Chronic Multisymptom Illness in Gulf War Veterans Case Definitions Reexamined (2014) / Chapter Skim
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4 Factor Analysis and its Use in Studies of Symptoms in Gulf War Veterans
4 Factor Analysis and its Use in Studies of Symptoms in Gulf War Veterans
Pages 67-86
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From page 67... ...
The two most frequently used statistical methods have been factor analysis and cluster analysis. Researchers began using statistical analyses to evaluate whether symptoms found in Gulf War veterans might constitute a unique syndrome.
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From page 68... ...
It is an empirical method." Factor Analysis for Data Reduction Factor analysis has been used in studies of Gulf War veterans, initially to see whether a unique "Gulf War syndrome" could be identified and later to inform case definitions of CMI. In attempting to reduce the amount of data that would be gathered (the large and varied number of symptoms)
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From page 69... ...
The other approach is to conduct separate factor analyses in the reference and comparison groups, derive factor scores for everyone on the basis of both factor-analytic models, and then compare the resulting scores; this is not an accepted method of comparing factor structures. The studies that have used factor analysis to investigate symptoms in Gulf War veterans have used several analytic strategies.
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From page 70... ...
It should be noted that each successive factor that is extracted in a factor analysis accounts for less of the variance than the previous one. In the deployed sample, the sixth factor, labeled neurologic impairment, accounted for only 3% of the total variance, compared with 79% for the first factor.
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From page 71... ...
grouped symptoms into categories suggestive of existing syndromes or disorders, such as fibromyalgia or depression. Its finding of a considerably higher prevalence of symptom groups suggestive of fibromyalgia, depression, and cognitive dysfunction in Gulf War veterans motivated the first applications of factor analysis to grouping and classifying veterans' symptoms.
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From page 72... ...
No difference was found in the neurologic factor scores, and appetite factor scores were significantly lower in the nondeployed cohort. None of the factors was exclusive to Gulf War veterans, so the investigators concluded that their findings did not support the existence of a new syndrome (Cherry et al., 2001)
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From page 73... ...
The study was the first to examine groupings of symptoms in Gulf War veterans with factor analysis. Through standardized symptom questionnaires and a two-stage exploratory factor analysis, the investigators defined what they considered to be either six syndromes or six variants of a single syndrome, which they labeled impaired cognition, confusion–ataxia, arthromyoneuropathy, phobia–apraxia, fever–adenopathy, and weakness–incontinence.
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From page 74... ...
Scores among the three analyses were similar for insecurity or minor depression; higher in Gulf War veterans for somatization, depression, and obsessive–compulsive; and higher in nondeployed Seabees for malaise.9 Somatization, depression, and obsessive–compulsive affected an excess of about 20% of Gulf War veterans. This indirect method of testing the differences in factor structure led them to conclude that deployed and nondeployed veterans report "more of the same symptoms and illnesses" and that "identifying a new syndrome such as the putative Gulf War syndrome is a difficult task and is unlikely to be accomplished by factor analysis, or any other statistical methodology, performed on a small, selected group of Gulf War Veterans." The authors also conducted a discriminant analysis to test the ability of the factors to discriminate between Gulf War–deployed and nondeployed veterans: the probability of misclassification was 7.4% in nondeployed veterans and 76.5% in Gulf War–deployed veterans.
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From page 75... ...
The authors conducted a principal components analysis of the first subsample, extracting 10 components with eigenvalues10 greater than 1.0; three of the components accounted for 39.1% of the total variance. (See Appendix A for a discussion of the relationship between principal components analysis and factor analysis.)
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From page 76... ...
Rather than showing the symptom means for each of the six clusters, they showed means by cluster of seven factor scores, which they had derived from a factor analysis of the same 95 symptoms, thus complicating the interpretation of the cluster analysis. Cluster 1 was composed primarily of well people and had a smaller proportion of Gulf War veterans (36.4%)
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From page 77... ...
The researchers used the mean factor scores from their factor analysis to group respondents on the basis of severity of symptoms. Examining the two randomly divided subsamples five times each but using cluster analysis, they identified two stable clusters.
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From page 78... ...
. The committee notes that neither factor analysis nor cluster analysis alone can directly produce a case definition; such definitions are the product of postprocessing of factoranalytic model results (for example, dichotomization of factor scores to operationalize a case definition)
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From page 79... ...
data, so symptom survey items should include multiple response categories for collecting symptom data. • Account for the measurement level of the data when conducting factor analysis; for example, use a polychoric correlation matrix, rather than a Pearson correlation matrix, to account for ordinal-level data.
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From page 80... ...
% Variance Explained Kang et al., Active and Ordinal; 47 Iterative Oblique 6; % not reported Fatigue or depression, Factors similar, but 4 2002 retired, symptoms coded as principal- neurologic, neurologic symptoms n = 19,383 0 = none, 1 = mild, factor analysis musculoskeletal– loaded on neurologic or 2 = severe. rheumatologic, factor for deployed but gastrointestinal, not nondeployed.
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From page 81... ...
factor analysis musculoskeletal factor 1,979; US appeared in US GW GW veterans, veteran data. n = 1,163 Forbes et Active and Ordinal; 63 Unknown Orthogonal 3; 47.1% Psychophysiologic No.
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From page 82... ...
factors; 29% by using structural estimating equations. Some models also fitted higher-order factor "Gulf War syndrome" and loaded 4 additional symptoms (chronic fatigue involving excessive muscle weakness, chronic fever and night sweats, middle and terminal insomnia, and chronic watery diarrhea)
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From page 83... ...
Oblique rotation algorithms allow factors to correlate with one another. b Principal-axis factor analysis is a common kind of factor analysis.
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From page 84... ...
2001. Symptom factor analysis, clinical findings, and functional status in a population-based case control study of Gulf War unexplained illness.
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From page 85... ...
2000. Factor analysis of self-reported symptoms: Does it identify a Gulf War syndrome?
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