1 Psych 5500/6500 Populations, Samples, Sampling Procedures, and Bias Fall, 2008

2 Populations Population 1.A large set of items. 2.It is the group we are trying to find out about when we run a study.

3 Populations (cont.) A population does not need to be an existing group of people, it can be: 1.Animals 2.Objects 3.Hypothetical 4.Scores on some measure

4 Samples Sample 1.A subset of the population. 2.Those members of the population that we actually measure.

5 Basic Approach Basic approach: use the sample to tell us about the larger population from which the sample was drawn. representative sample: a sample that is similar to the population in terms of the variables in which we are interested. non-representative (‘biased’) sample: a sample that is not similar to the population in terms of the variables in which we are interested.

6 Basic Problem Basic problem: we don’t know whether or not the sample we have drawn is representative of the population (to know that we would have to compare the sample to the population, but if we know that much about the population then why are we sampling?)

7 The Solution The solution is to turn to probability theory. We can only use probability theory, however, if certain conditions are met. First, we must determine the possible reasons for obtaining a non-representative sample.

8 The Two Biases Random bias: obtaining a non-representative sample purely due to chance (this can be modeled using probability theory). Systematic bias: obtaining a non-representative sample for any reason other than chance (this can not be modeled using probability theory).

9 Handling Bias Statistical procedures cannot cope with systematic bias, it is a case of garbage in and garbage out. Statistical procedures can, however, cope with the possibility of random bias, for the probability of getting a non-representative sample due to chance can be determined mathematically

10 Determining the Type of Bias that Might Arise The type of bias that might arise is determined by how we sample from the population. Random sampling is a way to avoid systematic bias. There are many forms of random sampling, we’ll take a look at one just to develop the idea.

11 Random Sampling Simple Random Sampling 1.Everyone in the population has an equal chance of being included in the sample. 2.The selections are independent of each other. If we successfully randomly sample then we will only have random bias to worry about (no systematic bias).

12 Simple Random Sampling 1) Everyone in the population has an equal chance of being included in the sample: a)Selection must be random. Random not the same as haphazard. b)Subject attrition (someone drops out of the study or refuses to participate) might bias the sample. More on that next:

13 Subject Attrition If attrition is due to non-random factors than that can introduce systematic bias. If, for example, people with a particular viewpoint refuse to participate than they no longer have an equal chance of being in the data and the sample will be nonrepresentative for a systematic (non-random) reason.

14 Random Sampling (cont.) 2) The selections are independent of each other: a)Selecting every 10 th name from an alphabetical list could bias the results. The probability of sampling people with the same last name (who might be related) would be affected. b)Technically we should sample with replacement, but we don’t. See next slide.

15 Sampling with Replacement Sampling with Replacement means that once someone has been selected their name goes back into the pool of people to select from (like returning a card to a deck after it has been selected before selecting again). This preserves the criteria that each selection is independent of other selections. We don’t do this in psychology because measuring the same person twice creates other problems. If our samples are small compared to the population there is only a trivial difference in the independence of selections between sampling with replacement and sampling without replacement and so sampling without replacement is ok.

16 Sampling with Replacement Example: if we draw ten cards from a deck without replacement then the earlier draws influence the later draws. For example, if you draw four aces then the chances of drawing another ace is zero. If, however, we shuffle 500 decks together and draw ten cards, then the fact that we draw four aces earlier in the sample does not have a big influence on the probability of drawing another ace.

17 Reducing Random Bias When we randomly sample we can never know for sure whether or not we have a representative sample. We can, however, increase the probability of obtaining a representative sample by: 1.Increasing the size of our sample. 2.Selecting a variable that has low variability in its values.

18 Hite Report: A Cautionary Tale Back Off Buddy: A New Hite Report stirs up a furor over sex and love in the 1980's. Time Magazine Oct 12,  95% of the women respondents reported emotional and psychological harassment from the men they love.  98% want to make "basic changes" in their love relationship  70% of women married 5 years or more have had an affair.

19 But only 4.5% of the 100,000 questionnaires mailed were returned. If you put in the whole group sent a questionnaire then. 1) 4.2% reported harassment 0.3% did not report harassment 95.5% did not respond 2) 4.4% want to make basic changes in their relationship 0.1% do not 95.5% did not respond 3) 3.2% have had an affair 1.4% have not 95.5% did not respond

20 Hite’s Response When questioned about the low return rate, Hite responded that a reply of 4,500 was a sufficiently large enough N. What both Hite and the interviewer didn’t understand is that if you have systematic bias in your sample (in this case from attrition) then increasing N doesn’t help. If, for example, your population consists of all voters, and you only sample from one political party, then it doesn’t matter how big N is, it still won’t be a representative sample.

21 Random Sampling in Psychology We can almost never randomly sample in psychology. 1.It usually requires a complete and up-to-date list of everyone in the population. If your population, for example, is ‘women’, good luck at getting a list of all women on the earth 2.Subject attrition for non-random reasons violates the principle of everyone having an equal chance of being measured. Good luck at getting people from Mongolia to come to your study.

22 Convenience Sampling Convenience sampling: a non-random sampling procedure based upon using who is convenient to recruit to be in your study (e.g. Intro to Psych students). This is about as non-random as you can get. How do we justify this? It is based on the idea that just because you open the door (wide) to systematic bias doesn’t mean that it will come in. It is up to you to keep systematic bias out of your sample. There are at least three ways of accomplishing that.

23 Controlling Systematic Bias (1) 1) Design a study where it would be reasonable to argue that you would have obtained a similar result if you could have randomly sampled. This will be influenced by what you are measuring. For example, if you are measuring the effect of a chemical on pupil dilation then Intro to Psych students would probably be as representative as a random sample from a larger population of the same age range. If, on the other hand, you are measuring support for higher education among voters then Intro to Psych students would probably be a biased sample.

24 Controlling Systematic Bias (2) 2) Create a representative sample. An example of this would be the selection of institutions in the article On Being Sane in Insane Places (described in the lecture).

25 Controlling Systematic Bias (3) 3)Get your sample, and then decide what population it represents (i.e. determine to what population you would feel comfortable generalizing the results). This seems to be the most common approach in psychology.

26 Summary We are interested in populations, but they are too big to measure, so we rely on samples to tell us what the populations are like. We don’t, however, know if the sample is representative of the population. Statistic procedures were essentially invented to solve this problem, but they only work if you do not have systematic bias in your sample.