chapter 7 the logic of sampling

Question 1

nonprobability sampling

Answer 1

any technique in which samples are selected in someway not suggested by probability theory. Examples include reliance on available subjects as well as. purposive (judgmental), quota, and snowball sampling.

Question 2

Purposive (judgmental) sampling

Answer 2

A type of nonprobability sampling in. which the units to be observed are selected on the basis of the researcher’s judgement about which ones will be the most useful or representative.

Question 3

snowball sampling

Answer 3

A nonprobability sampling method, often employed in field research, whereby each person interviewed may be asked to suggest additional people for interviewing.

Question 4

quota sampling

Answer 4

A type of nonprobability sampling in which units are selected into a sample on the basis of prespecified characteristics, so that the total sample will have the same distribution of characteristics assumed to exist in the population being studied.

Question 5

informant

Answer 5

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. Not to be confused with a respondent.

Question 6

probability sampling

Answer 6

the general term for samples selected in accord with probability theory, typically involving some random-selection mechansim. Specific types of probability sampling include Equal probablity of selection method,PPS, simple random sampling, and systematic sampling.

Question 7

representativeness

Answer 7

that qualtiy of a sample of having the same distribution of characteristics as the population from which it was selected. By implication, descriptions, and explanations derived from an analysis of the sample may be assumed to represent similar ones in the population. Representativeness is enchanced by probability sampling and provideds for generalizability and the use of inferential statistics.

Question 8

EPSEM (equal probability of selection method)

Answer 8

A sample design in which each member of a population has the same chance of being selected into the sample.

Question 9

element

Answer 9

That unit of which a population is composed and which is selected in a sample. Distinguished from units of analysis, which are used in data anaylsis.

Question 10

population

Answer 10

The theoretically specified aggregation of the elements in a study.

Question 11

study population

Answer 11

That aggregation of elements from which a sample is actually selected.

Question 12

random selection

Answer 12

A sampling method in which each element has an equal chance of selection independent of any other event in the selection process.

Question 13

sampling unit

Answer 13

That element or set of elements considered for selection in some stage of sampling.

Question 14

parameter

Answer 14

The summary description of a given variable in a population

Question 15

statistic

Answer 15

The summary description of a variable in a sample, used to estimate a population parameter.

Question 16

sampling error

Answer 16

The degree of error to be expected by virtue of studying a sample instead of everyone. For probability sampling, the maximum error depends on three factors: the sample size, the diversity of the population, and the confidence level.

Question 17

confidence level

Answer 17

The estimated probability that a population parameter lies within a given confidence interval. Thus, we might be 95 percent confident that between 33 and 45 percent of all voters favor. candidate A.

Question 18

confidence interval

Answer 18

The range of values within which a population parameter is estimated to lie.

Question 19

sampling frame

Answer 19

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.

Question 20

simple random sampling (SRS)

Answer 20

A type of probability sampling in which the units composing a population are assigned numbers. A set of random numbers is then generated, and the units having those numbers are included in the sample.

Question 21

systematic sampling

Answer 21

A type of probability sampling in which every Kth unit in a list is selected for inclusion in the sample- for example, every 25th student in the college directory of students. You compute K by dividing the size of the population by the desired sample size; K is called the sampling interval. Within certain constraints, systematic sampling is a functional equivalent of simple random sampling and is usually easier to do. Typically, the first unit is selected at random.

Question 22

sampling interval

Answer 22

The standard distance between elements selected from a population for a sample.

Question 23

sampling ratio

Answer 23

the proportion of elements in the population that are selected to be in a sample.

Question 24

stratification

Answer 24

The grouping of the units composing a population into homogeneous groups (or strata) 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.

Question 25

cluster sampling

Answer 25

a multistage sampling in which natural groups (clusters) are sampled initially, with the members of each selected group being sampled afterward. For example you might select a sample of U.S. colleges and universites from a directory, get lists of the students at all the selected schools, than draw samples of students from each.

Question 26

PPS (probability proportionate to size)

Answer 26

This refers to a type of multistage cluster sample in which clusters are selected, not with equal probabilities (see EPSEM) but with probabilities proportionate to their sizes- as measured by the number of units to be subsampled.

Question 27

weighting

Answer 27

Assigning different weights to cases that were selected into a sample with different probabilities of selection. In the simplest scenario, each case is given a weight equal to the inverse of its probability of selection. When all cases have the same chance of selection, no weighting is necessary.