Statistics for Categorical, Nonparametric, and Distribution-Free Data

Jesse Egbert, Geoffrey T. LaFlair

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Scopus citations

Abstract

Researchers in applied linguistics frequently encounter data that could be considered nontraditional, including categorical data, data that does not fit a traditional parametric model, and data that may not fit any distribution (distribution free). In this chapter we describe statistical methods for handling such data. We begin by introducing methods for analyzing categorical data, including the use of basic descriptive statistics such as measures of central tendency, measures of dispersion, frequency counts, and normed rates of occurrence. We then introduce when, why, and how to use alternatives to traditional parametric statistical tests, including nonparametric analogs, permutation tests, and bootstrapping.

Original languageEnglish (US)
Title of host publicationThe Palgrave Handbook of Applied Linguistics Research Methodology
PublisherPalgrave Macmillan
Pages523-539
Number of pages17
ISBN (Electronic)9781137599001
ISBN (Print)9781137598998
DOIs
StatePublished - Jan 1 2018

Keywords

  • Bootstrapping
  • Categorical data analysis
  • Distribution-free data
  • Nonparametric statistics
  • Permutation tests

ASJC Scopus subject areas

  • Arts and Humanities(all)
  • Social Sciences(all)

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