1. 1 Chapter 24 Scale Development and Statistical Analysis Methods for Scale Data

2. 2 Content Introduction Scale development methods Scale evaluation methods Statistical analysis of scale data

3. 3 §1 Introduction 1 Definition Measurement instrument that are collections of items combined into a composite score, and to reveal levels of a certain state, behavior or attitude of the research objects are referred to as scale.

4. 4 Example24-1 How to evaluate the treatment of pain? Or how can we measure different degree of pain?

5. 5 Characteristics of scale: Quantitative Standardization

6. 6 Ways to obtain the variable value: 1) Measurement ----to get the quantitative data 2) questionnaire or inquiring---- to get the quantitative or qualitative data

7. 7 Structure design of a scale is similar to a questionnaire, but they are different: questionnaire ----different independent contents can be included in scale----describe one characteristic of the object, and the items are associated with each other

8. 8 2 Scales are used in the situations of: measure variable that can not assess directly. Eg: some physical, psychological and social characteristics. 1) Variables can not be measured directly 2) Nonobjective conceptions and attitudes 3) Complicated behaviors or psychological states

9. 9 3 merits and demerits of the scale measurement merits ： Strong objectivity, standardized process, comparable and easy to implement demerits ： large variation with different individual, high quality are request in scale development

10. 10 1 Structure of a scale Scale----domain(subscale) ---- facet ---- item §2 Methods of scale development

11. 11 2 principle of scale development An ideal scale: exactly measure the aim characteristics, and obtain reliable data （ 1 ） principle of suitability （ 2 ） principle of validity （ 3 ） principle of feasibility

12. 12 3 Approaches of scale development 1) Make sure the research aim and measurement content nominal group focus group 2) Define dimensions and facets

13. 13 3) Establish item pool and select items 4) Design operable items Make sure the format of items and response

14. 14 Table 24-1 response scale analysis of i mportance quantifiers quantifiersAverage score quantifiersAverage score Very unimportant0.90important5.96 unimportant1.10More important7.14 More unimportant2.40Quite important8.04 important4.41Very important8.59 Some important4.72highly important8.69

15. 15 5) Qualitative evaluation of scales Expert consultation methods Delphi method

16. 16 6) Pilot test of the scale and it ’ s quantitative evaluation 7) Establish the norm

17. 17 4 quantitative analysis and selection of items Similar to the indicator selection in comprehensive evaluation, a good item should have good importance, sensitivity, independency, and acceptability,and be representative, ascertain.

18. 18 Methods of item selection: （ 1 ） subjective evaluation method--importance （ 2 ） discrete tendency method—sensitivity （ 3 ） correlation coefficient method– representative and independency （ 4 ） principal components analysis and factor analysis method-- representative

19. 19 （ 5 ） clustering analysis method-- representative （ 6 ） stepwise selection method based on importance （ 7 ） stepwise regression -- importance （ 8 ） stepwise discriminant method – discriminative ability

20. 20 Example 24-2 Analyze the quality of 12 items of 3 facets (ache, energy and sleeping) in physical domain of 206 cases of general people and hypertension patients

21. 21 Table 24-2results of item selection item Correlation coefficient CVFactor loadings Stepwise regression Stepwise discrimin ant (%)F1F1F2F2F3F3 F11-0.166*40.900.2220.013-0.478 F12-0.226* 42.510.762-0.046-0.143 F13-0.240* 44.250.812-0.069-0.175 F14-0.08940.580.7060.020-0.265  F210.243* 42.06-0.1120.2330.801  F22-0.181*39.090.657-0.255-0.197 F23 0.227* 32.24-0.1260.2240.812 F24-0.276* 41.150.741-0.293-0.016 F310.212* 32.140.0250.8300.297 F32-0.317* 51.740.512-0.665-0.017  F330.237* 35.24-0.0370.8240.308 F34-0.316* 55.730.480-0.6690.112

22. 22 5 Notice 1) Quantity of items 30-50 2) wording of items should be explicit and material 3) Objective index and subjective index

23. 23 §3 Methods of scale evaluation

24. 24 1 qualitative evaluation of scale experts colloquia or experts consultation Improve the structure of the scale, wording of items, item selection, and so on.

25. 25 2 reliability analysis Reliability is used to evaluate the accuracy, stability and consistency of a scale. （ 1 ） test-retest reliability ： ≥0.7 。 （ 2 ） split-half reliability ： （ 24-1 ） （ 3 ） Cronbach’s alpha coefficient ： （ 24-2 ） K-number of items ， -the score variance of the ith item ， - the total score variance of a scale

26. 26 Example 24-4 A doctor measured Quality of life with 50 people using WHOQOL-100 ， and a retest was done one week later, results were showed in table 24-4.

27. 27 … Table 24-4 QOL score of 50 persons

28. 28 Results: ① test-retest reliability ： r=0.82 ； ② mean difference = 3.87 ， t=1.544 ， P=0.129 ； ③ Cronbach’s alpha coefficient ：

29. 29 3 validity analysis of a scale Validity is used to evaluate the veracity, validity of a scale. The accordance of practice results and the anticipate results.

30. 30 1) content validity 2) criterion-related validity 3) contract validity (evaluating:confirmatory factor analysis, CFA)

31. 31 4 responsibility analysis of a scale Responsibility is the capability of a scale to observe the changes between different objects or the changes with in an object at different time. The statistic is effect size: （ 24-3) : Scale socre Example 24-5

32. 32 §4 statistical analysis methods of scale data

33. 33 1 characteristic of scale data 1) grouping the objects and compare the scores between groups; 2) Repeated measure data; 3) multi-domain data

34. 34 1) inevitable 2) Deal with missing data Missing data:

35. 35 2 statistical analysis of scale data Statistical description Statistical inference

36. 36 Statistical inference 1)For situation if one time point, more than one group one variable analysis: t-test, F test, rank sum test

37. 37 some comprehensive evaluation methods: Fuzzy discriminant method, O ’ Brien synthetic method, rank sum method, TOPSIS method.

38. 38 2) Compare of longitudinal data Hotelling T 2 test, multi-variable variance analysis, variance analysis of repeated measure, profile analysis

39. 39 3 Examples of scale data analysis Example 24-6, Example 24-7

40. 40 4 Application of scales psychology, pedagogy, and sociology----medicine （ 1 ） psychology and psychiatry （ 2 ） evaluation of disease treatment in clinical research

41. 41 （ 3 ） disease statistics and health statistics （ 4 ） nursing research （ 5 ） health management

42. 42 THANKS ！