What about executive functions and CHC theory: New research for discussionKevin McGrew
This module contains a subset of slides that were only briefly touched on as part of a larger "Beyond CHC" presentation at the Australian Psychological Society (APS) 2016 Annual Congress. Time was limited. Thus, the complete subset of slides are presented here for FYI and discussion purposes.
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.
"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
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
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.
CHC model of inteligence revised (v2.4). Has Glr been incorrectly conceptual...Kevin McGrew
This presentation contains a historical overview of the derivation of the Glr ability domain in contemporary CHC theory. It then presents new data, as well as historical conclusions of the CHC masters, that makes a strong case for replacing the stratum II broad ability domain of Glr with two separate broad ability domains of Gl (learning efficiency) and Gr (retrieval fluency). How to obtain WJ IV scores for these two broad abilities is presented, as well as other possible Gl and Gr tests indicators from the CHC cross-battery literature.
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.
What about executive functions and CHC theory: New research for discussionKevin McGrew
This module contains a subset of slides that were only briefly touched on as part of a larger "Beyond CHC" presentation at the Australian Psychological Society (APS) 2016 Annual Congress. Time was limited. Thus, the complete subset of slides are presented here for FYI and discussion purposes.
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.
"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
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
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.
CHC model of inteligence revised (v2.4). Has Glr been incorrectly conceptual...Kevin McGrew
This presentation contains a historical overview of the derivation of the Glr ability domain in contemporary CHC theory. It then presents new data, as well as historical conclusions of the CHC masters, that makes a strong case for replacing the stratum II broad ability domain of Glr with two separate broad ability domains of Gl (learning efficiency) and Gr (retrieval fluency). How to obtain WJ IV scores for these two broad abilities is presented, as well as other possible Gl and Gr tests indicators from the CHC cross-battery literature.
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.
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.
CHC Theory Codebook 1: Cognitive definitionsKevin McGrew
A presentation of the most up-to-date CHC broad and narrow ability definitions as adapted from McGrew & Schneider (2012) and McGrew, LaForte and Schrank (2014). One of two. See CHC Codebook 2 for additional information
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.
A presentation on deciding when the scores from two tests, which are from the same CHC domain (e.g., Gwm), and which may have the same narrow CHC classifications, are different enough to warrant clinical interpretation.
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.)
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)
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.
Deep neural networks with remarkably strong generalization performances are usually over-parameterized. Despite explicit regularization strategies are used for practitioners to avoid over-fitting, the impacts are often small. Some theoretical studies have analyzed the implicit regularization effect of stochastic gradient descent (SGD) on simple machine learning models with certain assumptions. However, how it behaves practically in state-of-the-art models and real-world datasets is still unknown. To bridge this gap, we study the role of SGD implicit regularization in deep learning systems. We show pure SGD tends to converge to minimas that have better generalization performances in multiple natural language processing (NLP) tasks. This phenomenon coexists with dropout, an explicit regularizer. In addition, neural network's finite learning capability does not impact the intrinsic nature of SGD's implicit regularization effect. Specifically, under limited training samples or with certain corrupted labels, the implicit regularization effect remains strong. We further analyze the stability by varying the weight initialization range. We corroborate these experimental findings with a decision boundary visualization using a 3-layer neural network for interpretation. Altogether, our work enables a deepened understanding on how implicit regularization affects the deep learning model and sheds light on the future study of the over-parameterized model's generalization ability.
Part I: Beyond the CHC tipping point: Back to the futureKevin McGrew
An overview of the CHC (Cattell-Horn-Carroll) theory of intelligence within a historical and "waves of interpretation" context. Presents idea that CHC has reached the "tipping point" in school psychology..and...this is allowing assessment practitioners to realize past attempts to engage in individual strength and weakness interpretation of CHC based test profiles
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.
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
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.
CHC Theory Codebook 1: Cognitive definitionsKevin McGrew
A presentation of the most up-to-date CHC broad and narrow ability definitions as adapted from McGrew & Schneider (2012) and McGrew, LaForte and Schrank (2014). One of two. See CHC Codebook 2 for additional information
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.
A presentation on deciding when the scores from two tests, which are from the same CHC domain (e.g., Gwm), and which may have the same narrow CHC classifications, are different enough to warrant clinical interpretation.
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.)
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)
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.
Deep neural networks with remarkably strong generalization performances are usually over-parameterized. Despite explicit regularization strategies are used for practitioners to avoid over-fitting, the impacts are often small. Some theoretical studies have analyzed the implicit regularization effect of stochastic gradient descent (SGD) on simple machine learning models with certain assumptions. However, how it behaves practically in state-of-the-art models and real-world datasets is still unknown. To bridge this gap, we study the role of SGD implicit regularization in deep learning systems. We show pure SGD tends to converge to minimas that have better generalization performances in multiple natural language processing (NLP) tasks. This phenomenon coexists with dropout, an explicit regularizer. In addition, neural network's finite learning capability does not impact the intrinsic nature of SGD's implicit regularization effect. Specifically, under limited training samples or with certain corrupted labels, the implicit regularization effect remains strong. We further analyze the stability by varying the weight initialization range. We corroborate these experimental findings with a decision boundary visualization using a 3-layer neural network for interpretation. Altogether, our work enables a deepened understanding on how implicit regularization affects the deep learning model and sheds light on the future study of the over-parameterized model's generalization ability.
Part I: Beyond the CHC tipping point: Back to the futureKevin McGrew
An overview of the CHC (Cattell-Horn-Carroll) theory of intelligence within a historical and "waves of interpretation" context. Presents idea that CHC has reached the "tipping point" in school psychology..and...this is allowing assessment practitioners to realize past attempts to engage in individual strength and weakness interpretation of CHC based test profiles
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.
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
IQ Score Interpretation in Atkins MR/ID Death Penalty Cases: The Good, Bad a...Kevin McGrew
I presented this at the 2012 Habeas Assistance Training Seminar in Washington DC, Aug, 2012. It reviews a number of psychometric issues in Atkins MR/ID death penalty cases using examples from a recent completed case and other cases as well.
New directions in neuropsychological assessment: Augmenting neuropsychologica...Kevin McGrew
This is a presentation I made at the 2010 CNN conference in Fremantle Australia. It is an attempt to integrate CHC with neuropsychological assessment, with an emphasis on how NP tests can be interpreted from the CHC intelligence model which serves as a foundation for follow-up testing of NP tests with CHC measures.
Similar to "Intelligent" intelligence testing with the WJ IV COG: Why do some individuals obtain markedly different scores on the various WJ IV Ga tests?
In this presentation I will show a set of important topics about Software Engineering Empirical Studies that can be useful for increasing quality on your thesis and monographs in general. You can read this presentation and to think about how to do a good experimentation by apply its objectives, validation methods, questions, answers expected, define metrics and measuring it.I will exhibit how the researchers selected the data for avoid case studies in a biased way using a GQM methodology to sort the study in a simpler view as well.
Using VarSeq to Improve Variant Analysis Research WorkflowsDelaina Hawkins
Many questions must be answered when analyzing DNA sequence variants: How do I determine which variants are potentially deleterious? Is the sequencing quality sufficient? How do I prioritize the results? Which annotation sources may help answer my research question?
In this webinar presentation, we will review workflow strategies for quality control and analysis of DNA sequence variants using the VarSeq software package from Golden Helix. VarSeq is a powerful platform for analysis of DNA sequence variants in clinical and translational research settings. VarSeq provides researchers with easy access to curated public databases of variant annotation information, and also enables users to incorporate their own local databases or downloaded information about variants and genomic regions.
The presentation will include interactive demonstrations using VarSeq to analyze variants found by exome sequencing of an extended family with a complex disease. We will review strategies for assessing variant quality, applying genomic annotations, incorporating custom annotation sources, and creating variant filters in VarSeq. We will also demonstrate the PhoRank gene ranking algorithm and its application for prioritizing variants.
Using VarSeq to Improve Variant Analysis Research WorkflowsGolden Helix Inc
In this webinar presentation, we will review workflow strategies for quality control and analysis of DNA sequence variants using the VarSeq software package from Golden Helix. VarSeq is a powerful platform for analysis of DNA sequence variants in clinical and translational research settings. VarSeq provides researchers with easy access to curated public databases of variant annotation information, and also enables users to incorporate their own local databases or downloaded information about variants and genomic regions.
Presented in this document is a short discussion on using IMPL’s SLPQPE algorithm to solve process optimization problems in either off- or on-line environments also known as real-time optimization (RTO). Process optimization is somewhat different than production optimization in the sense that there are more “constitutive relations” involving only intensive variables. Both types of optimizations involve “conservation laws” and “correlative equations” which usually involve a mix of extensive and intensive variables (Kelly, 2004). Whereas production optimization deals more with material, meta-material (nonlinear), logic and logistics (discrete) balances (Zyngier and Kelly, 2009 and Kelly and Zyngier, 2015), process optimization is inherently more detailed and includes energy, exergy, momentum, hydraulics, equilibrium, diffusion, kinetics and other types of transport phenomena which involve nonlinear and perhaps discontinuous functions (Pantelides and Renfro, 2012).
Regulatory and Linguistic Analysis For a New Proprietary Drug NameBrand Acumen, LLC
Brand Acumen's Regulatory and Linguistic Analysis For a New Proprietary Drug Name. A look into what goes into the creation of a new pharmaceutical name. A Case Study: Janage
A hybrid composite features based sentence level sentiment analyzerIAESIJAI
Current lexica and machine learning based sentiment analysis approaches
still suffer from a two-fold limitation. First, manual lexicon construction and
machine training is time consuming and error-prone. Second, the
prediction’s accuracy entails sentences and their corresponding training text
should fall under the same domain. In this article, we experimentally
evaluate four sentiment classifiers, namely support vector machines (SVMs),
Naive Bayes (NB), logistic regression (LR) and random forest (RF). We
quantify the quality of each of these models using three real-world datasets
that comprise 50,000 movie reviews, 10,662 sentences, and 300 generic
movie reviews. Specifically, we study the impact of a variety of natural
language processing (NLP) pipelines on the quality of the predicted
sentiment orientations. Additionally, we measure the impact of incorporating
lexical semantic knowledge captured by WordNet on expanding original
words in sentences. Findings demonstrate that the utilizing different NLP
pipelines and semantic relationships impacts the quality of the sentiment
analyzers. In particular, results indicate that coupling lemmatization and
knowledge-based n-gram features proved to produce higher accuracy results.
With this coupling, the accuracy of the SVM classifier has improved to
90.43%, while it was 86.83%, 90.11%, 86.20%, respectively using the three
other classifiers.
Identifying Key Terms in Prompts for Relevance Evaluation with GPT Modelskevig
Relevance evaluation of a query and a passage is essential in Information Retrieval (IR). Recently, numerous studies have been conducted on tasks related to relevance judgment using Large Language Models (LLMs) such as GPT-4, demonstrating significant improvements. However, the efficacy of LLMs is considerably influenced by the design of the prompt. The purpose of this paper is to identify which specific terms in prompts positively or negatively impact relevance evaluation with LLMs. We employed two types of prompts: those used in previous research and generated automatically by LLMs. By comparing the performance of these prompts in both few-shot and zero-shot settings, we analyze the influence of specific terms in the prompts. We have observed two main findings from our study. First, we discovered that prompts using the term ‘answer’ lead to more effective relevance evaluations than those using ‘relevant.’ This indicates that a more direct approach, focusing on answering the query, tends to enhance performance. Second, we noted the importance of appropriately balancing the scope of ‘relevance.’ While the term ‘relevant’ can extend the scope too broadly, resulting in less precise evaluations, an optimal balance in defining relevance is crucial for accurate assessments. The inclusion of few-shot examples helps in more precisely defining this balance. By providing clearer contexts for the term ‘relevance,’ few-shot examples contribute to refine relevance criteria. In conclusion, our study highlights the significance of carefully selecting terms in prompts for relevance evaluation with LLMs.
Identifying Key Terms in Prompts for Relevance Evaluation with GPT Modelskevig
Relevance evaluation of a query and a passage is essential in Information Retrieval (IR). Recently, numerous studies have been conducted on tasks related to relevance judgment using Large Language Models (LLMs) such as GPT-4,
demonstrating significant improvements. However, the efficacy of LLMs is considerably influenced by the design of the prompt. The purpose of this paper is to
identify which specific terms in prompts positively or negatively impact relevance
evaluation with LLMs. We employed two types of prompts: those used in previous
research and generated automatically by LLMs. By comparing the performance of
these prompts in both few-shot and zero-shot settings, we analyze the influence of
specific terms in the prompts. We have observed two main findings from our study.
First, we discovered that prompts using the term ‘answer’ lead to more effective
relevance evaluations than those using ‘relevant.’ This indicates that a more direct
approach, focusing on answering the query, tends to enhance performance. Second,
we noted the importance of appropriately balancing the scope of ‘relevance.’ While
the term ‘relevant’ can extend the scope too broadly, resulting in less precise evaluations, an optimal balance in defining relevance is crucial for accurate assessments.
The inclusion of few-shot examples helps in more precisely defining this balance.
By providing clearer contexts for the term ‘relevance,’ few-shot examples contribute
to refine relevance criteria. In conclusion, our study highlights the significance of
carefully selecting terms in prompts for relevance evaluation with LLMs.
May 2024 - Top10 Cited Articles in Natural Language Computingkevig
Natural Language Processing is a programmed approach to analyze text that is based on both a set of theories and a set of technologies. This forum aims to bring together researchers who have designed and build software that will analyze, understand, and generate languages that humans use naturally to address computers.
Recent and Robust Query Auto-Completion - WWW 2014 Conference Presentationstewhir
These are the presentation slides used for the WWW 2014 (Web Search) full paper: "Recent and Robust Query Auto-Completion".
The PDF full paper is available from: http://www.stewh.com/wp-content/uploads/2014/02/fp539-whiting.pdf
Similar to "Intelligent" intelligence testing with the WJ IV COG: Why do some individuals obtain markedly different scores on the various WJ IV Ga tests? (20)
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 Model of Achievement Competence Motivation (MACM) is a series of slide modules. This is the third (Part C) 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 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)
Implications of 20 Years of CHC Cognitive-Achievement Research: Back-to-the...Kevin McGrew
Much has been learned about CHC CHC COG-ACH relations during the past 20 years (McGrew & Wendling’s, 2010). This presentation, made at the First Richard Woodcock Institute on Cognitive Assessment (Sept 29, 2012), built on this extant research by first clarifying the definitions of abilities, cognitive abilities, achievement abilities, and aptitudes. Differences between domain-general and domain-specific CHC predictors of school achievement were defined. The promise of Kafuman’s “intelligent” intelligence testing approach was illustrated with two approaches to CHC-based selective referral-focused assessment (SRFA). Next, a number of new intelligent test design (ITD) principles were described and demonstrated via a series of exploratory data analyses that employed a variety of data analytic tools (multiple regression, SEM causal modeling, multidimensional scaling). The ITD principles and analyses resulted in the proposal to construct developmentally-sensitive CHC-consistent scholastic aptitude clusters, measures that can play an important role in contemporary third method (pattern of strength and weakness) approaches to SLD identification.
The need to move beyond simplistic conceptualizations of COG COG-ACH relations and SLD identification models was argued and demonstrated via the presentation and discussion of CHC COG-ACH causal SEM models. Another example was the proposal to identify and quantify cognitive-aptitude-achievement trait complexes (CAATCs). A revision in current PSW third-method SLD models was proposed that would integrate CAATCs. Finally, the need to incorporate the degree of cognitive complexity of tests and composite scores within CHC domains in the design and organization of intelligence test batteries (to improve the prediction of school achievement) was proposed. The various proposals presented in this paper represented a mixture of (a) a call to return to old ideas with new methods (Back-to-the-Future) or (b) the embracing of new ideas, concepts and methods that require psychologists to move beyond the confines of the dominant CHC taxonomy of human cognitive abilities (i.e., Beyond CHC).
Kevin McGrew IM Keynote Oct 2012. Use of movement in slides is not present in this static SlideShare show ..the red circle bounces around in the live presentation.
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
3. Recent WJ IV Ga-test related interpretation
question posted to the IAP CHC listserv
(8-13-16; some edits made to original for clarity)
I recently evaluated a fourth grader with a history of dyslexia
and phonics remediation, who scored at the 5th percentile on
the WJ IV Phonological Processing test but who did very well
on the Segmentation and Sound Blending tests (in the
advanced and average range respectively). Can anyone give
me an explanation as to why Phonological Processing would be
significantly lower? Can reading remediation affect the
Segmentation and Blending tests more than the Phonological
Processing tests?
4. A number of others responded. One response, by Dr. Joel Schneider, provides an
important insight into a possible answer. His response indicates that it is important to
know what the three subtests that comprise the Phonological Processing test measure.
The WJ IV Phonological Processing score is what I call a "forced
composite" score. It combines several subscores but does not tell you
how well the person did on each subscore, just the combined score.
Two of the tasks would likely function well as retrieval fluency tasks
and the other is more like a traditional Ga task like segmentation and
sound blending paradigms. It is possible that Ga is fine and retrieval
fluency is not.
I would try giving the Verbal Fluency subtests from the DKEFS to see if
naming words that start with a specific letter is a problem and if
divergent processing tests in general are a problem. Gs tests might help
you know if speeded tasks in general are a problem, too.
5. What the Phonological Processing test/subtests measure
(Schrank, 2016)
• Word Access subtest: “the depth of word access from phonemic cues”
• Word Fluency subtest: “the breadth and fluency of work activation from
phonemic cues”
• Substitution subtest: “lexical substitution from phonemic cues in working
memory”
• “This test is also cognitively complex because it invokes multiple cognitive
operations and parameters of cognitive efficiency in phonological
processing”
• Inferred cognitive processes: “Phonological activation and access to
stored lexical entries; speeded lexical network activation; phonological
working memory”
The fact that the PP test measures multiple cognitive operations is consistent with Schneider’s
designation of this test as a “forced composite” –that is, it is a test deliberately constructed to measure
multiple abilities. It is not a “pure” narrow ability test indicator as is conceptualized in CHC-driven assessment
6. The technical manual can be
your friend !
A good technical manual
frequently includes information
to help answer interpretation
questions
McGrew, LaForte & Schrank (2014)
7. PPSUB – Substitution
PPACC – Word Access
PPFLU – Word Fluency
VZSPRL – Spatial Relations
VZBLKR – Block Rotation
GIWHAT – What
GIWHER – Where
OVANT – Antonyms
OVSYN – Synonyms
RVANT – Antonyms
RVSYN – Synonyms
Test and
subtest name
abbreviations
used in
analysis and
results
included in
this PPT
module
9. It is important to remember that
just because a collection of tests
load on a common factor (e.g., Ga)
this does not mean they are
measuring the same ability. This
only means that the different
narrow abilities measured by each
test share a common latent ability
trait (factor) different from other
latent ability traits (factors; e.g.,
Gc). Differences between tests
within CHC domains are to be
expected.
.39
10. NWREP (Nonword Repetition) had .62 secondary loading on Gwm,
suggesting that it is a mixed measure of a narrow Ga ability and
working memory (Gwm)—possibly the “phonological or
articulatory loop” or “phonological short-term memory” as in
some classic models of working memory (McGrew et al., 2014).
SNDAWR (Sound Awareness) test had secondary loading of .39 on
Grw—but it does not require reading to perform.
CFA of WJ IV norm data (example here is for ages 9-13) supported a single
Ga factor. Models with Ga narrow factors, specified in the model-
development sample, were not possible to fit.
From WJ IV technical manual (McGrew et al., 2014)
.39
11. CFA of WJ IV norm data (example here is for
ages 9-13) supported a single Ga factor. Models
with Ga narrow factors, specified in the model-
development sample, were not possible to fit.
However, a narrow speed of lexical access (LA)
factor was suggested in a broad+narrow ability
alternative model.
PHNPRO (Phonological Processing) had a
secondary loading (.43) on the LA factor,
indicating that a portion of the PHNPRO test
(most likely the Word Fluency subtest) measures
common abilities with the Retrieval Fluency
(RETFLU) and Rapid Picture Naming (RPCNAM)
tests (viz., speed of lexical access)
.38
16. The WJ IV technical manual
includes special MDS analysis
results for all major age groups
reported (McGrew et al., 2014)
In MDS the magnitude of the
relationship between tests is
represented by spatial proximity.
Tests that are far apart are
weakly correlated. Test that are
close together are more highly
correlated.
However, the MDS plots in the
technical manual did not include
the component “subtests” of
“tests” comprised of subtests
(e.g., PHNPRO)