What are the Odds?

Sampling Theory and Logic

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

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

Random Samples

“Equal chance of being chosen” Sample size Simple random sampling

Random number generation (p. 681) Systematic random sampling (kth

sampling)

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”

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

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”

Non-random Sampling

Convenience or availability sampling Purposive sampling Snowball sampling Quota sampling