Data Configuration, Analysis, and Management

Information and resources for secure data storage and data encryption, as well as guidance for deidentification and cleaning data, maintaining integrity of study data, strategies and software for data analysis, maintaining records of data manipulations, and options of data visualization. Main menu | Comments/Suggestions

Basics of managing research data

Minicourse: Introduction to Research Data Management

Participant Recruitment Tracker Example

Data management and storage – Best practice for data storage, guidance for security and privacy measures based on type of data

General guidelines:

· Save both an original and working copy of your data

· Never work with your original data file, only a copy

· Back-up all data and working files in at least two secure locations

Office of Research Integrity: Guidelines for responsible data management in scientific research

Minicourse: Responsible Data Planning, Use, and Sharing

Research Data Services – Free campus services for data storage, analysis and sharing

Using Box and additional security measures for storing PHI

REDCap: Secure, web-based application used for building and managing data collection forms

Data security, management and retention

Data encryption

Issues to Consider Before Implementing Encryption

UW-Madison IT storage and encryption standard

Free encryption for cloud storage

Data integrity – Practices to ensure data are truthful and accurate, as well as processes to preserve and most accurately present data

Overview of data management

Data management and research integrity

Ensuring the Integrity, Accessibility, and Stewardship of Research Data in the Digital Age

Deidentifying data and working with protected health information

UW IRB Identifiability Guide

De-Identifying Protected Health Information

Health Information Privacy

HIPAA Basics

PREP: Professional Research Education Program – In-person and on-demand training for conducting safe and compliant human subjects research at UW-Madison

Data cleaning and transformation – The process of removing duplicates, correcting errors, and correcting formatting so you can more easily analyze your data

Data cleaning: Detecting, diagnosing, and editing data abnormalities

What are dummy variables?

Dummy variables – Indicator variables used with categorical data

Collapsing or recategorizing variables – Changing the scale of a variable or reducing the number of categories

Data transformation – Process of adding or multiplying a constant number, or applying a mathematical function to the value of a variable to facilitate data analysis

Analyzing data: There are two main forms of data analysis, descriptive and inferential analysis.

Descriptive analysis involves summarizing and describing the data whereas inferential analysis uses sample data to make generalizations about the larger population.

Statistics: Descriptive and Inferential

Descriptive and inferential statistics

Understanding descriptive and inferential statistics – Lynda.com

Glossary of statistical terms

Back to basics: An introduction to statistics

Approaches to the analysis of survey data

Quantitative data collection and analysis

Qualitative data collection and analysis

Data analysis in qualitative research

Qualitative research: Data collection, analysis, and management

Data analysis protocols and records

The importance of documenting what analysis you have done and how you did them cannot be overstated. Recording your data analysis protocol will allow others to verify your work.

NIH Scientific Record Keeping

Software for quantitative statistical analysis

LinkedIn Learning – Excel

The Beginner’s Guide to Excel – Excel Basics Tutorial

LinkedIn Learning – Access 2016: Essential Training

Getting Started in Data Analysis using Stata and R

LinkedIn Learning – SPSS Statistics Essential Training

SAS Essential Training – Descriptive Analysis for Healthcare Research

NCSS Training Videos

OpenEpi

UW-Madison Social Science Computing Cooperative Training Classes

UW-Madison Social Science Computing Cooperative Handbook and Statistics Resources