B - 7 Factor Structure of the Beck Depression Inventory-II in Epilepsy

Abstract Objective This study aimed to explore the factor structure of the Beck Depression Inventory, Second Edition (BDI-II) in persons with epilepsy (PWE) and assess its correspondence with validated BDI-II cognitive and somatic-affective factors associated with depression in other populations. Method Retrospective data from 69 PWE (ages 18–72) who completed the BDI-II at a National Association of Epilepsy Centers level 4 epilepsy center were analyzed. Patients with diagnoses of bipolar disorder and schizophrenia spectrum disorders were excluded. An Exploratory Factor Analysis (EFA) using the psych package in R Studio was conducted, allowing factors to correlate with oblique factor rotation. The number of factors was determined using eigenvalues >1 and scree plots. Results Statistical support is strongest for a one-factor solution with eigenvalue 7.91; however, a two-factor solution (Factor two eigenvalue = 1.16) also met criteria. These factors accounted for 30% and 10% of the variance, respectively. Due to the low eigenvalue, few item loadings (3), and low variance explained by the second factor, item-level considerations were implemented to determine whether a two-factor solution added clinically relevant information. Conclusion Although a one-factor solution was best supported by the data, a second factor including sadness, feeling of punishment, and suicidal thoughts may be clinically relevant given elevated suicide risk and qualitative research reporting punishment feelings among PWE.

Authors

Publication Details

Journal
Archives of Clinical Neuropsychology
Published
2024-09-12
DOI
https://doi.org/10.1093/arclin/acae067.168
Primary Topic
Epilepsy research and treatment
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

B - 7 Factor Structure of the Beck Depression Inventory-II in Epilepsy

Bern G. Lee, Emily Brickell, Chantal Muller-Cohn, Anneliese Boettcher et al.
Archives of Clinical Neuropsychology
Epilepsy research and treatment
article

B - 7 Factor Structure of the Beck Depression Inventory-II in Epilepsy

Bern G. Lee, Emily Brickell, Chantal Muller-Cohn, Anneliese Boettcher, Mark Buckman
article en

Abstract

Abstract Objective This study aimed to explore the factor structure of the Beck Depression Inventory, Second Edition (BDI-II) in persons with epilepsy (PWE) and assess its correspondence with validated BDI-II cognitive and somatic-affective factors associated with depression in other populations. Method Retrospective data from 69 PWE (ages 18–72) who completed the BDI-II at a National Association of Epilepsy Centers level 4 epilepsy center were analyzed. Patients with diagnoses of bipolar disorder and schizophrenia spectrum disorders were excluded. An Exploratory Factor Analysis (EFA) using the psych package in R Studio was conducted, allowing factors to correlate with oblique factor rotation. The number of factors was determined using eigenvalues >1 and scree plots. Results Statistical support is strongest for a one-factor solution with eigenvalue 7.91; however, a two-factor solution (Factor two eigenvalue = 1.16) also met criteria. These factors accounted for 30% and 10% of the variance, respectively. Due to the low eigenvalue, few item loadings (3), and low variance explained by the second factor, item-level considerations were implemented to determine whether a two-factor solution added clinically relevant information. Conclusion Although a one-factor solution was best supported by the data, a second factor including sadness, feeling of punishment, and suicidal thoughts may be clinically relevant given elevated suicide risk and qualitative research reporting punishment feelings among PWE.

Archives of Clinical Neuropsychology
Openalex Percentile: Top 79%
Epilepsy research and treatment
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.