Transcript Week 10 Lecture Powerpoint

University of Warwick, Department of Sociology, 2012/13
SO 201: SSAASS (Surveys and Statistics) (Richard Lampard)
Survey Design:
Some Implications for
Secondary Analysis
(Week 10)
Weighting
• Sample designs often do not select
respondents with a single, consistent
probability of selection.
• Unequal selection probabilities need to be
taken into account at the analysis stage, to
allow the results to be properly
generalisable to the population of interest.
Complex Samples
and Sampling Error
• Weighting can increase the expected
amount of sampling error.
• Stratification can reduce the expected
amount of sampling error.
• Clustering within samples can increase the
expected amount of sampling error.
But...
• Secondary analysts frequently treat
samples as if they were simple random
samples.
• Hence the resulting analyses may underestimate (or over-estimate) the likely
amount of sampling error.
• In consequence the p-values may not be
as reliable as they should be.
The solution...
• Recent versions of SPSS (and various
other forms of software) allow one to
incorporate these aspects of sample
design into the analysis, and thus take
account of their effect on the standard
errors of estimates in an appropriate way.
Merging files
• Sometimes the design of a survey and/or a
research focus means that a secondary analyst
needs to merge data from two or more files.
• For example, information on aspects of
individuals’ life histories may be included in
separate files.
• Sometimes information about household
members needs to be matched by saving
subsets of members and matching them (e.g.
matching individuals with their resident partners).
Example
• In the case of a repeated survey like the
British Social Attitudes survey, different
years which contain some of the same
questions can be matched to facilitate
examinations of social change.