A formal framework for measuring change in sets of dichotomous data is developed and implications of the principle of specific objectivity of results within this framework are investigated. Building upon the concept of specific objectivity as introduced by G. Rasch, three equivalent formal definitions of that postulate are given, and it is shown that they lead to latent additivity of the parametric structure. If, in addition, the observations are assumed to be locally independent realizations of Bernoulli variables, a family of models follows necessarily which are isomorphic to a logistic model with additive parameters, determining an interval scale for latent trait measurement and a ratio scale for quantifying change. Adding the further assumption of generalizability over subsets of items from a given universe yields a logistic model which allows a multidimensional description of individual differences and a quantitative assessment of treatment effects; as a special case, a unidimensional parameterization is introduced also and a unidimensional latent trait model for change is derived. As a side result, the relationship between specific objectivity and additive conjoint measurement is clarified.

The partial credit model is considered under the assumption of a certain linear decomposition of the item × category parameters δih into “basic parameters” αj. This model is referred to as the “linear partial credit model”. A conditional maximum likelihood algorithm for estimation of the αj is presented, based on (a) recurrences for the combinatorial functions involved, and (b) using a “quasi-Newton” approach, the so-called Broyden-Fletcher-Goldfarb-Shanno (BFGS) method; (a) guarantees numerically stable results, (b) avoids the direct computation of the Hesse matrix, yet produces a sequence of certain positive definite matrices Bk, k = 1, 2, ..., converging to the asymptotic variance-covariance matrix of the \documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\hat \alpha _j $$\end{document}. The practicality of these numerical methods is demonstrated both by means of simulations and of an empirical application to the measurement of treatment effects in patients with psychosomatic disorders.

Necessary and sufficient conditions for the existence and uniqueness of a solution of the so-called “unconditional” (UML) and the “conditional” (CML) maximum-likelihood estimation equations in the dichotomous Rasch model are given. The basic critical condition is essentially the same for UML and CML estimation. For complete data matrices A, it is formulated both as a structural property of A and in terms of the sufficient marginal sums. In case of incomplete data, the condition is equivalent to complete connectedness of a certain directed graph. It is shown how to apply the results in practical uses of the Rasch model.

Two linearly constrained logistic models which are based on the well-known dichotomous Rasch model, the ‘linear logistic test model’ (LLTM) and the ‘linear logistic model with relaxed assumptions’ (LLRA), are discussed. Necessary and sufficient conditions for the existence of unique conditional maximum likelihood estimates of the structural model parameters are derived. Methods for testing composite hypotheses within the framework of these models and a number of typical applications to real data are mentioned.

The polytomous unidimensional Rasch model with equidistant scoring, also known as the rating scale model, is extended in such a way that the item parameters are linearly decomposed into certain basic parameters. The extended model is denoted as the linear rating scale model (LRSM). A conditional maximum likelihood estimation procedure and a likelihood-ratio test of hypotheses within the framework of the LRSM are presented. Since the LRSM is a generalization of both the dichotomous Rasch model and the rating scale model, the present algorithm is suited for conditional maximum likelihood estimation in these submodels as well. The practicality of the conditional method is demonstrated by means of a dichotomous Rasch example with 100 items, of a rating scale example with 30 items and 5 categories, and in the light of an empirical application to the measurement of treatment effects in a clinical study.

This paper discusses a new form of specifying and normalizing a Linear Logistic Test Model (LLTM) as suggested by Bechger, Verstralen, and Verhelst (Psychometrika, 2002). It is shown that there are infinitely many ways to specify the same normalization. Moreover, the relationship between some of their results and equivalent previous results in the literature is clarified, and it is shown that the goals of estimating and testing a single element of the weight matrix, for which they propose new methods, can be reached by means of simple, well-known tools already implemented in published LLTM software.

The paper addresses three neglected questions from IRT. In section 1, the properties of the “measurement” of ability or trait parameters and item difficulty parameters in the Rasch model are discussed. It is shown that the solution to this problem is rather complex and depends both on general assumptions about properties of the item response functions and on assumptions about the available item universe. Section 2 deals with the measurement of individual change or “modifiability” based on a Rasch test. A conditional likelihood approach is presented that yields (a) an ML estimator of modifiability for given item parameters, (b) allows one to test hypotheses about change by means of a Clopper-Pearson confidence interval for the modifiability parameter, or (c) to estimate modifiability jointly with the item parameters. Uniqueness results for all three methods are also presented. In section 3, the Mantel-Haenszel method for detecting DIF is discussed under a novel perspective: What is the most general framework within which the Mantel-Haenszel method correctly detects DIF of a studied item? The answer is that this is a 2PL model where, however, all discrimination parameters are known and the studied item has the same discrimination in both populations. Since these requirements would hardly be satisfied in practical applications, the case of constant discrimination parameters, that is, the Rasch model, is the only realistic framework. A simple Pearson x2 test for DIF of one studied item is proposed as an alternative to the Mantel-Haenszel test; moreover, this test is generalized to the case of two items simultaneously studied for DIF.

The LLRA (linear logistic model with relaxed assumptions; Fischer, 1974, 1977a, 1977b, 1983a) was developed, within the framework of generalized Rasch models, for assessing change in dichotomous item score matrices between two points in time; it allows to quantify change on latent trait dimensions and to explain change in terms of treatment effects, treatment interactions, and a trend effect. A remarkable feature of the model is that unidimensionality of the item set is not required. The present paper extends this model to designs with any number of time points and even with different sets of items presented on different occasions, provided that one unidimensional subscale is available per latent trait. Thus unidimensionality assumptions within subscales are combined with multidimensionality of the item set. Conditional maximum likelihood methods for parameter estimation and hypothesis testing are developed, and a necessary and sufficient condition for unique identification of the model, given the data, is derived. Finally, a sample application is presented.

Objectives: Patients with mild cognitive impairment (MCI) employ compensatory cognitive processes to maintain independence in day-to-day functioning as compared to patients with Alzheimer’s dementia (AD). The dorsolateral prefrontal cortex (DLFPC) supports cognitive compensation in normal aging and MCI. Using Paired Associative Stimulation combined with Electroencephalography (PAS-EEG) we have previously shown that patients with AD have impaired DLPFC plasticity compared to healthy control (HC) individuals. The aim of this study is to examine whether DLPFC plasticity in individuals with MCI is preserved compared to those with AD and HC, serving as a potential mechanism underlying cognitive compensation in MCI.

Methods: We analyzed a combined cross-sectional data of 47 AD, 16 MCI, and 40 HC participants from three different studies that assessed their DLPFC plasticity using PAS-EEG. PAS-EEG assesses DLPFC plasticity via the induction of Long Term Potentiation (LTP)-like activity, thereby referred to as PAS-LTP. Using multiple regression, we compared PAS-LTP in MCI to PAS-LTP in AD and HCs, after adjusting for age andgender.

Results: Among the 47 participants with AD (mean [SD] age = 75.3 [7] years), 29 were women and 18 were men; among the 16 participants with MCI (mean [SD] age = 74.8 [6] years), 11 were women and 5 were men; and among the 40 HCs (mean [SD] age = 76.4 [5.1] years), 22 were women and 18 were men. After adjusting for age and gender, there was an impact of diagnostic group on PAS-LTP [F (2,95) = 4.19, p = 0.018, between-group comparison η2 = 0.81]. Post-hoc comparisons showed that participants with MCI had a higher PAS-LTP (mean [SD] = 1.31 [0.49]) than those with AD (mean [SD] = 1.09 [0.28]) (Bonferroni corrected p = 0.042) but not different from PAS-LTP in HCs (mean [SD] = 1.25 [0.33]) (Bonferroni corrected p = 1.0).

Conclusions: Our findings indicate that plasticity is preserved in the DLPFC among individuals with MCI, supporting the hypothesis that DLPFC plasticity contributes to cognitive compensation towards delaying progression to AD. Thus, further enhancement of longer preservation of DLPFC plasticity in individuals with MCI could further delay the onset of AD in this population.