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