Sampling Chapter 5

Introduction Sampling The process of drawing a number of individual cases from a larger population A way to learn about a larger population by obtaining information from a subset of a larger population

Why Sample? To learn something about a large group without having to study every member of that group Time and cost Studying every single instance of a thing is impractical or too expensive

Why Sample? Improve data quality Obtain in-depth information about each subject rather than superficial data on all

Why Sample? We want to minimize the number of things we examine or maximize the quality of our examination of those things we do examine.

Why Sample? When is sampling unnecessary? The number of things we want to sample is small Data is easily accessible Data quality is unaffected by the number of things we look at

Why Sample? Elements A kind of thing the researcher wants to look at

Why Sample? Population The group of elements from which a researcher samples and to which she or he might like to generalize

Why Sample? Sample A number of individual cases drawn from a larger population

Sampling Frames, Probability versus Nonprobability Samples Target population A population of theoretical interest

Sampling Frames, Probability versus Nonprobability Samples Sampling frame or study population The group of elements from which a sample is actually selected

Sampling Frames, Probability versus Nonprobability Samples Nonprobability Samples A sample that has been drawn in a way that doesn’t give every member of the population a known chance of being selected

Sampling Frames, Probability versus Nonprobability Samples Probability Samples A sample drawn in a way to give every member of the population a known (nonzero) chance of inclusion Probability samples are usually more representative than nonprobability samples of the populations from which they are drawn

Sampling Frames, Probability versus Nonprobability Samples Biased Samples A sample that is not representative from the population which it is drawn Probability samples are LESS likely to be biased samples

Sampling Frames, Probability versus Nonprobability Samples Generalizability The ability to apply the results of a study to groups or situations beyond those actually studied A probability sample tends to be more generalizable because it increases the chances that samples are representative of the populations from which they are drawn.

Sources of Error Associated with Sampling Types of Survey Error – due to sampling Coverage Error Nonresponse Error Sampling Error

Sources of Error Associated with Sampling Coverage Errors Errors that results from differences between the sampling frame and the target population

Sources of Error Associated with Sampling Coverage Errors People are typically left out, if samples are drawn from phone books, car registrations, etc… Unlisted Phone Numbers – one of the greatest potentials for coverage error  Pollsters use random digit dial to avoid unlisted numbers  Random-digit dialing  A method for selecting participants in a telephone survey that involves randomly generating telephone numbers

Sources of Error Associated with Sampling Coverage Errors Parameter A summary of a variable characteristic in a population

Sources of Error Associated with Sampling Coverage Errors Statistic A summary of a variable in a sample

Sources of Error Associated with Sampling Nonresponse Error Errors that result from differences between nonreponders and responders to a survey

Sources of Error Associated with Sampling Sampling Error Any difference between the characteristics of a sample and the characteristics of the population from which the sample is drawn

Sources of Error Associated with Sampling Sampling Error Sampling Variability The variability in sample statistics that occurs when different samples are drawn from the same population

Sources of Error Associated with Sampling Sampling Error Simple Random Sample A probability sample in which every member of a study population has been given an equal chance of selection

Sources of Error Associated with Sampling Sampling Error Sampling Distribution The distribution of a sample statistic computed from many samples

Sources of Error Associated with Sampling Sampling Error Margin of error Suggestion of how far away the actual population parameter is likely to be from the statistic

Types of Probability Sampling Simple Random Sampling Systematic Sampling Stratified Sampling Cluster Sampling Multistage Sampling

Types of Probability Sampling Simple Random Sampling

Types of Probability Sampling Systematic Sampling A probability sampling procedure that involves selecting every kth element from a list of population elements, after the first element has been randomly selected

Types of Probability Sampling Stratified Sampling A probability sampling procedure that involves dividing the population in groups or strata defined by the presence of certain characteristics and then random sampling from each stratum

Types of Probability Sampling Stratified Sampling Steps to draw a stratified random sample 1. Group the study population into strata or into groups that share a given characteristic 2. Enumerate each group separately 3. Randomly sample within each strata

Types of Probability Sampling Cluster Sampling A probability sampling procedure that involves randomly selecting clusters of elements from a population and subsequently selecting every element in each selected cluster for inclusion in the sample Cluster sampling is an option if data collection involves visits to sites that are far apart

Types of Probability Sampling Multistage Sampling A probability sampling procedure that involves several stages, such as randomly selecting clusters from a population, then randomly selecting elements from each of the clusters

Types of Nonprobabilty Sampling Purposive Sampling Quota Sampling Snowball Sampling Convenience Sampling

Types of Nonprobability Sampling Purposive Sampling A nonprobability sampling procedure that involves selecting elements based on a researcher's judgment about which elements will facilitate his or her investigation

Types of Nonprobability Sampling Quota Sampling A nonprobability sampling procedure that involves describing the target population in terms of what are thought to be relevant criteria and then selecting sample elements to represent the “relevant” subgroups in proportion to their presence in the target population

Types of Nonprobability Sampling Snowball Sampling A nonprobability sampling procedure that involves using members of the group of interest to identify other members of the group

Types of Nonprobability Sampling Convenience Sampling A nonprobability sampling procedure that involves selecting elements that are readily accessible to the researcher Sometimes called an available-subjects sample

Choosing a Sampling Technique Is it desirable to sample at all or can the whole population be used? Is it important to generalize to a larger population? Do you have the access and ability to perform probability sampling? Major considerations Methods Theory Practicality Ethics

Summary Sampling is a means to an end. We sample because studying every element in our population is frequently beyond our means or would jeopardize the quality of our. On the other hand, we don’t need to sample when studying every member of our population is feasible.

Quiz – Question 1 Why does the Census use the full population and not use a sample?

Quiz – Question 2 In the case of presidential elections in the United States the population is ________ and the elements of this population are _________.

Quiz – Question 3 The local television station conducted a study of TV viewers in the local viewing region. A list of all residential customers who subscribed to cable TV was obtained from the cable company. The list had 200,000 households as subscribers. The TV station samples every 40 th household on the subscriber list. An interviewer visited each household and conducted the survey on viewing habits of household members. What is the sampling frame of the study?

Quiz – Question 4 You want to draw a sample of the employees at a large university ensuring that in your sample you have people represented from all personnel categories including administrators, faculty, secretarial staff, cleaning staff, mail room staff, technicians, and students. What type of probability sample would be best?