Literature DB >> 24079924

Why the resistance to statistical innovations? Bridging the communication gap.

Donald Sharpe1.   

Abstract

While quantitative methodologists advance statistical theory and refine statistical methods, substantive researchers resist adopting many of these statistical innovations. Traditional explanations for this resistance are reviewed, specifically a lack of awareness of statistical developments, the failure of journal editors to mandate change, publish or perish pressures, the unavailability of user friendly software, inadequate education in statistics, and psychological factors. Resistance is reconsidered in light of the complexity of modern statistical methods and a communication gap between substantive researchers and quantitative methodologists. The concept of a Maven is introduced as a means to bridge the communication gap. On the basis of this review and reconsideration, recommendations are made to improve communication of statistical innovations. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Mesh:

Year:  2013        PMID: 24079924     DOI: 10.1037/a0034177

Source DB:  PubMed          Journal:  Psychol Methods        ISSN: 1082-989X


  16 in total

1.  Increasing Literacy in Quantitative Methods: The Key to the Future of Canadian Psychology.

Authors:  Alyssa Counsell; Robert A Cribbie; Lisa L Harlow
Journal:  Can Psychol       Date:  2016-08

Review 2.  Single-Case Research Methods: History and Suitability for a Psychological Science in Need of Alternatives.

Authors:  Camilo Hurtado-Parrado; Wilson López-López
Journal:  Integr Psychol Behav Sci       Date:  2015-09

3.  The fickle P value generates irreproducible results.

Authors:  Lewis G Halsey; Douglas Curran-Everett; Sarah L Vowler; Gordon B Drummond
Journal:  Nat Methods       Date:  2015-03       Impact factor: 28.547

4.  Statistical learning theory for high dimensional prediction: Application to criterion-keyed scale development.

Authors:  Benjamin P Chapman; Alexander Weiss; Paul R Duberstein
Journal:  Psychol Methods       Date:  2016-07-25

Review 5.  Towards the automatic classification of neurons.

Authors:  Rubén Armañanzas; Giorgio A Ascoli
Journal:  Trends Neurosci       Date:  2015-03-09       Impact factor: 13.837

6.  Interpreting Interaction Effects in Generalized Linear Models of Nonlinear Probabilities and Counts.

Authors:  Connor J McCabe; Max A Halvorson; Kevin M King; Xiaolin Cao; Dale S Kim
Journal:  Multivariate Behav Res       Date:  2021-02-01       Impact factor: 3.085

7.  Time series analysis for psychological research: examining and forecasting change.

Authors:  Andrew T Jebb; Louis Tay; Wei Wang; Qiming Huang
Journal:  Front Psychol       Date:  2015-06-09

8.  Difficult Decisions: A Qualitative Exploration of the Statistical Decision Making Process from the Perspectives of Psychology Students and Academics.

Authors:  Peter J Allen; Kate P Dorozenko; Lynne D Roberts
Journal:  Front Psychol       Date:  2016-02-16

9.  On the reproducibility of meta-analyses: six practical recommendations.

Authors:  Daniël Lakens; Joe Hilgard; Janneke Staaks
Journal:  BMC Psychol       Date:  2016-05-31

10.  Predictors of Citation Rate in Psychology: Inconclusive Influence of Effect and Sample Size.

Authors:  Paul H P Hanel; Jennifer Haase
Journal:  Front Psychol       Date:  2017-07-11
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