1. Using statistics in the analysis of quantitative data A good way to use this material for detailed study is to print the whole file then to run the slide show, while reading the text from the printed version. This will allow you to use the links and animations that are included in some of the slides. Suggested Print settings for use in the print dialogue box: Print Range: All Print what: Notes Pages (from the drop down box) Then tick:Black & White, Scale to fit paper

2. Types of data Data typeExample Nominal or Categorical Eye colour Ordinal Job seniority Interval: parametric non-parametric Language comprehension test score; IQ Ratio parametric non-parametric Age

3. Uses of statistics Use of statistics Inferential or Non- inferential Describing a sampleNon-inferential Looking for relationships between variable in a sample Non-inferential Estimating parameters in a population Inferential Testing hypothesesUsually used inferentially but can be used non- inferentially

4. SPSS task Entering data

5. Describing a sample

7. SPSS calculation of mean

8. Finding the spread of scores in a sample

9. Standard Deviation

11. Finding how scores are distributed

12. Distribution of attitude scores

15. Properties of the Normal Distribution

16. Checking normality

17. An overall test for normality

18. Describing ordinal data - Frequencies

19. Median and Mode for ordinal data

20. Describing ordinal data Bar charts (no gaps)

21. Describing nominal data - Frequencies

22. Nominal data - Mode

23. Describing nominal data – Bar Chart

24. Describing nominal data – Pie Chart

25. Exploring relationships between data

26. Correlation

28. Review of meaning and importance of linearity http://www.aiaccess.net/English/Glossarie s/GlosMod/Flash/e_gm_fla_covariance.ht mhttp://www.aiaccess.net/English/Glossarie s/GlosMod/Flash/e_gm_fla_covariance.ht m http://www.fon.hum.uva.nl/Service/Statistics.html

29. Extreme groups – a warning

30. Correlation - effect of measurement error motivation Test result Actual points

31. Correlation - effect of measurement error motivation Test result Actual points Measured points

32. Correlation - effect of measurement error motivation Test result

33. Correlation & Regression

34. Spearman Correlation Ordinal data

35. Chi squared test of association Nominal data

36. Chi squared showing an association

37. Calculating chi-squared from cell values http://www.physics.csbsju.edu/stats/ contingency.html

38. Item analysis, reliability and validity

39. Cronbach’s Alpha

40. Estimating population values

41. Terminology Population (described by parameters) Sample (described by statistics)

42. Estimating population values

43. Sampling Samples that allow statistical generalisation random systematic stratified random cluster multi-stage Samples that don’t allow statistical generalisation quota convenience snowball

44. Sampling Samples that allow statistical generalisation random systematic stratified random cluster multi-stage Samples that don’t allow statistical generalisation quota convenience snowball

45. Making it practicable whilst retaining validity

46. Calculating required sample sizes http://StatPages.org http://www.jalt.org/test/bro_25.htm and related web pageshttp://www.jalt.org/test/bro_25.htm

47. Statistics and parameters Statistics of sample Mean = m Standard Deviation = s Correlation = r Parameters of population Mean = μ Standard deviation = σ correlation = ρ

48. Statistics and parameters Statistics of sample m s r Parameters of population Best estimate is… μ = m σ = ρ = r (for large samples >30)

49. 95% confidence limits for the population mean - large samples

50. Calculation of confidence intervals Mean http://glass.ed.asu.edu/stats/analysis/mci.h tmlhttp://glass.ed.asu.edu/stats/analysis/mci.h tml Correlation http://glass.ed.asu.edu/stats/analysis/rci.ht mlhttp://glass.ed.asu.edu/stats/analysis/rci.ht ml Standard deviation Walpole R (1982) Introduction to statistics 3rd Edition p277-8;482

51. Confidence interval for  2 Walpole R. (1982) Introduction to Statistics 3 rd Edn New York: Macmillan pp277-8

52. As long as the population is at least ten times as large as the sample, the size of the population has almost no influence on the accuracy of sample estimates. The margin of error for a sample size of 1000 is about 3% whether the number of people in the population is 30,000 or 200 million. You can make a good check on how salty a well stirred bowl of soup is by tasting one spoonful – whatever the size of the bowl What’s the surprise? There is no effect! The Surprising Effect of Population Size *.