 0.05 but considers a p-value threshold of 0.08, which is unconventional. Typically, statistical significance is set at p

Inferences That Will Be Made from Data

The conclusions to be drawn—whether professional development is deemed desirable and whether perceptions differ between the groups—are valid and directly relevant to organizational decision-making. However, the interpretation must consider the context: statistical significance does not imply practical importance. Effect sizes should be calculated to understand the magnitude of differences, providing a more nuanced interpretation of the results.

Furthermore, incorporating additional analyses, such as confidence intervals around mean differences, could strengthen the findings. The employee's plan lacks mention of checking assumptions for the t-test, which is a critical step to ensure valid results. Also, considering non-parametric alternatives like the Mann-Whitney U test might be appropriate if data do not meet parametric assumptions.

In conclusion, the employee’s plan contains solid foundational ideas but requires adjustments for accuracy and robustness. Using more appropriate visualizations, clarifying statistical assumptions, adhering to standard significance thresholds, and including measures of effect size will improve the validity and utility of the analysis. These modifications will ensure that the results are both statistically sound and meaningful for organizational stakeholders.

References

• Field, A. (2013). Discovering statistics using IBM SPSS statistics. Sage.
• Gravetter, F. J., & Wallnau, L. B. (2017). Statistics for the behavioral sciences. Cengage Learning.
• Haidt, J. (2017). Why we see what isn’t there. Scientific American.
• Keselman, H. J., et al. (2013). Statistical considerations in the analysis of data from questionnaires. European Journal of Psychology.
• Levine, S., & Buja, A. (2014). Visualizing data: Exploring and understanding data with graphical representations. Annual Review of Statistics and Its Application.
• Moore, D. S., et al. (2014). The practice of statistics. W. H. Freeman.
• Shea, J. (2018). Analyzing Likert data: The use of t-tests and alternative methods. Journal of Applied Statistics.
• Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics. Pearson.
• Welkowitz, J., et al. (2012). Statistical reasoning for the behavioral sciences. Academic Press.
• Field, A. (2013). Discovering statistics using IBM SPSS statistics. Sage.