A note on replication of experiments

2014 ◽  
Vol 63 ◽  
pp. 138
Author(s):  
Christian Ritz ◽  
Jens Carl Streibig
2014 ◽  
Vol 45 (3) ◽  
pp. 223-231 ◽  
Author(s):  
Iris L. Žeželj ◽  
Biljana R. Jokić

Eyal, Liberman, and Trope (2008) established that people judged moral transgressions more harshly and virtuous acts more positively when the acts were psychologically distant than close. In a series of conceptual and direct replications, Gong and Medin (2012) came to the opposite conclusion. Attempting to resolve these inconsistencies, we conducted four high-powered replication studies in which we varied temporal distance (Studies 1 and 3), social distance (Study 2) or construal level (Study 4), and registered their impact on moral judgment. We found no systematic effect of temporal distance, the effect of social distance consistent with Eyal et al., and the reversed effect of direct construal level manipulation, consistent with Gong and Medin. Possible explanations for the incompatible results are discussed.


HortScience ◽  
2005 ◽  
Vol 40 (4) ◽  
pp. 1009B-1009
Author(s):  
Marc W. van Iersel

Do you accurately measure and report the growing conditions of your controlled environment experiments? Conditions in controlled environment plant growth rooms and chambers should be reported in detail. This is important to allow replication of experiments on plants, to compare results among facilities, and to avoid artefacts due to uncontrolled variables. The International Committee for Controlled Environment Guidelines, with representatives from the U.K. Controlled Environment Users' Group, the North American Committee on Controlled Environment Technology and Use (NCR-101), and Australasian Controlled Environment Working Group (ACEWG), has developed guidlines to report environmental conditions in controlled environment experiments. These guidelines include measurements of light, temperature, humidity, CO2, air speed, and fertility. A brochure with these guidelines and a sample paragraph on how to include this information in a manuscript will be available.


2005 ◽  
Vol 62 (1) ◽  
pp. 1-55 ◽  
Author(s):  
Melvyn Usselman ◽  
Alan Rocke ◽  
Christina Reinhart ◽  
Kelly Foulser

2015 ◽  
Vol 22 (4) ◽  
pp. 137-146 ◽  
Author(s):  
Martin F. Stoelen ◽  
Virginia Fernandez de Tejada ◽  
Alberto Jardon Huete ◽  
Carlos Balaguer ◽  
Fabio Paolo Bonsignorio

2021 ◽  
pp. 026921552110432
Author(s):  
Stefano Negrini ◽  
William Mark Magnus Levack ◽  
Thorsten Meyer ◽  
Carlotte Kiekens

Purpose: Responding to a recent editorial arguing against defining rehabilitation, we discuss the reasons for developing a classification of rehabilitation for research purposes, its philosophical background and some of the possible risks. Why define: Science requires the definition and classification of phenomena to allow replication of experiments and studies, and to allow interpretation and use of the findings. As understanding increases, the definitions can be refined. Defining rehabilitation does run the risk of excluding some interventions or practices that are either considered rehabilitation (perhaps wrongly) or are rehabilitation interventions; when identified, these errors in definition can be remedied. Defining rehabilitation for research purposes should not inhibit but could (possibly) orient research. Risk of not: Without a definition, rehabilitation will remain in a permanent limbo. Experts will (apparently) know what it is, while others are left guessing or failing to comprehend or recognise it. This uncertainty may reassure some people, because all possible interventions are included; we argue that it downgrades the understanding of our field because interventions that are not rehabilitation are, nonetheless, called rehabilitation. In an era of international collaboration, and of undertaking systematic reviews with metanalysis, we need a shared definition. Conclusion: Terminology is often controversial, but definition enables progress in understanding such that terms themselves can evolve over time.


2014 ◽  
Vol 56 (8) ◽  
pp. 1033-1048 ◽  
Author(s):  
Omar S. Gómez ◽  
Natalia Juristo ◽  
Sira Vegas

2021 ◽  
Author(s):  
Shaun Killen ◽  
Emil Christensen ◽  
Daphne Cortese ◽  
Libor Zavorka ◽  
Lucy Cotgrove ◽  
...  

Interest in the measurement of metabolic rates is growing rapidly, due to the relevance of metabolism in understanding organismal physiology, behaviour, evolution, and responses to environmental change. The study of metabolism in aquatic organisms is experiencing an especially pronounced expansion, with more researchers utilizing intermittent-closed respirometry as a research tool than ever before. Despite this, there remain no published guidelines on the reporting of methodological details when using intermittent-closed respirometry. Using a survey of the existing literature, we show that this lack of recommendations has led to incomplete and inconsistent reporting of methods for intermittent-closed respirometry over the last several decades. We also provide the first guidelines for reporting such methods, in the form of a checklist of details that are the minimum required for the interpretation, evaluation, and replication of experiments using intermittent-closed respirometry. This should increase consistency of the reporting of methods for studies that use this research technique. With the steep increase in studies using intermittent-closed respirometry over the last several years, now is the ideal time to standardise the reporting of methods so that data can be properly assessed by other scientists and conservationists.


2011 ◽  
Vol 25 (1) ◽  
pp. 165-169 ◽  
Author(s):  
David C. Blouin ◽  
Eric P. Webster ◽  
Jason A. Bond

The replication of experiments over multiple environments such as locations and years is a common practice in field research. A major reason for the practice is to estimate the effects of treatments over a variety of environments. Environments are frequently classed as random effects in the model for statistical analysis, while treatments are almost always classed as fixed effects. Where environments are random and treatments are fixed, it is not always necessary to include all possible interactions between treatments and environments as random effects in the model. The rationale for decisions about the inclusion or exclusion of fixed by random effects in a mixed model is presented. Where the effects of treatments over broad populations of environments are to be estimated, it is often most appropriate to include only those fixed by random effects that reference experimental units.


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