In the previous chapter, you learned when and how to compute several measures of central tendency. This chapter explores three other important properties of data: dispersion, skewness, and kurtosis. Measures of dispersion represent the spread or scatter of scores around a central point or the distinguishability of scores.
Four measures of this important property are described: range, semi-interquartile range, standard deviation, and index of dispersion. You will learn when and how to compute each of the measures.
Measures of skewness and kurtosis represent, respectively, the asymmetry and peakedness of data.
Knowledge of these two characteristics, along with knowledge of central tendency and dispersion, provide a fairly complete description of one’s data.
By now you have probably discovered how easy it is to enter wrong numbers in your calculator or transpose numbers when you read the calculator display. Such errors are a fact of life. Some errors are obvious; others are more difficult to detect. You will learn several statistical procedures for detecting scores that differ sufficiently from the main body of data as to raise questions about their accuracy.
After reading the chapter, you should know the following:
■ How to compute and interpret the range, semi-interquartile range, standard deviation, and index of dispersion
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The advantages of the four measures of dispersion and when to use each
■ How to detect scores whose accuracy is questionable
■ How to compute and interpret a measure of skewness
■ How to compute and interpret a measure of kurtosis