Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Naturalistic Observations02:30

Naturalistic Observations

18.0K
If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
18.0K
Data Collection by Observations01:08

Data Collection by Observations

15.6K
Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
15.6K
Observational Studies01:11

Observational Studies

11.4K
Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One...
11.4K
Ordinal Level of Measurement00:55

Ordinal Level of Measurement

36.8K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks...
36.8K
Observational Learning01:12

Observational Learning

1.2K
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
1.2K
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

17.0K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
17.0K

You might also read

Related Articles

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

Sort by
Same author

Ecological assessment of transdiagnostic clinical symptoms in serious mental illness with daily smartphone surveys.

Translational psychiatry·2026
Same author

Sentiment Analysis of Naturalistic Speech Using Open-Weight Large Language Models.

Affective science·2026
Same author

Testing affect regulation theories of dreaming.

Sleep·2026
Same author

<i>GeSTICS</i>: A Multimodal Corpus for Studying Gesture Synthesis in Two-party Interactions with Contextualized Speech.

Proceedings of the ... ACM International Conference on Intelligent Virtual Agents. IVA (Conference)·2025
Same author

Representation Learning for Interpersonal and Multimodal Behavior Dynamics: A Multiview Extension of Latent Change Score Models.

Proceedings of the ... ACM International Conference on Multimodal Interaction. ICMI (Conference)·2025
Same author

Examining the Role of Insomnia in Maintaining Binge Eating and Restricting in United States Veterans.

European eating disorders review : the journal of the Eating Disorders Association·2025

Related Experiment Video

Updated: Mar 24, 2026

Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger
05:50

Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger

Published on: January 16, 2020

6.3K

A Primer on Observational Measurement.

Jeffrey M Girard1, Jeffrey F Cohn1

  • 11 University of Pittsburgh, Pittsburgh, PA, USA.

Assessment
|March 3, 2016
PubMed
Summary
This summary is machine-generated.

This article offers a guide to observational measurement, covering standardized instruments, validity, and common challenges. It also explores recent technological advances and future research directions in this scientific method.

Keywords:
affective computingcomputational behavioral sciencecontemporary validity theoryinterobserver reliabilityobservational measurement

More Related Videos

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools
11:29

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

Published on: June 20, 2020

9.9K
Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

10.4K

Related Experiment Videos

Last Updated: Mar 24, 2026

Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger
05:50

Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger

Published on: January 16, 2020

6.3K
Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools
11:29

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

Published on: June 20, 2020

9.9K
Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

10.4K

Area of Science:

  • Interdisciplinary applications of observational measurement in biology, psychology, sociology, education, medicine, and marketing.

Background:

  • Observational measurement is crucial across many scientific fields.
  • Standardized instruments are key to consistent data collection.

Purpose of the Study:

  • To provide an interdisciplinary primer on observational measurement.
  • To highlight recent advances and challenges in observational methodology.

Main Methods:

  • Detailing types of instruments for standardizing measurements.
  • Discussing validity approaches based on contemporary theory.
  • Outlining challenges: measurement drift, observer reactivity, reliability, time/expense.

Main Results:

  • Recent advances include computer-assisted and automated measurement.
  • Improved statistical data analysis techniques are emerging.
  • Key directions for future observational research are identified.

Conclusions:

  • Addressing challenges and leveraging technological advancements are vital for robust observational measurement.
  • Future research should focus on refining methods and expanding applications.