Psych 311 Unit 4

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Target population

The group defined by the researcher's specific interests


Accessible population

The population from which the sample is selected



The extent to which the characteristics of the sample accurately reflect the characteristics of the population


Sampling bias

Occurs when participants or subjects are selected in a manner that increases the probability of obtaining a biased sample


Law of large numbers

The larger the sample size, the more likely it is that values obtained form the sample are similar to the actual values for the population


Probability sampling

THe odds of selecting a particular individual are known and can be calculated

1. Exact size of population is known and it is possible to list all individuals

2. Each individual in the population must have a specified probability of selection

3. 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


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


Random process

A procedure that produces one unpredictable outcome from a set of possible outcomes and each possible outcomes is equally likely to occur


Simple random sampling

Each individual in the population has an equal chance of being selected and sometimes it is required that each selection is independent of the others

1. Clearly define the population

2. List all members

3. Use a random process to select individuals from the list


Sampling with replacement

This method requires that an individual selected for the sample be recorded as a sample member, and then returned to the population (replaced) before the next selection is made


Sampling without replacement

This method removes each selected individual from the population before the next selection is made, this does not produce independent selections


Systematic sampling

Begins by listing all the individuals in the population, then randomly picking a starting point on the list and obtain the sample by moving down the list, selecting every nth name.

- Violates principle of independence`


Stratified random sampling

First identify the specific subgroups then select equal-sized samples from each of the subgroup using the same steps as in simple random sampling and finally combine the subgroup samples into one overall sample

- Could be faulty if men and women end up being represented equally in the sample but are not equal in the population


Proportionate stratified random sampling

Begin by identifying a set of subgroups, determine what proportion of the population corresponds to each subgroup and obtain a sample such that the proportions in the sample exactly match the proportions in the overall population `


Cluster sampling

When the individuals in the population are already clustered in preexisting groups and a researcher can randomly select group instead of selecting individuals

- Can raise concerns about the independence of the individual scores


Combined strategy sampling

When combing two or more sampling strategies to select participants


Convenience sampling

Researchers try to ensure their samples are reasonably representative and also provide a clear description of how the sample was obtained and who the participants are


Quota sampling

A researcher can guarantee equal representation of subgroups by establishing quotas for the number of individuals to be selected from each subgroup

- Not the same as stratified and proportionate stratified sampling because it does not randomly select individuals from the population