Bartlett's test of sphericitg: was applied to a correlation matrix computed on random normal deviates by Armstrong and Soelberg (1968), and returned a chi square value indicating that the matrix could have been generated from a population where the correlation coefficients are zero. These results re- emphasize the desirability of computing this test prior to proceeding t o factor extraction, and in accord with the findings of other writers, indicate this test to be sensitive in detecting results which can be ascribed to chance.

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A full-information item factor analysis model for EM algorithm for estimation of the model parameters is multidimensional polytomously scored item response presented and results of the analysis of item response data is developed as an extension of previous work by data by a computer program incorporating this algoseveral authors. The model is expressed both in factorrithm are presented. Index terms: EM algorithm, fullanalytic and item response theory parameters.information item factor analysis, multidimensionalReckase's multidimensional parameters for the model item response theory, polytomous response data.also are discussed as well as the related geometry. AnThe use of Likert items on questionnaires is very popular in psychological and sociological measure-B ment. In the typical analysis of data from such a questionnaire, frequencies of endorsements to points on B the scale are tabulated and percentages are computed separately for each item and point of the scale. These descriptive statistics are inconvenient when used to compare and interpret persons' responses as measures on the construct being assessed. Comparisons among items become more cumbersome as the number of items or the number of points on the scale increase. The points on the scale are frequently assumed to constitute an equal interval scale and persons' scores are treated as constituting a continuous variable, although they are in fact discrete. Item response theory (IRT) models have provided a solution to problems such as these in other contexts.A variety of IRT models applicable to scales consisting of dichotomously scored items measuring a single trait have been developed and are now in widespread use. The model developed here is based on two extensions of the basic IRT model. Models that can incorporate polytomously scored items have been proposed and used by several researchers (extended IRT models for dichotomously scored items to the multidimensional case (several traits) and developed an EM algorithm (Dempster, Laird, & Rubin, 1977) to estimate the parameters of the model based on the normal ogive. McKinley & Reckase (1983) proposed a multidimensional model based on the logistic function. In this paper, a multidimen-s ional IRT model for polytomously scored items, based on Samejima's graded response model (GRM) and I using the normal ogive, is developed. An EM algorithm that may be used to estimate the parameters of the model also is discussed. LT he Polytomous Full-Information Factor Analysis Model Development of the Model Bock & Aitkin (1981) and Bock, Gibbons, & Muraki (1988) assumed that the interaction of item i and person j results in a response process variable, y,, that is a linear combination of M latent traits. Using vector notation in which 0 is an M-dimensional vector of latent traits (common factors), and a is a vector APPLIED PSYCHOLOGICAL MEASUREMENT

IntroductionPulmonary arterial hypertension (PAH) is a life-limiting condition characterized by progressive vascular obliteration leading to right heart failure and ultimately death. Recent research has highlighted altered cellular and systemic metabolism as a key feature promoting pulmonary vascular disease and right heart BACKGROUND. Pulmonary arterial hypertension (PAH) is a deadly disease of the small pulmonary vasculature with an increased prevalence of insulin resistance (IR). Insulin regulates both glucose and lipid homeostasis. We sought to quantify glucose-and lipid-related IR in human PAH, testing the hypothesis that lipoprotein indices are more sensitive indices of IR in PAH. METHODS.Oral glucose tolerance testing in PAH patients and triglyceride-matched (TG-matched) controls and proteomic, metabolomics, and lipoprotein analyses were performed in PAH and controls. Results were validated in an external cohort and in explanted human PAH lungs. RESULTS. PAH patients were similarly glucose intolerant or IR by glucose homeostasis metrics compared with control patients when matched for the metabolic syndrome. Using the insulinsensitive lipoprotein index, TG/HDL ratio, PAH patients were more commonly IR than controls. Proteomic and metabolomic analysis demonstrated separation between PAH and controls, driven by differences in lipid species. We observed a significant increase in long-chain acylcarnitines, phosphatidylcholines, insulin metabolism-related proteins, and in oxidized LDL receptor 1 (OLR1) in PAH plasma in both a discovery and validation cohort. PAH patients had higher lipoprotein axis-related IR and lipoprotein-based inflammation scores compared with controls. PAH patient lung tissue showed enhanced OLR1 immunostaining within plexiform lesions and oxidized LDL accumulation within macrophages.CONCLUSIONS. IR in PAH is characterized by alterations in lipid and lipoprotein homeostasis axes, manifest by elevated TG/HDL ratio, and elevated circulating medium-and long-chain acylcarnitines and lipoproteins. Oxidized LDL and its receptor OLR1 may play a role in a proinflammatory phenotype in PAH.

Type 2 diabetes and tooth loss are linked both epidemiologically and pathophysiologically. We applied label-free differential protein expression analysis using multidimensional liquid chromatography/tandem mass spectrometry (2D-LC-MS/MS) to explore the proteomic profile of saliva samples collected from selected type 2 diabetic edentulous patients and non-diabetic controls. Ninety-six peptides corresponding to 52 proteins were differentially expressed between the diabetic edentulous patients and controls (p < 0.05). Some diabetes-related inflammatory biomarkers including glyceraldehyde-3-phosphate dehydrogenase and serum amyloid A were detected with levels increased in diabetic samples. Other biomarkers including amylase, palate, lung and nasal epithelium associated protein (PLUNC), and serotransferrin levels were decreased in diabetic samples. In contrast with previous findings, salivary carbonic anhydrase 6 and alpha-2 macroglobulin levels, however, were decreased in this diabetic patient population. Cluster analysis and principle component analysis demonstrated a differential pattern of protein biomarker expression between diabetic and control subjects. Western blot analysis was completed to confirm the relatively lower expression level of two biomarkers, including PLUNC and amylase in the diabetic group compared to control subjects. The presence of salivary biomarkers specific for diabetes in edentulous subjects mimics those in serum, especially those related to inflammatory/lipid metabolism. While this exploratory study requires further validation with a larger population, it provides proof-of-principle for salivary proteomics for edentulous subjects with diabetes.