全国高等学校英语教师 教育与发展系列研修班

1. 全国高等学校英语教师教育与发展系列研修班 Statistics in action: A listening strategies project 25 July 2010 Victoria University of Wellington 中国外语教育研究中心 顾永琦 peter.gu@vuw.ac.nz

2. In this session… Part 1: Feedback to your journals Part 2: Exercises 1 and 2 demo Part 3: Statistics in a listening strategy project

3. Part 1 Your journals • 收获 • 问题 • 建议

4. 问题 运用实例，看什么研究问题用什么统计方法，生成什么样的图表，如何解读 一些关键术语还是模糊，如nominal, ordinal, interval scales, p值，相关正负值 Comment：realistic expectations

5. 建议 加强练习与实际操作 SPSS操练手册？ 提供references 提前把材料放在网上

6. Part 2 Exercises 1 and 2 demo

8. If you run Frequencies on the data, you get the descriptive statistics for all 44 cases, i.e., for both classes. What you need is mean scores and SD for both classes. You can get the descriptive statistics you want from the Explore command under Descriptive statistics.

15. Reporting your descriptive statistics

16. Interpreting your findings The mean score of the graduate student’s class (Mean=58.13) was indeed higher than that of the professor’s class (Mean=55.29). In general, the graduate student’s class outperformed the professor’s class. The professor’s class also had a much larger standard deviation than the student’s class, indicating that the professor’s class showed a wider spread of scores than the other class. NB: you can also show the spread visually on a histogram.

17. Exercises 2 Correlation

19. Choose your options to include descriptive statistics

20. Report the descriptive statistics first

21. Report the correlation table

22. Interpreting your correlation findings The findings showed that candidates’ abilities were positively correlated with the salary package offered to them. All four ability measures were found to be significantly correlated to the candidates’ annual salary. The highest correlation existed between the candidates’ IQ scores and their salary (r=.844, p<.001). In other words, someone’s IQ score explains about 65% of the salary s/he earns. Communication score revealed the second highest correlation with salary (r=.813, p<.001)… This is encouraging news for business school students, in that hard work, good marks, and an intelligent mind are indeed highly related to the salary they get after they graduate.

23. Part 3: Stats in action 3.1. Developing and validating the Strategy Battery for Young Learners of English (SBYLE) 3.2. Statistics in a listening strategy project

24. Strategy Battery for Young Learners of English (SBYLE) Purpose: Profiling English language learner strategies used by upper primary school pupils in Singapore • Listening Strategy Questionnaire • Reading Strategy Questionnaire • Writing Strategy Questionnaire

25. Designing the SBYLE • The listening strategies questionnaire as an example

26. Version numbers for the Strategy Battery for Young Learners of English

27. Version 1.0: Creation of questionnaire items

28. Where do Questionnaire items come from? Phase I studies • Participants: 4 schools, all 6 grades in each school, one high, one medium, and one low EL proficiency pupil from each grade (4x6x3=72) • Materials: • Listening, reading, writing tasks • Think-aloud protocols • Analysis: • Coding and pattern exploration

29. Version 1.0 Example

30. Validating the SBYLE

31. Validity checks • Content validity Consult experts, teachers, target group on • relevance • coverage • representativeness, and • exactness of wording • Construct validity • Comparison with theory • Factor analysis • Response validity • Participating pretest: paired think-aloud

32. Reliability checks

33. Version 1.1: Validation by experts

34. Changes made at this stage: Examples

35. Version 1.2: Validation by teachers

36. Version 1.3: Validation by target sample(intended meaning = perceived meaning?)

37. Version 1.4: Construct validity and internal consistency reliability • Formal piloting among 293 pupils (P4-P6) • Elicit student and teacher comments • Revise, after factor analysis and item analysis: delete, add, change, collapse categories, re-categorize items • Fine-tune survey administration procedures and detect problems (Guidelines for Questionnaire Administration)

38. Version 1.4: Reliability analysis

39. Deleted item: #38

40. Deleted item #2

41. Obtaining variables from individual items COMPUTE LSELFINI=(L11+L12+L14 +L18)/4. COMPUTE LPLAN=(L3+L15+L23)/3. COMPUTE LMONITOR =(L21+L32+L5+L9+L36)/5. COMPUTE LPERCEPT =(L8+L24 +L34+L37)/4. COMPUTE LINFER =(L1+L4+L17+L26 +L31+L27+L33)/7. COMPUTE LPREDICT =(L6+L7+L13+L29+L35)/5. COMPUTE LUTILISE =(L16+L19+L20+L22+L30)/5. COMPUTE LSOCIAL=(L2+L10+L28+L25+L38)/5.

42. Running item analysis and reliability in SPSS • What we are doing: • Checking if items that make up a variable are consistent among themselves. • Doing item-total correlation to see how each item in the variable is correlated to the total variable score. • Determining which item should be removed from the variable (alpha is item deleted) • Example: the monitoring variable LMONITOR was made up of 5 items L21, L32, L5, L9, and L36.

43. Getting internal-consistency reliability

44. Locate all items in the variable

45. Specifying the statistics you want

47. Deciding if any item does not belong to the variable

48. Version 1.4: Problems and solutions

49. Version 2.0: Final Administration

50. Statistics in the listening strategy project:Deciding what stats to use