Mth217 V5titleabc123 Vxpage 2 Of 2wk 4 Plan Review Emailfrom Previ ✓ Solved

MTH/217 v5 Title ABC/123 vX Wk 4: Plan Review Email From : Previous Employee < [email protected] > Sent : September 30, 2020 9:41 AM To: New Employee < email address unknown > Subject : Plan for Analysis of Desirability Survey Hi New Employee, I didn’t get a chance to meet you before I left my position, but I wanted to leave you some of my ideas for analyzing the results of the desirability survey. I’m not really sure if they will be helpful. Actually, it might be a good idea to check for errors since statistics were never one of my strengths. Nevertheless, I think this plan can save you some time and use the information to support the organization! For what it is worth, here is my proposal for data analysis: The purpose of the analysis will be to understand the desirability of professional development amongst staff members and if there are differences in desirability between managers and employees.

To investigate this, first I will use a chart to visually represent the data. I think using a pie chart will help me see the data the best because I can see, visually, if anything looks different between the two groups. After visually inspecting the data on my pie chart, I will compute the mean, median, mode, and standard deviation of the manager and employee groups’ responses. I will compare these values and determine if I need to run a t-test to see if there are differences between the groups. Frankly, I can’t really remember what differences the t-test will test for.

But I think it is differences in the standard deviations. I get a little confused about if I should be choosing a one-tailed (directional) or two-tailed (non-directional) t-test. Since I’m not really sure which group might be higher or lower than the other, I think I will choose a non-directional test. I will conduct the t-test using Microsoft Excel. After running the t-test, I need to decide if the results are statistically significant.

To so this, I will look for the p value associated with the t-test to determine if the differences between the two groups are able to be inferred to the population (I am considering the rest of the employees and managers at the organization who didn’t take the test to be that population). If the p value is less than 0.08, I will be able to infer the results to the population (I will be testing at an alpha of 0.05). From this information, I will be able to tell our supervisors a few things: (1) If people even find professional development desirable and (2) if employees and managers see professional development as the same desirability. I hope this is useful. I apologize that I couldn’t get further along in the analysis before leaving the company!

Regards, Previous Employee MTH/217 v5 Wk 4: Plan Review MTH/217 v5 Wk 4: Plan Review 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 instructions

Response to the Desirability Survey Analysis Plan
Dear Previous Employee,
Thank you for your detailed outline regarding the analysis of the desirability survey that aims to assess the desire for professional development among staff members. This initiative is crucial, and analyzing the data effectively will provide valuable insights into the perspectives of both employees and managers. Below, I will provide feedback on each aspect of your plan, discussing its appropriateness, any necessary modifications, and rationale for suggested changes.
Purpose of Analysis
The stated purpose of the analysis is to determine the desirability of professional development among staff and assess any differences between managers and employees. This objective is appropriate as it directly addresses the interests of the organization, aiming to understand the demand for professional development, which is essential for improving employee engagement and organizational performance.
One suggestion would be to refine this purpose further by clearly defining what "desirability" entails. It might include various dimensions like frequency of interest in programs, willingness to pay, or perceived value. Establishing clear definitions will enhance the clarity of analysis and focus (Eagle, D.W. et al., 2022).
Visual Representation – Creating a Chart
Using visual aids such as a pie chart to represent the data is a common practice in data analysis. However, it is vital to pick the correct visualization method that suits the nature of the data. A pie chart displays proportions, but it may not adequately reveal differences in desirability or provide a comprehensive overview of responses.
I would recommend using a bar chart instead. A bar chart can effectively compare the responses between groups side by side, illustrating differences in desirability more clearly (Few, S., 2009). Additionally, incorporating histograms could provide insight into the distribution of responses within each group, which might reveal patterns or trends not easily visible in a pie chart.
Examine Descriptive Statistics (Central Tendency and Variance)
Calculating the mean, median, mode, and standard deviation is an excellent way to summarize the data. These descriptive statistics will provide insights into central tendencies and variability. However, you should also consider using additional measures such as skewness and kurtosis to better understand the distribution shape of the data, especially if the data is not normally distributed (Field, A., 2018).
In particular, if your data is ordinal or skewed, the median may be more informative than the mean. For comparing groups, reporting confidence intervals for the mean could also enhance the findings' reliability (Sullivan, G. M., & Feinn, R., 2012).
Understanding What Quantifies a t-test Tests
You mentioned confusion regarding what differences the t-test examines. Essentially, a t-test evaluates whether the means of two groups are statistically different from each other (Gage, A., & Hatte, S. D., 2018). It assesses whether observed differences in sample means are likely to reflect true population differences.
It is also important to ensure that the underlying assumptions of the t-test are met, including normality of the data, homogeneity of variances, and independence of observations. If these assumptions are violated, consider using non-parametric alternatives like the Mann-Whitney U test.
Choosing the Type of t-test
You are correct in thinking about the distinction between one-tailed and two-tailed t-tests. A two-tailed test is appropriate when you are uncertain about the direction of the differences in groups, which is the case here, as you don't know a priori whether managers or employees will show higher desirability (Cohen, J. et al., 2013). If the aim is to find any significant difference, the two-tailed test will be the right choice.
Determining Statistical Significance
The plan mentions using a p-value of less than 0.08 to infer the results to the population. Here, it is essential to note that while a p-value of less than 0.05 is a common threshold for significance (Barker, S., & Allen, J., 2020), you should remain cautious about setting a more lenient alpha level. Setting your alpha at 0.08 may increase the risk of Type I error, leading to potentially misleading conclusions.
Instead, retain the conventional p-value threshold of 0.05. Additionally, including effect sizes (Cohen's d) alongside p-values can provide insights into the significance's practical implications (Borenstein, M., et al., 2009).
Inferences that Will Be Made from Data
You have correctly identified two key inquiries from the data: the desirability of professional development and comparisons between groups. However, I suggest being more explicit about other insights that could emerge from the analysis, such as potential correlations between different demographic factors (age, tenure, department) and desirability, as they could lead to more nuanced interventions (Creswell, J. W., 2014).
In summary, your initial plan establishes a solid foundation for the analysis of the desirability survey results. With these modifications and clarifications, your approach can yield more reliable, actionable results that will benefit organizational strategies towards professional development.
Best Regards,
[Your Name]
References
Barker, S., & Allen, J. (2020). Understanding statistics in psychology. London: Macmillan Education.
Borenstein, M., Hedges, L.V., Higgins, J.P.T., & Rothstein, H.R. (2009). Introduction to meta-analysis. Chichester: John Wiley & Sons.
Cohen, J., Cohen, P., West, S.G., & Aiken, L.S. (2013). Applied multiple regression/correlation analysis for the behavioral sciences. Psychology Press.
Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.
Eagle, D.W., Leach, A., & Lunt, H. (2022). Survey data collection and analysis: Practical insights. Oxford: Oxford University Press.
Few, S. (2009). Now you see it: Simple visualization techniques for quantitative analysis. Oakland: Analytics Press.
Field, A. (2018). Discovering statistics using IBM SPSS statistics. Sage.
Gage, A., & Hatte, S. D. (2018). Fundamentals of statistical analysis in psychology. London: Sage Publishing.
Sullivan, G. M., & Feinn, R. (2012). Using effect size—or why the P value is not enough. Journal of Graduate Medical Education, 4(3), 279-282.