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Lecture 2 - PowerPoint PPT Presentation

Survey Methodology Sampling. EPID 626 Lecture 2. What is sampling?. Population: The collection of all possible measurements that could be used to address the study question. Sample: (v.) To select a small subset of a population representative of the whole population. (Fowler, 1993).

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Survey MethodologySampling

EPID 626

Lecture 2

• Population: The collection of all possible measurements that could be used to address the study question.

• Sample: (v.) To select a small subset of a population representative of the whole population.(Fowler, 1993)

Critical sampling issues nearly all) population members the same (or a known) chance of being sampled, and to use probability methods for choosing the sample.

• Whether or not to use a probability sample

• The sample frame (those who actually have a chance to be sampled)

• The size of the sample

Critical sampling issues (con’t) nearly all) population members the same (or a known) chance of being sampled, and to use probability methods for choosing the sample.

• The sample design (the particular strategy used for sampling people or household)

• The rate of response (the percentage of those sampled for whom data are actually collected)(Fowler, 1995)

Sample frame nearly all) population members the same (or a known) chance of being sampled, and to use probability methods for choosing the sample.

• The set of people that has a chance to be selected, given the sampling approach that is chosen.

• Question: How well does the sample frame correspond to the population you want to describe?(Fowler, 1993)

Examples of sampling frames nearly all) population members the same (or a known) chance of being sampled, and to use probability methods for choosing the sample.

• List of registered drivers in Louisiana

• List of patients who have been treated at a clinic in the past year

• Greater New Orleans residential phone listing

• List of all public schools in Virginia

Here is our sampling scenario nearly all) population members the same (or a known) chance of being sampled, and to use probability methods for choosing the sample.

• Population: Roosevelt High School studentsN=99

• Sampling frame: List of students, numbered 01-99

• Desired sample size: n=33

Sampling strategies nearly all) population members the same (or a known) chance of being sampled, and to use probability methods for choosing the sample.

• One-stage sampling

• Simple random sampling

• Systematic sampling

• Stratified sampling

• Multi-stage sampling

• Area probability sampling

Simple random sampling nearly all) population members the same (or a known) chance of being sampled, and to use probability methods for choosing the sample.

• Each member of the study population has an equal probability of being selected.

• Analogous to drawing a number from a hat.

• Each sample is sampled from the sampling frame one at a time, independent of one another, and without replacement.

Simple random sampling nearly all) population members the same (or a known) chance of being sampled, and to use probability methods for choosing the sample.

• We do it by numbering the sample frame, then using a computer, a table of random numbers, or another random generator to randomly choose observations from the list.

Systematic random sample strategy nearly all) population members the same (or a known) chance of being sampled, and to use probability methods for choosing the sample.

• Each member of the study population is listed, a random start is designated, then members of the population are selected at equal intervals.(Henry, 1990)

Systematic random sampling nearly all) population members the same (or a known) chance of being sampled, and to use probability methods for choosing the sample.

• Select a random start between 0 and i

• Select every ith person

• Cautionary note about ordered lists

Roosevelt systematic random sampling nearly all) population members the same (or a known) chance of being sampled, and to use probability methods for choosing the sample.

• i=99/33=3

• (Round down if i is not an integer)

• Select a random start from 1 to 3

• Select every 3rd student from the random start

• So if start is 2, select 2, 5, 8 etc.

Stratified sampling strategy nearly all) population members the same (or a known) chance of being sampled, and to use probability methods for choosing the sample.

• Each member of the study population is assigned to a group or stratum, then a simple or systematic random sample is selected from each stratum.

• This reduces normal sampling variation and ensures that the sample reflects the total population with regard to the stratifying variable.

Stratified Disproportionate Sampling nearly all) population members the same (or a known) chance of being sampled, and to use probability methods for choosing the sample.

• Can oversample a stratum with high variability to increase the precision of an estimate

• Oversample a particular stratum to increase the n for the subpopulation without a corresponding increase in the total N.

• Important to weight data accordingly for analysis

Disproportionate Sampling nearly all) population members the same (or a known) chance of being sampled, and to use probability methods for choosing the sample.