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Chapter 9

Ch. 9 - Research Participants.pdf

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PSYC 2520
Josee Rivest

Ch. 9 - Research Participants Sunday, December 16, 2012 12:36 PM PSYC 2520: Introduction to Experimental Psychology Beginning Behavioral Research: A Conceptual Primer (7th ed. 2012) Rosnow & Rosenthal Chapter 9: Survey Research and Subject Recruitment What are opportunity and probability samples? • Opportunity samples are the first units that are available, whereas probability sampling plans use a random procedure for selecting a sample that is expected to be representative of the target population. However, to be absolutely sure that a sample is representative, we would have to know the true population value in advance, in which case (practically speaking) there would be no reason to study the sample. • Researchers who use a sample can never be 100% sure of their generalizations; they can make a reasonable guess, however, by first developing an accurate sampling frame that defines the target population and then relying on a carefully designed blueprint (the sampling plan) to select the sample by means of probability sampling ○ The term probability sampling implies that randomness enters into the selection process at some stage so that the laws of mathematical probability apply ○ Probability refers to the mathematical chance of an event's occurring What is meant by bias and instability in survey research? • Two important statistical requirements of a probability sampling plan are ○ That the sample be unbiased ○ That there be stability in the samples • A biased sample overestimates or underestimates the true population ○ Bias = systematic error • An unstable sample is characterized by sampling units that vary greatly from one another (sample is highly variable) ○ Generally speaking, the more homogenous the population, the fewer the sampling units needed ○ Stability is estimated by statistical procedures such as variance and standard deviation Why do we not know " for sure" the bias in sampling? • We can never know for sure the bias in sampling results unless we know the results for the entire population How is simple randomsampling done? • In simple random sampling, the sample is selected from an undivided population (or from a relatively homogenous stratum), and each unit has the same chance of being selected on any draw ○ In order for this to occur, the selection of one unit must have no influence on the selection of other units ○ A further assumption is that we have an understanding of the existence of all the units in the population ○ Simple random sampling is useful when the population is known to be homogeneous or when its precise composition is unknown • Sampling with replacement means that the selected units are placed in the selection pool again and may be reselected on subsequent draws ○ Every unit continues to have the same probability of being chosen every time a number is read • In sampling without replacement , a previously selected unit cannot be reselected and the population shrinks each time you remove a unit, but all the units remaining have the same likelihood of being drawn on the next occasion What are stratified random sampling and area probability sampling? • When we know something about the exact composition of the population, a more efficient method of sampling is to sample from the different substrates of the population (this is known as stratified random sampling) ○ A separate sample is randomly selected from each homogeneous stratum (or "layer") for the entire population • A popular variant of this sampling approach is called area probability sampling because the population is divided into geographic areas What did the Literary Digest case teach pollsters? • Literary Digest predicted the repub
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