#SPSS 23 EXPORT ALGORITHM PROFESSIONAL#
However, access to professional statistical expertise is not always available. Our aim is not to replace that gold standard if professional help is at hand then that will always be the best route for data analysis. In an ideal world, the data generated from energy-balance experiments would be analysed with the help of a professional statistician. Therefore, the aim of this paper is to provide an algorithm as a step-by-step guide for performing this type of analysis.
#SPSS 23 EXPORT ALGORITHM HOW TO#
Thus, a common framework for this analysis is now widely agreed on by researchers from the entire field, from those working with model organisms such as mice and flies, to those studying humans.ĭespite this agreement of what should be done to analyse energy-balance data, researchers do not necessarily know how to do it.
![spss 23 export algorithm spss 23 export algorithm](https://i.ytimg.com/vi/HcMZ2BLdGw0/maxresdefault.jpg)
More recently, the same consensus has emerged among many researchers studying energy balance in small mammals ( Kaiyala and Schwartz, 2011 Tschöp et al., 2012). Rather, the optimal approach is to correct for mass effects using a regression-based approach called analysis of covariance (ANCOVA) or general linear modelling (GLM). expenditure divided by body mass, or lean body mass) because these approaches do not adequately normalise for the mass effect ( Allison et al., 1995 Poehlman and Toth, 1995). It was agreed that the best way forward was not to perform simple ratio calculations (e.g.
![spss 23 export algorithm spss 23 export algorithm](https://methods.sagepub.com/images/virtual/sage-encyclopedia-of-educational-research-measurement-evaluation/10.4135_9781506326139-fig158.jpg)
However, a consensus on this issue emerged in the 1990s in human studies. These issues are not new – the discussion of the optimal methods by which to normalise for body-mass effects began at least a century ago ( Rubner, 1883 Kleiber, 1932 Kleiber, 1961). A particular concern in recent studies of small mammals has been how to normalise intake or expenditure data for differences in body mass or body composition of the animals ( Arch et al., 2006 Butler and Kozak, 2010 Kaiyala and Schwartz, 2011 Tschöp et al., 2012). This will mean that data derived using different approaches and organisms can be interpreted and compared with greater consistency. The diversity of experimental approaches and organisms used to investigate energy balance calls for harmonisation in how data are analysed. We hope that this algorithm will assist in the statistically appropriate analysis of such data, a field in which there has been much confusion and some controversy. We also provide interpretations of the results obtained at each step. It can be used in combination with any commercial statistics package however, to assist with analysis, we have included detailed instructions for performing each step for three popular statistics packages (SPSS, MINITAB and R). The algorithm can be used to analyse data from either humans or experimental animals, such as small mammals or invertebrates.
![spss 23 export algorithm spss 23 export algorithm](https://bookdown.org/mwheymans/bookmi/images/fig1.18.png)
![spss 23 export algorithm spss 23 export algorithm](https://windows-cdn.softpedia.com/screenshots/SPSS-Statistics-Developer_6.png)
To facilitate using these best-practice methods, we present here an algorithm that provides a step-by-step guide for analysing energy-intake or -expenditure data. In the last few years, a consensus has been reached regarding the best methods for analysing such data. Although generating energy-intake or -expenditure data is relatively straightforward, the most appropriate way to analyse the data has been an issue of contention for many decades. The epidemics of obesity and diabetes have aroused great interest in the analysis of energy balance, with the use of organisms ranging from nematode worms to humans.