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Cited by 465Data Visualization and AnalyticsStatistics Education and MethodologiesStatistical Methods and Applications

The DOI matches its Crossref record; 119 of the 119 references it cites were found in a registry or an index.

Principles and procedures of exploratory data analysis.

Behrens, John T.·1997·Psychological Methods
DOI 10.1037/1082-989x.2.2.131
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Abstract

Exploratory data analysis (EDA) is a well-established statistical tradition that provides conceptual and computational tools for discovering patterns to foster hypothesis development and refinement. These tools and attitudes complement the use of significance and hypothesis tests used in confirmatory data analysis (CDA). Although EDA complements rather than replaces CDA, use of CDA without EDA is seldom warranted. Even when well-specified theories are held, EDA helps one interpret the results of CDA and may reveal unexpected or misleading patterns in the data. This article introduces the central heuristics and computational tools of EDA and contrasts it with CDA and exploratory statistics in general. EDA techniques are illustrated using previously published psychological data. Changes in statistical training and practice are recommended to incorporate these tools. The widespread availability of software for graphical data analysis and calls for increased use of exploratory data analysis (EDA) on epistemic grounds (e.g. Cohen, 1994) have increased the visibility of EDA. Nevertheless, few psychologists receive explicit training in the beliefs or procedures of this tradition. Huberty (1991) remarked that statistical texts are likely to give cursory references to common EDA techniques such as stem-and-leaf plots, box plots, or residual analysis and yet seldom integrate these techniques throughout a book. A survey of graduate training programs in psychology corroborates such an impression

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Crossref · 10.1037/1082-989x.2.2.131

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119 of 119 references verified0 citation errors · 0 not checkable

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  • Outliers were adjusted to 1.5 times the interquartile range below or above the 25th or 75th percentile, and this resulted in satisfactory skew and kurtosis for all variables.

    “Outliers were adjusted to 1.5 times the interquartile range below or above the 25th or 75th percentile, resulting in satisfactory skew/kurtosis for all variables (Behrens, 1997).”

    From Relationship of Mindfulness to Distress and Cortisol Response in Adolescent Girls At-Risk for Type 2 Diabetes. (2018) · cites this paper as Behrens, 1997

  • Statisticians recommend adjusting outliers to 1.5 times the interquartile range because it minimizes their influence, changes the overall distribution only minimally, and avoids bias from removing outliers.

    “This strategy has been recommended by statisticians in order to minimize outliers’ influence on the distribution, minimally change the distribution overall, and avoid potential bias associated with eliminating outliers (Behrens, 1997).”

    From Relationship of Mindfulness to Distress and Cortisol Response in Adolescent Girls At-Risk for Type 2 Diabetes. (2018) · cites this paper as Behrens, 1997

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Metadata

Authors
Behrens, J. T.
Affiliations
Arizona State University (US)
Corresponding author
Behrens, John T.
ORCID
Behrens, John T.
Venue
Psychological Methods
Publisher
American Psychological Association (APA)
Published
1997-06
Volume
2
Issue
2
Pages
131-160
ISSN
  • 1082-989X (ISSN-L)
  • 1939-1463
DOI
10.1037/1082-989x.2.2.131
OpenAlex
W2090847193
Cited by
465
References
119
Citations by year
2026: 14 · 2025: 17 · 2024: 27 · 2023: 28 · 2022: 37 · 2021: 45 · 2020: 36 · 2019: 34 · 2018: 19 · 2017: 26 · 2016: 21 · 2015: 17 · 2014: 18 · 2013: 17 · 2012: 17
FWCI
8.26
Type
journal article
Language
en
Open access
closed
Locations
Psychological Methods (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Data Visualization and Analytics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistics Education and Methodologies
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
Keywords
exploratory data analysis, confirmatory data analysis, exploratory statistics, statistical training, hypothesis development, graphical data analysis, stem-and-leaf plots, box plots, residual analysis

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