1 Sampling This lecture ties into Terre Blanche chapter 15 Now you have a design, and a protocol plus measures – who do you run it on? Selecting a group of people is called “sampling” – many strategies

2 Samples & Populations Population: All possible observations of a particular variable Eg: all people on earth (past, present & future) Cannot experiment on populations! Sample: subset of a population selected to estimate the behaviour of the population Looking at a few people, we can “guess” what is happening in the group as a whole

3 Sample Ex-sample What percentage of these dots are black and which are white?

4 Sample example In this small sub section (the sample), There are: 53% black, 47% white In the big picture, there are in fact: 59% black, 41% white Even though we used a sample, we approximated the big picture well!!

5 Two big questions about samples How many people (n=?) Depends on which analysis tool you use Calculating statistical power will tell you About 20 per group in the design is good Who to use Depends on the population you want to speak of (pretty complicated)

6 Importance of sampling properly A sample exists to represent its parent population We must know what the actual parent population is, otherwise we draw false conclusions If we sample only women, we cannot safely make claims about men Eg. “Student sampling” problem – is psychology about people or about students?

7 Deciding who to choose Two basic sampling methods: 1. Probability sampling Each member of the population has a certain probability of being selected 2. Non-probability sampling Members selected not by mathematical rules, but by other means (eg. Conveninece, access)

8 Probability sampling strategies There are several different ways of doing probability sampling We look at 3: 1. Systematic sampling 2. Random sampling 3. Stratified sampling

9 Systematic sampling Put all your population on a list Select every nth subject (eg every 12 th ) n is determined by desired sample size Eg. With a population of 300, if we want a sample of 10, choose every 30 th case Only useful if you have a complete list of the population Eg: classlist; customer database

10 Random sampling Random: without a rule or method Assign each person a probability of being chosen Try to match the ratios that exist in the population Eg: if you suspect 60% males, 40% females, then assign those odds to selecting the next person

11 Stratified sampling Expands on random sampling Build sub categories, then sample randomly inside each one Eg: decide you will have 10 men and 10 women Random sampling cannot ensure equal group size; stratification can

12 Non-probability sampling Methods used when distribution of the sample is not important Used when sampling frame is not known Cannot be used for most statistical analyses Well suited for qualitative research, where distribution is not important

13 NP sampling strategies Haphazard sampling: use the first people at hand Most convenient method, cheapest Quota sampling: “Stratified haphazard” sampling Like haphazard, but ensures subpopulations are included

14 NP sampling strategies (2) Purposive sampling: Use expert judges to identify candidates, select those Very rare populations Snow-ball sampling: Recruit one subject, that subject identifies others, who identify others… Used for covert subpopulations, non- cooperative groups