Journal Alert - PSYCHOMETRIKA

> Title:
> Seeking a Balance Between the Statistical and Scientific Elements in Psychometrics
>
> Authors:
> Wilson, M
>
> Source:
> *PSYCHOMETRIKA*, 78 (2):211-236; APR 2013
>
> Abstract:
> In this paper, I will review some aspects of psychometric projects that
> I have been involved in, emphasizing the nature of the work of the
> psychometricians involved, especially the balance between the
> statistical and scientific elements of that work. The intent is to seek
> to understand where psychometrics, as a discipline, has been and where
> it might be headed, in part at least, by considering one particular
> journey (my own). In contemplating this, I also look to psychometrics
> journals to see how psychometricians represent themselves to themselves,
> and in a complementary way, look to substantive journals to see how
> psychometrics is represented there (or perhaps, not represented, as the
> case may be). I present a series of questions in order to consider the
> issue of what are the appropriate foci of the psychometric discipline.
> As an example, I present one recent project at the end, where the roles
> of the psychometricians and the substantive researchers have had to
> become intertwined in order to make satisfactory progress. In the
> conclusion I discuss the consequences of such a view for the future of
> psychometrics.
>
> ========================================================================
>
>
> *Pages: 237-239 (Biographical-Item)
> *View Full Record: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=CCC&DestLinkType=FullRecord;KeyUT=CCC:000316344000002
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>
> Title:
> In Memoriam Joseph B. Kruskal 1928-2010
>
> Authors:
> Carroll, JD; Arabie, P
>
> Source:
> *PSYCHOMETRIKA*, 78 (2):237-239; APR 2013
>
> ========================================================================
>
>
> *Pages: 240-242 (Editorial Material)
> *View Full Record: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=CCC&DestLinkType=FullRecord;KeyUT=CCC:000316344000003
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>
> Title:
> Modeling fMRI Data: Challenges and Opportunities
>
> Authors:
> Maydeu-Olivares, A; Brown, G
>
> Source:
> *PSYCHOMETRIKA*, 78 (2):240-242; APR 2013
>
> Abstract:
> We offer an introduction to the five papers that make up this special
> section. These papers deal with a range of the methodological challenges
> that face researchers analyzing fMRI data-the spatial, multilevel, and
> longitudinal nature of the data, the sources of noise, and so on. The
> papers all provide analyses of data collected by a multi-site
> consortium, the Function Biomedical Informatics Research Network. Due to
> the sheer volume of data, univariate procedures are often applied, which
> leads to a multiple comparisons problem (since the data are necessarily
> multivariate). The papers in this section include interesting
> applications, such as a state-space model applied to these data, and
> conclude with a reflection on basic measurement problems in fMRI. All in
> all, they provide a good overview of the challenges that fMRI data
> present to the standard psychometric toolbox, but also to the
> opportunities they offer for new psychometric modeling.
>
> ========================================================================
>
>
> *Pages: 243-259 (Article)
> *View Full Record: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=CCC&DestLinkType=FullRecord;KeyUT=CCC:000316344000004
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>
> Title:
> Extracting Intrinsic Functional Networks with Feature-Based Group Independent Component Analysis
>
> Authors:
> Calhoun, VD; Allen, E
>
> Source:
> *PSYCHOMETRIKA*, 78 (2):243-259; APR 2013
>
> Abstract:
> There is increasing use of functional imaging data to understand the
> macro-connectome of the human brain. Of particular interest is the
> structure and function of intrinsic networks (regions exhibiting
> temporally coherent activity both at rest and while a task is being
> performed), which account for a significant portion of the variance in
> functional MRI data. While networks are typically estimated based on the
> temporal similarity between regions (based on temporal correlation,
> clustering methods, or independent component analysis [ICA]), some
> recent work has suggested that these intrinsic networks can be extracted
> from the inter-subject covariation among highly distilled features, such
> as amplitude maps reflecting regions modulated by a task or even
> coordinates extracted from large meta analytic studies. In this paper
> our goal was to explicitly compare the networks obtained from a
> first-level ICA (ICA on the spatio-temporal functional magnetic
> resonance imaging (fMRI) data) to those from a second-level ICA (i.e.,
> ICA on computed features rather than on the first-level fMRI data).
> Convergent results from simulations, task-fMRI data, and rest-fMRI data
> show that the second-level analysis is slightly noisier than the
> first-level analysis but yields strikingly similar patterns of intrinsic
> networks (spatial correlations as high as 0.85 for task data and 0.65
> for rest data, well above the empirical null) and also preserves the
> relationship of these networks with other variables such as age (for
> example, default mode network regions tended to show decreased low
> frequency power for first-level analyses and decreased loading
> parameters for second-level analyses). In addition, the best-estimated
> second-level results are those which are the most strongly reflected in
> the input feature. In summary, the use of feature-based ICA appears to
> be a valid tool for extracting intrinsic networks. We believe it will
> become a useful and important approach in the study of the
> macro-connectome, particularly in the context of data fusion.
>
> ========================================================================
>
>
> *Pages: 260-278 (Article)
> *View Full Record: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=CCC&DestLinkType=FullRecord;KeyUT=CCC:000316344000005
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>
> Title:
> A Hierarchical Modeling Approach to Data Analysis and Study Design in a Multi-site Experimental fMRI Study
>
> Authors:
> Zhou, B; Konstorum, A; Duong, T; Tieu, KH; Wells, WM; Brown, GG; Stern,
> HS; Shahbaba, B
>
> Source:
> *PSYCHOMETRIKA*, 78 (2):260-278; APR 2013
>
> Abstract:
> We propose a hierarchical Bayesian model for analyzing multi-site
> experimental fMRI studies. Our method takes the hierarchical structure
> of the data (subjects are nested within sites, and there are multiple
> observations per subject) into account and allows for modeling
> between-site variation. Using posterior predictive model checking and
> model selection based on the deviance information criterion (DIC), we
> show that our model provides a good fit to the observed data by sharing
> information across the sites. We also propose a simple approach for
> evaluating the efficacy of the multi-site experiment by comparing the
> results to those that would be expected in hypothetical single-site
> experiments with the same sample size.
>
> ========================================================================
>
>
> *Pages: 279-307 (Article)
> *View Full Record: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=CCC&DestLinkType=FullRecord;KeyUT=CCC:000316344000006
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>
> Title:
> State-Space Analysis of Working Memory in Schizophrenia: An FBIRN Study
>
> Authors:
> Janoos, F; Brown, G; Morocz, IA; Wells, WM
>
> Source:
> *PSYCHOMETRIKA*, 78 (2):279-307; APR 2013
>
> Abstract:
> The neural correlates of working memory (WM) in schizophrenia (SZ) have
> been extensively studied using the multisite fMRI data acquired by the
> Functional Biomedical Informatics Research Network (fBIRN) consortium.
> Although univariate and multivariate analysis methods have been
> variously employed to localize brain responses under differing task
> conditions, important hypotheses regarding the representation of mental
> processes in the spatio-temporal patterns of neural recruitment and the
> differential organization of these mental processes in patients versus
> controls have not been addressed in this context. This paper uses a
> multivariate state-space model (SSM) to analyze the differential
> representation and organization of mental processes of controls and
> patients performing the Sternberg Item Recognition Paradigm (SIRP) WM
> task. The SSM is able to not only predict the mental state of the
> subject from the data, but also yield estimates of the spatial
> distribution and temporal ordering of neural activity, along with
> estimates of the hemodynamic response. The dynamical Bayesian modeling
> approach used in this study was able to find significant differences
> between the predictability and organization of the working memory
> processes of SZ patients versus healthy subjects. Prediction of some
> stimulus types from imaging data in the SZ group was significantly lower
> than controls, reflecting a greater level of
> disorganization/heterogeneity of their mental processes. Moreover, the
> changes in accuracy of predicting the mental state of the subject with
> respect to parametric modulations, such as memory load and task
> duration, may have important implications on the neurocognitive models
> for WM processes in both SZ and healthy adults. Additionally, the SSM
> was used to compare the spatio-temporal patterns of mental activity
> across subjects, in a holistic fashion and to derive a low-dimensional
> representation space for the SIRP task, in which subjects were found to
> cluster according to their diagnosis.
>
> ========================================================================
>
>
> *Pages: 308-321 (Article)
> *View Full Record: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=CCC&DestLinkType=FullRecord;KeyUT=CCC:000316344000007
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>
> Title:
> An Introduction to Normalization and Calibration Methods in Functional MRI
>
> Authors:
> Liu, TT; Glover, GH; Mueller, BA; Greve, DN; Brown, GG
>
> Source:
> *PSYCHOMETRIKA*, 78 (2):308-321; APR 2013
>
> Abstract:
> In functional magnetic resonance imaging (fMRI), the blood oxygenation
> level dependent (BOLD) signal is often interpreted as a measure of
> neural activity. However, because the BOLD signal reflects the complex
> interplay of neural, vascular, and metabolic processes, such an
> interpretation is not always valid. There is growing evidence that
> changes in the baseline neurovascular state can result in significant
> modulations of the BOLD signal that are independent of changes in neural
> activity. This paper introduces some of the normalization and
> calibration methods that have been proposed for making the BOLD signal a
> more accurate reflection of underlying brain activity for human fMRI
> studies.
>
> ========================================================================
>
>
> *Pages: 322-340 (Article)
> *View Full Record: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=CCC&DestLinkType=FullRecord;KeyUT=CCC:000316344000008
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>
> Title:
> A New Zero-Inflated Negative Binomial Methodology for Latent Category Identification
>
> Authors:
> Blanchard, SJ; DeSarbo, WS
>
> Source:
> *PSYCHOMETRIKA*, 78 (2):322-340; APR 2013
>
> Abstract:
> We introduce a new statistical procedure for the identification of
> unobserved categories that vary between individuals and in which objects
> may span multiple categories. This procedure can be used to analyze data
> from a proposed sorting task in which individuals may simultaneously
> assign objects to multiple piles. The results of a synthetic example and
> a consumer psychology study involving categories of restaurant brands
> illustrate how the application of the proposed methodology to the new
> sorting task can account for a variety of categorization phenomena
> including multiple category memberships and for heterogeneity through
> individual differences in the saliency of latent category structures.
>
> ========================================================================
>
>
> *Pages: 341-379 (Article)
> *View Full Record: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=CCC&DestLinkType=FullRecord;KeyUT=CCC:000316344000009
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>
> Title:
> Identification of the 1PL Model with Guessing Parameter: Parametric and Semi-parametric Results
>
> Authors:
> San Martin, E; Rolin, JM; Castro, LM
>
> Source:
> *PSYCHOMETRIKA*, 78 (2):341-379; APR 2013
>
> Abstract:
> In this paper, we study the identification of a particular case of the
> 3PL model, namely when the discrimination parameters are all constant
> and equal to 1. We term this model, 1PL-G model. The identification
> analysis is performed under three different specifications. The first
> specification considers the abilities as unknown parameters. It is
> proved that the item parameters and the abilities are identified if a
> difficulty parameter and a guessing parameter are fixed at zero. The
> second specification assumes that the abilities are mutually independent
> and identically distributed according to a distribution known up to the
> scale parameter. It is shown that the item parameters and the scale
> parameter are identified if a guessing parameter is fixed at zero. The
> third specification corresponds to a semi-parametric 1PL-G model, where
> the distribution G generating the abilities is a parameter of interest.
> It is not only shown that, after fixing a difficulty parameter and a
> guessing parameter at zero, the item parameters are identified, but also
> that under those restrictions the distribution G is not identified. It
> is finally shown that, after introducing two identification
> restrictions, either on the distribution G or on the item parameters,
> the distribution G and the item parameters are identified provided an
> infinite quantity of items is available.
>
> ========================================================================
>
>
> *Pages: 380-394 (Article)
> *View Full Record: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=CCC&DestLinkType=FullRecord;KeyUT=CCC:000316344000010
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>
> Title:
> Factor Analysis with EM Algorithm Never Gives Improper Solutions when Sample Covariance and Initial Parameter Matrices Are Proper
>
> Authors:
> Adachi, K
>
> Source:
> *PSYCHOMETRIKA*, 78 (2):380-394; APR 2013
>
> Abstract:
> Rubin and Thayer (Psychometrika, 47:69-76, 1982) proposed the EM
> algorithm for exploratory and confirmatory maximum likelihood factor
> analysis. In this paper, we prove the following fact: the EM algorithm
> always gives a proper solution with positive unique variances and factor
> correlations with absolute values that do not exceed one, when the
> covariance matrix to be analyzed and the initial matrices including
> unique variances and inter-factor correlations are positive definite. We
> further numerically demonstrate that the EM algorithm yields proper
> solutions for the data which lead the prevailing gradient algorithms for
> factor analysis to produce improper solutions. The numerical studies
> also show that, in real computations with limited numerical precision,
> Rubin and Thayer's (Psychometrika, 47:69-76, 1982) original formulas for
> confirmatory factor analysis can make factor correlation matrices
> asymmetric, so that the EM algorithm fails to converge. However, this
> problem can be overcome by using an EM algorithm in which the original
> formulas are replaced by those guaranteeing the symmetry of factor
> correlation matrices, or by formulas used to prove the above fact.
>
>