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Quantitative analysis
Sonia WilliamsNorthern College of Acupuncture
19th February 2011
Numbers, numbers……
Measureable values
• Height, weight, age
• Can calculate:
• Average/mean
• Median
• Mode
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.
Distribution curve: height, weight, IQ, etc.Continuous variable
Comparing 2 groups
E.g. shoe sizes men/women?
Is there a statistically significant difference between them?
Parametric stats.
e.g. t-testsComparing means
And standard deviations
Other uses of numbers in quantitative data……
Categorical data
• E.g. gender
• Yes/no answers
Presenting categorical data
• 4 categories• Visually presented
Comparing categorical data
Sample size = 100Comparing…….• 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
30 10 40male
30 30 60female
60Apuc-
40Acup+
100total
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)
Probability values (P)
• How many times would you need to get consecutive tails to reach a probability value less than 0.05?
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
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
Sources of error in statisticswhich one will be true?
Sources of error in statisticswhich one will be true?
• Type 1 error. The one you thought was true was not
Sources of error in statisticswhich one will be true?
• Type 2 error:
• The one you thought would not be true was
Data entry: hardware?
Punch card machine
Data analysis
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
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)
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
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
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
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)
Outcomes?
• Frequencies?
• Cross tabulations?
• Visual display?
• Statistical analysis?
• Is amenable to enter into Word, if necessary