Summary of NOUN Stakeholders' Perceptions on 21st Century Skills

• Data Source: The analysis is based on data from an Excel file named Book1- 21st Century Skills Practice Exercise - Practical 9.xlsx, specifically from Sheet1.
• Data Structure: The sheet contains participant biodata and their responses to various questions related to 21st Century Skills. The data required cleaning for better readability, which involved setting descriptive column names from the first row and removing it afterwards.
• Next Steps: To analyze stakeholders' perceptions, we'll examine the responses to relevant questions in the cleaned data. This will involve identifying patterns, averages, or specific insights related to their views on 21st Century Skills.

1. Identifying Relevant Questions: Pinpointing which questions from the dataset directly ask about 21st Century Skills or can infer stakeholders' perceptions on these skills.
2. Analyzing Responses: Calculating averages, identifying trends, and summarizing responses to these questions to understand the overall perception.
3. Drawing Insights: Based on the analysis, we'll summarize key insights on stakeholders' perceptions regarding 21st Century Skills.

Analysis of NOUN Stakeholders' Perceptions on 21st Century Skills

• Identified Question: The analysis focuses on the question '1. What is your level of familiarity with the 21st Century Skills?' as it directly relates to stakeholders' perceptions.
• Next Steps: To further understand the stakeholders' perceptions, we will analyze the responses to this question, looking at the distribution of familiarity levels among the participants.

Correlation Analysis: Familiarity Level and Department

• Conversion: Familiarity levels were converted to numerical values for analysis.
• Next Step: We will calculate and analyze the correlation between the numerical familiarity levels and departments. This involves grouping the data by department and calculating the average familiarity level for each department to see if there's a noticeable trend or pattern.

1. Ensure Data Suitability: Check if our data meets the assumptions for an ANOVA test, including independence of observations, normality, and homogeneity of variances.
2. Prepare Data: Group the data by faculty and calculate familiarity levels for each group.
3. Conduct ANOVA Test: Use a statistical package to perform the test and interpret the results.

Preparing Data for ANOVA Test

• Grouped Data: We've grouped the data by faculty and calculated the mean familiarity level for each faculty.
• Next Steps: Before conducting the ANOVA test, we need to check if our data meets the assumptions for an ANOVA test. This includes checking for normality and homogeneity of variances among the groups. If these assumptions are met, we can proceed with the ANOVA test to evaluate our hypothesis.

ANOVA Test Result and Hypothesis Evaluation

• ANOVA Test Result: The ANOVA test yielded an F-Statistic of approximately 1.36 and a P-value of approximately 0.090.
• Interpretation: Given that the P-value is greater than the common alpha level of 0.05, we fail to reject the null hypothesis. This suggests that there is no statistically significant difference in the perceived 21st Century Skills across different faculties among NOUN stakeholders.
• Conclusion: The hypothesis that there is no significant difference between NOUN stakeholders regarding their perceived 21st Century Skills across their faculties is supported by the ANOVA test results.