Manage Your Research Data: Data Analysis Software
This guide provides a primer on the fundamentals of data management.
Data Analysis Tools (Selected Examples)
Below are some popular data analysis tools that may be of interest.
Please note that the PSU Library is unable to provide support or training in data analysis tools.
Some of the resources require individual subscriptions, which are not covered by the library.
- ArcGIS (Accessible on PSU Computers)"ArcGIS Online enables you to connect people, locations, and data using interactive maps. Work with smart, data-driven styles and intuitive analysis tools that deliver location intelligence. Share your insights with the world or specific groups. "
- MATLAB (Accessible on PSU computers)MATLAB® combines a desktop environment tuned for iterative analysis and design processes with a programming language that expresses matrix and array mathematics directly. It includes the Live Editor for creating scripts that combine code, output, and formatted text in an executable notebook.
- R Project"R is a free software environment for statistical computing and graphics. R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity."
- SAS (Accessible on PSU Computers)"SAS is an integrated software suite for advanced analytics, business intelligence, data management, and predictive analytics. You can use SAS software through both a graphical interface and the SAS programming language, or Base SAS.
With SAS software, you can complete these tasks: access data in almost any format, including SAS tables, Microsoft Excel tables, and database files.
manage and manipulate your existing data to get the data that you need. For example, you can subset your data, combine it with other data, and create new columns.
analyze your data using statistical techniques ranging from descriptive measures like correlations to logistic regression and mixed models to sophisticated methods such as modern model selection and Bayesian hierarchical models.
present the results of your analyses in a meaningful report that you can share with others. The reports that you create can be saved in a wide variety of formats, including HTML, PDF, and RTF." - SPSS (Accessible on PSU computers)"With SPSS Statistics you can: Analyze and better understand your data, and solve complex business and research problems through a user friendly interface.
Understand large and complex data sets quickly with advanced statistical procedures that help ensure high accuracy and quality decision making.
Use extensions, Python and R programming language code to integrate with open source software.
Select and manage your software easily, with flexible deployment options." - Stata (Subscription fee)"tatistical software for data science. Stata is a complete, integrated software package that provides all your data science needs—data manipulation, visualization, statistics, and automated reporting."
Further Readings
Data Analysis Using SAS (ebook) by ISBN: 9781412956741Publication Date: 2008=This book provides students with a hands-on, exercise-heavy method for learning basic to intermediate SAS commands while understanding how to apply statistics and reasoning to real-world problems.
Data Management Using Stata (2nd ed) by Call Number: PSU Library Shelves -- 5th floor QA276.45.S73 M58 2020ISBN: 1597180769Publication Date: 2020This book focuses on tasks that bridge the gap between raw data and statistical analysis. This second edition has been updated throughout to reflect new data management features that have been added over the last 10 years.
Data Mining (ebook) by ISBN: 9780128042915Publication Date: 2016Offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations.
(E-Book) JavaScript and Open Data by Call Number: E-BookISBN: 9781786302045Publication Date: 2018-09-05"This book will teach you how to take advantage of the JavaScript language to process data provided on the Internet. Much attention is given to the main JavaScript backbone: prototype based objects, and functional capabilities, while common features (loops, etc.) are summarized in a few cheat-sheets. Only operational features are detailed through the coding of several applications -the second and largest part of the book-, on free-access datasets (e.g. World Bank). It includes: cartography (SVG or API's based), data-sheets access (via Ajax or Jsonp), video data and post-synchronization, and animation examples."
(E-Book) Longitudinal and Panel Data by Call Number: E-BookISBN: 9780521535380Publication Date: 2004-08-16"This focuses on models and data that arise from repeated observations of a cross-section of individuals, households or companies. These models have found important applications within business, economics, education, political science and other social science disciplines. The author introduces the foundations of longitudinal and panel data analysis at a level suitable for quantitatively oriented graduate social science students as well as individual researchers. He emphasizes mathematical and statistical fundamentals but also describes substantive applications from across the social sciences, showing the breadth and scope that these models enjoy. The applications are enhanced by real-world data sets and software programs in SAS and Stata."
The Nvivo Qualitative Project Book by Call Number: Shelves - Basement H61.3 .B39 2000ISBN: 0761969993Publication Date: 2000-12-19"This text invites readers to learn how to use qualitative data analysis software in the context of doing their research project. The reader follows basic steps for creating and conducting a real project with real data, using QSR NVivo."
Qualitative Data Analysis with ATLAS. ti by Call Number: PSU Library Shelves -- 5th floor Q180.55.E4 F75 2019ISBN: 9781526446237Publication Date: 2019-04-22"Now updated to cover latest versions and featuring instructions for both Mac and Windows users, this book is still the go-to source of support for getting to grips with qualitative data analysis using ATLAS.ti. With over twenty years of experience using, developing, and teaching ATLAS.ti, Susanne Friese is the perfect data analysis tour guide. Aware of common challenges and sticking points, she eases readers from readying and organizing data into coding and querying it, providing not only tips on how to prepare for analysis, but also the tools and technical know-how needed to observe, examine, and discuss data. Placing quick software 'skills training' tutorials alongside different stages of the data analysis process, she gives readers the opportunity to integrate software training with their actual analysis."
(E-Book) Qualitative Research Design for Software Users by Call Number: E-BookISBN: 9780335225217Publication Date: 2009-01-01"
Shows readers how to organize, manage and analyze data from research projects so as to gain the benefits of software use. This title illustrates how electronic projects need to be articulated and organized. It uses case studies and examples - divided in to 'Simple Cases', 'Complex Cases' and 'Compound Complex Cases'."
(E-Book) Qualitative Text Analysis by Call Number: E-BookISBN: 1446267741Publication Date: 2014-02-06Providing students and researchers with a guide to systematic analysis, with an emphasis on using software, this is a hands-on introduction to the central steps involved with qualitative analysis.
Data Management in R : a guide for social scientists by Call Number: HA32 .E44 2021ISBN: 9781526459978Publication Date: 2021-03-09"This book shows students how to recode data, combine data from different sources, document data, and import data from statistical packages other than R. It explores both qualitative and quantitative data and is packed with a range of supportive learning features such as code examples, overview boxes, images, tables, and diagrams."
