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Fall 2022: Egocentric Analysis of NCAA Division I Student-Athlete’s Perceived Social Support And Network

Affiliations: Aggie Research Mentoring Program
Project Leader: Baykal Altiner
baltiner@tamu.edu
Neuroscience and Experimental Therapeutics
Faculty Mentor: Marlene Dixon, Ph.D.
Meeting Times:
M, T, W, F between 10 AM & 3 PM
Team Size:
3
Open Spots: 0
Special Opportunities:
This is a great opportunity to get involved with a rapidly emerging methodology in the social sciences. The data collected for this specific study may also lead to multiple publications derived from the same data
Team Needs:
Prior experience is not necessary. I am simply looking for motivated students that are interested in learning fundamentals of data collection, organization, and interpretation
Description:
The purpose of the study is to examine the factors of Division I student-athletes’ social network in relation to the athlete’s perception of support and health seeking behaviors.
Previous research suggests that university athletic programs develop initiatives and establish resources toward improving the holistic well-being of student-athletes. Despite this, evidence indicates student-athlete engagement with such resources are minimal. Thus, student-athletes are not mitigating or overcoming mental, emotional, or physical health issues.
Essentially, we are asking the question, what characteristics and dynamics of a student-athlete’s social circle within the athletic department plays a role or influences help-seeking behaviors. For example, does the inclusion of a coach or athletic staff member in the social circle have a positive or negative impact on whether or not the student-athlete will seek help for serious mental or emotional issues?
Methodology: Egocentric Social Network Analysis
The study will collect demographic data, the perception of their environment (athlete culture and coach connection), and the composition of each student-athlete’s social support network

Written by:
Andrew McNeely
Published on:
August 26, 2022

Categories: Full

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