assessing-working-memory-children-comprehensive-assessment-battery

Assessing Working Memory in Children: The Comprehensive Assessment Battery for Children &#8211; Working Memory (CABC-WM)

The Comprehensive Assessment Battery for Children - Working Memory (CABC-WM) is a computer-based battery designed to assess different components of ...

Arizona State University

Transient errors are caused by variations in feelings, moods, and mental states over time. If these errors are present, coefficient alpha is an inflated estimate of reliability. A true-score model is presented that incorporates transient errors for test-retest data, and a reliability estimate is derived. This estimate, referred to as the test-retest alpha, is less than coefficient alpha if transient error is present and is less susceptible to effects due to item recall than a test-retest correlation. An assumption underlying the test-retest alpha is essential tau equivalency of items. A test-retest split-half coefficient is presented as an alternative to the test-retest alpha when this assumption is violated. The test-retest alpha is the mean of all possible test-retest split-half coefficients.

A coefficient alpha for test-retest data.

Mice deficient in apolipoprotein-E (apoE-/-) experience severe hypercholesterolemia that is exacerbated by a high-fat Western-type diet and atherosclerotic lesions spontaneously develop. In addition, we have reported that deficiency of P-selectin dramatically protects against neointimal lesion formation after arterial injury in apoE-/- mice. To define the mechanism, bone marrow transplantation (BMT) after lethal irradiation was used to generate apoE-/- chimeric mice deficient in platelet, but not endothelial, P-selectin.

Critical role of platelet P-selectin in the response to arterial injury in apolipoprotein-E-deficient mice.

Les souris d&eacute;ficientes en apolipoprot&eacute;ine E (apoE-/-) hypercholest&eacute;rol&eacute;mie s&eacute;v&egrave;re qui est aggrav&eacute;e par une alimentation de type occidental riche en mati&egrave;res grasses d&#39;exp&eacute;rience et de d&eacute;veloppent des l&eacute;sions ath&eacute;roscl&eacute;reuses spontan&eacute;ment. En outre, nous avons rapport&eacute; que d&eacute;ficience en P-s&eacute;lectine prot&egrave;ge consid&eacute;rablement contre la formation de l&eacute;sions n&eacute;ointimale apr&egrave;s la l&eacute;sion art&eacute;rielle souris apoE-/. Pour d&eacute;finir le m&eacute;canisme, greffe de moelle osseuse (GMO) apr&egrave;s irradiation l&eacute;tale a &eacute;t&eacute; utilis&eacute;e pour g&eacute;n&eacute;rer apoE-/-chim&eacute;riques souris d&eacute;ficientes en plaquettes, mais pas endoth&eacute;liale, P-s&eacute;lectine.

R&ocirc;le essentiel des plaquettes P-s&eacute;lectine dans la r&eacute;action &agrave; une l&eacute;sion art&eacute;rielle chez les souris d&eacute;ficientes en apolipoprot&eacute;ine-E.

Kritische Rolle der Blutpl&#xE4;ttchen P-Selektin in der Antwort auf eine arterielle Verletzung im Apolipoprotein E-defiziente M&#xE4;use.

Topi carenti di apolipoproteina E (apoE-/-) esperienza ipercolesterolemia grave che &egrave; aggravata da una dieta di tipo occidentale ad alto contenuto di grassi e lesioni aterosclerotiche sviluppare spontaneamente. Inoltre, abbiamo segnalato che il deficit di P-Selectina drammaticamente protegge contro la formazione di lesione neointimale dopo lesioni arteriose in apoE-/-mice. Per definire il meccanismo, trapianto di midollo osseo (BMT) dopo irradiazione letale &egrave; stato utilizzato per generare apoE-/-chimerici topi carenti di piastrine, ma non endoteliale, P-Selectina.

Ruolo critico di piastrine P-Selectina in risposta a lesioni arteriose in apolipoproteina-E-deficient mice.

&#x5C0F;&#x9F20;&#x7F3A;&#x4E4F;&#x8F7D;&#x8102;&#x86CB;&#x767D; E (apoE-/-) &#x7ECF;&#x9A8C;&#x4E25;&#x91CD;&#x9AD8;&#x80C6;&#x56FA;&#x9187;&#x8840;&#x75C7;&#x662F;&#x7531;&#x9AD8;&#x8102;&#x80AA;&#x897F;&#x65B9;&#x578B;&#x7684;&#x996E;&#x98DF;&#x4E60;&#x60EF;&#x800C;&#x52A0;&#x5267;&#x548C;&#x52A8;&#x8109;&#x7CA5;&#x6837;&#x786C;&#x5316;&#x75C5;&#x53D8;&#x81EA;&#x53D1;&#x5730;&#x53D1;&#x5C55;&#x3002;&#x6B64;&#x5916;&#xFF0C;&#x6211;&#x4EEC;&#x5DF2;&#x62A5;&#x544A;&#x7684; P-&#x9009;&#x62E9;&#x7D20;&#x865A;&#x6781;&#x5927;&#x5730;&#x9632;&#x6B62; apoE/&#x5C0F;&#x9F20;&#x52A8;&#x8109;&#x635F;&#x4F24;&#x540E;&#x5185;&#x819C;&#x75C5;&#x53D8;&#x5F62;&#x6210;&#x3002;&#x82E5;&#x8981;&#x5B9A;&#x4E49;&#x673A;&#x5236;&#xFF0C;&#x81F4;&#x547D;&#x7684;&#x8F90;&#x5C04;&#x540E;&#x9AA8;&#x9AD3;&#x79FB;&#x690D; (BMT) &#x7528;&#x4E8E;&#x751F;&#x6210; apoE-/-&#x5D4C;&#x5408;&#x5C0F;&#x9F20;&#x8840;&#x5C0F;&#x677F;&#xFF0C;&#x4F46;&#x6CA1;&#x6709;&#x5185;&#x76AE;&#x3001; P-&#x9009;&#x62E9;&#x7D20;&#x6709;&#x4E0D;&#x8DB3;&#x4E4B;&#x5904;&#x3002;

&#x8840;&#x5C0F;&#x677F; P-&#x9009;&#x62E9;&#x7D20;&#x5BF9;&#x8F7D;&#x8102;&#x86CB;&#x767D; E &#x57FA;&#x56E0;&#x7F3A;&#x9677;&#x5C0F;&#x9F20;&#x52A8;&#x8109;&#x635F;&#x4F24;&#x53CD;&#x5E94;&#x4E2D;&#x7684;&#x5173;&#x952E;&#x4F5C;&#x7528;&#x3002;

&#x30A2;&#x30DD;&#x30EA;&#x30DD;&#x86CB;&#x767D; E &#x306E;&#x6B20;&#x4E4F;&#x30DE;&#x30A6;&#x30B9; (&#x30A2;&#x30DD;-/-&#xFF09; &#x9AD8;&#x8102;&#x80AA;&#x897F;&#x6D0B;&#x578B;&#x306E;&#x98DF;&#x4E8B;&#x306B;&#x3088;&#x3063;&#x3066;&#x60AA;&#x5316;&#x3057;&#x3066;&#x3044;&#x308B;&#x91CD;&#x5EA6;&#x306E;&#x9AD8;&#x30B3;&#x30EC;&#x30B9;&#x30C6;&#x30ED;&#x30FC;&#x30EB;&#x8840;&#x75C7;&#x3092;&#x7D4C;&#x9A13;&#x3057;&#x3001;&#x81EA;&#x767A;&#x7684;&#x306B;&#x52D5;&#x8108;&#x786C;&#x5316;&#x75C5;&#x5909;&#x3092;&#x958B;&#x767A;&#x3002;&#x3055;&#x3089;&#x306B;&#x3001;&#x6211;&#x3005; &#x306F; P-&#x30BB;&#x30EC;&#x30AF;&#x30C1;&#x30F3;&#x306E;&#x6B20;&#x4E4F;&#x304C;&#x5287;&#x7684;&#x306B;&#x30A2;&#x30DD;/&#x30DE;&#x30A6;&#x30B9;&#x306E;&#x52D5;&#x8108;&#x969C;&#x5BB3;&#x5F8C;&#x306E;&#x65B0;&#x751F;&#x5185;&#x819C;&#x75C5;&#x5909;&#x5F62;&#x6210;&#x304B;&#x3089;&#x4FDD;&#x8B77;&#x3059;&#x308B;&#x3053;&#x3068;&#x3092;&#x5831;&#x544A;&#x3057;&#x307E;&#x3057;&#x305F;&#x3002;&#x30E1;&#x30AB;&#x30CB;&#x30BA;&#x30E0;&#x3092;&#x5B9A;&#x7FA9;&#x3059;&#x308B;&#x306B;&#x306F;&#x3001;&#x9AA8;&#x9AC4;&#x79FB;&#x690D; &#xFF08;BMT&#xFF09; &#x81F4;&#x6B7B;&#x7167;&#x5C04;&#x5F8C;&#x4F7F;&#x7528;&#x3055;&#x308C;&#x305F;&#x30A2;&#x30DD;-&#x3092;&#x751F;&#x6210;&#x3059;&#x308B;/-&#x30AD;&#x30E1;&#x30E9;&#x30DE;&#x30A6;&#x30B9;&#x8840;&#x5C0F;&#x677F;&#x3001;&#x3057;&#x304B;&#x3057;&#x3001;&#x306A;&#x3044;&#x5185;&#x76AE;&#x3001;P-&#x30BB;&#x30EC;&#x30AF;&#x30C1;&#x30F3;&#x306E;&#x6B20;&#x4E4F;&#x3002;

&#x8840;&#x5C0F;&#x677F;&#x306E; P-&#x30BB;&#x30EC;&#x30AF;&#x30C1;&#x30F3; &#x30A2;&#x30DD; E &#x6B20;&#x640D;&#x30DE;&#x30A6;&#x30B9;&#x306E;&#x52D5;&#x8108;&#x969C;&#x5BB3;&#x3078;&#x306E;&#x5FDC;&#x7B54;&#x306E;&#x91CD;&#x8981;&#x306A;&#x5F79;&#x5272;&#x3002;

Apolipoprotein E &#xACB0;&#xD54D; &#xC0DD;&#xC950; (apoE-/-) &#xACBD;&#xD5D8; &#xB192;&#xC740; &#xC9C0;&#xBC29; &#xC11C;&#xC591; &#xD615; &#xC2DD;&#xB2E8;&#xC5D0; &#xC758;&#xD574; &#xC545;&#xD654; &#xC2EC;&#xAC01;&#xD55C; &#xCF5C;&#xB808;&#xC2A4;&#xD14C;&#xB864; &#xD608; &#xC99D; &#xBC0F; &#xB3D9;&#xB9E5; &#xACBD; &#xD654;&#xC131; &#xBCD1; &#xBCC0;&#xC774; &#xC800;&#xC808;&#xB85C; &#xAC1C;&#xBC1C;. &#xB610;&#xD55C;, &#xC6B0;&#xB9AC;&#xAC00; &#xBCF4;&#xACE0; P selectin&#xC758; &#xBD80;&#xC871; &#xADF9;&#xC801;&#xC73C;&#xB85C; apoE/&#xC0DD;&#xC950;&#xC5D0;&#xC11C; &#xB3D9;&#xB9E5; &#xC190;&#xC0C1; &#xD6C4; neointimal &#xBCD1; &#xBCC0; &#xD615;&#xC131;&#xC5D0; &#xB300;&#xD558;&#xC5EC; &#xBCF4;&#xD638; &#xD55C;&#xB2E4;. &#xBA54;&#xCEE4;&#xB2C8;&#xC998;&#xC744; &#xC815;&#xC758; &#xD558;&#xB824;&#xBA74; &#xCE58;&#xBA85;&#xC801;&#xC778; &#xBC29;&#xC0AC;&#xC120; &#xC870;&#xC0AC; &#xD6C4; &#xACE8; &#xC218; &#xC774;&#xC2DD; (BMT) apoE-&#xC0DD;&#xC131; &#xD558;&#xB294; &#xB370; &#xC0AC;&#xC6A9;&#xD55C; /-chimeric &#xC950; &#xC548; &#xB0B4; &#xD53C;, P selectin &#xD558;&#xC9C0;&#xB9CC; &#xD608;&#xC18C;&#xD310;&#xC758; &#xACB0;&#xD54D;.

&#xD608;&#xC18C;&#xD310; P selectin apolipoprotein E &#xACB0;&#xD54D; &#xC0DD;&#xC950;&#xC5D0;&#xC11C; &#xB3D9;&#xB9E5; &#xBD80;&#xC0C1;&#xC5D0; &#xB300; &#xD55C; &#xC751;&#xB2F5;&#xC5D0;&#xC11C;&#xC758; &#xC911;&#xC694; &#xD55C; &#xC5ED;&#xD560;.

Ratones deficientes en la apolipoprote&iacute;na E (apoE-/-) experiencia hipercolesterolemia severa que se ve agravada por una dieta alta en grasa de tipo occidental y las lesiones ateroscler&oacute;ticas desarrollan espont&aacute;neamente. Adem&aacute;s, nos han informado de que la deficiencia de P-selectina dram&aacute;ticamente protege contra la formaci&oacute;n de la lesi&oacute;n de neointimal despu&eacute;s de lesi&oacute;n arterial en apoE-/-ratones. Para definir el mecanismo, trasplante de m&eacute;dula &oacute;sea (BMT) despu&eacute;s de la irradiaci&oacute;n letal se utiliz&oacute; para generar apoE-/-quim&eacute;ricas ratones deficientes en plaquetas, pero no endotelial, P-selectina.

Papel cr&iacute;tico de la plaqueta P-selectina en la respuesta a la lesi&oacute;n arterial en ratones de la apolipoprote&iacute;na E-deficientes.

Multivariate methods for analyzing group differences in means on dependent variables include multivariate analysis of variance, discriminant analysis, and multivariate analysis of factor means. To make appropriate choices among these methods, researchers should understand the statistical models underlying them. We present these models using path diagrams within a structural equation modeling framework. Results for the different methods are presented for an example concerning coping with asthma.

Structural equation modeling for conducting tests of differences in multiple means.

We present an introduction to the basic concepts essential to understanding confirmatory factor analysis (CFA). We initially discuss the underlying mathematical model and its graphical representation. We then show how parameters are estimated for the CFA model based on the maximum likelihood function. Finally, we discuss several ways in which model fit is evaluated as well as introduce the concept of model identification. In our presentation, we use an example to illustrate the application of CFA to psychosomatic research and touch on the more general role of structural equation modeling in psychosomatic research.

Confirmatory factor analysis: an introduction for psychosomatic medicine researchers.

Here we investigate the response of soils and litter to 5 years of experimental additions of ammonium (NH4), nitrate (NO3), and ammonia (NH3) to an ombrotrophic peatland. We test the importance of direct (via soil) and indirect (via litter) effects on phosphatase activity and efflux of CO2. We also determined how species representing different functional types responded to the nitrogen treatments. Our results demonstrate that additions of NO3, NH4 and NH3 all stimulated phosphatase activity but the effects were dependent on species of litter and mechanism (direct or indirect). Deposition of NH3 had no effect on efflux of CO2 from Calluna vulgaris litter, despite it showing signs of stress in the field, whereas both NO3 and NH4 reduced CO2 fluxes. Our results show that the collective impacts on peatlands of the three principal forms of nitrogen in atmospheric deposition are a result of differential effects and mechanisms on individual components.

Direct and indirect effects of ammonia, ammonium and nitrate on phosphatase activity and carbon fluxes from decomposing litter in peatland.

Practising standardised team communication in the anatomy laboratory.

Sijtsma and van der Ark (2015) focused in their lead article on three frameworks for reliability estimation in nursing research: classical test theory (CTT), factor analysis (FA), and generalizability theory. We extend their presentation with particular attention to CTT and FA methods. We first consider the potential of yielding an overly negative or an overly positive assessment of reliability based on coefficient alpha. Next, we discuss other CTT methods for estimating reliability and how the choice of methods affects the interpretation of the reliability coefficient. Finally, we describe FA methods, which not only permit an understanding of a measure's underlying structure but also yield a variety of reliability coefficients with different interpretations. On a more general note, we discourage reporting reliability as a two-choice outcome--unsatisfactory or satisfactory; rather, we recommend that nursing researchers make a conceptual and empirical argument about when a measure might be more or less reliable, depending on its use.

Further discussion on reliability: the art of reliability estimation.

Several novel techniques have been developed recently to assess the breadth of a speaker's vocabulary exhibited in a language sample. The specific aim of this study was to increase our understanding of the validity of the scores generated by different lexical diversity (LD) estimation techniques. Four techniques were explored: D, Maas, measure of textual lexical diversity, and moving-average type-token ratio.

Psychometric Evaluation of Lexical Diversity Indices: Assessing Length Effects.

Reliabilities of scores for experimental tasks are likely to differ from one study to another to the extent that the task stimuli change, the number of trials varies, the type of individuals taking the task changes, the administration conditions are altered, or the focal task variable differs. Given that reliabilities vary as a function of the design of these tasks and the characteristics of the individuals taking them, making inferences about the reliability of scores in an ongoing study based on reliability estimates from prior studies is precarious. Thus, it would be advantageous to estimate reliability based on data from the ongoing study. We argue that internal consistency estimates of reliability are underutilized for experimental task data and in many applications could provide this information using a single administration of a task. We discuss different methods for computing internal consistency estimates with a generalized coefficient alpha and the conditions under which these estimates are accurate. We illustrate use of these coefficients using data for three different tasks.

Use of internal consistency coefficients for estimating reliability of experimental task scores.

The root mean square error of approximation (RMSEA) and the comparative fit index (CFI) are two widely applied indices to assess fit of structural equation models. Because these two indices are viewed positively by researchers, one might presume that their values would yield comparable qualitative assessments of model fit for any data set. When RMSEA and CFI offer different evaluations of model fit, we argue that researchers are likely to be confused and potentially make incorrect research conclusions. We derive the necessary as well as the sufficient conditions for inconsistent interpretations of these indices. We also study inconsistency in results for RMSEA and CFI at the sample level. Rather than indicating that the model is misspecified in a particular manner or that there are any flaws in the data, the two indices can disagree because (a) they evaluate, by design, the magnitude of the model's fit function value from different perspectives; (b) the cutoff values for these indices are arbitrary; and (c) the meaning of "good" fit and its relationship with fit indices are not well understood. In the context of inconsistent judgments of fit using RMSEA and CFI, we discuss the implications of using cutoff values to evaluate model fit in practice and to design SEM studies.

The Problem with Having Two Watches: Assessment of Fit When RMSEA and CFI Disagree.

This study investigated the structure of working memory in young school-age children by testing the fit of three competing theoretical models using a wide variety of tasks. The best fitting models were then used to assess the relationship between working memory and nonverbal measures of fluid reasoning (Gf) and visual processing (Gv) intelligence. One hundred sixty-eight English-speaking 7-9 year olds with typical development, from three states, participated. Results showed that Cowan's three-factor embedded processes model fit the data slightly better than Baddeley and Hitch's (1974) three-factor model (specified according to Baddeley, 1986) and decisively better than Baddeley's (2000) four-factor model that included an episodic buffer. The focus of attention factor in Cowan's model was a significant predictor of Gf and Gv. The results suggest that the focus of attention, rather than storage, drives the relationship between working memory, Gf, and Gv in young school-age children. Our results do not rule out the Baddeley and Hitch model, but they place constraints on both it and Cowan's model. A common attentional component is needed for feature binding, running digit span, and visual short-term memory tasks; phonological storage is separate, as is a component of central executive processing involved in task manipulation. The results contribute to a zeitgeist in which working memory models are coming together on common ground (cf. Cowan, Saults, & Blume, 2014; Hu, Allen, Baddeley, & Hitch, 2016).

The Structure of Working Memory in Young Children and Its Relation to Intelligence.

The purpose of this study is to investigate word learning in children with dyslexia to ascertain their strengths and weaknesses during the configuration stage of word learning.

Word Learning Deficits in Children With Dyslexia.

In children with dyslexia, deficits in working memory have not been well-specified. We assessed second-grade children with dyslexia, with and without concomitant specific language impairment, and children with typical development. Immediate serial recall of lists of phonological (non-word), lexical (digit), spatial (location) and visual (shape) items were included. For the latter three modalities, we used not only standard span but also running span tasks, in which the list length was unpredictable to limit mnemonic strategies. Non-word repetition tests indicated a phonological memory deficit in children with dyslexia alone compared with those with typical development, but this difference vanished when these groups were matched for non-verbal intelligence and language. Theoretically important deficits in serial order memory in dyslexic children, however, persisted relative to matched typically developing children. The deficits were in recall of (1) spoken digits in both standard and running span tasks and (2) spatial locations, in running span only. Children with dyslexia with versus without language impairment, when matched on non-verbal intelligence, had comparable serial order memory, but differed in phonology. Because serial orderings of verbal and spatial elements occur in reading, the careful examination of order memory may allow a deeper understanding of dyslexia and its relation to language impairment. Copyright © 2017 John Wiley & Sons, Ltd.

Samuel Green

Sanford School of Social and Family Dynamics

Arizona State University

Assessing Working Memory in Children: The Comprehensive Assessment Battery for Children – Working Memory (CABC-WM)

Samuel Green

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Sanford School of Social and Family Dynamics

Arizona State University

Assessing Working Memory in Children: The Comprehensive Assessment Battery for Children – Working Memory (CABC-WM)

Sanford School of Social and Family Dynamics