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  • Generalized linear models (GLMs) are increasingly used in modern statistical analyses of sex ratio variation because they are able to determine variable design effects on binary response data. However, in applying GLMs, authors frequently neglect the hierarchical structure of sex ratio data, thereby increasing the likelihood of committing 'type I' error. Here, we argue that whenever clustered (e.g., brood) sex ratios represent the desired level of statistical inference, the clustered data structure ought to be taken into account to avoid invalid conclusions. Neglecting the between-cluster variation and the finite number of clusters in determining test statistics, as implied by using likelihood ratio-based chi (2)-statistics in conventional GLM, results in biased (usually overestimated) test statistics and pseudoreplication of the sample. Random variation in the sex ratio between clusters (broods) can often be accommodated by scaling residual binomial (error) variance for overdispersion, and using F-te
  • Generalized linear models (GLMs) are increasingly used in modern statistical analyses of sex ratio variation because they are able to determine variable design effects on binary response data. However, in applying GLMs, authors frequently neglect the hierarchical structure of sex ratio data, thereby increasing the likelihood of committing 'type I' error. Here, we argue that whenever clustered (e.g., brood) sex ratios represent the desired level of statistical inference, the clustered data structure ought to be taken into account to avoid invalid conclusions. Neglecting the between-cluster variation and the finite number of clusters in determining test statistics, as implied by using likelihood ratio-based chi (2)-statistics in conventional GLM, results in biased (usually overestimated) test statistics and pseudoreplication of the sample. Random variation in the sex ratio between clusters (broods) can often be accommodated by scaling residual binomial (error) variance for overdispersion, and using F-te (en)
Title
  • Analysis of brood sex ratios: implications of offspring clustering.
  • Analysis of brood sex ratios: implications of offspring clustering. (en)
skos:prefLabel
  • Analysis of brood sex ratios: implications of offspring clustering.
  • Analysis of brood sex ratios: implications of offspring clustering. (en)
skos:notation
  • RIV/68081766:_____/01:67010007!RIV/2003/AV0/A67003/N
http://linked.open.../vavai/riv/strany
  • 293;301
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GA524/01/1316), P(GA524/96/1095), Z(AV0Z6093917)
http://linked.open...iv/cisloPeriodika
  • 4
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 673075
http://linked.open...ai/riv/idVysledku
  • RIV/68081766:_____/01:67010007
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • generalized linear mixed models; random coefficients; multilevel analysis (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • DE - Spolková republika Německo
http://linked.open...ontrolniKodProRIV
  • [2F790E1CA211]
http://linked.open...i/riv/nazevZdroje
  • Behavioral Ecology and Sociobiology
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...ocetUcastnikuAkce
http://linked.open...nichUcastnikuAkce
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • N/A
http://linked.open...iv/tvurceVysledku
  • Tkadlec, Emil
  • Krackow, S.
http://linked.open...n/vavai/riv/zamer
issn
  • 0340-5443
number of pages
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