The Politics of Rights Douglas Brown Pol Sci 100 March 2009.
The Scientific Study of Politics (POL 51)
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Transcript of The Scientific Study of Politics (POL 51)
The Scientific Study of Politics (POL 51)
Professor B. Jones
University of California, Davis
Today
Sampling Plans Survey Research
Populations
Key Concepts Population
– Defined by the research– “All U.S. citizens age 18 or older.”– All democratic countries– Counties in the United States
Characteristics of a Population– Bounded and definable– If you can’t define the population, you probably don’t have a
well formed research question!
Populations vs. Samples
Populations are often unattainable– TOO BIG (U.S. population)– Very Costly to Obtain– May not be necessary
The beauty of statistical theory
Samples– Simply Defined: a subset of the population
chosen in some manner– How you choose is the important question!
Moving Parts of a Sample
Units of Analysis– J is the population– i is a member of J – Then i is a “sample element”
Sampling Frames– The actual source of the data– Literary Digest Poll (1936)– “Dewey Defeats Truman” (1948)– Exit Polls
More Moving Parts
Sampling Unit– Could be same as sample element (Unit of
Analysis)– But it could be collections of elements (cluster,
stratified sampling)
Sampling Plan– Random? Nonrandom?
Kinds of Samples
Simple Random Sample– Major Characteristic: Every sample element has
an equi-probable chance of selection.– If done properly, maximizes the likelihood of a
representative sample. What if your assumptions of randomness
goes badly? Nonrandom samples (often) produce
nonrepresentative surveys.
Why Randomness is Goodness
Nonprobability Sampling– Probability of “getting into” the sample is unknown– All bets are off; inference most likely impossible– Highly unreliable!
Simple Random Sampling– Every sample element has the same probability of
being selected: Pr(selection)=1/N– In practice, not always easy to guarantee or achieve
An Example of a Bad Assumption
Some Data
y = -7.0524x + 229.42
R2 = 0.7519
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More Data
y = 6.7867x + 90.803
R2 = 0.7005
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Getting Probability Samples Wrong
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Vietnam Draft LotteryLottery Numbers and Deaths by Month of Birth
Lottery Number Deaths
Draft Lottery
Simple random sampling did not exist.– Avg. Lottery Number Jan.-June: 206– Avg. Lottery Number July-Dec.: 161– Avg. Deaths Jan.-June: 159– Avg. Deaths July-Dec.: 111
Differences highly significant. Its absence had profound consequences. Randomness should have ensured an equal chance of draft, invariant
to birth date. It didn’t. By analogy, suppose college admissions were based on this kind of
lottery… Those of you born later in the year would be less likely to be admitted. Would you consider that fair?
How to Achieve Randomness
Random number generation– Modern computers are really good at this. – Assign sample elements a number– Generate a random numbers table– Use a decision rule upon which to select sample.
The Key: sampled units are randomly drawn. Why Important? Randomness helps ensure
REPRESENTATIVENESS! Absent this, all bets are off:
– Convenience Polls– Push Polls– Person-on-the-Street Interviews
A Population and Some “Samples”
A “Population”– Striations represent “attitudes”
Some “Samples”
Other Kinds of Sampling Strategies
Stratified Samples: a probability sample in which elements sharing some characteristic are grouped and then sample elements are randomly chosen from each group.
Benefit? Can ensure more representative sample with smaller sample sizes.
Why might this be the case?
Sampling come to life in…R!!!
Suppose we have a population of 100,000 And in that population, we have 4 groups
– Group 1: 13,000 (13 percent)– Group 2: 12,000 (12 percent)– Group 3: 4,000 ( 4 percent)– Group 4: 70,000 (70 percent)
Racial/Ethnic Characteristics in the US: US Census– White (69.13 percent)– Black (12.06 percent)– Hispanic (12.55 percent)– Asian (3.6 percent)
Some R Code
R
#Creating a population of 100,000 consisting of 4 groups set.seed(535126235)population<- rep(1:4,c(13000, 12000, 4000, 70000))
#Tabulating the population (ctab requires package catspec)
ctab(table(population))
#Tabulating the population (ctab requires package catspec)(btw, not sure why percents are not whole numbers)
ctab(table(population)) Count Total %population 1 13000.00 13.132 12000.00 12.123 4000.00 4.044 70000.00 70.71
Sampling
What do we expect from random sampling? That each sample reproduces the population
proportions. Let’s consider SIMPLE RANDOM
SAMPLES. Also, let’s consider small samples (size 100) …which is a .001 percent sample.
R: 3 samples of n=100
#Three Simple Random Samples without Replacement; n=100 which is a .001 percent sample#The set.seed command ensures I can exactly replicate the simulations
set.seed(15233)srs1<-sample(population, size=100, replace=FALSE) ctab(table(srs1))
set.seed(5255563)srs2<-sample(population, size=100, replace=FALSE) ctab(table(srs2))
set.seed(5255)srs3<-sample(population, size=100, replace=FALSE) ctab(table(srs3))
R: Sample Results
> set.seed(15233)> srs1<-sample(population, size=100, replace=FALSE)> ctab(table(srs1)) Count Total %srs1 1 19 192 13 133 5 54 63 63 > set.seed(5255563)> srs2<-sample(population, size=100, replace=FALSE)> ctab(table(srs2)) Count Total %srs2 1 16 162 8 83 4 44 72 72
> set.seed(5255)> srs3<-sample(population, size=100, replace=FALSE)> ctab(table(srs3)) Count Total %srs3 1 12 122 9 93 1 14 78 78
Implications?
Small samples? Variability in proportion of groups. Why does this occur? Let’s understand stratification. What does it do? You’re sampling within strata. Suppose we know the population
proportions?
R: Identifying Strata and then Sampling from them.
#Stratified Sampling #Creating the Groupings strata1<- rep(1,c(13000)) strata2<- rep(1,c(12000)) strata3<- rep(1,c(4000)) strata4<- rep(1,c(70000)) #Sampling by strata #Selection observations proportional to known population values: Proportionate Sampling set.seed(52524425)
srs4<-sample(strata1, size=13, replace=FALSE) ctab(table(srs4)) set.seed(4244225)srs5<-sample(strata2, size=12, replace=FALSE) ctab(table(srs5)) set.seed(33325)srs6<-sample(strata3, size=4, replace=FALSE) ctab(table(srs6)) set.seed(1114225)srs7<-sample(strata4, size=70, replace=FALSE) ctab(table(srs7))
R: Results? Proportional Sampling w/small samples.
> srs4<-sample(strata1, size=13, replace=FALSE)> ctab(table(srs4)) Count Total %srs4 1 13 100> > set.seed(4244225)> srs5<-sample(strata2, size=12, replace=FALSE)> ctab(table(srs5)) Count Total %srs5 1 12 100> > set.seed(33325)> srs6<-sample(strata3, size=4, replace=FALSE)> ctab(table(srs6)) Count Total %srs6 1 4 100> > set.seed(1114225)> srs7<-sample(strata4, size=70, replace=FALSE)> ctab(table(srs7)) Count Total %srs7 1 70 100
Proportionate Sampling
What do we see? If we know the proportions of the relevant
stratification variable(s)… Then sample from the groups. SMALL SAMPLES can reproduce certain
characteristics of the sample. But of course, it is probabilistic.
Disproportionate Sampling
Why? “Oversampling” may be of interest when
research centers on small pockets in the population.
Race is often an issue in this context.
R: Disproportionate Sampling
> #Sampling by strata> #Selection observations disproportional to known population values: disproportionate Sampling> #"Oversampling by Race" > set.seed(5555425)> srs8<-sample(strata1, size=24, replace=FALSE)> ctab(table(srs8)) Count Total %srs8 1 24 100> > set.seed(4222225)> srs9<-sample(strata2, size=22, replace=FALSE)> ctab(table(srs9)) Count Total %srs9 1 22 100> > set.seed(103325)> srs10<-sample(strata3, size=14, replace=FALSE)> ctab(table(srs10)) Count Total %srs10 1 14 100> > set.seed(11534)> srs11<-sample(strata4, size=70, replace=FALSE)> ctab(table(srs7)) Count Total %srs7 1 70 100>
Disproportionate Samples
What did I ask R to do? I “oversampled” for some groups. Again, understand why we, as researchers,
might want to do this.
Side-trip: Sample Sizes
Who is happy with a .001 percent SRS? On the other hand… What do we get from a stratified sample? Suppose we increase n in a SRS? It’s R time!
R: SRS with a 1 percent sample
> #Sample Size=1000> > set.seed(1775233)> srs1<-sample(population, size=1000, replace=FALSE)> ctab(table(srs1)) Count Total %srs1 1 129.0 12.92 97.0 9.73 46.0 4.64 728.0 72.8> > set.seed(5200563)> srs2<-sample(population, size=1000, replace=FALSE)> ctab(table(srs2)) Count Total %srs2 1 117.0 11.72 127.0 12.73 41.0 4.14 715.0 71.5> > set.seed(52909)> srs3<-sample(population, size=1000, replace=FALSE)> ctab(table(srs3)) Count Total %srs3 1 147.0 14.72 126.0 12.63 39.0 3.94 688.0 68.8>
Implications?
Sample Size MATTERS What do we see? Note, again, what stratification “buys” us. The issues with stratification? Another R example (code posted on website)
R
We have again 4 sample elements > set.seed(52352) > urn<-sample(c(1,2,3,4),size=1000, replace=TRUE) > > ctab(table(urn)) Count Total % urn 1 239.0 23.9 My Population 2 253.0 25.3 3 268.0 26.8 4 240.0 24.0
R version of a person-on-the-street interview
> #Convenience Sample: What shows up> > con<-matrixurn[1:10]; con [1] 1 1 1 3 4 2 4 3 4 3> > ctab(table(con)) Count Total %con 1 3 302 1 103 3 304 3 30
R and Samples, redux
What do we find? Very unreliable sample: we oversample
some groups, undersample others. Useless data more than likely. What do you imagine happens when we
increase the sample sizes?
R and SRS with samples of size N
/*Sample: Sizes 10, 50, 75, 100, 200, 250, 900, 1000*/
set.seed(562)s1<-sample(urn, 10, replace=FALSE)ctab(table(s1))
set.seed(58862)s1a<-sample(urn, 50, replace=FALSE)ctab(table(s1a))
set.seed(562657)s1b<-sample(urn, 75, replace=FALSE)ctab(table(s1b))
set.seed(58862)s2<-sample(urn, 100, replace=FALSE)ctab(table(s2))
set.seed(58862)s3<-sample(urn, 200, replace=FALSE)ctab(table(s3))
set.seed(10562)s4<-sample(urn, 250, replace=FALSE)ctab(table(s4))
set.seed(22562)s5<-sample(urn, 900, replace=FALSE)ctab(table(s5))
set.seed(56882)s6<-sample(urn, 1000, replace=FALSE)ctab(table(s6))
> /*Sample: Sizes 10, 50, 75, 100, 200, 250, 900, 1000*/Error: unexpected '/' in "/"> > set.seed(562)> s1<-sample(urn, 10, replace=FALSE)> ctab(table(s1)) Count Total %s1 1 2 202 4 403 2 204 2 20> > set.seed(58862)> s1a<-sample(urn, 50, replace=FALSE)> ctab(table(s1a)) Count Total %s1a 1 13 262 13 263 13 264 11 22>
Sampling and Sample Size
> > > set.seed(562657)> s1b<-sample(urn, 75, replace=FALSE)> ctab(table(s1b)) Count Total %s1b 1 22.00 29.332 18.00 24.003 22.00 29.334 13.00 17.33> > set.seed(58862)> s2<-sample(urn, 100, replace=FALSE)> ctab(table(s2)) Count Total %s2 1 27 272 24 243 22 224 27 27>
Sample Sizes
> set.seed(58862)> s3<-sample(urn, 200, replace=FALSE)> ctab(table(s3)) Count Total %s3 1 54 272 48 243 48 244 50 25> > > set.seed(10562)> s4<-sample(urn, 250, replace=FALSE)> ctab(table(s4)) Count Total %s4 1 62.0 24.82 67.0 26.83 56.0 22.44 65.0 26.0>
Sample Size
Sample Size
> set.seed(22562)> s5<-sample(urn, 900, replace=FALSE)> ctab(table(s5)) Count Total %s5 1 220.00 24.442 231.00 25.673 234.00 26.004 215.00 23.89> > set.seed(56882)> s6<-sample(urn, 1000, replace=FALSE)> ctab(table(s6)) Count Total %s6 1 239.0 23.92 253.0 25.33 268.0 26.84 240.0 24.0> >
R: What did we learn?
Sample size seems to have some impact here.
But there are trade-offs.
Important Moving Parts
Randomness (covered!) Sampling Frame
– Random sampling from a bad sampling frame produces bad samples.
Sample Size– What is your intuition about sample sizes?
Must they always be large?– Not necessarily so…although…
Bad Sampling
Person-on-the-Street Interviews What do these imply? Small samples and inherently nonrandom Likely poor inference. Other examples? Not all non-random samples are
necessarily bad Purposive Samples