Quality Point Analysis (QPA)

Transcript

1 NCAA Division I Progress -Toward -Degree Waivers Quality-Point Analysis . Background Based on research used by the NCAA Division I Academic Consultants gathered through the NCAA Academic Performance Census, the NCAA Division I A cademics/Eligibility/C ompliance quality points as a way to guide staff and committee perceptions of a Cabinet endorsed -athlete 's academic record. student Based on a directive approved annually by t he NCAA Division I Academic Cabinet , quality , the s ize of waiver request, points are one of several factors, including mitigating circumstances and overall academic record ttee when evaluating a considered by the staff and subcommi -toward waiver request. progress -degree The analysis of quality points allows the staff and NCAA Division I Committee on Progress - -Degree W aiver Toward -athlete 's likelihood of graduation within five years s to project a student through research, rather than basing the decision on prior decisions of the Committee on Progress -Toward -Degree Waivers or the committee members ' perception of a student -athlete 's academic record. -Point Analysis ( ). How to Calculate the Quality QPA redits e arned times ( x ) cumulative grade -point a verage = t otal Total number of c . quality points Example: Julie earned 40 total credits with a 2.000 grade after attending the institution for -point average two academic years. Total number of credits earned = 40 . Grade -point average = 2.000. 40 x 2.000 = 80 quality points. How is the QPA Used ? The staff, after calculating the student -athlete' s quality points, will determine the student 's predicated probability of gradua tion by using the range charts on the subsequent -athlete pages :

2 NCAA Division I Progress -Toward -Degree -Point Analysis Waivers Quality 2 Page No. _________ Range Chart – Semesters . Quality Points After Two Years Quality Points After One Year of Enrollment of Enrollment Total Total Total Quality Total Predicted Probability of Quality Points Predicted Probability Points of Graduation Graduation Less than 29 Less than 76 Less than 25% Less than 25% Less than 41 Less than 33% Less than 91 Less than 33% Above 50% Above 61 Above 50% Above 116 Points After Three Years Points After Four Years Quality Quality of Enrollment of Enrollment Total Total Quality Total Total Quality Points Predicted Probability Predicted Probability of Points of Graduation Graduation Less than 124 Less than 25% Less than 174 Less than 25% Less than 139 Less than 33% Less than 189 Less than 33% Above 50% Above 165 Above 50% Above 215 Examples . Below are several hypothetical situations to help understand how to calculate quality points for a -athlete and to identify what the academic record of a student student -athlete would look like based on earned quality p oints. 1. -athlete A completed hi s or her first year of collegiate enrollment and has 16 credit Student hours with a 1.710 grade -point average : -athlete 's quality points, he or she has less than a 25 percent predicated Based on the student probability of graduating. Student -athlete A has 27.36 quality points (16 credit hours x 1.710 cumulative grade -point average = 27.36 quality points ).

3 NCAA Division I Progress -Toward -Degree -Point Analysis Waivers Quality 3 Page No. _________ 2. credit Student -athlete B completed his or her second year of collegiate enrollment and has 40 . hours with a 2.200 grade -point average Based on the student s quality points, he or she has a 25 to 33 percent predicated -athl ete' Student -athlete B has 88 quality points (40 credit hours x 2.200 probability of graduating. ulative grade -point average cum quality points ). = 88 -athlete C completed his or her thir d year of collegiate enrollment and has 53 credit 3. Student hours with a 3.000 grade -point average . Based on the student -athlete' s quality points, he or she has a 33 to 50 percent predic ated -athlete C has 159 quality points (53 probability of graduating. Student x 3.000 credit hours cum grade -point average = 159 quality points ). ulative What A bout Q uarter S chools ? The quality s (QPR) are based on semester hours so quarter hours must be converted -point range . Simply multiply the student -athlete into semester hours 's total number of quarter hours by the . The resulting number is then multiplied by 2/3. -point average cumulative grade Example: -time quarters with a 2.00 0 grade -point average Jean earned 36 quarter hours in three full . x 36 2.000 = 72 quality points. x 2/3 = 48 quality points 72 (This is the quality point to be compared to the QPR located in the charts .) Mid year Analysis . For those waivers in which the student -athlete has a deficiency at midyear (e.g., midyear six using the range QP R must be identified enrollee, missed term, -hour deficiency), a midyear charts . Example: Tasha has completed 30 credit hours in three full -time semesters with a 2.50 0 grade- point average . Based on the calculation, the QP R for midyear between years and has 75 quality points one and two, the student -athlete has a 33 to 50 percent chance of graduating from the certifying institution.

4 NCAA Division I Progress -Toward -Degree -Point Analysis Waivers Quality 4 Page No. _________ Mid . year Range Chart Mid year Quality Points During Mid year Quality Points During Year One Year Two Enrollment of Enrollment Total Predicted Total Predicted Probability of Total Total Quality Points Quality Probability of Graduation Graduation Points Less than 14 Less than 52 Less than 25% Less than 25% Less than 20 Less than 33% Less than 33% Less than 66 Above 50% Above 30 Above 50% Above 88 Mid Points During Mid year Quality Points During year Quality Year Three of Enrollment of Enrollment Year Four Total Total Predicted Predicted Probability of Total Probability of Quality Points Total Quality Graduation Points Graduation Less than Less than 100 Less than 25% Less than 25% 149 Less than Less than 115 Less than 33% Less than 33% 164 Above 50% Above 140 Above 50% Above 190 bout T ? What A ransfers -year or a four- year transfer student, two QPAs should be calculated . The Generally, for a two Credits earned at the certifying institution first is an overall QPA for all institutions attended. and credits which transferred into the certifying institutio n are used in the calculation. Credits earned at the previous institution that did not transfer are not used. The second QPA is calculated credit hours earned subsequent to enrolling at the institution the student is for the currently attending. Using this approach, both the student -athlete 's overall progress and the progress at the current institution can be consider ed. Example:

5 NCAA Division I Progress -Toward -Degree -Point Analysis Waivers Quality Page No. 5 _________ transferable- credit hours. He George attended a two -year college for two years and earned 48 academic year and earned 20 credit hours. His has attended the certifying institution for one overall grade (the two -year college grade -point average does not transfer -point average is 2.300 to the institution). x 2.300 = 156.4 total quality points. 68 x 2.300 = 46 quality points at the certifying institution. 20 four year into (going three year The total quality points should be compared to the ) QPR in the chance of . Based on the QPR tables, the student -athlete has a 33 to 50 percent QPR tables graduating overall. The certifying institution quality points should be compar ed to the year one year has only spent one ) QPR, as the student -athlete academic year at the (going into two 50 percent certifying institution. This analysis indicates the student -athlete also has a 33 to chance of graduating from the certifying institution. in order to meet the -athlete *NOTE: In situations where t he waiver is being filed for a student -time one transfer exception, a QPA should be conducted for the previous institution, as generally A at the certifying institution no data exists to conduct a QP . The National Collegiate Athletic Association KEY: June 11, 2010 bh

Related documents

Fourth National Report on Human Exposure to Environmental Chemicals Update

Fourth National Report on Human Exposure to Environmental Chemicals Update

201 8 Fourth National Report on Human Exposure to Environmental Chemicals U pdated Tables, March 2018 , Volume One

More info »
2018 Physical Activity Guidelines Advisory Committee Scientific Report

2018 Physical Activity Guidelines Advisory Committee Scientific Report

2018 Physical Activity Guidelines Advisory Committee Scientific Report To the Secretary of Health and Human Services

More info »
The Health Consequences of Smoking   50 Years of Progress: A Report of the Surgeon General

The Health Consequences of Smoking 50 Years of Progress: A Report of the Surgeon General

The Health Consequences of Smoking—50 Years of Progress A Report of the Surgeon General U.S. Department of Health and Human Services

More info »
DoD7045.7H

DoD7045.7H

DoD 7045.7-H EPARTMENT OF D EFENSE D F UTURE Y EARS D EFENSE P ROGRAM (FYDP) S TRUCTURE Codes and Definitions for All DoD Components Office of the Director, Program Analysis and Evaluation A pril 2004

More info »
AcqKnowledge 4 Software Guide

AcqKnowledge 4 Software Guide

® Acq 4 Software G uide Knowledge Check BIOPAC.COM > Sup port > Manuals for updates For Life Science Research Applications Data Acquisition and Analysis with BIOPAC Hardware Systems Reference Manual f...

More info »
435 441 458 467r e

435 441 458 467r e

WT/DS435/R, WT/DS441/R WT/DS458/R, WT/DS467/R 28 June 2018 Page: (18 - 1/884 4061 ) Original: English AUSTRALIA CERTAIN MEASURES CON CERNING TRADEMARKS, – PACKAGING IONS AND OTHER PLAIN GEOGRAPHICAL I...

More info »
June2018CUR

June2018CUR

CHANCELLOR'S UNIVERSITY REPORT JUNE 25 2018

More info »
Justification Book

Justification Book

UNCLASSIFIED Department of Defense Fiscal Year (FY) 2019 Budget Estimates February 2018 Office of the Secretary Of Defense Defense-Wide Justification Book Volume 3B of 5 Research, Development, Test & ...

More info »
Computer Vision: Algorithms and Applications

Computer Vision: Algorithms and Applications

Computer Vision: Algorithms and Applications Richard Szeliski September 3, 2010 draft c © 2010 Springer This electronic draft is for non-commercial personal use only, and may not be posted or re-distr...

More info »
Joint Software Systems Safety Handbook

Joint Software Systems Safety Handbook

DEPARTMENT OF DEFENS E JOINT SOFTWARE SYSTEMS SAFETY ENGINEERING HANDBOOK ----------------------------------------- THE JOINT SOFTWARE S YSTEMS SAFETY DEVELOPED BY ENGINEERING WORKGROUP Original publi...

More info »
tsa4

tsa4

i i “tsa4_trimmed” — 2017/12/8 — 15:01 — page 1 — #1 i i Springer Texts in Statistics Robert H. Shumway David S. Sto er Time Series Analysis and Its Applications With R Examples Fourth Edition i i i ...

More info »
812182 humanfactorseval l2l3 automdrivingconcepts

812182 humanfactorseval l2l3 automdrivingconcepts

DOT HS 812 182 August 2015 Human Factors Evaluation of Level 2 and Level 3 Automated Driving Concepts

More info »
Draft Environmental Impact Statement for the Safer Affordable Fuel Efficient (SAFE) Vehicles Rule for Model Year 2021 2026 Passenger Cars and Light Trucks

Draft Environmental Impact Statement for the Safer Affordable Fuel Efficient (SAFE) Vehicles Rule for Model Year 2021 2026 Passenger Cars and Light Trucks

The Safer Affordable Fuel-Efficient (SAFE) Vehicles Rule for Model Year 2021–2026 Passenger Cars and Light Trucks Draft Environmental Impact Statement July 2018 Docket No. NHTSA-2017-0069

More info »
Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation

Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation

MANAGING THE RISKS OF EXTREME EVENTS AND DISASTERS TO ADVANCE CLIMATE CHANGE ADAPTATION SPECIAL REPORT OF THE INTERGOVERNMENTAL PANEL ON CLIMATE CHANGE

More info »
Microsoft Word    a. Cover 2 14 18

Microsoft Word a. Cover 2 14 18

TOWN & COUNTRY APARTMENTS AND TOWNHOMES MITIGATED NEGATIVE DECLARATION NO. 1855-17 Lead Agency: City of Orange Community Development Department • Planning Division 300 East Chapman Avenue Orange, CA 9...

More info »
networks-book

networks-book

Networks, Crowds, and Markets: Reasoning about a Highly Connected World David Easley Jon Kleinberg Dept. of Economics Dept. of Computer Science Cornell University Cornell University Cambridge Universi...

More info »