Evidence map›Paper›PMID 36737582›Full record

ReviewBehavior research methods2024

An extended emotion-eliciting film clips set (EGEFILM): assessment of emotion ratings for 104 film clips in a Turkish sample.

Elvan Arıkan İyilikci, Merve Boğa, Elif Yüvrük, Yıldız Özkılıç, Osman İyilikci, Sonia Amado

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Review in Behavior research methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Elvan Arıkan İyilikciDepartment of Psychology, Ege University, Bornova, 35400, Izmir, Turkey. elvan.arikan@ege.edu.tr.
Merve BoğaDepartment of Psychology, Ege University, Bornova, 35400, Izmir, Turkey.
Elif YüvrükDepartment of Psychology, Ege University, Bornova, 35400, Izmir, Turkey.
Yıldız ÖzkılıçDepartment of Psychology, İzmir Bakırçay University, Izmir, Turkey.
Osman İyilikciDepartment of Psychology, Manisa Celal Bayar University, Manisa, Turkey.
Sonia AmadoDepartment of Psychology, Ege University, Bornova, 35400, Izmir, Turkey.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The primary aim of this study was to test emotion-elicitation levels of widely used film clips in a Turkish sample and to expand existing databases by adding several new film clips with the capacity to elicit a wide range of emotions, including a rarely studied emotion category, i.e., calmness. For this purpose, we conducted a comprehensive review of prior studies and collected a large number of new suggestions from a Turkish sample to select film clips for eight emotion categories: amusement, tenderness, calmness, anger, sadness, disgust, fear, and neutrality. Furthermore, we aimed to assess emotion-eliciting levels of short video clips, mostly taken by amateur video footage. In total, 104 film clips were tested online by rating several affective dimensions. Self-reported emotional experience was assessed in terms of intensity, discreteness, valence, and arousal. It was found that at least one of the existing film clips, most of the new film clips, and the short video clips were successful at eliciting medium to high levels of target emotions. However, we also observed overlaps between certain emotions (e.g., tenderness-sadness, anger-sadness-disgust, or fear-anxiety). The current results are mostly in line with previous databases, suggesting that film clips are efficient at eliciting a wide range of emotions where cultural background might play a role in the elicitation of certain emotions (e.g., amusement, anger, etc.). We hope that this extended emotion-eliciting film clips set (EGEFILM) will provide a rich resource for future emotion research both in Turkey and the international area.

Indexed as

EmotionsMotion PicturesAngerFearHumansTurkeyAffect dimensionsDiscrete emotionsEmotion elicitationEmotion-eliciting film clips

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.