Taylor Owings Marcy Towns Purdue University

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Statistical analysis of a one-semester general chemistry approach for students entering the pharmacy field

Taylor OwingsMarcy TownsPurdue University

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Goal•The goal of this research was to evaluate

the newly introduced CHM 109 on its effectiveness for preparing students for organic chemistry

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Introduction• Change in the MCAT encourages more biochemistry

for premedical and pre-pharmacy students▫AAMC mandated

• Recommendation for a 1-2-1 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

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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 1-2-1 chemistry series for students▫1 general chemistry course CHM 109▫2 organic courses (MCMP (Medicinal Chemistry &

Molecular Pharmacology) 204-205)▫1 biochemistry course

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Participants•Students enrolled in MCMP 204/205 from

fall 2002 - fall 2011▫Classified by year enrolled in MCMP 204▫Information collected on all chemistry

classes students had enrolled in

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Data collection•Data collected included

▫Demographic information▫SAT/ACT scores▫Grades in all Purdue chemistry courses

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Research question•How do students enrolled in MCMP

204/205 perform in organic chemistry based on prior enrollment in CHM 109 and CHM 115-116?

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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 115-116 or CHM 109

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Explaining differencesSignificant difference Practical 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

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Formulas

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SAT test scores

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012400

450

500

550

600

650

700

SAT Scores by year

VerbalMath

Year in MCMP 204

SAT

Scor

e

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Preliminary standardized test scoresMean F-statistic Effect size

(η2)SAT Verbal 576 6.79 .0338SAT Math 623 10.8 .0524ACT English 26.4 9.16 .0809ACT Math 27.9 5.20 .0476ACT English/Writing 26.0 7.41 .00010

Effect Size: Small ~ .01, Medium ~ .06, Large ~ .14All listed standardized test scores were significantly different (p <.001)

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Demographics analysis of chemistry GPA

F-Statistic p-value Effect Size (η2)

Gender .852 .427 .00062Ethnicity 5.46 <.001 .0125Effect size: Small ~ .01, Medium ~ .06, Large ~ .14Significant difference: p < .05

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Average course gradesCourse Mean Standard

deviationF-statistic Effect size (η2)

CHM 109 3.10 .806 16.6 .0342CHM 115 3.26 .699 12.8 .0556CHM 116 3.32 .757 19.7 .0768

CHM 109 data is from 2010-2011CHM 115/116 data is from 2002-2011Effect size: Small ~ .01, Medium ~ .06, Large ~ .14All courses had a significant difference (p <.001)

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Student performance in MCMP 204

Mean Standard deviation115 Students 2.78 1.11116 Students 2.84 1.08109 Students 2.76 1.03

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Effect size: Small = .1, Medium = .25, Large = .4Significant difference: p < .05

t-statistic p-value Effect size (Cohen’s d)

CHM 115 vs. CHM 109

.289 .772 .00001

CHM 116 vs. CHM 109

1.08 .278 .0745

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Mean Standard deviation115 Students 2.83 1.02116 Students 2.87 .999109 Students 2.87 .978

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Effect size: Small = .1, Medium = .25, Large = .4Significant difference: p < .05

t-statistic p-value Effect size (Cohen’s d)

CHM 115 vs. CHM 109

-.372 .711 .0394

CHM 116 vs. CHM 109

.007 .994 .0018

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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.

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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

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Acknowledgements•HHMI Nexus team at Purdue

▫Dr. Marc Loudon▫Dr. Chris Hrycyna

•Research and assessment grant 2011•Towns research group

▫Dr. Marcy Towns

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Participant demographicsN

Male 1059Female 1707White Non-Hispanic 1680Hispanic/ Latino 43African American 87Asian American/ Pacific Islander

289

Native American 5Other 65

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Formulas

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CHM 109Mean Standard Deviation N

2010-2011 3.27 .7387 204

2011-2012 2.97 .8315 266

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CHM 109Mean Standard

DeviationN

Male 3.05 .7986 168Female 3.12 .8096 302

Other demographics were unavailable for a significant portion of population

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CHM 109F-Statistic P-value Effect Size

(η2)Gender .922 .337 .00197Ethnicity N/A N/A N/A

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2002-2003 2.99 .718 220

2003-2004 3.13 .716 234

2004-2005 3.17 .632 205

2005-2006 3.15 .696 185

2006-2007 3.28 .729 233

2007-2008 3.51 .623 264

2008-2009 3.36 .650 221

2009-2010 3.38 .665 188

2010-2011 3.80 .422 10

2011-2012 2.33 1.155 3

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DeviationN

Male 3.29 .668 665Female 3.24 .717 1095White Non-Hispanic

3.28 .686 1323

Hispanic/ Latino

3.30 .877 30

African American

2.89 .657 73

Asian American/ Pacific Islander

3.16 .735 208

Other 3.50 6.79 40

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(η2)Gender 1.231 .267 .00080Ethnicity 4.973 .000 .01681

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2002-2003 3.04 .788 186

2003-2004 3.33 .746 216

2004-2005 3.08 .806 190

2005-2006 3.15 .762 183

2006-2007 3.25 .763 235

2007-2008 3.44 .739 256

2008-2009 3.56 .604 207

2009-2010 3.68 .556 195

2010-2011 3.4 .894 5

2011-2012 1.5 2.121 2

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DeviationN

Male 3.35 .735 646Female 3.31 .770 1027White Non-Hispanic

3.34 .744 1257

Hispanic/ Latino

3.17 .966 29

African American

2.92 .802 61


3.28 .774 204

Other 3.53 .726 45

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MCMP 204Mean Standard Deviation N

2002-2003 2.33 1.320 222

2003-2004 2.73 1.048 230

2004-2005 2.85 1.152 259

2005-2006 2.75 1.175 238

2006-2007 2.76 1.183 208

2007-2008 3.03 1.013 266

2008-2009 2.69 1.263 270

2009-2010 2.99 .983 238

2010-2011 2.75 1.011 417

2011-2012 2.00 0 2

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MCMP 204Mean Standard

DeviationN

Male 2.80 1.16 896Female 2.75 1.12 1449White Non-Hispanic

2.80 1.13 1596

Hispanic/ Latino

2.71 1.20 37

African American

2.43 1.09 81


2.63 1.21 280

Other 3.05 1.18 61

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MCMP 204F-Statistic P-value Effect Size


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MCMP 205Mean Standard Deviation N

2002-2003 2.65 .894 169

2003-2004 2.79 .983 184

2004-2005 2.76 .978 195

2005-2006 2.75 1.031 175

2006-2007 2.91 .9713 148

2007-2008 3.03 1.043 216

2008-2009 3.07 .897 185

2009-2010 2.69 1.134 188

2010-2011 2.91 1.117 169

2011-2012 2.86 .989 174

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MCMP 205Mean Standard

DeviationN

Male 2.87 1.012 700Female 2.82 1.018 1098White Non-Hispanic

2.87 .998 1213

Hispanic/ Latino

2.71 .937 28

African American

2.33 1.203 60


2.77 1.048 212

Other 3.14 .833 50

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MCMP 205F-Statistic P-value Effect Size

(η2)Gender .796 .372 .00059Ethnicity 5.478 .000 .01387

Taylor Owings Marcy Towns Purdue University

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