Teaching Undergraduate Statistics without Math Peter A. Kindle, PhD, CPA, LMSW The University of South Dakota Presented at the 29th Annual Meeting of the Association of Baccalaureate Program Directors , Inc. Portland, Oregon – March 16, 2012

Today We will summarize existing research related to reducing statistics anxiety in students. We will examine one approach to teaching descriptive and inferential statistics at the undergraduate level without requiring student facility with mathematics. We will evaluate the pedagogical approach presented to determine its suitability for use in other statistics courses.

Statistics Anxiety Underwhelming research: Prior knowledge of stats  lower anxiety Low math skills  higher anxiety Perceptions of inadequacy  higher anxiety Gender and age effects  higher anxiety Majority of graduate students report uncomfortable levels of anxiety. Statistics Anxiety: A performance characterized by extensive worry, intrusive thoughts, mental disorganization, tension, and physiological arousal when exposed to statistics content, problems, instructional situations, or evaluative contexts, and is commonly claimed to debilitate performance in a wide variety of academic situations by interfering with the manipulation of statistics data and solution of statistics problems (Ziedner, 1990, p. 319).

Anxiety-Expectation Mediation Model (simplified) Lower Student Expectations Lower Statistics Achievement Less Study Statistics Anxiety (Test and Class Anxiety)

American Statistical Association Timeline Fisher’s first textbook Stats as Probabilities RECOMMENDATIONS Statistical literacy Use real data Conceptual understanding Active learning Technology Integrate Assessment Freeman et al., and Moore Traditional intro courses 1992 1925 1978 Aliaga, M., Cobb, G., Cuff, C., Garfield, J., Gould, R., Lock, R., . . . Witmer, J. (2010). Guidelines for assessment and instruction in statistics education: College report. Alexandria, VA: American Statistical Association. Retrieved from http://www.amstat.org/education/gaise/index.cfm The Cobb Report

ASA Recommendations Emphasize statistical literacy and develop statistical thinking Use real data Stress conceptual understanding, rather than mere knowledge of procedures Foster active learning in the classroom Use technology for developing conceptual understanding and analyzing data Use assessments to improve and evaluate student learning

Instructional Model Classroom Setting Inputs Outcomes Real data Statistical literacy Conceptual understanding Active Learning Technology Assessment

Selected Outcomes Students should understand . . . How to obtain and generate data How to graph data Random selection and generalization Random assignment and causation The basic ideas of statistical inference Sampling distribution, statistical significance, and confidence intervals How to interpret statistical results in context How to critique news stories and journal articles

Implementation Team based (pairs) Downloadable statistical software (OpenStat) Instructor support (high tolerance for chaos) Scaffolded (increasingly difficult ala Blooms Taxonomy)

Scaffolding I Remembering - Direct questions in stat terms. What is the mean of Age? What is the range of the overall score on the Satisfaction with Life scale? What is the variability of GPA scores? Understanding - Using stat terms to understand an article. Which variable on Table 1 has the greatest variability? Which variable on Table 2 is the best example of a ceiling effect? How many respondents are older than one standard deviation beyond the mean?

Scaffolding II Applying - Asking questions in research terminology that require statistical answers Test the hypothesis: Total student stress is associated with lower satisfaction of life among USD students. This study will attempt to confirm the results of other studies that indicate that, USD students who performed better on the ACT exam tend to receive higher course grades. What is your finding? Did you reject the Null?

Transition Points Strengths Weaknesses Data transformations and scales of measurement Correlation and variance Correlation and effect size Statistical Significance Relating stat tests to hypotheses (esp. the Null) Distinguishing between variable and between group tests

Lessons Learned OpenStat and Textbook deficiencies Pairs Multiple flashdrives with data sets Not Apple friendly Pairs Absentee issue Group expansion over time Regularity of schedule Need better integration of journal articles

Contact Information for Questions, Comments, or Suggestions: Peter.Kindle@usd.edu 605-677-5585