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Summary Statistics Rules Overview

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  • Course
  • Introduction to Research Methodology (SCOM77613902AY)
  • Institution
  • Universiteit Van Amsterdam (UvA)
  • Book
  • Discovering Statistics Using IBM SPSS

A short overview of all rules relevant for the statistics course of the Communication Science pre-master. What were the critical values again? What does Levene's test mean? Is my effect size small or big....? Don't hesitate, just check this overview!! (Got a 7.3 for the course)

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  • Unknown
  • August 15, 2018
  • 2
  • 2017/2018
  • Summary

Subjects

  • pre master uva
  • stats uva
  • rmtt uva
  • statistics rules
  • statistic overview

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  • Universiteit van Amsterdam (UvA)
  • Communicatiewetenschap
  • Introduction to Research Methodology (SCOM77613902AY)
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By: alisahkamps • 1 year ago

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The Rules of Thumb – A Compilaton


1.Critial Values
 95%  1.95
 99%  2.58
 99.9%  3.29

2.Cohen’s d of effeit size
 0.2 small
 0.5 moderate
 0.8 large

3.Pearson’s Correlaton Coefiient r
 0.10  small
 0.30  moderate
 0.50  large
 Here, in general, 0 means no assoiiaton, whereas 1 indiiates a perfeit one

4.Levene’s Test
 If the result is >.05 (non-signifiantt, the assumpton of homogeneity is met and the
varianies are equal  read from the upper row, “equal varianies assumed”
 If the result is below .05, the assumpton of homogeneity is not met  read from the
lower row, “equal varianies not assumed”
5.Eta Squared
 0.01  small
 0.09  medium
 0.25  large
 Eta, same as Pearson’s r, only ranges from 0 to 1

6.R-Squared
 R-Squared ian be interpreted the same way r is  it ranges from 0 (0% ian be
explained by the variables in questont to 1 (100% ian be explained by the variablest
 The small r tells you, by the way, only how muih a single variable aiiounts for the
total varianie
7.Communality (Faitor Analysist
 Communality of 0  variable shares none of its varianie with another variable
 Communality of 1  variable has no unique varianie whatsoever

8.Kaiser’s Criterion (Faitor Analysist
 Requires you to retain all faitors with an eigenvalue greater than 1

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