STAT 344 – Sample Surveys was actually a very enjoyable course for me. Unlike many theory-based statistics courses, stat 344 gives concrete and practical examples of how surveys are conducted. This gave me a feeling of accomplishment because I could get a sense of how what I was learning could be applied towards real-life situations. A common question that was asked on practice exams: we are given a table of data and we are asked to treat the data as a

a) stratified sample

b) panel study

c) aggregation of polls

d) cluster sample

and find their relative estimates and standard errors.

Some topics or concepts covered in this course (Off the top of my head):

- Recommending a sample size in order to satisfy an employer’s preferred accuracy level
- Bias (an example that helped me understand bias was “say you were sampling random people on the street and asking them the number of people in their household” but this is a biased way of sampling because larger households have a better chance of being approached by you)
- Ratio vs. Regression vs. “Vanilla” estimation
- Panel study (has a co-variance term)
- Stratified sampling
- One-stage cluster sampling (Simple random sample clusters, then sample everyone in selected cluster)
- Two-stage cluster sampling (Simple random sample of clusters, then another random sampling within the cluster)
- Aggregate polls (poll of polls)
- House-effects (τ)
- Weighted sampling
- Proportional/optimal allocation
- Cluster sampling with probability-proportional-to-size (this was tricky!)
- Non-responders
- 3 types of missing data – missing at random (MAR): the chance of participation varies with the helper variables but not with the variable of interest, missing completely at random (MCAR): the chance of participating is constant and does not depend of the variable of interest, non-ignorable missing (NMAR): chance of participation varies with the variable of interest and helper variables.