Sampling Flashcards
Target Population
- Group defined by what researcher is interested in
- Not easily available - even if we could reach them, it would be unlikely to get ALL of them
Accessible Population
- For a variety of reasons we may not be able yo access everyone
- geographically remote areas
- might not be ethically ok to research some groups (isolated tribes exposed to our germs)
- the accessible population are those we can get to
The Sample
- Those who we can actually recruit FROM the accessible population
Representative Samples
A key Goal is to generalize results from a sample to the entire population
The representatives of a sample refer to the extent to which the characteristics of the sample accurately reflect the characteristics of the population; therefore a representative sample is a sample with the same characteristics as the population.
Sampling Error:
The naturally occurring difference between s sample and the population. It can be reduced by increasing the size of the sample or by stratification (independently sample each subsection of a population)
Biased Sample
Is a sample with the characteristics different from those of the population. Generally occurs when there has been sampling bias (when participants or subjects are selected in a manner that increases the probability of obtaining a biased sample - huge threat to external validity)
Non-sampling error or measurement error:
A non sampling error is an error that results solely from the manner in which the observations are made, for example measurement might be inaccurate due to malfunctioning instruments or poor experimental procedures.
Four Key Types can Occur:
- The effect of the interviewer: The same person may provide difference answers to different interviewers
- The respondent effect: Respondents might give incorrect answers to impress the interviewer
- Knowling the study purpose: Knowing why a study is being conducted may create incorrect responses. For example, consider the question What is your income? If a government agency is asking, a different figure may be provided than the respondent would give on an application for a home mortgage.
- Induced bias: In designing a questionnaire, questions may be slanted In a such a way that a particular response will be obtained even though it is inaccurate
Probability Based Sampling techniques - Overview
In probability sampling, the odds of selecting a particular individual are known can can be calculated. There are three important conditions:
- The exact size of the population must be known and it must be possible to list all of the individuals
- Each individual in the population mist have a specified probability of selection
- When a group of individuals are all assigned the same probability, the selection process must be unbiased so that all group members have an equal chance of being selected. Selection must be a random process, which simply means that every possible outcome is equally likely.
The have a very good chance of producing a representative sample, but are not often used.
why?
- Extremely time consuming and tedious
- Require that the researcher “knows” the entire population and has access to it
Probability Based Sampling techniques - Types
Simple Random Sampling: each individual in the population has an equal and independent chance of selection
- Sampling with replacement: When an individuals is selected, they are placed back into the population to ensure that all members still have the sample probability of being selected at a given time
- Sampling without replacement: Participants are not replaced. Although probability of being selected increases with reach section, this method ensures that a participant will not be selected twice
Systematic sampling: a sample is obtained by selecting every nth participant from a list containing the total population after a random starting point.
Stratified random sampling: involves identifying specific subgroups to be included in the sample and then selecting equal random sample from each pre-identified subgroup
Proportionate stratified random sampling: involves identifying a specific subgroups to be included, determining what proportion of the population corresponds to each subgroup, and randomly selecting individuals to that the proportion for each subgroup in the sample exactly matches the corresponding proportion in the population.
Cluster sampling: The random selection of groups instead of individuals from a population. Can be a problem as groups may not contain independent participants and may affect reach other.
Non-Probability based Sampling - Overview
In nonprobability sampling, the odds of selecting a particular individual are not known because the researcher does not know the population size and cannot list the members of the population. In addition, in nonprobability sampling, the researcher does not use an unbiased method of selection.
- For example, a researcher who wants to study the behaviour of university students may go to a local university where university students are already assembled – well, perhaps in the earlier weeks of trimester! Because the researcher does not ensure that all university students have an equal chance of being selected, this sample has an increased chance of being biased. If the university includes only law and psychology students, then the sample definitely does not represent the target population of all university students. In general, nonprobability sampling has a greater risk of producing a biased sample than does probability sampling.
In general, nonprobability sampling has a greater risk of producing a biased sample than does probability sampling.
Non-Probability based Sampling - Types
Convenience sampling: is a nonprobability sampling method involving selection of individuals on the basis of their availability and willingness to respond; that is, because they are easy to get, e.g., a class of university students.◦Pros - you get participants fast and easily◦Cons: the sample is not necessarily representative of the population.
Quota sampling: a type of convenience sampling involving identifying specific subgroups to be included in the sample and then establishing quotas for individuals to be sampled from each group.
Purposeful Sampling: finding a sample that is ‘information rich’.
