##### Psych 311 Unit 4 study guide

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1

Population versus sample (including reasons to use a sample rather than the whole population)

Population: Overall group of interest to the researcher

Sample: Set of individuals selected from a population, goal is to be representative of a population

Reasons to use a sample rather than the whole population: population is too large, cost, feasibility

Target population: Group defined by research interests

Accessible population: Subset of target population that is accessible to researcher

2

Generalization, representativeness, representative sample, biased sample, sampling bias and selection bias

Generalization: The process of using the results of a research sample to infer information about a population

Representativeness: How accurately a sample reflects the characteristics of the population

Representative sample: Sample with same characteristics as population, allows the researcher to make generalizations

Biased sample: Sample with characteristics different from population, does not allow the researcher to make generalizations, caused by Sampling Bias (or Selection Bias)

Selection bias: Participants are selected in a manner that increases the probability of a biased sample

3

Sample size: law of large numbers, general recruitment guidelines for numbers of participants, practical considerations that affect sample size

Law of Large Numbers: the larger the sample size, the more likely the sample accurately represents the population

Feasibility: What is actually possible?

Level of accuracy: Is there a specific target needed? Example: Poll during an election period, voter preferences (±5%)

4

Probability sampling methods and three conditions

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5

Simple random sampling

• Each individual has an equal chance of being selected, selection of one individual is independent of the selection of others
• Process
• Clearly define population
• List all members of population
• Use a random process to select
• Two methods: With replacement and Without replacement
• Strengths: Process is fair and unbiased
• Weaknesses: Need to know all members of population, No guarantee that the sample is representative
6

Stratified random sampling

• Uses identifiable subgroups of a population, goal is to be representative of subgroups
• Process:
• Identify subgroups of the population
• Select equal-sized random sample of the subgroups, using the same steps as random sampling
• Combine subgroups into one overall sample
• Strengths: Guarantees that each subgroup will have representation
• Weaknesses: Need to know all members of population, selection not really random or independent, overall sample is usually NOT representative of the population
7

Proportionate Stratified Random Sampling

• Sample obtained by subdividing a population into strata, then randomly selecting from the strata so that proportions in the sample correspond to those in the population
• Process:
• Identify subgroups of the population
• Determine what proportion of the population corresponds to each subgroup
• Select proportioned random samples of the subgroups
• Combine subgroups into one overall sample
• Strengths: Composition of the sample (representative strata) will be proportionate to the population
• Weaknesses: Need to know all members of population, selection not really random or independent, some strata may have limited representation within the sample
8

Nonprobability sampling methods

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9

Convenience sampling

• Participants are selected on the basis of availability and willingness to respond, Population is not known, no random process
• Safeguarding against bias: Can be attempted through selecting a broad cross-section of people, explicitly describe the sample methodology for use in limitations and understanding generalization
• Process:
• Identify individuals who are easy to reach and willing to participate
• Recruit participants
• Strengths:
• Don’t need to know all members of the population
• An easy (and sometimes inexpensive) method for obtaining a sample
• Weaknesses:
• Selection not random or independent
• Result is likely a biased sample
10

Quota sampling

Ensures that subgroups are represented in a convenience sample, variation can mimic proportionate stratified sampling

Safeguarding against bias: Can be attempted through selecting a broad cross-section, explicitly describe sample methodology for use in limitations and understanding generalization

Process:

• Identify subgroups to be included
• Establish quotas for subgroups
• Identify individuals who are easy to obtain and willing to participate
• Recruit participants

Strengths:

• Don’t need to know all members of the population
• Allows researcher to control the composition of a convenience sample

Weaknesses:

• Selection not random or independent
• Result is likely a biased sample