Preliminary Multilevel Confirmatory Factor Analysis of the Startle and Surprise Inventories using Simulated Flight Scenarios

This study was designed to validate the factor structure of the Startle and Surprise Inventories using multilevel confirmatory factor analysis in an ecologically valid flightdeck setting. The Startle and Surprise Inventories were developed to assess self-report startle and surprise to target stimuli. As their use expands in operational settings, construct validity should be further examined in contexts with ecological validity. 208 observations were collected from 26 professional pilots exposed to eight scenarios with varied levels of startle and surprise in a motion-based simulator. After each scenario, pilots completed the Startle and Surprise Inventories. A two-factor model, comprising the constructs Startle and Surprise, demonstrated superior and acceptable fit over a one-factor model. All items demonstrated significant factor loadings at both within- and between-scenario levels in the two-factor solution. McDonald’s ω ranged from ω = 0.88 to ω = 0.96 for the Startle Inventory, and ω = 0.77 to ω = 0.96 for the Surprise Inventory, indicating acceptable to excellent internal consistency. The findings offer empirical support for the construct validity and reliability of the Startle and Surprise Inventories in a highly ecologically-valid setting. The validated and reliable measures can inform evidence-based safety training protocols and interventions in aviation and other safety-critical domains.

Authors

Institutions

Publication Details

Journal
Journal of Cognitive Engineering and Decision Making
Published
2025-11-29
DOI
https://doi.org/10.1177/15553434251401784
Citations
1
Primary Topic
Aerospace and Aviation Technology
Type
article
Field-Weighted Citation Impact
4.99
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Preliminary Multilevel Confirmatory Factor Analysis of the Startle and Surprise Inventories using Simulated Flight Scenarios

Max Mulder, Jiayu Chen, Annemarie Landman, Alexis Derumigny et al.
1 citations
Journal of Cognitive Engineering and Decision Making
Aerospace and Aviation Technology
4.99
article

Preliminary Multilevel Confirmatory Factor Analysis of the Startle and Surprise Inventories using Simulated Flight Scenarios

Max Mulder, Jiayu Chen, Annemarie Landman, Alexis Derumigny, M. M. van Paassen, Olaf Stroosma
article en
1 citations

Abstract

This study was designed to validate the factor structure of the Startle and Surprise Inventories using multilevel confirmatory factor analysis in an ecologically valid flightdeck setting. The Startle and Surprise Inventories were developed to assess self-report startle and surprise to target stimuli. As their use expands in operational settings, construct validity should be further examined in contexts with ecological validity. 208 observations were collected from 26 professional pilots exposed to eight scenarios with varied levels of startle and surprise in a motion-based simulator. After each scenario, pilots completed the Startle and Surprise Inventories. A two-factor model, comprising the constructs Startle and Surprise, demonstrated superior and acceptable fit over a one-factor model. All items demonstrated significant factor loadings at both within- and between-scenario levels in the two-factor solution. McDonald’s ω ranged from ω = 0.88 to ω = 0.96 for the Startle Inventory, and ω = 0.77 to ω = 0.96 for the Surprise Inventory, indicating acceptable to excellent internal consistency. The findings offer empirical support for the construct validity and reliability of the Startle and Surprise Inventories in a highly ecologically-valid setting. The validated and reliable measures can inform evidence-based safety training protocols and interventions in aviation and other safety-critical domains.

Journal of Cognitive Engineering and Decision Making
Netherlands Organisation for Applied Scientific Research (NL), Delft University of Technology (NL)
Openalex Percentile: Top 4%
Aerospace and Aviation Technology
4.99
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.