The Videotest of Emotion Recognition by Lyusin and Ovsyannikova: A new scoring method

The article discusses how emotional intelligence tests are scored if the correct answers are based on expert assessments. As an example, the convergent validity of the Videotest of Emotion Recognition by Lyusin and Ovsyannikova is assessed using the author's and new key. In the author's version of the key, calculation of the accuracy of emotion recognition and the sensitivity to emotion intensity is based on the estimates of the distance between the respondent’s and experts’ scores. An alternative key was proposed for the reason that this method of calculation does not take into account the respondent’s errors when she notes that there was no emotion (0 points), but, according to experts, an emotion was present (from 1 to 5 points), and vice versa. In the standardization sample (N = 654), accuracy and sensitivity scores obtained using these two keys correlated at the 0.7 level. A slight advantage was shown for the new key: the accuracy of emotion recognition had stronger relationships with general emotional intelligence, identification with general emotional intelligence, identification of emotion (N = 120– 165, MSCEIT), as well as accuracy of basic emotion recognition in facial expressions (N = 112, JACFEE). With a higher accuracy of emotion recognition, a person is maximally sensitive to the intensity of another person’s emotions or slightly underestimates their intensity, which was more pronounced when using the author’s key.

Authors

Publication Details

Journal
Психология Журнал Высшей школы экономики
Published
2025-01-01
DOI
https://doi.org/10.17323/1813-8918-2025-2-209-221
Primary Topic
Emotion and Mood Recognition
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Videotest of Emotion Recognition by Lyusin and Ovsyannikova: A new scoring method

Alina Pankratova
Психология Журнал Высшей школы экономики
Emotion and Mood Recognition
article

The Videotest of Emotion Recognition by Lyusin and Ovsyannikova: A new scoring method

Alina Pankratova
article en

Abstract

The article discusses how emotional intelligence tests are scored if the correct answers are based on expert assessments. As an example, the convergent validity of the Videotest of Emotion Recognition by Lyusin and Ovsyannikova is assessed using the author's and new key. In the author's version of the key, calculation of the accuracy of emotion recognition and the sensitivity to emotion intensity is based on the estimates of the distance between the respondent’s and experts’ scores. An alternative key was proposed for the reason that this method of calculation does not take into account the respondent’s errors when she notes that there was no emotion (0 points), but, according to experts, an emotion was present (from 1 to 5 points), and vice versa. In the standardization sample (N = 654), accuracy and sensitivity scores obtained using these two keys correlated at the 0.7 level. A slight advantage was shown for the new key: the accuracy of emotion recognition had stronger relationships with general emotional intelligence, identification with general emotional intelligence, identification of emotion (N = 120– 165, MSCEIT), as well as accuracy of basic emotion recognition in facial expressions (N = 112, JACFEE). With a higher accuracy of emotion recognition, a person is maximally sensitive to the intensity of another person’s emotions or slightly underestimates their intensity, which was more pronounced when using the author’s key.

Психология Журнал Высшей школы экономикиVol. 22(2)
Openalex Percentile: Top 86%
Emotion and Mood Recognition
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.