Variable reporting and quantitative reviews: a comparison of three meta‐analytical techniques

By M. Lajeunesse, M. Forbes. 2003

150 citations recorded by the source corpus.

Subfields: Public Health, Environmental and Occupational Health, Social Psychology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/6e71a9704c08e02504b21d1c12f1d957274570ad

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Public Health, Environmental and Occupational Health

1. G. Markó
2. James T. Carlton
3. G. Herczeg
4. A. Agrawal
5. Oswald J. Schmitz
6. A. Rosenblatt
7. L. Garamszegi
8. C. Sorte

In Social Psychology

1. G. Markó
2. James T. Carlton
3. G. Herczeg
4. Oswald J. Schmitz
5. A. Power
6. A. Rosenblatt
7. L. Garamszegi
8. C. Sorte

In Statistics and Probability

1. G. Markó
2. James T. Carlton
3. G. Herczeg
4. A. Agrawal
5. Oswald J. Schmitz
6. William Godsoe
7. L. Packer
8. A. Rosenblatt

Citations in the corpus

• ENEMY RELEASE? AN EXPERIMENT WITH CONGENERIC PLANT PAIRS AND DIVERSE ABOVE- AND BELOWGROUND ENEMIES (2005, methodology): “However, if effects of multiple enemies frequently occur in opposing directions, they may cancel out the advantage for introduced species.”
• Consumer effects decline with prey diversity (2004, unknown): “Nonetheless, following the recommendations of Lajeunesse & Forbes (2003), we considered both measures of grazer impact in our analyses to preclude the possibility that observed patterns might result from the unique statistical properties of any given metric.”
• Relative effects of grazers and nutrients on seagrasses: a meta-analysis approach (2004, background): “Decisions to include or exclude particular studies can have a large impact on the results of a meta-analysis, particularly if the number of studies is small (Englund et al. 1999, Gates 2002, Lajeunesse & Forbes 2003).”
• WHAT DETERMINES THE STRENGTH OF A TROPHIC CASCADE (2005, methodology): “…we have included a greater number of studies rather than limiting the analysis to studies with precision estimates, because increased sample sizes decrease the probability of Type I estimation errors in meta-analyses (Arnqvist and Wooster 1995b, Englund et al. 1999, Lajeunesse and Forbes 2003).”
• Changing philosophies and tools for statistical inferences in behavioral ecology (2009, unknown): “Such information can be used for effective biological interpretations because the comparison of these metrics in the form of evidence ratio permits evidentiary statements about the plausibility of different hypotheses, given the data (see examples in Figures 1 and 2).”
• Interactive effects of multiple climate change variables on trophic interactions: a meta-analysis (2014, methodology): “We chose the log response ratio over other metrics, such as Hedge’s d [71], because of 1) its ability to detect true effects [72], 2) its ability to remain more normally distributed even with small sample sizes [69], 3) its linear nature [69], and 4) its more explicit ties to ecological dynamics [73”
• Increased genetic differentiation in a specialist versus a generalist bee: implications for conservation (2006, background): “This lack of significance might be due to statistical noise in the dataset generated from pooling data over many taxa regardless of phylogenetic relationships (Carpenter 1992), and through errors arising from variable reporting of data inherent in meta-analysis methodologies (Lajeunesse and Forbes, ”
• Impacts of Dreissena invasions on benthic macroinvertebrate communities: a meta‐analysis (2007, background): “Nonetheless, by including a greater number of sites we have reduced the probabilities of both type I and type II errors in our meta-analysis (Lajeunesse & Forbes, 2003).”
• Marine range shifts and species introductions: comparative spread rates and community impacts (2010, methodology): “We compared range shift and introduction impact studies by calculating the log-transformed response ratio (ln RR), a commonly used meta-analytical metric that reduces measures on variable scales to proportional values (Hedges et al., 1999; Lajeunesse & Forbes, 2003).”
• A meta-analysis of correlated behaviours with implications for behavioural syndromes: mean effect size, publication bias, phylogenetic effects and the role of mediator variables (2012, methodology): “The meta-analytical approach is a useful tool to make a quantitative summary from the published literature that can serve as a statistical basis for theoretical generalizations (Arnquist and Wooster 1995; Lajeunesse and Forbes 2003; Adams 2008; Borenstein et al. 2009).”