Statistical analysis of a one- semester general chemistry approach for students entering the pharmacy field Taylor Owings Marcy Towns Purdue University 1

Goal The goal of this research was to evaluate the newly introduced CHM 109 on its effectiveness for preparing students for organic chemistry 2

Introduction Change in the MCAT encourages more biochemistry for premedical and pre-pharmacy students ▫AAMC mandated Recommendation for a format, with inclusion of Biochemistry in place of a second semester of general chemistry CHM 109 implemented at Purdue to accommodate the one semester general chemistry requirement ▫5 credit hours, 3 lectures a week 3

Background on the CHM 109 course 5 credit hour course Created to replace the 2 semester equivalent (CHM 115 and CHM 116) Designed to be part of new chemistry series for students ▫1 general chemistry course CHM 109 ▫2 organic courses (MCMP (Medicinal Chemistry & Molecular Pharmacology) ) ▫1 biochemistry course 4

Participants Students enrolled in MCMP 204/205 from fall fall 2011 ▫Classified by year enrolled in MCMP 204 ▫Information collected on all chemistry classes students had enrolled in 5

Data collection Data collected included ▫Demographic information ▫SAT/ACT scores ▫Grades in all Purdue chemistry courses 6

Research question How do students enrolled in MCMP 204/205 perform in organic chemistry based on prior enrollment in CHM 109 and CHM ? 7

Statistical analysis Analysis to determine if comparisons can be made between student groups Analysis to determine if students course outcomes in MCMP 204/205 varied based on prior enrollment in CHM or CHM 109 8

Explaining differences Significant differencePractical difference Difference is determined by significance test ▫ANOVA, t-test, etc… Answers the question “are they different” Degrees of freedom included in equation Different tests that accompany significance tests ▫Cohen’s d, eta squared, etc… Looks at how different values are ▫Small, medium, or large Answers the question “Does the difference matter” Degrees of freedom not included in equation 9

Formulas 10

SAT test scores 11

Preliminary standardized test scores MeanF-statisticEffect size (η 2 ) SAT Verbal SAT Math ACT English ACT Math ACT English/Writing Effect Size: Small ~.01, Medium ~.06, Large ~.14 All listed standardized test scores were significantly different (p <.001)

Demographics analysis of chemistry GPA F-Statisticp-valueEffect Size (η 2 ) Gender Ethnicity5.46< Effect size: Small ~.01, Medium ~.06, Large ~.14 Significant difference: p <.05

Average course grades CourseMeanStandard deviation F-statisticEffect size (η 2 ) CHM CHM CHM CHM 109 data is from CHM 115/116 data is from Effect size: Small ~.01, Medium ~.06, Large ~.14 All courses had a significant difference (p <.001)

Student performance in MCMP 204 MeanStandard deviation 115 Students Students Students

Student performance in MCMP Effect size: Small =.1, Medium =.25, Large =.4 Significant difference: p <.05 t-statisticp-valueEffect size (Cohen’s d) CHM 115 vs. CHM CHM 116 vs. CHM

Student performance in MCMP 205 MeanStandard deviation 115 Students Students Students

Student performance in MCMP Effect size: Small =.1, Medium =.25, Large =.4 Significant difference: p <.05 t-statisticp-valueEffect size (Cohen’s d) CHM 115 vs. CHM CHM 116 vs. CHM

Conclusion Analysis demonstrates no significant or practical differences exist in performance in MCMP 204/205 based upon general chemistry preparation in CHM 109 and CHM 115/116. Thus, the new course sequence (one semester general chemistry) supports student success in Purdue’s pre-pharmacy curriculum. 19

Implications Demonstrates efficacy of one semester gen chemistry course in pre-medical and pre- pharmacy curriculum Research finding provide research based support for curriculum augmentation 20

Acknowledgements HHMI Nexus team at Purdue ▫Dr. Marc Loudon ▫Dr. Chris Hrycyna Research and assessment grant 2011 Towns research group ▫Dr. Marcy Towns 21

Participant demographics N Male1059 Female1707 White Non-Hispanic1680 Hispanic/ Latino43 African American87 Asian American/ Pacific Islander289 Native American5 Other65 22

Formulas 23

CHM 109 MeanStandard DeviationN

CHM 109 MeanStandard Deviation N Male Female Other demographics were unavailable for a significant portion of population 25

CHM 109 F-StatisticP-valueEffect Size (η 2 ) Gender EthnicityN/A 26

CHM 115 MeanStandard DeviationN

CHM 115 MeanStandard Deviation N Male Female White Non- Hispanic Hispanic/ Latino African American Asian American/ Pacific Islander Other

CHM 115 F-StatisticP-valueEffect Size (η 2 ) Gender Ethnicity

CHM 116 MeanStandard DeviationN

CHM 116 MeanStandard Deviation N Male Female White Non- Hispanic Hispanic/ Latino African American Asian American/ Pacific Islander Other

CHM 116 F-StatisticP-valueEffect Size (η 2 ) Gender Ethnicity

MCMP 204 MeanStandard DeviationN

MCMP 204 MeanStandard Deviation N Male Female White Non- Hispanic Hispanic/ Latino African American Asian American/ Pacific Islander Other

MCMP 204 F-StatisticP-valueEffect Size (η 2 ) Gender Ethnicity

MCMP 205 MeanStandard DeviationN

MCMP 205 MeanStandard Deviation N Male Female White Non- Hispanic Hispanic/ Latino African American Asian American/ Pacific Islander Other

MCMP 205 F-StatisticP-valueEffect Size (η 2 ) Gender Ethnicity