Class notes SWRK 344
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This 3 page Class Notes was uploaded by Kirsten Swikert on Saturday September 24, 2016. The Class Notes belongs to SWRK 344 at Western Kentucky University taught by Dr. Getch in Fall 2016. Since its upload, it has received 2 views. For similar materials see Social Work Statistics and Data Analysis in Social Work at Western Kentucky University.
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Date Created: 09/24/16
Skewness The degree that a distribution of a variable (and the variable’s frequency polygon) are not symmetrical Skewness can be in either direction Positively skewed distributions have a tail to the right Negatively skewed distributions have a tail to the left Positively Skewed Negatively Skewed Distributions Kurtosis Is simply the degree of peakness of a distribution The variables measured cluster at the center When this happens the distribution is described as leptokurtic Platykurtic Distribution is flatter There are more at either end of the distribution While there is a slight rise near the center, there is not a heavy clustering around the center The Normal Curve (Mesokurtic) Referred to as the “bell shaped curve” or a normal distribution Distributions are more evenly distributed without bunching at the center or at the tails on either end Measurements at the center are the most common or frequent Measurements (scores) taper gradually as they get further from the mean The Standard Normal Distribution Is theoretical Is based on the normal curve It is perfectly symmetrical This does not occur “perfectly” Use of the Standard Normal Distribution We can substitute the mean of our data sample for 0 then use our sample’s calculated standard deviation to see how it falls within the distribution Ex: in our sample our mean was calculated to be 70, we calculated the standard deviation to be 7 o This means we can substitute 0 as the mean for 70 o 70+7=77 this is +1 standard deviation from the mean o 70-7=63 this is -1 standard deviation from the mean o we know that scores falling between 63-77 are 1 standard deviation from the mean and that the majority of our variables attributes will fall within that range
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