An Introduction to the General Monotone Model with Application to Two Problematic Data Sets

Bibliographic Data

ID6169673
AuthorsMichael R Dougherty (0000-0001-9547-1937, University of Maryland, College Park, MD, USA), Rick P Thomas (0000-0003-3610-2722, Georgia Institute of Technology), Ryan P Brown (0000-0003-2401-6077, University of Oklahoma, Norman, OK, USA), Jeffrey S Chrabaszcz (0000-0002-8434-9511, University of Maryland, College Park, MD, USA), Joe W Tidwell (University of Maryland, College Park, MD, USA)
Year2015
Volume45
Issue1
Pages223-271
Publication date2015-01-21
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueSociological Methodology (JOURNAL)
Journal identifiersISSN: 0081-1750 • E-ISSN: 1467-9531
PublisherSAGE Publishing (PUBLISHER • US)
DOI10.1177/0081175014562589
OpenAlexW2123280642
LanguageEN
Citations received2
References cited47

We argue that the mismatch between data and analytical methods, along with common practices for dealing with “messy” data, can lead to inaccurate conclusions. Specifically, using previously published data on racial bias and culture of honor, we show that manifest effects, and therefore theoretical conclusions, are highly dependent on how researchers decide to handle extreme scores and nonlinearities when data are analyzed with traditional approaches. Within LS approaches, statistical effects appeared or disappeared on the basis of the inclusion or exclusion of as little as 1.5% (3 of 198) of the data, and highly predictive variables were masked by nonlinearities. We then demonstrate a new statistical modeling technique called the general monotone model (GeMM) and show that it has a number of desirable properties that may make it more appropriate for modeling messy data: It is more robust to extreme scores, less affected by outlier analyses, and more robust to violations of linearity on both the response and predictor variables compared with a variety of well-established statistical algorithms and frequently possesses greater statistical power. We argue that using procedures that make fewer assumptions about the data, such as GeMM, can lessen the need for researchers to use data-editing strategies (e.g., to apply transformations or to engage outlier analyses) on their data to satisfy often unrealistic statistical assumptions, leading to more consistent and accurate conclusions about data than traditional approaches of data analysis

Data mining · Econometrics · Machine learning · Monotone polygon · Outlier · Statistical model · Advanced Statistical Methods and Models · Artificial Intelligence · Computer Science · Mathematics · Qualitative Comparative Analysis Research · Social and Intergroup Psychology

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