Chapter 5 Flashcards
Accidental Sampling
A non-probability sample technique in which researchers gather data from individuals whom they “accidentally” encounter or who are convenient; also known as a sample of convenience or haphazard sample
Cluster Sampling
A probability sampling technique in which the researcher divides the population into a number of subgroups (i.e., clusters) and then randomly selects clusters within which to randomly sample
Confidence Interval
The estimated range of values within which the population parameter is likely to fall.
Confidence Level
The probability that the sample statistic is an accurate estimate of the population parameter; also known as alpha level.
Margin of error:
A range around the estimate, expressed in percentage terms, that likely contains the population parameter; used by researchers to state their sample statistics as a confidence interval
Non-probability sampling
Sampling techniques that are not based on probability theory; sample selection is not random and some cases in the population are more likely than others to be selected for participation.
Population (or universe):
In research, the group that a researcher wishes to generalize about.
Population parameter
Population characteristics, expressed in numeric terms when the responses of each member (or case) of the population are measured.
Probability sampling
Sampling techniques that are based on probability theory; sample selection is random and each case in the population has an equal chance to be selected for participation.
Purposive sampling:
A non-probability sampling technique in which researchers use their judgment to select cases that will provide the greatest amount of information; also known as judgmental sampling.
Quota sampling
A non-probability sampling technique in which the researcher combines purposive or accidental sampling with stratification.
Random sampling
A selection technique in which all cases in a population have an equal opportunity for inclusion in the sample
Representative Sample
A sample that accurately reflects the larger population from which it was drawn.
Sampling
Choosing a number of cases or available texts from a larger population rather than analyzing the entire population.
Sampling distribution
The theoretical distribution of a sample statistic for a given sample size.
Sampling error
The difference between the sample statistic and the population parameter.
Self-selection
A process in which individuals select themselves for participation in a sample (e.g. radio call-in programs).
Simple random sampling
The process by which every case in the population is listed and the sample is selected randomly from this list.
Snowball (or network) sampling
A non-probability sampling technique in which the researcher begins by identifying a few cases and, from these, gets referrals for other cases and continues to branch out.
Statistic
A numeric estimate of the population parameter.
Stratified sampling
A probability sampling technique in which the researcher breaks the population into mutually exclusive subgroups, or strata, and then randomly samples from each group.
Systemic sampling
A probability sampling technique in which the researcher calculates a selection interval and uses it to select cases from the sampling frame.
Census
All members/units of the population are included in the study
Unit of analysis
The primary focus of analysis for your research; what or who will constitute cases for your study (e.g. political parties, individuals, governments, newspaper stories, tweets, etc.)
Transferability
The extent to which researchers can export lessons drawn from the study to develop conclusions about another set of cases
Portable
said of a study in which the results may be used to draw conclusions about other cases not immediately under investigation
Sample size
the number of cases included in the full sample
Design weights
mathematical corrections to compensate for the fact that respondents probabilities of being selected were influenced by the design research
Proportionate stratified random sampling
a probability sampling technique in which the researcher breaks the population into mutually exclusive subgroups, or strata, and then randomly selects samples from each group to produce a sample that reflects their relative proportion within the population
Disproportionate stratified random sampling
a probability sampling technique in which the researcher breaks the population into mutually exclusive subgroups, or strata, and then oversamples a larger proportion of smaller subgroups to ensure that these groups include enough cases to produce meaningful statistics
If you conduct a census study, then you include __ of the population in the study.
all members
In order to identify the population be studied, you must determine the __, the geographic period, and the time period under consideration.
unit of analysis
We do not need to consider __ to determine an appropriate sample size.
the nature of the research question
A sampling frame is a list of all the units in the target population
TRUE
In sampling, the group we wish to generalize about is known as the __.
population
___ refers to how similar a population is with respect to the variables of interest.
Homogeneity
Researchers use __ to estimate population parameters.
sample statistics
P(A) = r/n is used to calculate the __.
probability of a single event
As qualitative approaches often have smaller samples, the selection of cases is unimportant.
False
Probabilities can range from __.
0 to 1
