Note on the Factor Structure of the Millon Behavioral Health Inventory

1987 ◽  
Vol 60 (3) ◽  
pp. 799-802 ◽  
Author(s):  
Michael A. Murphy ◽  
Donald J. Tosi ◽  
Pamela Sharratt Wise ◽  
Dennis M. Eshbaugh
Keyword(s):  
Principal Component Analysis ◽  
Graduate Students ◽  
Behavioral Health ◽  
Factor Structure ◽  
Principal Component ◽  
Varimax Rotation ◽  
Components Analysis ◽  
The Stability ◽  
Pain Patients ◽  
Millon Behavioral Health Inventory

The factor structure of the Millon Behavioral Health Inventory was examined. Millon, Green, and Meagor in 1982 factor analyzed the responses to the inventory using a principal component analysis which gave four factors. Here procedures used by Millon, et al. were replicated. Principal components analysis ( N = 26 pain patients and 37 graduate students in counseling) with a varimax rotation resulted in four factors which essentially replicated the earlier findings. However, further study of the inventory should determine the stability of these factors with larger samples.

Factor Structure of the 28-Item General Health Questionnaire in a Sample of Antenatal Women

1996 ◽  
Vol 26 (3) ◽  
pp. 263-269 ◽  
Author(s):  
Yekeen A. Aderibigbe ◽  
William Riley ◽  
Terry Lewin ◽  
Oye Gureje
Keyword(s):  
Principal Component Analysis ◽  
Factor Structure ◽  
General Health ◽  
General Health Questionnaire ◽  
Severe Depression ◽  
Principal Component ◽  
Health Questionnaire ◽  
Varimax Rotation ◽  
Anxiety Depression ◽  
Anxiety Subscale

Objective: The factor structure of responses to the twenty-eight-item General Health Questionnaire (GHQ-28) in a sample of 277 Nigerian antenatal women was examined. Method: Principal Component Analysis (PCA) and Varimax rotation were used. Results: A four factor structure interpretable as social dysfunction, somatic-anxiety, depression-anxiety, and severe depression was obtained. Conclusion: Although the factor structure in this sample is similar to that previously reported with this instrument, the factor loadings, particularly for the anxiety subscale differed. Thus, the factor structures of the GHQ may differ depending on the cultural background of the sample.

Stability and Factor Structure of the Jackson Personality Inventory-Revised

2000 ◽  
Vol 86 (2) ◽  
pp. 421-428 ◽  
Author(s):  
Joseph A. Doster ◽  
Susan E. Wilcox ◽  
Paul L. Lambert ◽  
Maria F. Rubino-Watkins ◽  
Arthur J. Goven ◽  
...  
Keyword(s):  
Continuing Education ◽  
Factor Structure ◽  
Big Five ◽  
Cognitive Styles ◽  
Value Orientation ◽  
Personality Inventory ◽  
Varimax Rotation ◽  
Interpersonal Patterns ◽  
Components Analysis ◽  
Patterns Of Interaction

The Jackson Personality Inventory-Revised comprises 15 bipolar scales and five cluster scores concerning an individual's interpersonal patterns of interaction, cognitive styles, and value orientation. Recent reviews of this revised version raise questions about test-retest stability as well as the factor structure on which cluster scores are based. 74 men and 33 women (29–63 years of age, M = 42.3) completed the inventory while participating in a continuing education program. Of these 45 participated in a second session 13 wk. later. Test-retest correlations are significant, with 12 of the 15 scales having correlations > .75. Intercorrelations among all subscales indicate that the Jackson subscales for the most part remain distinct from each other ranging from .01 to .59. A Principal Components Analysis with a varimax rotation yielded three factors that parallel the NEO big five, i.e., Openness, Neuroticism, and Extroversion and replicated previous factor structure found for both versions of the Jackson inventory. The fourth and fifth factors here were labeled Trustworthy and Organization; however, the composition of these factors across several studies appears to be unstable, suggesting optimal certainty when interpreting the clusters of subscales associated only with Openness, Neuroticism, and Extroversion. Further research may help clarify the instability associated with the other factors of this inventory.

Assessing Teacher Manageability: A Factor Analytic Approach

1988 ◽  
Vol 13 (4) ◽  
pp. 245-252 ◽  
Author(s):  
Stephen P. Safran ◽  
Joan S. Safran ◽  
Robert S. Barcikowski
Keyword(s):  
Principal Component Analysis ◽  
Statistical Analysis ◽  
Problem Behaviors ◽  
Principal Component ◽  
Analytic Approach ◽  
Future Research ◽  
Maladaptive Behaviors ◽  
Varimax Rotation ◽  
Behaviorally Disordered ◽  
Factor Analytic

An ecologically valid appraisal of students' problem behaviors must include assessment of the teacher's role as perceiver on various levels. This study analyzes the teacher manageability construct, examining educators' beliefs about their ability to manage 39 generally maladaptive behaviors within their own classroom. To address measurement limitations of previous investigations (including nonfactor analytic clustering of behaviors), a principal component analysis followed by a varimax rotation was administered on teacher manageability ratings (N = 182). This statistical analysis yielded nine component subtests (the Teacher Manageability Scale) and demonstrated that by changing the method used to group behaviors, you also modify the structure of a teacher checklist. Lack of Communication was identified as the most difficult behavior to manage. Implications for professionals working with students identified as behaviorally disordered and for future research are discussed.

scholarly journals Measuring the Systematic Risk of Sectors within the US Market Via Principal Components Analysis: Before and during the COVID-19 Pandemic

2022 ◽  
Author(s):  
Jaime González Maiz Jiménez ◽  
Adán Reyes Santiago
Keyword(s):  
Principal Component Analysis ◽  
Stock Market ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Systematic Risk ◽  
Principal Component ◽  
Component Analysis ◽  
Market Capitalization ◽  
The Us ◽  
Components Analysis

This research measures the systematic risk of 10 sectors in the American Stock Market, discerning the COVID-19 pandemic period. The novelty of this study is the use of the Principal Component Analysis (PCA) technique to measure the systematic risk of each sector, selecting five stocks per sector with the greatest market capitalization. The results show that the sectors that have the greatest increase in exposure to systematic risk during the pandemic are restaurants, clothing, and insurance, whereas the sectors that show the greatest decrease in terms of exposure to systematic risk are automakers and tobacco. Due to the results of this study, it seems advisable for practitioners to select stocks that belong to either the automakers or tobacco sector to get protection from health crises, such as COVID-19.

scholarly journals Analysis of infrared spectra of blood serum of patients with multiple myeloma

2021 ◽  
Vol 2103 (1) ◽  
pp. 012052
Author(s):  
D A Chernyshev ◽  
E S Mikhailets ◽  
E A Telnaya ◽  
L V Plotnikova ◽  
A D Garifullin ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Blood Serum ◽  
Infrared Spectra ◽  
Early Stage ◽  
Principal Component ◽  
Healthy Donors ◽  
Components Analysis ◽  
Serious Disease

Abstract Multiple myeloma (MM) is a serious disease that is difficult to diagnose especially at early stage. Infrared spectroscopy is a promising approach for diagnosing MM. The principal component analysis (PCA) allows us to reduce the dimension of the data and keep only the important variables. In this study, we apply principal components analysis to infrared (IR) spectra of blood serum from healthy donors and multiple myeloma patients. As a result of the analysis by PCA, it was possible to visualize the separation of patient’s and donor’s samples into two clusters. The result indicates that this method is potentially applicable for diagnosis of multiple myeloma.

Combat Deployment Experiences and Soldier Mental Health: Examining the Factor Structure of a Combat Experiences Scale

2021 ◽  
Author(s):  
Haley Sherman ◽  
Nicky Frye-Cox ◽  
Mallory Lucier-Greer
Keyword(s):  
Mental Health ◽  
Principal Component Analysis ◽  
Depressive Symptoms ◽  
Health Outcomes ◽  
Factor Structure ◽  
Principal Component ◽  
Mental Health Outcomes ◽  
Mental Health Symptoms ◽  
Coping With Stress ◽  
Health Symptoms

ABSTRACT Introduction Researchers and practitioners are invested in understanding how deployment experiences impact the nearly 193,000 U.S. service members who deploy in a given year. Yet, there remains a need to adequately identify salient deployment experiences through survey measurement tools and understand how differential experiences are uniquely related to mental health outcomes. Therefore, this study examined the factor structure of an established combat experiences measure from the Army Study to Assess Risk and Resilience in Service members (Army STARRS) dataset to identify underlying survey constructs that reflect nuanced deployment experiences. Then, we examined the association between diverse combat experiences and current mental health symptoms (i.e., anxiety and depressive symptoms) and the mediating role of coping. Materials and Methods Data were drawn from the Army STARRS data (N = 14,860 soldiers), specifically the All Army Study component. A principal component analysis (PCA) was conducted to examine the dimensionality of the combat experiences scale, and then a path model was conducted to examine the relationships between combat experiences, coping with stress following a deployment, and mental health symptoms while controlling for relevant individual and interpersonal factors. Results Results from the principal component analysis suggested that the Army STARRS combat experiences scale encompasses two components, specifically: “Expected combat experiences” and “Responsible for non-enemy deaths.” Both “Expected combat experiences” and “Responsible for non-enemy deaths” were associated with higher levels of anxiety and depressive symptoms, respectively, and “Responsible for non-enemy deaths” was also indirectly linked to these mental health outcomes through coping with stress after deployment. Conclusions These findings provide insight into the dimensionality of combat experiences and offer practitioners a more nuanced understanding of how to process unique combat experiences that differentially relate to mental health symptoms.

Heuristic Principal Component Analysis-Based Unsupervised Feature Extraction and Its Application to Bioinformatics

2015 ◽  
pp. 138-162 ◽  
Author(s):  
Y-H. Taguchi ◽  
Mitsuo Iwadate ◽  
Hideaki Umeyama ◽  
Yoshiki Murakami ◽  
Akira Okamoto
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Principal Component ◽  
Component Analysis ◽  
Circulating Microrna ◽  
Biomarker Identification ◽  
Typical Situation ◽  
Aberrant Dna Methylation ◽  
The Stability ◽  
Unsupervised Feature Extraction

Feature Extraction (FE) is a difficult task when the number of features is much larger than the number of samples, although that is a typical situation when biological (big) data is analyzed. This is especially true when FE is stable, independent of the samples considered (stable FE), and is often required. However, the stability of FE has not been considered seriously. In this chapter, the authors demonstrate that Principal Component Analysis (PCA)-based unsupervised FE functions as stable FE. Three bioinformatics applications of PCA-based unsupervised FE—detection of aberrant DNA methylation associated with diseases, biomarker identification using circulating microRNA, and proteomic analysis of bacterial culturing processes—are discussed.

Developing Attitude Statements toward Illegal Immigration: Transcultural Reliability and Utility

2007 ◽  
Vol 100 (3) ◽  
pp. 901-914 ◽  
Author(s):  
Reidar Ommundsen ◽  
Kees van der Veer ◽  
Hao Van Le ◽  
Krum Krumov ◽  
Knud S. Larsen
Keyword(s):  
Principal Component Analysis ◽  
Sex Differences ◽  
Item Analysis ◽  
Illegal Immigration ◽  
Principal Component ◽  
Component Analysis ◽  
Illegal Immigrants ◽  
State University ◽  
Varimax Rotation ◽  
Two Samples

This is a report on the utility of a scale measuring attitudes toward illegal immigrants in two samples from nations that have more people moving out of the country than moving into the country. The Attitude toward Illegal Immigrants Scale was administered to 219 undergraduates from Sofia University in Bulgaria, and 179 undergraduates from Hanoi State University in Vietnam. Results yielded a scale with no sex differences, and acceptable alpha coefficients. Item analysis identified the most contributory and least contributory items, with considerable overlap in the two samples. A principal component analysis with varimax rotation was carried out to examine the structure.

Überprüfung eines Familien-Assessment-Instruments auf der Grundlage der Theorie des systemischen Gleichgewichts

Pflege ◽  
2006 ◽  
Vol 19 (1) ◽  
pp. 23-32
Author(s):  
Christina Köhlen ◽  
Marie-Luise Friedemann
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Screening Instrument ◽  
Assessment Instrument ◽  
Assessment Instruments ◽  
Varimax Rotation ◽  
Cronbach Alpha

In diesem Beitrag wird die Überprüfung des Assessment-Instruments zur Einschätzung der Wirksamkeit familiärer Strategien (ASF-E) für die Anwendung in Deutschland und der deutschsprachigen Schweiz beschrieben. Das ASF-E ist ein Screening-Instrument für Familiengesundheit, wie sie in der Theorie des systemischen Gleichgewichts definiert ist (Friedemann, 1991). Zunächst wurde das Instrument in den frühen 1990er Jahren in der Schweiz unter Berücksichtigung kultureller Unterschiede ins Deutsche übersetzt. Die vorliegende Testung war die erste in Deutschland und die zweite in der Schweiz. Das Instrument hatte ursprünglich 26 Items, wobei jedes drei Aussagen beinhaltet, die Familienstrategien ausdrücken und von denen die Probanden dasjenige auswählen sollten, das am ehesten auf ihre Familiensituation zutrifft. Die Aussagen sind von 1 bis 3 gestaffelt, wobei der Wert 3 für optimale, zufrieden stellende Gesundheit steht. In Deutschland wurde das Instrument von 343 und in der Schweiz von 209 Befragten aus der Gemeinde ausgefüllt, die sowohl unterschiedlichen Alters als auch unterschiedlicher ökonomischer Herkunft waren. Eine Principal Component Analysis mit Varimax Rotation brachte vier Faktoren mit einem Eigenwert > 1 hervor. Acht Items mussten herausgenommen werden, da sie eine unzureichende Verteilung oder zu schwache Faktorladungen aufwiesen. Das endgültige Assessment-Instrument hat 18 Items mit einem akzeptablen Wert für Reliabilität (Cronbach Alpha). Das ASF-E kann in Deutschland und in der Schweiz genutzt werden, um Forschung mit Familien zu begleiten und Familiengesundheit in Verbindung mit Pflegeinterventionen einzuschätzen.

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