1. What are the Odds? Sampling Theory and Logic

2. Let’s Be Realistic… It’s unlikely you’ll be in a position to do much sampling in your daily work Important to know sampling theory when consuming research

3. The Logic of Probability The universe and the sample The theoretical distribution curve (bell- shaped curve)  random distribution about the mean Mean and standard deviation, true and sample  Natural variation of the sample mean about the true mean

4. Random Samples “Equal chance of being chosen” Sample size Simple random sampling  Random number generation (p. 681)  Systematic random sampling (kth sampling)

5. Stratified Sampling Breaks the cardinal rule: unequal chance of being chosen In social work, interested in minority opinion Disproportionate stratified sampling captures minority voice “Stratify for variables of interest, randomize the rest”

6. Strategies for Stratification Divide population into homogeneous subgroups of interest Disproportionate stratification  Sample based on subgroup size: simple random or systematic (p. 269)  Simple oversampling  Must not generalize to larger population

7. Gender and Cultural Bias “When one group is the norm, the other group is the deviant” Systematic vs. random errors Source of systematic errors  Population frame bias  Insufficient sample size  Generalization errors – up and down “Stratify variables of interest, randomize the rest”

8. Non-random Sampling Convenience or availability sampling Purposive sampling Snowball sampling Quota sampling