This document provides an example of a selective testing scenario using the Woodcock-Johnson III (WJ III) battery for a student referred for basic reading skills difficulties between the ages of 6-8. It outlines a branching path of additional tests that could be administered within the cognitive domain of Crystallized Intelligence (Gc) based on initial test results to further evaluate broad versus narrow abilities. The path includes examining Listening Comprehension (Gc-LS), General Information (Gc-K0), and Academic Knowledge through comparison of cluster scores.
CHC Cog-Ach Relations Research SynthesisKevin McGrew
This presentation presents an update of the "CHC COG-ACH correlates research synthesis" project described and hosted at IQ's Corner (www.intelligencetesting.blogspot.com) and IAP (www.iapsych.com). The viewer should first read the background materials regarding this project at these sites (how to access is also included in first slide). The current slides present my preliminary analysis and conclusions of the relations between CHC cognitive abilities and basic reading skills, reading comprehension, basic math skills, and math reasoning as a function of age (developmental status). The results are part of a manuscript that is in preparation. Revisit IQ's Corner to keep abreast of updates.
CHC theory 101: From general intelligence (g) to CHC theoryKevin McGrew
The second in the CHC Theory 101 series. This brief module traces, in broad strokes, the history of psychometric theories of intelligence from Spearman's g to contemporary Cattell-Horn-Cattell (CHC) theory
Beyond IQ: Model of Academic Competence and Motivation (MACM)Kevin McGrew
The current slides supplement the on-line background paper “Beyond IQ: A Model of Academic Competence and Motivation” (Kevin McGrew, 2008), which is presented in the form of an Institute for Applied Psychometrics (IAP) Evolving Web of Knowledge (EWOK).
All materials are part of the Beyond IQ Project, which is housed at IQ’s Corner Blog
www.intelligencetesting.blogspot.com
http://tinyurl.com/3ygdsw
Updates and announcements can be found by routinely monitoring IQ’s Corner Blog.
These slides can be used without permission for educational and training purposes (not commercial use or for-profit activities)
Overview of the WJ IV Cognitive Battery: GIA and CHC ClustersKevin McGrew
This slideshow provides an overview of the composition of the WJ IV Cognitive clusters. It outlines the design principles used to assign tests to the GIA and CHC clusters, and also presents summary statistics as per the primary design principles used in constructing the COG clusters.
"intelligent" intelligence testing: Why do some individuals obtain markedly ...Kevin McGrew
This is the second in a series. Please view the first ("intelligent" intelligence testing: Evaluating wihtin CHC domain test score differences) to better appreciate this module
Data and theory-based hypotheses for evaluating differences between scores on the different WJ IV tests of Gwm
MDS Analysis of the CHC-based WJ III Battery: Implications for possible refin...Kevin McGrew
The WJ III Battery is comprised of both cognitive (intelligence) and achievement components. As reported in the technical manual, the Cattell-Horn-Carroll (CHC) theory of cognitive abilities organizational structure of the WJ III has been validated. The current investigation analyzed the cognitive and achievement tests for all WJ III norm subjects from ages 6-18 years of age. Multidimensional scaling (MDS—Guttman Radex model) of the 50 WJ III tests suggested new facets from which to interpret the WJ III. The results suggested three to four higher-order intermediate CHC model stratum abilities that varied along the dimensions of (a) controlled vs automatic cognitive processing and (b) product- vs process-dominant abilities. The results, together with recent similar analysis of the WAIS-IV, support Woodcock’s Cognitive Performance Model (CPM). Implications for possible minor changes in the CPM model are suggested. More importantly, the WJ III and WAIS-IV results collectively suggest hypothesized refinements and extensions of the CHC intelligence framework. Research focused on exploring the compatibility of a combined CHC and Berlin Model of Intelligence Structure (BIS) theory is recommended.
CHC Cog-Ach Relations Research SynthesisKevin McGrew
This presentation presents an update of the "CHC COG-ACH correlates research synthesis" project described and hosted at IQ's Corner (www.intelligencetesting.blogspot.com) and IAP (www.iapsych.com). The viewer should first read the background materials regarding this project at these sites (how to access is also included in first slide). The current slides present my preliminary analysis and conclusions of the relations between CHC cognitive abilities and basic reading skills, reading comprehension, basic math skills, and math reasoning as a function of age (developmental status). The results are part of a manuscript that is in preparation. Revisit IQ's Corner to keep abreast of updates.
CHC theory 101: From general intelligence (g) to CHC theoryKevin McGrew
The second in the CHC Theory 101 series. This brief module traces, in broad strokes, the history of psychometric theories of intelligence from Spearman's g to contemporary Cattell-Horn-Cattell (CHC) theory
Beyond IQ: Model of Academic Competence and Motivation (MACM)Kevin McGrew
The current slides supplement the on-line background paper “Beyond IQ: A Model of Academic Competence and Motivation” (Kevin McGrew, 2008), which is presented in the form of an Institute for Applied Psychometrics (IAP) Evolving Web of Knowledge (EWOK).
All materials are part of the Beyond IQ Project, which is housed at IQ’s Corner Blog
www.intelligencetesting.blogspot.com
http://tinyurl.com/3ygdsw
Updates and announcements can be found by routinely monitoring IQ’s Corner Blog.
These slides can be used without permission for educational and training purposes (not commercial use or for-profit activities)
Overview of the WJ IV Cognitive Battery: GIA and CHC ClustersKevin McGrew
This slideshow provides an overview of the composition of the WJ IV Cognitive clusters. It outlines the design principles used to assign tests to the GIA and CHC clusters, and also presents summary statistics as per the primary design principles used in constructing the COG clusters.
"intelligent" intelligence testing: Why do some individuals obtain markedly ...Kevin McGrew
This is the second in a series. Please view the first ("intelligent" intelligence testing: Evaluating wihtin CHC domain test score differences) to better appreciate this module
Data and theory-based hypotheses for evaluating differences between scores on the different WJ IV tests of Gwm
MDS Analysis of the CHC-based WJ III Battery: Implications for possible refin...Kevin McGrew
The WJ III Battery is comprised of both cognitive (intelligence) and achievement components. As reported in the technical manual, the Cattell-Horn-Carroll (CHC) theory of cognitive abilities organizational structure of the WJ III has been validated. The current investigation analyzed the cognitive and achievement tests for all WJ III norm subjects from ages 6-18 years of age. Multidimensional scaling (MDS—Guttman Radex model) of the 50 WJ III tests suggested new facets from which to interpret the WJ III. The results suggested three to four higher-order intermediate CHC model stratum abilities that varied along the dimensions of (a) controlled vs automatic cognitive processing and (b) product- vs process-dominant abilities. The results, together with recent similar analysis of the WAIS-IV, support Woodcock’s Cognitive Performance Model (CPM). Implications for possible minor changes in the CPM model are suggested. More importantly, the WJ III and WAIS-IV results collectively suggest hypothesized refinements and extensions of the CHC intelligence framework. Research focused on exploring the compatibility of a combined CHC and Berlin Model of Intelligence Structure (BIS) theory is recommended.
WJ IV NASP 2014 workshop: Cognitive and Oral Language batteries by Dr. Kevin...Kevin McGrew
This document provides an overview of the WJ IV Tests of Cognitive Ability given by Dr. Kevin McGrew. It discusses the new and revised tests in the WJ IV as well as some select data analysis results. The presentation covers the organization of the WJ IV cognitive and oral language batteries including the clusters available from each. It also discusses the contemporary CHC broad and narrow ability content coverage in the WJ IV cognitive battery.
Peter krusche population based targeted validation of structural variant brea...GenomeInABottle
This document summarizes Illumina's process for validating structural variants using population data. It describes several community resources that are used, including Platinum Genomes and Polaris datasets. The validation process involves jointly calling variants in these populations and checking for Mendelian consistency and Hardy-Weinberg equilibrium. Variants are compared between calls sets to identify representations needing improvement. Future plans include genome-wide graph realignment and validation and improving targeted validation tools.
This document summarizes research on enhancing gene expression programming (GEP) for Reynolds-averaged Navier-Stokes equations turbulence modeling with unsupervised clustering. It presents a GEP-enhanced multi-model framework that uses feature selection, dimensionality reduction, and clustering to assign different turbulence models to distinct regions of a flow, improving simulation accuracy. Results show the approach produces more accurate mean velocities and Reynolds stresses for a body-of-revolution testcase compared to baseline and GEP-driven models. Ongoing work includes optimizing the framework configuration and extending it to 3D domains.
Many of today's researchers are generating DNA sequence data for large numbers of samples in population-based experiments. This may include whole genomes, exomes, or targeted regions. The Golden Helix SNP and Variation Suite (SVS) provides a powerful computing environment for analyzing these data and performing association tests at the gene and/or variant level.
In this presentation, Dr. Christensen will review fundamentals of population-based variant analysis and demonstrate some of the tools available in SVS for analysis of both common and rare variants. The presentation will feature the recently implemented SKAT-O method, as well as other functions for annotation, visualization, quality control and statistical analysis of DNA sequence variants.
This document outlines John Theodore Goetz's PhD defense presentation on hyperon photoproduction from threshold to 5.4 GeV using the CEBAF Large Acceptance Spectrometer. The presentation has two parts, with part I discussing cascade hyperons, the g12 experiment that collected data on hyperon photoproduction, and g12 kaon data analysis. Part II will present results from g12, including excitation functions of the Ξ hyperon, searches for higher mass Ξ* states and iso-exotic particles, and conclusions.
The WJ IV Measurement of Auditory Processing (Ga)Kevin McGrew
The WJ IV Cognitive and Oral Language include new measures of auditory processing (Ga) that are much more cognitively complex auditory measures of intelligence. This short presentation provides an overview of the WJ IV Ga tests and presents evidence supporting the importance of Ga as a major component of human intelligence.
FAST Approaches to Scalable Similarity-based Test Case Prioritizationbrenoafmiranda
Many test case prioritization criteria have been proposed for speeding up fault detection. Among them, similarity-based approaches give priority to the test cases that are the most dissimilar from those already selected. However, the proposed criteria do not scale up to handle the many thousands or even some millions test suite sizes of modern industrial systems and simple heuristics are used instead. We introduce the FAST family of test case prioritization techniques that radically changes this landscape by borrowing algorithms commonly exploited in the big data domain to find similar items. FAST techniques provide scalable similarity-based test case prioritization in both white-box and black-box fashion. The results from experimentation on real world C and Java subjects show that the fastest members of the family outperform other black-box approaches in efficiency with no significant impact on effectiveness, and also outperform white-box approaches, including greedy ones, if preparation time is not counted. A simulation study of scalability shows that one FAST technique can prioritize a million test cases in less than 20 minutes.
Towards the comparative analysis of genomic variants with JalviewJim Procter
Invited talk at the first 'Fantastic Forces' Computational Evolutionary Biology at the University of St. Andrews on 5th June, 2019. In 45 minutes, I introduced key concepts in multiple sequence alignment analysis, Jalview's major capabilities for working with DNA, RNA and Protein sequences, structures and phylogenetic trees, and how they could be applied to filter and visualise genomic variant data imported from VCF and added to alignments as positional annotation.
I also highlighted MacGowan and Barton's ongoing research program (published on biorxiv) into the inference of functional regions of proteins by analysis of the relationship between the distribution of missense variants and conservation in Pfam multiple sequence alignments involving paralogous domains from human and other organisms.
https://fantastic4cesworkshop.wordpress.com/programme/
This document discusses forecasting covariance matrices using the Dynamic Conditional Correlation (DCC) GARCH model. It begins with an overview of univariate GARCH models and the GARCH(1,1) specification. It then introduces the DCC model, which models the conditional covariance matrix indirectly through the conditional correlation matrix. The document evaluates how forecasts from the DCC model perform compared to a covariance matrix based only on historical data. It presents an empirical application comparing the two approaches using different datasets. The conclusion discusses how the DCC model tends to outperform the historical covariance matrix in the short-run but the reverse is true in the long-run.
Learning LWF Chain Graphs: A Markov Blanket Discovery ApproachPooyan Jamshidi
LWF Chain graphs were introduced by Lauritzen, Wermuth, and Frydenberg as a generalization of graphical models based on undirected graphs and DAGs. From the causality point of view, in an LWF CG: Directed edges represent direct causal effects. Undirected edges represent causal effects due to interference, which occurs when an individual’s outcome is influenced by their social interaction with other population members, e.g., in situations that involve contagious agents, educational programs, or social networks. The construction of chain graph models is a challenging task that would be greatly facilitated by automation.
Markov blanket discovery has an important role in structure learning of Bayesian network. It is surprising, however, how little attention it has attracted in the context of learning LWF chain graphs. In this work, we provide a graphical characterization of Markov blankets in chain graphs. The characterization is different from the well-known one for Bayesian networks and generalizes it. We provide a novel scalable and sound algorithm for Markov blanket discovery in LWF chain graphs. We also provide a sound and scalable constraint-based framework for learning the structure of LWF CGs from faithful causally sufficient data. With the use of our algorithm, the problem of structure learning is reduced to finding an efficient algorithm for Markov blanket discovery in LWF chain graphs. This greatly simplifies the structure-learning task and makes a wide range of inference/learning problems computationally tractable because our approach exploits locality.
The document discusses Adaptable Constrained Genetic Programming (ACGP), which aims to automate the discovery of heuristics to guide the genetic programming search. It describes how ACGP develops first-order and second-order heuristics based on patterns observed in high-performing individuals, and uses these heuristics to bias mutation, crossover and regrowth. Experimental results on a target equation with explicit second-order structure show that ACGP with second-order heuristics outperforms both standard GP and ACGP with only first-order heuristics. The document concludes that ACGP is effective at discovering and exploiting problem structure through its adaptive heuristic approach.
This document discusses the use of GEANT4 and the GATE toolkit for simulating emission tomography systems like PET and SPECT. It provides an overview of why Monte Carlo simulations are useful for imaging, the architecture of GATE which is based on GEANT4, and how GATE handles geometry, time, physics processes, digitization and more. It also provides examples of how GATE has been validated against real patient and preclinical imaging systems and discusses its potential applications and current limitations like long simulation times.
NIST-JARVIS infrastructure for Improved Materials DesignKAMAL CHOUDHARY
The document describes the NIST-JARVIS infrastructure for materials design using computational methods. It provides electronic structure databases containing properties of thousands of materials calculated using DFT. Tools include JARVIS-DFT for electronic structure calculations, ALIGNN for developing machine learning models to predict material properties from structure, and AtomVision for analyzing microscopy images. The infrastructure aims to accelerate materials discovery and design through automation, collaboration and open access to computational data and tools.
This document provides information about the Computational Chemistry 126 course offered at UC Santa Barbara in Fall 2011, including the instructor's contact information, course objectives, required readings and assignments. The course focuses on learning principles of computational chemistry and molecular modeling techniques. Students will learn algorithms for geometry optimization, transition state location, and prediction of molecular properties using software for quantum chemical calculations and molecular simulations.
Some slides from a recent overview of the CRU climate scenario generator 'ClimGen' of which I am a co-developer. EU HELIX, ERMITAGE & TOPDAD FP7 & NERC projects have all recently supported development rounds.
The document discusses online tools for interpreting NMR and MS spectra to determine the structure of compounds. It describes databases like SugaBase that contain NMR data and can be used to identify structures by comparison to known compounds. It also describes CASPER, which simulates NMR spectra based on possible structures and linkages to identify candidate structures. While databases provide simple identification of known structures, CASPER allows analysis of unknown structures by comparing experimental data to simulated spectra. The accuracy of these methods depends on the quality of the data and additional information provided about the compound.
Toward a Unified Approach to Fitting Loss ModelsJacques Rioux
The document discusses developing a unified approach for fitting loss models to insurance data. It proposes using a limited set of standard distributions, including the exponential, gamma, lognormal and Pareto distributions. These distributions could be combined in mixtures to better fit different parts of the data. The document presents an example of fitting a mixture of a lognormal and exponential distribution to insurance payment data. Graphs and statistical tests show the model provides an acceptable fit to the data. Automating the process of fitting mixtures and comparing models is an area for further development.
This thesis investigates methods for integrative analysis of multiple data types. It extends the Joint and Individual Variation Explained (JIVE) method by incorporating a fused lasso penalty. A novel rank selection algorithm is also proposed. The methods are evaluated on simulated data and applied to analyze The Cancer Genome Atlas glioblastoma data to identify shared mutational processes between chromosomes.
The Model of Achievement Competence Motivation (MACM) Part E: Crossing the R...Kevin McGrew
The Model of Achievement Competence Motivation (MACM) is a series of slide modules. This is the fifth (Part E) in the series. It is brief...only 11 slides. The modules will serve as supplemental materials to "The Model of Achievement Competence Motivation (MACM)--Standing on the shoulders of giants" (McGrew, in press, 2021 - in a forthcoming special issue on motivation in the Canadian Journal of School Psychology)
The Model of Achievement Competence Motivation (MACM): Part D: The volition ...Kevin McGrew
The Model of Achievement Competence Motivation (MACM) is a series of slide modules. This is the fourth (Part D) in the series. The modules will serve as supplemental materials to "The Model of Achievement Competence Motivation (MACM)--Standing on the shoulders of giants" (McGrew, in press, 2021 - in a forthcoming special issue on motivation in the Canadian Journal of School Psychology)
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Peter krusche population based targeted validation of structural variant brea...GenomeInABottle
This document summarizes Illumina's process for validating structural variants using population data. It describes several community resources that are used, including Platinum Genomes and Polaris datasets. The validation process involves jointly calling variants in these populations and checking for Mendelian consistency and Hardy-Weinberg equilibrium. Variants are compared between calls sets to identify representations needing improvement. Future plans include genome-wide graph realignment and validation and improving targeted validation tools.
This document summarizes research on enhancing gene expression programming (GEP) for Reynolds-averaged Navier-Stokes equations turbulence modeling with unsupervised clustering. It presents a GEP-enhanced multi-model framework that uses feature selection, dimensionality reduction, and clustering to assign different turbulence models to distinct regions of a flow, improving simulation accuracy. Results show the approach produces more accurate mean velocities and Reynolds stresses for a body-of-revolution testcase compared to baseline and GEP-driven models. Ongoing work includes optimizing the framework configuration and extending it to 3D domains.
Many of today's researchers are generating DNA sequence data for large numbers of samples in population-based experiments. This may include whole genomes, exomes, or targeted regions. The Golden Helix SNP and Variation Suite (SVS) provides a powerful computing environment for analyzing these data and performing association tests at the gene and/or variant level.
In this presentation, Dr. Christensen will review fundamentals of population-based variant analysis and demonstrate some of the tools available in SVS for analysis of both common and rare variants. The presentation will feature the recently implemented SKAT-O method, as well as other functions for annotation, visualization, quality control and statistical analysis of DNA sequence variants.
This document outlines John Theodore Goetz's PhD defense presentation on hyperon photoproduction from threshold to 5.4 GeV using the CEBAF Large Acceptance Spectrometer. The presentation has two parts, with part I discussing cascade hyperons, the g12 experiment that collected data on hyperon photoproduction, and g12 kaon data analysis. Part II will present results from g12, including excitation functions of the Ξ hyperon, searches for higher mass Ξ* states and iso-exotic particles, and conclusions.
The WJ IV Measurement of Auditory Processing (Ga)Kevin McGrew
The WJ IV Cognitive and Oral Language include new measures of auditory processing (Ga) that are much more cognitively complex auditory measures of intelligence. This short presentation provides an overview of the WJ IV Ga tests and presents evidence supporting the importance of Ga as a major component of human intelligence.
FAST Approaches to Scalable Similarity-based Test Case Prioritizationbrenoafmiranda
Many test case prioritization criteria have been proposed for speeding up fault detection. Among them, similarity-based approaches give priority to the test cases that are the most dissimilar from those already selected. However, the proposed criteria do not scale up to handle the many thousands or even some millions test suite sizes of modern industrial systems and simple heuristics are used instead. We introduce the FAST family of test case prioritization techniques that radically changes this landscape by borrowing algorithms commonly exploited in the big data domain to find similar items. FAST techniques provide scalable similarity-based test case prioritization in both white-box and black-box fashion. The results from experimentation on real world C and Java subjects show that the fastest members of the family outperform other black-box approaches in efficiency with no significant impact on effectiveness, and also outperform white-box approaches, including greedy ones, if preparation time is not counted. A simulation study of scalability shows that one FAST technique can prioritize a million test cases in less than 20 minutes.
Towards the comparative analysis of genomic variants with JalviewJim Procter
Invited talk at the first 'Fantastic Forces' Computational Evolutionary Biology at the University of St. Andrews on 5th June, 2019. In 45 minutes, I introduced key concepts in multiple sequence alignment analysis, Jalview's major capabilities for working with DNA, RNA and Protein sequences, structures and phylogenetic trees, and how they could be applied to filter and visualise genomic variant data imported from VCF and added to alignments as positional annotation.
I also highlighted MacGowan and Barton's ongoing research program (published on biorxiv) into the inference of functional regions of proteins by analysis of the relationship between the distribution of missense variants and conservation in Pfam multiple sequence alignments involving paralogous domains from human and other organisms.
https://fantastic4cesworkshop.wordpress.com/programme/
This document discusses forecasting covariance matrices using the Dynamic Conditional Correlation (DCC) GARCH model. It begins with an overview of univariate GARCH models and the GARCH(1,1) specification. It then introduces the DCC model, which models the conditional covariance matrix indirectly through the conditional correlation matrix. The document evaluates how forecasts from the DCC model perform compared to a covariance matrix based only on historical data. It presents an empirical application comparing the two approaches using different datasets. The conclusion discusses how the DCC model tends to outperform the historical covariance matrix in the short-run but the reverse is true in the long-run.
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LWF Chain graphs were introduced by Lauritzen, Wermuth, and Frydenberg as a generalization of graphical models based on undirected graphs and DAGs. From the causality point of view, in an LWF CG: Directed edges represent direct causal effects. Undirected edges represent causal effects due to interference, which occurs when an individual’s outcome is influenced by their social interaction with other population members, e.g., in situations that involve contagious agents, educational programs, or social networks. The construction of chain graph models is a challenging task that would be greatly facilitated by automation.
Markov blanket discovery has an important role in structure learning of Bayesian network. It is surprising, however, how little attention it has attracted in the context of learning LWF chain graphs. In this work, we provide a graphical characterization of Markov blankets in chain graphs. The characterization is different from the well-known one for Bayesian networks and generalizes it. We provide a novel scalable and sound algorithm for Markov blanket discovery in LWF chain graphs. We also provide a sound and scalable constraint-based framework for learning the structure of LWF CGs from faithful causally sufficient data. With the use of our algorithm, the problem of structure learning is reduced to finding an efficient algorithm for Markov blanket discovery in LWF chain graphs. This greatly simplifies the structure-learning task and makes a wide range of inference/learning problems computationally tractable because our approach exploits locality.
The document discusses Adaptable Constrained Genetic Programming (ACGP), which aims to automate the discovery of heuristics to guide the genetic programming search. It describes how ACGP develops first-order and second-order heuristics based on patterns observed in high-performing individuals, and uses these heuristics to bias mutation, crossover and regrowth. Experimental results on a target equation with explicit second-order structure show that ACGP with second-order heuristics outperforms both standard GP and ACGP with only first-order heuristics. The document concludes that ACGP is effective at discovering and exploiting problem structure through its adaptive heuristic approach.
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This document provides information about the Computational Chemistry 126 course offered at UC Santa Barbara in Fall 2011, including the instructor's contact information, course objectives, required readings and assignments. The course focuses on learning principles of computational chemistry and molecular modeling techniques. Students will learn algorithms for geometry optimization, transition state location, and prediction of molecular properties using software for quantum chemical calculations and molecular simulations.
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Toward a Unified Approach to Fitting Loss ModelsJacques Rioux
The document discusses developing a unified approach for fitting loss models to insurance data. It proposes using a limited set of standard distributions, including the exponential, gamma, lognormal and Pareto distributions. These distributions could be combined in mixtures to better fit different parts of the data. The document presents an example of fitting a mixture of a lognormal and exponential distribution to insurance payment data. Graphs and statistical tests show the model provides an acceptable fit to the data. Automating the process of fitting mixtures and comparing models is an area for further development.
This thesis investigates methods for integrative analysis of multiple data types. It extends the Joint and Individual Variation Explained (JIVE) method by incorporating a fused lasso penalty. A novel rank selection algorithm is also proposed. The methods are evaluated on simulated data and applied to analyze The Cancer Genome Atlas glioblastoma data to identify shared mutational processes between chromosomes.
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The Model of Achievement Competence Motivation (MACM) Part E: Crossing the R...Kevin McGrew
The Model of Achievement Competence Motivation (MACM) is a series of slide modules. This is the fifth (Part E) in the series. It is brief...only 11 slides. The modules will serve as supplemental materials to "The Model of Achievement Competence Motivation (MACM)--Standing on the shoulders of giants" (McGrew, in press, 2021 - in a forthcoming special issue on motivation in the Canadian Journal of School Psychology)
The Model of Achievement Competence Motivation (MACM): Part D: The volition ...Kevin McGrew
The Model of Achievement Competence Motivation (MACM) is a series of slide modules. This is the fourth (Part D) in the series. The modules will serve as supplemental materials to "The Model of Achievement Competence Motivation (MACM)--Standing on the shoulders of giants" (McGrew, in press, 2021 - in a forthcoming special issue on motivation in the Canadian Journal of School Psychology)
The Model of Achievement Competence Motivation (MACM) Part C: The motivation...Kevin McGrew
The document provides an overview of the Model of Achievement Competence Motivation (MACM) which defines key motivation domains. The MACM model integrates affective and conative construct domains and defines three major motivation domains: achievement orientations, interests and task values, and self-beliefs. Each domain is further defined by constructs such as intrinsic motivation, academic goal orientation, need for cognition, academic self-efficacy, and locus of control. The model represents motivational processes as occurring in preparatory, deliberation, and action commitment stages focused on goals, reasons for goals, and expectancies of competence.
The Model of Achievement Competence Motivation (MACM): Part B - An overview ...Kevin McGrew
The Model of Achievement Competence Motivation (MACM) is a series of slide modules. This is the second (Part B) in the series. The modules will serve as supplemental materials to "The Model of Achievement Competence Motivation (MACM)--Standing on the shoulders of giants" (McGrew, in press, 2021 - in a forthcoming special issue on motivation in the Canadian Journal of School Psychology)
The Model of Achievement Competence Motivation (MACM): Part A Introduction o...Kevin McGrew
The Model of Achievement Competence Motivation (MACM) is a series of slide modules. This is the first (Part A) in the series. The modules will serve as supplemental materials to "The Model of Achievement Competence Motivation (MACM)--Standing on the shoulders of giants" (McGrew, in press, 2021 - in a forthcoming special issue on motivation in the Canadian Journal of School Psychology)
The WJ IV Cognitive GIA in iintellectual disability (ID) assessmentKevin McGrew
This is a brief presentation that explains why the WJ IV (and WJ III) GIA IQ score is an appropriate and valid indicator of general intelligence that can be used in possible intellectual disability (ID) determinations
The Evolution of the Cattell-Horn-Carrol (CHC) Theory of Intelligence: Schne...Kevin McGrew
This presentation is based on Schneider, W. J., & McGrew, K. S. (in press). The Cattell-Horn-Carroll Theory of Cognitive Abilities. This presentation includes a portion of key material to be published in a forthcoming CHC update/revision chapter-->In D. P. Flanagan & Erin M .McDonough (Eds.), Contemporary intellectual assessment: Theories, tests and issues (4thed.,) New York: Guilford Press.
This is only a small amount of the chapter. Also, I have inserted some new material related to test interpretation that is not included in the to-be-published chapter. The tentative date for publication of the Flanagan book is spring 2018.
Beyond cognitive abilities: An integrative model of learning-related persona...Kevin McGrew
For centuries educational psychologists have highlighted the importance of "non-cognitive" variables in school learning. The presentation is a "big picture" overview of how cognitive abilities and non-cognitive factors can be integrated into an over-arching conceptual framework. The presentation also illustrates how the big picture framework can be used to conceptualize a number of contemporary "buzz word" initiatives related to building 21st century educationally important skills (social-emotional learning, critical thinking, creativity, complex problem solving, etc.)
What about executive functions and CHC theory: New research for discussionKevin McGrew
1. The document discusses recent research on how executive functions fit within the Cattell-Horn-Carroll (CHC) theory of cognitive abilities and their relationship to general intelligence (g).
2. Studies have found that white matter integrity, processing speed (Gt), and executive functions are strongly related to general fluid intelligence (Gf) and that processing speed may mediate the relationship between white matter integrity and intelligence.
3. The Parietal-Frontal Integration Theory (P-FIT) model proposes that the frontal and parietal lobes are important for constructs like working memory, attention, and executive functions that relate to general intelligence.
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What is "intelligent" intelligence testingKevin McGrew
This document discusses the history and evolution of intelligence test interpretation. It outlines four waves of interpretation: 1) Quantification of general intelligence, 2) Clinical profile analysis, 3) Psychometric profile analysis, and 4) Applying psychological theory. It highlights Dr. Alan Kaufman's 1979 book "Intelligent Testing with the WISC-R" as pioneering the third wave of psychometric profile analysis. The document emphasizes that intelligent testing requires incorporating both quantitative test data and clinical expertise to develop interventions that improve individuals' lives.
This document discusses evaluating differences between scores on tests that measure the same cognitive ability. It notes that tests within a domain, like working memory, may share a moderate amount of variance but also have unique aspects. Differences between two tests' scores need to be evaluated in terms of how unusual they are statistically. The base rate or standard deviation of differences provides important context for interpreting score discrepancies within an ability domain. Understanding the reliability and shared variance between tests is key to determining if differences warrant interpretation.
How to evaulate the unusualness (base rate) of WJ IV cluster or test score di...Kevin McGrew
The WJ IV provides two primary methods for comparing tests or cluster scores. One is based on a predictive model (the variation and comparison procedures) and the other allows comparisons of SEM confidence bands, which takes into account each measures reliability. A third method for comparing scores, one that takes into account the correlation between compared measures (ability cohesion model) is not provided, but is frequently used by assessment professionals. The three types of score comparison methods are described and new information, via a "rule of thumb" summary slide and nomograph, are provided to allow WJ IV users to evaluate scores via all three methods.
The WJ IV and Beyond CHC Theory: Kevin McGrew's NASP mini-skills workshopKevin McGrew
This presentation represents the slides Dr. Kevin McGrew presented at his WJ IV and Beyond CHC theory mini-skills workshop at the 2015 NASP convention in Orlando, Florida. The show includes more and newer slides than were presented at the live session.
CHC Theory Codebook 2: Cognitive definitionsKevin McGrew
The document outlines the CHC Periodic Table of Human Abilities which categorizes human cognitive abilities into three domains: domain-independent capacities, acquired knowledge systems, and sensory-motor domain-specific abilities. It was adapted from the works of Schneider & McGrew from 2012 and McGrew, LaForte and Schrank from 2014 and is copyrighted by the Institute for Applied Psychometrics under Dr. Kevin McGrew from April 23rd, 2014.
CHC Theory Codebook 1: Cognitive definitionsKevin McGrew
The document defines and provides details on several broad cognitive abilities according to the Cattell-Horn-Carroll (CHC) model of intelligence. It discusses fluid reasoning (Gf), short-term working memory (Gwm), long-term retrieval (Glr), processing speed (Gs), comprehension-knowledge (Gc), and visual processing (Gv). Each broad ability is defined and their narrow abilities are outlined, such as induction, deduction, and quantitative reasoning under Gf. The document serves as a reference for understanding the factors and structure of the CHC model.
WJ IV Battery Introduction and OverviewKevin McGrew
A brief introduction to the WJ IV Battery revision. This does include some slides posted previously in the three WJ IV NASP presentation slides I posted.
WJ IV NASP 2014 workshop: Variation and comparison procedures & PSW models i...Kevin McGrew
The document discusses procedures for using the Woodcock-Johnson IV Tests of Cognitive Abilities and Achievement (WJ IV) to identify specific learning disabilities (SLD), including ability-achievement discrepancies and the pattern of strengths and weaknesses (PSW) approach. It describes five discrepancy procedures and four variation procedures that can be used with the WJ IV to document intra-individual differences in cognitive abilities and achievement. The procedures allow comparisons between broad cognitive composites and specific achievement domains, as well as comparisons among narrower cognitive and oral language abilities. Identifying a student's unique pattern of strengths and weaknesses can help inform appropriate instructional accommodations.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
1. This is the second half of the above presentation made at NASP 2009. The first half of this presentation is also available at Kevin McGrew’s SlideShare space and is called “ CHC-Cog-Ach Relations Research Synthesis” ---- the current module is an attempt to demonstrate selective testing (branching-tree) referral-focused testing scenarios based on the results of the CHC Cog-Ach relations research synthesis, using the WJ III battery as the illustrative instrument. The viewer should first view the CHC Cog-Ach Relations Research Synthesis module prior to viewing this module. CHC cognitive and achievement relations research synthesis: Part B: Selective testing (branching tree) referral-focused assessment scenarios
2. Conflict of interest disclosure for Kevin McGrew Dr. Kevin McGrew has a financial interest in the WJ III as a co-author of the WJ III Battery, the battery that is featured in the current slide show
6. The big picture Where we are going in example Let’s break down the steps Yes: Gc-LS No yes Yes: Gc-K0 no No Yes Administer BRS (6-8) suggested start tests What are prelim concerns? Gc (VrCmp+GnInf) -Give PicVoc & OrlCmp -Get Gc~4 & Gc-LS cls. Gc narrow concerns? -Give StRec -Get Gc- LS~3 cls Examine Gc-K0~2 (GnInf+PicVoc) Gc-K0~2 concern? Stop testing in Gc-K0 -Give AcdKnw -Get Know. (Gc-K0+) and Gc-K0~3 cls Other areas (Ga, etc.) See other flow charts Compare broad and narrow Gc cls Make final broad Gc interp. Make final broad vs narrow Gc interp. Possible Gc retrieval/ access (Glr) problems? Examine Glr-NA~2 (ReFl+RPNam) Glr-NA~2 concern? Stop testing in Glr-NA -Give DecSpd -Get Cognitive Fluency cls Compare Glr-NA2 and/or Cog Flu to broad & narrow Gc clusters Make final broad & narrow Gc (level) and rate (fluency) of retrieval (Glr) interp.
7. (Gc-LD/VL) (Glr-MA) (Gs-P) (Gf-I) ( Ga-PC ) (Gv-SR) ( Gsm-MW ) (Gc-K0) (Glr-MA/MM) (Gsm-MW) (Ga-PC) (Gs-R4/RE) (Glr-FI) (Gv-MV) (Ga-US/UR) (Gf-RG) (Gsm-MS) (Glr-NA) (Gv-SS) (Gs-AC/EF) ( Gsm-MW /Gc-LS) (Glr-MM) (Gc-VL) (Gc-LS) ( Gsm-MW / Ga-PC ) (Gc-LS) . BRS (6-8 yrs): Suggested starting point tests for WJ III referral-focused selective testing (based on CHC Cog-Ach correlates research synthesis ; McGrew & Wendling, 2009) WJ III tests suggested as most relevant for Basic Reading Skills referrals from ages 6-8
8. (Gc-LD/VL) (Glr-MA) (Gs-P) (Gf-I) ( Ga-PC ) (Gv-SR) ( Gsm-MW ) (Gc-K0) (Glr-MA/MM) (Gsm-MW) (Ga-PC) (Gs-R4/RE) (Glr-FI) (Gv-MV) (Ga-US/UR) (Gf-RG) (Gsm-MS) (Glr-NA) (Gv-SS) (Gs-AC/EF) ( Gsm-MW /Gc-LS) (Glr-MM) (Gc-VL) (Gc-LS) ( Gsm-MW / Ga-PC ) (Gc-LS) Gc . Glr-NA Gs~2 Glr-NA~2 Cognitive efficiency (Std) Ga-PC~2 Gsm-MW~3 BRS (6-8 yrs): Suggested starting point tests for WJ III referral-focused selective testing (based on CHC Cog-Ach correlates research synthesis; McGrew & Wendling, 2009 ) Glr WJ III norm-based CHC cluster scores available (@ starting point) plus other special theoretical/ empirical cluster composites (designated with ~ # notation which designates “special” cluster status and the # of tests in each special cluster).
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10. Psychometric approach . Calculate actual cluster SS with psychometrically sound formula’s (i.e., those that take into account each tests reliability and the inter-correlation among the tests in the clusters; NOT the simple arithmetic average of test SS’s)
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16. Administer BRS (6-8) Suggested Start Tests What are Preliminary Concerns? Gc (VC + GI) (UD) Other areas (Ga, etc.) See other flowcharts
17. BRS (6-8 yrs): WJ III Gc branching test scenarios: Lets “break it down” into the parts of possible selective testing scenarios Warning/Caution: The following set of branching tree-based selective testing examples are intended NOT to be THE sequence to follow. This is not a cookbook . All sequential scenarios are an attempt to model one possible research/expert-based possibility. Each referral and case is unique. It is the modeled logic and decisions that are important in the following slides. In reality, referral-based focused testing is more of a non-linear iterative cognitive testing hypothesis method based on the skills of the clinician (“We are the instrument”; K. McGrew; date unknown)
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20. BRS (6-8 yrs): Gc branching test scenario - AB (Gc-LD/VL) (Glr-MA) (Gs-P) (Gf-I) (Ga-PC) (Gv-SR) (Gsm-MW) ( Gc-K0 ) (Gsm-MW) (Ga-PC) (Gs-R4/RE) (Glr-FI) (Gv-MV) (Ga-US/UR) (Gf-RG) (Gsm-MS) (Glr-NA) (Gv-SS) (Gs-AC/EF) (Gsm-MW/ Gc-LS ) (Glr-MM) (Gc-VL/ K0?) ( Gc-LS ) (Gsm-MW/Ga-PC) (Gc-LS) Gc Suggested additional tests to administer if Gc (comp-knowledge) appears to be the major problem (based on primary Gc tests; background information; teacher info; other test info; etc.) These tests allow for calculation of the WJ III Listening Comprehension (Gc-LS) cluster and Gc~4 Gc-LS Gc~4
21. No Give PV and OC Get Gc -LS and Gc ~4 Gc narrow concerns? Make final broad Gc interpretation A. Possible generalized problem in Gc
22. Yes : Gc-LS Give PV and OC Get Gc -LS and Gc ~4 clusters Gc narrow concerns? Give Story Recall Get Gc -LS~3 cluster Compare broad and narrow Gc clusters Make final broad vs. narrow Gc interpretation B. Possible problem in narrow ability ( Gc- LS)
23. BRS (6-8 yrs): Gc branching test scenario - AB (Gc-LD/VL) (Glr-MA) (Gs-P) (Gf-I) (Ga-PC) (Gv-SR) (Gsm-MW) ( Gc-K0 ) (Gsm-MW) (Ga-PC) (Gs-R4/RE) (Glr-FI) (Gv-MV) (Ga-US/UR) (Gf-RG) (Gsm-MS) (Glr-NA) (Gv-SS) (Gs-AC/EF) (Gsm-MW/ Gc-LS ) (Glr-MM) (Gc-VL/ K0?) ( Gc-LS ) (Gsm-MW/Ga-PC) (Gc-LS) Gc Gc-LS Compare Gc, Gc~4 and Gc-LS for possible specific vs broad Gc problems If listening ability (Gc-LS) needs further study administer Story Recall and examine compare broad Gc (Gc, Gc~4) and narrow Gc-LS (Gc-LS; Gc-LS~3) Gc-LS~3 Gc~4
24. Yes: Gc -K0 No Give PV & OC Get Gc -LS & Gc ~4 clusters Gc narrow concerns? Examine Gc -K0~2 (GI + PV) Gc -K0~2 concern? Stop testing in Gc -K0 B. Possible problem in narrow ability ( Gc- K0) Suggested general information (clinical) narrow cluster ( Gc -K0 ~ 2): General Information Picture Vocabulary
25. BRS (6-8 yrs): Gc branching test scenario - AB Gc Gc-LS Gc-K0~2 Gc-LS~3 Suggested general information (clinical) narrow cluster (Gc-K0~2) to evaluate if Gc (comp-knowledge) appears to be the major problem (based on primary Gc tests; background information; teacher info; other test info; etc.) Compare Gc (Gc4~4), Gc-LS (LS~3) and Gc-K0~2 for possible broad vs narrow Gc problems Gc~4 (Gc-LD/VL) (Glr-MA) (Gs-P) (Gf-I) (Ga-PC) (Gv-SR) (Gsm-MW) ( Gc-K0 ) (Gsm-MW) (Ga-PC) (Gs-R4/RE) (Glr-FI) (Gv-MV) (Ga-US/UR) (Gf-RG) (Gsm-MS) (Glr-NA) (Gv-SS) (Gs-AC/EF) Gc (Gsm-MW/ Gc-LS ) (Glr-MM) (Gc-VL/ K0?) ( Gc-LS ) (Gsm-MW/Ga-PC) (Gc-LS)
26. Give AK Get Knowledge ( Gc -K0) and Gc -K0~3 clusters Yes: Gc -K0 Give PV & OC Get Gc -LS & Gc ~4 clusters Gc narrow concerns? Examine Gc -K0~2 (GI + PV) Gc -K0~2 concern? Stop testing in Gc -K0 No If general information ( Gc -K0~2) needs further study, administer Academic Knowledge test and examine Knowledge ( Gc -K0+) and Gc -K0~3 clusters. B. Possible problem in narrow ability ( Gc- K0) Yes
27. BRS (6-8 yrs): Gc branching test scenario - AB (Gsm-MW/ Gc-LS ) (Glr-MM) (Gc-VL/ K0?) ( Gc-LS ) (Gsm-MW/Ga-PC) (Gc-LS) Gc Gc-LS Gc-K0~2 Gc-LS~3 If general information (Gc-K0~2) needs further study administer Academic Knowledge test and examine Knowledge (Gc-K0+) and Gc-K0~3 clusters Compare Gc (Gc~4), Gc-LS (LS~3) and Knowledge (Gc-K0+) and Gc-K0~2 (K0~3) for possible broad vs specific Gc problems Test 19: Academic Knowledge (Gc-K0/1/2) Gc~4 Gc (Gc-LD/VL) (Glr-MA) (Gs-P) (Gf-I) (Ga-PC) (Gv-SR) (Gsm-MW) ( Gc-K0 ) (Gsm-MW) (Ga-PC) (Gs-R4/RE) (Glr-FI) (Gv-MV) (Ga-US/UR) (Gf-RG) (Gsm-MS) (Glr-NA) (Gv-SS) (Gs-AC/EF) Gc Knowl (Gc-K0+) Gc-K0~3
28. Yes : Gc-LS Give PV and OC Get Gc -LS and Gc ~4 clusters Gc narrow concerns? Give Story Recall Get Gc -LS~3 cluster Compare broad and narrow Gc clusters Make final broad vs. narrow Gc interpretation Examine Gc -K0~2 (GI + PV) Gc -K0~2 concern? Stop testing in Gc -K0 Yes: Gc -K0 No Give AK Get Knowledge ( Gc -K0 and Gc -K0~3 clusters) Yes
29. What are prelim concerns? Gc (VC + GI) Other areas (Ga, etc .) Possible Gc r retrieval/ access (Glr) problems? Examine Glr-NA ~2 (RF+RPN) Glr-NA~2 concern? Make final broad & narrow Gc (level) and rate (fluency) of retrieval ( Glr ) interpretation No C. Exploring possible problem in rate (fluency) of retrieval Stop testing in Glr-NA Compare Glr-NA ~ 2 to broad & narrow Gc clusters
30. BRS (6-8 yrs): Gc branching test scenario - C (Gc-LD/VL) (Glr-MA) (Gs-P) (Gf-I) (Ga-PC) (Gv-SR) (Gsm-MW) ( Gc-K0 ) (Glr-MA/MM) (Gsm-MW) (Ga-PC) (Gs-R4/RE) (Glr-FI) (Gv-MV) (Ga-US/UR) (Gf-RG) (Gsm-MS) (Glr-NA) (Gv-SS) (Gs-AC/EF) (Gsm-MW/ Gc-LS ) (Glr-MM) (Gc-VL/ K0?) ( Gc-LS ) (Gsm-MW/Ga-PC) (Gc-LS) Gc Gc-LS Gc-K0~2 Gc-LS~3 Test 19: Academic Knowledge (Gc-K0/1/2) Knowl (Gc-K0+) Gc-K0~3 Compare amount ( level ) of broad Gc (Gc, Gc~4) and narrow (Gc-LS/LS~3; Knowledge; Gc-K0~2/K0~3) abilities with fluency ( rate ) of retrieval from Gc for possible problem in naming facility (speed/fluency of cognitive/semantic-lexical access; Glr-NA~2; RAN?) If speed/fluency of cognitive/semantic-lexical access appears a problem, consider additional verification via administration of another test that taps semantic processing speed (R4 ) [continued on next slide] Glr-NA ~2 Gc~4
31. What are prelim concerns? Gc (VC + GI) Other areas (Ga, etc . Possible Gc retrieval/ access (Glr) problems? Examine Glr-NA ~2 (RF+RPN) Glr-NA~2 concern? Stop testing in Glr-NA No C. Exploring possible problem in rate (fluency) of retrieval Yes If speed/fluency of cognitive/semantic-lexical access appears a problem, consider additional verification via administration of another test that taps semantic processing speed (R4 ). Give DS Compare Glr-NA ~ 2 and/or Cog FL to broad & narrow Gc clusters Make final broad & narrow Gc (level) and rate (fluency) of retrieval ( Glr ) interpretation Get Cog FL cluster
32. BRS (6-8 yrs): Gc branching test scenario - C (Gc-LD/VL) (Glr-MA) (Gs-P) (Gf-I) (Ga-PC) (Gv-SR) (Gsm-MW) ( Gc-K0 ) (Glr-MA/MM) (Gsm-MW) (Ga-PC) (Gs-R4/RE) (Glr-FI) (Gv-MV) (Ga-US/UR) (Gf-RG) (Gsm-MS) (Glr-NA) (Gv-SS) (Gs-AC/EF) (Gsm-MW/ Gc-LS ) (Glr-MM) (Gc-VL/ K0?) ( Gc-LS ) (Gsm-MW/Ga-PC) (Gc-LS) Gc Gc-LS Gc-K0~2 Gc-LS~3 Test 19: Academic Knowledge (Gc-K0/1/2) Knowl (Gc-K0+) Gc-K0~3 Glr-NA ~2 Gc~4 [ continued from prior slide] Administer Decision Speed - allows for calculation of Cognitive Fluency cluster Compare Cognitive Fluency and naming facility (Glr-NA~2; RAN) with Gc to determine if problem is specific to speed/fluency of cognitive/semantic-lexical access Cognitive Fluency
33. The big picture: Gc scenario Yes: Gc-LS No yes Yes: Gc-K0 no No Yes Administer BRS (6-8) suggested start tests What are prelim concerns? Gc (VrCmp+GnInf) -Give PicVoc & OrlCmp -Get Gc~4 & Gc-LS cls. Gc narrow concerns? -Give StRec -Get Gc- LS~3 cls Examine Gc-K0~2 (GnInf+PicVoc) Gc-K0~2 concern? Stop testing in Gc-K0 -Give AcdKnw -Get Know. (Gc-K0+) and Gc-K0~3 cls Other areas (Ga, etc.) See other flow charts Compare broad and narrow Gc cls Make final broad Gc interp. Make final broad vs narrow Gc interp. Possible Gc retrieval/ access (Glr) problems? Examine Glr-NA~2 (ReFl+RPNam) Glr-NA~2 concern? Stop testing in Glr-NA -Give DecSpd -Get Cognitive Fluency cls Compare Glr-NA2 and/or Cog Flu to broad & narrow Gc clusters Make final broad & narrow Gc (level) and rate (fluency) of retrieval (Glr) interp.
34. BRS (6-8 yrs): WJ III Cognitive Efficiency branching test scenarios: Lets “break it down” into the parts of a possible selective testing scenario Warning/Caution: The following set of branching tree-based selective testing examples are intended NOT to be THE sequence to follow. This is not a cookbook . All sequential scenarios are an attempt to model one possible research/expert-based possibility. Each referral and case is unique. It is the modeled logic and decisions that are important in the following slides. In reality, referral-based focused testing is more of a non-linear iterative cognitive testing hypothesis method based on the skills of the clinician (“We are the instrument” ; K. McGrew; date unknown)
35.
36. (Gc-LD/VL) (Glr-MA) (Gs-P) (Gf-I) ( Ga-PC ) (Gv-SR) ( Gsm-MW ) (Gc-K0) (Glr-MA/MM) (Gsm-MW) (Ga-PC) (Gs-R4/RE) (Glr-FI) (Gv-MV) (Ga-US/UR) (Gf-RG) (Gsm-MS) (Glr-NA) (Gv-SS) (Gs-AC/EF) ( Gsm-MW /Gc-LS) (Glr-MM) (Gc-VL) (Gc-LS) ( Gsm-MW / Ga-PC ) (Gc-LS) Gs~2 Gsm-MW~3 BRS (6-8 yrs): Cognitive efficiency branching test scenario - ABC Compare Cog Eff (Std), cognitive processing speed (Gs~2), and working memory (Gsm-MW~3) to ascertain if there is a posisible generalized deficit in cognitive efficiency (Gsm+Gs) Are all Gs~2 and Gsm-MW~3 scores low? If not, consider administering additional Gs and Gsm-MW tests to help differentiate Gs and Gsm-MW functioning [continued on next slide] Cognitive efficiency (Std)
37. (Gc-LD/VL) (Glr-MA) (Gs-P) (Gf-I) ( Ga-PC ) (Gv-SR) ( Gsm-MW ) (Gc-K0) (Glr-MA/MM) (Gsm-MW) (Ga-PC) (Gs-R4/RE) (Glr-FI) (Gv-MV) (Ga-US/UR) (Gf-RG) (Gsm-MS) (Glr-NA) (Gv-SS) (Gs-AC/EF) ( Gsm-MW /Gc-LS) (Glr-MM) (Gc-VL) (Gc-LS) ( Gsm-MW / Ga-PC ) (Gc-LS) Gs~2 Gsm-MW~3 BRS (6-8 yrs): Cognitive efficiency branching test scenario - ABC [continued from prior slide] Administer Auditory Working Memory – allows for calculation of Working Memory (Gsm-MW ) cluster and Gsm-MW~4 [continued on next slide] Gsm-MW Cognitive efficiency (Std) Gsm-MW~4
38. (Gc-LD/VL) (Glr-MA) (Gs-P) (Gf-I) ( Ga-PC ) (Gv-SR) ( Gsm-MW ) (Gc-K0) (Glr-MA/MM) (Gsm-MW) (Ga-PC) (Gs-R4/RE) (Glr-FI) (Gv-MV) (Ga-US/UR) (Gf-RG) (Gsm-MS) (Glr-NA) (Gv-SS) (Gs-AC/EF) ( Gsm-MW /Gc-LS) (Glr-MM) (Gc-VL) (Gc-LS) ( Gsm-MW / Ga-PC ) (Gc-LS) Gs Gsm-MW~3 BRS (6-8 yrs): Cognitive efficiency branching test scenario - ABC [ continued from prior slide] Administer Decision Speed (if not already administered ) - allows for calculation of Cognitive Efficiency (Ext) and Processing Speed (Gs; Gs~3) clusters Compare Cog Eff (Ext), cog. processing speed (Gs; Gs~3), and working memory (Gsm-MW; Gsm-MW~3,; Gsm-MW~4) to try isolate if problem is a generalized deficit in cognitive efficiency (Gsm+Gs) or specific to cognitive processing speed or working memory . [continued on next slide] Gsm-MW Cognitive efficiency (Ext) Gsm-MW~4 Gs~3
39. (Gc-LD/VL) (Glr-MA) (Gs-P) (Gf-I) ( Ga-PC ) (Gv-SR) ( Gsm-MW ) (Gc-K0) (Glr-MA/MM) (Gsm-MW) (Ga-PC) (Gs-R4/RE) (Glr-FI) (Gv-MV) (Ga-US/UR) (Gf-RG) (Gsm-MS) (Glr-NA) (Gv-SS) (Gs-AC/EF) ( Gsm-MW /Gc-LS) (Glr-MM) (Gc-VL) (Gc-LS) ( Gsm-MW / Ga-PC ) (Gc-LS) Gs Gsm-MW~3 BRS (6-8 yrs): Cognitive efficiency branching test scenario - D [ continued from prior slide] Administering Decision Speed also allows for calculation of Cognitive Fluency cluster Compare Cog Eff (Ext), cog. processing speed (Gs; Gs~3), and working memory (Gsm-MW; Gsm-MW~3; Gsm-MW~4) with Cognitive Fluency and naming facility (RAN?; Glr-NA~2) to determine if problem is specific to speed/fluency of cognitive/semantic-lexical access Gsm-MW Cognitive efficiency (Ext) Gsm-MW~4 Gs~3 Glr-NA ~2 Cognitive Fluency
40. BRS (6-8 yrs): WJ III Cognitive Efficiency branching test scenarios: Delving even deeper into the cognitive speed/fluency domain: An example of even more selective and diagnostic possibilities Warning/Caution: The following set of branching tree-based selective testing examples are intended NOT to be THE sequence to follow. This is not a cookbook . All sequential scenarios are an attempt to model one possible research/expert-based possibility. Each referral and case is unique. It is the modeled logic and decisions that are important in the following slides. In reality, referral-based focused testing is more of a non-linear iterative cognitive testing hypothesis method based on the skills of the clinician (“We are the instrument”; K. McGrew; date unknown)
42. (Gc-LD/VL) (Glr-MA) (Gs-P) (Gf-I) (Ga-PC) (Gv-SR) (Gsm-MW) (Gc-K0) (Glr-MA/MM) (Gsm-MW) (Ga-PC) (Gs-R4/RE) (Glr-FI) (Gv-MV) (Ga-US/UR) (Gf-RG) (Gsm-MS) (Glr-NA) (Gv-SS) (Gs-AC/EF) Gs BRS (6-8 yrs): Cognitive efficiency branching test scenario - E If problem seems to be even more narrowly focused in cognitive speed/fluency, and further exploration is desirable, consider administering additional tests (from WJ III Diagnostic Supplement) and comparing the relevant speeded/fluency broad and narrow clusters within domains. Administer Cross Out - allows for calculation of Perceptual Speed (Gs-P) cluster, as well as possible Gs~4 Compare broadest cognitive processing speed clusters (Gs; Gs~3; Gs~4) with narrower Perceptual Speed cluster (Gs-P) [continued on next slide] Gs-P Gs~3 Test 26: Cross Out (Gs-P) Gs~4
43. (Gc-LD/VL) (Glr-MA) (Gs-P) (Gf-I) (Ga-PC) (Gv-SR) (Gsm-MW) (Gc-K0) (Glr-MA/MM) (Gsm-MW) (Ga-PC) (Gs-R4/RE) (Glr-FI) (Gv-MV) (Ga-US/UR) (Gf-RG) (Gsm-MS) (Glr-NA) (Gv-SS) (Gs-AC/EF) Glr-MA BRS (6-8 yrs): Cognitive efficiency branching test scenario - E Glr-NA ~2 [continued from prior slide] If problem seems to be focused in cognitive speed/fluency, and further exploration is desirable, consider administering additional tests (from WJ III Diagnostic Supplement) and compare the relevant speeded/fluency broad and narrow clusters within domains. Administer Memory for Names – allows for calculation of Associative Memory (Gs-MA) cluster. Compare broadest fluency of recall/cog fluency clusters (Cognitive Fluency; Glr) with narrower Associative Memory (Glr-MA) and naming facility (Glr-NA~2; RAN?) Test 30: Memory for Names (Glr-MA) Cognitive Fluency Glr
47. (Gc-LD/VL) (Glr-MA) (Gs-P) (Gf-I) ( Ga-PC ) (Gv-SR) ( Gsm-MW ) (Gc-K0) (Glr-MA/MM) (Gsm-MW) ( Ga-PC ) (Gs-R4/RE) (Glr-FI) (Gv-MV) (Ga-US/UR) (Gf-RG) (Gsm-MS) (Glr-NA) (Gv-SS) (Gs-AC/EF) ( Gsm-MW /Gc-LS) (Glr-MM) (Gc-VL) (Gc-LS) ( Gsm-MW / Ga-PC ) (Gc-LS) BRS (6-8 yrs): Ga branching test scenario AB [continued from prior slide] Administer Incomplete Words – allows for calculation of Phonemic Awareness (PA) and Phonemic Awareness 3 (PA3) cluster Administer Auditory Attention – allows for calculation of broad Auditory Processing (Ga) cluster Compare Ga, PA and PA3 to determine if problem is possible generalized auditory processing problem (all are low) or if isolated to narrow phonetic coding (Ga-PC---PA and PA3) Phon. Awr.-PA (Ga-PC) Phon. Awr3-PA3 (Ga-PC) Ga
48. (Gc-LD/VL) (Glr-MA) (Gs-P) (Gf-I) ( Ga-PC ) (Gv-SR) ( Gsm-MW ) (Gc-K0) (Glr-MA/MM) (Gsm-MW) (Ga-PC) (Gs-R4/RE) (Glr-FI) (Gv-MV) (Ga-US/UR) (Gf-RG) (Gsm-MS) (Glr-NA) (Gv-SS) (Gs-AC/EF) ( Gsm-MW /Gc-LS) (Glr-MM) (Gc-VL) (Gc-LS) ( Gsm-MW / Ga-PC ) (Gc-LS) Gsm-MW~3 BRS (6-8 yrs): Gsm branching test scenario AC Ga-PC~2 If Gs-MW~3 is possible problem (lower than (Ga-PC~2) administer additional Gsm tests: [continued on next slide]
49. (Gc-LD/VL) (Glr-MA) (Gs-P) (Gf-I) ( Ga-PC ) (Gv-SR) ( Gsm-MW ) (Gc-K0) (Glr-MA/MM) (Gsm-MW) (Ga-PC) (Gs-R4/RE) (Glr-FI) (Gv-MV) (Ga-US/UR) (Gf-RG) (Gsm-MS) (Glr-NA) (Gv-SS) (Gs-AC/EF) ( Gsm-MW /Gc-LS) (Glr-MM) (Gc-VL) (Gc-LS) ( Gsm-MW / Ga-PC ) (Gc-LS) Gsm-MW~3 [continued from prior slide] Administer Auditory Working Memory – allows for calculation of Working Memory (Gsm-MW ) cluster and Gsm-MW~4 Administer Memory for Words (Gsm-MS) – allows for calculation of broad Short-term Memory (Gsm) cluster Compare broad Short-term Memory (Gsm) with various working memory (Gsm-MW; Gsm-MW~3; Gsm-MW~4) clusters to attempt to differentiate problem as generalized broad Gsm problem or more narrow working memory (Gsm-MW) problem [continued on next slide] Gsm-MW Gsm-MW~4 BRS (6-8 yrs): Gsm branching test scenario AC Gsm
51. BRS (6-8 yrs): WJ III Ga/Gsm branching test scenario: Delving even deeper into the Ga/Gsm domains: An example of even more selective and diagnostic possibilities Warning/Caution: The following set of branching tree-based selective testing examples are intended NOT to be THE sequence to follow. This is not a cookbook . All sequential scenarios are an attempt to model one possible research/expert-based possibility. Each referral and case is unique. It is the modeled logic and decisions that are important in the following slides. In reality, referral-based focused testing is more of a non-linear iterative cognitive testing hypothesis method based on the skills of the clinician (“We are the instrument”; K. McGrew; date unknown)
53. (Gc-LD/VL) (Glr-MA) (Gs-P) (Gf-I) ( Ga-PC ) (Gv-SR) (Gsm-MW) (Gc-K0) (Glr-MA/MM) (Gsm-MW) (Ga-PC) (Gs-R4/RE) (Glr-FI) (Gv-MV) (Ga-US/UR) (Gf-RG) (Gsm-MS) (Glr-NA) (Gv-SS) (Gs-AC/EF) ( Gsm-MW /Gc-LS) (Glr-MM) (Gc-VL) (Gc-LS) ( Gsm-MW / Ga-PC ) (Gc-LS) Gsm-MW BRS (6-8 yrs): Gsm-MW vs MS branching test scenario B Gsm-MS If it appears necessary to differentiate Memory Span (Gsm-MS) from Working Memory (Gsm-MW), consider administering additional tests (from WJ III Diagnostic Supplement) and comparing the narrow clusters within Gsm Administer Memory for Sentences Compare Working Memory (Gsm-MW) and Memory Span (Gsm-MS) clusters Test 27: Memory for Sentences (Gsm-MS)
54. (Gc-LD/VL) (Glr-MA) (Gs-P) (Gf-I) (Ga-PC) (Gv-SR) (Gsm-MW) (Gc-K0) (Glr-MA/MM) (Gsm-MW) (Ga-PC) (Gs-R4/RE) (Glr-FI) (Gv-MV) (Ga-US/UR) (Gf-RG) (Gsm-MS) (Glr-NA) (Gv-SS) (Gs-AC/EF) BRS (6-8 yrs): Ga (PC vs US/3) branching test scenario A If it appears necessary to differentiate Phonemic Awareness (Ga-PC) from general Sound Discrimination (Ga-US/U3), consider administering additional tests (from WJ III Diagnostic Supplement) and comparing the relevant broad and narrow clusters within domains. Administer Sound Patterns-Voice and Sound Patterns-Music – allows for calculation of Sound Discrimination (Ga-US/U3) cluster Compare Phonemic Awareness (Ga-PC) and Sound Discriminatio n (Ga-US/U3) clusters Test 23: Sound Patterns-Voice (Ga-U3/UR) Test 29: Sound Patterns-Music (Ga-U1/U8/U9) Ga-US/U3 Ga-PC
55. WJ III branching test scenarios PSYCHOLOGIST GENERAL’S WARNING: These are NOT to be used in a cook-book manner. The examples are intended to demonstrate modeled logical and decision-making. All cases are unique. Referral-based focused testing is a non-linear iterative cognitive testing hypothesis method based on the skills and expertise of the clinician.
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57. To what extent are the following similarly classified narrow indicators of Induction (I under Gf) and Working Memory (MW under Gsm) exchangeable? How correlated are scores from the indicators that are classified as measuring the same narrow abilities? We don’t know !!!!!!!!!!!!!!!!!!
63. No significant Gf Arithmetic loading Arithmetic loaded high on Gq (.69) and low on Gs (.20) Phelps et al. (2005) WISC-III/WJ III CB CFA
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68. We now know that the world is not flat---and, we know have an empirically based workable taxonomic “map” of the terrain of cognitive abilities. We are now charting a more accurate course in our search for the “holy grail” of COG-ACH abilities research We have learned something about empirical support for CHC COG-ACH relations over the past 20 years. VCI/POI/FFD/PS
69. Detailed information re: the CHC COG-ACH research synthesis is available on the web www.iapsych.com
72. “ Tests do not think for themselves, nor do they directly communicate with patients. Like a stethoscope, a blood pressure gauge, or an MRI scan, a psychological test is a dumb tool , and the worth of the tool cannot be separated from the sophistication of the clinician who draws inferences from it and then communicates with patients and professionals” Meyer et al. (2001). Psychological testing and psychological assessment. American Psychologist,
73. Broad Domain Markers Basic Reading Skills – ages 6 to 8 Gc Crystallized Intelligence Gsm Short-Term Memory Ga Auditory Processing Gs Processing Speed Glr Long-Term Retrieval Short-term Memory Working Memory Processing Speed Perceptual Speed-DS Comp-Knowledge Listening Comp. Phonemic Awareness Phonemic Awareness 3 Most Relevant WJ III Clusters Long-term Retrieval Associative Memory-DS Cognitive Fluency Work Mem (MW) Lang. Dev. (LD) Listen. Ability (LS) Gen. Info. (K0) Lex. Know. (VL) Phonetic Coding (PC) Perc. Speed (P) Narrow Domain Markers Assoc. Mem. (MA) Naming Fac. (NA) Numbers Reversed (MW) Understanding Dir (MW/LS ) Aud. Working. Mem. (MW) Visual Matching (P) Verbal Comp. (LD/VL) Oral Comp. (LS) General Info (K0) Picture Vocab. (VL) Snd. Aware. (PC/MW) Snd. Blending (PC) Most Relevant WJ III Tests Vis.-Aud.-Lrng. (MA) Rapid. Pic. Nam. (NA) Retrieval Fluency (FI) (NA) Research foundation: From McGrew @ Wendling (2009) CHC COG-ACH relations research synthesis (prior slides) Bridge research – real world practice WJ III clusters/test selected based on McGrew & Wendling (2009) plus expert-knowledge and clinical experience with WJ III battery These are only tools
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75. CHC Selective Referral-Focused Assessment Worksheet (McGrew, 2009) Age/grade: _____ Academic referral concern ___________________________________________ CHC res. syn. based Non-CHC res. based Broad/Narrow CHC Abilities Referral Relevant domain? Selective/focused set of starting tests Selective/focused possible additional tests Gsm Memory Span (MS) Y N Working Memory (MW) Y N Gs Perceptual Speed (P) Y N Number Facility (N) Y N Glr Associative Memory (MA) Y N Naming Facility (NA) Y N Meaningful Memory (MM) Y N Gc Language Development (LD) Y N General Information (K0) Y N Listening Ability (LS) Y N Lexical Knowledge (VL) Y N Ga Phonetic Coding (PC) Y N Spch-Snd Disc/Res to ASD (US/UR) Gf Gen. Seq. Reasoning (RG) Y N Quantitative Reasoning (RQ) Y N EF Vigilance/inhibition/planning/ concentration, self-regulation, etc Y N Gkn Domain-specific knowledge (__) Y N Gv Visualization (Vz)/Spat Rel (SR)/ Visual Memory (MV)/Imagery (IM) Y N ??? Orthographic processing (???) Y N These are only tools
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77. Yes: Gc-LS No yes Yes: Gc-K0 no No Yes Administer BRS (6-8) suggested start tests What are prelim concerns? Gc (VrCmp+GnInf) -Give PicVoc & OrlCmp -Get Gc~4 & Gc-LS cls. Gc narrow concerns? -Give StRec -Get Gc- LS~3 cls Examine Gc-K0~2 (GnInf+PicVoc) Gc-K0~2 concern? Stop testing in Gc-K0 -Give AcdKnw -Get Know. (Gc-K0+) and Gc-K0~3 cls Other areas (Ga, etc.) See other flow charts Compare broad and narrow Gc cls Make final broad Gc interp. Make final broad vs narrow Gc interp. Possible Gc retrieval/ access (Glr) problems? Examine Glr-NA~2 (ReFl+RPNam) Glr-NA~2 concern? Stop testing in Glr-NA -Give DecSpd -Get Cognitive Fluency cls Compare Glr-NA2 and/or Cog Flu to broad & narrow Gc clusters Make final broad & narrow Gc (level) and rate (fluency) of retrieval (Glr) interp. PSYCHOLOGIST GENERAL’S WARNING: These are NOT to be used in a cookbook manner. The examples are intended to demonstrate modeled logical and decision-making. All cases are unique. Referral-based focused testing is a non-linear iterative cognitive testing hypothesis method based on the skills and expertise of the clinician. (“We are the instrument”; K. McGrew; date unknown)