Rapid naming and phonological processing as predictors of reading and spelling.
Rapid automatic naming, as first conceptualized by Denckla and Rudel (1976a; 1976b), consisted of four separate series of letters, numbers, colors, and objects presented over and over in random order on a 50-item matrix. Students would be asked to name the series as quickly as possible, and separate times were kept for each. Rapid naming is considered by some researchers to be subsumed under phonological skills (Felton & Brown, 1990; Wagne, Torgesen, & Rashotte, 1999; Shaywitz, 2003) and by others as a marker for processing speed (Ackerman, Holloway, Youngdahl & Dykman, 2001; Hammill & Mather, 2003). It has also been shown to predict reading development. Poor readers are slower at rapid naming of letters, digits, colors and familiar objects (Ackerman, Dykman, & Gardner, 1990; Denckla & Rudel, 1976; Fawcett & Nicolson, 2001; Spring & Capps, 1974; Spring & Farmer, 1975; Spring & Davis, 1988; Torgesen & Houck, 1980; Wolf, 1986; Wolf, 1991; Wolf & Obergon, 1992). Naming speed differences distinguish reading disabled children from those with other learning disabilities and from children with attention deficit disorders (Denckla & Rudel, 1976, Felton, Wood, Brown & Campbell, 1987). It has been shown to account for a significant amount of variance in reading skills beyond that accounted for by a phonological processing measure (Manis & Freedman, 2001; Schatschneider, Carlson, Francis, Foorman, & Fletcher, 2002; Spring & Davis, 1988). Naming speed can be evaluated in both discrete (the time necessary to produce the name for one item) and continuous (the time necessary to produce the names of a series of items) naming trials. Differences have been found under both conditions (Wolf & Goodglass, 1986; Wolf & Obergon, 1992); however, serial naming tasks have generally been seen as a stronger predictor of future reading success (Allor, 2002; Bowers & Swanson, 1991; Walsh, Price & Gillingham, 1988; Wolf, 1991). Wagner et al (1993) found a significant correlation between word identification with serial naming of both letters and digits but not with isolated naming. Spring & Davis (1988) suggested that continuous naming tasks are more like reading than discrete trial naming because of the necessity of overlapping cognitive demands (naming one while accessing next).
Bowers (1996) also proposed that "naming speed influences the ability to learn the orthographic pattern of words" (p.1). In a study requiring subjects to recall letter strings of nonsense words briefly flashed, she found a relationship between the facilitative effects of orthographic redundancy and rapid naming that was independent of phonological processing. That is, orthographic redundancy was more helpful for those students who were slower on rapid naming tasks.
The relationship between these two variables, phonological processing and rapid automatic naming, remains unclear. Wolf (1996) sees these two variables as being markers for separate cognitive processes. Along with other researchers (Bowers, 1996, 2001; Bowers & Wolf, 1993; Spring, personal communication; Wolf, 2001; Wolf & Bowers, 1999), she has suggested a double deficit theory of reading disability to account for the common co-occurrence of deficits in rapid automatic naming and phonological processing that are seen in disabled readers. According to this view, the two tasks represent independent cognitive functions and the most severely disabled readers are deficient in both. Naming speed has been found to have long-term predictive power, independent of phonological processing skills, for measures of reading proficiency (Newhaus & Swank, 2002; Spring & Davis, 1988; Torgeson et al, 1997). In a dyslexia subtyping study, Morris and Shaywitz (1998) identified seven subtypes of reading disability based on a series of cognitive assessments. Six of these subtypes displayed a core deficit in phonological processing while the seventh was categorized as displaying a rate deficit. Further complicating the picture, the relationship between naming speed and reading skills changes across levels of reading skill and across age of the reader (Manis, Seidenberg & Doi, 1999; Meyer, Wood, Hart & Felton, 1998a, 1998b; Torgesen, Wagner, Rashotte, Burgess & Hecht, 1997). In their review of evidence regarding the double deficit hypothesis Vukovic and Siegal (2006) concluded that "the existence of a naming speed only subtype of dyslexia has not been consistently documented" (p. 44). Vukovic and Siegal also note that the research on naming speed deficits is difficult to interpret due to considerable variation in how samples are chosen and defined. Vukovic and Siegal's review of the literature highlights the need for studies that more clearly explicate how naming speed and phonological processing may differentially affect different aspects of literacy.
Bowers (1996) in studying recall of orthographic patterns found an interaction effect between phonological processing and rapid naming. Students who were only deficient in phonemic awareness were better able to recall briefly presented letter strings than those who were poor at both rapid naming and phonological tasks. In addition, for students with phonological processing problems, proficiency in rapid naming appeared to improve performance.
Researchers have also found differential responses to intervention depending on whether the reader is deficient in one or both areas (and which one area) (Bowers, 1993; Bowers & Wolf, 1993; Levy, Bourassa & Horn, 1999). Such studies suggest that rather than being indicators of a single phonological core deficit (Torgeson, Wagner & Rashotte, 1994) naming speed and phonological processing may represent two different cognitive processes.
It is also unclear which skill is more critical to reading and which to spelling (Cossu et al, 1993; Perin, 1983). Both phonological processing and rapid naming performance are linked to reading and spelling performance across ages (Adams, 1990; Wagner & Torgeson, 1987). Bowers, Sunseth and Golden (1999) investigated one possible mechanism for this link. They developed and administered the Quick Spelling Test in which students reported the letters they had seen in briefly (250 msec) presented words, pseudowords and nonwords. The authors found that third graders with naming speed deficits were less successful at finding letter strings in illegal words than third graders with phonological deficits only. They suggest that naming speed deficits interfere with the ability to learn orthographic patterns. Levy, Bourassa and Horn (1999) found that children with naming speed deficits were slower to learn words, particularly when learning the words as whole units. Spring and Davis (1988) see naming speed as measuring the automaticity of lower level processes that are critical in developing word recognition. For the beginning reader, decoding words requires producing beginning phonemes while accessing the ones following. This is necessary because following phonemes affect the articulation of the preceding one. Thus, accurate blending is dependent upon fluent and rapid identification of constituent phonemes. Children with slow naming speed will have difficulty with this and thus require more learning trials with each individual word in order to develop accurate automatic recognition (even if they have good phonological processing and accurate decoding skills).
More information is needed regarding the uniqueness and co-occurrence of these deficits in relationship to both reading and spelling. The purpose of this study was to further investigate the relationships between phonological processing, rapid naming, reading and spelling in a specific group: children who had been referred for difficulties in reading and/or spelling. Specifically, this study examined the predictive value of naming speed and phonological processing for single word reading, decoding nonsense words, reading comprehension and spelling.
Participants were children in grades 2 through 5 who had been referred to one of two clinics (Raskob Learning Institute at College of Holy Names and School Diagnostic Clinic at California State University, California) for assessment and possible intervention in regard to difficulties in reading or spelling. Data were collected from 65 students. Students were predominantly white and from urban settings. The socio-economic status of the homes was mixed. All students were native English speakers.
Cognitive ability scores were used to screen out students whose reading difficulties might be due to overall cognitive or language deficits. Students whose verbal scores fell below 80 were not included in the data analysis.
WISC-III (Wechsler, 1991): The WISC-III is an individually administered test of general cognitive ability. The 12 subtests on this test are divided between a Verbal and a Performance Scale. Standard scores are derived for a Full Scale, Verbal and Performance I.Q. with mean = 100 and SD = 15.
Wechsler Individual Achievement Test (WIAT) (Wechsler, 1991): Basic Reading: Students are presented with a series of individual, real words, printed on a card, and are asked to read the words in a row-by-row manner. WIAT Comprehension: The child reads a series of brief passages and responds orally to questions presented by the examiner. The test measures the ability to recall detail and make inferences. WIAT Dictation: Dictation is a measure of spelling skill in which words are presented orally and the student is asked to write the words.
Woodcock-Johnson Word Attack (Woodcock & Johnson, 1989): Students are required to read nonsense words that follow standard orthographic patterns. Words are presented on a flip chart card in groups of six. This test assesses skill in applying knowledge of phonics and in orthographic analysis.
Test of Auditory Analysis Skills (TAAS) (Rosner, 1975): The TAAS consists of 13 items evaluating a child's phonological processing ability through manipulation of constituent sounds in words orally presented to the child. The test items increase in complexity. Beginning items require the child to split compound words; complex items require removing phonemes and saying the new word.
Digit Naming Speed (DNS) (Spring & Davis, 1988). On the DNS, students are presented with a 5" x 8" card on which 50 digits are presented. Students are instructed to read the digits as quickly as possible. Two trials (with different number series) are given. Time to read all digits is recorded. The DNS is a measure developed for use in research projects only and does not have psychometric data available (Spring & Davis, 1988). However the format is similar to that of other naming speed measures (Denckla & Rudel, 1976; Compton, Olson, deFries & Pennington, 2002: Wagner, Torgesen and Rashotte, 1999). Wagner and colleagues report test-retest reliabilities for the Rapid Digit Naming Test on the Comprehensive Test of Phonological Processing to range from .80 to .91, depending on age group.
All students were tested individually in quiet rooms. Additional psychoeducational testing, unrelated to the present study, was also completed during the testing sessions. Testing was generally done over two-to-three testing sessions. No session lasted longer than 2 1/2 hours.
The means and standard deviations for all measures used in this analysis are presented in Table 1. All measures, except the TAAS, resulted in standardized age-normed scores. Therefore, prior to further statistical analysis, the TAAS raw scores were regressed on age and converted to a standard score. Because the TAAS is subject to ceiling effects the distribution of scores was analyzed for deviance from a normal distribution. The TAAS scores for the sample population were normally distributed (Pcs = -.42).
The inter-correlations among the measures are presented in Table 1. As expected there is strong correlation among these measures of literacy. The weakest correlations are between verbal IQ and the literacy measures. Because verbal IQ did not correlate at a significant level with any of the literacy measures reported it was not used in further analysis. DNS and TAAS are the variables used to measure the identified underlying cognitive processes of rapid naming and phonological processing, respectively. The correlation between these two variables was significant at the .05 level but weak (r=.28). This suggests that these measures are indeed measuring different skills. Given the stronger correlations between these two variables (DNS and TAAS) and measures of literacy (Dictation, Basic Reading and Reading Comprehension) than between the two variables themselves, it was expected that they would have independent contributions to predicting performance on these measures. In addition, both variables were more strongly correlated with Word Attack than with each other.
Regression analysis was used to test the predictive power of rapid naming, as measured by the DNS, and phonological processing, as measured by the TAAS, to Dictation, Basic Reading and Reading Comprehension. Each analysis was done in the same manner as described below.
* First, stepwise regression was performed with the selected literacy measure as dependent variables and both DNS and TAAS entered as independent variables. Stepwise regression determines the strongest predictor and builds models based upon the predictive value of each possible variable.
* Next, two-step forced entry regression was performed entering the weaker of the two predictors first to check for its contribution to prediction.
* To further analyze the independent and overlapping contributions of the two independent variables, partial correlations with the dependent variable were computed for each independent variable.
Basic Reading. The results of the regression analysis with Basic Reading as the dependent variable and DNS and TAAS as predictors are presented in Table 2. Only DNS contributed significantly to the prediction of the Basic Reading score. In this analysis, DNS accounted for 40% of the variance in performance on Basic Reading. Adding TAAS to the equation did not add any significant predictive power. When the order was reversed and TAAS was entered into the regression equation first, it accounted for 12% of the variance. , when entered into the regression equation second, it contributed an additional 31%. Partial correlation statistics revealed that both variables correlated significantly with Basic Reading independent of the other variable. Those values are also presented in Table 2.
Reading Comprehension. The results of the regression analysis with Reading Comprehension as the dependent variable and DNS and TAAS as predictors are presented in Table 3. Both DNS and TAAS contributed significantly to the prediction of the Comprehension score. DNS was the strongest predictor, accounting for 22% of the variance in scores. The TAAS accounted for an additional 9% when entered after DNS. When the order was reversed and TAAS was entered into the regression equation first, it accounted for 19% of the variance. DNS, when entered into the regression equation second, contributed an additional 13%. Partial correlation statistics revealed that both variables correlated significantly with Reading Comprehension independent of the other variable. Those values are also presented in Table 3.
Dictation. The results of the regression analysis with Dictation as the dependent variable and DNS and TAAS as predictors are presented in Table 4. Both DNS and TAAS contributed significantly to the prediction of the Dictation score. However, DNS was clearly the strongest predictor, accounting for 40% of the variance in scores. The TAAS accounted for an additional 7% when entered after DNS. When the order was reversed and TAAS was entered into the regression equation first, it accounted for 19% of the variance. DNS, when entered into the regression equation second, contributed an additional 28%. Partial correlation statistics revealed that both variables correlated significantly with Dictation independent of the other variable. Those values are also presented in Table 4.
Word Attack. Word Attack can be considered both a predictor of other literacy skills as well as a foundational literacy skill. Examination of the correlations presented in Table 1 shows that Word Attack was the variable most highly correlated with performance on the common markers of literacy: Basic Reading, Reading Comprehension, and Dictation. In addition, the correlations between Word Attack and both DNS and TAAS were stronger than the correlation between DNS and TAAS. This gives support to the proposal that DNS and TAAS are measuring independent cognitive processes, but that both are important in developing decoding skills. To further analyze the part that rapid naming and phonological processing play in developing decoding skills and likewise the part that decoding skills play in predicting literacy, regression analysis was performed with Word Attack included with DNS and TAAS as predictor variables. The results are presented in Table 5. Word attack proved to be a strong predictor of Dictation and Basic Reading. It also predicted scores on the Reading Comprehension measure.
Word Attack was also a stronger predictor for Basic Reading than either DNS or TAAS. It accounted for 62% of the variance in scores on Basic Reading. DNS added 8% to the predictive power of the model. TAAS, however, did not add to the predictive value of the model after the variance accorded to Word Attack and DNS was accounted for.
In predicting Reading Comprehension, Word Attack was also the strongest predictor of the three variables. However, its predictive power was much less: stepwise regression resulted in an R2 of .28 with Word Attack as the only predictor. At the next level, TAAS was found to significantly add to the power of the equation resulting in an R2 change of .08. The addition of DNS to the model did not add significantly to its predictive power.
Finally, Word Attack was a more potent predictor of Dictation than either DNS or TAAS, accounting for 53% of the variance in scores when entered alone. DNS added 8% more predictive value and the addition of TAAS as an additional predictor increased the amount of variance accounted for by 2%.
Word Attack was also investigated as a dependent variable, to measure the predictive power of rapid naming (DNS) and phonological processing (TAAS) to the development of decoding skills. DNS was the strongest predictor of performance on the Word Attack test (Table 6). It accounted for 26% of the variance in these scores. Adding TAAS to the model increased the amount of variance predicted by 7%. Partial correlations of DNS and TAAS with Word Attack indicated that both variables had significant independent correlation with Word Attack.
This study sought to further exam the double deficit hypothesis and the unique contribution of rapid naming and phonological processing to different aspects of literacy. It was expected that phonological processing (TAAS) would be most strongly predictive of Word Attack and Dictation. It was also expected that both TAAS and DNS would contribute significantly and uniquely to all aspects of literacy measured.
The results were surprising because of the clear superiority of DNS, a measure of rapid naming, in predicting scores on tests of spelling, word reading and reading comprehension. For each of these variables DNS accounted for considerably more variance than did TAAS, whether entered first or second into the regression equations. Indeed, TAAS added no additional power to the prediction of Word Reading. In contrast to the results of this study, the bulk of previous research shows phonological processing to be a strong and consistent predictor of literacy. It is likely that the results of this study are counter to this trend
because of the population sampled. Recall that the participants in this study were all students who had been referred for literacy-related problems. A review of their mean scores on the literacy-related measures reveals that these students were indeed performing below average on these tasks. With a Mean verbal IQ of 101, the group's mean scores on Word Reading, 90; Dictation, 88; and Reading Comprehension, 88 are lower than would be expected. Despite average verbal ability, these students are performing below average on literacy-related measures. Scarborough (1998) found that rapid naming was a stronger predictor of future reading success for disabled readers than for the normal reading population. The results of this study lend further support to the important role that rapid naming plays in developing reading for poor readers. However, it is still unclear exactly what these rapid naming measures are measuring. Shaywitz (2003) explains rapid naming as a measure of phonological accessing, Roberts and Mather (1997) as orthographic processing, and Fawcett and Nicolson (2000) as a variety of timing related deficits attributable to abnormal cerebellar functioning. It is clear that more research needs to be done to better understand this set of measures.
These results also strengthen the double deficit hypothesis. Rapid naming and phonological processing were found to have strong partial correlations with literacy measures, independent of each other. This provides support for conceptualizing rapid naming and phonological processing measures as markers for two separate cognitive processes, rather than as markers of a general underlying phonological processing disorder.
These students were deficient in both rapid naming and phonological processing in comparison to their peers. The mean for DNS was 85 (M=100). On the TAAS the mean raw score was approximately two grade levels below the students' mean grade placement. As a group they exhibited significant deficits in phonological processing and rapid naming.
The power of decoding skills as measured by Word Attack to predict success in reading and spelling was striking. It accounted for over 50% of the variance on both Dictation and Basic Reading. It was less strong in predicting reading comprehension, indicating that there are other important variables to consider as well. When Word Attack was entered into the regression equations predicting Dictation and Basic Reading, both DNS and TAAS lost much of their predictive significance. Understanding of sound-symbol relationships and the ability to use them on novel words is a strong predictor of reading success even for disabled readers.
The contribution of rapid naming and phonological processing to decoding skills was also analyzed. Surprisingly, rapid naming (DNS) was a stronger predictor of performance on the Word Attack test than was phonological processing (TAAS). However, with both variables in the model only 33% of the variance in scores was accounted for. It is possible that instructional differences may account for a significant amount of the remaining variance in decoding skills. The students were from many different schools; therefore, it is likely that the amount of direct instruction regarding phonics that the students received varied considerably amongst the participants.
Implications for school psychology practice
Referrals for reading difficulties are one of the most common referring problems encountered by school psychologists. Therefore, it is important for them to be aware of the cognitive processes that appear to underlie and predict success or failure in literacy activities. Such knowledge will lead to a more informed and useful diagnostic protocol. The results of this study suggest that psychologists should look beyond the overall measures of reading ability and assess underlying processes of rapid naming and phonological processing. This information will be useful in formulating interventions and projecting the level of support that might be needed. Several current tests provide measures of both phonological processing and rapid naming. These include comprehensive achievement test batteries such as the Kaufman Test of Educational Achievement II (Pearson Assessment) and the Woodcock Johnson Tests of Achievement and Tests of Cognitive Abilities (Riverside Publishing) and reading batteries such as the Comprehensive Test of Phonological Processing (Pro-Ed) and the Process Assessment of the Learner II (Psychological Corporation).
This study also emphasizes the importance of looking at the subskills of reading, particularly decoding of nonsense words. In doing so, diagnosticians will be have a more complete picture of a child's reading skill. The strength of decoding as a predictor of other reading related measures also underscores the importance of including direct instruction in decoding in the reading curriculum. This study suggests that it is particularly important for struggling readers.
These results strengthen the case for investigating the double deficit hypothesis more intensely and for recognizing the critical role of rapid naming in developing literacy. It is particularly important to consider rapid naming when investigating the struggles that disabled readers have and how to differentiate among these students. Rapid naming appears to be a marker for a cognitive process for which a threshold level of proficiency is critical to becoming a successful reader. That is, one just needs to be "good enough." Differences in rapid naming wash out in the general population--more is not necessarily better. However, in the disabled reading population they are of central importance. Students who are strong in rapid naming may be able to compensate for poor phonological processing skills.
As an example, consider the role that rapid naming might play in developing the decoding skills measured by Word Attack. Understanding and being able to internally manipulate the sounds of language makes gaining decoding skills easier. However, if a student struggles with phonological processing, he can still learn common sound-symbol patterns through repeated exposures to their association. If he is quick at recognizing individual letters and then forming common digraphs, he will link the letters to the sounds in the word. This word will become a sight word and the letter-sound associations will form a part of his data bank, accessible for identifying like words. If he is impaired in naming speed, he will not label and recognize the letters and digraphs as quickly and these associations will form more slowly. When asked to decode novel words this student will have a smaller store of common sound-symbol patterns to access.
Though this study did not explore differential response to interventions based upon deficits in rapid naming and/or phonological processing other studies suggest differential response. This study, however, in demonstrating the important role that rapid naming plays in developing literacy for disabled readers, adds support to the need to further investigate differential response to intervention.
Finally, this study did not include a measure of reading fluency. However, studies suggest that naming speed measures are good predictors of reading fluency (Kame'enui, Simmons, Good & Harn, 2001; Petrill, Deater-Deckard, Thompson, DeThorne, & Schatschneider, 2006). Further research in this area is needed as being able to differentiate between the components of proficient reading and the processes that are predictive of each will promote more targeted interventions.
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TABLE 1 Means (M). Standard Deviations (SD) and Zero-order Correlations of Studc Vanables (N=65) Measure M SD 1 2 1. Verbal 10 101 17 2. DNS 85 14 -165 3. TAAS 8.5 2.9 .168 .275 * 4. Word Attack 93 13.1 .083 .508 ** 5. Basic Read 90 13.1 .137 .616 ** 6. Comprehension 89 11.9 .241 .458 ** 7. Dictation 88 10 .238 .616 ** Measure 3 4 5 6 7 1. Verbal 10 2. DNS 3. TAAS 4. Word Attack .412 ** 5. Basic Read .349 ** .773 ** 6. Comprehension .430 ** .521 ** .686 ** 7. Dictation .449 ** .724 ** .760 ** .600 ** Note: DNS= Digit Naming Speed. TAAS= Test or Auditory Analysis Skills. Word Attack = a nonsense mord decoding task. Basic Readingn = a real mord reading task. Comprehension = a reading comprehension task. Dictation = a spelling task. TABLE 2 Regression Analysis Predicting WIAT Basic Reading Variable [R.sup.2] (Change) F Change DNS .400 (.400) 42.592 Forced Order with TAAS First TAAS .117(.117) 8.471 DNS .430(.313) 34.643 Variable Sig. F Change Partial [r.sup.2] DNS .000 .632 Forced Order with TAAS First TAAS .005 .226 DNS .000 .596 Note: DNS = Digit Naming Speed. TAAS = Test of Auditory TABLE 3 Regression Analysis Predicting WIAT Reading Comprehension DNS .223 (.223) 16.911 .000 .397 TAAS .316 (.094) 7.934 .007 .347 Forced Order with TAAS First TAAS .188 (.188) 13.701 .000 .347 DNS .316 (.128) 10.845 .002 .397 Note: DNS = Digit Naming Speed. TAAS = Test of Auditory Analysis Skills. WIAT = Wechsler Individual Achievement Test TABLE 4 Regression Analysis Predicting WIAT Dictation Variable [R.sup.2] (Change) F Change DNS .395 (.395) 41.74 TAAS .466 (.071) 8.198 Forced Order with TAAS First TAAS .188 (.188) 14.567 DNS .466 (.278) 32.277 Variable Sig. F Change Partial [r.sup.2] DNS .000 .585 TAAS .006 .342 Forced Order with TAAS First TAAS .000 .342 DNS .000 .585 Note: DNS = Digit Naming Speed; TAAS = Test of Auditory Analysis Skills; WIAT = Wechsler Individual Achievement Test TABLE 5 Regression Analysis Predicting WIAT Scores Including Word Attack Scores Variable [R.sup.2] (Change) F Change Predicting WIAT Basic Reading Word Attack .616 (.616) 94.486 DNS .677 (.061) 10.977 Predicting WIAT Dictation Word Atlack .531 (.531) 66.845 DNS .612 (.081) 12.143 TAAS .799 (.026) 4.178 Predicting WIAT Comprehension Word Attack .278 (.278) 21.203 TAAS .362 (.084) 7.099 Variable Sig. F Change Partial [r.sup.2] Predicting WIAT Basic Reading Word Attack .000 .695 DNS .002 .399 Predicting WIAT Dictation Word Atlack .000 .565 DNS .001 .398 TAAS .046 .261 Predicting WIAT Comprehension Word Attack .000 .341 TAAS .010 .291 Note: Word Attack = a nonsense word decoding task; DNS = Digit Naming Speed; TAAS = Test of Auditory Analysis Skills; WIAT = Wechsler Individual Achievement Test TABLE 6 Regression Analysis Predicting Woodcock-Johnson Word Attack Scores Variable [R.sup.2] (Change) F Change DNS .259 (.259) 20.594 TAAS .325 (.066) 5.690 Variable Sig. F Change Partial [r.sup.2] DNS .000 .441 TAAS .020 .299 Note: DNS = Digit Naming Speed; TAAS = Test of Auditory Analysis Skills
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|Author:||Christo, Catherine; Davis, Jack|
|Publication:||The California School Psychologist|
|Date:||Jan 1, 2008|
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