Statistical Validation of Prerequisites and Corequisites : Approaches from the Field

1. 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 Keith Wurtz, Dean, Institutional Effectiveness, Research & Planning, Crafton Hills College Anne Danenberg, Research Analyst Planning, Research and Institutional Effectiveness Office, Sacramento City College

2. 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

3. 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

4. The Prerequisite Research Framework

5. Post-Implementation

6. Pre-Implementation Content Analysis

7. 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

8. Content Review: Sample Rating Template Number of skills with a mean rating of ≥ 4.0: 4 Percent of skills with a mean rating of ≥ 4.0: 80%

9. 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

10. Pre-Implementation Statistical Analysis

11. 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

12. 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)

13. Sample Statistical Options for Researchers

14. Sample Statistical Options for Researchers

15. Chaffey CollegePrerequisite Validation Approach Three-Pronged Approach: • Comparison of Performance in the Target Course of Students Who Did and Did Not Meet the Prerequisite • Effect Size (accounts for influence of sample size) and Average Percent Gain • 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

16. Chaffey CollegePrerequisiteData Table

17. 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)

18. 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

19. Classification and Regression Tree (CART)Example Tree

20. 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

21. Post-Implementation Statistical Analysis

22. 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?

23. Post-Implementation Statistical Analysis: Methodology • 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

24. Post-Implementation Statistical Analysis: Findings • Did the EMS-020 course success rate increase after the READ-078 prerequisite was implemented? • 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%).

25. Post-Implementation Statistical Analysis: Findings 2. What is the racial/age/gender/disability makeup of the course post implementation compared to pre implementation? • Gender, ethnicity, age, and disability status were not substantially different prior to or after the implementation of the READ-078 prerequisite.

26. Post-Implementation Statistical Analysis: Findings 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?

27. Post-Implementation Statistical Analysis: Findings 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? • 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. • In addition, female, African American, and Native American students were slightly more likely to successfully complete EMS-020 post-implementation.

28. Post-Implementation Statistical Analysis: Findings 4. Was there disproportionate impact? • Used Classification and Regression Tree (CART) modeling to analyze disproportionate impact by gender, ethnicity, age, and disability status

29. Post-Implementation Statistical Analysis: Findings 5. What effect did the implementation have on overall course enrollment? • The overall course enrollment in EMS-020 did not decrease as a result of the implementation of the prerequisite • The decline in enrollments and section offerings was due to the statewide budget cuts and comparable to the cuts that occurred college wide.

30. Sacramento City College Example • History Department, 2011—2014 • 17 courses • 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 courses • Same minimum writing word count requirement for all SCC HIST courses

31. 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?

32. SCC Example (continued…) Now that prerequisite has been in placefor 3 semesters…

33. SCC Example (continued…) Ethnicity before and after prerequisite

34. Communicating Results and Evidence-Based Decision Making

35. 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

36. Questions

37. 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. Rancho Cucamonga, CA: Institutional Research • Chaffey College. (2010). Philosophy 76 Prerequisite Validation Study: English 1A Prerequisite. Rancho Cucamonga, CA: Institutional Research. • 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