Shifting ability predicts math and reading performance in children: A meta-analytical study
TL;DR: In this article, the authors examined children's shifting ability in relation to their performance in math and reading and concluded that the links between shifting ability, academic skills, and intelligence are domain-general.
About: This article is published in Learning and Individual Differences.The article was published on 2013-02-01. It has received 282 citations till now.
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TL;DR: In this paper, the authors systematically review what is known empirically about the association between executive function and student achievement in both reading and math and critically assesses the evidence for a causal association between the two.
Abstract: This article systematically reviews what is known empirically about the association between executive function and student achievement in both reading and math and critically assesses the evidence for a causal association between the two. Using meta-analytic techniques, the review finds that there is a moderate unconditional association between executive function and achievement that does not differ by executive function construct, age, or measurement type but finds no compelling evidence that a causal association between the two exists.
434 citations
Cites methods from "Shifting ability predicts math and ..."
...Prior research has demonstrated an association between each of these subcomponents and achievement (Blair & Razza, 2007; Gathercole et al., 2004; Raghubar, Barnes, & Hecht, 2010; Yeniad et al., 2013)....
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...Finally, Yeniad et al. (2013)...
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TL;DR: In this paper, a meta-analysis was conducted to investigate the strength of the relation between mathematics and working memory components and to establish whether the variation in the association is caused by tests, sample characteristics and study and other methodological characteristics.
383 citations
TL;DR: The importance of executive functioning (EF) skills in mathematical achievement is well established, and researchers have moved from just measuring working memory or updating to an inclusion of other EF skills, namely, inhibition and shifting.
Abstract: The importance of executive functioning (EF) skills in mathematical achievement is well established, and researchers have moved from just measuring working memory or updating to an inclusion of other EF skills, namely, inhibition and shifting. In this article, we review studies that have taken different approaches to measuring EF (e.g., using single vs. multiple indicators) and those that have applied different analytical techniques to conceptualize the structure of EF (e.g., exploratory vs. confirmatory techniques). Across studies, updating is often a unique predictor of math achievement at many ages; the findings relating to inhibition and switching are less conclusive. We discuss these findings in relation to age-related variance in EF structure, the nature of inhibitory and shifting task requirements, and the role of updating as a limiting factor or a common resource for inhibition and shifting.
376 citations
TL;DR: The authors reviewed the literature to assess concurrent relationships between mathematics and executive function skills, and highlighted key theoretical issues within the field and identified future avenues for research, highlighting the role of executive function skill in the performance of mathematical calculations.
374 citations
Cites background from "Shifting ability predicts math and ..."
...Inhibition, the ability to suppress distracting information and unwanted responses [16,40,58,84], and shifting, the ability to flexibly switch attention between different tasks [98], have also been implicated in mathematics achievement....
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...A recent meta-analysis demonstrated that shifting ability does predict performance in mathematics [98], however it remains unclear whether shifting is an independent predictor of mathematics over and above general intelligence....
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TL;DR: The aim of this review is to illustrate the role of working memory and executive functions for scholastic achievement as an introduction to the question of whether and how workingMemory and executive control training may improve academic abilities.
Abstract: The aim of this review is to illustrate the role of working memory and executive functions for scholastic achievement as an introduction to the question of whether and how working memory and executive control training may improve academic abilities. The review of current research showed limited but converging evidence for positive effects of process-based complex working-memory training on academic abilities, particularly in the domain of reading. These benefits occurred in children suffering from cognitive and academic deficits as well as in healthy students. Transfer of training to mathematical abilities seemed to be very limited and to depend on the training regime and the characteristics of the study sample. A core issue in training research is whether high- or low-achieving children benefit more from cognitive training. Individual differences in terms of training-related benefits suggested that process-based working memory and executive control training often induced compensation effects with larger benefits in low performing individuals. Finally, we discuss the effects of process-based training in relation to other types of interventions aimed at improving academic achievement.
295 citations
References
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Book•
01 Jan 1983
TL;DR: In this Section: 1. Multivariate Statistics: Why? and 2. A Guide to Statistical Techniques: Using the Book Research Questions and Associated Techniques.
Abstract: In this Section: 1. Brief Table of Contents 2. Full Table of Contents 1. BRIEF TABLE OF CONTENTS Chapter 1 Introduction Chapter 2 A Guide to Statistical Techniques: Using the Book Chapter 3 Review of Univariate and Bivariate Statistics Chapter 4 Cleaning Up Your Act: Screening Data Prior to Analysis Chapter 5 Multiple Regression Chapter 6 Analysis of Covariance Chapter 7 Multivariate Analysis of Variance and Covariance Chapter 8 Profile Analysis: The Multivariate Approach to Repeated Measures Chapter 9 Discriminant Analysis Chapter 10 Logistic Regression Chapter 11 Survival/Failure Analysis Chapter 12 Canonical Correlation Chapter 13 Principal Components and Factor Analysis Chapter 14 Structural Equation Modeling Chapter 15 Multilevel Linear Modeling Chapter 16 Multiway Frequency Analysis 2. FULL TABLE OF CONTENTS Chapter 1: Introduction Multivariate Statistics: Why? Some Useful Definitions Linear Combinations of Variables Number and Nature of Variables to Include Statistical Power Data Appropriate for Multivariate Statistics Organization of the Book Chapter 2: A Guide to Statistical Techniques: Using the Book Research Questions and Associated Techniques Some Further Comparisons A Decision Tree Technique Chapters Preliminary Check of the Data Chapter 3: Review of Univariate and Bivariate Statistics Hypothesis Testing Analysis of Variance Parameter Estimation Effect Size Bivariate Statistics: Correlation and Regression. Chi-Square Analysis Chapter 4: Cleaning Up Your Act: Screening Data Prior to Analysis Important Issues in Data Screening Complete Examples of Data Screening Chapter 5: Multiple Regression General Purpose and Description Kinds of Research Questions Limitations to Regression Analyses Fundamental Equations for Multiple Regression Major Types of Multiple Regression Some Important Issues. Complete Examples of Regression Analysis Comparison of Programs Chapter 6: Analysis of Covariance General Purpose and Description Kinds of Research Questions Limitations to Analysis of Covariance Fundamental Equations for Analysis of Covariance Some Important Issues Complete Example of Analysis of Covariance Comparison of Programs Chapter 7: Multivariate Analysis of Variance and Covariance General Purpose and Description Kinds of Research Questions Limitations to Multivariate Analysis of Variance and Covariance Fundamental Equations for Multivariate Analysis of Variance and Covariance Some Important Issues Complete Examples of Multivariate Analysis of Variance and Covariance Comparison of Programs Chapter 8: Profile Analysis: The Multivariate Approach to Repeated Measures General Purpose and Description Kinds of Research Questions Limitations to Profile Analysis Fundamental Equations for Profile Analysis Some Important Issues Complete Examples of Profile Analysis Comparison of Programs Chapter 9: Discriminant Analysis General Purpose and Description Kinds of Research Questions Limitations to Discriminant Analysis Fundamental Equations for Discriminant Analysis Types of Discriminant Analysis Some Important Issues Comparison of Programs Chapter 10: Logistic Regression General Purpose and Description Kinds of Research Questions Limitations to Logistic Regression Analysis Fundamental Equations for Logistic Regression Types of Logistic Regression Some Important Issues Complete Examples of Logistic Regression Comparison of Programs Chapter 11: Survival/Failure Analysis General Purpose and Description Kinds of Research Questions Limitations to Survival Analysis Fundamental Equations for Survival Analysis Types of Survival Analysis Some Important Issues Complete Example of Survival Analysis Comparison of Programs Chapter 12: Canonical Correlation General Purpose and Description Kinds of Research Questions Limitations Fundamental Equations for Canonical Correlation Some Important Issues Complete Example of Canonical Correlation Comparison of Programs Chapter 13: Principal Components and Factor Analysis General Purpose and Description Kinds of Research Questions Limitations Fundamental Equations for Factor Analysis Major Types of Factor Analysis Some Important Issues Complete Example of FA Comparison of Programs Chapter 14: Structural Equation Modeling General Purpose and Description Kinds of Research Questions Limitations to Structural Equation Modeling Fundamental Equations for Structural Equations Modeling Some Important Issues Complete Examples of Structural Equation Modeling Analysis. Comparison of Programs Chapter 15: Multilevel Linear Modeling General Purpose and Description Kinds of Research Questions Limitations to Multilevel Linear Modeling Fundamental Equations Types of MLM Some Important Issues Complete Example of MLM Comparison of Programs Chapter 16: Multiway Frequency Analysis General Purpose and Description Kinds of Research Questions Limitations to Multiway Frequency Analysis Fundamental Equations for Multiway Frequency Analysis Some Important Issues Complete Example of Multiway Frequency Analysis Comparison of Programs
53,113 citations
TL;DR: The results suggest that it is important to recognize both the unity and diversity ofExecutive functions and that latent variable analysis is a useful approach to studying the organization and roles of executive functions.
12,182 citations
"Shifting ability predicts math and ..." refers background in this paper
...Instead, it has been proposed that the relatively pure EF components can be extracted by confirmatory factor analysis (Lehto et al., 2003; Miyake et al., 2000) and the nonexecutive processes operated by EF tasks should be accounted for by using control tasks, which are quite similar to their EF correspondents except that they do not require the operation of the given EF component (Van der Sluis et al....
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...The term may encompass at least three separate but related components: inhibition, working memory and shifting (Lehto, Juujärvi, Kooistra, & Pulkkinen, 2003; Miyake et al., 2000)....
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...For instance, it is important to note that the substantial variety in shifting tasks remains a methodological challenge mostly due to task impurity (Miyake et al., 2000)....
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TL;DR: In this paper, a rank-based data augmentation technique is proposed for estimating the number of missing studies that might exist in a meta-analysis and the effect that these studies might have had on its outcome.
Abstract: We study recently developed nonparametric methods for estimating the number of missing studies that might exist in a meta-analysis and the effect that these studies might have had on its outcome. These are simple rank-based data augmentation techniques, which formalize the use of funnel plots. We show that they provide effective and relatively powerful tests for evaluating the existence of such publication bias. After adjusting for missing studies, we find that the point estimate of the overall effect size is approximately correct and coverage of the effect size confidence intervals is substantially improved, in many cases recovering the nominal confidence levels entirely. We illustrate the trim and fill method on existing meta-analyses of studies in clinical trials and psychometrics.
9,163 citations
Book•
18 Aug 2000TL;DR: This paper presents a meta-analysis procedure called “Meta-Analysis Interpretation for Meta-Analysis Selecting, Computing and Coding the Effect Size Statistic and its applications to Data Management Analysis Issues and Strategies.
Abstract: Introduction Problem Specification and Study Retrieval Selecting, Computing and Coding the Effect Size Statistic Developing a Coding Scheme and Coding Study Reports Data Management Analysis Issues and Strategies Computational Techniques for Meta-Analysis Data Interpreting and Using Meta-Analysis Results
6,930 citations
Book•
05 Sep 2011
TL;DR: The present article is a commencement at attempting to remedy this deficiency of scientific correlation, and the meaning and working of the various formulæ have been explained sufficiently, it is hoped, to render them readily usable even by those whose knowledge of mathematics is elementary.
Abstract: All knowledge—beyond that of bare isolated occurrence—deals with uniformities. Of the latter, some few have a claim to be considered absolute, such as mathematical implications and mechanical laws. But the vast majority are only partial; medicine does not teach that smallpox is inevitably escaped by vaccination, but that it is so generally; biology has not shown that all animals require organic food, but that nearly all do so; in daily life, a dark sky is no proof that it will rain, but merely a warning; even in morality, the sole categorical imperative alleged by Kant was the sinfulness of telling a lie, and few thinkers since have admitted so much as this to be valid universally. In psychology, more perhaps than in any other science, it is hard to find absolutely inflexible coincidences; occasionally, indeed, there appear uniformities sufficiently regular to be practically treated as laws, but infinitely the greater part of the observations hitherto recorded concern only more or less pronounced tendencies of one event or attribute to accompany another. Under these circumstances, one might well have expected that the evidential evaluation and precise mensuration of tendencies had long been the subject of exhaustive investigation and now formed one of the earliest sections in a beginner’s psychological course. Instead, we find only a general naı̈ve ignorance that there is anything about it requiring to be learnt. One after another, laborious series of experiments are executed and published with the purpose of demonstrating some connection between two events, wherein the otherwise learned psychologist reveals that his art of proving and measuring correspondence has not advanced beyond that of lay persons. The consequence has been that the significance of the experiments is not at all rightly understood, nor have any definite facts been elicited that may be either confirmed or refuted. The present article is a commencement at attempting to remedy this deficiency of scientific correlation. With this view, it will be strictly confined to the needs of practical workers, and all theoretical mathematical demonstrations will be omitted; it may, however, be said that the relations stated have already received a large amount of empirical verification. Great thanks are due from me to Professor Haussdorff and to Dr. G. Lipps, each of whom have supplied a useful theorem in polynomial probability; the former has also very kindly given valuable advice concerning the proof of the important formulæ for elimination of ‘‘systematic deviations.’’ At the same time, and for the same reason, the meaning and working of the various formulæ have been explained sufficiently, it is hoped, to render them readily usable even by those whose knowledge of mathematics is elementary. The fundamental procedure is accompanied by simple imaginary examples, while the more advanced parts are illustrated by cases that have actually occurred in my personal experience. For more abundant and positive exemplification, the reader is requested to refer to the under cited research, which is entirely built upon the principles and mathematical relations here laid down. In conclusion, the general value of the methodics recommended is emphasized by a brief criticism of the best correlational work hitherto made public, and also the important question is discussed as to the number of ‘‘cases’’ required for an experimental series.
3,687 citations