GSSR
Research Methodology and Methods of Social Inquiry
www.socialinquiry.wordpress.com
November 29, 2010
Representation of Units of Analysis. Sampling
Population & Sample
Population:
everybody whom we want to generalize to.
There are no strict rules to follow, and the researcher must
rely on logic and judgment.
The population is defined in keeping with the objectives of
the study.
Census study: data are gathered on every member of the
population.
Parameters: If you measure the entire population and
calculate a value like a mean or average, we don't refer
to this as a statistic, we call it a parameter of the
population
Problems: time-consuming; expensive.
Sampling
The sample should reflect the characteristics of the
population from which it is drawn.
Sampling Frame: the listing of the (reachable) population
from which we draw the sample.
Sample: the group of people who we select to be in our
study.
Statistic: When we look across the responses that we get
for our entire sample, we use a statistic
- nonrespondents; dropouts.
Sampling methods: the method by which units of
observation are selected
random (probability) or non-random sampling.
Random Sampling
Basic requirements of a random sample:
Every case has:
1) the same chance of being selected
2) the chance of selection does not change (i.e. constant
probability).
The case that is selected (theoretically) is put back in the pot =
“sampling with replacement”.
- allows calculating the probability that we have
represented the population well.
- automatically eliminates selection bias in large N studies.
Basic Terms
N = number of cases in the sampling frame
n = number of cases in the sample
NCn = number of combinations (subsets) of n from N
f = n/N = the sampling fraction
Simple random sampling
Stratified random sampling (proportional/quota random
sampling)
Systematic random sampling
Cluster (Area) random sampling
Random route sampling
Multi-stage random sampling
See www.socialresearchmethods.net/kb/sampprob.php
Nonrandom Samples
- selected cases do not have an equal chance of
selection (some people have a greater, but
unknown, chance than others to be selected)
Convenience sampling
Purposive sampling
- likely to overweight subgroups in the population that are
more readily accessible.
Modal Instance Sampling
- sampling the most frequent case (i.e. "typical" case)
Expert Sampling
- sample of persons with known or demonstrable
experience and expertise in some area: "panel of
experts;"
- often, used in combination with modal sampling, as
means of validation of the former.
Quota Sampling
- non-random selection of people according to some fixed
quota.
Proportional quota sampling: want to represent the major
characteristics of the population by sampling a proportional
amount of each.
Ex: Population has 40% women, 60% men. We want N = 100.
Problem: decide the specific characteristics on which to base the
quota.
Non-proportional quota sampling: specify the minimum no. of
sampled units we want in each category.
Goal: have enough cases to assure that you will be able to talk
about even small groups in the population.
- non-probabilistic analogue of stratified random sampling (i.e.
used to assure that smaller groups are adequately
represented in the sample).
Heterogeneity (diversity) Sampling
- want to include all opinions or views, without concern
about representing these views proportionately.
Ex: brainstorming (including concept mapping)
Snowball Sampling
- begin by identifying someone who meets the criteria for
inclusion in your study; then ask them to recommend
others who they may know who also meet the criteria;
Respondent Driven Sampling (RDS)
- combines "snowball sampling" with a mathematical
model that weights the sample to compensate for the
fact that the sample was collected in a non-random way.
Heckathorn, Douglas D. 1997." Respondent-Driven Sampling: A New
Approach to the Study of Hidden Populations." Social Problems.
http://www.respondentdrivensampling.org/
Selection Bias
-
distortion of analysis, resulting from the method of collecting the
-
sample;
can occur at various(multiple) points in the research design
Selecting on the DV
- allow for the possibility of at least some variation on DV!
If we do not take into account other instances when DV takes other
values, we can learn nothing about the causes of the DV.
King’s discussion of Porter’s (1990) cross-national work on competitive
advantage for contemporary industries and firms (see p. 133-134 in
King et al. 1994)
Selection on the DV is general difficulty for researchers using historical
records as data source: History differentially selects what it ‘keeps’
according to a set of rules that are not always clear from the record.
Ex: survival of sculpture in stone vs. sculpture in wood;
Indeterminate Research Designs
Research design: plan that shows, through the discussion
of the causal model (theoretical) and the data, how we
expect to make inferences.
A research design should not be indeterminate!
1. More Inferences than Observations (units of
observations/cases)
Rule: One fact (observation) cannot give independent
information about more than another fact.
Ex: a study with 1 observation (units), and 2 causal (IV)
variables, cannot determine which, if any of the
hypotheses is correct.
Observations
(cases)
1
Variables
X1
X2
Y (DV)
3
5
35
Y = expected value of the DV
Regression eqation: Y = X1*B1 + X2*B2 + e
Prediction equation E(Y) = X1*B1 + X2*B2
Where E(Y) = expected value of Y; here E(Y) = 35.
In practice, we never know this, because of the
randomness inherent in Y (i.e. because of e)
Observations
(cases)
1
Variables
X1
X2
Y (DV)
3
5
35
E(Y) = X1*B1 + X2*B2
This equation has no unique solution.
B1 = 10, B2 =1;
B1 = -10, B2 = 13
35 = 3* B1 + 5*B2
DV: successful joint collaboration on capital-defense
projects (high-tech weapon system) King et al.. P. 119-122
Observations
(cases)
1: Countries A
&B
2: Countries
C&D
3: Countries
D&E
Variables
Geographical
proximity
Frequency
Of
Negotiations
Ec.
Resources
Type of
pol.
regime
Existence
of
other coop
...
…
Final product
(cooperation or
not)