Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Data Collection by Survey

Data Collection by Survey

The systematic method of obtaining and analyzing accurate information of a population is called data collection. A survey is a standard method of data collection that involves collecting information from a target human population about their experience, opinion, or knowledge of a product, service, or process. The responses are recorded and interpreted. The most common survey examples are written questionnaires, face-to-face or telephonic conversations, focus groups, and electronic (e-mail or...

Surveys

Surveys

Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally. Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.

Data Reporting and Recording

Data Reporting and Recording

Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...

Taxonomy

Taxonomy

Taxonomy is the science of defining and naming groups of biological organisms based on shared characteristics. It uses a hierarchy of increasingly inclusive categories with Latin names. The smallest units of taxonomy, species and genus, are used to assign a formal, taxonomic name to each species in a system. This classification system, referred to as binomial nomenclature, was formalized by Carolus Linnaeus in the 18th century.
Hierarchy of Taxonomy
The hierarchy that Carolus Linnaeus first...

How Data are Classified: Categorical Data

How Data are Classified: Categorical Data

A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...

How Data are Classified: Numerical Data

How Data are Classified: Numerical Data

Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Promoting and inhibiting factors for the implementation of evidence-based non-invasive treatment programs for patients with knee- and/or hip-osteoarthritis: a rapid review.

BMC health services research·2026

Same author

Exploring accessibility, user experience and engagement of digital media among older patients with depression: a pilot and observational screening study protocol of the DiGA4Aged study.

BMJ open·2024

Same author

Mapping the Bibliometrics Landscape of AI in Medicine: Methodological Study.

Journal of medical Internet research·2023

Same author

Involving Health Care Professionals in the Development of Electronic Health Records: Scoping Review.

JMIR human factors·2023

Same author

Do multimodal search cues help or hinder teleoperated search and rescue missions?

Ergonomics·2022

Same author

Why cumulative loading calculated using non-weighted integration may not be suitable for assessing physical stress of the lower back: an empirical investigation of strain during lifting and lowering tasks.

Ergonomics·2021

Search research articles

Related Experiment Video

Updated: Feb 8, 2026

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Published on: December 9, 2022

Task-Data Taxonomy for Health Data Visualizations: Web-Based Survey With Experts and Older Adults.

Sabine Theis¹, Peter Wilhelm Victor Rasche¹, Christina Bröhl¹

¹Human Factors Engineering and Ergonomics in Healthcare, Chair and Institute of Industrial Engineering and Ergonomics, RWTH Aachen University, Aachen, Germany.

JMIR Medical Informatics

|July 11, 2018

Summary

This summary is machine-generated.

This study developed a health data visualization taxonomy based on older adults' input, improving eHealth system design and user understanding. It confirms time-dependent data

Keywords:

classification computer graphics data display human factors medicine task performance and analysis telemedicine user/machine systems

More Related Videos

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Published on: June 10, 2021

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

Related Experiment Videos

Last Updated: Feb 8, 2026

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Published on: December 9, 2022

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Published on: June 10, 2021

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

Area of Science:

Human-Computer Interaction
Health Informatics
Usability Engineering

Background:

eHealth increasingly uses data visualizations for user health understanding.
Existing task-data taxonomies lack the perspective of prospective eHealth users, especially older adults.
User-centered design requires understanding user tasks and data types for effective visualization.

Purpose of the Study:

To construct a task-data taxonomy for health data visualizations incorporating older adults' perspectives.
To provide an orientation for system requirement analysis and empirical evaluation in eHealth.
To facilitate a common understanding and language in eHealth data visualization.

Main Methods:

Quantitative analysis of online survey responses from 98 participants (51 older adults, 47 eHealth experts).
Comparison of opinions between older adults and eHealth experts.
Synthesis of data into a comprehensive task-data taxonomy for health data visualizations.

Main Results:

Confirmed consultation, diagnosis, mentoring, and monitoring as key eHealth tasks.
Identified significant disagreements between older adults and experts on the importance of mentoring and monitoring.
Time-dependent data emerged as the most relevant data type across all eHealth tasks.

Conclusions:

An empirically developed task-data taxonomy for health data visualizations now includes prospective user perspectives.
The taxonomy offers a framework for user-centered system design and evaluation in eHealth.
The functionality dimension of the taxonomy for telemedicine was confirmed.