Concepts (A)

Model-based approach

The model-based approach to statistical analysis is heavily reliant on the specification of a probability model for data generation, typically assuming that data come from an infinite population that follows a specific distribution, such as the Normal distribution. Inferences about the population, including point estimates and hypothesis testing, are made based on how well the sample data fit these model assumptions.

Design-based approach

The design-based approach emphasizes the use of sampling methods and the design of the study itself to make inferences about a real/finite population. The design-based approach takes into account the actual structure of the data collection process to make inferences, ensuring that each unit in the population has a known and often non-zero chance of being included in the sample, thus addressing the potential biases and variance issues arising from the sampling design. This approach is critical in understanding and analyzing data from surveys with complex designs, including those with stratification, clustering, and weighting.

Reading list

Key reference:

Optional reading:

Video Lessons

Model-based approach

Review materials from pre-requisite statistics courses (optional)

Design-based approach

What is included in this Video Lesson:

  • Model-based approach review: 0:00
  • Design-based approach: 1:15
  • Types of sampling techniques 6:46
  • Statistical inference 8:25
  • NHANES 12:02
  • Survey weight 20:40
  • CCHS download 23:45
  • NHANES download 24:50
  • NHANES sampling design 27:24
  • How to find NHANES data from CDC website 27:42

The timestamps are also included in the YouTube video description.

Video Lesson Slides

References

Bilder, Christopher R, and Thomas M Loughin. 2014. Analysis of Categorical Data with r. CRC Press.
Heeringa, Steven G, Brady T West, and Patricia A Berglund. 2017. Applied Survey Data Analysis. Chapman; Hall/CRC.
Lumley, Thomas. 2011. Complex Surveys: A Guide to Analysis Using r. Vol. 565. John Wiley & Sons.
Vittinghoff, Eric, David V Glidden, Stephen C Shiboski, and Charles E McCulloch. 2011. Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models. Springer Science & Business Media.