Using Profile Analysis via Multidimensional Scaling (PAMS) to identify core profiles from the WMS-III.
Department of Educational, School, and Counseling Psychology, University of Missouri-Columbia, USA. Psychological Assessment
(Impact Factor: 2.99).
03/2008; 20(1):1-9. DOI: 10.1037/1040-3518.104.22.168
Profile Analysis via Multidimensional Scaling (PAMS) is a procedure for extracting latent core profiles in a multitest data set. The PAMS procedure offers several advantages compared with other profile analysis procedures. Most notably, PAMS estimates individual profile weights that reflect the degree to which an individual's observed profile approximates the shape and scatter of latent core profiles. The PAMS procedure was applied to index scores of nonreplicated participants from the standardization sample (N = 1,033) for the Wechsler Memory Scale--Third Edition (D. Tulsky, J. Zhu, & M. F. Ledbetter, 2002). PAMS extracted discrepant visual memory and auditory memory versus working memory core profiles for the complete 16- to 89-year-old sample and discrepant working memory and auditory memory versus working memory core profiles for the 75- to 89-year-old cohort. Implications for use of PAMS in future research are discussed.
Available from: David F Tolin
- "Therefore, ordinary MDS provides less detailed information on observed measures with dimensions when compared to factor analysis (MacCallum, 1974). To bypass this disadvantage, Davison (1996) introduced the PAMS model, which has been utilized in the assessment of personality (e.g., Kim, Davison, & Frisby, 2007) and cognitive ability (e.g., Davison, Gasser, & Ding, 1996; Frisby & Kim, 2008; Kim et al., 2004), to reveal associations between observed scores of individuals and model-identified dimensions. A detailed description of the PAMS approach can be found in Kim et al. (2004, 2007). "
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ABSTRACT: Contemporary cognitive models of obsessive-compulsive disorder (OCD) emphasize the importance of various types of dysfunctional beliefs in contributing to OC symptoms, such as beliefs about excessive personal responsibility, perfectionism, and intolerance for uncertainty. The present study seeks to further our understanding of the role of these beliefs by identifying the common profiles of such beliefs, using profile analysis via multidimensional scaling (PAMS). In Study 1, a large student sample (N=4079) completed the 44-item obsessive beliefs questionnaire. One major profile, control of thoughts and perfectionism, was extracted. Study 2 examined profiles of the 87-item obsessive beliefs questionnaire in people with obsessive-compulsive disorder (OCD; n=398), other anxiety disorders (n=104), and a sample of undergraduate students (n=285). Inflated responsibility was a prominent subscale in the profiles of all three groups. Only control over thoughts was a unique subscale in the profile obtained for the OCD group, with this group having lower scores compared to the other groups. The results suggest that while inflated responsibility is a significant subscale in the profile of individuals with OCD, it is not a unique contributor; instead, control over thoughts is unique to OCD. The data, as well as recent research investigating obsessive beliefs, suggest the need to revise the contemporary cognitive model of OCD.
Journal of anxiety disorders 03/2014; 28(4):352-357. DOI:10.1016/j.janxdis.2014.03.004 · 2.68 Impact Factor
Available from: James K Wyatt
- "To identify the predominant daytime symptom profiles in our sample we used a procedure called profile analysis via multidimensional scaling (PAMS) (Davison et al., 1996, Kim et al., 2004, Frisby and Kim, 2008). Following the guidelines of Davidson et al. (1996), we submitted a 332-person × 6 daytime measure standardized-score matrix (scores on the ESS, FSS, IDD, SHAPS, Tension-anxiety and Anger-hostility POMS subscales) to a simple nonmetric multidimensional scaling analysis using the ASCAL program, available in SPSS version 15.0 (SPSS, Chicago, IL). "
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ABSTRACT: The type and severity of daytime symptoms reported by insomnia sufferers may vary markedly. Whether distinctive daytime symptom profiles are related to different insomnia diagnoses has not been studied previously. Using profile analysis via multidimensional scaling, we investigated the concurrent validity of ICSD-2 insomnia diagnoses by analysing the relationship of prototypical profiles of daytime symptoms with a subset of ICSD-2 diagnoses, such as insomnia associated to a mental disorder, psychophisiological insomnia, paradoxical insomnia, inadequate sleep hygiene, idiopathic insomnia, obstructive sleep apnea and restless legs syndrome. In a sample of 332 individuals meeting research diagnostic criteria for insomnia (221 women, M(age) =46 years.), the profile analysis identified four prototypical patterns of daytime features. Pearson correlation coefficients indicated that the diagnoses of insomnia associated to a mental disorder and idiopathic insomnia were associated with a daytime profile characterized by mood disturbance and low sleepiness; whereas the diagnoses of psychophysiological insomnia and inadequate sleep hygiene were related to a profile marked by poor sleep hygiene, daytime tension and low fatigue. Furthermore, whereas paradoxical insomnia was consistently associated to lower daytime impairment, insomnia associated to a mental disorder appeared as the most severe daytime form of insomnia. This classification of insomnia sufferers along multiple defining dimensions provides initial validation for two basic insomnia subtypes, with a presumably distinct aetiology: insomnia characterized mainly by an 'internal' component, and a 'learned' insomnia. Research to determine which dimensions are critical for inclusion or differential weighting for defining a general typological system for insomnia sufferers is warranted.
Journal of Sleep Research 09/2011; 20(3):425-33. DOI:10.1111/j.1365-2869.2010.00905.x · 3.35 Impact Factor
Available from: Se-Kang Kim
- "PAMS first identifies major profiles among people using items 9 items proximity data (as opposed to a persons 9 persons proximity matrix), and then interprets each individual's observed score profile based on the major profile patterns. This new approach has been applied to educational (e.g., Ding et al. 2005; Frisby et al. 2005; Kim et al. 2004) and psychological areas (e.g., Frisby and Kim 2008; Kim et al. 2007). "
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ABSTRACT: The aim of the study is to compare longitudinal patterns from Mathematics and Reading data from the direct child assessment
of Early Child Longitudinal Study, Kindergarten (ECLS-K, US Department of Education, National Center for Education Statistics
2006), utilizing Profile Analysis via Multidimensional Scaling (PAMS). PAMS has been used initially to discover profile patterns
in crosssectional data, and further applied to uncover longitudinal patterns by considering each time point as a coordinate
of longitudinal patterns. The ECLS-K data analyzed here included longitudinal information about student achievement. The current
study applied longitudinal PAMS to the data and examined how much the longitudinal patterns predict the fifth-grade achievement
scores. Results showed that the longitudinal patterns that depicted the growing trend and the growing–decaying trend were
significantly related to the fifth-grade achievement scores. Educational implications and discussions of longitudinal patterns
KeywordsProfile Analysis via Multidimensional Scaling (PAMS)-Mathematics and Reading longitudinal profile patterns-Regression analysis
Asia Pacific Education Review 06/2010; 11(2):189-198. DOI:10.1007/s12564-010-9074-4 · 0.47 Impact Factor
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