Measurement and Statistics_Dasch_Date__031210

Measurement and Statistics_Dasch_Date__031210 - convert to...

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Exploring Assumptions - errors when we assume normal distribution but there actually isn’t homogeneity- when the variance is the same throughout the data Assumptions of Parametric Tests - interval data is not categorical - data is independent Assessing Normality Graphically - histogram - probability-probability plot plots the cumulative probability of a variable versus the cumulative probability of a distribution assume a normal distribution actual z score plotted against expected z score expect 68% of z scores between -1 and 1 skewed data plot to right and down (positive), up and left (negative - z score of 0 is normal
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Unformatted text preview: convert to z score by dividing value by standard error compare to absolute value of 1.96 (if greater, then significant skew or kurtosis - K-S test (Kolmogorov-Smirnov) tests if data differ from a normal distribution significant = non normal data non-significant = normal data SPSS P-P plot: analyze descriptive stats p plot Only need normal plots Z scores: descriptive stats frequencies stats skew and kurtosis data split file output organized by groups (gender) after, turn off split file (data split file analyze all data)...
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This note was uploaded on 08/30/2010 for the course PSYC 209 taught by Professor Hoffman during the Spring '08 term at University of Delaware.

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