PROBABILITY SAMPLING- is selected according to mathematical guidelines whereby each unit’s chance for selection is known.
(types of probability sampling)
RANDOM SAMPLING (probability): where each subject or unit in the population has an equal chance of being selected
Pros: detailed knowledge of the population is not required
External validity may be statistically inferred.
A representative group is easily obtainable
The possibility of classification error is eliminated
Cons: a list of the population must be compiled
A representative sample may not result in all cases
The procedure can be more expensive than other methods
Example: obtaining a given population and randomly select and individual from that population
STRATIFIED SAMPLE: approach used to get adequate representation of a subsample. Before sampling, the population is divided into characteristics of importance for the research.
- Example: males and females, or managers and
- non-managers. The researcher first identifies the relevant stratums and their actual representation in the population.
Pros: representation of relevant variables is ensured
Comparison can be made to other populations
Selection is made from a homogenous group
Sampling error is reduced
Cons: a knowledge of the population prior to selection is required
The procedure can be costly and time consuming
It can be difficult to find a sample if incidence is low
Variables that define strata may not be relevant
CLUSTER SAMPLING: selecting samples in groups or categories
Example: a researcher wants to survey academic performance of high school students in Spain. He can divide the entire population (population of Spain) into different clusters (cities). Then the researcher selects a number of clusters depending on his research
Pros: only part of the population need be counted
Cost are reduced if clusters are well defined
Estimates of cluster parameters are made and compared by the population
Cons: sampling error are likely
Clusters may not be representative of the population
Each subject or unit must be assigned to a specific cluster
- SNOWBALL SAMPLING: qualifies respondents are randomly contacted, then asked for the names of friends, relatives, and acquaintances they know
- that may qualify for the research study
- Example: researcher is studying environmental engineers but can only find five. She asks
- these engineers if they know any more. They give her several further referrals, who in turn provides additional contacts.
Pros: process is cheap and simple
Cons: sample may be bias, sample may consist of respondents from club or organization
Researcher has little control over sampling method
(Wimmer/Dominick 2006)