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Statistical Conclusion Validity: Some Common Threats and Simple Remedies

The ultimate goal of research is to produce dependable knowledge or to provide the evidence that may guide practical decisions. Statistical conclusion validity (SCV) holds when the conclusions of a research study are founded on an adequate analysis of the data, generally meaning that adequate statis...

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Autor principal: García-Pérez, Miguel A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Research Foundation 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3429930/
https://www.ncbi.nlm.nih.gov/pubmed/22952465
http://dx.doi.org/10.3389/fpsyg.2012.00325
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author García-Pérez, Miguel A.
author_facet García-Pérez, Miguel A.
author_sort García-Pérez, Miguel A.
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description The ultimate goal of research is to produce dependable knowledge or to provide the evidence that may guide practical decisions. Statistical conclusion validity (SCV) holds when the conclusions of a research study are founded on an adequate analysis of the data, generally meaning that adequate statistical methods are used whose small-sample behavior is accurate, besides being logically capable of providing an answer to the research question. Compared to the three other traditional aspects of research validity (external validity, internal validity, and construct validity), interest in SCV has recently grown on evidence that inadequate data analyses are sometimes carried out which yield conclusions that a proper analysis of the data would not have supported. This paper discusses evidence of three common threats to SCV that arise from widespread recommendations or practices in data analysis, namely, the use of repeated testing and optional stopping without control of Type-I error rates, the recommendation to check the assumptions of statistical tests, and the use of regression whenever a bivariate relation or the equivalence between two variables is studied. For each of these threats, examples are presented and alternative practices that safeguard SCV are discussed. Educational and editorial changes that may improve the SCV of published research are also discussed.
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spelling pubmed-34299302012-09-05 Statistical Conclusion Validity: Some Common Threats and Simple Remedies García-Pérez, Miguel A. Front Psychol Psychology The ultimate goal of research is to produce dependable knowledge or to provide the evidence that may guide practical decisions. Statistical conclusion validity (SCV) holds when the conclusions of a research study are founded on an adequate analysis of the data, generally meaning that adequate statistical methods are used whose small-sample behavior is accurate, besides being logically capable of providing an answer to the research question. Compared to the three other traditional aspects of research validity (external validity, internal validity, and construct validity), interest in SCV has recently grown on evidence that inadequate data analyses are sometimes carried out which yield conclusions that a proper analysis of the data would not have supported. This paper discusses evidence of three common threats to SCV that arise from widespread recommendations or practices in data analysis, namely, the use of repeated testing and optional stopping without control of Type-I error rates, the recommendation to check the assumptions of statistical tests, and the use of regression whenever a bivariate relation or the equivalence between two variables is studied. For each of these threats, examples are presented and alternative practices that safeguard SCV are discussed. Educational and editorial changes that may improve the SCV of published research are also discussed. Frontiers Research Foundation 2012-08-29 /pmc/articles/PMC3429930/ /pubmed/22952465 http://dx.doi.org/10.3389/fpsyg.2012.00325 Text en Copyright © 2012 García-Pérez. http://www.frontiersin.org/licenseagreement This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.
spellingShingle Psychology
García-Pérez, Miguel A.
Statistical Conclusion Validity: Some Common Threats and Simple Remedies
title Statistical Conclusion Validity: Some Common Threats and Simple Remedies
title_full Statistical Conclusion Validity: Some Common Threats and Simple Remedies
title_fullStr Statistical Conclusion Validity: Some Common Threats and Simple Remedies
title_full_unstemmed Statistical Conclusion Validity: Some Common Threats and Simple Remedies
title_short Statistical Conclusion Validity: Some Common Threats and Simple Remedies
title_sort statistical conclusion validity: some common threats and simple remedies
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3429930/
https://www.ncbi.nlm.nih.gov/pubmed/22952465
http://dx.doi.org/10.3389/fpsyg.2012.00325
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