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:
- (Heeringa, West, and Berglund 2017) (chapter 2)
Optional reading:
- (Lumley 2011) (chapter 1)
- (Vittinghoff et al. 2011) (chapter 12)
- (Bilder and Loughin 2014) (section 6.3)
Video Lessons
Review materials from pre-requisite statistics courses (optional)
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
Links
- Google Slides
- PDF Slides
- Model-based approach (Review/optional content)
- Design-based approach