AER (version 1.2-9)

EquationCitations: Number of Equations and Citations for Evolutionary Biology Publications

Description

Analysis of citations of evolutionary biology papers published in 1998 in the top three journals (as judged by their 5-year impact factors in the Thomson Reuters Journal Citation Reports 2010).

Usage

data("EquationCitations")

Format

A data frame containing 649 observations on 13 variables.

journal

Factor. Journal in which the paper was published (The American Naturalist, Evolution, Proceedings of the Royal Society of London B: Biological Sciences).

authors

Character. Names of authors.

volume

Volume in which the paper was published.

startpage

Starting page of publication.

pages

Number of pages.

equations

Number of equations in total.

mainequations

Number of equations in main text.

appequations

Number of equations in appendix.

cites

Number of citations in total.

selfcites

Number of citations by the authors themselves.

othercites

Number of citations by other authors.

theocites

Number of citations by theoretical papers.

nontheocites

Number of citations by nontheoretical papers.

Details

Fawcett and Higginson (2012) investigate the relationship between the number of citations evolutionary biology papers receive, depending on the number of equations per page in the cited paper. Overall it can be shown that papers with many mathematical equations significantly lower the number of citations they receive, in particular from nontheoretical papers.

References

Fawcett, T.W. and Higginson, A.D. (2012). Heavy Use of Equations Impedes Communication among Biologists. PNAS -- Proceedings of the National Academy of Sciences of the United States of America, 109, 11735--11739. http://dx.doi.org/10.1073/pnas.1205259109

See Also

PhDPublications

Examples

# NOT RUN {
## load data and MASS package
data("EquationCitations", package = "AER")
library("MASS")

## convenience function for summarizing NB models
nbtable <- function(obj, digits = 3) round(cbind(
  "OR" = exp(coef(obj)),
  "CI" = exp(confint.default(obj)),
  "Wald z" = coeftest(obj)[,3],
  "p" = coeftest(obj)[, 4]), digits = digits)


#################
## Replication ##
#################

## Table 1
m1a <- glm.nb(othercites ~ I(equations/pages) * pages + journal,
  data = EquationCitations)
m1b <- update(m1a, nontheocites ~ .)
m1c <- update(m1a, theocites ~ .)
nbtable(m1a)
nbtable(m1b)
nbtable(m1c)

## Table 2
m2a <- glm.nb(
  othercites ~ (I(mainequations/pages) + I(appequations/pages)) * pages + journal,
  data = EquationCitations)
m2b <- update(m2a, nontheocites ~ .)
m2c <- update(m2a, theocites ~ .)
nbtable(m2a)
nbtable(m2b)
nbtable(m2c)


###############
## Extension ##
###############

## nonlinear page effect: use log(pages) instead of pages+interaction
m3a <- glm.nb(othercites ~ I(equations/pages) + log(pages) + journal,
  data = EquationCitations)
m3b <- update(m3a, nontheocites ~ .)
m3c <- update(m3a, theocites ~ .)

## nested models: allow different equation effects over journals
m4a <- glm.nb(othercites ~ journal / I(equations/pages) + log(pages),
  data = EquationCitations)
m4b <- update(m4a, nontheocites ~ .)
m4c <- update(m4a, theocites ~ .)

## nested model best (wrt AIC) for all responses
AIC(m1a, m2a, m3a, m4a)
nbtable(m4a)
AIC(m1b, m2b, m3b, m4b)
nbtable(m4b)
AIC(m1c, m2c, m3c, m4c)
nbtable(m4c)
## equation effect by journal/response
##           comb nontheo theo
## AmNat     =/-  -       +
## Evolution =/+  =       +
## ProcB     -    -       =/+
# }