1. Sampling
Lecture 10

2. Readings are good.for.us
Going forward, most readings are in Freedman et al. textbook
It is essential that you have read the material before class, otherwise I cannot tell what really needs emphasis and what is just obvious from the reading.
Freedman text is probably best I have used; It is especially good and I don’t want to waste your time! More than we can talk about in one semester.

3. Non-probability and Probability Sampling
Non-probability Sampling
Probability Sampling
Probability Distributions
Info 271B

4. Why Sample? Drawing populations versus ‘samples’ Reducing Error
E.g., survey of iSchool masters students
Reducing Error
Consider Bernard’s example of interviewing all doctors in a hospital
Difference between a census and survey?
Info 271B

5. A Few Key Concepts: Units of Analysis (elements, cases, participants)
Selected Sample
Actual Sample
Sampling Design
Selected
Actual
Selected: who we target
Actual: who responds

6. Method Matters…
Method of choosing sample and “planned introduction of chance”
Literary Digest Poll revisited
The problem of bias
May not be detectible in the valid data
If sampling method is biased, larger sample will not help

7. Design: Non-Probability Sampling
Quotas
Pick key groups of interest and find individuals to fill specific goals (i.e., 100 people in each key group).
Quotas are fulfilled without using random sampling
Purposive Sampling
Find key groups and only study them
TWO requirements of random sampling (and one or both are violated in non-probability sampling):
Researcher has no discretion over who is included
The procedure for selecting sample is definite and it involves planned use of chance
Purposive Sampling: that guy at Safeway. But also in a lot of field research
Info 271B

8. More Non-Probability Sampling
Convenience Sampling
Taking anyone you can get to participate
Snowball Sampling
Find starting point and use these individuals to get next participant…and so on
Info 271B

9. “Judgment and choice usually show bias, while chance is impartial
“Judgment and choice usually show bias, while chance is impartial. That is why probability methods work better than judgment”
Freedman et al., p. 342
So why probability???

10. Design: Probability Sampling
Sampling Frames
A list of units of analysis from which you take a sample
Directories, local census, registered users of an online system, etc
But, often cannot get an adequate sampling frame
Field research
Question: how does sampling frame relate to generalizability?
Info 271B

11. Randomized Samples Simple Random Sample
Requires numbering all potential participants in a given sampling frame (N)
Pull random numbers from any source, use these as the sample (n)
Select n units out of N such that each NCn has and equal chance of being selected.
Issues…
True randomization?
Replacement “in the field”
Simple random sampling = drawing without replacement
True Randomization -> How do you insure that you are truly being random?
Replacement Issues-> Example of door-to-door sampling (homogeneity of replacement)
Info 271B

12. Systematic Random Samples
Random start and sampling interval
(Sample x Interval = Population)
Issues
Periodicity
Poor sampling frames
Why Use Systematic Random Samples?
- Do not have ability (or resources) to literally number the population
Divide total sample frame (N) by the sample you want (n), which gives you the sampling interval
PERIODICITY
-Occurs when there is a systematic difference that occurs throughout the sample (i.e., the sample frame isnt random) (
Info 271B

13. Bias! From voter survey examples alone…
Nonvoters…Undecided…Response bias…Non-response bias…Checking the data…Interviewer control
The point is not that bias is everywhere in sampling. The point is that we have to anticipate it, find it even when we didn’t anticipate it, and deal with it.

14. Chance Error Estimate = parameter + bias + chance error
Chance error = sampling error: we expect this! It happens in all samples
Bias = non-sampling error: we try to do everything possible to avoid it, kill it, stamp it out.

15. How Large of a Sample? Sample Accuracy versus Sample Precision
Key Issues for Determining Sample Size:
Heterogeneity of Population
Number of Subgroups
Size of Subgroup
Precision of sample statistics
Sample Accuracy = Every element has an EQUAL chance of being selected
Sample Precision = Increasing size of an unbiased sample
Heterogeneity: How much do people differ on variables of interest?
Number of subgroups and size of them: (SEE NEXT 2 SLIDES)
Info 271B

16. Sampling (no subgroups): Attitudes About the President (1-5 Likert Scale)
Suppose we want to know the mean about attitudes re: president…

17. SaDo men and women differ in their assessment of the US president?
But what happens when we want to split into sub-groups?

18. Stratified Random Sampling
Key Issue: Representation of salient sub-populations
Maximize between-group variance while minimizing within-group variance
Proportionate Samples
Do you know the key independent variables?
If not, may be better off avoiding stratification
Disproportionate Samples
Weighting
Info 271B

19. Complex Sampling Designs
Cluster Sampling
No available frames
Based on areas, institutions, or ‘clusters’
Randomly select clusters, then sample from those clusters (or, sample the entire cluster)
Info 271B