chapter 7- research methods

President Thomas E. Dewey

George Gallup had used quota sampling to predict earlier presidential winners. Because of the massive changes in the population following WW II, the quotas were no longer accurate, and he incorrectly predicted that Thomas Dewey would defeat the incumbent, Harry Truman

President Alf Landon

Literary Digest conducted a poll to determine the winner of the 1936 presidential election and predicted that Alf Landon would unseat President Roosevelt. They overrepresented wealthy voters who favored Landon, and thus their prediction was incorrect.

Confidence levels and confidence intervals

a confidence interval provides an estimate of the range around the population parameter, while a confidence level indicates as a percentage how confident the researcher is that the confident interval captures the true population parameter.

Representativeness and probability of selection

a representative sample is one that is similar to the population of interest on the characteristics the researcher is interested in. Probability of selection means that each member of the population has a known probability of being selected in the sample.

Conscious and subconscious sampling bias

a sample is biased if it does not represent the population it purports to represent. A researcher may knowingly ignore certain segments of the population (conscious bias) or may fail to consider the actual characteristics and behaviors of the population, which could result in subconscious bias.

Probability proportionate to size (PPS) sampling

because certain primary sampling units have more elements than others, the elements in those large units ultimately has a smaller chance of being selected. Probability proportionate to size sampling weighs each primary sampling unit by the number of elements it has.

Multistage designs and sampling error

because multistage cluster design involves multiple probability samples, the sampling error for the final is compounded by the number of samples drawn.

An illustration: sampling university students

describes the process of sampling university studies.

Simple random sampling

each element of the population is assigned a number, and a table of random numbers (now typically computers perform this task) is used to generate a random sample.

Sample selection

employs a systematic sample to select students.

The sampling distribution of ten cases

example of drawing a sample ten times and examining the results of those ten samples. Illustrates how sampling distributions work.

Random selection

in random selection, each member or element of the population has the same chance of being sampled; the specific characteristics of the elements do not affect selection.

Implicit stratification in systematic sampling

in systematic sampling, if the list is arranged in some meaningful way, then it is already stratified and the resulting sample will be improved.

Quota sampling

in this sampling strategy the researcher knows the characteristics of the population he or she wishes to sample. The researcher then selects subjects that represent the population.

Systematic sampling

in this sampling strategy, the elements are arranged in a list and every nth element from the list is selected. N is estimated by dividing the size of the population by the size of the desired sample.

Stratification in multistage cluster sampling

it is possible to stratify the primary sampling units by important characteristics and then draw a sample from each subunit, just as in a simple stratified sample.

Two type of sampling methods

nonprobability and probability sampling are the two major types of sampling strategies available to sociologists. Each has its own particular advantages and disadvantages.

Sample modification

occasionally the sample must be altered due to unforeseen circumstances, such as budget shortfalls

Selecting informants

often in field research, sociologists rely on a few individuals as a source of data on the group, organization, or social phenomenon that is being examined. Because informants provide much of the information in the study, they must be trustworthy and knowledgeable.

Probability theory, sampling distributions, and estimates of sampling error

probability theory allows researchers to estimate how close to the population their sample is on a given dimension. It is based on sampling distributions, which tell the given estimate of a population had a large number of samples been taken.

Sampling distributions and estimates of sampling error

sampling distributions allow the sociologists to calculate the sampling error; the amount of error made when trying to estimate a measure of the population using a sample

Snowball sampling

snowball sampling uses subjects as a way to identify other potential subjects to be included in the sample; it is especially useful for studying populations whose members are difficult to locate.

Stratified sampling

sociologists can increase the accuracy of their sample by dividing up the population into relevant subunits, and then selecting a random sample from each subunit, based on the portion of the population the subunit encompasses.

Disproportionate sampling and weighting

taking one step further, it is possible to overweight certain primary units if they are of special interest to the researcher.

Study population and sampling frame

the first step is defining the group of students that the researcher is interested in drawing conclusions about (the study population) and then identifying all potential students to be included in the sample (the sampling frame).

Purposive or judgmental sampling

this sampling strategy entails identifying a small subset of the population the researcher is interested in and then sampling those subjects.

Reliance on available subjects

this sampling strategy uses people (or groups, organizations, or social artifacts) that are readily accessible to the researcher

Stratification

what criterion should be used to stratify the sample.