Abstract
The NPAR procedure allows the user to carry out a variety of non-parametric statistical tests. Unlike parametric statistics (including the t-test, anova, the product-moment correlation, etc.) non-parametric statistics make few assumptions about the nature of the data. They may be particularly suitable where there are relatively few cases in the population we wish to examine, or where a frequency distribution is badly skewed (rather than symmetrical). Whereas a procedure like ANOVA demands that the dependent variable be truly numerical, non-parametric tests can handle data at other levels of measurement. In particular they allow us to analyze nominal and ordinal data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Rights and permissions
Copyright information
© 1987 Neil Frude
About this chapter
Cite this chapter
Frude, N. (1987). Statistical Analysis V: Non-parametric Statistics. In: A Guide to SPSS/PC+. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-8985-0_13
Download citation
DOI: https://doi.org/10.1007/978-1-4757-8985-0_13
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4757-8987-4
Online ISBN: 978-1-4757-8985-0
eBook Packages: Springer Book Archive