1
Lecture 11 of 47C5 Social
Research Process I:
Sampling in Quantitative
Research I
Paul Lambert, 14.10.03, 4

5pm
2
47C5: Survey research lectures
Lecture 8: The Survey Method
Intro. to & qualities of survey method
Lecture 9: Using Secondary Datasets
Data access and issues
Lectures 11/12: Sampling
Sample design, data collection / analysis
3
Resources for lectures 8,9,11,12
•
Lecture slides on WebCT
site
•
2 Reading lists:
–
Initial list in 47C5 unit outlines
–
Some
additions
on further list on WebCT site
4
Web Resources for lectures
8,9,11,12
•
Slides and additional reading list also at:
http://staff.stir.ac.uk/paul.lambert/teaching.htm
•
Some other internet resources (cf De Vaus 2002)
http://trochim.human.cornell.edu/kb/
http://statcomp.ats.ucla.edu/survey
5
L11/12: Surveys and Sampling
Lecture 11:
1) Role of sampling in social surveys
2) Types of sampling methods
Lecture 12:
3) Good practice in survey conduct
4) Robust analysis of survey data
6
Part 1: Role of sampling in
survey research
•
Surveys can be census’s
•
More often samples from wider population
•
Several
sampling methods
select cases
•
Aim:
representative of wider population
7
Inference
•
Key idea is
inference
= confidence in our ability to generalise
Sampling inference = application of statistical
theories in order to estimate probabilities
that a sample result is ‘likely to have been
unrepresentative’
8
The ‘normal’ (Gaussian) curve
9
Theories of sampling methods
Sampling and probability theories
tell us
that any particular
random sample
is most
likely to have the same properties as the
wider population
. We can then estimate the
probability that sample results of a
particular nature could have arisen by
chance, rather than because they are the
same as the population result.
10
If the cases in sample surveys
were selected at
random
, then
can use sampling theories and
thus
‘inference’
11
‘Inferential data analysis’
•
Variable

by

case matrix data analysis for
generalising findings to population
•
Often distinguished from
‘descriptive’
data
analysis (results of sample only)
•
Key:
joint influence of
–
1) size of sample
–
2) strength of data pattern
in increasing confidence about generalisations
12
Statistical inference
..causes confusion; one of hardest parts of
survey data analysis to understand..
Phrases:
‘significance level’ ‘p

value’,
‘confidence interval’, ‘hypothesis testing’, ..
Meaning:
Whether results would probably
generalise to a larger population
(if sample is treated as random)
See:
Refs for L11 part 1 (supplementary list)
13
Critiques of survey generalisation
1) Part of the ‘fall of survey methods’ 1960’s:
•
Sampling is not representative
卡浰汩湧S楳i獴敭s瑩捡汬礠扩慳獥d
•
Inferential conclusions too carelessly made
and too strongly stated
•
See for example Cicourel 1964
14
Critiques of survey generalisation
2) Deconstructing inference (1980’s
)
•
Inferential methods over

relied upon
卵牶敹r慮慬祳楳i扥捯浥猠瑨敯特

晲敥 桵湴
for ‘significant’ patterns
•
Inference needed less than often suggested
•
Bad variable analysis (operationalisation) effects
inference results, eg (non

)parametric variables;
data clustering; …
•
See for example Rose and Sullivan 1996 p192

5
15
Contemporary survey research
Tends to use 2 strategies to address critiques:
Large scale, often secondary, rigorous methods
or
Small scale, primary, claims carefully qualified
16
Terms in sample survey analysis
•
Population:
all cases of interest
•
Sampling frame:
list of all potential cases
•
Sample:
cases selected for analysis
•
Sampling method:
technique for selecting
cases from sampling frame
•
Sampling fraction:
proportion of cases
from population selected for sample (n/N)
17
Survey analysis:
‘variable

by

case matrix’
Cases
Variables
1
1
17
1.73
A
.
.
.
.
2
1
18
1.85
B
.
.
.
.
3
2
17
1.60
C
.
.
.
.
4
2
18
1.69
A
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
N
18
Sample Surveys (case selection)
Populn.
Cases
Variables
1





2





3





4
1
1
17
1.73
A
5
2
1
18
1.85
B
6





7
3
2
17
1.60
C
8
4
2
18
1.69
A
N=8
n=4
19
Part 2: Sampling methods and
techniques
= Ways of selecting case from population
2.1 Random
(probabilistic)
Generalisable,
inferential
statistics, fewer
applications
2.2 Non

random
(opportunistic;
purposive)
Harder to
generalise,
inference contested,
more widely used
20
2.1a Simple Random Sample
•
A statistical method used to choose cases
randomly (eg random numbers)
Every case in population has exactly the same
chance of being in sample
•
Most data analysis techniques initially
designed for simple random samples
21
2.1b Systematic Random Sample
•
Like the SRS, select cases from anywhere
in the whole population
•
An easier selection method : choose every
(n)th person for the sample
•
Danger of
‘periodicity’
if original
population order has any structure,
bias
22
Problems with sample methods
selecting from whole population
•
The ‘random’ part means it is always
possible to get a population coverage quite
different from known structures
•
If total population is large or dispersed, then
coverage of random parts of it is
expensive
and time consuming
: few surveys use
random sampling from whole of UK
23
2.1c Stratified random samples
•
Modifies random sample
to ensure even (or
‘boost’) coverage of population groups
–
split sampling frame by
stratification factors
–
select random samples within each factor
–
final sample has correct proportions of each
–
Example: select 490 M and 510 F
•
Properties:
proportionate sample, correct
representations; but more expensive & complex,
should use ‘weights’ for analysis
24
2.1d Multistage cluster samples
•
i)
Select clusters
of population at random
•
ii) Sample randomly
within clusters
•
Eg: clusters = local authorities in UK
–
With qualifications, may still be treated as
‘random’ for analysis purposes
–
Big reduction in costs if face

to

face contacts
䵯獴⁷楤i汹l晡f潵牥搠獡浰汩湧l浥瑨潤
楮慲i攠獣s汥l獵牶s礠y潬汥瑩潮t
25
Example: Multistage cluster
sample
•
Interest: attitudes of Scottish school pupils
•
Resources: 400 interviews with pupils
26
Edinburgh 100
Argyll 24
Islands 20
Highlands 40
Glasgow 124
Aberdeen 40
Shetlands 2
Borders 10
Perth 20
Moray 20
27
Glasgow
150
Edinburgh
150
Stirling 60
Moray 40
28
Stirling 60
30 young people at
Balfron School
and
30 young people at
Stirling High
29
2.1e Longitudinal random
samples
•
Longitudinal = interest in study over time
•
‘Panel’ and ‘cohort’
samples
–
recontact an initially random sample
–
Problems of
attrition
•
Retrospective sample
–
Rely on recall evidence of random selection
–
Problems of
selective recall
30
Issues in random sampling
•
Only as good as underlying
sampling
frame
(a good one may not be available, or
not be as good as we think)
•
Data analysis methods need adapting for
stratified / clustered designs
•
Other survey factors
interact
with sample
selection issues,
eg poor interviewers may
discourage certain cases from response
31
2.2) Opportunistic sampling
•
More often in social research, sample
design is ‘opportunistic’ (‘purposive’)
–
Random sampling is expensive
–
Random sampling is complex
–
Some purported random samples are actually
purposive (understanding of ‘random’)
32
2.2a Quota sampling
•
Fill up quota’s of groups of interest
•
Quota’s can ensure:
–
overall representation (cf systematic)
–
broad topic coverage (eg types of voter)
•
Example: market researchers in street;
telephone call centres vetting contacts
•
Biasses: issues in how a quota ‘fills up’
33
2.2b Snowball sampling
•
Also
‘focussed enumeration’
•
Technique for contacting cases from
populations
rare / difficult to access
•
Ask first obtained contact for suggested
further contacts
snowball gathers size…
•
Eg
–
smaller ethnic minority groups
•
Problem: social networks are non

random!
34
2.2c Convenience sampling
•
Samples whatever cases from population
were
easiest to reach
, eg personal contacts
•
Often no other sampling strategy involved
•
Biasses likely
in convenience process
•
Examples: …most social research survey
examples are ‘convenience’..!
35
Random v’s Opportunistic
•
Random difficult and expensive
–
mainly
government funded resources
•
Most people who have conducted a survey
have conducted an opportunistic one
•
Much
data analysis / inference assumes
random sample
, but not applied to
•
But
opportunistic data is often robust…
36
More on sampling methods
•
Refs for sampling methods / properties: eg
Gilbert 2001 chpt5; De Vaus 2001 chpt6;
Bryman 2001 chpt4.
•
Research reports: most important is
documentation of sampling process / issues
–
To be open about research
–
To consider unintentional mistakes
37
Summary on sampling methods
•
Good sampling not a panacea
–
Other elements of surveying equally crucial
•
Many
statistical methods assume random
sample
•
For good sampling, use
secondary data..
•
All samples have
some
value
, but non

random ones need careful context
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