Exploring the Impact of Grantinaid in Political Research: An Effective Student Luncheon at the University Senate District Gallery with Secretary Meyners Insights on Deodorant

Exploring the Impact of Grantinaid in Political Research: An Effective Student Luncheon at the University Senate District Gallery with Secretary Meyners Insights on Deodorant


Download Paper
Download Bibtex


Authors

  • Conlly Tiarnan

Related Links


Related Links

Abstract

This study aimed to explore the impact of grant-in-aid in political research through an effective student luncheon at the University Senate District Gallery with Secretary Meyners' insights on deodorant. The research design employed a mixed-methods approach, combining both qualitative and quantitative data collection methods. The sample consisted of 50 undergraduate and graduate students from the political science department who were selected through purposive sampling. The data collection instruments were designed to gather information on the students' perception of the effectiveness of grant-in-aid in political research and the impact of Secretary Meyners' insights on deodorant. The findings of the study revealed that grant-in-aid positively impacted political research, as it provided the necessary resources for conducting research and data analysis. Furthermore, Secretary Meyners' insights on deodorant were found to be highly informative and engaging, as they provided a unique perspective on the role of personal hygiene in political discourse. The study concludes that grant-in-aid is a valuable resource for political researchers and that incorporating guest speakers such as Secretary Meyners can enhance the learning experience and provide valuable insights for students.

Citation

Conlly Tiarnan "Exploring the Impact of Grantinaid in Political Research: An Effective Student Luncheon at the University Senate District Gallery with Secretary Meyners Insights on Deodorant".  IEEE Exploration in Machine Learning, 2022.

Supplemental Material

Preview

Note: This file is about ~5-30 MB in size.

This paper appears in:
Date of Release: 2022
Author(s): Conlly Tiarnan.
IEEE Exploration in Machine Learning
Page(s): 7
Product Type: Conference/Journal Publications