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pierce

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Question
Answer
null hypothesis   show
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show that something other than chance caused the results to look they way they do. It’s not just an accident that the results look the way they do. Something made it happen.  
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show The researcher has to decide just how unlikely the null hypothesis has to be to get them to not believe that it’s true. The odds the researcher decides to use in this situation are referred to as the this for the decision.  
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show the frequency you would expect to get by chance  
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observed frequency   show
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construct   show
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show the place where the person really is on the scale going from the lowest possible score to the highest possible score. The score that we obtain is just a best guess as to what the person’s true score really is.  
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construct validity   show
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show The measure is strongly correlated with other measures of the same construct. Example: a new measure of depression is strongly correlated with other established measures of depression.  
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show The new test is uncorrelated with other tests that are clearly designed to measure different constructs.  
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positive relationship   show
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show it’s a situation where higher scores on one variable tend to go along with having lower scores on the other variable.  
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show the two variables have nothing to do with each other, as in this case, one’s answer about the direction of the relationship  
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perfect relationship   show
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show If you run a straight line through the points in the scatterplot and line runs pretty close to those points, but not right through all of them you’d say that you were dealing with this  
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cross-product   show
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show the name for the mean of a bunch of cross-products  
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show is that the researcher may not have measured scores for this third variable, in which case the researcher has no way of knowing the true cause of the relationship between X and Y.  
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reliability   show
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validity   show
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domain of sampling   show
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where does the error come from   show
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show the measure may contain info from a different construct and not obtain all the info relevant to the construct  
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test administrationg   show
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internal consistancy   show
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show average correlation among the items  
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show cause====>effect, cant know which variable occurred first  
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loaded question   show
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show attempts to influence the response. EX: shouldnt first year students be aloud to bring a car to campus?  
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double barreled question   show
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show leaves it up to the subject of info theyre going to give you, not restricting range of responses theyre going to give you. EX: counselor---> ask patient  
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show ask someone specifically what meds the person takes, give options like 8 meds and they check ones that apply to them, researcher determines possbile responses  
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show "other" is added as an alternate response. questionnaire that says race and gives you a list and at the bottom it says other in case none apply to you.  
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rating scale   show
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likert rating scale   show
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show every member of the population has an equal chance of being in the sample  
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stratified random sample   show
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test-retest reliability   show
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show compare odd and even scores  
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