Podcast
Questions and Answers
What is the difference between nonprobability sampling and probability sampling? When is each sampling method appropriate?
What is the difference between nonprobability sampling and probability sampling? When is each sampling method appropriate?
Nonprobability sampling does not give all individuals an equal chance of being selected, while probability sampling does. Nonprobability sampling is appropriate when random selection is impractical or unnecessary.
What is the difference between random sampling and random assignment?
What is the difference between random sampling and random assignment?
Random sampling involves selecting a subset from a population using a random method, while random assignment is assigning participants to groups randomly during the study.
How does one select a simple random sample from a population? How would one go about selecting an appropriate sampling frame and sampling ratio?
How does one select a simple random sample from a population? How would one go about selecting an appropriate sampling frame and sampling ratio?
Select an accurate sampling frame and then randomly select sampled elements from that frame. The sampling ratio is the ratio of sample size to population size.
Give 5 examples of probability samples: Simple random, _____, _____, _____, _____
Give 5 examples of probability samples: Simple random, _____, _____, _____, _____
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Give 8 examples of nonprobability samples: Convenience, _____, _____, _____, _____, _____, _____, _____
Give 8 examples of nonprobability samples: Convenience, _____, _____, _____, _____, _____, _____, _____
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What are the advantages of using probability sampling techniques over nonprobability sampling techniques when making inferences to a population?
What are the advantages of using probability sampling techniques over nonprobability sampling techniques when making inferences to a population?
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What is the central limit theorem?
What is the central limit theorem?
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What are the seven elements of an experiment? Treatment or independent variable, dependent variable, pretest, posttest, _____, _____, _____
What are the seven elements of an experiment? Treatment or independent variable, dependent variable, pretest, posttest, _____, _____, _____
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Which elements are present in all experimental designs?
Which elements are present in all experimental designs?
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What distinguishes pre-experimental designs from classical designs?
What distinguishes pre-experimental designs from classical designs?
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Study Notes
Sampling Methods
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Nonprobability Sampling vs. Probability Sampling:
- Nonprobability sampling does not ensure each member of a population has a chance to be included, making it less representative.
- Probability sampling ensures equal chances, allowing for better generalization to the population.
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Random Sampling vs. Random Assignment:
- Random sampling selects individuals from a population, ensuring representativeness.
- Random assignment allocates these selected individuals into different groups for experimental comparison.
Selecting a Sample
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Simple Random Sample:
- Requires an accurate sampling frame and utilizes a random process to select elements.
- Sampling ratio is defined as the size of the sample relative to the target population.
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Probability Sample Examples:
- Simple Random: Uses a complete sampling frame for random selection.
- Stratified: Samples from identified categories within the population.
- Cluster Sampling: Targets larger units, followed by sampling within those units.
- Systematic Sampling: Selects every kth individual from a list.
- Random-Digit Dialing: A method used for telephone surveys.
Nonprobability Samples
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Examples:
- Convenience: Samples chosen for ease of access.
- Quota: Fulfills specific quotas across categories, lacking randomness.
- Purposive: Selects individuals based on predefined criteria.
- Snowball: Utilizes referrals from existing participants.
- Deviant Case: Focuses on unique or atypical cases.
- Sequential: Continues sampling until information saturation.
- Theoretical: Aims to understand key theoretical constructs.
- Adaptive: Adjusts sampling based on earlier findings.
Advantages and Theorems
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Benefits of Probability Sampling:
- Produces statistically valid representative samples, enhancing inferential accuracy and minimizing sampling errors.
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Central Limit Theorem:
- With repeated random samples, the sampling distribution approaches a normal distribution, allowing estimation of population parameters.
Experimental Design Components
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Seven Elements of an Experiment:
- Treatment (Independent Variable), Dependent Variable, Pretest, Posttest, Experimental Group, Control Group, Random Assignment.
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Core Elements of Experimental Designs:
- All designs incorporate independent and dependent variables, posttests, and experimental groups.
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Pre-experimental vs. Classical Designs:
- Pre-experimental designs lack random assignment and are applied when classical designs are impractical, often resulting in validity concerns.
Studying That Suits You
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Description
Prepare for your Methods final exam with these flashcards covering key concepts of sampling techniques. Understand the distinctions between nonprobability and probability sampling, as well as random sampling and random assignment. Perfect for review and study in preparation for your exam.
