| Table 1 |
Background
Characteristics, Retention, Graduation, Grade Point Averages, and
Credit Hours Earned of Learning
Community and First Year Program Participants - Fall 1997 Cohort |
| Table 2 |
Background
Characteristics, Retention, Graduation, Grade Point Averages, and
Credit Hours Earned of Learning
Community and First Year Program Participants - Fall 1998 Cohort |
| Table 3 |
Background Characteristics,
Retention, Grade Point Averages, and Credit Hours Earned of Learning
Community
and First Year Program Participants - Fall 1999 Cohort |
| Table 4 |
Background
Characteristics, Retention, Grade Point Averages, and Credit Hours
Earned of Learning Community
and First Year Program Participants - Fall 2000 Cohort |
| Table 5 |
Background
Characteristics, Retention, Grade Point Averages, and Credit Hours
Earned of Learning Community
and First Year Program Participants - Fall 2001 Cohort |
| Table 6 |
Summary of Logistic Regression Analysis
Predicting One-Year Retention After Controlling for Gender, Race, and
High School GPA |
| Table 7 |
Summary of Logistic Regression Analysis
Predicting Two-Year Retention After Controlling for Gender, Race, and
High School GPA |
| Table 8 |
Summary of Logistic Regression Analysis
Predicting Three-Year Retention After Controlling for Gender, Race,
and High School GPA |
| Table 9 |
Summary of Logistic Regression Analysis
Predicting Four-Year Graduation After Controlling for Gender, Race,
and High School GPA |
| Table 10 |
Summary of Regression Analysis
Predicting Cumulative GPA at the End of the First Academic Year After
Controlling for Gender, Race, and High School GPA |
| Table 11 |
Summary of Regression Analysis
Predicting Cumulative GPA at the End of the Second Academic Year After
Controlling for Gender, Race, and High School GPA |
| Table 12 |
Summary of Regression Analysis
Predicting Cumulative GPA at the End of the Third Academic Year After
Controlling for Gender, Race, and High School GPA |
| Table 13 |
Summary of Regression Analysis
Predicting Cumulative GPA at the End of the Fourth Academic Year After
Controlling for Gender, Race, and High School GPA |
| Table 14 |
Summary of Regression Analysis
Predicting Cumulative Student Credit Hours Earned at the End of the
First Academic Year After Controlling for Gender, Race, and High
School GPA |
| Table 15 |
Summary of Regression Analysis
Predicting Cumulative Student Credit Hours Earned at the End of the
Second Academic Year After Controlling for Gender, Race, and High
School GPA |
| Table 16 |
Summary of Regression Analysis
Predicting Cumulative Student Credit Hours Earned at the End of the
Third Academic Year After Controlling for Gender, Race, and High
School GPA |
| Table 17 |
Summary of Regression Analysis
Predicting Cumulative Student Credit Hours Earned at the End of the
Fourth Academic Year After Controlling for Gender, Race, and High
School GPA |
| Table 18 |
Summary of Logistic Regression Analysis
Predicting One-Year Retention After Controlling for Race and High
School GPA and Examining the Interaction of Program Participation and
Gender |
| Table 19 |
Summary of Logistic Regression Analysis
Predicting One-Year Retention After Controlling for Gender and High
School GPA and Examining the Interaction of Program Participation and
Race |
| Table 20 |
Summary of Logistic Regression Analysis
Predicting One-Year Retention After Controlling for Gender and Race
and Examining the Interaction of Program Participation and High School
GPA |
| Table 21 |
Summary of Logistic Regression Analysis
Predicting Two-Year Retention After Controlling for Race and High
School GPA and Examining the Interaction of Program Participation and
Gender |
| Table 22 |
Summary of Logistic Regression Analysis
Predicting Two-Year Retention After Controlling for Gender and High
School GPA and Examining the Interaction of Program Participation and
Race |
| Table 23 |
Summary of Logistic Regression Analysis
Predicting Two-Year Retention After Controlling for Gender and Race
and Examining the Interaction of Program Participation and High School
GPA |
| Table 24 |
Summary of Logistic Regression Analysis
Predicting Three-Year Retention After Controlling for Race and High
School GPA and Examining the Interaction of Program Participation and
Gender |
| Table 25 |
Summary of Logistic Regression Analysis
Predicting Three-Year Retention After Controlling for Gender and High
School GPA and Examining the Interaction of Program Participation and
Race |
| Table 26 |
Summary of Logistic Regression Analysis
Predicting Three-Year Retention After Controlling for Gender and Race
and Examining the Interaction of Program Participation and High School
GPA |
| Table 27 |
Summary of Logistic Regression Analysis
Predicting Four-Year Graduation After Controlling for Race and High
School GPA and Examining the Interaction of Program Participation and
Gender |
| Table 28 |
Summary of Logistic Regression Analysis
Predicting Four-Year Graduation After Controlling for Gender and High
School GPA and Examining the Interaction of Program Participation and
Race |
| Table 29 |
Summary of Logistic Regression Analysis
Predicting Four-Year Graduation After Controlling for Gender and Race
and Examining the Interaction of Program Participation and High School
GPA |
| Table 30 |
Summary of Regression Analysis
Predicting Cumulative GPA at the End of the First Academic Year After
Controlling for Race and High School GPA and Examining the Interaction
of Program Participation and Gender |
| Table 31 |
Summary of Regression Analysis
Predicting Cumulative GPA at the End of the First Academic Year After
Controlling for Gender and High School GPA and Examining the
Interaction of Program Participation and Race |
| Table 32 |
Summary of Regression Analysis
Predicting Cumulative GPA at the End of the First Academic Year After
Controlling for Gender and Race and Examining the Interaction of
Program Participation and High School GPA |
| Table 33 |
Summary of Regression Analysis
Predicting Cumulative GPA at the End of the Second Academic Year After
Controlling for Race and High School GPA and Examining the Interaction
of Program Participation and Gender |
| Table 34 |
Summary of Regression Analysis
Predicting Cumulative GPA at the End of the Second Academic Year After
Controlling for Gender and High School GPA and Examining the
Interaction of Program Participation and Race |
| Table 35 |
Summary of Regression Analysis
Predicting Cumulative GPA at the End of the Second Academic Year After
Controlling for Gender and Race and Examining the Interaction of
Program Participation and High School GPA |
| Table 36 |
Summary of Regression Analysis
Predicting Cumulative GPA at the End of the Third Academic Year After
Controlling for Race and High School GPA and Examining the Interaction
of Program Participation and Gender |
| Table 37 |
Summary of Regression Analysis
Predicting Cumulative GPA at the End of the Third Academic Year After
Controlling for Gender and High School GPA and Examining the
Interaction of Program Participation and Race |
| Table 38 |
Summary of Regression Analysis
Predicting Cumulative GPA at the End of the Third Academic Year
Controlling for Gender and Race and Examining the Interaction of
Program Participation and High School GPA |
| Table 39 |
Summary of Regression Analysis
Predicting Cumulative GPA at the End of the Fourth Academic Year After
Controlling for Race and High School GPA and Examining the Interaction
of Program Participation and Gender |
| Table 40 |
Summary of Regression Analysis
Predicting Cumulative GPA at the End of the Fourth Academic Year After
Controlling for Gender and High School GPA and Examining the
Interaction of Program Participation and Race |
| Table 41 |
Summary of Regression Analysis
Predicting Cumulative GPA at the End of the Fourth Academic Year After
Controlling for Gender and Race and Examining the Interaction of
Program Participation and High School GPA |
| Table 42 |
Summary of Regression Analysis
Predicting Cumulative Student Credit Hours Earned at the End of the
First Academic Year After Controlling for Race and High School GPA and
Examining the Interaction of Program Participation and Gender |
| Table 43 |
Summary of Regression Analysis
Predicting Cumulative Student Credit Hours Earned at the End of the
First Academic Year After Controlling for Gender and High School GPA
and Examining the Interaction of Program Participation and Race |
| Table 44 |
Summary of Regression Analysis
Predicting Cumulative Student Credit Hours Earned at the End of the
First Academic Year After Controlling for Gender and Race and
Examining the Interaction of Program Participation and High School GPA |
| Table 45 |
Summary of Regression Analysis
Predicting Cumulative Student Credit Hours Earned at the End of the
Second Academic Year After Controlling for Race and High School GPA
and Examining the Interaction of Program Participation and Gender |
| Table 46 |
Summary of Regression Analysis
Predicting Cumulative Student Credit Hours Earned at the End of the
Second Academic Year After Controlling for Gender and High School GPA
and Examining the Interaction of Program Participation and Race |
| Table 47 |
Summary of Regression Analysis
Predicting Cumulative Student Credit Hours Earned at the End of the
Second Academic Year After Controlling for Gender and Race and
Examining the Interaction of Program Participation and High School GPA |
| Table 48 |
Summary of Regression Analysis
Predicting Cumulative Student Credit Hours Earned at the End of the
Third Academic Year After Controlling for Race and High School GPA and
Examining the Interaction of Program Participation and Gender |
| Table 49 |
Summary of Regression Analysis
Predicting Cumulative Student Credit Hours Earned at the End of the
Third Academic Year After Controlling for Gender and High School GPA
and Examining the Interaction of Program Participation and Race |
| Table 50 |
Summary of Regression Analysis
Predicting Cumulative Student Credit Hours Earned at the End of the
Third Academic Year Controlling for Gender and Race and Examining the
Interaction of Program Participation and High School GPA |
| Table 51 |
Summary of Regression Analysis
Predicting Cumulative Student Credit Hours Earned at the End of the
Fourth Academic Year After Controlling for Race and High School GPA
and Examining the Interaction of Program Participation and Gender |
| Table 52 |
Summary of Regression Analysis
Predicting Cumulative Student Credit Hours Earned at the End of the
Fourth Academic Year After Controlling for Gender and High School GPA
and Examining the Interaction of Program Participation and Race |
| Table 53 |
Summary of Regression Analysis
Predicting Cumulative Student Credit Hours Earned at the End of the
Fourth Academic Year After Controlling for Gender and Race and
Examining the Interaction of Program Participation and High School GPA |
| Table 54 |
Summary of Regression Analysis
Predicting New Student Transition Questionnaire Scale Scores After
Controlling for Gender, Race, and High School GPA |
| Table 55 |
Summary of Regression Analysis
Predicting National Survey of Student Engagement Benchmark Scale
Scores After Controlling for Gender, Race, and High School GPA |
| Table 56 |
Income vs. Expense Analysis for Chapman
Learning Community in 2000-2001 |
| Table 57 |
Income vs. Expense
Analysis for the Health Sciences Residential Community in 2000-2001 |
| Table 58 |
Income vs. Expense Analysis for the
Honors Program in 2000-2001 |
| Table 59 |
Income vs. Expense Analysis for The
Springboard Program in 2000-2001 |
| Table 60 |
Income vs. Expense Analysis for UNIV100
in 2000-2001 |
| |
|
* The files
are in PDF format. Click here
to download the free Adobe Acrobat Reader necessary for reading the
files. |
