DOCSLIB.ORG

PSPP Developers Guide GNU PSPP Statistical Analysis Software Release 1.4.1

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
PSPP Developers Guide GNU PSPP Statistical Analysis Software Release 1.4.1 PSPP Developers Guide GNU PSPP Statistical Analysis Software Release 1.4.1 Ben Pfaff John Darrington This manual is for GNU PSPP version 1.4.1, software for statistical analysis. Copyright c 1997, 1998, 2004, 2005, 2007, 2010, 2014, 2015, 2016 Free Software Foundation, Inc. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.3 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license is included in the section entitled "GNU Free Documentation License". i Table of Contents 1 Introduction ::::::::::::::::::::::::::::::::::::: 1 2 Basic Concepts :::::::::::::::::::::::::::::::::: 2 2.1 Values:::::::::::::::::::::::::::::::::::::::::::::::::::::::::: 2 2.1.1 Numeric Values :::::::::::::::::::::::::::::::::::::::::::: 2 2.1.2 String Values :::::::::::::::::::::::::::::::::::::::::::::: 3 2.1.3 Runtime Typed Values::::::::::::::::::::::::::::::::::::: 3 2.2 Input and Output Formats ::::::::::::::::::::::::::::::::::::: 4 2.2.1 Constructing and Verifying Formats:::::::::::::::::::::::: 5 2.2.2 Format Utility Functions::::::::::::::::::::::::::::::::::: 6 2.2.3 Obtaining Properties of Format Types ::::::::::::::::::::: 6 2.2.4 Numeric Formatting Styles::::::::::::::::::::::::::::::::: 9 2.2.5 Formatted Data Input and Output:::::::::::::::::::::::: 10 2.3 User-Missing Values ::::::::::::::::::::::::::::::::::::::::::: 11 2.3.1 Testing for Missing Values :::::::::::::::::::::::::::::::: 12 2.3.2 Creation and Destruction ::::::::::::::::::::::::::::::::: 12 2.3.3 Changing User-Missing Value Set Width :::::::::::::::::: 13 2.3.4 Inspecting User-Missing Value Sets:::::::::::::::::::::::: 13 2.3.5 Modifying User-Missing Value Sets:::::::::::::::::::::::: 14 2.4 Value Labels :::::::::::::::::::::::::::::::::::::::::::::::::: 15 2.4.1 Creation and Destruction ::::::::::::::::::::::::::::::::: 16 2.4.2 Value Labels Properties::::::::::::::::::::::::::::::::::: 16 2.4.3 Adding and Removing Labels ::::::::::::::::::::::::::::: 16 2.4.4 Iterating through Value Labels:::::::::::::::::::::::::::: 17 2.5 Variables :::::::::::::::::::::::::::::::::::::::::::::::::::::: 18 2.5.1 Variable Name:::::::::::::::::::::::::::::::::::::::::::: 18 2.5.2 Variable Type and Width ::::::::::::::::::::::::::::::::: 18 2.5.3 Variable Missing Values::::::::::::::::::::::::::::::::::: 19 2.5.4 Variable Value Labels::::::::::::::::::::::::::::::::::::: 20 2.5.5 Variable Print and Write Formats ::::::::::::::::::::::::: 21 2.5.6 Variable Labels ::::::::::::::::::::::::::::::::::::::::::: 22 2.5.7 GUI Attributes ::::::::::::::::::::::::::::::::::::::::::: 22 2.5.8 Variable Leave Status::::::::::::::::::::::::::::::::::::: 23 2.5.9 Dictionary Class :::::::::::::::::::::::::::::::::::::::::: 24 2.5.10 Variable Creation and Destruction ::::::::::::::::::::::: 25 2.5.11 Variable Short Names ::::::::::::::::::::::::::::::::::: 26 2.5.12 Variable Relationships ::::::::::::::::::::::::::::::::::: 27 2.5.13 Variable Auxiliary Data ::::::::::::::::::::::::::::::::: 28 2.5.14 Variable Categorical Values :::::::::::::::::::::::::::::: 29 2.6 Dictionaries ::::::::::::::::::::::::::::::::::::::::::::::::::: 29 2.6.1 Accessing Variables ::::::::::::::::::::::::::::::::::::::: 30 2.6.2 Creating Variables :::::::::::::::::::::::::::::::::::::::: 30 2.6.3 Deleting Variables :::::::::::::::::::::::::::::::::::::::: 31 ii 2.6.4 Changing Variable Order ::::::::::::::::::::::::::::::::: 32 2.6.5 Renaming Variables :::::::::::::::::::::::::::::::::::::: 32 2.6.6 Weight Variable :::::::::::::::::::::::::::::::::::::::::: 33 2.6.7 Filter Variable :::::::::::::::::::::::::::::::::::::::::::: 33 2.6.8 Case Limit ::::::::::::::::::::::::::::::::::::::::::::::: 34 2.6.9 Split Variables :::::::::::::::::::::::::::::::::::::::::::: 34 2.6.10 File Label ::::::::::::::::::::::::::::::::::::::::::::::: 35 2.6.11 Documents :::::::::::::::::::::::::::::::::::::::::::::: 35 2.7 Coding Conventions ::::::::::::::::::::::::::::::::::::::::::: 36 2.8 Cases ::::::::::::::::::::::::::::::::::::::::::::::::::::::::: 36 2.9 Data Sets ::::::::::::::::::::::::::::::::::::::::::::::::::::: 36 2.10 Pools::::::::::::::::::::::::::::::::::::::::::::::::::::::::: 36 3 Parsing Command Syntax ::::::::::::::::::::: 37 4 Processing Data :::::::::::::::::::::::::::::::: 38 5 Presenting Output ::::::::::::::::::::::::::::: 39 6 Internationalisation :::::::::::::::::::::::::::: 40 6.1 The working locales ::::::::::::::::::::::::::::::::::::::::::: 40 6.1.1 The user interface locale :::::::::::::::::::::::::::::::::: 40 6.1.2 The output locale::::::::::::::::::::::::::::::::::::::::: 40 6.1.3 The data locale ::::::::::::::::::::::::::::::::::::::::::: 40 6.2 System files ::::::::::::::::::::::::::::::::::::::::::::::::::: 40 6.3 GUI::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: 41 6.3.1 Filenames :::::::::::::::::::::::::::::::::::::::::::::::: 41 6.4 Existing locale handling functions:::::::::::::::::::::::::::::: 41 6.5 Quirks :::::::::::::::::::::::::::::::::::::::::::::::::::::::: 42 7 Function Index ::::::::::::::::::::::::::::::::: 43 8 Concept Index:::::::::::::::::::::::::::::::::: 46 Appendix A Portable File Format :::::::::::::: 47 A.1 Portable File Characters :::::::::::::::::::::::::::::::::::::: 47 A.2 Portable File Structure:::::::::::::::::::::::::::::::::::::::: 47 A.3 Portable File Header :::::::::::::::::::::::::::::::::::::::::: 48 A.4 Version and Date Info Record ::::::::::::::::::::::::::::::::: 51 A.5 Identification Records ::::::::::::::::::::::::::::::::::::::::: 51 A.6 Variable Count Record :::::::::::::::::::::::::::::::::::::::: 51 A.7 Precision Record :::::::::::::::::::::::::::::::::::::::::::::: 51 A.8 Case Weight Variable Record:::::::::::::::::::::::::::::::::: 51 A.9 Variable Records :::::::::::::::::::::::::::::::::::::::::::::: 51 A.10 Value Label Records ::::::::::::::::::::::::::::::::::::::::: 52 iii A.11 Document Record:::::::::::::::::::::::::::::::::::::::::::: 52 A.12 Portable File Data ::::::::::::::::::::::::::::::::::::::::::: 52 Appendix B System File Format :::::::::::::::: 54 B.1 System File Record Structure ::::::::::::::::::::::::::::::::: 55 B.2 File Header Record ::::::::::::::::::::::::::::::::::::::::::: 56 B.3 Variable Record::::::::::::::::::::::::::::::::::::::::::::::: 58 B.4 Value Labels Records ::::::::::::::::::::::::::::::::::::::::: 61 B.5 Document Record ::::::::::::::::::::::::::::::::::::::::::::: 62 B.6 Machine Integer Info Record :::::::::::::::::::::::::::::::::: 62 B.7 Machine Floating-Point Info Record ::::::::::::::::::::::::::: 64 B.8 Multiple Response Sets Records ::::::::::::::::::::::::::::::: 64 B.9 Extra Product Info Record :::::::::::::::::::::::::::::::::::: 66 B.10 Variable Display Parameter Record :::::::::::::::::::::::::: 67 B.11 Long Variable Names Record::::::::::::::::::::::::::::::::: 68 B.12 Very Long String Record ::::::::::::::::::::::::::::::::::::: 69 B.13 Character Encoding Record :::::::::::::::::::::::::::::::::: 70 B.14 Long String Value Labels Record::::::::::::::::::::::::::::: 71 B.15 Long String Missing Values Record::::::::::::::::::::::::::: 72 B.16 Data File and Variable Attributes Records ::::::::::::::::::: 73 B.16.1 Variable Roles :::::::::::::::::::::::::::::::::::::::::: 74 B.17 Extended Number of Cases Record ::::::::::::::::::::::::::: 74 B.18 Other Informational Records ::::::::::::::::::::::::::::::::: 75 B.19 Dictionary Termination Record :::::::::::::::::::::::::::::: 75 B.20 Data Record ::::::::::::::::::::::::::::::::::::::::::::::::: 75 Appendix C SPSS/PC+ System File Format :: 79 C.1 Record 0: Main Header Record:::::::::::::::::::::::::::::::: 80 C.2 Record 1: Variables Record ::::::::::::::::::::::::::::::::::: 81 C.3 Record 2: Labels Record :::::::::::::::::::::::::::::::::::::: 83 C.4 Record 3: Data Record:::::::::::::::::::::::::::::::::::::::: 83 C.5 Records 4 and 5: Data Entry:::::::::::::::::::::::::::::::::: 84 Appendix D SPSS Viewer File Format ::::::::: 85 D.1 Structure Member Format :::::::::::::::::::::::::::::::::::: 86 D.1.1 The heading Element :::::::::::::::::::::::::::::::::::: 88 D.1.2 The label Element :::::::::::::::::::::::::::::::::::::: 90 D.1.3 The container Element ::::::::::::::::::::::::::::::::: 90 D.1.4 The text Element (Inside container):::::::::::::::::::: 90 D.1.5 The html Element ::::::::::::::::::::::::::::::::::::::: 91 D.1.6 The table Element :::::::::::::::::::::::::::::::::::::: 91 D.1.7 The graph Element :::::::::::::::::::::::::::::::::::::: 92 D.1.8 The model Element :::::::::::::::::::::::::::::::::::::: 93 D.1.9 The tree Element ::::::::::::::::::::::::::::::::::::::: 93 D.1.10 Path Elements :::::::::::::::::::::::::::::::::::::::::: 93 D.1.11 The pageSetup Element :::::::::::::::::::::::::::::::: 94 D.1.12 The text Element (Inside pageParagraph) :::::::::::::: 95 iv D.2 Light Detail Member Format:::::::::::::::::::::::::::::::::: 96 D.2.1 Header::::::::::::::::::::::::::::::::::::::::::::::::::: 97 D.2.2 Titles :::::::::::::::::::::::::::::::::::::::::::::::::::: 98 D.2.3 Footnotes :::::::::::::::::::::::::::::::::::::::::::::::: 98 D.2.4 Areas :::::::::::::::::::::::::::::::::::::::::::::::::::: 98 D.2.5 Borders :::::::::::::::::::::::::::::::::::::::::::::::::: 99 D.2.6 Print Settings::::::::::::::::::::::::::::::::::::::::::: 100 D.2.7 Table Settings :::::::::::::::::::::::::::::::::::::::::: 101 D.2.8 Formats :::::::::::::::::::::::::::::::::::::::::::::::: 102
Load more

Recommended publications
  • Python for Economists Alex Bell
    Python for Economists Alex Bell [email protected] http://xkcd.com/353 This version: October 2016. If you have not already done so, download the files for the exercises here. Contents 1 Introduction to Python 3 1.1 Getting Set-Up................................................. 3 1.2 Syntax and Basic Data Structures...................................... 3 1.2.1 Variables: What Stata Calls Macros ................................ 4 1.2.2 Lists.................................................. 5 1.2.3 Functions ............................................... 6 1.2.4 Statements............................................... 7 1.2.5 Truth Value Testing ......................................... 8 1.3 Advanced Data Structures .......................................... 10 1.3.1 Tuples................................................. 10 1.3.2 Sets .................................................. 11 1.3.3 Dictionaries (also known as hash maps) .............................. 11 1.3.4 Casting and a Recap of Data Types................................. 12 1.4 String Operators and Regular Expressions ................................. 13 1.4.1 Regular Expression Syntax...................................... 14 1.4.2 Regular Expression Methods..................................... 16 1.4.3 Grouping RE's ............................................ 18 1.4.4 Assertions: Non-Capturing Groups................................. 19 1.4.5 Portability of REs (REs in Stata).................................. 20 1.5 Working with the Operating System....................................
    [Show full text]
  • TSP 5.0 Reference Manual
    TSP 5.0 Reference Manual Bronwyn H. Hall and Clint Cummins TSP International 2005 Copyright 2005 by TSP International First edition (Version 4.0) published 1980. TSP is a software product of TSP International. The information in this document is subject to change without notice. TSP International assumes no responsibility for any errors that may appear in this document or in TSP. The software described in this document is protected by copyright. Copying of software for the use of anyone other than the original purchaser is a violation of federal law. Time Series Processor and TSP are trademarks of TSP International. ALL RIGHTS RESERVED Table Of Contents 1. Introduction_______________________________________________1 1. Welcome to the TSP 5.0 Help System _______________________1 2. Introduction to TSP ______________________________________2 3. Examples of TSP Programs _______________________________3 4. Composing Names in TSP_________________________________4 5. Composing Numbers in TSP _______________________________5 6. Composing Text Strings in TSP_____________________________6 7. Composing TSP Commands _______________________________7 8. Composing Algebraic Expressions in TSP ____________________8 9. TSP Functions _________________________________________10 10. Character Set for TSP ___________________________________11 11. Missing Values in TSP Procedures _________________________13 12. LOGIN.TSP file ________________________________________14 2. Command summary_______________________________________15 13. Display Commands _____________________________________15
    [Show full text]
  • Towards a Fully Automated Extraction and Interpretation of Tabular Data Using Machine Learning
    UPTEC F 19050 Examensarbete 30 hp August 2019 Towards a fully automated extraction and interpretation of tabular data using machine learning Per Hedbrant Per Hedbrant Master Thesis in Engineering Physics Department of Engineering Sciences Uppsala University Sweden Abstract Towards a fully automated extraction and interpretation of tabular data using machine learning Per Hedbrant Teknisk- naturvetenskaplig fakultet UTH-enheten Motivation A challenge for researchers at CBCS is the ability to efficiently manage the Besöksadress: different data formats that frequently are changed. Significant amount of time is Ångströmlaboratoriet Lägerhyddsvägen 1 spent on manual pre-processing, converting from one format to another. There are Hus 4, Plan 0 currently no solutions that uses pattern recognition to locate and automatically recognise data structures in a spreadsheet. Postadress: Box 536 751 21 Uppsala Problem Definition The desired solution is to build a self-learning Software as-a-Service (SaaS) for Telefon: automated recognition and loading of data stored in arbitrary formats. The aim of 018 – 471 30 03 this study is three-folded: A) Investigate if unsupervised machine learning Telefax: methods can be used to label different types of cells in spreadsheets. B) 018 – 471 30 00 Investigate if a hypothesis-generating algorithm can be used to label different types of cells in spreadsheets. C) Advise on choices of architecture and Hemsida: technologies for the SaaS solution. http://www.teknat.uu.se/student Method A pre-processing framework is built that can read and pre-process any type of spreadsheet into a feature matrix. Different datasets are read and clustered. An investigation on the usefulness of reducing the dimensionality is also done.
    [Show full text]
  • 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 .
    [Show full text]
  • Data Workflows with Stata and Python
    In [1]: from IPython.display import IFrame import ipynb_style from epstata import Stpy import pandas as pd from itertools import combinations from importlib import reload In [2]: reload(ipynb_style) ipynb_style.clean() #ipynb_style.presentation() #ipynb_style.pres2() Out[2]: Data Workflows in Stata and Python (http://www.stata.com) (https://www.python.org) Data Workflows in Stata and Python Dejan Pavlic, Education Policy Research Initiative, University of Ottawa Stephen Childs (presenter), Office of Institutional Analysis, University of Calgary (http://ucalgary.ca) (http://uottawa.ca/en) (http://socialsciences.uottawa.ca/irpe- epri/eng/index.asp) Introduction About this talk Objectives know what Python is and what advantages it has know how Python can work with Stata Please save questions for the end. Or feel free to ask me today or after the conference. Outline Introduction Overall Motivation About Python Building Blocks Running Stata from Python Pandas Python language features Workflows ETL/Data Cleaning Stata code generation Processing Stata output About Me Started using Stata in grad school (2006). Using Python for about 3 years. Post-Secondary Education sector University of Calgary - Institutional Analysis (https://oia.ucalgary.ca/Contact) Education Policy Research Initiative (http://socialsciences.uottawa.ca/irpe-epri/eng/index.asp) - University of Ottawa (a Stata shop) Motivation Python is becoming very popular in the data world. Python skills are widely applicable. Python is powerful and flexible and will help you get more done,
    [Show full text]
  • Gretl User's Guide
    Gretl User’s Guide Gnu Regression, Econometrics and Time-series Allin Cottrell Department of Economics Wake Forest university Riccardo “Jack” Lucchetti Dipartimento di Economia Università Politecnica delle Marche December, 2008 Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.1 or any later version published by the Free Software Foundation (see http://www.gnu.org/licenses/fdl.html). Contents 1 Introduction 1 1.1 Features at a glance ......................................... 1 1.2 Acknowledgements ......................................... 1 1.3 Installing the programs ....................................... 2 I Running the program 4 2 Getting started 5 2.1 Let’s run a regression ........................................ 5 2.2 Estimation output .......................................... 7 2.3 The main window menus ...................................... 8 2.4 Keyboard shortcuts ......................................... 11 2.5 The gretl toolbar ........................................... 11 3 Modes of working 13 3.1 Command scripts ........................................... 13 3.2 Saving script objects ......................................... 15 3.3 The gretl console ........................................... 15 3.4 The Session concept ......................................... 16 4 Data files 19 4.1 Native format ............................................. 19 4.2 Other data file formats ....................................... 19 4.3 Binary databases ..........................................
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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
    [Show full text]
  • Introduction to Stata
    Introduction to Stata Christopher F Baum Faculty Micro Resource Center Boston College August 2011 Christopher F Baum (Boston College FMRC) Introduction to Stata August 2011 1 / 157 Strengths of Stata What is Stata? Overview of the Stata environment Stata is a full-featured statistical programming language for Windows, Mac OS X, Unix and Linux. It can be considered a “stat package,” like SAS, SPSS, RATS, or eViews. Stata is available in several versions: Stata/IC (the standard version), Stata/SE (an extended version) and Stata/MP (for multiprocessing). The major difference between the versions is the number of variables allowed in memory, which is limited to 2,047 in standard Stata/IC, but can be much larger in Stata/SE or Stata/MP. The number of observations in any version is limited only by memory. Christopher F Baum (Boston College FMRC) Introduction to Stata August 2011 2 / 157 Strengths of Stata What is Stata? Stata/SE relaxes the Stata/IC constraint on the number of variables, while Stata/MP is the multiprocessor version, capable of utilizing 2, 4, 8... processors available on a single computer. Stata/IC will meet most users’ needs; if you have access to Stata/SE or Stata/MP, you can use that program to create a subset of a large survey dataset with fewer than 2,047 variables. Stata runs on all 64-bit operating systems, and can access larger datasets on a 64-bit OS, which can address a larger memory space. All versions of Stata provide the full set of features and commands: there are no special add-ons or ‘toolboxes’.
    [Show full text]
  • 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.
    [Show full text]