Sampling

Types of probability samples:

Complex designs can be necessary to extract valid or more precise information from a sample we want to represent a target population.

In a simple random sample each individual has equal chance of being selected, but in clustering we need to weight samples if the clusters are different sizes.

Missing Data

Missing Completely at Random (MCAR)

The probability an individual value will be missing does not depend on the outcome, any collected variables, variables not collected or the survey design

Missing at Random (MAR)

The probability an individual value is missing is independent of the outcome of interest and unobserved variables, but depends on the covariates in the model. In other words the response rate only depends on observed data. 

Non-ignorable missing data

The probability an individual value is missing depends on unobserved variables and cannot be completely explained by variables that have been collected

What to do?

  1. Ignore it
    • Worst approach as it reduces sample size and power
  2. Prevent it
    • Try to design the survey to minimize non-response
  3. Statistical methods
    • Imputation - Estimating missing values from information from other observations
      • Divide data into homogenous strata and determine the variables to impute

Revision #3
Created 8 November 2022 17:06:47 by Elkip
Updated 21 November 2022 01:25:01 by Elkip