"R Coding and Modeling" In: the Encyclopedia of Archaeological
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Sigmaplot 11: Now with Total Sigmastat Integration
SigmaPlot 11: Now with Total SigmaStat Integration Imagine my joy as I discovered a complete package of publication-quality graphics software with analytic and presentation tools John A. Wass, Ph.D., in: Scientific Computing International, Jan/Feb 2009 The SYSTAT people who market this product have thrown me a curve. For years, I have bemoaned the fact that most of the upgrade and development efforts that went into the SigmaPlot/SigmaStat software seemed to be biased to the plot side. When I observed that the new package was merely named SigmaPlot, and I further failed to find SigmaStat integration features (the stuff that connects the two programs), the Figure 1: SigmaPlot graphics and wizards, including the Quick Start natural conclusion seemed to be that the statistical Menu (upper right) and the graph program was jettisoned in favor of the graphics. wizard (bottom center) The above introductory narrative is intended to alert the reader to this editor’s long- time love affair with SigmaStat. It was the first statistical software that I used, (seemingly) the first to make a seamless transition from DOS to Windows, and the very first to offer that wonderful Wizard to we befuddled amateur statisticians. My introduction to SigmaPlot came much later, and use of that was only stimulated when the two became integrated. Later on, a pharmacology menu was added and the usage of the plotting software was greatly extended. Of course, the new version has added further graphics and helps to make an already useful program even easier to use. It is now a complete package of publication-quality graphics software with analytic and presentation tools. -
R for Beginners
R for Beginners Emmanuel Paradis Institut des Sciences de l'Evolution´ Universit´e Montpellier II F-34095 Montpellier c´edex 05 France E-mail: [email protected] I thank Julien Claude, Christophe Declercq, Elo´ die Gazave, Friedrich Leisch, Louis Luangkesron, Fran¸cois Pinard, and Mathieu Ros for their comments and suggestions on earlier versions of this document. I am also grateful to all the members of the R Development Core Team for their considerable efforts in developing R and animating the discussion list `rhelp'. Thanks also to the R users whose questions or comments helped me to write \R for Beginners". Special thanks to Jorge Ahumada for the Spanish translation. c 2002, 2005, Emmanuel Paradis (12th September 2005) Permission is granted to make and distribute copies, either in part or in full and in any language, of this document on any support provided the above copyright notice is included in all copies. Permission is granted to translate this document, either in part or in full, in any language provided the above copyright notice is included. Contents 1 Preamble 1 2 A few concepts before starting 3 2.1 How R works . 3 2.2 Creating, listing and deleting the objects in memory . 5 2.3 The on-line help . 7 3 Data with R 9 3.1 Objects . 9 3.2 Reading data in a file . 11 3.3 Saving data . 14 3.4 Generating data . 15 3.4.1 Regular sequences . 15 3.4.2 Random sequences . 17 3.5 Manipulating objects . 18 3.5.1 Creating objects . -
Introduction to the British Social Attitudes Survey Practical Exercises
Introduction to the British Social Attitudes Survey Practical exercises UK Data Service 21 September 2016 QUESTION SHEETS Exploring internet use, trust and political affiliation using BSA 2013 1. Finding out about the dataset using documentation Go to the UK Data Service website www.ukdataservice.ac.uk. Find the British Social Attitudes Survey from among the Key surveys via the Get data menu. Click on the title of the survey to go to the Series page. Under DATA ACCESS, click on the survey name and from the list that appears, click on BSA 2013 to go to the catalogue page for the BSA 2013. The catalogue page gives summary information about the survey, contains detailed documentation from the survey providers and links to download the data. TASK: Explore the BSA 2013 catalogue page to find out more about the data Q1. Where do you click to download the data? (but don’t download the data now) Q2. What extra modules were included in the BSA in 2013? Scroll down to find the questionnaire and user guide. If you have time, you can explore these documents to see what they contain. They are searchable using Control-F. Q3. What is the name of the weighting variable in the BSA? 1 Introduction to the British Social Attitudes Survey 2013 using PSPP: Question sheet 2. Exploring survey data online in Nesstar Nesstar is a tool that allows you to explore survey data online. You can also conduct some basic analyses, create graphs and download subsets of the data in Nesstar. Anyone can explore the variables in Nesstar but for most other tasks you must log in to the UK Data Service first. -
The Coding Manual for Qualitative Researchers for Manual Coding The
2E Second Edition The Coding Manual for Qualitative Researchers ‘This book fills a major gap in qualitative research methods courses. Saldaña has accomplished what has not been done before - creating a text that clearly identifies the many choices one has in coding their data. I wish I had this book when I started conducting qualitative research. It should be required reading for all.’ Mark Winton, Criminal Justice Instructor, University of Central Florida ‘An excellent handbook that helps demystify the coding process with a comprehensive assessment of different coding types, examples and exercises. As such it is a valuable teaching resource and it will also be of use to anyone undertaking qualitative analysis.’ Kevin Meethan, Associate Professor in Sociology, Plymouth University The ‘The Coding Manual describes the qualitative coding process with clarity and expertise. Its wide array of strategies, from the more straightforward to the more complex, are skillfully explained and exemplified. This extremely usable manual is a must-have resource for qualitative researchers at all levels.’ Coding Manual for Tara M. Brown, Assistant Professor of Education, Brandeis University The second edition of Johnny Saldaña’s international bestseller provides an in-depth guide to the Qualitative Researchers multiple approaches available for coding qualitative data. Fully up-to-date, it includes new chapters, more coding techniques and an additional glossary. Clear, practical and authoritative, the book: • Describes how coding initiates qualitative data analysis • Demonstrates the writing of analytic memos • Discusses available analytic software • Suggests how best to use The Coding Manual for Qualitative Researchers for particular studies In total, 32 coding methods are profiled that can be applied to a range of research genres from grounded theory to phenomenology to narrative inquiry. -
Benefits to Qualitative Data Quality with Multiple Coders: Two Case Studies in Multi-Coder Data Analysis." Journal of Rural Social Sciences, 34(1): Article 2
Journal of Rural Social Sciences Volume 34 Issue 1 Volume 34, Issue 1 Article 2 8-9-2019 Benefits ot Qualitative Data Quality with Multiple Coders: Two Case Studies in Multi-coder Data Analysis Sarah P. Church Montana State University, [email protected] Michael Dunn Centre for Ecosystems, Society and Biosecurity, Forest Research, Northern Research Station (UK), [email protected] Linda S. Prokopy Purdue University, [email protected] Follow this and additional works at: https://egrove.olemiss.edu/jrss Part of the Demography, Population, and Ecology Commons, and the Rural Sociology Commons Recommended Citation Church, Sarah, Michael Dunn, and Linda Prokopy. 2019. "Benefits to Qualitative Data Quality with Multiple Coders: Two Case Studies in Multi-coder Data Analysis." Journal of Rural Social Sciences, 34(1): Article 2. Available At: https://egrove.olemiss.edu/jrss/vol34/iss1/2 This Research Note is brought to you for free and open access by the Center for Population Studies at eGrove. It has been accepted for inclusion in Journal of Rural Social Sciences by an authorized editor of eGrove. For more information, please contact [email protected]. Benefits ot Qualitative Data Quality with Multiple Coders: Two Case Studies in Multi-coder Data Analysis Cover Page Footnote Please address all correspondence to Dr. Sarah Church ([email protected]). This research note is available in Journal of Rural Social Sciences: https://egrove.olemiss.edu/jrss/vol34/iss1/2 Church et al.: Benefits to Qualitative Data Quality with Multiple Coders Benefits to Qualitative Data Quality with Multiple Coders: Two Case Studies in Multi-coder Data Analysis Sarah P. -
Statistics with Free and Open-Source Software
Free and Open-Source Software • the four essential freedoms according to the FSF: • to run the program as you wish, for any purpose • to study how the program works, and change it so it does Statistics with Free and your computing as you wish Open-Source Software • to redistribute copies so you can help your neighbor • to distribute copies of your modified versions to others • access to the source code is a precondition for this Wolfgang Viechtbauer • think of ‘free’ as in ‘free speech’, not as in ‘free beer’ Maastricht University http://www.wvbauer.com • maybe the better term is: ‘libre’ 1 2 General Purpose Statistical Software Popularity of Statistical Software • proprietary (the big ones): SPSS, SAS/JMP, • difficult to define/measure (job ads, articles, Stata, Statistica, Minitab, MATLAB, Excel, … books, blogs/posts, surveys, forum activity, …) • FOSS (a selection): R, Python (NumPy/SciPy, • maybe the most comprehensive comparison: statsmodels, pandas, …), PSPP, SOFA, Octave, http://r4stats.com/articles/popularity/ LibreOffice Calc, Julia, … • for programming languages in general: TIOBE Index, PYPL, GitHut, Language Popularity Index, RedMonk Rankings, IEEE Spectrum, … • note that users of certain software may be are heavily biased in their opinion 3 4 5 6 1 7 8 What is R? History of S and R • R is a system for data manipulation, statistical • … it began May 5, 1976 at: and numerical analysis, and graphical display • simply put: a statistical programming language • freely available under the GNU General Public License (GPL) → open-source -
Comparative Analysis of Statistic Software Used in Education of Non- Statisticians Students
Recent Advances in Computer Engineering, Communications and Information Technology Comparative analysis of statistic software used in education of non- statisticians students KLARA RYBENSKA; JOSEF SEDIVY, LUCIE KUDOVA Department of Technical subjects, Departement of Informatics Faculty of Education, Fakulty of Science, Faculty of Arts University of Hradec Kralove Rokitanskeho 62, 500 03 Hradec Kralove CZECH REPUBLIC [email protected] http://www.uhk.cz [email protected] http://www.uhk.cz, [email protected] http://www.uhk.cz Abstract: - Frequently used tool for processing of statistical data in the field of science and humanities IBM SPSS program. This is a very powerful tool, which is an unwritten standard. Its main disadvantage is the high price, which is restrictive for use in an academic environment, not only in teaching but also in the case of individual student work on their own computers. Currently, there are two tools that could at least partially IBM SPSS for teaching science disciplines to replace. These are programs PSPP (http://www.gnu.org/software/pspp/) and alternative (SOFA http://www.sofastatistics.com). Both are available under a license that permits their free use not only for learning but also for commercial purposes. This article aims to find out which are the most common ways of using IBM SPSS program at the University of Hradec Králové and suggest a possible alternative to the commercial program to use in teaching non-statistical data processing student study programs. Key-Words: - statistic software, open source, IBM SPSS, PSPP, data processing, science education. 1 Introduction using formal symbolic system. They then certainly Quantitative research uses logical reasoning process one of the methods of mathematical , statistical and Very simply, you can lay out this process in a few other, but such methods of abstract algebra , formal steps: Getting Started formulation of the research logic, probability theory, which are not restricted to problem, which should be read plenty of literature, numerical data nature. -
The Use of PSPP Software in Learning Statistics. European Journal of Educational Research, 8(4), 1127-1136
Research Article doi: 10.12973/eu-jer.8.4.1127 European Journal of Educational Research Volume 8, Issue 4, 1127 - 1136. ISSN: 2165-8714 http://www.eu-jer.com/ The Use of PSPP Software in Learning Statistics Minerva Sto.-Tomas Darin Jan Tindowen* Marie Jean Mendezabal University of Saint Louis, PHILIPPINES University of Saint Louis, PHILIPPINES University of Saint Louis, PHILIPPINES Pyrene Quilang Erovita Teresita Agustin University of Saint Louis, PHILIPPINES University of Saint Louis, PHILIPPINES Received: July 8, 2019 ▪ Revised: August 31, 2019 ▪ Accepted: October 4, 2019 Abstract: This descriptive and correlational study investigated the effects of using PSPP in learning Statistics on students’ attitudes and performance. The respondents of the study were 200 Grade 11 Senior High School students who were enrolled in Probability and Statistics subject during the Second Semester of School Year 2018-2019. The respondents were randomly selected from those classes across the different academic strands that used PSPP in their Probability and Statistics subject through stratified random sampling. The results revealed that the students have favorable attitudes towards learning Statistics with the use of the PSPP software. The students became more interested and engaged in their learning of statistics which resulted to an improved academic performance. Keywords: Probability and statistics, attitude, PSPP software, academic performance, technology. To cite this article: Sto,-Tomas, M., Tindowen, D. J, Mendezabal, M. J., Quilang, P. & Agustin, E. T. (2019). The use of PSPP software in learning statistics. European Journal of Educational Research, 8(4), 1127-1136. http://doi.org/10.12973/eu-jer.8.4.1127 Introduction The rapid development of technology has brought remarkable changes in the modern society and in all aspects of life such as in politics, trade and commerce, and education. -
R Programming for Data Science
R Programming for Data Science Roger D. Peng This book is for sale at http://leanpub.com/rprogramming This version was published on 2015-07-20 This is a Leanpub book. Leanpub empowers authors and publishers with the Lean Publishing process. Lean Publishing is the act of publishing an in-progress ebook using lightweight tools and many iterations to get reader feedback, pivot until you have the right book and build traction once you do. ©2014 - 2015 Roger D. Peng Also By Roger D. Peng Exploratory Data Analysis with R Contents Preface ............................................... 1 History and Overview of R .................................... 4 What is R? ............................................ 4 What is S? ............................................ 4 The S Philosophy ........................................ 5 Back to R ............................................ 5 Basic Features of R ....................................... 6 Free Software .......................................... 6 Design of the R System ..................................... 7 Limitations of R ......................................... 8 R Resources ........................................... 9 Getting Started with R ...................................... 11 Installation ............................................ 11 Getting started with the R interface .............................. 11 R Nuts and Bolts .......................................... 12 Entering Input .......................................... 12 Evaluation ........................................... -
The Second Step in Data Analysis: Coding Qualitative Research Data
Methodological Issues in Social Health and Diabetes Research The second step in data analysis: Coding qualitative research data Heather L. Stuckey Department of Medicine and Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, USA ABSTRACT Coding is a process used in the analysis of qualitative research, which takes time and creativity. Three steps will help facilitate this process: 1. Reading through the data and creating a storyline; 2. Categorizing the data into codes; and 3. Using memos for clarifi cation and interpretation. Remembering the research question or storyline, while coding will help keep the qualitative researcher focused on relevant codes. A data dictionary can be used to defi ne the meaning of the codes and keep the process transparent. Coding is done using either predetermined (a priori) or emergent codes, and most often, a combination of the two. By using memos to help clarify how the researcher is constructing the codes and his/her interpretations, the analysis will be easier to write in the end and have more consistency. This paper describes the process of coding and writing memos in the analysis of qualitative data related to diabetes research. Key words: Coding, memos, methods, qualitative research IÄãÙÊçã®ÊÄ One of the largest datasets for which I created a coding dictionary was the second Diabetes Attitudes, Wishes and In earlier issues, we discussed data collection through Needs (DAWN2) study. In that dataset, there were over three types of interviews, and the first step in the analysis 15,000 participants who responded to an average of three was transcribing and managing qualitative research data. -
JASP: Graphical Statistical Software for Common Statistical Designs
JSS Journal of Statistical Software January 2019, Volume 88, Issue 2. doi: 10.18637/jss.v088.i02 JASP: Graphical Statistical Software for Common Statistical Designs Jonathon Love Ravi Selker Maarten Marsman University of Newcastle University of Amsterdam University of Amsterdam Tahira Jamil Damian Dropmann Josine Verhagen University of Amsterdam University of Amsterdam University of Amsterdam Alexander Ly Quentin F. Gronau Martin Šmíra University of Amsterdam University of Amsterdam Masaryk University Sacha Epskamp Dora Matzke Anneliese Wild University of Amsterdam University of Amsterdam University of Amsterdam Patrick Knight Jeffrey N. Rouder Richard D. Morey University of Amsterdam University of California, Irvine Cardiff University University of Missouri Eric-Jan Wagenmakers University of Amsterdam Abstract This paper introduces JASP, a free graphical software package for basic statistical pro- cedures such as t tests, ANOVAs, linear regression models, and analyses of contingency tables. JASP is open-source and differentiates itself from existing open-source solutions in two ways. First, JASP provides several innovations in user interface design; specifically, results are provided immediately as the user makes changes to options, output is attrac- tive, minimalist, and designed around the principle of progressive disclosure, and analyses can be peer reviewed without requiring a “syntax”. Second, JASP provides some of the recent developments in Bayesian hypothesis testing and Bayesian parameter estimation. The ease with which these relatively complex Bayesian techniques are available in JASP encourages their broader adoption and furthers a more inclusive statistical reporting prac- tice. The JASP analyses are implemented in R and a series of R packages. Keywords: JASP, statistical software, Bayesian inference, graphical user interface, basic statis- tics. -
Catalogo Generale ADALTA N. 25
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