Objectives/Goals: Depression is common among people living with HIV (PLWH). This study explored the link between reduced metacognitive awareness and depression in PLWH. It utilized a positive emotion regulation task to compare brain activation during viewing versus upregulating positive emotions. Methods/Study Population: Depressed PLWH (N = 24; mean age  =  53; HAM-D mean  =  19) participated in an emotion regulation task while blood oxygen-level-dependent (BOLD) responses were recorded. In the emotional regulation task, participants were shown the International Affective Picture System (IAPS) a series of positive, negative, and neutral images. Participants were asked to view these images and given instructions to either negatively reappraise (RN) or positively reappraise (RP). In the RP condition, participants were no longer shown the image and asked to upregulate their positive emotional responses associated with it. Ten onset times were included for each trial. Results/Anticipated Results: A one-sample t-test was conducted to analyze contrasts between reappraisal of positive images and viewing positive images (RP > VP). Results showed significantly greater activation in the posterior cingulate and angular gyrus during the RP condition (peak MNI: 18, -52, 34; p < 0.001, uncorrected, k > 10 voxels). In comparing the reappraisal of negative images to viewing negative images (RN > VN), there was increased activation in the right supramarginal gyrus (peak MNI: 50, -28, 22; p < 0.001, uncorrected, k > 10 voxels). When contrasting the reappraisal of positive to negative images (RP > RN), BOLD signals were higher in the left dorsolateral prefrontal cortex (peak MNI: 40, -38, 32; p < 0.001, uncorrected, k > 10 voxels). Discussion/Significance of Impact: Findings underscore that depressed PLWH demonstrates BOLD responses in brain regions linked to appetitive motivation and meta-cognitive awareness during the RP condition which demands more executive resources among those with depression, highlighting the complexity of emotional regulation in this population.

Rapid blood culture identification is most effective with antimicrobial stewardship feedback, which is limited during non-business hours. We implemented overnight review of Blood Culture Identification 2 panel results by intensive care unit pharmacists and demonstrated reduced time to evaluation (3.6 vs 9.3 hours, P < .01).

We ask (1) why the United States adopted the car more quickly than other countries before 1929, and (2) why in the United States the car changed from a luxury to a mass-market good between 1909 and 1919. The answer is in part the success of the Model T in the United States. Mass production of the Model T began in 1913; by 1917, more than 40 percent of U.S. cars were Model Ts. Tariffs and difficulties producing outside Detroit made the U.S. success of the Model T difficult to replicate abroad.

Substantive research in the Social Sciences regularly investigates signed networks, where edges between actors are positive or negative. One often-studied example within International Relations for this type of network consists of countries that can cooperate with or fight against each other. These analyses often build on structural balance theory, one of the earliest and most prominent network theories. While the theorization and description of signed networks have made significant progress, the inferential study of link formation within them remains limited in the absence of appropriate statistical models. We fill this gap by proposing the Signed Exponential Random Graph Model (SERGM), extending the well-known Exponential Random Graph Model (ERGM) to networks where ties are not binary but positive or negative if a tie exists. Since most networks are dynamically evolving systems, we specify the model for both cross-sectional and dynamic networks. Based on hypotheses derived from structural balance theory, we formulate interpretable signed network statistics, capturing dynamics such as “the enemy of my enemy is my friend”. In our empirical application, we use the SERGM to analyze cooperation and conflict between countries within the international state system. We find evidence for structural balance in International Relations.

Bounds are established for log odds ratios (log cross-product ratios) involving pairs of items for item response models. First, expressions for bounds on log odds ratios are provided for one-dimensional item response models in general. Then, explicit bounds are obtained for the Rasch model and the two-parameter logistic (2PL) model. Results are also illustrated through an example from a study of model-checking procedures. The bounds obtained can provide an elementary basis for assessment of goodness of fit of these models.

A large class of rank tests, which includes the familiar sign test and the Wilcoxon signed-ranks test, is described and discussed. This class of distribution-free tests provides a flexible basis for testing research hypotheses of various forms. Exact small sample and approximate large sample procedures are considered. Applications of these procedures are presented, including simple numerical examples.

The problem of characterizing the manifest probabilities of a latent trait model is considered. The item characteristic curve is transformed to the item passing-odds curve and a corresponding transformation is made on the distribution of ability. This results in a useful expression for the manifest probabilities of any latent trait model. The result is then applied to give a characterization of the Rasch model as a log-linear model for a 2J- contingency table. Partial results are also obtained for other models. The question of the identifiability of “guessing” parameters is also discussed.

The Dutch Identity is a useful way to reexpress the basic equations of item response models that relate the manifest probabilities to the item response functions (IRFs) and the latent trait distribution. The identity may be exploited in several ways. For example: (a) to suggest how item response models behave for large numbers of items—they are approximate submodels of second-order loglinear models for 2J tables; (b) to suggest new ways to assess the dimensionality of the latent trait—principle components analysis of matrices composed of second-order interactions from loglinear models; (c) to give insight into the structure of latent class models; and (d) to illuminate the problem of identifying the IRFs and the latent trait distribution from sample data.

It is shown that measurement error in predictor variables can be modeled using item response theory (IRT). The predictor variables, that may be defined at any level of an hierarchical regression model, are treated as latent variables. The normal ogive model is used to describe the relation between the latent variables and dichotomous observed variables, which may be responses to tests or questionnaires. It will be shown that the multilevel model with measurement error in the observed predictor variables can be estimated in a Bayesian framework using Gibbs sampling. In this article, handling measurement error via the normal ogive model is compared with alternative approaches using the classical true score model. Examples using real data are given.

We give an account of Classical Test Theory (CTT) in terms of the more fundamental ideas of Item Response Theory (IRT). This approach views classical test theory as a very general version of IRT, and the commonly used IRT models as detailed elaborations of CTT for special purposes. We then use this approach to CTT to derive some general results regarding the prediction of the true-score of a test from an observed score on that test as well from an observed score on a different test. This leads us to a new view of linking tests that were not developed to be linked to each other. In addition we propose true-score prediction analogues of the Dorans and Holland measures of the population sensitivity of test linking functions. We illustrate the accuracy of the first-order theory using simulated data from the Rasch model, and illustrate the effect of population differences using a set of real data.

Item response theory (IT) models are now in common use for the analysis of dichotomous item responses. This paper examines the sampling theory foundations for statistical inference in these models. The discussion includes: some history on the “stochastic subject” versus the random sampling interpretations of the probability in IRT models; the relationship between three versions of maximum likelihood estimation for IRT models; estimating θ versus estimating θ-predictors; IRT models and loglinear models; the identifiability of IRT models; and the role of robustness and Bayesian statistics from the sampling theory perspective.

Jansen and Roskam (1986) discussed the compatibility of the unidimensional polytomous Rasch model with dichotomization of the response continuum. They derived a rather strict condition in which dichotomization of multicategory data that fit the unidimensional polytomous Rasch model, results in dichotomous data which fit the dichotomous Research model with effectively the same subject parameter. In this paper a more general dichotomization condition is derived for the polytomous Rasch model, which appears less restrictive, but upholds that the intrinsic logic of the unidimensional polytomous Rasch model defies dichotomization in general. The robustness of dichotomous analysis investigated in a simulation study. It shows a close relation with the two-parameters (Birnbaum) model. Theoretical and methodological implications are discussed.

Recent studies pertaining to an extended class of matched pairs tests based on powers of ranks are discussed. Previous questions regarding the asymptotic properties for this class of tests are clarified and a generalization of this class is described. This generalization raises a previously unanticipated concern about whether or not the analytic comparisons resulting from these tests correspond with an intuitive notion of what is being compared.

For the case of the one-element Markov learning model for which the guessing parameter p is assumed known the efficiency of Bower's method of moments estimator c+ for the learning parameter, c, is found. It seems that although c+ is not efficient, for some practical value of c it is not very inefficient. If p and c are small, ĉ, the maximum likelihood estimate, and c+ have approximately the same first term when expanded in powers of p.

The Non-Equivalent groups with Anchor Test (NEAT) design involves missingdata that are missing by design. Three nonlinear observed score equating methods used with a NEAT design are the frequency estimation equipercentile equating (FEEE), the chain equipercentile equating (CEE), and the item-response-theory observed-score-equating (IRT OSE). These three methods each make different assumptions about the missing data in the NEAT design. The FEEE method assumes that the conditional distribution of the test score given the anchor test score is the same in the two examinee groups. The CEE method assumes that the equipercentile functions equating the test score to the anchor test score are the same in the two examinee groups. The IRT OSE method assumes that the IRT model employed fits the data adequately, and the items in the tests and the anchor test do not exhibit differential item functioning across the two examinee groups. This paper first describes the missing data assumptions of the three equating methods. Then it describes how the missing data in the NEAT design can be filled in a manner that is coherent with the assumptions made by each of these equating methods. Implications on equating are also discussed.