Statistics for Categorical, Nonparametric, and Distribution-Free Data

Jesse Egbert; Geoffrey T. LaFlair

doi:10.1057/978-1-137-59900-1_23

Statistics for Categorical, Nonparametric, and Distribution-Free Data

Jesse Egbert, Geoffrey T. LaFlair

English

Research output: Chapter in Book/Report/Conference proceeding › Chapter

5 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 language	English (US)
Title of host publication	The Palgrave Handbook of Applied Linguistics Research Methodology
Publisher	Palgrave Macmillan
Pages	523-539
Number of pages	17
ISBN (Electronic)	9781137599001
ISBN (Print)	9781137598998
DOIs	https://doi.org/10.1057/978-1-137-59900-1_23
State	Published - Jan 1 2018

Keywords

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

ASJC Scopus subject areas

General Arts and Humanities
General Social Sciences

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10.1057/978-1-137-59900-1_23

Cite this

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AB - 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.

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Statistics for Categorical, Nonparametric, and Distribution-Free Data

Abstract

Keywords

ASJC Scopus subject areas

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