Survey Methodology Sampling

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## Survey Methodology Sampling

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**Survey MethodologySampling**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)**It is assumed that only chance could cause the composition**of the sample to differ from the composition of the population in all aspects other than the quantity of data contained in each. (Hirsch and Riegelman, 1995)**The key to good sampling is finding a way to give all (or**nearly all) population members the same (or a known) chance of being sampled, and to use probability methods for choosing the sample.(Fowler, 1993)**Critical sampling issues**• 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)**• 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**• 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**• 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**• Population: Roosevelt High School studentsN=99 • Sampling frame: List of students, numbered 01-99 • Desired sample size: n=33**Sampling strategies**• One-stage sampling • Simple random sampling • Systematic sampling • Stratified sampling • Multi-stage sampling • Area probability sampling**Simple random sampling**• 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**• 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**• 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**• Determine your interval: i=N/n • Select a random start between 0 and i • Select every ith person • Cautionary note about ordered lists**Roosevelt systematic random sampling**• 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**• 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**• 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