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Chen LT, Chen YK, Yang TR, Chiang YS, Hsieh CY, Cheng C, Ding QW, Wu PJ, Peng CYJ. Examining the normality assumption of a design-comparable effect size in single-case designs. Behav Res Methods 2024; 56:379-405. [PMID: 36650402 DOI: 10.3758/s13428-022-02035-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/22/2022] [Indexed: 01/18/2023]
Abstract
What Works Clearinghouse (WWC, 2022) recommends a design-comparable effect size (D-CES; i.e., gAB) to gauge an intervention in single-case experimental design (SCED) studies, or to synthesize findings in meta-analysis. So far, no research has examined gAB's performance under non-normal distributions. This study expanded Pustejovsky et al. (2014) to investigate the impact of data distributions, number of cases (m), number of measurements (N), within-case reliability or intra-class correlation (ρ), ratio of variance components (λ), and autocorrelation (ϕ) on gAB in multiple-baseline (MB) design. The performance of gAB was assessed by relative bias (RB), relative bias of variance (RBV), MSE, and coverage rate of 95% CIs (CR). Findings revealed that gAB was unbiased even under non-normal distributions. gAB's variance was generally overestimated, and its 95% CI was over-covered, especially when distributions were normal or nearly normal combined with small m and N. Large imprecision of gAB occurred when m was small and ρ was large. According to the ANOVA results, data distributions contributed to approximately 49% of variance in RB and 25% of variance in both RBV and CR. m and ρ each contributed to 34% of variance in MSE. We recommend gAB for MB studies and meta-analysis with N ≥ 16 and when either (1) data distributions are normal or nearly normal, m = 6, and ρ = 0.6 or 0.8, or (2) data distributions are mildly or moderately non-normal, m ≥ 4, and ρ = 0.2, 0.4, or 0.6. The paper concludes with a discussion of gAB's applicability and design-comparability, and sound reporting practices of ES indices.
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Affiliation(s)
- Li-Ting Chen
- Department of Educational Studies, University of Nevada, Reno, Reno, NV, USA.
| | - Yi-Kai Chen
- Department of Psychology, National Taiwan University, Taipei, Taiwan
| | - Tong-Rong Yang
- Department of Psychology, National Taiwan University, Taipei, Taiwan
| | - Yu-Shan Chiang
- Department of Curriculum & Instruction, Indiana University Bloomington, Bloomington, IN, USA
| | - Cheng-Yu Hsieh
- Department of Psychology, National Taiwan University, Taipei, Taiwan
- Department of Psychology, Royal Holloway, University of London, Egham, UK
| | - Che Cheng
- Department of Psychology, National Taiwan University, Taipei, Taiwan
| | - Qi-Wen Ding
- Institute of Sociology, Academia Sinica, Taipei, Taiwan
| | - Po-Ju Wu
- Department of Counseling and Educational Psychology, Indiana University Bloomington, Bloomington, IN, USA
| | - Chao-Ying Joanne Peng
- Department of Psychology, National Taiwan University, Taipei, Taiwan
- Department of Counseling and Educational Psychology, Indiana University Bloomington, Bloomington, IN, USA
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Ren X, Li J, Liu J, Liu D, Zhao J. Intervention targeting different visual attention span components in Chinese children with developmental dyslexia: a study based on Bundesen's theory of visual attention. ANNALS OF DYSLEXIA 2023; 73:487-509. [PMID: 37422551 DOI: 10.1007/s11881-023-00288-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 07/02/2023] [Indexed: 07/10/2023]
Abstract
Within the framework of the theory of visual attention (TVA), the visual attention span (VAS) deficit among individuals with developmental dyslexia has been ascribed to the problems entailed by bottom-up (BotU) and top-down (TopD) attentional processes. The former involves two VAS subcomponents: the visual short-term memory storage and perceptual processing speed; the latter consists of the spatial bias of attentional weight and the inhibitory control. Then, what about the influences of the BotU and TopD components on reading? Are there differences in the roles of the two types of attentional processes in reading? This study addresses these issues by using two types of training tasks separately, corresponding to the BotU and TopD attentional components. Three groups of Chinese children with dyslexia-15 children each in the BotU training, TopD training, and non-trained active control groups were recruited here. Participants completed reading measures and a CombiTVA task which was used to estimate VAS subcomponents, before and after the training procedure. Results showed that BotU training improved both the within-category and between-category VAS subcomponents and sentence reading performance; meanwhile, TopD training enhanced character reading fluency through improving spatial attention capacity. Moreover, benefits on attentional capacities and reading skills in the two training groups were generally maintained three months after the intervention. The present findings revealed diverse patterns in the influences of VAS on reading within the TVA framework, which contributes to enriching the understanding of VAS-reading relation.
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Affiliation(s)
- Xiaoyu Ren
- Key Laboratory of Learning and Cognition, School of Psychology, Capital Normal University, Beijing, China
| | - Jie Li
- Key Laboratory of Learning and Cognition, School of Psychology, Capital Normal University, Beijing, China
| | - Jinqiu Liu
- Key Laboratory of Learning and Cognition, School of Psychology, Capital Normal University, Beijing, China
| | - Duo Liu
- Department of Special Education and Counselling, The Education University of Hong Kong, Hong Kong, SAR, China.
| | - Jing Zhao
- Key Laboratory of Learning and Cognition, School of Psychology, Capital Normal University, Beijing, China.
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Marchetti R, Pinto S, Spieser L, Vaugoyeau M, Cavalli E, El Ahmadi A, Assaiante C, Colé P. Phoneme Representation and Articulatory Impairment: Insights from Adults with Comorbid Motor Coordination Disorder and Dyslexia. Brain Sci 2023; 13:210. [PMID: 36831753 PMCID: PMC9954044 DOI: 10.3390/brainsci13020210] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 01/19/2023] [Accepted: 01/25/2023] [Indexed: 01/28/2023] Open
Abstract
Phonemic processing skills are impaired both in children and adults with dyslexia. Since phoneme representation development is based on articulatory gestures, it is likely that these gestures influence oral reading-related skills as assessed through phonemic awareness tasks. In our study, fifty-two young dyslexic adults, with and without motor impairment, and fifty-nine skilled readers performed reading, phonemic awareness, and articulatory tasks. The two dyslexic groups exhibited slower articulatory rates than skilled readers and the comorbid dyslexic group presenting with an additional difficulty in respiratory control (reduced speech proportion and increased pause duration). Two versions of the phoneme awareness task (PAT) with pseudoword strings were administered: a classical version under time pressure and a delayed version in which access to phonemic representations and articulatory programs was facilitated. The two groups with dyslexia were outperformed by the control group in both versions. Although the two groups with dyslexia performed equally well on the classical PAT, the comorbid group performed significantly less efficiently on the delayed PAT, suggesting an additional contribution of articulatory impairment in the task for this group. Overall, our results suggest that impaired phoneme representations in dyslexia may be explained, at least partially, by articulatory deficits affecting access to them.
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Affiliation(s)
- Rebecca Marchetti
- Laboratoire de Neurosciences Cognitives (LNC), French National Centre for Scientific Research (CNRS), Aix-Marseille University, 13007 Marseille, France
- Laboratoire de Psychologie Cognitive (LPC), French National Centre for Scientific Research (CNRS), Aix-Marseille University, 13003 Marseille, France
- Federation de Recherche 3C, French National Centre for Scientific Research (CNRS), Aix-Marseille University, 13003 Marseille, France
| | - Serge Pinto
- Laboratoire Parole et Langage (LPL), French National Centre for Scientific Research (CNRS), Aix-Marseille University, 13100 Aix-en-Provence, France
| | - Laure Spieser
- Laboratoire de Neurosciences Cognitives (LNC), French National Centre for Scientific Research (CNRS), Aix-Marseille University, 13007 Marseille, France
- Federation de Recherche 3C, French National Centre for Scientific Research (CNRS), Aix-Marseille University, 13003 Marseille, France
| | - Marianne Vaugoyeau
- Laboratoire de Neurosciences Cognitives (LNC), French National Centre for Scientific Research (CNRS), Aix-Marseille University, 13007 Marseille, France
- Federation de Recherche 3C, French National Centre for Scientific Research (CNRS), Aix-Marseille University, 13003 Marseille, France
| | - Eddy Cavalli
- Laboratoire d’Etude des Mécanismes Cognitifs (EA3082), University Lumière Lyon 2, 69007 Lyon, France
| | - Abdessadek El Ahmadi
- Laboratoire de Neurosciences Cognitives (LNC), French National Centre for Scientific Research (CNRS), Aix-Marseille University, 13007 Marseille, France
- Federation de Recherche 3C, French National Centre for Scientific Research (CNRS), Aix-Marseille University, 13003 Marseille, France
| | - Christine Assaiante
- Laboratoire de Neurosciences Cognitives (LNC), French National Centre for Scientific Research (CNRS), Aix-Marseille University, 13007 Marseille, France
- Federation de Recherche 3C, French National Centre for Scientific Research (CNRS), Aix-Marseille University, 13003 Marseille, France
| | - Pascale Colé
- Laboratoire de Psychologie Cognitive (LPC), French National Centre for Scientific Research (CNRS), Aix-Marseille University, 13003 Marseille, France
- Federation de Recherche 3C, French National Centre for Scientific Research (CNRS), Aix-Marseille University, 13003 Marseille, France
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