Chapter 6 Flashcards
Inferential Statistics
used to make statements (inferences) about the population based on the findings from our sample.
Sampling Frame
– a list of the population from which the sample is drawn.
Probability Sampling
Techniques for which you can specify the probability that a participant will be selected from a population.
Non-Probability Sampling
It is impossible to specify the probability of selecting any one individual.
The sample may or may not be representative of the population.
Random Sampling
a sample is drawn such that each member of the population has an equal probability of being included in the sample.
Random Assignment
requires that participants have been independently assigned to groups.
Systematic Sampling
the population size is divided by your sample size to provide you with a number, k, for example; then from a random starting point you select every kth individual
Stratified Sampling
the population is divided into strata based on some population characteristic and participants are randomly selected from each stratum (therefore each stratum is proportionally represented in the sample).
Cluster Sampling
can be used when a population list is not available and researchers simply identify a number of clusters or groups and include all participants in the cluster/group in the sample.
Multi-Stage Sampling
a cluster technique where smaller clusters are randomly selected from larger clusters that were randomly selected previously.
Convenience Sampling
using whatever participants are easily available.
Quota Sampling
convenience sampling in which the goal is to select participants with particular characteristics until you have enough.
Referral Sampling
– involves including participants in the sample who have been referred by other participants.
Sample Size Depends on:
the power of the statistic
your research design (how many conditions you have)
size of the effect
variability of the data
