# The Logic of Sampling. Political Polls and Survey Sampling In the 2000 Presidential election, pollsters came within a couple of percentage points of estimating.

## Presentation on theme: "The Logic of Sampling. Political Polls and Survey Sampling In the 2000 Presidential election, pollsters came within a couple of percentage points of estimating."— Presentation transcript:

The Logic of Sampling

Political Polls and Survey Sampling In the 2000 Presidential election, pollsters came within a couple of percentage points of estimating the votes of 100 million people. To gather this information, they interviewed fewer than 2,000 people.

Election Eve Polls - U.S. Presidential Candidates, 2000 DateAgencyGoreBushNaderBuchanan 11/6IDB/CSM474940 11/6CBS484741 11/6CNN/USA Today] 464841 11/6Reuters/ MSNBC 484651 11/6Voter. com 455140 11/7Results48 31

Observation and Sampling Polls and other forms of social research, rest on observations. The task of researchers is to select the key aspects to observe, or sampling. Generalizing from a sample to a larger population is called probability sampling and involves random selection.

Probability Sampling Used when researchers want precise, statistical descriptions of large populations. A sample of individuals from a population must contain the same variations that exist in the population.

Probability Sampling Most effective method for selection of study elements. Avoids researchers biases in element selection. Permits estimates of sampling error.

Populations and Sampling Frames Findings based on a sample of elements that compose a sampling frame. Sampling frames do not always include all the elements their names imply. All elements must have equal representation in the frame.

Types of Sampling Designs Simple random sampling (SRS) Systematic sampling Stratified sampling

Simple Random Sampling Every member of a population has an equal (non-zero) chance of being selected. Feasible only with the simplest sampling frame. Not always the most accurate method available.

Systematic Sampling Requires a good sampling frame (list of elements) Arrangement of elements in the list can result in a biased sample.

Stratified Sampling Rather than selecting sample for population at large, researcher draws from homogenous subsets of the population. –Urban/rural; male/female; race; geographic Results in a greater degree of representativeness by decreasing the probable sampling error.

Multistage Cluster Sampling Used when it's not possible or practical to create a list of all the elements that compose the target population. Involves repetition of two basic steps: listing and sampling. Highly efficient but less accurate.

Probability Proportionate to Size Sampling Used when clusters sampled are of greatly differing sizes (picking a small cluster and missing a large one) For example: If we wanted to do a Multistage cluster sample of Nevada we wouldn’t want to randomly select 50 residents from White Pine and 50 from Clark (people in Clark should have a higher probability of being selected)

Disproportionate Sampling & Weighting Oversampling population to get a large enough N Weighing the sample subgroup to approximate the true population (i.e. multiplying all blacks by 4) Problem is that if the small sample of African-Americans is not representative of the total population, weighing is not going to help

Types of Nonprobability Sampling Reliance on available subjects: Only justified if less risky sampling methods are not possible. Researchers must exercise caution in generalizing from their data when this method is used.

Types of Nonprobability Sampling Purposive or judgmental sampling Selecting a sample based on knowledge of a population, its elements, and the purpose of the study. Can be used when field researchers are interested in studying cases that don’t fit into regular patterns of attitudes and behaviors

Types of Nonprobability Sampling Snowball sampling Appropriate when members of a population are difficult to locate. Researcher collects data on members of the target population she can locate, then asks them to help locate other members of that population.

Types of Nonprobability Sampling Quota sampling Begin with a matrix of the population. Data is collected from people with the characteristics of a given cell. Each group is assigned a weight appropriate to their portion of the population. Data should provide a representation of the total population.

Similar presentations