A Comparative Statistical Analysis of Faculty Ratings<\/p>\n
Author: Ganesh Gaikwad<\/p>\n
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Introduction:<\/p>\n
In educational institutions, faculty performance plays a crucial role in shaping student outcomes and institutional reputation. Evaluating faculty ratings helps in understanding teaching effectiveness, consistency, and student satisfaction. Beyond classroom delivery, factors such as subject expertise, communication skills, and engagement levels significantly influence overall ratings. This study examines the perceived performance of different faculty members based on student feedback.<\/p>\n
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Objective:<\/p>\n
To analyze the ratings of faculty members using one-way ANOVA in order to determine whether significant differences exist among them.<\/p>\n
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Literature Review:<\/p>\n
Efficiency and Performance Metrics<\/p>\n
Singh (2011) discusses the performance evaluation of professionals based on multiple parameters. The study highlights that effectiveness is not only dependent on output but also on qualitative factors such as communication and engagement. It concludes that performance ratings vary significantly due to differences in individual efficiency levels.<\/p>\n
Comparative Methodology and Statistical Modeling<\/p>\n
Kumar and Nagorao (2022) examined statistical consistency using advanced techniques like ANOVA. The study emphasizes that comparing multiple groups simultaneously provides better insights than simple averages. It suggests that variation in ratings across groups can be effectively analyzed using ANOVA to identify significant differences.<\/p>\n
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Data Collection:<\/p>\n
The data for this study was collected using primary research methods via a structured survey. A total of 40 responses were recorded for each faculty member. Participants were asked to rate the faculty members\u2014CA. Kriti Utareja, Prof. Nikhil Ubale, Prof. Venkati Muttappa, and Dr. Jagdish Sachdeva\u2014on a scale of 1 to 10 based on teaching quality, subject knowledge, and overall effectiveness. A One-Way ANOVA was conducted on the collected data.<\/p>\n
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Data Analysis:<\/p>\n
Anova: Single Factor<\/p>\n
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SUMMARY<\/p>\n
Groups Count Sum Average Variance<\/p>\n
CA. Kriti Utareja 40 396 9.9 0.14359<\/p>\n
Prof. Nikhil Ubale 40 300 7.5 7.128205<\/p>\n
Prof. Venkati Muttappa 40 188 4.7 7.907692<\/p>\n
Dr. Jagdish Sachdeva 40 246 6.15 7.412821<\/p>\n
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ANOVA<\/p>\n
Source of Variation SS df MS F P-value F crit<\/p>\n
Between Groups 586.275 3 195.425 34.600272 0.000 2.662569<\/p>\n
Within Groups881.11565.647436<\/p>\n
Total1467.375159<\/p>\n
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Hypothesis:<\/p>\n
H0: All faculty have equal mean ratings<\/p>\n
H1: At least one faculty has a different mean rating<\/p>\n
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Conclusion:<\/p>\n
As calculated, F (34.600272) is greater than F crit (2.662569). Therefore, we reject the null hypothesis (H0) and accept the alternative hypothesis (H1).<\/p>\n
This indicates that there is a significant difference in the ratings of the faculty members. Among the groups, CA. Kriti Utareja has the highest average rating, indicating superior perceived performance, while Prof. Venkati Muttappa has comparatively lower ratings. This variation highlights differences in teaching effectiveness and student perception among faculty members.<\/p>\n
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References:<\/p>\n
Kumar, P., & Nagorao, C. G. (2022). A statistical study of performance analysis. Journal of Modern Mathematics and Statistics, 17(1), 1\u20138. https:\/\/doi.org\/10.59218\/makjmms.2023.1.8<\/p>\n
Singh, S. (2011). Measuring performance using statistical tools. American Journal of Operations Research, 1(03), 180\u2013184. https:\/\/doi.org\/10.4236\/ajor.2011.13020<\/p>\n
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