Research Data Access Resources: Terminology, Tutorials, Books

This research guide identifies electronic datasets to support statistical research in the social sciences.

Overview

Understanding how to work with data in analytic and visualization software is a key component to accessing data. The below resources cover important terminology, online tutorials, and books available at the library.

A list of upcoming library trainings and workshops can be found here.

Also see the Center for High Performance Computing training events and courses through the DELPHI Initiative.

If you still have questions, please email Madison Golden or Kaylee Alexander to schedule a consultation or request a workshop.

Selected Books on Data Analysis

A Conceptual Guide to Statistics Using SPSS by Berkman, Elliot
Call Number: HA32 .B47 2012; LVL 2: Ready Reference Knowledge Commons
Publication Date: 2012
A gentle introduction to stata by Acock, Alan C.
Call Number: HA32 .A26 2018; LVL 2: General Collection
Publication Date: 2018
Data analysis and graphics using R : an example-based approach by Maindonald, J. H.
Call Number: QA276.4 .M245 2010
Data analysis using SAS by Peng, C. Y. Joanne
Call Number: QA276.4 .P46 2009
Practical statistics : a quick and easy guide to IBM SPSS statistics, STATA, and other statistical software by Kremelberg, David
Call Number: HA32 .K74 2011; LVL 2: Ready Reference Knowledge Commons
Statistical analysis : Microsoft Excel 2010 by Carlberg, Conrad
Call Number: HF5548.4.M523 C3685 2011; LVL 2: Ready Reference Knowledge Commons
Statistical methods for the social sciences by Agresti, Alan
Call Number: QA276.12 .A34 2009
Text Mining with R by Julia Silge; David Robinson
ISBN: 1491981652
Publication Date: 2017-07-18
Much of the data available today is unstructured and text-heavy, making it challenging for analysts to apply their usual data wrangling and visualization tools. With this practical book, you'll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr.