Chapter 06

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

Referral sampling

Involves including participants in the sample who have been referred by other participants.

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.

Probability Sampling

Techniques for which you can specify the probability that a participant will be selected from a population.

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).

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.

Census

The whole population is researched

Inferential statistics

Used to make statements (inferences) about the population based on the findings from our sample.

Convenience sampling

Using whatever participants are easily available.

Multistage sampling

A cluster technique where smaller clusters are randomly selected from larger clusters that were randomly selected previously.

Sampling frame

A list of the population from which the sample is drawn.

Random sampling

A sample is drawn such that each member of the population has an equal probability of being included in the sample. - vs. random assignment - requires that participants have been independently assigned to groups.

What is the relationship between effect size and sample size? A. When the effect size is small, larger samples are needed. B. When the effect size is large, larger samples are needed. C. Regardless of effect size, large samples are generally necessary. D. Effect size and sample size are dependent on the level of power.

A. When the effect size is small, larger samples are needed.

What is the difference between cluster and multistage sampling? A. Cluster sampling uses several levels of clusters. B. Cluster sampling uses clusters whereas multistage sampling uses stages. C. Multistage sampling is a more complex method of cluster sampling. D. Multistage sampling and cluster sampling are two names for the same thing.

C. Multistage sampling is a more complex method of cluster sampling.

What is the assumption made when using inferential statistics? A. Qualitative variables were measured. B. Non-probability sampling techniques were used. C. The samples were randomly selected from the population. D. The sample was large.

C. The samples were randomly selected from the population.

Stratified sampling is used ____. A. only when simple random samples are inappropriate B. when systematic samples are not possible C. when you want to represent groups of different sizes D. as much as possible due to their superiority E. none of the above

C. when you want to represent groups of different sizes

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.

Quota sampling

Convenience sampling in which the goal is to select participants with particular characteristics until you have enough.

Jennifer is a 2nd grade teacher who conducts a reading study on her students. This sample is A. A convenient sample B. A nonprobability sampling method C. A cluster sampling method D. Both A and B E. Both A and C

D. Both A and B

Sheeba wants to make sure that her sample represents the actual frequency of important demographic variables (e.g., ethnicity, gender). What type of sampling procedure will insure this? A. Convenient sampling B. Multistage sampling C. Cluster sampling D. Quota sampling

D. Quota sampling

Random sampling, systematic sampling, and multistage sampling are examples of _____. A. methods requiring inferential statistics B. methods requiring descriptive statistics C. non-probability sampling techniques D. probability sampling techniques E. none of the above

D. probability sampling techniques