Ch7 Sampling Flashcards
Qualitative samples
Nonrandom samples - rarely determine the sample size in advance and have limited knowledge about the larger group or population from which the sample is taken
Nonprobability - haphazard
get any cases in any manner that is convenientineffective, highly unrepresentative, not recommendedcheap quick but lots of errors worse than no sample
Nonprobability - quota
Get preset number of cases in each of several predetermined categories that will reflect the diversity of the population using haphazard methodsimprovement over haphazard - decide how many to have in each category
Nonprobability - purposive
get all possible cases that fit particular criteria using various methods
best used in situations in which an expert uses judgment in selecting cases with a specific purpose in mind. - inappropriate to pick the typical schooloften used in explanatory or field research
Snowball
get cases using referrals from one or a few cases, then referrals from those cases and so forth.can be used to sample a network sociogram - diagram of circles connected with lines - map that shows the network of social relationships, influence patters or communication paths among a group of people or units
Sequential
get cases until there is no additional information or new characteristics
different that purposive, purposive tries to find as many relevant cases as possible until time, financial resources or their energy is exhausted. sequential - researcher continues to gather cases until the amount of new info or diversity of cases is filled - gathered until marginal utility or incremental benefit drops off
Purposive sampling appropriate in 3 situations:
- researcher uses it to select unique cases that are especially informative
- select members of a difficult to reach, specialized population
- when a researcher wants to identify particular types of cases for in-depth investigations
deviant case sampling
type of purposive sampling researcher seeks cases that differ from the dominant pattern or that differ from the predominant characteristics of other cases variety of techniques to locate cases with different characteristics not represented by the whole - studying unusual especially used by qualitative
Theoretical sampling
they continue to collect data until no new information emerges - until reaching theoretical saturation
Probability sampling
fundamentally different from nonprobability sampling because it is strongly linked to the science of math and probability draw samples from the same population in a manner so that every case has the same likelihood of being chosen
sampling element
unit of analysis or case in a populationcan be a person, group, organization, written document or symbolic message or social action
sampling ratio
ratio of the size of the sample to the size of the target population
sampling frame
operationalizes a population by developing a specific list that closely approximates all the elements in the population list of cases in a population or the best approximation of it
parameter
characteristic of the entire population that is estimated from a sample
statistic
a numberical estimate of a population parameter computed from a sample
sampling error
how much a sample deviates from being representative of the population
margin of error
an estimate about the amount of sampling error that exists in a survey’s results
Simple random sampling
a type of random sample in which a researcher creates a sampling frame and uses a pure random process to select cases. each sampling element in the population will have an equal probability of being selected
sampling distribution
distribution created by drawing many random samples from the same population
sampling distribution of sample means
distribution of sample means created by drawing many random samples from the same population
central limit theorem
mathematical relationship stating that whenever many random samples are drawn from a population and plotted, a normal distribution is formed, and the centre of such a distribution for a variable is equal to its population parameter
confidence interval
range of values usually a little higher and lower than a specific value found in a sample within which a researcher has specified and high degree of confidence that the population parameter lies
stratified sampling
random sample in which the researcher first identifies a set of mutually exclusive and exhaustive cateogies then uses a random selection method to select cases for each category more rep of population than simple random - percentages match population percentages
cluster sampling
random sample that uses multiple stages and is often used to cover wide geographic areas in which aggregated units are randomly selected; samples are then drawn from the sampled aggregated units or clustersless expensive but less accurate -must decide on the number of clusters and the number of elements within each clusterstage 1 - random sampling of big clustersstage 2 - random sampling of small clusters within each selected big cluster stage 3 - sampling of elements from within the sampled small clusters
probability proportionate to size
an adjustment made in cluster sampling when each cluster does not have the same number of sampling events
random digit dialling
a method of randomly selecting cases for telephone interviews that uses all possible telephone numbers as a sampling frame
hidden populations
people who engage in clandestine, deviant or concealed activities who are difficult to locate and study
How to address question of sample size
make assumptions about the population and use statistical equations about random sampling processes -
conventional or commonly accepted amount
principle of sample size
the smaller the population, the bigger the sampling ratio has to be for an accurate sample larger populations permit smaller sampling ratios for equally good samples - as population size grows the returns in accuracy for sample size shrink
decision about sample size depends on 3 things
- degree of accuracy required
- degree of variability or diversity in the population
- number of different variables examined simultaneously in data analysis
inferential stats
based on a random samplelets a researcher make precise statements about the level of confidence they have in the results of a sample being equal to the population parameter
