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S2E32: Lies, Damned Lies, and Random Samples with Laura Stapleton
Apr 27, 2021
Laura Stapleton, an economist and sampling design expert, joins the hosts to discuss lies in statistics and the different types of sampling issues. They explore the assumption of normality, stratified sampling, and the importance of understanding the population of inference. They also introduce the funded project called the generalizer, which helps researchers select schools for intervention research. The podcast concludes with expressions of gratitude and a promotional message.
54:30
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Quick takeaways
- Achieving simple random sampling in research is rare due to factors like convenience sampling and non-response rates.
- Design-based approaches focus on sampling design and provide inferences about a finite population, while model-based approaches consider an infinite population and use statistical modeling.
Deep dives
The Challenges of Sampling Designs
Sampling designs in research, particularly in the field of psychology, often face challenges in achieving simple random sampling. Factors such as convenient sampling, non-response rates, and specialized target populations can affect the generalizability of findings.
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