Transcript measurement: general principles

SAMPLE DESIGN: WHO WILL BE
IN THE SAMPLE ? (CONTINUED)
Lu Ann Aday, Ph.D.
The University of Texas
School of Public Health
COMBINED DESIGNS
A. Area probability sample design
(example: PPS)
B.
Random digit dialing (RDD)
C.
List sample
PROBABILITY PROPORTIONATE TO SIZE (PPS) –
EXAMPLE (Aday & Cornelius, 2006, Table 6.2)
STEPS
EXAMPLE
1. Estimate the desired sample size (n).
77
2. Fix the desired cluster size (nc).
7
3. Calculate the number of clusters (c ) needed to achieve the desired sample size: n/nc.
77/7 = 11
4. Estimate the total number of units in the universe from which the sample will be drawn (N).
2200
Col. B, Table
5. Calculate the cumulative total of the number of units across all clusters in the universe.
Col. C, Table
6. Calculate the sampling interval (k) for selecting clusters for the universe: N/c.
2200/11=200
7. Pick a random starting point (r ) to select clusters within the designated sampling interval
(Step 6), using a random numbers table.
50
8. Calculate the selection numbers (HU #) for the blocks to be sampled by entering the random
starting point, adding the sampling interval, and then repeat the process to identify sampled
blocks.
Col. D, Table
9. Assign cluster numbers to each designated block.
Col. E, Table
10. Confirm % in strata for sample agree with % in universe.
Col. B, E (%),
Table
RANDOM DIGIT DIALING:
WAKSBERG-MITOFSKY DESIGN
Target population: residents of the State of California
STAGE 1
1. Implement random or systematic selection of area code-central
office code combinations for area: (xxx) xxx.
2. Add two random digits to each area code-central office code
combination.
3. Prepare list of possible 8 digit numbers, which become PSUs
with clusters of 100 numbers each: (xxx) xxx-xx00 thru xx99.
4. Assign last two digits of the number randomly, such as (xxx)
xxx-xx24.
5. Dial the resulting number.
6a. Eligible household number—complete the interview. Retain
PSU of 100 numbers.
6b. Ineligible household number—terminate the interview.
Eliminate the PSU of 100 numbers from further calls.
RANDOM DIGIT DIALING:
WAKSBERG-MITOFSKY DESIGN
(cont.)
Target population: residents of the State of California
STAGE 2
7. Randomly assign two new digits to end of cluster of
numbers for same or new PSU (as appropriate).
8. Repeat process until desired sample size is reached.
LIST SAMPLE
Target population: U.S. dentists in active practice
STAGE 1: Identify eligible dentists.
STAGE 2:
1. Determine sampling fraction.
2. Draw systematic random sample of eligible
dentists.
SAMPLING RARE POPULATIONS




Screening- Ask respondents whether they/household have
the attribute of interest and drop those from sample that do
not.
Disproportionate sampling- Assign a higher sampling fraction
to stratum that has attribute of interest.
Network sampling- Ask the respondents if they know others
in family network (defined in certain way) who have attribute
of interest.
Dual frame sampling- Use a second sampling frame
containing elements with attribute of interest to supplement
original frame.
PROCEDURES FOR SELECTING THE
RESPONDENT: SELECTION TABLES

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Kish tables- Ask about all potentially eligible
individuals in the household, list them and then
use Kish tables.
Troldahl-Carter-Bryant (TCB)- Ask how many
persons live in the household, how many of them
are women, and then use TCB selection charts.
PROCEDURES FOR SELECTING THE
RESPONDENT: RESPONDENT
CHARACTERISTICS


Hagan and Collier Method- Ask to speak with one
of four types of age-sex individuals and if no one of
that gender, ask for counterpart of opposite gender
(youngest adult male/youngest adult
female/oldest adult male/oldest adult female).
Last/Next Birthday Method- Ask to speak with the
person who had a birthday last or who will have
one next.
SURVEY ERRORS: Deciding
Who Will Be in the Sample
Solutions
to errors
Systematic Errors
Variable
Errors
Noncoverage bias
(frame bias)
Noncoverage bias
(respondent
selection bias)
Design effects
Match the sample
frame to the target
population.
Employ methods for
randomly selecting
the study
respondents.
Try to balance the
complexity (especially
the cluster nature) of
the sample design
needed to address the
study objectives in
relationship to survey
costs.
Use multiple sample
frames, if needed, to
more fully capture
the target
population of
interest.
REFERENCES

Bennett, S., Woods, T., Liyanage,
W.M., & Smith, D.L. (1991). A
simplified general method for
cluster-sample surveys of health in
developing countries. World
Health Statistics Quarterly 44: 98106.