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the extent to which a measure is repeatable or consistent. If it is reliable, repeating that same measure on the same case/observation will yield the same values for the variable
r=t/(t+e) 


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test retestperform the same test multiple times
alternative formsame test/concept, different implementation/method and different time, reliability assesed by correlation
Intercoder Reliabilitydifferent observer using the same measure 


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a valid measure accurately represents the concept it is trying to measure. an invalid measure measures something else than what was intended 


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face validity, content validity construct validity 


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seems like a valid way to measure a concept on the face value 


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the amount to which a measure represents/addresses all faces of a concept 


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the amount to which it is related to other measures that it is required to be related to. 


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test retestperform the same test multiple times 


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alternative formsame test/concept, different implementation/method and different time, reliability assesed by correlation 


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Intercoder Reliabilitydifferent observer using the same measure 


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1. clear goal 2. reliable and valid measure 3. comparaility 4. know your data 


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characterisits, general and specific, declarative form, abt expected relationship, brief, direct, guided by a theory 


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measures symmetry, not skewed: cases tend to cluster symmetrically around the mean and taper off evenly
skewed: one tail is longer and skinnier, affects mean not median 


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right tail is longer and skinnier, pulls mean upward 


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left tail is longer and skinnier, pulls mean downward 


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1. categorical 2. ordinal 3. continuous 


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variables are clearly defined in seperate categories. cannot rank and there is no natural order 


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varaibles are clearly defined. you can rank. not equal unit differences ex. political identification left independent right 


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variables clearly defined, can rank, equal unit differences ex. annual income 


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organize and summarize data frequencies, porportions, percents, ratios, rates 


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estimation of the center of a distribution of values, can use mean or median 


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can be presented using a box and whisker plot 


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how spread out a distribution is var(y)= s^2(y)={the sum of (yi average y)^2} /[n1] 


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average (sum of Yi)/n "expected value" 


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more accurate measure of the distribtion, measures how far an observation is from the mean. average difference between each of our cases and the mean
st dev= s(y)= square root of variance= square root of [(sum yimean y)^2/n1] 


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sum (yi mean 7)=0 the sum of (all variables of y minus the mean) is equal to zero 


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the sum of (yimean y)^2 < the sum of (yic)^2 when c doesnt equal the mean of y 


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regardless of the distribution, the expected value(mean) for the sample will be normally distributed around the population expected value (mean) 


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skewness=0 kurtosis=0 mean=median=mode 1 st dev=68% of data 2 st dev=95% of data 3 st dev=99.7% of data 


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indicates the steepness of the statistical distribution positive=very steep negative=not steep, flatter 


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1. credible causal mechanism? 2. could y cause x? 3. covariation? 4. confounding variable z? 


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1.Don't let data drive the theory 2.consider only Empirical evidence 3.make your theories Causal 4.Avoid normative statements 5.Purse parsimony and generality 


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standard deviation of sample mean sy/square root of n 


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how to find the range of st deviation 

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mean of y +/ 1 x se= range for 1 st dev(68%of data)
mean of y +/ 2 x se= range for 2 st dev(95%of data)
mean of y +/ 3 x se= range for 3 st dev(99.7%of data) 

