1. 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

2. 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.

3. 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).

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

5. 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
The Cobb Report

6. 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

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

8. 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

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

10. 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?

11. 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?

12. 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

13. 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

14. Contact Information for Questions, Comments, or Suggestions: