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Chapter 5

Chapter 5

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David Nussbaum

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Chapter 5- sampling and survey research  Conducting a census studying the entire population of interest which avoids the problem of sampling (where only a limited number of people represent the entire population)  If we want to assess the cross-population generaliziability of our findings, we need to compare results obtained from samples of different populations  Elements example. The students on the list. Who or what we are studying. The elementary units.  this list, from which the elements of the population are selected is termed the sampling frame  representative sample a sample that ‘looks like’ the population from which it was selected in all respects potentially relevant to the study  sample generaliziability depends on the amount of sampling error- the difference between the characteristics of a sample and the characteristics of the population from which it was selected  the larger the sampling error, the less representative the sample and thus the less generalizable the findings obtained from that sample  for example you have 15 people, 5 of them are happy, 10 of them are unhappy. That’s 5 out of 15 people which is 33% that are happy. You choose a sample of 6 out of this 15. A representative sample of this would be if you took 2 happy people out of 6. That would also be 33% that is happy. But if you take 4 happy people out of the original population out of the 6 then it will show that 66% are happy which would become a unrepresentative sample. Of course, representation in a sample is never perfect, but it is important to provide information about how representative a given sample is  we can calculate the likely amount of sampling error and the tool to do this is called inferential statistics a mathematical tool for estimating how likely it is that a statistical result based on data from a random sample is representative of the population from which the sample is assumed to have been selected  sampling distributions for many statistics, including the mean have a ‘normal’ curve/shape.  so a normal distribution is symmetric (this shape is produced by random sampling error- variation owing purely to chance  random sampling error/chance sampling error differences between the population and the sample that are due only to chance factors (random error), not to systematic sampling error.  Random sampling error may or may not result in an unrepresentative sample. This magnitude of sampling error due to chance factors can be estimated statistically  In a sampling distribution, the most frequent value of the sample statistic—the statistic (such as the mean) computed from sample data—is identical to the population parameter—the statistic computed for the entire population  In other words, we can have a lot of confidence that the value at the peak of the bell curve represents the norm for the entire population  Sample statistic value of a statistic, such as a mean, computed from sample data  Population parameter the value of a statistic, such as a mean computed using the data for the entire population, a sample statistic is an estimate of a population parameter  The most important distinction that needs to be made about samples is whether they are based on a probability or a nonprobability sampling method  Sampling methods that allow us to know in advance how likely it is that any element of a population will be selected for the sample are termed probability sampling methods  You can only make a statistical estimate of a sampling error for a probability based sample  Sampling methods that do not let us know in advance the likelihood of selecting each element is termed nonprobability sampling methods  Probability sampling methods rely on a random, or chance, selection procedure  Probability of selection the likelihood that an element will be selected from the population for inclusion in the sample  Random sampling in which cases are selected only on the basis of chance, with a haphazard method of sampling. Where ‘leaving things up the chance’ seems to imply not exerting any control over the sampling method but to ensure that nothing but chance influences the selection of cases, the researcher must proceed methodically, leaving nothing to chance except the selection of the cases themselves. The researcher must follow carefully controlled procedures if a purely random process is to be the result Probability Sampling Methods  These methods randomly select elements and therefore have no systematic bias; nothing but chance determines which elements are included in the sample  This feature of probability samples makes them much more desirable than nonprobability samples when the goal is to generalize to a larger population  It is the number of cases that is most important (don’t make the mistake of thinking that a larger sample is better because it includes a greater proportion of the population)  The 4 most common methods of drawing random samples are 1. Simple random sampling 2. Systematic random sampling 3. Stratified random sampling 4. Cluster sampling  Simple random sampling requires some procedure that generates numbers or otherwise identifies cases strictly on the basis of chance  Organizations that conduct phone surveys often draw random samples using another automated procedure called random digit dialing (a machine dials random numbers within the phone prefixes corresponding to the area in which the survey is to be conducted)  Systematic random sampling is a variant of simple random sampling  the first element is selected randomly froma list or from sequential files, and then every nth element is selected  this is a convenient method for drawing a random sample when the population elements are arranged sequentially, such as in folders in filing cabinets  but you have to watch out for periodicity—that is, the sequence varies in some regular, period pattern (refer to phone book example from powerpoint)  sampl
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