{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

Measurement and Statistics_Dasch_Date__031210

Measurement and Statistics_Dasch_Date__031210 - • convert...

Info iconThis preview shows page 1. Sign up to view the full content.

View Full Document Right Arrow Icon
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
Background image of page 1
This is the end of the preview. Sign up to access the rest of the document.

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)...
View Full Document

{[ snackBarMessage ]}

Ask a homework question - tutors are online