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Common Sources of Bias : Selection bias Non-response bias Response bias Voluntary response (self-selected) sample Conven PowerPoint Presentation
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A study of the effects of running on personality involved 231 male runners who each ran about 20 miles a week. The runners were given the Cattell Sixteen Personality Factor Questionnaire, a 187-item multiple-choice test often used by psychologists. A news report (New York Times, February 15, 1988) stated, “The researchers found statistically significant personality differences between the runners and the 30-yr-old male population as a whole.” A headline on the article said, “Research has shown that running can alter one’s moods.”


Sample survey – a subgroup of a larger population is questioned on a set of topics.  The information we gather from the sample is used to make inferences about the population.There is no manipulation of the respondent’s behavior – this is an observational study.


Results from a sample survey are used as if they are representative of the larger population – and they will be IF the sample is chosen correctly and if those selected cooperate in responding.A representative sample is a sample in which the relevant characteristics of the sample members are generally the same as the characteristics of the population.


Population – the entire group of interestSample – the part of the pop’n that we actually examine in order to gather informationSampling Frame – the list of units from which the sample is selected


The key is to use a proper sampling design – the design refers to the method used to choose the sample from the population.     If a sample is selected in a manner that guarantees that it would not be representative of the entire population, we get bias, or systematic error, by favoring some parts of the population over others.


Generally, the term bias refers to any problem in the design or conduct of a statistical study that tends to favor certain outcomes.  We cannot trust the conclusions of a biased study.


Bias can arise in many ways:A sample can be biased if the members of the sample differ in some specific way from the members of the general population of interest.A researcher is biased if he or she has a personal stake in a particular outcome.  In that case, the researcher might intentionally or unintentionally distort the true meaning of the data.


The data set itself is biased if its values were collected (intentionally or unintentionally) in a way that makes the data unrepresentative of the population.


A good design will prevent bias by using impersonal chance to choose the sample.  Random selection of a sample eliminates bias by giving all individuals an equal chance to be chosen, just as randomization eliminates bias in assigning experimental subjects.


Sampling Designs:Voluntary ResponseSimple Random Sample (SRS)Stratified Random SampleSystematic SampleCluster SampleConvenience SampleMulti-Stage Sample


Common Sources of Bias:Selection biasNon-response biasResponse biasVoluntary response (self-selected) sampleConvenience sample

Other things to think about:Wording and ordering of questionsConfidentiality and anonymityDefining terms


Undercoverage occurs when some groups in the population are left out of the process of choosing the sample.Non-response occurs when an individual chosen for the sample can’t be contacted or does not cooperate.