Chapter Outline  Populations and Sampling Frames  Types of Sampling Designs  Multistage Cluster Sampling  Probability Sampling in Review

Political Polls and Survey Sampling  In the 2004 Presidential election, pollsters generally agreed that the election was “too close to call”.  To gather this information, they interviewed fewer than 2,000 people.

Election Eve Polls - U.S. Presidential Candidates, 2004 Date Begun AgencyBushKerry 10/28Fox/OpinDynamics50 10/28TIPP /28CBS/NYT /28ARG50 10/28ABC /29Fox/OpinDynamics4951

Election Eve Polls - U.S. Presidential Candidates, 2004 Date Begun AgencyBushKerry 10/29Gallup/CNN/USA51 10/29NBC/WSJ /29TIPP /29Harris /29Democracy Crops /29CBS5149

Election Eve Polls - U.S. Presidential Candidates, 2004 Date Begun AgencyBushKerry 10/30Fox/OpinDynamics /30TIPP /31Marist50 10/31 GWU Battleground /2Actual Vote5248

Bush Approval: Raw Poll Data

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 (sample).  Generalizing from a sample to a larger population is called probability sampling and involves random selection.

Nonprobability Sampling  Technique in which samples are selected in a way that is not suggested by probability theory.  Examples include reliance on available subjects as well as purposive (judgmental), quota, and snowball sampling.

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. 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 represent the total population.

Informant  Someone who is well versed in the social phenomenon that you wish to study and who is willing to tell you what he or she knows about it.

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.

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

A Population of 100 Folks  Sampling aims to reflect the characteristics and dynamics of large populations.  Let’s assume our total population only has 100 members.

Sample of Convenience: Easy but Not Representative

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

Representativeness  Representativeness - Quality of a sample having the same distribution of characteristics as the population from which it was selected.  EPSEM - Equal probability of selection method. A sample design in which each member of a population has the same chance of being selected into the sample.

Population  The theoretically specified aggregation of study elements.  Study population - Aggregation of elements from which the sample is actually selected.  Element - Unit about which information is collected and that provides the basis of analysis.

Random selection  Each element has an equal chance of selection independent of any other event in the selection process.

Sampling unit  Element or set of elements considered for selection in some stage of sampling.

Parameter  Summary description of a given variable in a population.

A Population of 10 People with $0–$9

The Sampling Distribution of Samples of 1  In this example, the mean amount of money these people have is $4.50 ($45/10).  If we picked 10 different samples of 1 person each, our “estimates” of the mean would range all across the board.

Sampling Distributions

Range of Possible Sample Study Results  Shifting to a more realistic example, let’s assume that we want to sample student attitudes concerning a proposed conduct code.  Let’s assume 50% of the student body approves and 50% disapproves - though the researcher doesn’t know that.

Results Produced by Three Hypothetical Studies  Assuming a large student body, let’s suppose we selected three different samples, each of substantial size.  We would not expect those samples to perfectly reflect attitudes in the whole student body, but they should come close.

Statistic  Summary description of a variable in a sample.

Sampling Error  The degree of error to be expected of a given sample design.

Confidence Level  The estimated probability that a population parameter lies within a given confidence interval.  Thus, we might be 95% confident that between 35 and 45% of all voters favor Candidate A.  Confidence interval - The range of values within which a population parameter is estimated to lie.

Sampling Frame  That list or quasi list of units composing a population from which a sample is selected.  If the sample is to be representative of the population, it is essential that the sampling frame include all (or nearly all) members of the population.

The Sampling Distribution  If we were to select a large number of good samples, we would expect them to cluster around the true value (50%), but given enough such samples, a few would fall far from the mark.

Review of Populations and Sampling Frames: Guidelines 1. Findings based on a sample represent only the aggregation of elements that compose the sampling frame. 2. Sampling frames do not include all the elements their names might imply. Omissions are inevitable. 3. To be generalized, all elements must have equal representation in the frame.

Simple Random Sampling  Feasible only with the simplest sampling frame.  Not the most accurate method available.

A Simple Random Sample

Systematic Sampling  Slightly more accurate than simple random sampling.  Arrangement of elements in the list can result in a biased sample.

Sampling ratio  Proportion of elements in the population that are selected.

Stratification  Grouping of units composing a population into homogenous groups before sampling.  This procedure, which may be used in conjunction with simple random, systematic, or cluster sampling, improves the representativeness of a sample, at least in terms of the stratification variables.

Stratified Sampling  Rather than selecting sample for population at large, researcher draws from homogenous subsets of the population.  Results in a greater degree of representativeness by decreasing the probable sampling error.

A Stratified, Systematic Sample with a Random Start.

Cluster Sampling  A multistage sampling in which natural groups are sampled initially with the members of each selected group being subsampled afterward.

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 (PPS) Sampling  Sophisticated form of cluster sampling.  Used in many large scale survey sampling projects.

Weighting  Giving some cases more weight than others.

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