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INF 397C Fall, 2003 Days 13

INF 397C Fall, 2003 Days 13. Designing Research. Finishing the exercise from last week . . . Question 3 (groups 5 and 6): Does design of a Web site affect visitors’ perception of the validity of the content?. Homework. http://www.environ.wa.gov.au/community/results.asp

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INF 397C Fall, 2003 Days 13

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  1. INF 397CFall, 2003Days 13

  2. Designing Research • Finishing the exercise from last week . . . • Question 3 (groups 5 and 6): Does design of a Web site affect visitors’ perception of the validity of the content?

  3. Homework • http://www.environ.wa.gov.au/community/results.asp • http://www.yogapoint.com/info/research.htm • http://www.main.nc.us/bcsc/Chess_Research_Study_I.htm • http://www.econ.upenn.edu/Centers/CARESS/CARESSpdf/00-15.pdf • http://www.georgetowncollege.edu/departments/education/portfolios/Franzen/research_paper.htm • http://www.pbats.com/articles/foot/Foot_Postures.pdf • http://www.cwrl.utexas.edu/currents/spring02/wakefield.html • http://www.neuhaus.org/paper3.htm

  4. Other “T scores” • http://www.rehabinfo.net/rrtc/publications/research_summaries/neropsychfunction/default.asp • “T scores” – “Standardized T scores for the WAIS-R, WISC-R, and HRB” • http://collection.nlc-bnc.ca/100/201/300/cdn_medical_association/cim/vol-21/issue-2/0094.htm • Yet new “T scores.”

  5. Other measures • Confidence intervals • http://www.hortnet.co.nz/publications/nzpps/proceedings/95/95_294.htm • Anova • http://www.ischool.utexas.edu/~adillon/publications/empirical.html

  6. Chi-squared • Hinton, p. 239

  7. Chi square test • Let’s work an example. • Just know that you use the chi square test when you have FREQUENCY data.

  8. Correlation • With correlation, we return to DESCRIPTIVE statistics. (This is counterintuitive. To me.) • We are describing the strength and direction of the relationship between two variables. • And how much one variable predicts the other.

  9. Hinton, p. 259

  10. Break

  11. Let’s conduct some REAL research • Research question: Is one of the two formulae for Standard Deviation easier to use? • H0: µ1 = µ2 • HA: µ1 ≠ µ2 • Population: iSchool Research Methods students • Sampling technique: Random • IV: Particular formula (derivative formula vs. computational formula • DVs: 1) Speed, 2) Accuracy

  12. Procedure • Who has a calculator? • Number off, 1 to N. • We’re gonna choose 10. • Randomly assign 5 to Group 1 and 5 to Group 2. • http://www.random.org/sform.html • Group 1 – Derivative formula first. • Group 2 – Computational formula first. • For each testee (wink), need one volunteer to time the completion. • Test participant: Please start when I say “Go,” and tell your observer when you think you have the answer.

  13. Test Materials -- A • Please calculate the Standard Deviation of the following distribution of scores: • 5 • 6 • 2 • 8 • 3 • 9 • 4

  14. Test Materials -- B • Please calculate the Standard Deviation of the following distribution of scores: • 4 • 6 • 8 • 7 • 3 • 9 • 2

  15. Scoring • Observers, please collect the following data: • Test Participant number • Materials A problem: Correct (y/n) • Materials A problem: Time to complete? • Materials B problem: Correct (y/n) • Materials B problem: Time to complete? • Difference score: Time for derivative (whether it was A or B) minus time for computational formula (whether it was A or B) • Let’s build a spreadsheet with the data.

  16. End game • Calculate t. • Reject or not? • Conclusion. • Now, REALLY, to whom do these results generalize?

  17. Let’s talk about the final • Course evaluation • Q-and-A session, Monday, December 1, 5:30 – 6:30, place TBD. • Sample problems handed out next week.

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