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# Survey Methodology Sampling, Part 2 - PowerPoint PPT Presentation

Survey Methodology Sampling, Part 2. EPID 626 Lecture 3. Random digit dialing. Delineate the geographic boundaries of the sampling area Identify all of the exchanges used in the geographic area Identify the distribution of prefixes with the sampling area

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### Survey MethodologySampling, Part 2

EPID 626

Lecture 3

• Delineate the geographic boundaries of the sampling area

• Identify all of the exchanges used in the geographic area

• Identify the distribution of prefixes with the sampling area

• Example: There may be 8 exchanges, but you may find that 3 of them are used for nearly two-thirds of residential lines.

• You may stratify based on the distribution of prefixes

• Ex. Take more samples of the 3 exchanges that account for the most residential lines

• Try to identify vacuous suffixes

• These are suffixes not yet assigned or assigned in large groups to a business

• Usually consider suffixes in 100s

• ex. 0000-0099, 0100-0199

• May randomly select the four-digit suffixes

• ex. use a random-numbers table

• Alternatively, you may use a plus-one approach

• When you reach residence, use the number as a seed, and add fixed digits (one or two) to get the next sample

• Provides a nonzero chance of reaching any household within a sampling area that has a telephone line regardless of whether the number is listed

• Is the probability of reaching every household equal?

• No. Households with more than one phone line will have a greater probability than households with one phone line.

• Adjust for unequal probability by weighting

• Advantages: Inexpensive and easy to do

• Disadvantages: 1. Large number of unfruitful calls2. Will exclude individuals without phones3. May be difficult to ascertain geographic area

• Each member of the study population is assigned to a group or cluster, then clusters are selected at random and all members of a selected cluster are included in the sample

• Clusters are often naturally-occurring groupings such as schools, households, or city blocks (Henry, 1990)

• Underlying concept is similar to cluster sampling

• Clusters are selected as in the cluster sample, then sample members are selected from the cluster members by simple and random sampling

• Clustering may be done at more than one stage (Henry, 1990)

• Assume 20,000 students; 40 schools

• Desired sample size=2,000 students (i=10)

• Select all schools, list all students, and select 1/10 of students (SRS)

• Select 1/2 of schools, then select 1/5 of students (multistage)

• Select 1/5 of schools, then select 1/2 of students (multistage)

• Select 1/10 of schools, then collect data on all students in those schools (cluster)

• Geographic basis

• Divide a total geographic area into exhaustive, mutually exclusive subareas

• Sample subareas

• List all housing units within selected subareas

• Sample from list or collect data from entire list (Fowler, 1993)

• City has 400 blocks with a total of 20,000 housing units

• Sample size is 2,000 housing units (i=10)

• Sample 80 blocks, list housing units, then sample 1/2 of the housing units

• Sample 40 blocks, list housing units, then sample all of them

• Choose a cluster size for the last stage of sampling (for example, 10 housing units)

• Estimate the number of housing units on each block

• Order the blocks so that similar ones are contiguous

• Create a cumulative count of housing units

• Determine the interval between clusters (I=i*cluster size)

• Choose a random start between 1 and I.

• Proceed through cumulative count, selecting every Ith block

• List units on the selected block and select cluster size (ex. 10)(Fowler, 1993)

Respondent selection block was wrong?

• Once you have selected a housing unit, how do you select the individual respondent?

• Who is the best person to provide the desired information?

• For self-reporting surveys, we use probability sampling to select the respondent.

• Convenience: select cases based on their availability for the study

• Most similar/dissimilar cases: select cases that are judged to represent similar conditions or, alternatively, very different conditions

• Typical cases: select cases that are known beforehand to be useful and not to be extreme

• Critical cases: select cases that are key or essential for overall acceptance or assessment

• Snowball: group members identify additional members to be included in sample

• Quota: interviewers select sample that yields the same proportions as the population proportions on easily identified variables(Henry, 1990)

Terminology unit

• Universe

• Population

• Survey population

• Sampling frame

• Sampling unit

• Observation unit