Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autor principal: Lazic, Stanley E
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
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
_version_ 1782366507588648960
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