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The current study examines whether R or SPSS output induces greater initial anxiety in students and whether anxiety toward one or both changes after being taught one type of software output throughout the course. The authors each ...
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The current study examines whether R or SPSS output induces greater initial anxiety in students and whether anxiety toward one or both changes after being taught one type of software output throughout the course. The authors each taught an introductory statistics course, with the first course (n = 43) teaching R output exclusively and the second course (n = 39) teaching SPSS output exclusively. Students in both courses were given surveys assessing their anxiety and confidence toward R and SPSS output on the first and last days of class. Students initially reported greater anxiety and lower confidence when viewing R compared to SPSS output. However, the initial difference between R- and SPSS-related anxiety and confidence level disappeared when students were taught R and decreased substantially when students were taught SPSS. The results suggest that although R output may seem more intimidating initially, students adapt to it as well as they do to SPSS.
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Two controversial topics related to the teaching of statistics to psychology students are (a) when to introduce statistical software and (b) which statistical software package to use. The current research looked at the use of stat...
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Two controversial topics related to the teaching of statistics to psychology students are (a) when to introduce statistical software and (b) which statistical software package to use. The current research looked at the use of statistical software in statistics classes from every university with a psychology program in Canada. Researchers collected data from 321 statistics courses offered to psychology students at 65 Canadian universities and coded the type of statistical software used (if any) in each course. Results show that slightly more than half of all universities introduce software at the introductory level. Point-and-click software is most popular, particularly SPSS. There is a considerable amount of variability in when and which software is introduced to students. Departments can use these data to inform their own practices.
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There are several fundamental problems with statistical software development in the academic community. In addition, the development and dissemination of academic software will become increasingly difficult due to a variety of rea...
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There are several fundamental problems with statistical software development in the academic community. In addition, the development and dissemination of academic software will become increasingly difficult due to a variety of reasons. To solve these problems, a new framework for statistical software development, maintenance, and publishing is proposed: it is based on the paradigm that academic and commercial software should be both cost-effectively created, maintained and published with Marketing Principles in mind. The framework has been seamlessly integrated into a highly successful website (http://www.wessa.net) that operates as a provider of free web-based statistical software. Finally it is explained how the R framework provides a platform for the development of a true compendium publishing system.
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Introduction In metabolomics studies, unwanted variation inevitably arises from various sources. Normalization, that is the removal of unwanted variation, is an essential step in the statistical analysis of metabolomics data. Howe...
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Introduction In metabolomics studies, unwanted variation inevitably arises from various sources. Normalization, that is the removal of unwanted variation, is an essential step in the statistical analysis of metabolomics data. However, metabolomics normalization is often considered an imprecise science due to the diverse sources of variation and the availability of a number of alternative strategies that may be implemented.
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? 2023 The AuthorsThe computing environment has revolutionized the management and analysis of data in sciences during the last decades. This study aimed to evaluate the use of R software in research articles addressing the study o...
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? 2023 The AuthorsThe computing environment has revolutionized the management and analysis of data in sciences during the last decades. This study aimed to evaluate the use of R software in research articles addressing the study of wildlife worldwide, particularly focusing on the research area “Veterinary Sciences”. For this purpose, a systematic review mainly performed in the Web of Science database was conducted. Out of a total of 509 articles reviewed, our results show an increasing trend of the number of publications using the R software over time, as well as a wide geographical distribution at a global scale, particularly in North America, Europe, Australia and China. Most publications were categorized in research areas related to “Biological Sciences”, while a minority of them was included in “Veterinary Sciences” (5.9%; 30/509). About the species groups assessed, many articles evaluated a single species group (96.5%), being mammals (50.7%) and birds (14.8%) the most studied ones. The present study showed a high variety of R-packages used in the publications reviewed, all of them related to data analysis, the study of genetic/phylogenetic information and graphical representation. Interestingly, the common use of packages between different research areas is indicative of the high interest of using R software in scientific articles. Our study points the R software as an open-source programming language that allows to support research addressing the study of wildlife, becoming a key software for many research areas, including “Veterinary Sciences”. However, an in-depth methodological description about the use of R software in publications to improve the tracking, reproducibility and transparency is encouraged.
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To understand the effect of climate on tree-ring features, such as width or density, tree-ring data have to be calibrated against instrumental records. The high degree of multicollinearity among monthly time series of climate data...
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To understand the effect of climate on tree-ring features, such as width or density, tree-ring data have to be calibrated against instrumental records. The high degree of multicollinearity among monthly time series of climate data violates the assumption of independent predictor variables in ordinary least squares regression. Bootstrapped confidence intervals of parameter estimates via regression against the principle components of the predictor variables are a possible solution to that problem. Package bootRes for R implements a flexible interface for bootstrapped response and correlation function analysis and tackles some shortcomings of currently available software. Given the increasing popularity of the free R software for statistical analysis, bootRes should facilitate both using R as a computational environment among tree-ring scientists and implementing new approaches to dendroclimatic calibration.
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Curran-Everett D. Explorations in statistics: hypothesis tests and P values. Adv Physiol Educ 33: 81-86, 2009; doi: 10.1152/advan.90218.2008.-Learning about statistics is a lot like learning about science: the learning is more mea...
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Curran-Everett D. Explorations in statistics: hypothesis tests and P values. Adv Physiol Educ 33: 81-86, 2009; doi: 10.1152/advan.90218.2008.-Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This second installment of Explorations in Statistics delves into test statistics and P values, two concepts fundamental to the test of a scientific null hypothesis. The essence of a test statistic is that it compares what we observe in the experiment to what we expect to see if the null hypothesis is true. The P value associated with the magnitude of that test statistic answers this question: if the null hypothesis is true, what proportion of possible values of the test statistic are at least as extreme as the one I got? Although statisticians continue to stress the limitations of hypothesis tests, there are two realities we must acknowledge: hypothesis tests are ingrained within science, and the simple test of a null hypothesis can be useful. As a result, it behooves us to explore the notions of hypothesis tests, test statistics, and P values.
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Multilevel modeling is an excellent tool among researchers with nested data. As statistical software improves, the ease of conducting these models has become more accessible. In particular, the second edition of Finch and colleagu...
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Multilevel modeling is an excellent tool among researchers with nested data. As statistical software improves, the ease of conducting these models has become more accessible. In particular, the second edition of Finch and colleagues' (2019) Multilevel Modeling using R illustrates a variety of multilevel models that can be estimated using R. This manuscript reviews that text by including a brief introduction, a chapter-by-chapter breakdown, the book's strengths, and whether the text enhances learning among graduate students.
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The Comprehensive R Archive Network (CRAN) is a network of sites acting as the primary web service distributing R sources and binaries, extension packages, and documentation. We discuss this functionality in more detail, with part...
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The Comprehensive R Archive Network (CRAN) is a network of sites acting as the primary web service distributing R sources and binaries, extension packages, and documentation. We discuss this functionality in more detail, with particular emphasis on the CRAN package repository, and its underlying design and operation principles.
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The vast majority of toxicological papers summarize experimental data as bar charts of means with error bars. While these graphics are easy to generate, they often obscure essential features of the data, such as outliers or subgro...
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The vast majority of toxicological papers summarize experimental data as bar charts of means with error bars. While these graphics are easy to generate, they often obscure essential features of the data, such as outliers or subgroups of individuals reacting differently to a treatment. In particular, raw values are of prime importance in toxicology; therefore, we argue they should not be hidden in messy supplementary tables but rather unveiled in neat graphics in the results section. We propose jittered boxplots as a very compact yet comprehensive and intuitively accessible way of visualizing grouped and clustered data from toxicological studies together with individual raw values and indications of statistical significance. A web application to create these plots is available online.
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