1. Sampling Neuman and Robson Ch. 7 Qualitative and Quantitative Sampling

2. Introduction Qualitative vs. Quantitative Sampling Non-Random Sampling Non-probability Not representative of population Random sampling Probability Representative of population The sampling distribution Used in probability sample to allow us to generalize from sample to population

3. Non-Probability Samples Haphazard, convenience or accidental Choose any convenient cases Highly distorted Quota Establish categories of cases Choose fixed number in each category Purposive (judgmental) Use expert judgment to pick cases Used for exploratory or field research

4. Non-Probability (cont.) Snowball Network or chain referral Use of sociograms to represent Other types Deviant case Choose cases for difference from dominant pattern Sequential Select cases until all possible information obtained

5. Probability Sampling Used for quantitative research Representative of population Can generalize from sample to population through use of sampling distribution

6. Logic Behind Probability Sampling Problem: The populations we wish to study are almost always so large that we are unable to gather information from every case.

7. Logic (cont.) Solution: We choose a sample -- a carefully chosen subset of the population – and use information gathered from the cases in the sample to generalize to the population.

8. Terminology Statistics are mathematical characteristics of samples. Parameters are mathematical characteristics of populations. Statistics are used to estimate parameters. PARAMETER STATISTIC

9. Probability Samples: Must be representative of the population. Representative: The sample has the same characteristics as the population. How can we ensure samples are representative? Samples drawn according to the rule of EPSEM (every case in the population has the same chance of being selected for the sample) are likely to be representative.

10. The Sampling Distribution We can use the sampling distribution to calculate our population parameter based on our sample statistic. The single most important concept in inferential statistics. Definition: The distribution of a statistic for all possible samples of a given size (N). The sampling distribution is a theoretical concept.

11. The Sampling Distribution Every application of inferential statistics involves 3 different distributions. Information from the sample is linked to the population via the sampling distribution. Population Sampling Distribution Sample

12. The Sampling Distribution: Properties 1. Normal in shape. 2. Has a mean equal to the population mean. μ x =μ 3. Has a standard deviation (standard error) equal to the population standard deviation divided by the square root of N. σ x = σ/√N

13. First Theorem Tells us the shape of the sampling distribution and defines its mean and standard deviation. If we begin with a trait that is normally distributed across a population (IQ, height) and take an infinite number of equally sized random samples from that population, the sampling distribution of sample means will be normal.

14. Central Limit Theorem For any trait or variable, even those that are not normally distributed in the population, as sample size grows larger, the sampling distribution of sample means will become normal in shape. Note: The Census is a sample of the entire population

15. Simple Random Sampling (SRS) Sampling frame and elements Selection techniques Table of random numbers Other types of samples are variants of the simple random sample

16. Other Probability Samples Systematic Random Sampling Stratified Random Sampling Cluster Sampling Random Route Sampling

17. Other Strategies and Issues Related to Random Sampling Random Digit Dialing (RDD) Hidden Populations Sampling Error and Bias Sample Size