Data Management Basics

These flashcards cover essential concepts and terms related to data management, including plans, processes, and structures instrumental for conducting statistical research.

Data Management

The systematic process of organizing and maintaining all records and information for a research study, from inception through data collection, analysis, and post-study activities.

Data Management Plan (DMP)

A formal document outlining strategies for handling data across a research project's lifecycle, detailing collection methods, organization, secure storage, and mechanisms for sharing with other researchers.

Codebook

A detailed reference guide describing each variable in a dataset, including names, labels, value definitions, measurement scales, and instructions for accurate data entry into a computer file.

Metadata

Descriptive information providing essential contextual details about data, such as collection methodology, instruments used, and processing steps, to help users understand and interpret the primary dataset.

Data Cleaning

The systematic process of identifying and correcting inaccuracies, inconsistencies, or errors in data files to enhance quality, reliability, and accuracy before statistical analysis.

Recoding

Generating new values for an existing variable, or creating a new variable, based on computations or transformations from one or more existing data columns. This simplifies data or forms new constructs for analysis.

Derived Variable

A new variable created during data analysis by combining, transforming, or calculating values from existing variables in the dataset. It helps address research questions not directly answerable by original variables.

Double-entry

A data entry method where two individuals independently enter the same data into separate files. Discrepancies are identified and resolved, significantly increasing accuracy and reducing errors.

Access and Sharing

Policies and procedures governing how research data is made available and disseminated to other researchers or the scientific community. This includes formats, repositories, licensing, and restrictions for ethical use.

Data Security

Technological and procedural measures implemented to protect research data from unauthorized access, loss, corruption, or destruction. This involves encryption, access controls, backups, and secure storage to ensure confidentiality, integrity, and availability.