Validation of Prerequisites and Corequisites: Approaches from the Field DAYLENE MEUSCHKE, DIRECTOR, INSTITUTIONAL RESEARCH, COLLEGE OF THE CANYONS JIM FILLPOT, DEAN, INSTITUTIONAL RESEARCH & RESOURCE DEVELOPMENT, CHAFFEY COLLEGE KEITH WURTZ, DEAN, INSTITUTIONAL EFFECTIVENESS, RESEARCH & PLANNING, CRAFTON HILLS COLLEGE ANNE DANENBERG, RESEARCH ANALYST PLANNING, RESEARCH AND INSTITUTIONAL EFFECTIVENESS OFFICE, SACRAMENTO CITY COLLEGE RP Conference April 11, 2014
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Statistical Validation of Prerequisites and Corequisites : Approaches from the Field
Statistical Validation of Prerequisites and Corequisites : Approaches from the Field. RP Conference April 11, 2014. Daylene Meuschke, Director, Institutional Research, College of the Canyons Jim Fillpot, Dean, Institutional Research & Resource Development, Chaffey College - PowerPoint PPT Presentation
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Statistical Validation of Prerequisites and Corequisites: Approaches from the Field
DAY L E N E M E U SC H K E , D I R E C T O R , I N S T IT U T I O N AL R E S E A RC H , C O L L E G E O F T H E C A N YO N S
J I M F I L L P O T , D E AN , I N S T I T U T I O N AL R E S E A RC H & R E S O U RC E D E V E LO P M E N T , C H A F F E Y C O L L E G E
K E I T H W U RT Z , D E A N , I N S T I T U T I O N AL E F F E C T IV E N E S S , R E S E ARC H & P L AN N I N G , C RA FT O N H I L L S C O L L E GE
AN N E DA N E N B E R G , R E SE A RC H A N ALY S T P L AN N IN G , R E SE A RC H A N D I N S T I T U T I O N A L E F F E C T I V E N E S S O F F I C E , S AC RAM E N T O C I T Y C O L L E GE
RP Conference April 11, 2014
Session Objectives Briefly review the Suggestions for CCC institutional Researchers Conducting Prerequisite Research
Review approaches for locally validating prerequisites pre-implementation and post-implementation
◦ Research methodologies◦ Data sources◦ Statistical techniques
Provide strategies for communicating prerequisite findings to internal audiences and facilitating evidence-based decision making
Overview of the Prerequisite Validation Guidelines
Intended to help researchers ◦ Execute the statistical analyses◦ Support faculty
Developed by the RP Group with input from the field
Input provided by various groups: matriculation, faculty, researchers
Incorporated information from Academic Senate and Chancellor’s office guidelines
The Prerequisite Research Framework
Post-Implementation
Pre-Implementation Content Analysis
The “Warm Up” (a.k.a. Preliminary Work)
Content Review/Validity (Pre-Implementation)◦ Develop a systematic but manageable approach◦ Researchers can help facilitate, if needed◦ Identify what is acceptable level of success◦ Ensure faculty have sufficient experience with the pre-requisite and target courses◦ Consider using a rating template
Percent of skills with a mean rating of ≥ 4.0: 80%
The “Warm Up” (a.k.a. Preliminary Work)
Preliminary Analyses (Pre-Implementation)◦ Is the pre-requisite reasonably likely to improve student success?◦ What enrollment/access issues might arise?◦ What impact might this have on other programs?
Information researchers need from faculty◦ Changes in curriculum◦ Inclusion/exclusion of different delivery modes◦ Outcome measures
Pre-Implementation Statistical Analysis
Initiating a Statistical Validation Request
Local Discussion Among:◦ Discipline Faculty◦ Faculty from Prerequisite Discipline◦ Student Services (Matriculation) Personnel◦ Curriculum Committee◦ Administration
•Knowing What the Process Entails
•Institutionally Approved Requestor/Process◦ Curriculum Committee◦ Approved Research Request Form with Appropriate Sign-Off
Some Useful Data Elements for Statistical Validation of Prerequisites
MIS Data Elements◦ GI03 (Term Identifier)◦ CB01 (Course-Department-Number)◦ SX01 (Enrollment-Effective-Date)◦ SX04 (Enrollment-Grade)◦ SB, SD, STD Data Elements (for Disproportionate Impact)
Assessment Data◦ Placement Recommendation (Communication and
Computational Skills Courses)
Sample Statistical Options for Researchers
Test Questions Answered with Prerequisite Validation Approach
T-Test Is their a statistically significant difference between the average GPA/success rate in the prerequisite and target courses? The observed difference may not be substantial.
Chi-Square Is there a statistically significant difference between GPA in the target course and completion of the prerequisite course? The observed difference may not be substantial.
Pearson Correlation
Is there a significant relationship between GPA in the prerequisite and target courses?
Sample Statistical Options for Researchers
Test Questions Answered with Prerequisite Validation Approach
Odds Ratio How likely is it that students who meet the prerequisite will succeed in the target course compared to those who do not meet the prerequisite?
Effect Size What is the strength of the relationship between successfully completing the prerequisite course and successfully completing the target course?
Average Percent Gain
What is the average percent gain in success in the target course of students who met the prerequisite over those who did not meet the prerequisite?
Chaffey CollegePrerequisite Validation Approach
Three-Pronged Approach:1. Comparison of Performance in the Target Course of Students Who Did
and Did Not Meet the Prerequisite
2. Effect Size (accounts for influence of sample size) and Average Percent Gain
3. Restricted Bivariate Correlation Coefficient and Corrections for Restriction of Range◦ Pearson’s r (Rule of Thumb: r ≥ .35, assuming p < .05)◦ Chaffey also recalculates to correct for restriction of range
Chaffey CollegePrerequisite Data Table
Examination of Disproportionate Impact Prior to Prerequisite Enforcement Disproportionate impact occurs when “the percentage of persons from a particular racial, ethnic, gender, age or disability group who are directed to a particular service or placement based on an assessment instrument, method, or procedure is significantly different from the representation of that group in the population of persons being assessed, and that discrepancy is not justified by empirical evidence demonstrating that the assessment instrument, method or procedure is a valid and reliable predictor of performance in the relevant educational setting.” [Title 5 Section 55502(d)]
When there is a disproportionate impact on any such group of students, the district shall, in consultation with the Chancellor, develop and implement a plan setting forth the steps the district will take to correct the disproportionate impact.” [Title 5 Section 55512(a)]
Disproportionate Impact Guide (CCCCO and RP Group, 2013)
Examination of Disproportionate Impact Prior to Prerequisite Enforcement Classification and Regression Trees (CART)
◦ divide a population into segments that differ with respect to a designated criterion.
◦ identifies the best predictor variable, conducting a splitting algorithm that further identifies additional statistically significant predictor variables and splits these variables into smaller subgroups
◦ ensures that cases in the same segment are homogeneous with respect to the segmentation criterion, while cases in different segments tend to be heterogeneous with respect to the segmentation criterion
Classification and Regression Tree (CART)Example Tree
Node 0Category % n
34.3 172Did Not Meet Prerequisite65.7 329Met Prerequisite
54.8 57Did Not Meet Prerequisite45.2 47Met Prerequisite
Total 20.8 104
40 to 49; 25 to 29; 30 to 34; 50 or Older
Node 2Category % n
29.0 115Did Not Meet Prerequisite71.0 282Met Prerequisite
Total 79.2 397
35 to 39; 19 or Younger; 20 to 24
Did Not Meet PrerequisiteMet Prerequisite
Chaffey CollegeDecision to Implement a Prerequisite
Green – Sufficient evidence exists to enforce prerequisite (at least two out of three measures are supported)
Yellow – Although evidence exists, only one out of three measures supports enforcement of the prerequisite. Further discussion should occur within the department and the Curriculum Committee before the prerequisite is enforced
Red – Data does not exist to support enforcement of the prerequisite. None of the measures explored meet pre-established criteria
Insufficient Data – While evidence may point to the efficacy of the prerequisite, the sample size is too small to render a reliable decision
Post-Implementation Statistical Analysis
Post-Implementation Statistical Analysis: Research Questions
The following questions were examined to determine the impact of implementing READ-078 as a prerequisite for EMS-020:
◦ Did the EMS-020 course success rate increase after the READ-078 prerequisite was implemented?
◦ What is the racial/age/gender/disability makeup of the course post implementation compared to pre implementation?
◦ Does the increased success of students in each protected category support the implementation if indeed the percentages of students in each group have changed?
◦ Was there disproportionate impact?◦ What effect did the implementation have on overall course enrollment?
An effect size statistic was used to indicate the size of the difference between course success for students who met and did not meet the prerequisite
At the time of the study the prerequisite had been enforced from Spring 2011 to Spring 2013 (i.e. 5 primary terms)
The performance of students who had to meet the prerequisite prior to taking EMS-020 was compared to students who earned a GOR in EMS-020 from Fall 2008 to Fall 2010
1. Did the EMS-020 course success rate increase after the READ-078 prerequisite was implemented?
a. Yes, students who met the reading prerequisite were statistically significantly (p < .001) and substantially (ES = .21) more likely to successfully complete EMS-020 (62%) than students who had not completed the prerequisite (51%).
To identify disproportionate impact can use the 80% rule or segmentation modeling (see page 17 in Wurtz & Riggs, 2010 for an example of segmentation modeling).
80% Rule
.80 * .828 (proportion of males pre-implementation) = .6624 or 66.2%. Is the proportion of males less than 66.2% post-implementation?
.80 * .166 (proportion of females pre-implementation) = .1328 or 13.3%. Is the proportion of females less than 13.3% post-implementation?
GenderPre-Implementation Post-Implementation Total
3. Does the increased success of students in each protected category support the implementation, if indeed the percentages of students in each group have changed?
a. Yes, male students, Hispanic Students, and students 24 years old or younger were substantially (ES >= .20) and statistically significantly (p < .01) more likely to successfully complete EMS-020 if they had met the reading prerequisite than students who had not met the prerequisite.
b. In addition, female, African American, and Native American students were slightly more likely to successfully complete EMS-020 post-implementation.
40-50 sections per semester offered pre-implementation, with 2,300 to 2,700 Census enrollments from 2009-2011
Studied largest-enrolled (U.S. HIST) courses based on Fall 2009 HIST enrollments and prior English preparation. A department-wide pre-req. of English 1-level-below-transfer was implemented in Fall 2012, based on a number of factors, including:Same SLOs for all SCC HIST coursesSame minimum writing word count requirement for all SCC HIST courses
Sacramento City College Example (continued…)
Enrollment impact estimation=16% (based on 2009 data)
Able to calibrate estimates against actuals to inform planning for other departments. How close were we? From Fall 2011 to Fall 2012 = 16.37%
No measurable change is success rates
How does enrollment composition look?
SCC Example (continued…) Now that prerequisite has been in placefor 3 semesters…
86.183.0
99.8
89.9
0.0
20.0
40.0
60.0
80.0
100.0
120.0
Percentage
SCC History Department Fall and Spring Fill Rate (Fall 2004 to Fall 2013)
Fall 2012: Pre-requisiteimplementation
SCC Example (continued…) Ethnicity before and after prerequisite
0
5
10
15
20
25
30
35
Fall 2009 Spring 2010 Fall 2010 Spring 2011 Fall 2011 Spring 2012 Fall 2012
African American
Asian
Filipino
Latino
Multi-
Native American
Other, non-wht
Pacific Islander
Unknown
White
Communicating Results and Evidence-Based Decision Making
Tips for CommunicatingPrerequisite Results
Affirm your common interest in supporting and enhancing student success
Affirm that you are there to support and assist faculty
Affirm that the research is not meant to substitute for faculty’s professional judgment (i.e. evidence-based decision making)
Making data accessible (i.e. do not use big statistical term, know your audience)
Recognize and communicate the limitations of the data/research in the beginning
Consider different approaches for how data is presented
Questions
Resources: College Examples
Cabrillo College◦ Cabrillo College. (2002). Validation of English 1A as a prerequisite for Psychology 1A. Aptos, CA: Borden, R. C.
Chaffey College◦ Chaffey College. (2011). Prerequisite Validation Studies: Impact of a Reading Prerequisite on HIST-1, HIST-2, and HIST-7.
Sacramento City College◦ Danenberg, A. (2011). Methodological and Data Considerations for a Communication or Computation Prerequisite
Implementation Study. Sacramento City College: Planning, Research, & Institutional Effectiveness.
Crafton Hills◦ Wurtz, K. A. (2014). Relationship of the EMS-020 Reading Prerequisite to EMS-020 Course Success. Retrieved March 25, 2014
from http://www.craftonhills.edu/~/media/Files/SBCCD/CHC/About%20CHC/Research%20and%20Planning/Research%20Briefs/Academic%20Success%20Studies/2013_July_EMS20_PrereqEval_Post2.pdf
◦ Wurtz, K. A., & Riggs, M. (2010). Prerequisite Validation Study: Examination of Reading as a Prerequisite to EMS-020 (Emergency Medication Technician-I / EMT – Basic). Retrieved November 16, 2012 from http://www.craftonhills.edu/~/media/Files/SBCCD/CHC/About%20CHC/Research%20and%20Planning/Research%20Reports/0910_EMS_Read_PrerequisiteStudy.ashx
RP Group for California Community Colleges◦ RP Group. (2013). SUGGESTIONS FOR CALIFORNIA COMMUNITY COLLEGE INSTITUTIONAL RESEARCHERS CONDUCTING
PREREQUISITE RESEARCH. Retrieved March 25, 2014 from http://www.rpgroup.org/sites/default/files/RPGroupPreqreqGuidelinesFNL.pdf