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ADN Prerequisite Validation Study Center for Student Success February 14 th 2002 Sacramento, California William Armstrong, Ph.D. Brad C. Phillips, Ph.D. Steve Spurling, M.A.
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Today’s Activities Review study purpose Review salient literature Examine ADN student cohort Review past ADN student success Review proposed model Provide feedback and direction Determine next steps
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ADN Project Study Purpose Review prerequisite research, emphasis on Nursing programs Determine what is meant by ADN program completion Examine ADN program completion in the last several years Statistically validate the use of the most powerful prerequisites to predict successful ADN program completion Make recommendations to the Nursing Directors whose colleges participated in the study
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Review of Prior Research 35 Years of Research Literature Literature Covers Era from 1964 to Present Journal Articles Professional Presentations Technical Reports Foundation Studies (Kellogg, Mellon) Institutional Research Reports (Community Colleges) Professional Associations
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Focus of Prior Research Improve Program and Student Outcomes Reduce Attrition Improve Access Under-represented Groups State Licensing Exams Accreditation
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Relevance of Prior ADN Research: Past as Prologue Similar Issues Found Across 35 Years of Research and Evaluation Studies and Current Project
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ADN Research Data Enhancing Student Outcomes Patricia Cross (1981) Taxonomy For Analysis of Data Institutional Variables Situational Variables Dispositional Variables
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Institutional Data Line of Research Focusing on Programmatic Aspects of Programs that Promote or Hinder Student Success Example: Method of Selection Lottery First-Come, First Served Full time vs. Part time requirements Costs
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Situational Data Focus on Particular Situation of Student as Mediator of Success Example: Hours Worked /Week Marital Status Support Network Transportation Needs Child Care
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Dispositional Data Most Predominant Line of Research Found in Literature Review Focus on Prior Performance of Student High School and College GPA Test Scores Completion of Prior Coursework Relevant Work Experience in Health Field
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Theoretical Considerations Point-to-Point Theory The greater the correspondence between elements found in the predictor and in the criterion Improves the power and reliability of prediction of success in training
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Relevance of Literature for this Study Importance of Dispositional Variables in Predicting Completion Consistent with Prior Research However Proposed Model Goes Beyond Prior Research by Estimating Probabilities for Success in ADN programs
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Program Completion Three Possibilities: Program courses completed satisfactory Degree received (this is how we started) License received Decided on course completion
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Examination of ADN Completion The cohort Demographics Success by year Success by method of selection
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Nursing Students Gender 1994 - 1998
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Nursing Students Ethnicity 1994 - 1998
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Nursing Students Primary Language 1994 - 1998
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Nursing Students Age 1994 - 1998
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Program Completion by Selection Method by Year
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Student Factors in Selection Student factors related to program completion – within college Factors Validity evidence Selection models compared Bias evidence
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Topics of Discussion Validity Evidence GPA College Core Biology (Anatomy, Physiology, Microbiology) English Repetitions Core Biology (Anatomy, Physiology, Microbiology) Minorities with No Repetitions
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Forming a Composite The Variables Relationship to Program Completion Some Examples
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Variables in the Composite GPA Core Biology (Anatomy, Physiology, Microbiology) College-wide English Repetitions in Core Biology Number of Repetitions divided by number of courses
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Some Examples of the Composite
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Selection Possibilities Program Selection Method Not Completed Completed SelectedBA UnselectedCD A + B = Selected Students C + D = Unselected Students A / (A + B) = Percent of Selected Students who Complete an ADN Program D / (C + D) = Percent of Unselected Students who Complete an ADN Program (A+D) / (A+B+C+D) = Current Completion Rate
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Unselected Percent, Composite, and Selected Completion Rate
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Bias Evidence The Composite prediction of Completion Ethnicity Gender Age Language Background Effect on Selection
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Original and New Completion Rates Compared at a Point That Unselects 18% of the Population
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Programmatic and Student Factors in Selection Future Research into Additional Selection Criteria to further improve success rates Interviews Work Experience Recommendations
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Setting a Cut Score Your Part The over-subscription to your programs Your comfort level with the rejection of potentially successful applicants Your target program completion rate Your willingness to change the demographics of your entry population
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Next Steps Where do we go from here?
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Additional Slides The formula The core biology model compared to the composite model Program Completion as a Function of GPA and Core Biology Repetitions
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The Formula
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