Accuracy and phonological access as reading comprehension predictors

Accuracy and phonological access as reading comprehension predictors | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Accuracy and phonological access as reading comprehension predictors Patrícia Silva Lúcio, Stephanie Pandjarjian Mekhitarian, Hugo Cogo-Moreira, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7849144/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Reading comprehension difficulties are common in neurodevelopmental disorders, yet their underlying cognitive predictors remain debated. This study examined the predictive roles of reading accuracy—operationalized as words read per minute—and rapid automatized serial naming (RAN) in reading comprehension among 637 Brazilian students from second to fifth grade (57% girls), primarily enrolled in public schools (87.4%). Participants were classified into two groups: those with reading comprehension difficulties (G1) and those without (G2). Assessments included oral reading of isolated words, RAN of objects, and reading of a narrative text. Results indicated that G1 exhibited significantly lower accuracy compared to G2. Regression analyses showed that reading accuracy, but not RAN, predicted reading comprehension, with predictive effects restricted to the early school years. These findings highlight reading accuracy as a relevant marker for monitoring reading comprehension development in early elementary education and may inform early identification and intervention strategies for children at risk of neurodevelopmental reading difficulties. Reading Reading Comprehension Mental Processes Elementary School INTRODUCTION Reading is a communicative activity and, as such, is guided by objectives that will lead to access to meaningful content encoded through a writing system (Wallot, 2014 ; Smith et al., 2021 ). This ability depends on the interrelationship of several neurocognitive and linguistic skills, components, and processes. In order to understand a read text, first of all, one would expect that auditory linguistic comprehension is adequate and decoding is efficient (Hoover & Gough, 1990 ). Decoding is understood as the perceptual-cognitive-linguistic process that culminates in the recognition of the written word, which, with the development of learning, should become automatic and allow fluent reading (Basso et al., 2019 ; Smith et al., 2021 ). Several components related to the idea of ​​time are added to the process: automaticity, processing speed, and accuracy in word recognition (Basso et al., 2019 ; Cooper et al., 2022 ; Kargin et al., 2024). Especially in the initial learning years, these processes demand from the reader efficient command of the writing system, vocabulary adequate in scope and depth (Oakhill et al., 2009 ; Sparks & Metsala, 2023 ), but, above all, precise and fast, if not automatic, access to the lexicon (Norton & Wolf, 2012 ). Reading comprehension, the outcome of the entire process from decoding onwards, requires coordinating multiple language and cognitive structures and skills (Hoover & Gough, 1990 ) to create the mental representation of the situation model proposed in the text (Bråten, Haverkamp & Anmarkrud, 2025 ; Kintsch, 1998 ; Landi, 2017 ; Yang, Xiong & Chen, 2023 ). A study by Cooper et al. ( 2022 ) argues that there is a strong relationship between automaticity in word reading and comprehension of what is read in the early school years. There is also evidence that the speed and accuracy of access to the mental lexicon can predict performance in word reading (Norton, Wolf, 2012 ; Lubineau et al., 2024 ). Although it is known that, throughout schooling, the strength of the correlation between decoding and reading comprehension decreases (Salles & Paula, 2016 ; Psyridou et al., 2022), it seems logical to assume that, especially in the early years, correct reading may be more important than speed to understand the text: the more errors are made while reading a text, the more difficult it becomes to understand (Alvarez-Cañizo, Suárez-Coalla, Cuetos & 2015; Oliveira & Starling-Alves, 2022). The initial reading assessment at the recognition or lexical decision level can provide clues about primary processes that are detrimental to text comprehension, already at the word level (Cogo-Moreira et al., 2023; Cutting et al., 2006; Oliveira, Germano & Capellini, 2016 ; Martins & Capellini, 2021 ). In this study, decoding was assessed by accuracy calculated by the number of words read correctly in one minute. The assessment of rapid naming ability is one of the ways to assess or estimate the speed of access to the mental lexicon and evaluate processes underlying the speed and accuracy with which written words are recognized (Bishop et al., 2009 ; Guo, MA, Pan & Zhang, 2023; Wolf, Katzir-Cohen, 2001). The ability to access the phonological lexicon defines how quickly and accurately one can name a sequence of familiar visual stimuli, including written items (Candal & Avila, in press; Norton and Wolf, 2012 ). This ability is assessed through rapid automatized naming tests and may be associated with decoding and automatic word recognition at a neurophysiological level (Guo, MA, Pan & Zhang, 2023). However, it does not appear to be sufficient by itself to explain difficulties in reading comprehension, especially with language disorders (Bishop et al., 2009 ). On the other hand, authors argue that deficits in accuracy and speed of access to the lexicon can compromise the comprehension of the text read (Álvarez-Cañizo, Suárez-Coalla & Cuetos, 2015; Cutting & Scarborough, 2006 ; Perfetti & Stafura, 2014 ). Thus, we could assume that the ability to process visual symbols more or less quickly — through lexical access — is correlated with the reading comprehension level (dos Santos & Capellini, 2020 ). This association could be explained by the relationship between rapid automatized naming and fluent reading and by the hypothesis that the more automated the ability to recognize words, the greater the availability of cognitive resources for comprehension processes (Álvarez-Cañizo, Suárez-Coalla & Cuetos, 2015; Bishop et al., 2009 ; Cunha et al., 2015 ; Perfetti & Stafura, 2014 ; Sparks & Metsala, 2023 ; Yang, Xiong & Chen, 2023 ). Since reading skills have a continuous distribution in the population (Snowling, Hulme, 2012 ; OECD, 2023 ), one would assume a significant variability in reading comprehension skills from the early schooling years. The reading comprehension performance variability can be explained by its multifactorial nature. This study investigated decoding characteristics and processing speed in accessing the lexicon, when comprehension occurs with better and worse results, in Elementary School I. The lack of information on the relationships between speed and accuracy in processing linguistic information and reading comprehension in Brazilian students justifies this work. Thus, considering the results obtained by applying different instruments that assess reading (Corso et al., 2015 ; Hua, Keenan, 2017 ), this study investigated two groups of students with different reading comprehension conditions, which skills, whether reading accuracy or lexicon access speed, measured by rapid automatized naming time, could predict comprehension performance. The study with Brazilian Elementary School I students is justified by the relevance of understanding whether the accuracy in recognizing written words or the speed of access to the mental lexicon can predict the performance in the reading comprehension of a text whose spelling is relatively transparent. Thus, even though it is a cross-sectional investigation, by evaluating children at different levels in the initial schooling phases, this study will support clarifying aspects of neurodevelopment related to the complex interactions observed between reading words through the lexical route, lexical access through naming figures and understanding texts in our language. METHODS This is a retrospective, cross-sectional study with quantitative analysis, complementary to the original research, approved by the Research Ethics Committee of the Federal University of São Paulo (CAAE: 00987412.4.0000.5505; opinion number 38406/12). Only children whose parents or guardians signed the Informed Consent Form participated in the study. All experiments were performed in accordance with relevant guidelines and regulations. Sampling procedures Sampling was performed through the proportional distribution representative of schools in São Paulo, based on the 2012 School Census (INEP, 2013 ). Twenty-one schools were randomly selected (nine municipal, 10 state, and four private) from the initial calculation of the required sample size. Teachers identified potential participants under the following criteria: no complaints of specific learning difficulties, behavioral problems, or cognitive deficits; no school retention; and no uncorrected hearing or visual problems. After this preliminary selection, letters were sent to parents or guardians inviting their children to participate in the research. Only children whose consent forms were returned signed participated in the study. Case analysis The present study’s sample has been retrieved from a larger study composed of 728 Elementary School second-to-fifth graders evaluated in several cognitive, language, and reading tasks (among them, oral reading, text comprehension, oral comprehension, rapid naming, and memory). In order to fulfill the objectives of this research, participants who met the inclusion criteria were initially screened by the oral reading rate value to ensure a minimum automatic word recognition level (Cogo-Moreira et al., 2023). The children then read a short text appropriate for their school year (ranging from 206 to 235 words). After the children had finished reading the first two paragraphs, the evaluator started counting the time and, after 60 seconds, recorded the point at which the child had finished reading. The reading rate (number of words read per minute – p.p.m) was then computed for the selected passage. Students with reading rate values ​​below: second grade – 50 words per minute (wpm); third grade – 66 wpm; fourth grade – 71 wpm; fifth grade – 95 wpm were excluded from the sample. Therefore, for the present study, we initially considered only the responses of participants who met this criterion and performed the reading comprehension task. As a result, the sample initially consisted of the evaluation protocols of 676 second-to-fifth-graders (58% girls) from the public (84%) and private schools in São Paulo (168 second-graders, 166 third-graders, 176 fourth-graders, and 166 fifth-graders, respectively). Thirty-nine children did not complete all the evaluations. The sample had a mean age of 9.12 years (standard deviation 1.0; minimum 07 years; maximum 11 years). Material Word reading accuracy Decoding accuracy was assessed using a reading-aloud task of 48 low-frequency words as a reference (Lúcio et al., 2018 ). The number of words read correctly was computed, and this value was divided by the total reading time (in seconds) and multiplied by 60, generating a value that represents the mean number of words that would be read in one minute. The words varied in regularity (regular and irregular words) and length (04 to 07 letters). The task was submitted to factor validation (Lúcio et al., 2018 ). Rapid Serial Automated Naming Test The task consisted of quickly naming pictures of six objects (egg, bread, ball, sun, key, and fork) randomly distributed on two boards, with 36 appearances, aligned horizontally, on each one. The children named the pictures contained on the boards as correctly and quickly as possible. Time and correct answers were collected for each board separately. The task was submitted to factor validation and establishment of norms (Lúcio et al., 2017 ). For the present study, the total naming time on both boards together was considered as a variable. Reading comprehension Reading comprehension was assessed by applying a narrative text (190 words) part of a reading assessment battery that was submitted to item analysis (Lúcio et al., 2021 ) and a validation study (Lúcio et al., 2015 ). Linguistic characteristics and readability and complexity parameters of the text were analyzed by the CohMetrix-Port platform (Kida, Avila, Capellini, 2015; Scarton, Aluísio, 2010 ). Thus, readability was measured using the Flesch Index (based on the length of the text's words and sentences). The text had a Flesch Index of 52.707, which characterized it as a reasonably complex text (Goldim, 2006 ). Syntactic complexity was characterized by the following parameters: number of words (190), number of sentences (17), number of sentences per paragraph (8.5), content words (578.947), pronouns per syntagm (0.034), and number of connectives (126.316). Finally, vocabulary complexity was assessed by the Type/Token Index (0.695), characterized by the number of unique words with the number of tokens of these words, where each word instance was a token. All second-to-fifth-graders in the sample read the narrative and then answered orally nine open-ended questions, also proposed orally, about the text read. The answers were analyzed through a correction sieve established by a panel of experts (speech therapists), whose reliability between evaluators was assessed through the Kappa coefficient (Lúcio et al., 2017 ). Group definition The children in the sample were grouped per their reading comprehension level: Group 1 (G1) children with difficulty and Group 2 (G2) children without difficulty understanding the text read. Comprehension difficulty was defined by the percentile in the comprehension task per the school grade. Children who did not achieve a score of at least the 25th percentile were defined as having low comprehension (G1), and the others comprised G2 with medium/superior performance, from now on, also called good comprehenders. Data analysis We conducted descriptive and inferential statistics using SPSS for Windows version 23.0. We reported mean, standard deviation, minimum, and maximum values ​​for each study variable and Spearman correlations between groups and metric variables. We investigated differences between groups (G1 and G2) by t-tests for independent samples for metric variables and chi-square for dichotomous variables (gender and school type). We performed Logistic regression analyses using Generalized Estimating Equations (GEE) to investigate the predictive power of naming time and accuracy in group membership (G1 was the reference group, classified as 0, and G2, classified as 1). The method used was Enter (the predictor variables of interest were forced to enter the equation). The effect sizes considered were Cohen's (2013) classification of 0.2 (small), 0.5 (medium), and 0.8 (large). As the analyses were performed separately by school grade, the p-value was corrected to control the effect of multiple comparisons. We adopted the Bonferroni correction method (Miller, 1981 ) for this purpose; that is, with 4 groups (school grades), the critical value of 0.05 was divided by 4, considering comparisons with p < 0.0125 as significant. RESULTS Data were complete on the reading (accuracy), comprehension, and naming tasks for 637 participants. The mean age of the children was 9.12 (SD = 1; minimum = 7; maximum = 11). Most children attended public school (87.4%) and were female (57%). The number of participants was similar across grades (second = 158, third = 155, fourth = 165, fifth = 159). There were no differences in age ( t (750) = 1.154, p = 0.249) or gender frequency (U = 36,068.00, p = 0.416) between children who performed all tasks and those with missing data. Table 1 presents the descriptive statistics of the study by group. The minimum score to reach the 25th percentile in the comprehension task was 2.0 points for the second grade, 3.0 points for the third grade, and 4.0 points for the fourth and fifth grades, and these scores were classified as having below-expected performance for the current grade. Using this criterion, 187 (29.4%) children comprised G1 (low comprehension group), and 450 (70.6%) comprised G2 (a group with medium/superior comprehension). The groups did not differ regarding age ( t (632) = 1.227, p = 0.220) or gender χ 2 (1) = 1.279 p = 0.292. However, they differed significantly regarding the frequency of school type ( χ 2 (1) = 6.188, p = 0.012), with 92.0% of children in public schools in G1 and 85.3% in G2, i.e., a reduction of almost 8% in the prevalence of children with good comprehension. Table 1 shows that the difference only occurs in the fourth grade, at the level of p < 0.05, but not at p < 0.0125; therefore, the school type was not considered in the logistic regression. Table 1 Descriptive statistics of study control variables by group (good/poor comprehenders) and school year Year Group Age % feminine Public 2º G1 (n = 53) G2 (n = 105) 7,77 (0,46) 53% 96,20% 7,85 (0,46) 50% 87,60% t (156) = 0,958, p = 0,340 χ 2 (1) = 0,878 p = 0,866 χ 2 (1) = 3,037 p = 0,093 3º G1 (n = 41) G2 (n = 114) 8,73 (0,45) 68% 87,50% 8,78 (0,42) 65% 90,40% t (153) = 0,634, p = 0,527 x2(1) = 0,153 p = 0,848 x2(1) = 0,258 p = 0,563 4º G1 (n = 54) G2 (n = 111) 9,92 (0,39) 56% 94,40% 9,70 (0,55) 50% 79,80% t (162) = 1,369, p = 0,173 x2(1) = 0,525 p = 0,509 x2(1) = 5,951 p = 0,019 5º G1 (n = 39) G2 (n = 120) 10,05 (0,23) 69% 89,70% 10,14 (0,40) 57% 83,30% t (155) = 1,333, p = 0,185 x2(1) = 1,932 p = 0,191 x2(1) = 0,258 p = 0,563 Note. G1 = low comprehension group; G2 = group with medium/superior comprehension. Table 2 presents the descriptions of the outcome variables and the comparisons between groups G1 and G2 in each year (t-tests for independent samples). G1 had the worst results in all years in accuracy and comprehension, and the most considerable effect was identified in the latter variable (as the groups were generated based on the comprehension scores, the high difference in effect size was expected, according to the “d” indices obtained). The serial automatized naming speed was relevant to differentiate the groups only for the second grade (p < 0.0125). The effects of the naming task, measured in time, were considered small, and the effects of accuracy were primarily medium (Cohen, 2013 ). Table 2 Descriptive statistics of the study's predictor variables by group (good/poor comprehenders) and school year and comparison results (t-tests). Year Task Group Mean (d.p) Minimum Maximum Comparison d 2º Accuracy G1 19,20 (11,75) 0,60 52,00 t(156) = 3,729, p < 0,001 0,66 G2 28,55 (16,22) 0,48 96,00 Comprehension G1 1,21 (0,74) 0,00 2,00 t(156) = 16,385, p < 0,001 3,02 G2 4,70 (1,46) 3,00 8,00 Serial Automatized Naming G1 79,87 (13,93) 47,00 124,00 t(156) = 2,612, p = 0,010 0,45 G2 73,90 (13,39) 36,00 116,00 3º Accuracy G1 31,00 (14,55) 10,91 75,88 t(153) = 2,890, p = 0,004 0,53 G2 39,64 (17,02) 15,54 99,23 Comprehension G1 2,02 (1,01) 0,00 3,00 t(153) = 16,854, p < 0,001 3,26 G2 5,87 (1,33) 4,00 9,00 Serial Automatized Naming (sec.) G1 66,98 (16,25) 46,00 121,00 t(153) = 0,517, p = 0,606 0,09 G2 65,78 (11,17) 46,00 121,00 4º Accuracy G1 40,83 (14,88) 9,62 92,00 t(163) = 2,331, p = 0,021 0,40 G2 47,64 (18,76) 7,87 94,29 Comprehension G1 3,09 (0,94) 0,00 4,00 t(163) = 18,617, p < 0,001 3,25 G2 6,72 (1,27) 5,00 9,00 Serial Automatized Naming (sec.) G1 65,05 (10,35) 46,00 96,00 t(163) = 1,338, p = 0,218 0,21 G2 62,62 (12,51) 44,00 105,00 5º Accuracy G1 46,40 (16,47) 13,66 81,29 t(157) = 3,078, p = 0,002 0,58 G2 56,25 (17,62) 21,48 108,46 Comprehension G1 3,18 (1,05) 0,00 4,00 t(157) = 17,191, p < 0,001 3,24 G2 6,76 (1,16) 5,00 9,00 Serial Automatized Naming (sec.) G1 63,26 (9,24) 49,00 83,00 t(157) = 2,291, p = 0,023 0,43 G2 59,23 (9,64) 42,00 102,00 Note. G1 = low comprehension group; G2 = = group with medium/superior comprehension. sec. = time in seconds. Two types of complementary statistical analyses were conducted to analyze the relationship between the predictor variables (accuracy and speed of serial automated naming) and the students' reading comprehension level: Spearman correlation and logistic regression with GEE models. Table 3 presents the Spearman correlation between the study variables and the categories of textual comprehension groups (G1 x G2). Except for the fourth grade, all correlations with accuracy were significant and positive (indicating that reading is more accurate in the G2 group). The correlation between naming time and the categories was significant only among the second-graders (indicating that children who understand better – G2 – name more quickly). Table 3 Spearman correlation between the comprehension categories by school year and the study's predictor variables. School Year Accuracy Naming Speed 2º year 0,313 ( p < 0,001) -0,198 ( p = 0,012) 3º year 0,244 ( p = 0,002) 0,035 ( p = 0,664) (NS) 4º year 0,170 ( p = 0,029) (NS) -0,137 p = 0,078) (NS) 5º year 0,211 ( p = 0,008) -0,185 ( p = 0,020) (NS) Note. Critical value of p < 0.0125. The analysis was conducted using a logistic GEE model to verify whether the variables selected in the study are predictors of the groups constituted by the reading comprehension level. Table 4 presents the regression coefficients obtained by the covariates. The value of β is given in log odds ; therefore, the value in the Exp(β) column is more easily interpretable, which mathematically represents the coefficient e (= 2.71828) raised to the value obtained from β (Heck, Thomas, & Tabata, 2013 ). The result is interpreted in terms of odds ratios. Exp(β) values above 1 indicate variables with an increased probability of belonging to G2, thus presenting protective effects. Exp(β) variables with values below 1 indicate a reduced probability of belonging to G2 (a risk classification). For example, if Exp(β) is 0.65, this implies a 35% lower probability (1–0.65 = 0.35 = 35%) of belonging to G2. If it is 1.65, the probability would be 65% higher (1 + 0.65 = 1.65). Table 4 Logistic regression models Year Predictor β S.E. Wald p -value Exp( β ) 2º year Constant 1,169 1,208 0,937 0,333 3,320 Accuracy 0,051 0,016 9,575 0,002 1,052 Naming -0,022 0,014 2,420 0,120 0,979 3º year Constant -0,357 1,192 0,090 0,764 0,699 Accuracy 0,038 0,014 7,275 0,007 1,039 Naming 0,001 0,015 0,003 0,954 1,001 4º year Constant -0,230 1,370 0,028 0,967 0,795 Accuracy 0,023 0,012 3,810 0,051 1,023 Naming -0,001 0,016 0,003 0,959 0,999 5º year Constant 1,099 1,609 0,467 0,495 3,001 Accuracy 0,029 0,013 5,523 0,019 1,030 Naming -0,024 0,021 1,371 0,242 0,976 Note. Degrees of freedom = 1 for all comparisons. In bold, significant comparisons (p < 0.0125). The accuracy variable could significantly predict the comprehension level in the initial years (second and third grades). A one-unit increase in the number of correct words read per minute increased the probability of belonging to G2 by 5.2% in the second grade and by 3.9% in the third grade. On the other hand, the speed of serial automatized naming was not a significant predictor in any of the school years. DISCUSSION The study investigated the role of reading accuracy and serial automatized naming speed in predicting reading comprehension performance among elementary school children in São Paulo (second-to-fifth grades). Logistic regression analyses showed that reading accuracy was a predictor of comprehension for younger children (second and third grades), indicating that this variable is relevant for monitoring reading comprehension progress in this age group. A one-unit increase in the number of correct words read per minute increased the probability of belonging to G2 (group without difficulty) by 5.2% in the second grade and by 3.9% in the third grade. In contrast, the serial automated naming speed was not a significant predictor beyond the predictive effect of accuracy. Difficulties in understanding the text read may be associated with low accuracy values ​​in rapid word recognition (Metsala & David, 2022 ; Smith et al., 2021 ). In the sample analyzed, a group of children scored below the 25th percentile in reading comprehension (G1), measured by the number of correct answers per minute in the text read. They made up 29.4% of the total sample and, thus, showed that despite having achieved the minimum reading speed and accuracy values ​​to participate in the research (Cogo-Moreira et al., 2023; Martins & Capellini, 2021 ), they struggled to understand the proposed text. The literature confirms the possibility of comprehension impairments even with good decoding performance (Catts, Hogan, & Fey, 2003 ; Keenan et al., 2014; Landi, 2017 ;), showing that this is not the only skill responsible for text comprehension (Bishop, 2009; Smith et al., 2021 ; Sparks & Metsala, 2023 ; Yang, Xiong & Chen, 2023 ). The identification of 29.4% of poor comprehenders in a randomized sample, with representation from public and private education networks, with no complaints related to reading or other learning, certainly generates apprehension that is further confirmed in results found in PISA - Programme for International Student Assessment (OECD, 2023 ), which indicated that most Brazilian adolescents had minimal reading proficiency. Notably, data for this research were collected before the COVID-19 pandemic, meaning that this situation is expected to deteriorate based on the results of the most recent national and international assessments. Regarding G2, the group of poor comprehenders had less favorable results in reading accuracy in all the evaluated years (Table 2 ). The correlation analysis between the variables showed that, except for the fourth grade, all correlations with accuracy were significant and positive (indicating that the reading of good comprehenders – G2 – is more accurate). Although the correlation indicates that good comprehenders have more accurate reading, the analysis of the prediction of variables on reading comprehension suggests that the predictive power of reading accuracy decreases as students advance in school grades. This situation may be because other higher-order skills and processes involved in comprehension, such as executive functions and working memory (Bråten, Haverkamp & Anmarkrud, 2025 ; Wu et al., 2020 ; Yang, Xiong & Chen, 2023 ), become more relevant in reading comprehension. These factors were not assessed in this study. Like the present one, other studies have also sought to understand the relationships between accuracy in visual word recognition and reading comprehension in the early schooling years, testing the hypothesis that achieving a specific accuracy value is a critical precondition for improving reading comprehension (Cogo-Moreira et al., 2023; Kargin et al., 2024; Martins & Capellini, 2019 ). Thus, the reading comprehension of children with a minimum accuracy level in the first grade developed significantly better than the reading comprehension of children who reached this accuracy level in later schooling stages or who did not reach this accuracy level until the end of the fourth grade (Karageorgos et al., 2020 ). In this sense, although accuracy lost its predictive power in the final years of this cross-sectional cohort study, it remains a relevant factor for those who are educators and clinicians (Psyridou et al., 2022). Previous studies have already indicated that the more automatic the reading, the more accessible the cognitive resources will be, which are fundamental for understanding the text (Bigozzi et al., 2017 ; Kim, Wagner & Lopes, 2012), which suggests that reading accuracy is an important indicator for early identification of students at risk of difficulty in reading comprehension (Kargin et al., 2024; Martins & Capellini, 2019 ; Oliveira & Starling-Alves, 2022). Furthermore, allowing cognitive-linguistic resources and other skills to be more available for reading comprehension from the earliest grades is relevant. For example, Wu et al. ( 2020 ) showed that the contribution of reading efficiency, vocabulary, and executive functions in the first grade predicted reading comprehension in the fourth grade. The serial automated naming speed was relevant to differentiate the groups only in the second grade (p < 0.0125), both in the quantitative comparison between good and poor comprehenders and considering the association between the variables. In other words, in the second grade, good comprehenders - G2 - named faster than poor comprehenders - G1. This result aligns with that found by Varizo et al. ( 2022 ), who analyzed the contribution of rapid automatized naming to the speed and comprehension of textual reading and found that such skills were correlated in the second grade. Another study also pointed to a correlation between fluency and reading accuracy with rapid automatized naming from the second to the fourth grades (Basso et al., 2019 ). Although serial automatized naming speed correlated with reading comprehension in the second grade, it was not a significant predictor in logistic regression models when controlled for accuracy. This result aligns with a previous study that did not observe a significant improvement in reading comprehension from automaticity training (Cooper et al., 2022 ). This fact suggests that the effect of this speed is secondary or mediated by other skills, such as reading accuracy itself. Another hypothesis is methodological. The present study considered accuracy as the number of words read correctly per minute and not only reading accuracy as a predictor. Since accuracy is a measure that considers time to be derived, this may have overlapped with the temporal measure obtained by serial automated naming speed, statistically canceling out the effect. Future studies should be conducted in this direction so that the relationship between naming speed, reading accuracy, and other skills can be explored in greater depth. Other research that verified the interaction between these skills showed that rapid naming of objects is the skill that best predicts reading speed in the second grade (Varizo et al., 2022 ). Thus, we could think that, at the beginning of schooling, quickly naming objects and accessing the semantic representation in the lexicon helps in the textual reading speed. We can argue, therefore, that rapid naming of objects requires conceptual processing beyond phonological representations and, therefore, is related to reading through the lexical route, indirectly influencing reading comprehension in the early schooling years (Donker et al., 2016 ; dos Santos & Capellini, 2020 ). ​​In a complementary manner, some studies argue that rapid serial automatized naming reflects efficiency in the rapid recognition of individual words. In contrast, others argue that its influence is related to the ability to process multiple words in sequence in a cascade processing model. Recent evidence suggests that Visual Attention Span (VAS) may play a moderating role in the relationship between rapid automatized naming and reading. Readers with low VAS tend to process words one by one and recognize a limited number of orthographic units. In contrast, those with high VAS can process multiple words at once because they see words as a whole unit, which influences how rapid serial automatized naming relates to reading in each case (Guo, MA, Pan, & Zhang, 2023). Knowing that fourth-and-fifth-graders show a predominance of the lexical route and the second-and-third graders still depend on the phonological route, this could explain why rapid naming differentiated the groups only in the second grade (Oliveira, Germano & Capellini, 2016 ). This study has some limitations that should be considered when interpreting the results. Since it is a cross-sectional study, it is impossible to establish causal relationships between accuracy and reading comprehension. However, it can indicate that children with greater accuracy have a greater probability of achieving good reading comprehension. The reading comprehension assessment was based only on narrative text, which may limit the generalization of the results to different text types. Moreover, the exclusion criterion based on the minimum reading rate may have restricted the representation of children with greater difficulties. Intonation, an important component of reading fluency, was also not considered in this study (Meggiato, Corso & Corso, 2021 ). Finally, other skills that are known to contribute to reading comprehension, such as working memory, vocabulary, and executive functions, were not included in the regression model. For example, language processing speed, measured by the verbal fluency task, contributed to reading comprehension directly and indirectly, mediated by working memory (Candal & Avila, 2025). This fact reinforces the idea that speed measures, such as the rapid naming task, should be better explored and probably interact in different ways and with other reading comprehension skills, thus enabling the automaticity of the processes involved in such an outcome. The findings of this study also have relevant contributions to analyzing the predictive factors of good comprehension in a more transparent language. The evidence found in this study shows that reading accuracy (number of words correctly read per minute) is a more robust predictor of reading comprehension than rapid serial automatized naming, especially in the early elementary school years (second and third grades). This fact suggests that even in relatively accessible orthographies, such as Brazilian Portuguese, accuracy must precede fluency to support comprehension — a pattern consistent with findings in both transparent and opaque orthographies (Kargin et al., 2024; Kim, Wagner, & Lopez, 2012 ). These results reinforce the idea that accurate decoding is a universal prerequisite for meaning construction across orthographic systems (Perfetti & Stafura, 2014 ). Cross-linguistic studies often contrast very opposite systems (English vs. Finnish, for example). Brazilian Portuguese, with its semi-transparent orthography, represents an intermediate case between these extremes. It generally presents regular correspondences between phonemes and graphemes, especially in reading, but also includes irregularities (e.g., homonyms, silent letters, and morphological complexities) that require phonological and lexical processing (Abreu & Saltini, 2017; Pinheiro, 2007). Thus, Brazilian Portuguese fills a gap between these extremes, allowing hypotheses to be tested more sensitively. The findings of this study have relevant implications for screening and early intervention in reading difficulties. The identification of accuracy as a significant predictor of reading comprehension in the early school years suggests that this variable can be used as a marker of risk in educational contexts. Simple word reading tasks can quickly and efficiently provide clues to future reading comprehension difficulties. Notably, 29.4% of the sample of students did not understand adequately despite having achieved the minimum text reading accuracy level expected for school age. Furthermore, the results support the importance of teaching strategies focused on reading accuracy in the early years and not only on reading speed and fluency. Finally, the data contribute theoretically by corroborating models that highlight the importance of efficient decoding as a basis for reading comprehension and indicate the need for longitudinal studies that explore the interaction between accuracy, lexical access speed, and higher cognitive skills. CONCLUSION This study showed that only accuracy in reading isolated words could predict performance in reading comprehension, corroborating the idea that imprecise reading compromises access to the overall meaning of the text. However, the effect of accuracy was restricted to the initial school years. Therefore, it can be considered an important marker for monitoring reading comprehension in the early elementary school years. Serial naming speed did not remain a significant predictor of reading comprehension when controlled for accuracy, which suggests that its effect on comprehension is secondary or mediated by other skills, such as reading accuracy itself. Declarations The research was approved by the Research Ethics Committee of the Federal University of São Paulo (CAAE: 00987412.4.0000.5505; number 38406/12). The document is attached to the submission with the title “PB_Parecer_Substanciado_CEP_38406”. All those responsible for the research participants signed the Free and Informed Consent Form and the document was attached to the submission with the title "TCLE". The research was funded by “Fundação de Amparo à Pesquisa do Estado de São Paulo – FAPESP” (Project: 2011/11369-0). Availability of data and materials: not applicable. Clinical trial number: not applicable. Author Contribution P.S.L. contributed to the writing of the article and performed the statistical analysis; S.P.M. wrote the text; H.G.M. assisted in writing the text; A.S.B.K. and C.A.F.C. collected the data; P.F.N. and D.B.C. assisted in writing the text; C.R.B.A. supervised the work. All authors reviewed the manuscript. Data Availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. References Álvarez‑Cañizo, A. , Suárez-Coalla, P., & Cuetos, F. (2015). The role of reading fluency in children’s text comprehension. Frontiers in Psychology, 6 , 1810. https://doi.org/10.3389/fpsyg.2015.01810 Basso, F. P. , Piccolo, L. R., Miná, C. S., & de Salles, J. F. (2019). Instrumento de Avaliação da Fluência de Leitura Textual: Da decodificação à compreensão de leitura. Letras de Hoje, 54 (2), 146–153. https://doi.org/10.15448/1984-7726.2019.2.32519 Bishop, D. V. M. , McDonald, D., Bird, S., & Hayiou‑Thomas, M. E. (2009). Children who read words accurately despite language impairment: Who are they and how do they do it? Child Development, 80 (2), 593–605. Bigozzi, L. , Tarchi, C., Vagnoli, L., Valente, E., & Pinto, G. (2017). Reading fluency as a predictor of school outcomes across grades 4–9. Frontiers in Psychology, 8 , 200. https://doi.org/10.3389/fpsyg.2017.00200 Bråten, I. , Haverkamp, Y. E., & Anmarkrud, Ø. (2025). Gaining a deeper understanding of the deep cloze reading comprehension test: Examining potential contributors and consequences. Reading and Writing, 38 , 425–446. https://doi.org/10.1007/s11145-024-10521-y Candal, D. B. , & Ávila, C. R. B. (no prelo). Phonological working memory and linguistic processing speed in inferential reading comprehension. Psicologia: Reflexão e Crítica . Catts, H. W. , Hogan, T. P., & Fey, M. E. (2003). Subgrouping poor readers on the basis of individual differences in reading‑related abilities. Journal of Learning Disabilities, 36 (2), 151–164. https://doi.org/10.1177/002221940303600208 Cogo‑Moreira, H. , Molinari, G. L., de Carvalho, C. A. F., Kida, A. S. B., Lúcio, P. S., & de Ávila, C. R. B. (2023). Cut‑off point, sensitivity and specificity for screening the reading fluency in children. CoDAS, 35 (3), e20210263. https://doi.org/10.1590/2317-1782/20232021263en Cohen, J. (2013). Statistical power analysis for the behavioral sciences (2ª ed.). Routledge. Cooper, S. , Hébert, M., Goodrich, J., Leiva, S., Lin, X., Peng, P., & Nelson, R. (2022). Effects of automaticity training on reading performance: A meta‑analysis. Journal of Behavioral Education, 33 (1), 1–30. https://doi.org/10.1007/s10864-022-09480-7 Corso, H. V. , Piccolo, L. R., Miná, C. S., & Salles, J. F. (2015). Normas de desempenho em compreensão de leitura textual para crianças de 1º ano à 6ª série. Psico PUCRS, 46 (1), 68–78. https://doi.org/10.15448/1980-8623.2015.1.16900 Cutting, L. E. , & Scarborough, H. S. (2006). Prediction of reading comprehension: Relative contributions of word recognition, language proficiency, and other cognitive skills can depend on how comprehension is measured. Scientific Studies of Reading, 10 (3), 277–299. https://doi.org/10.1207/s1532799xssr1003_5 Cunha, V. L. O. , Martins, M. A., & Capellini, S. A. (2015). Relação entre fluência e compreensão leitora em escolares com dificuldades de aprendizagem. Psicologia: Teoria e Pesquisa, 33 , 1–8. Donker, M. , Kroesbergen, E., Slot, E., Van Viersen, S., & De Bree, E. (2016). Alphanumeric and non‑alphanumeric rapid automatized naming in children with reading and/or spelling difficulties and mathematical difficulties. Learning and Individual Differences, 47 , 80–87. https://doi.org/10.1016/j.lindif.2015.12.011 dos Santos, B. , & Capellini, S. A. (2020). Programa de remediação com nomeação automática rápida e leitura para escolares com dislexia: elaboração e significância clínica. CoDAS, 32 (3), e20180127. https://doi.org/10.1590/2317-1782/20202018127 Goldim, J. R. (2006). Consentimento e informação: A importância da qualidade do texto utilizado. Revista HCPA, 26 (3), 117–122. Guo Y., MA, X., Pan, K., & Zhang, H. (2023) The influence of rapid automatized naming on reading and its mechanism. Advances in Psychological Science, 2023, 31(6): 1020-1029. Heck, R. H. , Thomas, S., & Tabata, L. (2013). Multilevel modeling of categorical outcomes using IBM SPSS . Routledge. Hoover, W. A. , & Gough, P. B. (1990). The Simple View of Reading. Reading and Writing: An Interdisciplinary Journal, 2 , 127–160. Hua, A. N. , & Keenan, J. M. (2017). Interpreting reading comprehension test results: Quantile regression shows that explanatory factors can vary with performance level. Scientific Studies of Reading . https://doi.org/10.1080/10888438.2017.1280675 INEP. (2013). Censo da educação básica: Resumo técnico . Brasília, DF: Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira. Recuperado em 22 de dezembro de 2020, de http://download.inep.gov.br/educacao_basica/censo_escolar/resumos_tecnicos/resumo_tecnico_censo_educacao_basica_2012.pdf Karageorgos, P. , Richter, T., Haffmans, M.-B., Schindler, J., & Naumann, J. (2020). The role of word-recognition accuracy in the development of word-recognition speed and reading comprehension in primary school: A longitudinal examination. Cognitive Development, 56 . https://doi.org/10.1016/j.cogdev.2020.100949 Kargın, T. , Güldenoğlu, B., Gengeç, H., & Alatlı, R. (2024). The role of word decoding speed and accuracy on reading comprehension in a highly transparent and morphologically complex orthography. Journal of Theoretical Educational Science, 17 (3), 592–615. https://doi.org/10.30831/akukeg.1386249 Kida, A. S. B. , Ávila, C. R. B., & Capellini, S. A. (2015). Marcadores sintáticos no reconto oral de escolares disléxicos. CoDAS, 27 (6), 557–564. https://doi.org/10.1590/2317-1782/20152015106 Kim, Y. S. , Wagner, R. K., & Lopez, D. (2012). Developmental relations between reading fluency and reading comprehension: A longitudinal study from Grade 1 to Grade 2. Journal of Experimental Child Psychology, 113 (1), 93–111. https://doi.org/10.1016/j.jecp.2012.03.002 Kintsch, W. (1998). Comprehension: A paradigm for cognition . Cambridge University Press. Landi, N. , & Ryherd, K. (2017). Understanding specific reading comprehension deficit: A review. Language and Linguistics Compass . https://doi.org/10.1111/lnc3.12234 Lubineau, M. , Potier‑Watkins, C., Glasel, H., & Dehaene, S. (2024, maio 5). Examining the impact of reading fluency on lexical decision results in French sixth graders. Open Mind: Discoveries in Cognitive Science . https://doi.org/10.1162/opmi_a_00140 Lúcio, O. S. , Kida, A. S. B., Carvalho, C. A. F., & Cogo‑Moreira, H. (2017). Rapid automatized naming of pictures for children: validity studies and intra‑group norms. Psico‑USF, 22 (1), 35–47. https://doi.org/10.1590/1413-82712017220104 Lúcio, P. S. , Cogo‑Moreira, H., Kida, A. S. B., Carvalho, C. A. F., Pinheiro, A. M. V., Mari, J. J., & Ávila, C. R. B. (2018). Word decoding task: Item analysis by IRT and within‑group norms. Psicologia: Teoria e Pesquisa, 34 , e3437. https://doi.org/10.1590/0102.3772e3437 Lúcio, P. S. , Coutinho, F. L., Cogo‑Moreira, H., Bandalos, D., Carvalho, C. A. F., Kida, A. S. B., & Ávila, C. R. B. (2021). Reading comprehension tests for children: Test equating and specific age‑interval reports. Frontiers in Psychology . https://doi.org/10.3389/fpsyg.2021.662192 Lúcio, P. S. , Kida, A. S. B., Carvalho, C. A. F., Cogo‑Moreira, H., & Ávila, C. R. B. (2015). Construção de uma prova para avaliação da compreensão leitora no ensino fundamental: Estudo piloto. Temas em Psicologia, 23 , 1035–1050. Martins, M. A. , & Capellini, S. A. (2019). Relation between oral reading fluency and reading comprehension. CoDAS, 31 (1), e20170244. https://doi.org/10.1590/2317-1782/20182018244 Martins, M. A. , & Capellini, S. A. (2021). Identification of struggling readers or at risk of reading difficulties with one‑minute fluency measures. Psicologia: Reflexão e Crítica, 34 , Article 10. https://doi.org/10.1186/s41155-021-00174-z Meggiato, A. O. , Corso, H. V., & Corso, L. V. (2021). Fluência de leitura: evolução do construto e relações com a compreensão. Cadernos de Pesquisa, 51 . https://doi.org/10.1590/198053147797 Metsala, J. L. , & David, M. D. (2022). Improving English reading fluency and comprehension for children with reading fluency disabilities. Dyslexia, 28 (1), 79–96. https://doi.org/10.1002/dys.1695 Miller, R. G. (1981). Simultaneous statistical inference . Springer‑Verlag. https://doi.org/10.1007/978-1-4613-8122-8 Norton, E. S. , & Wolf, M. (2012). Rapid automatized naming (RAN) and reading fluency: Implications for understanding and treatment of reading disabilities. Annual Review of Psychology, 63 , 427–452. https://doi.org/10.1146/annurev-psych-120710-100431 Oakhill, J. , Cain, K., & Elbro, C. (2009). Reading comprehension and comprehension difficulties . In C. Hulme & M. Snowling (Eds.), Reading development and difficulties: Bridging the gap between research and practice (pp. xx–xx). https://doi.org/10.1007/978-3-030-26550-2 OECD. (2023). PISA 2022 results (Volume II): Learning during – and from – disruption . OECD Publishing. https://doi.org/10.1787/a97db61c-e Oliveira, J. B. A . e.; Alves, I. S. (2022) The relationship between fluency and reading comprehension: evidence for Brazilian Portuguese speakers. Cadernos de Linguística, [S. l.], v. 3, n. 1, p. e631, 2022. Oliveira, A. M .; Germano, G. D.; Capellini, S. A. (2016) Desempenho de escolares em provas de processo de identificação de letras e do processo léxico. Revista CEFAC, v. 18, n. 5, p. 1121-32, 2016. Perfetti, C. (2010). Decoding, vocabulary, and comprehension: The golden triangle of reading skill. In M. G. McKeown & L. Kucan (Eds.), Bringing reading research to life (pp. 291–302). Guilford. Perfetti, C. , & Stafura, J. (2014). Word knowledge in a theory of reading comprehension. Scientific Studies of Reading, 18 (1), 22–37. https://doi.org/10.1080/10888438.2013.827687 Psyridou, M. , Tolvanen, A., Niemi, P., Aro, M., & Lerkkanen, M.-K. (2023). Development of silent reading fluency and reading comprehension across grades 1 to 9: Unidirectional or bidirectional effects between the two skills? Reading and Writing, 36 , 1969–1996. https://doi.org/10.1007/s11145-022-10371-6 Salles, J. F. , & Paula, F. D. (2016). Compreensão da leitura textual e sua relação com as funções executivas. Educação Revista, 62 , 53–67. Scarton, C. E. , & Aluísio, S. M. (2010). Análise da inteligibilidade de textos via ferramentas de processamento de língua natural: Adaptando as métricas do Coh-Metrix para o português. Linguamática, 2 (1), 45–61. Smith, R. , Snow, P., Serry, T., & Hammond, L. (2021). The role of background knowledge in reading comprehension: A critical review. Reading Psychology, 42 (6), 540–557. https://doi.org/10.1080/02702711.2021.1888348 Snowling, M. , & Hulme, C. (2012). (Special Issue: Developmental disorders and cognitive science). Japanese Psychological Research, 55 (2), 186–202. https://doi.org/10.1111/j.1468-5884.2012.00541.x Sparks, E. , & Metsala, J. L. (2023). Morphological awareness predicts reading comprehension in first grade students. Frontiers in Education, 8 , 1304688. https://doi.org/10.3389/feduc.2023.1304688 Varizo, S. , Correa, J., Mousinho, R., & Navas, A. L. (2022). A contribuição da nomeação automatizada rápida para a velocidade e compreensão de leitura textual em crianças brasileiras do ensino fundamental. Audiology – Communication Research, 27 . Wallot, S. (2014). From “cracking the orthographic code” to “playing with language”: Toward a usage‑based foundation of the reading process. Frontiers in Psychology . https://doi.org/10.3389/fpsyg.2014.00891 Weekes, B. S. (1997). Differential effects of number of letters on word and nonword naming latency. Quarterly Journal of Experimental Psychology, 50 (2), 439–456. https://doi.org/10.1080/027249897392170 Wolf, M. , & Katzir‑Cohen, T. (2001). Reading fluency and its intervention. Scientific Studies of Reading, 5 (3), 211–239. https://doi.org/10.1207/S1532799XSSR0503_2 Wu, Y. , Barquero, L. A., Pickren, S. E., Barber, A. T., & Cutting, L. E. (2020). The relationship between cognitive skills and reading comprehension of narrative and expository texts: A longitudinal study from Grade 1 to Grade 4. Learning and Individual Differences, 80 , 101848. https://doi.org/10.1016/j.lindif.2020.101848 Yang, L. , Xiong, Y., & Chen, Q. (2023). The role of linguistic and cognitive skills in reading Chinese as a second language: A path analysis modeling approach. Frontiers in Psychology, 14 , 1131913. https://doi.org/10.3389/fpsyg.2023.113191 Additional Declarations No competing interests reported. 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This ability depends on the interrelationship of several neurocognitive and linguistic skills, components, and processes.\u003c/p\u003e\u003cp\u003eIn order to understand a read text, first of all, one would expect that auditory linguistic comprehension is adequate and decoding is efficient (Hoover \u0026amp; Gough, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). Decoding is understood as the perceptual-cognitive-linguistic process that culminates in the recognition of the written word, which, with the development of learning, should become automatic and allow fluent reading (Basso et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Smith et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Several components related to the idea of ​​time are added to the process: automaticity, processing speed, and accuracy in word recognition (Basso et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Cooper et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kargin et al., 2024).\u003c/p\u003e\u003cp\u003eEspecially in the initial learning years, these processes demand from the reader efficient command of the writing system, vocabulary adequate in scope and depth (Oakhill et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Sparks \u0026amp; Metsala, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), but, above all, precise and fast, if not automatic, access to the lexicon (Norton \u0026amp; Wolf, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Reading comprehension, the outcome of the entire process from decoding onwards, requires coordinating multiple language and cognitive structures and skills (Hoover \u0026amp; Gough, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) to create the mental representation of the situation model proposed in the text (Br\u0026aring;ten, Haverkamp \u0026amp; Anmarkrud, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Kintsch, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Landi, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yang, Xiong \u0026amp; Chen, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA study by Cooper et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) argues that there is a strong relationship between automaticity in word reading and comprehension of what is read in the early school years. There is also evidence that the speed and accuracy of access to the mental lexicon can predict performance in word reading (Norton, Wolf, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Lubineau et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough it is known that, throughout schooling, the strength of the correlation between decoding and reading comprehension decreases (Salles \u0026amp; Paula, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Psyridou et al., 2022), it seems logical to assume that, especially in the early years, correct reading may be more important than speed to understand the text: the more errors are made while reading a text, the more difficult it becomes to understand (Alvarez-Ca\u0026ntilde;izo, Su\u0026aacute;rez-Coalla, Cuetos \u0026amp; 2015; Oliveira \u0026amp; Starling-Alves, 2022).\u003c/p\u003e\u003cp\u003eThe initial reading assessment at the recognition or lexical decision level can provide clues about primary processes that are detrimental to text comprehension, already at the word level (Cogo-Moreira et al., 2023; Cutting et al., 2006; Oliveira, Germano \u0026amp; Capellini, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Martins \u0026amp; Capellini, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this study, decoding was assessed by accuracy calculated by the number of words read correctly in one minute.\u003c/p\u003e\u003cp\u003eThe assessment of rapid naming ability is one of the ways to assess or estimate the speed of access to the mental lexicon and evaluate processes underlying the speed and accuracy with which written words are recognized (Bishop et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Guo, MA, Pan \u0026amp; Zhang, 2023; Wolf, Katzir-Cohen, 2001). The ability to access the phonological lexicon defines how quickly and accurately one can name a sequence of familiar visual stimuli, including written items (Candal \u0026amp; Avila, in press; Norton and Wolf, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis ability is assessed through rapid automatized naming tests and may be associated with decoding and automatic word recognition at a neurophysiological level (Guo, MA, Pan \u0026amp; Zhang, 2023). However, it does not appear to be sufficient by itself to explain difficulties in reading comprehension, especially with language disorders (Bishop et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOn the other hand, authors argue that deficits in accuracy and speed of access to the lexicon can compromise the comprehension of the text read (\u0026Aacute;lvarez-Ca\u0026ntilde;izo, Su\u0026aacute;rez-Coalla \u0026amp; Cuetos, 2015; Cutting \u0026amp; Scarborough, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Perfetti \u0026amp; Stafura, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Thus, we could assume that the ability to process visual symbols more or less quickly \u0026mdash; through lexical access \u0026mdash; is correlated with the reading comprehension level (dos Santos \u0026amp; Capellini, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This association could be explained by the relationship between rapid automatized naming and fluent reading and by the hypothesis that the more automated the ability to recognize words, the greater the availability of cognitive resources for comprehension processes (\u0026Aacute;lvarez-Ca\u0026ntilde;izo, Su\u0026aacute;rez-Coalla \u0026amp; Cuetos, 2015; Bishop et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Cunha et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Perfetti \u0026amp; Stafura, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sparks \u0026amp; Metsala, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yang, Xiong \u0026amp; Chen, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSince reading skills have a continuous distribution in the population (Snowling, Hulme, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; OECD, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), one would assume a significant variability in reading comprehension skills from the early schooling years. The reading comprehension performance variability can be explained by its multifactorial nature. This study investigated decoding characteristics and processing speed in accessing the lexicon, when comprehension occurs with better and worse results, in Elementary School I. The lack of information on the relationships between speed and accuracy in processing linguistic information and reading comprehension in Brazilian students justifies this work.\u003c/p\u003e\u003cp\u003eThus, considering the results obtained by applying different instruments that assess reading (Corso et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hua, Keenan, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), this study investigated two groups of students with different reading comprehension conditions, which skills, whether reading accuracy or lexicon access speed, measured by rapid automatized naming time, could predict comprehension performance.\u003c/p\u003e\u003cp\u003eThe study with Brazilian Elementary School I students is justified by the relevance of understanding whether the accuracy in recognizing written words or the speed of access to the mental lexicon can predict the performance in the reading comprehension of a text whose spelling is relatively transparent. Thus, even though it is a cross-sectional investigation, by evaluating children at different levels in the initial schooling phases, this study will support clarifying aspects of neurodevelopment related to the complex interactions observed between reading words through the lexical route, lexical access through naming figures and understanding texts in our language.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e This is a retrospective, cross-sectional study with quantitative analysis, complementary to the original research, approved by the Research Ethics Committee of the Federal University of S\u0026atilde;o Paulo (CAAE: 00987412.4.0000.5505; opinion number 38406/12). Only children whose parents or guardians signed the Informed Consent Form participated in the study. All experiments were performed in accordance with relevant guidelines and regulations.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSampling procedures\u003c/h2\u003e\u003cp\u003eSampling was performed through the proportional distribution representative of schools in S\u0026atilde;o Paulo, based on the 2012 School Census (INEP, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Twenty-one schools were randomly selected (nine municipal, 10 state, and four private) from the initial calculation of the required sample size. Teachers identified potential participants under the following criteria: no complaints of specific learning difficulties, behavioral problems, or cognitive deficits; no school retention; and no uncorrected hearing or visual problems. After this preliminary selection, letters were sent to parents or guardians inviting their children to participate in the research. Only children whose consent forms were returned signed participated in the study.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCase analysis\u003c/h3\u003e\n\u003cp\u003eThe present study\u0026rsquo;s sample has been retrieved from a larger study composed of 728 Elementary School second-to-fifth graders evaluated in several cognitive, language, and reading tasks (among them, oral reading, text comprehension, oral comprehension, rapid naming, and memory).\u003c/p\u003e\u003cp\u003e In order to fulfill the objectives of this research, participants who met the inclusion criteria were initially screened by the oral reading rate value to ensure a minimum automatic word recognition level (Cogo-Moreira et al., 2023). The children then read a short text appropriate for their school year (ranging from 206 to 235 words). After the children had finished reading the first two paragraphs, the evaluator started counting the time and, after 60 seconds, recorded the point at which the child had finished reading. The reading rate (number of words read per minute \u0026ndash; p.p.m) was then computed for the selected passage. Students with reading rate values ​​below: second grade \u0026ndash; 50 words per minute (wpm); third grade \u0026ndash; 66 wpm; fourth grade \u0026ndash; 71 wpm; fifth grade \u0026ndash; 95 wpm were excluded from the sample. Therefore, for the present study, we initially considered only the responses of participants who met this criterion and performed the reading comprehension task. As a result, the sample initially consisted of the evaluation protocols of 676 second-to-fifth-graders (58% girls) from the public (84%) and private schools in S\u0026atilde;o Paulo (168 second-graders, 166 third-graders, 176 fourth-graders, and 166 fifth-graders, respectively). Thirty-nine children did not complete all the evaluations. The sample had a mean age of 9.12 years (standard deviation 1.0; minimum 07 years; maximum 11 years).\u003c/p\u003e\n\u003ch3\u003eMaterial\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eWord reading accuracy\u003c/h2\u003e\u003cp\u003eDecoding accuracy was assessed using a reading-aloud task of 48 low-frequency words as a reference (L\u0026uacute;cio et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The number of words read correctly was computed, and this value was divided by the total reading time (in seconds) and multiplied by 60, generating a value that represents the mean number of words that would be read in one minute. The words varied in regularity (regular and irregular words) and length (04 to 07 letters). The task was submitted to factor validation (L\u0026uacute;cio et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eRapid Serial Automated Naming Test\u003c/h3\u003e\n\u003cp\u003eThe task consisted of quickly naming pictures of six objects (egg, bread, ball, sun, key, and fork) randomly distributed on two boards, with 36 appearances, aligned horizontally, on each one. The children named the pictures contained on the boards as correctly and quickly as possible. Time and correct answers were collected for each board separately. The task was submitted to factor validation and establishment of norms (L\u0026uacute;cio et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For the present study, the total naming time on both boards together was considered as a variable.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eReading comprehension\u003c/h2\u003e\u003cp\u003eReading comprehension was assessed by applying a narrative text (190 words) part of a reading assessment battery that was submitted to item analysis (L\u0026uacute;cio et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and a validation study (L\u0026uacute;cio et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Linguistic characteristics and readability and complexity parameters of the text were analyzed by the CohMetrix-Port platform (Kida, Avila, Capellini, 2015; Scarton, Alu\u0026iacute;sio, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Thus, readability was measured using the Flesch Index (based on the length of the text's words and sentences). The text had a Flesch Index of 52.707, which characterized it as a reasonably complex text (Goldim, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Syntactic complexity was characterized by the following parameters: number of words (190), number of sentences (17), number of sentences per paragraph (8.5), content words (578.947), pronouns per syntagm (0.034), and number of connectives (126.316). Finally, vocabulary complexity was assessed by the Type/Token Index (0.695), characterized by the number of unique words with the number of tokens of these words, where each word instance was a token. All second-to-fifth-graders in the sample read the narrative and then answered orally nine open-ended questions, also proposed orally, about the text read. The answers were analyzed through a correction sieve established by a panel of experts (speech therapists), whose reliability between evaluators was assessed through the Kappa coefficient (L\u0026uacute;cio et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eGroup definition\u003c/h3\u003e\n\u003cp\u003eThe children in the sample were grouped per their reading comprehension level: Group 1 (G1) children with difficulty and Group 2 (G2) children without difficulty understanding the text read. Comprehension difficulty was defined by the percentile in the comprehension task per the school grade. Children who did not achieve a score of at least the 25th percentile were defined as having low comprehension (G1), and the others comprised G2 with medium/superior performance, from now on, also called good comprehenders.\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eWe conducted descriptive and inferential statistics using SPSS for Windows version 23.0. We reported mean, standard deviation, minimum, and maximum values ​​for each study variable and Spearman correlations between groups and metric variables. We investigated differences between groups (G1 and G2) by t-tests for independent samples for metric variables and chi-square for dichotomous variables (gender and school type). We performed Logistic regression analyses using Generalized Estimating Equations (GEE) to investigate the predictive power of naming time and accuracy in group membership (G1 was the reference group, classified as 0, and G2, classified as 1). The method used was Enter (the predictor variables of interest were forced to enter the equation).\u003c/p\u003e\u003cp\u003eThe effect sizes considered were Cohen's (2013) classification of 0.2 (small), 0.5 (medium), and 0.8 (large). As the analyses were performed separately by school grade, the p-value was corrected to control the effect of multiple comparisons. We adopted the Bonferroni correction method (Miller, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1981\u003c/span\u003e) for this purpose; that is, with 4 groups (school grades), the critical value of 0.05 was divided by 4, considering comparisons with p\u0026thinsp;\u0026lt;\u0026thinsp;0.0125 as significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eData were complete on the reading (accuracy), comprehension, and naming tasks for 637 participants. The mean age of the children was 9.12 (SD\u0026thinsp;=\u0026thinsp;1; minimum\u0026thinsp;=\u0026thinsp;7; maximum\u0026thinsp;=\u0026thinsp;11). Most children attended public school (87.4%) and were female (57%). The number of participants was similar across grades (second\u0026thinsp;=\u0026thinsp;158, third\u0026thinsp;=\u0026thinsp;155, fourth\u0026thinsp;=\u0026thinsp;165, fifth\u0026thinsp;=\u0026thinsp;159). There were no differences in age (\u003cem\u003et\u003c/em\u003e(750)\u0026thinsp;=\u0026thinsp;1.154, p\u0026thinsp;=\u0026thinsp;0.249) or gender frequency (U\u0026thinsp;=\u0026thinsp;36,068.00, p\u0026thinsp;=\u0026thinsp;0.416) between children who performed all tasks and those with missing data.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the descriptive statistics of the study by group. The minimum score to reach the 25th percentile in the comprehension task was 2.0 points for the second grade, 3.0 points for the third grade, and 4.0 points for the fourth and fifth grades, and these scores were classified as having below-expected performance for the current grade. Using this criterion, 187 (29.4%) children comprised G1 (low comprehension group), and 450 (70.6%) comprised G2 (a group with medium/superior comprehension). The groups did not differ regarding age (\u003cem\u003et\u003c/em\u003e(632)\u0026thinsp;=\u0026thinsp;1.227, p\u0026thinsp;=\u0026thinsp;0.220) or gender \u003cem\u003eχ\u003c/em\u003e \u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;1.279 p\u0026thinsp;=\u0026thinsp;0.292. However, they differed significantly regarding the frequency of school type (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;6.188, p\u0026thinsp;=\u0026thinsp;0.012), with 92.0% of children in public schools in G1 and 85.3% in G2, i.e., a reduction of almost 8% in the prevalence of children with good comprehension. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that the difference only occurs in the fourth grade, at the level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, but not at p\u0026thinsp;\u0026lt;\u0026thinsp;0.0125; therefore, the school type was not considered in the logistic regression.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics of study control variables by group (good/poor comprehenders) and school year\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e% feminine\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePublic\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e2\u0026ordm;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eG1 (n\u0026thinsp;=\u0026thinsp;53)\u003c/p\u003e\u003cp\u003eG2 (n\u0026thinsp;=\u0026thinsp;105)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7,77 (0,46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e96,20%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7,85 (0,46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e87,60%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003et\u003c/em\u003e(156)\u0026thinsp;=\u0026thinsp;0,958, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0,340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;0,878 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0,866\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e (1)\u0026thinsp;=\u0026thinsp;3,037 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0,093\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e3\u0026ordm;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eG1 (n\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e\u003cp\u003eG2 (n\u0026thinsp;=\u0026thinsp;114)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8,73 (0,45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e87,50%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8,78 (0,42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e90,40%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003et\u003c/em\u003e(153)\u0026thinsp;=\u0026thinsp;0,634, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0,527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ex2(1)\u0026thinsp;=\u0026thinsp;0,153 p\u0026thinsp;=\u0026thinsp;0,848\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ex2(1)\u0026thinsp;=\u0026thinsp;0,258 p\u0026thinsp;=\u0026thinsp;0,563\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e4\u0026ordm;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eG1 (n\u0026thinsp;=\u0026thinsp;54)\u003c/p\u003e\u003cp\u003eG2 (n\u0026thinsp;=\u0026thinsp;111)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9,92 (0,39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e94,40%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9,70 (0,55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e79,80%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003et\u003c/em\u003e(162)\u0026thinsp;=\u0026thinsp;1,369, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0,173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ex2(1)\u0026thinsp;=\u0026thinsp;0,525 p\u0026thinsp;=\u0026thinsp;0,509\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ex2(1)\u0026thinsp;=\u0026thinsp;5,951 p\u0026thinsp;=\u0026thinsp;0,019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e5\u0026ordm;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eG1 (n\u0026thinsp;=\u0026thinsp;39)\u003c/p\u003e\u003cp\u003eG2 (n\u0026thinsp;=\u0026thinsp;120)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10,05 (0,23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e89,70%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10,14 (0,40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e57%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e83,30%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003et\u003c/em\u003e(155)\u0026thinsp;=\u0026thinsp;1,333, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0,185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ex2(1)\u0026thinsp;=\u0026thinsp;1,932 p\u0026thinsp;=\u0026thinsp;0,191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ex2(1)\u0026thinsp;=\u0026thinsp;0,258 p\u0026thinsp;=\u0026thinsp;0,563\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote. G1\u0026thinsp;=\u0026thinsp;low comprehension group; G2\u0026thinsp;=\u0026thinsp;group with medium/superior comprehension.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the descriptions of the outcome variables and the comparisons between groups G1 and G2 in each year (t-tests for independent samples). G1 had the worst results in all years in accuracy and comprehension, and the most considerable effect was identified in the latter variable (as the groups were generated based on the comprehension scores, the high difference in effect size was expected, according to the \u0026ldquo;d\u0026rdquo; indices obtained). The serial automatized naming speed was relevant to differentiate the groups only for the second grade (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0125). The effects of the naming task, measured in time, were considered small, and the effects of accuracy were primarily medium (Cohen, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics of the study's predictor variables by group (good/poor comprehenders) and school year and comparison results (t-tests).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTask\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean (d.p)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMinimum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMaximum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eComparison\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003ed\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u0026ordm;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAccuracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19,20 (11,75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0,60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e52,00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003et(156)\u0026thinsp;=\u0026thinsp;3,729, p\u0026thinsp;\u0026lt;\u0026thinsp;0,001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0,66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28,55 (16,22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0,48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e96,00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eComprehension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,21 (0,74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0,00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2,00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003et(156)\u0026thinsp;=\u0026thinsp;16,385, p\u0026thinsp;\u0026lt;\u0026thinsp;0,001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e3,02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4,70 (1,46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3,00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8,00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSerial Automatized Naming\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e79,87 (13,93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e47,00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e124,00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003et(156)\u0026thinsp;=\u0026thinsp;2,612, p\u0026thinsp;=\u0026thinsp;0,010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0,45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e73,90 (13,39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e36,00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e116,00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u0026ordm;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAccuracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31,00 (14,55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10,91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e75,88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003et(153)\u0026thinsp;=\u0026thinsp;2,890, p\u0026thinsp;=\u0026thinsp;0,004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0,53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39,64 (17,02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15,54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e99,23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eComprehension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,02 (1,01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0,00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3,00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003et(153)\u0026thinsp;=\u0026thinsp;16,854, p\u0026thinsp;\u0026lt;\u0026thinsp;0,001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e3,26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5,87 (1,33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4,00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9,00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSerial Automatized\u003c/p\u003e\u003cp\u003eNaming (sec.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66,98 (16,25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e46,00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e121,00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003et(153)\u0026thinsp;=\u0026thinsp;0,517, p\u0026thinsp;=\u0026thinsp;0,606\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0,09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65,78 (11,17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e46,00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e121,00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u0026ordm;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAccuracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40,83 (14,88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9,62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e92,00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003et(163)\u0026thinsp;=\u0026thinsp;2,331, p\u0026thinsp;=\u0026thinsp;0,021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0,40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47,64 (18,76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7,87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e94,29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eComprehension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3,09 (0,94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0,00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4,00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003et(163)\u0026thinsp;=\u0026thinsp;18,617, p\u0026thinsp;\u0026lt;\u0026thinsp;0,001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e3,25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6,72 (1,27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5,00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9,00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSerial Automatized Naming (sec.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65,05 (10,35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e46,00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e96,00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003et(163)\u0026thinsp;=\u0026thinsp;1,338, p\u0026thinsp;=\u0026thinsp;0,218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0,21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62,62 (12,51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e44,00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e105,00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u0026ordm;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAccuracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46,40 (16,47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13,66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e81,29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003et(157)\u0026thinsp;=\u0026thinsp;3,078, p\u0026thinsp;=\u0026thinsp;0,002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0,58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56,25 (17,62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e21,48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e108,46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eComprehension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3,18 (1,05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0,00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4,00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003et(157)\u0026thinsp;=\u0026thinsp;17,191, p\u0026thinsp;\u0026lt;\u0026thinsp;0,001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e3,24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6,76 (1,16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5,00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9,00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSerial Automatized Naming (sec.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63,26 (9,24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e49,00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e83,00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003et(157)\u0026thinsp;=\u0026thinsp;2,291, p\u0026thinsp;=\u0026thinsp;0,023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0,43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59,23 (9,64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e42,00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e102,00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote. G1\u0026thinsp;=\u0026thinsp;low comprehension group; G2\u0026thinsp;=\u0026thinsp;=\u0026thinsp;group with medium/superior comprehension. sec. = time in seconds.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTwo types of complementary statistical analyses were conducted to analyze the relationship between the predictor variables (accuracy and speed of serial automated naming) and the students' reading comprehension level: Spearman correlation and logistic regression with GEE models. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the Spearman correlation between the study variables and the categories of textual comprehension groups (G1 x G2). Except for the fourth grade, all correlations with accuracy were significant and positive (indicating that reading is more accurate in the G2 group). The correlation between naming time and the categories was significant only among the second-graders (indicating that children who understand better \u0026ndash; G2 \u0026ndash; name more quickly).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSpearman correlation between the comprehension categories by school year and the study's predictor variables.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSchool Year\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAccuracy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNaming Speed\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u0026ordm; year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0,313 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0,001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0,198 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0,012)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u0026ordm; year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0,244 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0,002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0,035 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0,664) (NS)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u0026ordm; year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0,170 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0,029) (NS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0,137 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0,078) (NS)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u0026ordm; year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0,211 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0,008)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0,185 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0,020) (NS)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote. Critical value of p\u0026thinsp;\u0026lt;\u0026thinsp;0.0125.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe analysis was conducted using a logistic GEE model to verify whether the variables selected in the study are predictors of the groups constituted by the reading comprehension level. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the regression coefficients obtained by the covariates. The value of \u003cem\u003eβ\u003c/em\u003e is given in \u003cem\u003elog odds\u003c/em\u003e; therefore, the value in the \u003cem\u003eExp(β)\u003c/em\u003e column is more easily interpretable, which mathematically represents the coefficient \u003cem\u003ee\u003c/em\u003e (=\u0026thinsp;2.71828) raised to the value obtained from \u003cem\u003eβ\u003c/em\u003e (Heck, Thomas, \u0026amp; Tabata, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The result is interpreted in terms of odds ratios. Exp(β) values above 1 indicate variables with an increased probability of belonging to G2, thus presenting protective effects. Exp(β) variables with values below 1 indicate a reduced probability of belonging to G2 (a risk classification). For example, if Exp(β) is 0.65, this implies a 35% lower probability (1\u0026ndash;0.65\u0026thinsp;=\u0026thinsp;0.35\u0026thinsp;=\u0026thinsp;35%) of belonging to G2. If it is 1.65, the probability would be 65% higher (1\u0026thinsp;+\u0026thinsp;0.65\u0026thinsp;=\u0026thinsp;1.65).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLogistic regression models\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eS.E.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWald\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eExp(\u003cem\u003eβ\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u0026ordm; year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0,937\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0,333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3,320\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAccuracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0,051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9,575\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0,002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1,052\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNaming\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0,022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2,420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0,120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0,979\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u0026ordm; year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0,357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0,090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0,764\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0,699\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAccuracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0,038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7,275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0,007\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1,039\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNaming\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0,001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0,003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0,954\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1,001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u0026ordm; year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0,230\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,370\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0,028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0,967\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0,795\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAccuracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0,023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3,810\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0,051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1,023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNaming\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0,001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0,003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0,959\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0,999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u0026ordm; year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,609\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0,467\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0,495\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3,001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAccuracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0,029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5,523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0,019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1,030\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNaming\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0,024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1,371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0,242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0,976\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote. Degrees of freedom\u0026thinsp;=\u0026thinsp;1 for all comparisons. In bold, significant comparisons (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0125).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe accuracy variable could significantly predict the comprehension level in the initial years (second and third grades). A one-unit increase in the number of correct words read per minute increased the probability of belonging to G2 by 5.2% in the second grade and by 3.9% in the third grade. On the other hand, the speed of serial automatized naming was not a significant predictor in any of the school years.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe study investigated the role of reading accuracy and serial automatized naming speed in predicting reading comprehension performance among elementary school children in S\u0026atilde;o Paulo (second-to-fifth grades). Logistic regression analyses showed that reading accuracy was a predictor of comprehension for younger children (second and third grades), indicating that this variable is relevant for monitoring reading comprehension progress in this age group. A one-unit increase in the number of correct words read per minute increased the probability of belonging to G2 (group without difficulty) by 5.2% in the second grade and by 3.9% in the third grade. In contrast, the serial automated naming speed was not a significant predictor beyond the predictive effect of accuracy.\u003c/p\u003e\u003cp\u003eDifficulties in understanding the text read may be associated with low accuracy values ​​in rapid word recognition (Metsala \u0026amp; David, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Smith et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the sample analyzed, a group of children scored below the 25th percentile in reading comprehension (G1), measured by the number of correct answers per minute in the text read. They made up 29.4% of the total sample and, thus, showed that despite having achieved the minimum reading speed and accuracy values ​​to participate in the research (Cogo-Moreira et al., 2023; Martins \u0026amp; Capellini, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), they struggled to understand the proposed text. The literature confirms the possibility of comprehension impairments even with good decoding performance (Catts, Hogan, \u0026amp; Fey, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Keenan et al., 2014; Landi, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e;), showing that this is not the only skill responsible for text comprehension (Bishop, 2009; Smith et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sparks \u0026amp; Metsala, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yang, Xiong \u0026amp; Chen, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe identification of 29.4% of poor comprehenders in a randomized sample, with representation from public and private education networks, with no complaints related to reading or other learning, certainly generates apprehension that is further confirmed in results found in PISA - Programme for International Student Assessment (OECD, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which indicated that most Brazilian adolescents had minimal reading proficiency. Notably, data for this research were collected before the COVID-19 pandemic, meaning that this situation is expected to deteriorate based on the results of the most recent national and international assessments.\u003c/p\u003e\u003cp\u003eRegarding G2, the group of poor comprehenders had less favorable results in reading accuracy in all the evaluated years (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The correlation analysis between the variables showed that, except for the fourth grade, all correlations with accuracy were significant and positive (indicating that the reading of good comprehenders \u0026ndash; G2 \u0026ndash; is more accurate).\u003c/p\u003e\u003cp\u003eAlthough the correlation indicates that good comprehenders have more accurate reading, the analysis of the prediction of variables on reading comprehension suggests that the predictive power of reading accuracy decreases as students advance in school grades. This situation may be because other higher-order skills and processes involved in comprehension, such as executive functions and working memory (Br\u0026aring;ten, Haverkamp \u0026amp; Anmarkrud, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yang, Xiong \u0026amp; Chen, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), become more relevant in reading comprehension. These factors were not assessed in this study.\u003c/p\u003e\u003cp\u003eLike the present one, other studies have also sought to understand the relationships between accuracy in visual word recognition and reading comprehension in the early schooling years, testing the hypothesis that achieving a specific accuracy value is a critical precondition for improving reading comprehension (Cogo-Moreira et al., 2023; Kargin et al., 2024; Martins \u0026amp; Capellini, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThus, the reading comprehension of children with a minimum accuracy level in the first grade developed significantly better than the reading comprehension of children who reached this accuracy level in later schooling stages or who did not reach this accuracy level until the end of the fourth grade (Karageorgos et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In this sense, although accuracy lost its predictive power in the final years of this cross-sectional cohort study, it remains a relevant factor for those who are educators and clinicians (Psyridou et al., 2022).\u003c/p\u003e\u003cp\u003ePrevious studies have already indicated that the more automatic the reading, the more accessible the cognitive resources will be, which are fundamental for understanding the text (Bigozzi et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kim, Wagner \u0026amp; Lopes, 2012), which suggests that reading accuracy is an important indicator for early identification of students at risk of difficulty in reading comprehension (Kargin et al., 2024; Martins \u0026amp; Capellini, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Oliveira \u0026amp; Starling-Alves, 2022). Furthermore, allowing cognitive-linguistic resources and other skills to be more available for reading comprehension from the earliest grades is relevant. For example, Wu et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) showed that the contribution of reading efficiency, vocabulary, and executive functions in the first grade predicted reading comprehension in the fourth grade.\u003c/p\u003e\u003cp\u003eThe serial automated naming speed was relevant to differentiate the groups only in the second grade (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0125), both in the quantitative comparison between good and poor comprehenders and considering the association between the variables. In other words, in the second grade, good comprehenders - G2 - named faster than poor comprehenders - G1. This result aligns with that found by Varizo et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who analyzed the contribution of rapid automatized naming to the speed and comprehension of textual reading and found that such skills were correlated in the second grade. Another study also pointed to a correlation between fluency and reading accuracy with rapid automatized naming from the second to the fourth grades (Basso et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough serial automatized naming speed correlated with reading comprehension in the second grade, it was not a significant predictor in logistic regression models when controlled for accuracy. This result aligns with a previous study that did not observe a significant improvement in reading comprehension from automaticity training (Cooper et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This fact suggests that the effect of this speed is secondary or mediated by other skills, such as reading accuracy itself. Another hypothesis is methodological. The present study considered accuracy as the number of words read correctly per minute and not only reading accuracy as a predictor. Since accuracy is a measure that considers time to be derived, this may have overlapped with the temporal measure obtained by serial automated naming speed, statistically canceling out the effect. Future studies should be conducted in this direction so that the relationship between naming speed, reading accuracy, and other skills can be explored in greater depth.\u003c/p\u003e\u003cp\u003eOther research that verified the interaction between these skills showed that rapid naming of objects is the skill that best predicts reading speed in the second grade (Varizo et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Thus, we could think that, at the beginning of schooling, quickly naming objects and accessing the semantic representation in the lexicon helps in the textual reading speed. We can argue, therefore, that rapid naming of objects requires conceptual processing beyond phonological representations and, therefore, is related to reading through the lexical route, indirectly influencing reading comprehension in the early schooling years (Donker et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; dos Santos \u0026amp; Capellini, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e​​In a complementary manner, some studies argue that rapid serial automatized naming reflects efficiency in the rapid recognition of individual words. In contrast, others argue that its influence is related to the ability to process multiple words in sequence in a cascade processing model. Recent evidence suggests that Visual Attention Span (VAS) may play a moderating role in the relationship between rapid automatized naming and reading. Readers with low VAS tend to process words one by one and recognize a limited number of orthographic units. In contrast, those with high VAS can process multiple words at once because they see words as a whole unit, which influences how rapid serial automatized naming relates to reading in each case (Guo, MA, Pan, \u0026amp; Zhang, 2023). Knowing that fourth-and-fifth-graders show a predominance of the lexical route and the second-and-third graders still depend on the phonological route, this could explain why rapid naming differentiated the groups only in the second grade (Oliveira, Germano \u0026amp; Capellini, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study has some limitations that should be considered when interpreting the results. Since it is a cross-sectional study, it is impossible to establish causal relationships between accuracy and reading comprehension. However, it can indicate that children with greater accuracy have a greater probability of achieving good reading comprehension. The reading comprehension assessment was based only on narrative text, which may limit the generalization of the results to different text types. Moreover, the exclusion criterion based on the minimum reading rate may have restricted the representation of children with greater difficulties. Intonation, an important component of reading fluency, was also not considered in this study (Meggiato, Corso \u0026amp; Corso, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Finally, other skills that are known to contribute to reading comprehension, such as working memory, vocabulary, and executive functions, were not included in the regression model.\u003c/p\u003e\u003cp\u003eFor example, language processing speed, measured by the verbal fluency task, contributed to reading comprehension directly and indirectly, mediated by working memory (Candal \u0026amp; Avila, 2025). This fact reinforces the idea that speed measures, such as the rapid naming task, should be better explored and probably interact in different ways and with other reading comprehension skills, thus enabling the automaticity of the processes involved in such an outcome.\u003c/p\u003e\u003cp\u003eThe findings of this study also have relevant contributions to analyzing the predictive factors of good comprehension in a more transparent language. The evidence found in this study shows that reading accuracy (number of words correctly read per minute) is a more robust predictor of reading comprehension than rapid serial automatized naming, especially in the early elementary school years (second and third grades). This fact suggests that even in relatively accessible orthographies, such as Brazilian Portuguese, accuracy must precede fluency to support comprehension \u0026mdash; a pattern consistent with findings in both transparent and opaque orthographies (Kargin et al., 2024; Kim, Wagner, \u0026amp; Lopez, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). These results reinforce the idea that accurate decoding is a universal prerequisite for meaning construction across orthographic systems (Perfetti \u0026amp; Stafura, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCross-linguistic studies often contrast very opposite systems (English vs. Finnish, for example). Brazilian Portuguese, with its semi-transparent orthography, represents an intermediate case between these extremes. It generally presents regular correspondences between phonemes and graphemes, especially in reading, but also includes irregularities (e.g., homonyms, silent letters, and morphological complexities) that require phonological and lexical processing (Abreu \u0026amp; Saltini, 2017; Pinheiro, 2007). Thus, Brazilian Portuguese fills a gap between these extremes, allowing hypotheses to be tested more sensitively.\u003c/p\u003e\u003cp\u003eThe findings of this study have relevant implications for screening and early intervention in reading difficulties. The identification of accuracy as a significant predictor of reading comprehension in the early school years suggests that this variable can be used as a marker of risk in educational contexts. Simple word reading tasks can quickly and efficiently provide clues to future reading comprehension difficulties. Notably, 29.4% of the sample of students did not understand adequately despite having achieved the minimum text reading accuracy level expected for school age. Furthermore, the results support the importance of teaching strategies focused on reading accuracy in the early years and not only on reading speed and fluency. Finally, the data contribute theoretically by corroborating models that highlight the importance of efficient decoding as a basis for reading comprehension and indicate the need for longitudinal studies that explore the interaction between accuracy, lexical access speed, and higher cognitive skills.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study showed that only accuracy in reading isolated words could predict performance in reading comprehension, corroborating the idea that imprecise reading compromises access to the overall meaning of the text. However, the effect of accuracy was restricted to the initial school years. Therefore, it can be considered an important marker for monitoring reading comprehension in the early elementary school years. Serial naming speed did not remain a significant predictor of reading comprehension when controlled for accuracy, which suggests that its effect on comprehension is secondary or mediated by other skills, such as reading accuracy itself.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe research was approved by the Research Ethics Committee of the Federal University of São Paulo (CAAE: 00987412.4.0000.5505; number 38406/12). The document is attached to the submission with the title “PB_Parecer_Substanciado_CEP_38406”.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll those responsible for the research participants signed the Free and Informed Consent Form and the document was attached to the submission with the title \"TCLE\".\u003c/p\u003e\n\u003cp\u003eThe research was funded by “Fundação de Amparo à Pesquisa do Estado de São Paulo – FAPESP” (Project: 2011/11369-0).\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials: not applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eP.S.L. contributed to the writing of the article and performed the statistical analysis; S.P.M. wrote the text; H.G.M. assisted in writing the text; A.S.B.K. and C.A.F.C. collected the data; P.F.N. and D.B.C. assisted in writing the text; C.R.B.A. supervised the work. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e\u003cstrong\u003e\u0026Aacute;lvarez‑Ca\u0026ntilde;izo, A.\u003c/strong\u003e, Su\u0026aacute;rez-Coalla, P., \u0026amp; Cuetos, F. (2015). The role of reading fluency in children\u0026rsquo;s text comprehension. \u003cem\u003eFrontiers in Psychology, 6\u003c/em\u003e, 1810. https://doi.org/10.3389/fpsyg.2015.01810\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eBasso, F. P.\u003c/strong\u003e, Piccolo, L. R., Min\u0026aacute;, C. S., \u0026amp; de Salles, J. F. (2019). Instrumento de Avalia\u0026ccedil;\u0026atilde;o da Flu\u0026ecirc;ncia de Leitura Textual: Da decodifica\u0026ccedil;\u0026atilde;o \u0026agrave; compreens\u0026atilde;o de leitura. \u003cem\u003eLetras de Hoje, 54\u003c/em\u003e(2), 146\u0026ndash;153. https://doi.org/10.15448/1984-7726.2019.2.32519\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eBishop, D. V. M.\u003c/strong\u003e, McDonald, D., Bird, S., \u0026amp; Hayiou‑Thomas, M. E. (2009). Children who read words accurately despite language impairment: Who are they and how do they do it? \u003cem\u003eChild Development, 80\u003c/em\u003e(2), 593\u0026ndash;605.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eBigozzi, L.\u003c/strong\u003e, Tarchi, C., Vagnoli, L., Valente, E., \u0026amp; Pinto, G. (2017). Reading fluency as a predictor of school outcomes across grades 4\u0026ndash;9. \u003cem\u003eFrontiers in Psychology, 8\u003c/em\u003e, 200. https://doi.org/10.3389/fpsyg.2017.00200\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eBr\u0026aring;ten, I.\u003c/strong\u003e, Haverkamp, Y. E., \u0026amp; Anmarkrud, \u0026Oslash;. (2025). Gaining a deeper understanding of the deep cloze reading comprehension test: Examining potential contributors and consequences. \u003cem\u003eReading and Writing, 38\u003c/em\u003e, 425\u0026ndash;446. https://doi.org/10.1007/s11145-024-10521-y\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCandal, D. B.\u003c/strong\u003e, \u0026amp; \u0026Aacute;vila, C. R. B. (no prelo). Phonological working memory and linguistic processing speed in inferential reading comprehension. \u003cem\u003ePsicologia: Reflex\u0026atilde;o e Cr\u0026iacute;tica\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCatts, H. W.\u003c/strong\u003e, Hogan, T. P., \u0026amp; Fey, M. E. (2003). Subgrouping poor readers on the basis of individual differences in reading‑related abilities. \u003cem\u003eJournal of Learning Disabilities, 36\u003c/em\u003e(2), 151\u0026ndash;164. https://doi.org/10.1177/002221940303600208\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCogo‑Moreira, H.\u003c/strong\u003e, Molinari, G. L., de Carvalho, C. A. F., Kida, A. S. B., L\u0026uacute;cio, P. S., \u0026amp; de \u0026Aacute;vila, C. R. B. (2023). Cut‑off point, sensitivity and specificity for screening the reading fluency in children. \u003cem\u003eCoDAS, 35\u003c/em\u003e(3), e20210263. https://doi.org/10.1590/2317-1782/20232021263en\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCohen, J.\u003c/strong\u003e (2013). \u003cem\u003eStatistical power analysis for the behavioral sciences\u003c/em\u003e (2\u0026ordf; ed.). Routledge.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCooper, S.\u003c/strong\u003e, H\u0026eacute;bert, M., Goodrich, J., Leiva, S., Lin, X., Peng, P., \u0026amp; Nelson, R. (2022). Effects of automaticity training on reading performance: A meta‑analysis. \u003cem\u003eJournal of Behavioral Education, 33\u003c/em\u003e(1), 1\u0026ndash;30. https://doi.org/10.1007/s10864-022-09480-7\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCorso, H. V.\u003c/strong\u003e, Piccolo, L. R., Min\u0026aacute;, C. S., \u0026amp; Salles, J. F. (2015). Normas de desempenho em compreens\u0026atilde;o de leitura textual para crian\u0026ccedil;as de 1\u0026ordm; ano \u0026agrave; 6\u0026ordf; s\u0026eacute;rie. \u003cem\u003ePsico PUCRS, 46\u003c/em\u003e(1), 68\u0026ndash;78. https://doi.org/10.15448/1980-8623.2015.1.16900\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCutting, L. E.\u003c/strong\u003e, \u0026amp; Scarborough, H. S. (2006). Prediction of reading comprehension: Relative contributions of word recognition, language proficiency, and other cognitive skills can depend on how comprehension is measured. \u003cem\u003eScientific Studies of Reading, 10\u003c/em\u003e(3), 277\u0026ndash;299. https://doi.org/10.1207/s1532799xssr1003_5\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCunha, V. L. O.\u003c/strong\u003e, Martins, M. A., \u0026amp; Capellini, S. A. (2015). Rela\u0026ccedil;\u0026atilde;o entre flu\u0026ecirc;ncia e compreens\u0026atilde;o leitora em escolares com dificuldades de aprendizagem. \u003cem\u003ePsicologia: Teoria e Pesquisa, 33\u003c/em\u003e, 1\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eDonker, M.\u003c/strong\u003e, Kroesbergen, E., Slot, E., Van Viersen, S., \u0026amp; De Bree, E. (2016). Alphanumeric and non‑alphanumeric rapid automatized naming in children with reading and/or spelling difficulties and mathematical difficulties. \u003cem\u003eLearning and Individual Differences, 47\u003c/em\u003e, 80\u0026ndash;87. https://doi.org/10.1016/j.lindif.2015.12.011\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003edos Santos, B.\u003c/strong\u003e, \u0026amp; Capellini, S. A. (2020). Programa de remedia\u0026ccedil;\u0026atilde;o com nomea\u0026ccedil;\u0026atilde;o autom\u0026aacute;tica r\u0026aacute;pida e leitura para escolares com dislexia: elabora\u0026ccedil;\u0026atilde;o e signific\u0026acirc;ncia cl\u0026iacute;nica. \u003cem\u003eCoDAS, 32\u003c/em\u003e(3), e20180127. https://doi.org/10.1590/2317-1782/20202018127\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eGoldim, J. R.\u003c/strong\u003e (2006). Consentimento e informa\u0026ccedil;\u0026atilde;o: A import\u0026acirc;ncia da qualidade do texto utilizado. \u003cem\u003eRevista HCPA, 26\u003c/em\u003e(3), 117\u0026ndash;122.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eGuo Y.,\u003c/strong\u003e MA, X., Pan, K., \u0026amp; Zhang, H. (2023) The influence of rapid automatized naming on reading and its mechanism. Advances in Psychological Science, 2023, 31(6): 1020-1029.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eHeck, R. H.\u003c/strong\u003e, Thomas, S., \u0026amp; Tabata, L. (2013). \u003cem\u003eMultilevel modeling of categorical outcomes using IBM SPSS\u003c/em\u003e. Routledge.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eHoover, W. A.\u003c/strong\u003e, \u0026amp; Gough, P. B. (1990). The Simple View of Reading. \u003cem\u003eReading and Writing: An Interdisciplinary Journal, 2\u003c/em\u003e, 127\u0026ndash;160.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eHua, A. N.\u003c/strong\u003e, \u0026amp; Keenan, J. M. (2017). Interpreting reading comprehension test results: Quantile regression shows that explanatory factors can vary with performance level. \u003cem\u003eScientific Studies of Reading\u003c/em\u003e. https://doi.org/10.1080/10888438.2017.1280675\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eINEP.\u003c/strong\u003e (2013). \u003cem\u003eCenso da educa\u0026ccedil;\u0026atilde;o b\u0026aacute;sica: Resumo t\u0026eacute;cnico\u003c/em\u003e. Bras\u0026iacute;lia, DF: Instituto Nacional de Estudos e Pesquisas Educacionais An\u0026iacute;sio Teixeira. Recuperado em 22 de dezembro de 2020, de http://download.inep.gov.br/educacao_basica/censo_escolar/resumos_tecnicos/resumo_tecnico_censo_educacao_basica_2012.pdf\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eKarageorgos, P.\u003c/strong\u003e, Richter, T., Haffmans, M.-B., Schindler, J., \u0026amp; Naumann, J. (2020). The role of word-recognition accuracy in the development of word-recognition speed and reading comprehension in primary school: A longitudinal examination. \u003cem\u003eCognitive Development, 56\u003c/em\u003e. https://doi.org/10.1016/j.cogdev.2020.100949\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eKargın, T.\u003c/strong\u003e, G\u0026uuml;ldenoğlu, B., Genge\u0026ccedil;, H., \u0026amp; Alatlı, R. (2024). The role of word decoding speed and accuracy on reading comprehension in a highly transparent and morphologically complex orthography. \u003cem\u003eJournal of Theoretical Educational Science, 17\u003c/em\u003e(3), 592\u0026ndash;615. https://doi.org/10.30831/akukeg.1386249\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eKida, A. S. B.\u003c/strong\u003e, \u0026Aacute;vila, C. R. B., \u0026amp; Capellini, S. A. (2015). Marcadores sint\u0026aacute;ticos no reconto oral de escolares disl\u0026eacute;xicos. \u003cem\u003eCoDAS, 27\u003c/em\u003e(6), 557\u0026ndash;564. https://doi.org/10.1590/2317-1782/20152015106\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eKim, Y. S.\u003c/strong\u003e, Wagner, R. K., \u0026amp; Lopez, D. (2012). Developmental relations between reading fluency and reading comprehension: A longitudinal study from Grade 1 to Grade 2. \u003cem\u003eJournal of Experimental Child Psychology, 113\u003c/em\u003e(1), 93\u0026ndash;111. https://doi.org/10.1016/j.jecp.2012.03.002\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eKintsch, W.\u003c/strong\u003e (1998). \u003cem\u003eComprehension: A paradigm for cognition\u003c/em\u003e. Cambridge University Press.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eLandi, N.\u003c/strong\u003e, \u0026amp; Ryherd, K. (2017). Understanding specific reading comprehension deficit: A review. \u003cem\u003eLanguage and Linguistics Compass\u003c/em\u003e. https://doi.org/10.1111/lnc3.12234\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eLubineau, M.\u003c/strong\u003e, Potier‑Watkins, C., Glasel, H., \u0026amp; Dehaene, S. (2024, maio 5). Examining the impact of reading fluency on lexical decision results in French sixth graders. \u003cem\u003eOpen Mind: Discoveries in Cognitive Science\u003c/em\u003e. https://doi.org/10.1162/opmi_a_00140\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eL\u0026uacute;cio, O. S.\u003c/strong\u003e, Kida, A. S. B., Carvalho, C. A. F., \u0026amp; Cogo‑Moreira, H. (2017). Rapid automatized naming of pictures for children: validity studies and intra‑group norms. \u003cem\u003ePsico‑USF, 22\u003c/em\u003e(1), 35\u0026ndash;47. https://doi.org/10.1590/1413-82712017220104\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eL\u0026uacute;cio, P. S.\u003c/strong\u003e, Cogo‑Moreira, H., Kida, A. S. B., Carvalho, C. A. F., Pinheiro, A. M. V., Mari, J. J., \u0026amp; \u0026Aacute;vila, C. R. B. (2018). Word decoding task: Item analysis by IRT and within‑group norms. \u003cem\u003ePsicologia: Teoria e Pesquisa, 34\u003c/em\u003e, e3437. https://doi.org/10.1590/0102.3772e3437\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eL\u0026uacute;cio, P. S.\u003c/strong\u003e, Coutinho, F. L., Cogo‑Moreira, H., Bandalos, D., Carvalho, C. A. F., Kida, A. S. B., \u0026amp; \u0026Aacute;vila, C. R. B. (2021). Reading comprehension tests for children: Test equating and specific age‑interval reports. \u003cem\u003eFrontiers in Psychology\u003c/em\u003e. https://doi.org/10.3389/fpsyg.2021.662192\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eL\u0026uacute;cio, P. S.\u003c/strong\u003e, Kida, A. S. B., Carvalho, C. A. F., Cogo‑Moreira, H., \u0026amp; \u0026Aacute;vila, C. R. B. (2015). Constru\u0026ccedil;\u0026atilde;o de uma prova para avalia\u0026ccedil;\u0026atilde;o da compreens\u0026atilde;o leitora no ensino fundamental: Estudo piloto. \u003cem\u003eTemas em Psicologia, 23\u003c/em\u003e, 1035\u0026ndash;1050.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eMartins, M. A.\u003c/strong\u003e, \u0026amp; Capellini, S. A. (2019). Relation between oral reading fluency and reading comprehension. \u003cem\u003eCoDAS, 31\u003c/em\u003e(1), e20170244. https://doi.org/10.1590/2317-1782/20182018244\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eMartins, M. A.\u003c/strong\u003e, \u0026amp; Capellini, S. A. (2021). Identification of struggling readers or at risk of reading difficulties with one‑minute fluency measures. \u003cem\u003ePsicologia: Reflex\u0026atilde;o e Cr\u0026iacute;tica, 34\u003c/em\u003e, Article 10. https://doi.org/10.1186/s41155-021-00174-z\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eMeggiato, A. O.\u003c/strong\u003e, Corso, H. V., \u0026amp; Corso, L. V. (2021). Flu\u0026ecirc;ncia de leitura: evolu\u0026ccedil;\u0026atilde;o do construto e rela\u0026ccedil;\u0026otilde;es com a compreens\u0026atilde;o. \u003cem\u003eCadernos de Pesquisa, 51\u003c/em\u003e. https://doi.org/10.1590/198053147797\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eMetsala, J. L.\u003c/strong\u003e, \u0026amp; David, M. D. (2022). Improving English reading fluency and comprehension for children with reading fluency disabilities. \u003cem\u003eDyslexia, 28\u003c/em\u003e(1), 79\u0026ndash;96. https://doi.org/10.1002/dys.1695\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eMiller, R. G.\u003c/strong\u003e (1981). \u003cem\u003eSimultaneous statistical inference\u003c/em\u003e. Springer‑Verlag. https://doi.org/10.1007/978-1-4613-8122-8\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eNorton, E. S.\u003c/strong\u003e, \u0026amp; Wolf, M. (2012). Rapid automatized naming (RAN) and reading fluency: Implications for understanding and treatment of reading disabilities. \u003cem\u003eAnnual Review of Psychology, 63\u003c/em\u003e, 427\u0026ndash;452. https://doi.org/10.1146/annurev-psych-120710-100431\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eOakhill, J.\u003c/strong\u003e, Cain, K., \u0026amp; Elbro, C. (2009). \u003cem\u003eReading comprehension and comprehension difficulties\u003c/em\u003e. In C. Hulme \u0026amp; M. Snowling (Eds.), \u003cem\u003eReading development and difficulties: Bridging the gap between research and practice\u003c/em\u003e (pp. xx\u0026ndash;xx). https://doi.org/10.1007/978-3-030-26550-2\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eOECD.\u003c/strong\u003e (2023). \u003cem\u003ePISA 2022 results (Volume II): Learning during \u0026ndash; and from \u0026ndash; disruption\u003c/em\u003e. OECD Publishing. https://doi.org/10.1787/a97db61c-e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eOliveira, J. B. A\u003c/strong\u003e. e.; Alves, I. S. (2022) The relationship between fluency and reading comprehension: evidence for Brazilian Portuguese speakers. Cadernos de Lingu\u0026iacute;stica, [S. l.], v. 3, n. 1, p. e631, 2022. \u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eOliveira, A. M\u003c/strong\u003e.; Germano, G. D.; Capellini, S. A. (2016) Desempenho de escolares em provas de processo de identifica\u0026ccedil;\u0026atilde;o de letras e do processo l\u0026eacute;xico. Revista CEFAC, v. 18, n. 5, p. 1121-32, 2016. \u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003ePerfetti, C.\u003c/strong\u003e (2010). Decoding, vocabulary, and comprehension: The golden triangle of reading skill. In M. G. McKeown \u0026amp; L. Kucan (Eds.), \u003cem\u003eBringing reading research to life\u003c/em\u003e (pp. 291\u0026ndash;302). Guilford.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003ePerfetti, C.\u003c/strong\u003e, \u0026amp; Stafura, J. (2014). Word knowledge in a theory of reading comprehension. \u003cem\u003eScientific Studies of Reading, 18\u003c/em\u003e(1), 22\u0026ndash;37. https://doi.org/10.1080/10888438.2013.827687\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003ePsyridou, M.\u003c/strong\u003e, Tolvanen, A., Niemi, P., Aro, M., \u0026amp; Lerkkanen, M.-K. (2023). Development of silent reading fluency and reading comprehension across grades 1 to 9: Unidirectional or bidirectional effects between the two skills? \u003cem\u003eReading and Writing, 36\u003c/em\u003e, 1969\u0026ndash;1996. https://doi.org/10.1007/s11145-022-10371-6\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eSalles, J. F.\u003c/strong\u003e, \u0026amp; Paula, F. D. (2016). Compreens\u0026atilde;o da leitura textual e sua rela\u0026ccedil;\u0026atilde;o com as fun\u0026ccedil;\u0026otilde;es executivas. \u003cem\u003eEduca\u0026ccedil;\u0026atilde;o Revista, 62\u003c/em\u003e, 53\u0026ndash;67.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eScarton, C. E.\u003c/strong\u003e, \u0026amp; Alu\u0026iacute;sio, S. M. (2010). An\u0026aacute;lise da inteligibilidade de textos via ferramentas de processamento de l\u0026iacute;ngua natural: Adaptando as m\u0026eacute;tricas do Coh-Metrix para o portugu\u0026ecirc;s. \u003cem\u003eLinguam\u0026aacute;tica, 2\u003c/em\u003e(1), 45\u0026ndash;61.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eSmith, R.\u003c/strong\u003e, Snow, P., Serry, T., \u0026amp; Hammond, L. (2021). The role of background knowledge in reading comprehension: A critical review. \u003cem\u003eReading Psychology, 42\u003c/em\u003e(6), 540\u0026ndash;557. https://doi.org/10.1080/02702711.2021.1888348\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eSnowling, M.\u003c/strong\u003e, \u0026amp; Hulme, C. (2012). (Special Issue: Developmental disorders and cognitive science). \u003cem\u003eJapanese Psychological Research, 55\u003c/em\u003e(2), 186\u0026ndash;202. https://doi.org/10.1111/j.1468-5884.2012.00541.x\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eSparks, E.\u003c/strong\u003e, \u0026amp; Metsala, J. L. (2023). Morphological awareness predicts reading comprehension in first grade students. \u003cem\u003eFrontiers in Education, 8\u003c/em\u003e, 1304688. https://doi.org/10.3389/feduc.2023.1304688\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eVarizo, S.\u003c/strong\u003e, Correa, J., Mousinho, R., \u0026amp; Navas, A. L. (2022). A contribui\u0026ccedil;\u0026atilde;o da nomea\u0026ccedil;\u0026atilde;o automatizada r\u0026aacute;pida para a velocidade e compreens\u0026atilde;o de leitura textual em crian\u0026ccedil;as brasileiras do ensino fundamental. \u003cem\u003eAudiology \u0026ndash; Communication Research, 27\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eWallot, S.\u003c/strong\u003e (2014). From \u0026ldquo;cracking the orthographic code\u0026rdquo; to \u0026ldquo;playing with language\u0026rdquo;: Toward a usage‑based foundation of the reading process. \u003cem\u003eFrontiers in Psychology\u003c/em\u003e. https://doi.org/10.3389/fpsyg.2014.00891\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eWeekes, B. S.\u003c/strong\u003e (1997). Differential effects of number of letters on word and nonword naming latency. \u003cem\u003eQuarterly Journal of Experimental Psychology, 50\u003c/em\u003e(2), 439\u0026ndash;456. https://doi.org/10.1080/027249897392170\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eWolf, M.\u003c/strong\u003e, \u0026amp; Katzir‑Cohen, T. (2001). Reading fluency and its intervention. \u003cem\u003eScientific Studies of Reading, 5\u003c/em\u003e(3), 211\u0026ndash;239. https://doi.org/10.1207/S1532799XSSR0503_2\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eWu, Y.\u003c/strong\u003e, Barquero, L. A., Pickren, S. E., Barber, A. T., \u0026amp; Cutting, L. E. (2020). The relationship between cognitive skills and reading comprehension of narrative and expository texts: A longitudinal study from Grade 1 to Grade 4. \u003cem\u003eLearning and Individual Differences, 80\u003c/em\u003e, 101848. https://doi.org/10.1016/j.lindif.2020.101848\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eYang, L.\u003c/strong\u003e, Xiong, Y., \u0026amp; Chen, Q. (2023). The role of linguistic and cognitive skills in reading Chinese as a second language: A path analysis modeling approach. \u003cem\u003eFrontiers in Psychology, 14\u003c/em\u003e, 1131913. https://doi.org/10.3389/fpsyg.2023.113191\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Reading, Reading Comprehension, Mental Processes, Elementary School","lastPublishedDoi":"10.21203/rs.3.rs-7849144/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7849144/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eReading comprehension difficulties are common in neurodevelopmental disorders, yet their underlying cognitive predictors remain debated. This study examined the predictive roles of reading accuracy\u0026mdash;operationalized as words read per minute\u0026mdash;and rapid automatized serial naming (RAN) in reading comprehension among 637 Brazilian students from second to fifth grade (57% girls), primarily enrolled in public schools (87.4%). Participants were classified into two groups: those with reading comprehension difficulties (G1) and those without (G2). Assessments included oral reading of isolated words, RAN of objects, and reading of a narrative text. Results indicated that G1 exhibited significantly lower accuracy compared to G2. Regression analyses showed that reading accuracy, but not RAN, predicted reading comprehension, with predictive effects restricted to the early school years. These findings highlight reading accuracy as a relevant marker for monitoring reading comprehension development in early elementary education and may inform early identification and intervention strategies for children at risk of neurodevelopmental reading difficulties.\u003c/p\u003e","manuscriptTitle":"Accuracy and phonological access as reading comprehension predictors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-06 18:49:29","doi":"10.21203/rs.3.rs-7849144/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d382cb67-1cfd-4453-ad25-a000a02c0e90","owner":[],"postedDate":"November 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-13T15:53:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-06 18:49:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7849144","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7849144","identity":"rs-7849144","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}