Factor Analysis of the General Health Questionnaire

1994 ◽  
Vol 75 (2) ◽  
pp. 979-983 ◽  
Author(s):  
Richard S. Epstein ◽  
Carol S. Fullerton ◽  
Robert J. Ursano
Keyword(s):  
Factor Analysis ◽  
Principal Components Analysis ◽  
Factor Structure ◽  
Principal Components ◽  
General Health ◽  
Depressive Symptomatology ◽  
General Health Questionnaire ◽  
Health Questionnaire ◽  
Components Analysis ◽  
Army Soldiers

We present the factor structure of the General Health Questionnaire-60 as derived from a population of 2115 Army soldiers. An eight-factor principal components analysis provided the most clinically relevant solution and explained 58.0% of the variance. We distinguished two types of depressive symptomatology, suggesting the questionnaire may be useful in differentiating shame-ridden dysphoria from anergic disinterest.

THE FACTOR STRUCTURE OF THE 60 ITEM GENERAL HEALTH QUESTIONNAIRE

1986 ◽  
Vol 14 (2) ◽  
pp. 123-131 ◽  
Author(s):  
Clive Layton ◽  
John Rust
Keyword(s):  
Factor Structure ◽  
School Children ◽  
Principal Components ◽  
General Health ◽  
General Health Questionnaire ◽  
Principal Component ◽  
Questionnaire Data ◽  
Health Questionnaire ◽  
Oblique Rotation ◽  
Components Analysis

Male school children (N 241), all aged 16 years, and 144 men facing redundancy completed the 60 item version of the General Health Questionnaire. Data from the two groups was analysed separately using unrotated first principal components analysis followed by oblique rotation. The unrotated first principal component accounted for 23% of the variance in the school group, and 13.2% in the group facing redundancy. No subsequent component accounted for more than 6.1% of the variance. For both samples the first five components were subjected to an oblique rotation. Results were discussed in relation to previous findings of the GHQ. The factor structure was found to be unstable across groups. The implications of these findings were considered in the context of proposed subscales of the GHQ.

scholarly journals On the Factor Structure of a Reading Comprehension Test

2011 ◽  
Vol 4 (2) ◽  
pp. 242 ◽  
Author(s):  
Mohammad Salehi
Keyword(s):  
Factor Analysis ◽  
Reading Comprehension ◽  
Construct Validity ◽  
Principal Components Analysis ◽  
Exploratory Factor Analysis ◽  
Factor Structure ◽  
Principal Components ◽  
High Stakes ◽  
Comprehension Test ◽  
Components Analysis

To investigate the construct validly of a section of a high stakes test, an exploratory factor analysis using principal components analysis was employed. The rotation used was varimax with the suppression level of .30. Eleven factors were extracted out of 35 reading comprehension items. The fact that these factors emerged speak to the construct validity of the test. However the problem of over-factoring was obvious. This may be attributable to different paradigms of testing on which the items were based. In other words, the test constructer opted for passages from TOEFL, FCE and IELTS books with much alteration.

Factor Analysis and Rotation of Responses to the Junior Eysenck Personality Inventory

1977 ◽  
Vol 40 (2) ◽  
pp. 599-601 ◽  
Author(s):  
Robert A. C. Stewart
Keyword(s):  
Factor Analysis ◽  
New Zealand ◽  
Principal Components Analysis ◽  
Factor Structure ◽  
Principal Components ◽  
Personality Inventory ◽  
Varimax Rotation ◽  
Eysenck Personality Inventory ◽  
Components Analysis ◽  
Affect Factor

To investigate further the basic item-factor structure of the Junior Eysenck Personality Inventory, a principal components analysis and varimax rotation were conducted on responses of 866 children (aged 7 to 16 yr.) from schools in the Rotorua area of New Zealand. Ten factors were extracted of which 7 were interpretable. These were named: Factor 1. Neuroticism I (Neurotic affect), Factor 2. Extraversion I (Impulsivity), Factor 3. Lie Scale, Factor 4. Extraversion II (Introversion), Factor 5. Extraversion III (Jocularity), Factor 6. Extraversion IV (Sociability), Factor 8. Neuroticism II (Neurotic ideation).

Preliminary Examination of Psychometric Properties of the Korean Version of the Specific Interpersonal Trust Scale

2007 ◽  
Vol 100 (2) ◽  
pp. 355-364 ◽  
Author(s):  
Sook-Jeong Lee
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Psychometric Properties ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Interpersonal Trust ◽  
Confirmatory Factor ◽  
Components Analysis ◽  
Original Scale ◽  
Trust Scale

The purpose of this study was to examine the psychometric properties of the Specific Interpersonal Trust scale of Johnson-George and Swap in Korean samples as a part of the process of providing an exemplary tool for intercultural studies of trust. A translated version of the original scale was administered to 337 university students (157 males, 180 females) in Seoul, Korea. Data were subjected to a principal components analysis and a confirmatory factor analysis. In principal components analysis for the Korean sample ( n = 167), three factors were identified and labeled: Overall Trust (Cronbach α=.89), Emotional Trust (Cronbach α = .88), and Reliableness (Cronbach α=.84). A confirmatory factor analysis ( n=170) showed that the three-factor model was valid for the sample (χ2/ df= 1.78, RMR=.06, RMSEA = .07, TLI=.92, CFI=.93). Internal consistency reliability and factorial validity were satisfactory in the case of the Korean sample. The Korean version of the Specific Interpersonal Trust Scale made good use of three factors of trust and appeared to be valid without sex differences, while the original scale distinguished the Males subscale from the Females subscale. Implications and limitations of this study were discussed.

Beyond principal components: a critical comparison of factor analysis methods for subspace modelling in chemistry

2021 ◽  
Vol 13 (37) ◽  
pp. 4188-4219
Author(s):  
Peter D. Wentzell ◽  
Cannon Giglio ◽  
Mohsen Kompany-Zareh
Keyword(s):  
Analytical Chemistry ◽  
Factor Analysis ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Critical Comparison ◽  
Analysis Methods ◽  
Analysis Tools ◽  
Components Analysis

Principal components analysis (PCA) is widely used in analytical chemistry, but is only one type of broader range of factor analysis tools that are described in this article.

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