1. Quantitative analysis Sonia Williams Northern College of Acupuncture 19 th February 2011

2. Numbers, numbers…… Measureable values Height, weight, age Can calculate: Average/mean Median Mode

3. Parametric statistics Continuous variables –height, weight, age expressed in exact terms –e.g. 1.67m; 71.5Kg; 25.5years. Non-continuous variables –height, weight, age expressed in groupings –e.g. 1.5-1.7m; 70-75Kg; 20<25yrs.

4. Distribution curve: height, weight, IQ, etc. Continuous variable

5. Comparing 2 groups E.g. shoe sizes men/women? Is there a statistically significant difference between them? Parametric stats. e.g. t-tests Comparing means And standard deviations

6. Other uses of numbers in quantitative data…… Categorical data E.g. gender Yes/no answers

7. Presenting categorical data 4 categories Visually presented

8. Comparing categorical data Sample size = 100 Comparing……. 40 males & 60 females 40 had received acupuncture while 60 had not. Was there a significant difference in the proportion of males & females receiving acupuncture? Chi squared test used ANSWER=? Ask SPSS 301040 male 30 60 female 60 Apuc- 40 Acup+ 100 total

9. Probability values (P) Probability of heads OR tails = 1 in 2 or 50% (or 0.5) Probability of 2 consecutive heads = 1 in 2 AND 1 in 2 = 1 in 4 or 25% (or 0.25)

10. Probability values (P) How many times would you need to get consecutive tails to reach a probability value less than 0.05?

11. Probability values (P) P<0.05 becomes biologically important. There is only a 5% chance that this result occurred by chance or 1 in 20 P<0.01 is 1% or 1 in 100 P<0.001 is 0.1% or 1 in 1000

12. Sources of error in statistics Assuming that an association is the same as causation. The link may be spurious There may be a confounding variable

13. Sources of error in statistics which one will be true?

14. Type 1 error. The one you thought was true was not

15. Sources of error in statistics which one will be true? Type 2 error: The one you thought would not be true was

16. Data entry: hardware?

17. Punch card machine

18. Data analysis

19. Life is easier now & less noisy! SPSS Comprehensive set of flexible tools that can be used to accomplish a wide variety of data analysis tasks. Data collection instrument Data analysis Graphic presentations Statistical analysis

20. Creating datasets What experimental design? Which variables? What values do these variables assume? How can the data be coded to make data entry easier? Devise a code book to help you Make sure you ‘clean’ the data, as errors in data entry can occur (10% check + frequency check)

21. Choose appropriate scales & measures Questionnaires Closed questions: easy to code: inflexible Semi-structured questions: harder to code: more flexible May need to add to dataset as ‘unexpected answers’ become apparent Open-ended questions: bit of a nightmare: need to go through & document all possible answers before devising suitable coding system

22. Questionnaires: try to avoid… Long complex questions Double negatives Double-barrelled questions Jargon or abbreviations Culture-specific terms Words with double meanings Leading questions Emotionally loaded words

23. Developing a codebook Decide how you will go about: –Defining and labelling each of the variables –Assigning numbers to each of the possible responses –Each question or section of a question must have a variable name which: Must be unique, begin with a letter, cannot include punctuation

24. Data entry: issues to consider Variables: Categorical Continuous/discrete Whether you are dealing with how to deal with multiple responses (where more than one response may be given to a single question)

27. Outcomes? Frequencies? Cross tabulations? Visual display? Statistical analysis? Is amenable to enter into Word, if necessary