Consistent Differential Discrimination Model Estimation

<p>A novel factor-analytic model—the differential discrimination model—for assessing individual differences in scale use has been recently introduced, together with a three-stage estimation approach for model fitting. Unfortunately, the second-stage estimator and, as a consequence, the third-stage estimator of this procedure are not consistent. In this article we show that (a) the differential discrimination model can be expressed in a structural equation model framework, and (b) consistent and simultaneous estimation of all model parameters can be achieved using standard SEM software.</p

supplement_2_code – Supplemental material for A Graded Response Model Framework for Questionnaires With Uniform Response Formats

<p>Supplemental material, supplement_2_code for A Graded Response Model Framework for Questionnaires With Uniform Response Formats by Dirk Lubbe and Christof Schuster in Applied Psychological Measurement</p

supplement_1_simulation – Supplemental material for A Graded Response Model Framework for Questionnaires With Uniform Response Formats

<p>Supplemental material, supplement_1_simulation for A Graded Response Model Framework for Questionnaires With Uniform Response Formats by Dirk Lubbe and Christof Schuster in Applied Psychological Measurement</p

LDA classifications for the cross-validation between Bonn and Marburg.

<p>Table 3 shows cross tables of the estimated vs. true AD and HC participants. Estimates for the LDA were gathered from one site and applied to classify the participants from the other site (LDA: Bonn to Marburg and LDA: Marburg to Bonn).</p><p>LDA classifications for the cross-validation between Bonn and Marburg.</p

Clinical characteristics of patients with Alzheimer’s disease and healthy controls.

<p>All values are arithmetic means with standard deviations in parentheses, except for sex, smoker status, drug treatment, and cerebrospinal fluid taken.</p><p>Abbreviations</p><p>^a AD Alzheimer’s disease</p><p>^b PD Parkinson’s disease</p><p>^c HC healthy control</p><p>^d n/a not applicable</p><p>^e MMSE mini-mental state examination.</p><p>^f pTau hyperphosphorylated tau protein</p><p>^g Aβ₄₂ amyloid-beta 1–42</p><p>^h Aβ₄₀ amyloid-beta 1–40</p><p>ⁱ n.s. not significant.</p><p>Clinical characteristics of patients with Alzheimer’s disease and healthy controls.</p

LDA^a classifications for the leave-one-out cross-validations.

<p>Table 2 shows cross tables of the estimated vs. true AD and HC participants from the two sites (Marburg and Bonn). Estimations were drawn from the outcome of a leave-one-out cross-validation of the LDA model.</p><p>Abbreviations</p><p>^a LDA Linear discriminant analysis</p><p>^b AD Alzheimer’s disease</p><p>^c HC healthy control</p><p>LDA<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0132227#t002fn003" target="_blank">^a</a> classifications for the leave-one-out cross-validations.</p

Linear discriminant analysis.

<p>In Fig 1, we tested whether differentiating among patients with two neurodegenerative disorders and healthy controls is possible using the eNose. Linear discriminant analysis (LDA) was used to distinguish among groups. Repeated measurements were evaluated using median values and normalised to a range of 0 to 1. LD = linear discriminant, ad = Alzheimer's disease, pd = Parkinson's disease, hc = healthy control.</p