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Analytical strategies for the marble burying test: avoiding impossible predictions and invalid p-values
BACKGROUND: The marble burying test is used to measure repetitive and anxiety-related behaviour in rodents. The number of marbles that animals bury are count data (non-negative integers), which are bounded below by zero and above by the number of marbles present. Count data are often analysed using...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2015
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4395904/ https://www.ncbi.nlm.nih.gov/pubmed/25890220 http://dx.doi.org/10.1186/s13104-015-1062-7 |
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author | Lazic, Stanley E |
author_facet | Lazic, Stanley E |
author_sort | Lazic, Stanley E |
collection | PubMed |
description | BACKGROUND: The marble burying test is used to measure repetitive and anxiety-related behaviour in rodents. The number of marbles that animals bury are count data (non-negative integers), which are bounded below by zero and above by the number of marbles present. Count data are often analysed using normal linear models, which include the t-test and analysis of variance (ANOVA) as special cases. Linear models assume that the data are unbounded and that the variance is constant across groups. These requirements are rarely met with count data, leading to 95% confidence intervals that include impossible values (less than zero or greater than the number of marbles present), misleading p-values, and impossible predictions. Transforming the data or using nonparametric methods are common alternatives but transformations do not perform well when many zero values are present and nonparametric methods have several drawbacks. FINDINGS: The problems with using normal linear models to analyse marble burying data are demonstrated and generalised linear models (GLMs) are introduced as more appropriate alternatives. CONCLUSIONS: GLMs have been specifically developed to deal with count and other types of non-Gaussian data, are straightforward to use and interpret, and will lead to more sensible inferences. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13104-015-1062-7) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4395904 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-43959042015-04-14 Analytical strategies for the marble burying test: avoiding impossible predictions and invalid p-values Lazic, Stanley E BMC Res Notes Technical Note BACKGROUND: The marble burying test is used to measure repetitive and anxiety-related behaviour in rodents. The number of marbles that animals bury are count data (non-negative integers), which are bounded below by zero and above by the number of marbles present. Count data are often analysed using normal linear models, which include the t-test and analysis of variance (ANOVA) as special cases. Linear models assume that the data are unbounded and that the variance is constant across groups. These requirements are rarely met with count data, leading to 95% confidence intervals that include impossible values (less than zero or greater than the number of marbles present), misleading p-values, and impossible predictions. Transforming the data or using nonparametric methods are common alternatives but transformations do not perform well when many zero values are present and nonparametric methods have several drawbacks. FINDINGS: The problems with using normal linear models to analyse marble burying data are demonstrated and generalised linear models (GLMs) are introduced as more appropriate alternatives. CONCLUSIONS: GLMs have been specifically developed to deal with count and other types of non-Gaussian data, are straightforward to use and interpret, and will lead to more sensible inferences. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13104-015-1062-7) contains supplementary material, which is available to authorized users. BioMed Central 2015-04-11 /pmc/articles/PMC4395904/ /pubmed/25890220 http://dx.doi.org/10.1186/s13104-015-1062-7 Text en © Lazic; licensee BioMed Central. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Technical Note Lazic, Stanley E Analytical strategies for the marble burying test: avoiding impossible predictions and invalid p-values |
title | Analytical strategies for the marble burying test: avoiding impossible predictions and invalid p-values |
title_full | Analytical strategies for the marble burying test: avoiding impossible predictions and invalid p-values |
title_fullStr | Analytical strategies for the marble burying test: avoiding impossible predictions and invalid p-values |
title_full_unstemmed | Analytical strategies for the marble burying test: avoiding impossible predictions and invalid p-values |
title_short | Analytical strategies for the marble burying test: avoiding impossible predictions and invalid p-values |
title_sort | analytical strategies for the marble burying test: avoiding impossible predictions and invalid p-values |
topic | Technical Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4395904/ https://www.ncbi.nlm.nih.gov/pubmed/25890220 http://dx.doi.org/10.1186/s13104-015-1062-7 |
work_keys_str_mv | AT lazicstanleye analyticalstrategiesforthemarbleburyingtestavoidingimpossiblepredictionsandinvalidpvalues |