Three multivariate models: Factor analysis, latent structure analysis, and latent profile analysis

Psychometrika ◽  
1959 ◽  
Vol 24 (3) ◽  
pp. 229-252 ◽  
Author(s):  
W. A. Gibson
Keyword(s):  
Factor Analysis ◽  
Structure Analysis ◽  
Latent Profile Analysis ◽  
Profile Analysis ◽  
Latent Structure ◽  
Multivariate Models ◽  
Latent Structure Analysis ◽  
Latent Profile

Clinical target syndromes in psychiatry: latent structure analysis versus factor analysis

1991 ◽  
Vol 6 (6) ◽  
pp. 301-306 ◽  
Author(s):  
P Bech
Keyword(s):  
Factor Analysis ◽  
Structure Analysis ◽  
Statistical Models ◽  
Rating Scales ◽  
Rating Scale ◽  
Latent Structure ◽  
Depression And Anxiety ◽  
Sufficient Statistics ◽  
Latent Structure Analysis ◽  
Symptom Rating

SummaryThe algorithms for the demonstration of shared phenomenology of psychiatric syndromes in DSM-III are resistant to quantification. In contrast, the rating scale approach quantifies clinical target syndromes in psychiatry. The two most useful statistical models for quantifying shared phenomenology by symptom rating scales have been reviewed; namely factor analysis and latent structure analysis. Results have shown that factor analysis has demonstrated dimensions of dementia, delirium, schizophrenia, mania, outward aggression, depression and anxiety. Latent structure analysis has confirmed that the items of brief rating scales (such as the Melancholia Scale) are additively related implying that their total scores are sufficient statistics for the measurement of these factors or dimensions. Latent structure analysis should be considered as a psychometric “glasnost” compared to algorithm-resistant logic of quantification in DSM-III.

FACTOR ANALYSIS, LATENT STRUCTURE ANALYSIS AND MENTAL TYPOLOGY

1961 ◽  
Vol 14 (1) ◽  
pp. 29-34 ◽  
Author(s):  
Curtis R. Miller ◽  
Richard K. Eyman ◽  
Harvey F. Dingman
Keyword(s):  
Factor Analysis ◽  
Structure Analysis ◽  
Latent Structure ◽  
Latent Structure Analysis

scholarly journals A latent profile analysis of the typology of bulimic symptoms in an indigenous Pacific population: evidence of cross-cultural variation in phenomenology

2010 ◽  
Vol 41 (1) ◽  
pp. 195-206 ◽  
Author(s):  
J. J. Thomas ◽  
R. D. Crosby ◽  
S. A. Wonderlich ◽  
R. H. Striegel-Moore ◽  
A. E. Becker
Keyword(s):  
Latent Profile Analysis ◽  
Profile Analysis ◽  
Signs And Symptoms ◽  
Cultural Orientation ◽  
Mental Health Research ◽  
Self Report ◽  
Latent Structure ◽  
Cultural Variation ◽  
Clinical Impairment ◽  
Latent Profile

BackgroundPrevious efforts to derive empirically based eating disorder (ED) typologies through latent structure modeling have been limited by the ethnic and cultural homogeneity of their study populations and their reliance on DSM-IV ED signs and symptoms as indicator variables.MethodEthnic Fijian schoolgirls (n=523) responded to a self-report battery assessing ED symptoms, herbal purgative use, co-morbid psychopathology, clinical impairment, cultural orientation, and peer influences. Participants who endorsed self-induced vomiting or herbal purgative use in the past 28 days (n=222) were included in a latent profile analysis (LPA) to identify unique subgroups of bulimic symptomatology.ResultsLPA identified a bulimia nervosa (BN)-like class (n=86) characterized by high rates of binge eating and self-induced vomiting, and a herbal purgative class (n=136) characterized primarily by the use of indigenous Fijian herbal purgatives. Both ED classes endorsed greater eating pathology and general psychopathology than non-purging participants, and the herbal purgative class endorsed greater clinical impairment than either the BN-like or non-purging participants. Cultural orientation did not differ between the two ED classes.ConclusionsIncluding study populations typically under-represented in mental health research and broadening the scope of relevant signs and symptoms in latent structure models may increase the generalizability of ED nosological schemes to encompass greater cultural diversity.

Factor Analysis of Spontaneous Speech in Aphasia

2020 ◽  
Vol 63 (12) ◽  
pp. 4127-4147
Author(s):  
Jean K. Gordon
Keyword(s):  
Factor Analysis ◽  
Language Production ◽  
Latent Profile Analysis ◽  
Profile Analysis ◽  
Ecological Validity ◽  
Spontaneous Speech ◽  
Factor Scores ◽  
Linear Discriminant ◽  
The Impact ◽  
Latent Profile

Purpose Spontaneous speech tasks are critically important for characterizing spoken language production deficits in aphasia and for assessing the impact of therapy. The utility of such tasks arises from the complex interaction of linguistic demands (word retrieval, sentence formulation, articulation). However, this complexity also makes spontaneous speech hugely variable and difficult to assess. The current study aimed to simplify the problem by identifying latent factors underlying performance in spontaneous speech in aphasia. The ecological validity of the factors was examined by examining how well the factor structures corresponded to traditionally defined aphasia subtypes. Method A factor analysis was conducted on 17 microlinguistic measures of narratives from 274 individuals with aphasia in AphasiaBank. The resulting factor scores were compared across aphasia subtypes. Supervised (linear discriminant analysis) and unsupervised (latent profile analysis) classification techniques were then conducted on the factor scores and the solutions compared to traditional aphasia subtypes. Results Six factors were identified. Two reflected aspects of fluency, one at the phrase level (Phrase Building) and one at the narrative level (Narrative Productivity). Two other factors reflected the accuracy of productions, one at the word level (Semantic Anomaly) and one at the utterance level (Grammatical Error). The other two factors reflected the complexity of sentence structures (Grammatical Complexity) and the use of repair behaviors (Repair), respectively. Linear discriminant analyses showed that only about two thirds of speakers were classified correctly and that misclassifications were similar to disagreements between clinical diagnoses. The most accurately diagnosed syndromes were the largest groups—Broca's and anomic aphasia. The latent profile analysis also generated profiles similar to Broca's and anomic aphasia but separated some subtypes according to severity. Conclusions The factor solution and the classification analyses reflected broad patterns of spontaneous speech performance in a large and representative sample of individuals with aphasia. However, such data-driven approaches present a simplified picture of aphasia patterns, much as traditional syndrome categories do. To ensure ecological validity, a hybrid approach is recommended, balancing population-level analyses with examination of performance at the level of theoretically specified subgroups or individuals. Supplemental Material https://doi.org/10.23641/asha.13232354

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