rapid naming speed components and reading development in a consistent orthography

Journal of Experimental Child Psychology 112 (2012) 1–17

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Journal of Experimental ChildPsychology

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Rapid naming speed components and reading developmentin a consistent orthography

George K. Georgiou a,⇑, Timothy C. Papadopoulos b,c, Argyro Fella b,Rauno Parrila a

a Department of Educational Psychology, University of Alberta, Edmonton, Alberta, Canada T6G 2G5b Department of Psychology, University of Cyprus, CY 1678, Nicosia, Cyprusc Centre for Applied Neuroscience, University of Cyprus, CY 1678, Nicosia, Cyprus

a r t i c l e i n f o a b s t r a c t

Article history:Received 21 January 2011Revised 16 November 2011Available online 31 January 2012

Keywords:Rapid automatized namingReading fluencyLongitudinalOrthographic consistencyPhonological awarenessOrthographic processingSpeed of processing

0022-0965/$ - see front matter � 2011 Elsevier Indoi:10.1016/j.jecp.2011.11.006

⇑ Corresponding author.E-mail address: [email protected] (G.K. Geo

We examined how rapid automatized naming (RAN) components—articulation time and pause time—predict word and text readingfluency in a consistent orthography (Greek). In total, 68 childrenwere followed from Grade 2 to Grade 6 and were assessed threetimes on RAN (Digits and Objects), phonological awareness, ortho-graphic processing, speed of processing, and reading fluency. BothRAN components were strongly related to reading fluency and,with few exceptions, accounted for unique variance over and abovethe contribution of speed of processing, phonological awareness,and orthographic processing. The amount of predictive varianceshared between the components and the cognitive processingskills varied across time. The implications of these findings forthe RAN–reading relationship are discussed.

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Introduction

Rapid automatized naming (RAN), defined as the ability to name as fast as possible highly familiarsymbols such as digits, letters, colors, and objects, has been found to be a robust predictor of readingacquisition (e.g., Compton, 2003; de Jong & van der Leij, 1999; Kirby, Parrila, & Pfeiffer, 2003; Landerl &Wimmer, 2008; Manis, Doi, & Bhadha, 2000; Parrila, Kirby, & McQuarrie, 2004; Savage & Frederickson,2005). However, the mechanism that is responsible for the relationship between RAN and readingremains unclear.

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2 G.K. Georgiou et al. / Journal of Experimental Child Psychology 112 (2012) 1–17

Different theories have emerged to explain the RAN–reading relationship (see Kirby, Georgiou,Martinussen, & Parrila, 2010, for a review). Torgesen, Wagner, and colleagues (e.g., Torgesen, Wagner,& Rashotte, 1994; Torgesen, Wagner, Rashotte, Burgess, & Hecht, 1997), for example, hypothesizedthat RAN is an index of the rate of access to stored phonological information in long-term memoryand is related to reading via the more general construct of phonological processing. Bowers and col-leagues (e.g., Bowers & Newby-Clark, 2002; Bowers & Wolf, 1993), in turn, proposed that RAN is re-lated to reading because of its connection to orthographic processing. Children with slow namingspeed process individual letters in a word too slowly to enable associations between the letters tobe formed, thereby hindering the formation of good-quality representations of orthographic patternsthat commonly occur in written English. Finally, Kail and colleagues (e.g., Kail & Hall, 1994; Kail, Hall,& Caskey, 1999) attributed the relationship between RAN and reading to a global speed of processingfactor because skilled performance in both RAN and reading depends, in part, on the rapid execution ofthe underlying subprocesses.

Although phonological processing, orthographic processing, and speed of processing have beenconsidered as possible mediators in the RAN–reading relationship, they have rarely been consideredtogether in a study (see Cutting & Denckla, 2001; Georgiou, Parrila, & Kirby, 2009, for exceptions). Thisis particularly important in light of evidence that RAN accounts for variance in reading beyond the ef-fects of phonological processing (e.g., Parrila et al., 2004), orthographic processing (e.g., Cutting &Denckla, 2001), and speed of processing (e.g., Bowey, McGuigan, & Ruschena, 2005). In addition, asshown in Georgiou and Parrila’s (2007) study, RAN predicted reading through phonological processingand orthographic processing only when speed of processing was not included in the regression model.Thus, there is a need for a study that examines how much of RAN’s predictive variance in reading isshared with the candidate mediators.

The majority of studies examining the nature of the relationship between RAN and reading havebeen conducted in English (e.g., Georgiou, Parrila, Kirby, & Stephenson, 2008; Jones, Obregón, Kelly,& Branigan, 2008; Powell, Stainthorp, Stuart, Garwood, & Quinlan, 2007; Savage, Pillay, & Melidona,2007). English, however, lies at the extreme end of the orthographic consistency continuum (e.g.,Seymour, Aro, & Erskine, 2003), and what is known for reading in English may be the exception ratherthan the rule (e.g., Share, 2008). The orthography in which children are learning to read may affect therelationship between RAN and reading in at least three ways. First, it has been argued that the effectsof phonological awareness on reading in consistent orthographies are limited to the first 1 or 2 years ofschooling (e.g., de Jong & van der Leij, 1999; Georgiou, Parrila, & Papadopoulos, 2008; Leppänen,Niemi, Aunola, & Nurmi, 2006). If RAN influences reading through the effects of phonologicalawareness (see Bowey et al., 2005; Savage et al., 2007; Torgesen et al., 1997, for proponents of thisposition), then it should have a time-limited effect as well. This argument is in contrast to severalstudies in consistent orthographies showing that RAN continues to predict reading beyond theelementary school years (e.g., Morfidi, van der Leij, de Jong, Scheltinga, & Bekebrede, 2007; Protopapas& Skaloumbakas, 2008; van den Bos, Zijlstra, & Spelberg, 2002).

Second, there is evidence that children in consistent orthographies use orthographic knowledge toread words already by the end of first grade (e.g., Burani, Marcolini, & Stella, 2002; Cuetos &Suárez-Coalla, 2009; Orsolini, Fanari, Tosi, De Nigris, & Carrieri, 2006). If RAN is related to readingbecause of its connection to orthographic processing (Bowers & Wolf, 1993), then an association withorthographic processing should be observed already by the end of Grade 1. This is in contrast tostudies in consistent orthographies showing that RAN was weakly related to measures of orthographicprocessing (e.g., Moll, Fussenegger, Willburger, & Landerl, 2009; Papadopoulos, Georgiou, & Kendeou,2009) and that spelling deficits can exist in the absence of RAN deficits (Moll & Landerl, 2009;Wimmer & Mayringer, 2002).

Third, because reading accuracy in consistent orthographies reaches ceiling after the first fewmonths of formal reading instruction (e.g., Seymour et al., 2003), reading fluency measures are oftenused as reading outcomes. This may give an advantage to RAN over the other predictors of readingbecause both RAN and reading fluency are speeded measures. To the extent that the common speedfactor between RAN and reading fluency is captured by the speed of processing measures, the contri-bution of speed of processing to the RAN–reading relationship should also be higher in consistentorthographies than in English, where reading accuracy measures are used as dependent variables.

G.K. Georgiou et al. / Journal of Experimental Child Psychology 112 (2012) 1–17 3

The limited empirical evidence available suggests that this might not be the case. For example, speedof processing correlated more highly with measures of RAN and word reading accuracy in English (Bo-wey et al., 2005) than with measures of RAN and word reading fluency in Dutch (van den Bos, Zijlstra,& van den Broeck, 2003).

RAN components

In most previous research, RAN has been measured as a unitary construct by obtaining a single per-formance time for the entire test. Such a time contains multiple sources of error (e.g., off-task behav-ior) and possibly conceals important individual differences in reading-related processes such asphonological processing and speed of processing. Neuhaus, Foorman, Francis, and Carlson (2001) ar-gued that measuring total performance time fails to provide the precision needed to adequately deter-mine the nature of RAN tasks and that interest should be turned to intratask components such as thepause time between the named stimuli and the articulation time for each stimulus. Neuhaus and col-leagues defined articulation time as the sum of the length of all correctly articulated RAN stimuliand defined pause time as the sum of the length of pauses that are the intervals between the correctlysequenced articulations.

Previous research on RAN components with normally developing children has produced mixedfindings (e.g., Clarke, Hulme, & Snowling, 2005; Georgiou, Parrila, & Kirby, 2006; Georgiou et al.,2009; Neuhaus & Swank, 2002; Neuhaus et al., 2001). For example, Neuhaus and colleagues (2001)examined RAN components and reading with an unselected sample of 50 children in Grades 1 and2. They used three RAN tasks (Letters, Digits, and Objects) and found that the letter naming pause timewas the only measure that consistently predicted decoding accuracy and reading comprehension inGrade 1. Letter naming pause time was a significant predictor of reading even after controlling forthe object naming pause time. In contrast, Clarke and colleagues (2005), in a study with older children,showed that for digit naming only the articulation time was significantly related to exception wordand nonword reading accuracy, whereas for letter naming both articulation and pause times were re-lated to exception word reading but not to nonword reading.

In a series of studies in English, Georgiou and colleagues (2006, 2009; Georgiou, Parrila, Kirby, et al.,2008) examined the impact of RAN components on reading (accuracy and fluency) and how RAN com-ponents relate to other cognitive processing skills. Briefly, these studies demonstrated a number ofthings. First, in kindergarten and Grade 1, articulation time was not significantly related to reading;it started to correlate with reading fluency only after Grade 2, and by Grade 5 it correlated equally wellwith reading fluency as pause time. Second, the relationship between RAN components and readingaccuracy decreased across time, and by Grade 3 it was no longer significant; in contrast, the relation-ship with reading fluency remained strong across time. Third, a significant amount of predictive var-iance in reading was shared with speed of processing across time. Fourth, the amount of predictivevariance shared with phonological processing decreased across time, and the amount of shared vari-ance with orthographic processing increased across time.

The current study

The purpose of this study was to examine how RAN components—articulation time and pausetime—predict word and text reading fluency1 in an orthographically consistent language (Greek).2 Threequestions were asked:

1. Does the predictive value of RAN components, when assessed relatively early (Grade 2), increase ordecrease across time?

1 We focused on reading fluency because reading accuracy reaches ceiling by the end of Grade 2 in Greek (e.g., Georgiou et al.,2008; Papadopoulos et al., 2009).

2 Protopapas & Vlahou (2009) estimated forward consistency (from graphemes to phonemes) in Greek to be 95.1% and backwardconsistency (from phonemes to graphemes) to be 80.3%.


2. Does the concurrent relationship between RAN components and reading fluency change acrosstime (Grades 2, 4, and 6)?

3. How much of RAN components’ predictive variance in reading fluency is shared with speed of pro-cessing, phonological awareness, and orthographic processing?

The developmental relationship between RAN and reading remains a controversial subject. In a lon-gitudinal study from kindergarten to Grade 5, Kirby and colleagues (2003) found that the relative con-tribution of RAN—assessed in kindergarten—to word reading increased with age. Other studies havesupported this trend (e.g., Landerl & Wimmer, 2008; Vaessen & Blomert, 2010; van de Bos et al.,2002). In contrast, Torgesen and colleagues (1997) found that RAN has its primary influence on read-ing in Grades 1 and 2 and that its influence diminishes in Grades 3 to 5 (see Georgiou, Parrila, Kirby,et al., 2008; Meyer, Wood, Hart, & Felton, 1998, for similar results). What may be the key in this con-troversy is the time when RAN is first measured. When measured relatively early (kindergarten toGrade 2), RAN may contribute more to later reading than when measured in later elementary years.An explanation could be the increased variability in RAN performance during the early grades.

The current study extends the previous research in three important ways. First, to our knowledge,this is the first longitudinal study to systematically examine the RAN components and their relation-ship to reading in a consistent orthography. It is possible that the component that drives the relation-ship between RAN and reading is different across languages varying in orthographic consistency.Georgiou, Parrila, and Liao (2008) provided preliminary support for this argument by showing thatpause time was more important for reading in Chinese than in English or Greek and that articulationtime was more important for reading in Greek than in English or Chinese. Second, compared with mostprevious longitudinal studies in English that monitored the development of the RAN–reading relation-ship across time (e.g., Manis et al., 2000; Parrila et al., 2004; Torgesen et al., 1997), this study covers abroader developmental span. We followed the same children from Grade 2, when they were still in theprocess of developing fluency, to Grade 6, when they were fluent readers. Finally, we have assessedphonological awareness, orthographic processing, and speed of processing at each measurementpoint, allowing us to examine whether their effect on the RAN–reading relationship changes acrosstime.

Method

Participants

Participants were 70 randomly selected Greek-speaking Cypriot children (40 girls and 30 boys,mean age = 95.04 months, SD = 3.36, at the end of Grade 2) who were followed from Grade 2 to Grade6. The children were Caucasian and came from middle to upper middle socioeconomic backgrounds(based on the location of the schools and reports from the teachers), and none had previously beendiagnosed with emotional, behavioral, or sensory deficits. General cognitive ability, measured withBlock Design and Expressive Vocabulary in Grade 4 (Wechsler Intelligence Scale for Children [WISCIII; Wechsler, 1991]; Greek adaptation: Georgas, Paraskevopoulos, Bezevegis, & Giannitsas, 1997),was within the average range (mean standard score for Block Design = 11.52, SD = 3.69, and meanstandard score for Expressive Vocabulary = 9.12, SD = 3.19). By Grade 6, the sample consisted of 68children after 2 children withdrew from the study in Grade 2. The final sample (40 girls and 28 boys)had a mean age of 94.97 months (SD = 3.37) in Grade 2, 116.69 months (SD = 3.43) in Grade 4, and141.87 months (SD = 3.44) in Grade 6. School and parental consent for participation in the studywas obtained prior to testing.

Measures

Rapid automatized namingTwo measures of RAN were administered: Digits and Objects. Both tasks were adapted in Greek

from existing test batteries in English by substituting some of the stimuli in the RAN tasks. In Grade


2, both RAN tasks were adapted from the Comprehensive Test of Phonological Processing (CTOPP;Wagner, Torgesen, & Rashotte, 1999) and required participants to name as fast as possible six recur-ring digits (2, 3, 4, 5, 7, and 8) or objects (ball, cat, chicken, ship, tree, and pencil) that were arrangedsemirandomly in four rows of nine. In Grades 4 and 6, both RAN tasks were adapted from the RapidAutomatized Naming and Rapid Alternative Stimulus (RAN/RAS) test battery (Wolf & Denckla, 2005)and required participants to name as fast as possible five recurring digits (2, 4, 5, 7, and 9) or objects(ball, cat, tree, apple, and chicken) that were arranged semirandomly in five rows of ten. Prior to begin-ning the timed naming, each participant was asked to name the digits or objects in a practice trial toensure familiarity. The time needed to name all of the stimuli was the a participant’s score. The num-ber of naming errors was negligible, and for this reason it was not considered further. The responses ofchildren on both RAN tasks were recorded to allow the sound analysis described below. Test–retestreliability coefficients of Digit Naming for a subsample of children in our study (n = 23) were .87,.87, and .90 for Grades 2, 4, and 6, respectively. For Object Naming, test–retest reliability coefficientswere .83 in Grade 2, .79 in Grade 4, and .80 in Grade 6. RAN Letters was not administered because itdoes not work properly in Greek due to the fact that children in school are first taught the lettersounds, not the letter names.

Phonological awarenessPhonological awareness was assessed with two tasks at each measurement point: Blending

(administered only in Grade 2), Phoneme Elision (administered in all grades), and Spoonerism (admin-istered in Grades 4 and 6). Blending was adapted in Greek from the CTOPP (Wagner et al., 1999). Itrequired children to listen to a series of separate sounds and then put the sounds together to makea word. There were 5 practice items and 20 test items; of the test items, 3 items required participantsto put together two syllables to make a word (e.g., what /mi/ and /lo/ make?), 5 items required partic-ipants to put an onset and a rime together to make a word (e.g., what /m/ and /as/ make?), and theremaining 12 items required participants to put individual sounds together to make a word (e.g., what/k/, /a/, /l/, and /o/ make?). The number of sounds to be blended ranged from 2 to 10. Testing was dis-continued after three consecutive errors, and a participant’s score was the number of correct itemsproduced. Cronbach’s alpha reliability coefficient in our sample was .84.

Phoneme Elision was also adapted in Greek from the CTOPP (Wagner et al., 1999). There were 3practice items and 29 test items; of the test items, 4 items were two-syllable words and required par-ticipants to say the word without saying one of the syllables (e.g., Say the word so9pi; [/topi/; ball], nowsay the word /topi/ after deleting the sound /pi/ ?so [/to/; the), and the remaining 25 items requiredparticipants to say a word without saying a designated sound in the word. The position of the pho-neme to be removed varied across those 25 items. After deleting the target phoneme, the remainingphonemes formed a word (e.g., Say the word sx9qa; [/tora/; now], now say the word /tora/ after delet-ing the sound /t/ ?x9qa [/ora/; time]). Cronbach’s alpha reliability coefficients in our sample were .82in Grade 2, .88 in Grade 4, and .85 in Grade 6.

Spoonerism was adapted in Greek from Frederickson, Frith, and Reason (1997). In this task, chil-dren heard pairs of words with the instruction to repeat the two words after having swapped the ini-tial sounds around (e.g., basket and lemon repeated as lasket and bemon). The resulting pair consistedof nonwords. The first six pairs of words consisted of two-syllable, highly familiar words, and the lastsix pairs consisted of three-syllable, highly familiar words. All 24 words used in the task were selectedon the basis that they had clear syllable divisions and no consonant clusters in their onsets. The taskwas discontinued after four consecutive mistakes. Children were given 1 point for each correctly re-versed pair of words. No partial credit was given. Cronbach’s alpha reliability coefficient in our samplewas .90 in both Grades 4 and 6.

Speed of processingVisual Matching and Cross-Out, adopted from the Woodcock–Johnson Tests of Cognitive Ability

(Woodcock & Johnson, 1989), were used in this study to measure speed of processing. In Visual Match-ing, individuals were asked to circle identical numbers dispersed in 60 rows. Each of the 60 rows in thetask consisted of six digits, two of which were identical (e.g., 8 9 5 2 9 7), and children were asked tocircle the identical digits in each row. Children completed four practice items prior to timed testing.


The performance measure was the number of rows completed correctly within a 3-min time limit.Woodcock, McGrew, and Mather (2001) reported a test–retest reliability coefficient of .87 for 7- to11-year-olds. In our sample (n = 23), test–retest reliability coefficients were .79, .83, and .85 for Grades2, 4, and 6, respectively.

In Cross-Out, the children were asked to scan 30 rows of items, each containing 20 similar geomet-ric figures. To the left of each row was a target figure. Children’s task was to scan the row of 19 similargeometric figures to identify the five figures identical to the target figure embedded within the rowbefore proceeding to the next row. Children were instructed to mark identified items by ‘‘crossingthem out’’ with a line. Children were given four practice items prior to timed testing. A participant’sscore was the number of rows correctly completed within a 3-min time limit. Woodcock and col-leagues (2001) reported a test–retest reliability coefficient of .87 for 7- to 11-year-olds. In our sample(n = 23), test–retest reliability coefficients were .70, .71, and .79 for Grades 2, 4, and 6, respectively.

Orthographic processingOrthographic processing was assessed with two tasks: Orthographic Choice (administered in all

grades) and Spelling Dictation (administered in Grades 4 and 6). In Orthographic Choice, childrenviewed pairs of letter strings that sounded similar (e.g., /rvokei9o/ [school] vs. /rvoki9o/) and wereasked to circle the one that was spelled correctly. In total, 30 pairs of phonologically identical letterstrings were presented to children on a sheet of paper. For the construction of the task in Grade 2,we selected words from the Grade 1 to 3 language textbooks. In turn, for the construction of the taskin Grade 4 (we used the same task in Grade 6), we selected words from the Grade 4 to 6 language text-books. An individual’s score was the number of correctly selected real words. Cronbach’s alpha reli-ability coefficients in our sample were .65 in Grade 2, .74 in Grade 4, and .76 in Grade 6.

The Spelling Dictation task was adopted from Nunes, Aidinis, and Bryant (2006) and required chil-dren to write on a form with numbered spaces a word that was dictated to them. The examiner firstread the word aloud, then read a sentence in which the target word was embedded, and then repeatedthe target word. The task contained 64 Greek words that were derived from children’s Grade 1 to 6language textbooks. The words were ordered in terms of difficulty, and there was no discontinuationrule. A participant’s score was the number of correctly spelled words. Cronbach’s alpha reliability coef-ficients in our sample were .93 and .94 for Grades 4 and 6, respectively.

Reading fluencyReading fluency was assessed with three measures: word reading efficiency, phonemic decoding

efficiency, and text reading fluency. The word reading efficiency and phonemic decoding efficiencytasks were adapted in Greek from the Test of Word Reading Efficiency (TOWRE; Torgesen, Wagner,& Rashotte, 1999). In the TOWRE task, children were asked to read as fast as possible a list of 104words, divided into four columns of 26 words each. In the phonemic decoding efficiency task, childrenwere asked to read as fast as possible a list of 63 nonwords. A short eight-word/nonword practice listwas presented before each subtest. In each task, a child’s score was the number of correct words/non-words read within a 45-s time limit. In our sample, test–retest reliability coefficients for word readingefficiency were .92 for Grades 2 and 4 and .93 for Grade 6. Test–retest reliability coefficients for pho-nemic decoding efficiency were .86 in Grade 2 and .89 in Grades 4 and 6.

In the text reading fluency task, participants were asked to read as fast and accurately as possibletwo short texts. The texts were selected so that one would be well within the reading ability of nearlyall children and one would be a bit more challenging. All participants read the same two texts. In total,the texts contained 53 words in Grade 2, 122 words in Grade 4, and 215 words in Grade 6. An individ-ual’s score was the time taken to read both texts. Reading accuracy was not considered because only afew reading errors occurred. Test–retest reliability coefficients in our sample were .81 in Grade 2, .89in Grade 4, and .83 in Grade 6.

Procedure

Participants were examined in April/May of their school year when they were in Grades 2, 4, and 6.All participants were tested individually in their schools during school hours by the first author and a


trained experimenter. Testing was divided into two sessions lasting roughly 40 min each. Half of theparticipants received Session A first, whereas the other half received first Session B first. The order oftasks was fixed across participants so that individual differences would not be confounded with dif-ferences in task order.

Manipulation of sound files

The sound files containing the Digit Naming and Object Naming responses for each participantwere analyzed using a sound editing program (GoldWave version 4.26). Data extraction was com-pleted following the procedure described in detail by Georgiou and colleagues (2006). Before estimat-ing the means for RAN articulation time and pause time, four types of cleaning of RAN componentstook place. First, if there was an incorrect articulation, then the preceding pause time, the incorrectarticulation, and the following pause time were removed. Second, if there was a self-correction, theneverything between the two correct articulations was removed. Third, if the child skipped a stimulus,then the pause time between the two correct articulations and the articulation time that followed theskip were removed. Fourth, in cases where off-task behavior (e.g., coughing, self-encouragement) wasobserved between two articulations, the specific pause time was removed.

Articulation time in this study represents the mean of those articulation times that were correctlyverbalized and were not preceded by a skipped stimulus. The maximum numbers of articulation timeswere 36 in Grade 2 and 50 in Grades 4 and 6. In Digit Naming, 2% of the articulation times were re-moved in Grade 2, .06% in Grade 4, and .08% in Grade 6. In Object Naming, .08% of the articulationtimes were removed in Grade 2, .06% in Grade 4, and .08% in Grade 6.

Pause time in this study is considered to be the mean of the pause times that occurred between twocorrectly articulated stimuli. The maximum numbers of pause times were 35 in Grade 2 and 49 inGrades 4 and 6. In Digit Naming, 6% of the pause times were removed in Grade 2, 2.6% in Grade 4,and 3.6% in Grade 6. In Object Naming, 3.9% of the pause times were removed in Grade 2, 2.8% in Grade4, and .06% in Grade 6.

To establish interrater reliability for the RAN components, the first author reanalyzed a subsample(n = 10) of the sound files in each RAN task at each grade level. Interrater reliability coefficients rangedfrom .92 to .95 for articulation time and from .89 to .95 for pause time.

Results

Preliminary data analysis

Table 1 presents the descriptive statistics for all of the measures used in the study separately foreach grade. An examination of the distributional properties of the measures revealed some problems.The RAN total times, the RAN components, and the text reading fluency times were positively skewed.In the case of RAN total times, we converted the total time into symbols per second, which normalizedthe distribution. In the case of RAN components and text reading fluency, a log transformation wascalculated (Tabachnick & Fidell, 2001). The log transformation normalized the distributions.

Correlations between RAN components and reading outcomes

Table 2 presents the correlations among the RAN components, the RAN total times, and the readingoutcomes across time. All of the correlations were significant, and most were quite high. For example,the correlations between the RAN Digits articulation time and word reading efficiency ranged from�.63 to �.76 and the correlations between the RAN Digits pause time and word reading efficiency ran-ged from �.60 to �.70. In general, the correlations between the RAN Objects components and thereading fluency measures were lower than the corresponding ones with the RAN Digits components.For example, the correlations between the RAN Objects articulation time and word reading efficiencyranged from �.42 to �.64 and the correlations between the RAN Objects pause time and word readingefficiency ranged from �.49 to �.69.

Table 1Descriptive statistics for all measures used in the study.

Grade 2 Grade 4 Grade 6

M SD M SD M SD

RAN Digits totala 1.74 0.41 2.08 0.40 2.35 0.50RAN Digits ATb 0.43 0.08 0.39 0.07 0.34 0.07RAN Digits PTb 0.16 0.06 0.11 0.05 0.11 0.04RAN Objects totala 0.89 0.22 1.18 0.23 1.28 0.26RAN Objects ATb 0.67 0.10 0.57 0.09 0.52 0.08RAN Objects PTb 0.49 0.28 0.30 0.13 0.28 0.12Speed of processing

Visual Matching 28.52 6.92 35.79 6.99 42.59 6.16Cross-Out 12.10 3.56 15.26 3.64 20.71 3.85

Phonological awarenessPhoneme Blending 14.44 4.69Phoneme Elision 18.29 4.10 24.94 4.46 27.29 1.59Spoonerism 5.61 3.77 7.04 3.62

Orthographic processingOrthographic Choice 26.72 2.84 19.48 4.34 22.43 4.56Spelling Dictation 38.53 11.34 44.18 12.16

Reading fluencyWord reading efficiency 43.65 11.97 59.48 12.48 68.99 14.87Phonemic decoding efficiency 29.46 9.31 36.42 8.61 41.51 8.91Text reading fluencyb 44.11 21.87 63.65 25.53 106.84 47.78

Note. AT, articulation time; PT, pause time. In word reading efficiency and phonemic decoding efficiency, a higher score reflectsa higher reading speed, whereas in text reading fluency, this score reflects lower reading speed.

a Number of symbols per second.b Time in seconds.

Table 2Correlations between RAN components and reading outcomes.

Grade 2 Grade 4 Grade 6

WRE PDE TRF WRE PDE TRF WRE PDE TRF

RAN Digits Grade 2Total time .68** .74** –.60** .67** .71** –.68** .72** .74** –.59**

Articulation time –.71** –.77** .70** –.71** –.73** .72** –.76** –.73** .67**

Pause time –.60** –.65** .55** –.61** –.68** .60** –.64** –.69** .53**

RAN Digits Grade 4Total time .74** .80** –.72** .79** .80** –.66**

Articulation time –.63** –.70** .61** –.71** –.70** .59**

Pause time –.70** –.72** .66** –.66** –.68** .63**

RAN Digits Grade 6Total t ime .81** .80** –.66**

Articulation time –.70** –.69** .59**

Pause time –.68** –.65** .56**

RAN Objects Grade 2Total time .50** .52** –.49** .54** .42** –.53** .61** .48** –.51**

Articulation time –.42** –.40** .50** –.48** –.32** .41** –.54** –.36** .47**

Pause time –.49** –.55** .46** –.52** –.47** .52** –.56** –.51** .48**

RAN Objects Grade 4Total time .68** .62** –.67** .70** .63** –.66**

Articulation time –.58** –.53** .58** –.64** –.54** .60**

Pause time –.54** –.49** .53** –.53** –.49** .53**

RAN Objects Grade 6Total time .71** .68** –.62**

Articulation time –.58** –.57** .50**

Pause time –.69** –.65** .63**

Note. WRE, word reading efficiency; PDE, phonemic decoding efficiency; TRF, text reading fluency.** p < .01.


Table 3Correlations between RAN components and measures of speed of processing, phonological awareness, and orthographicprocessing.

RAN Digits RAN Objects

Total time Articulation time Pause time Total time Articulation time Pause time

Grade 21. Visual Matching .52** –.59** –.42** .48** –.43** –.53**

2. Cross-Out .33** –.37** –.29* .42** –.26* –.45**

3. Phoneme Elision .26* –.38** –.26* .30* –.32** –.33**

4. Phoneme Blending .40** –.45** –.32** .36** –.33** –.39**

5. Orthographic Choice .31* –.36** –.32* .33** –.29* –.33**


2. Cross-Out .31* –.33** –.24* .50** –.36** –.43**

3. Phoneme Elision .39** –.39** –.36** .31* –.26* –.34**

4. Spoonerism .27* –.18 –.25* .29* –.18 –.24*

5. Orthographic Choice .31** –.24* –.37** .34** –.20 –.29*

6. Spelling Dictation .46** –.38** –.45** .48** –.37** –.39**


2. Cross-Out .57** –.50** –.44** .48** –.43** –.46**

3. Phoneme Elision .32** –.29* –.23 .40** –.36** –.41**

4. Spoonerism .37** –.43** –.39** .31* –.23 –.32**

5. Orthographic Choice .30* –.39** –.38** .29* –.27* –.32**

6. Spelling Dictation .46** –.40** –.39** .44** –.35** –.47**

Note. N = 68.* p < .05.

** p < .01.


Both RAN components and reading measures were highly stable across time. The correlations ran-ged from .51 to .78 for RAN articulation time, from .55 to .75 for RAN pause time, and from .85 to .90for the reading measures. It is worth noting that the stability for the RAN components was similar tothe size of the relationship between RAN components and reading at a subsequent point in time.

Correlations between RAN components and measures of speed of processing, phonological awareness, andorthographic processing

The correlations between the RAN components and measures of speed of processing, phonologicalawareness, and orthographic processing are presented in Table 3. With few exceptions, all of the cor-relations between the RAN components and the cognitive processing measures were significant. Thehighest correlations were observed between the RAN components and Visual Matching (rs rangedfrom �.42 to �.60). The correlations with Cross-Out, the second measure of speed of processing, wereinitially weak but increased across time (this is likely due to the restriction of range observed in Cross-Out in earlier grades). Both RAN components correlated moderately with the measures of phonolog-ical awareness and orthographic processing. However, there was no clear trend in the direction ofthese correlations across time.

Regression analyses

To examine the predictive value of the RAN components, we conducted a series of hierarchicalregression analyses. First, we used the Grade 2 RAN components to predict word and text reading flu-ency in Grades 2, 4, and 6 (see Table 4). Second, we used the Grade 4 and 6 RAN components to predictword and text reading fluency concurrently (see Table 5).

Before conducting the regression analyses, we computed composite scores for phonological aware-ness, orthographic processing, and speed of processing by averaging the z scores of the independent

Table 4Results of hierarchical regression analyses with Grade 2 RAN Digits and RAN Objects components as predictors of word and text reading fluency in Grades 2, 4, and 6.

Step Variable Word reading fluency Text reading fluency

RAN Digits RAN Objects RAN Digits RAN Objects

Grade 2 Grade 4 Grade 6 Grade 2 Grade 4 Grade 6 Grade 2 Grade 4 Grade 6 Grade 2 Grade 4 Grade 6DR2 DR2 DR2 DR2 DR2 DR2 DR2 DR2 DR2 DR2 DR2 DR2

1. Articulation time .59*** .55*** .59*** .18*** .17*** .21*** .50*** .52*** .44*** .25*** .16** .22***

1. Pause time .42*** .44*** .48*** .29*** .26*** .30*** .31*** .36*** .29*** .21*** .27*** .23***

1. Speed of processing .37*** .38*** .36*** .37*** .38*** .36*** .31*** .36*** .39*** .31*** .36*** .39***

2. Articulation time .28*** .24*** .28*** .05* .04* .07** .24*** .23*** .16*** .10** .04* .06**

2. Pause time .20*** .21*** .25*** .07** .05* .08** .14*** .16*** .10** .06* .06* .03

2. Phonological awareness .13*** .05* .06* .13*** .05* .06* .14*** .05* .12*** .14*** .05* .12***

3. Articulation time .20*** .20*** .24*** .02 .02 .04* .16*** .19*** .10*** .06** .03* .03*

3. Pause time .16*** .19*** .22*** .04* .04* .06* .10*** .13*** .07** .04* .04* .02

2. Orthographic processing .10*** .09*** .06* .10*** .09** .06* .20*** .10*** .16*** .20*** .10*** .16***

3. Articulation time .24*** .21*** .26*** .03* .03 .05* .19*** .20*** .13*** .07** .03 .04*

3. Pause time .20*** .21*** .25*** .06** .04* .07** .13*** .15*** .10*** .03 .05* .03

1. Speed of processingPhonological awarenessOrthographic processing .54*** .50*** .45*** .54*** .50*** .45*** .57*** .48*** .60*** .57*** .48*** .60***

2. Articulation time .20*** .19*** .23*** .02 .02 .03 .15*** .18*** .10*** .05** .03* .03*

2. Pause time .17*** .19*** .23*** .04* .03* .06* .11*** .14*** .08*** .04* .04* .02

1. Autoregressor .80*** .77*** .80*** .77*** .77*** .83*** .77*** .83***

2. Articulation time .01* .02* .00 .01 .02* .00 .00 .002. Pause time .01* .03** .00 .01 .02* .00 .01 .01

Note. N = 68.* p < .05.

** p < .01.*** p < .001.

10G

.K.G

eorgiouet

al./Journalof

Experimental

ChildPsychology

112(2012)

1–17

Table 5Results of hierarchical regression analyses with Grade 4 and 6 RAN Digits and RAN Objects components as concurrent predictors ofword and text reading fluency.

Step Variable Word reading fluency Text reading fluency

RAN Digits RAN Objects RAN Digits RAN Objects

Grade 4 Grade 6 Grade 4 Grade 6 Grade 4 Grade 6 Grade 4 Grade 6DR2 DR2 DR2 DR2 DR2 DR2 DR2 DR2

1. Articulation time .48*** .52*** .33*** .35*** .37*** .35*** .34*** .25***

1. Pause time .53*** .47*** .28*** .49*** .44*** .31*** .29*** .39***

1. Speed of processing .39*** .54*** .39*** .54*** .33*** .57*** .33*** .57***

2. Articulation time .20*** .12*** .10** .06** .15*** .03* .12*** .022. Pause time .26*** .14*** .05* .13*** .21*** .05** .07** .07**

2. Phonological awareness .05* .04* .05* .04* .11** .09*** .11** .09***

3. Articulation time .18*** .11*** .10** .06** .12*** .02 .12*** .013. Pause time .23*** .13*** .05* .11*** .16*** .04** .06** .05**

2. Orthographic processing .13*** .06** .13*** .06** .14*** .15*** .14*** .15***

3. Articulation time .18*** .12*** .09*** .06** .13*** .03* .11*** .023. Pause time .19*** .12*** .04* .11*** .13*** .03** .06** .05***

1. Speed of processingPhonological awarenessOrthographic processing .53*** .61*** .53*** .61*** .53*** .74*** .53*** .74***

2. Articulation time .17*** .11*** .09*** .06*** .11*** .02* .11*** .02*

2. Pause time .17*** .12** .05* .10*** .11*** .03** .05** .04***

Note. N = 68.* p < .05.

** p < .01.*** p < .001.


measures for each construct.3 In Grade 2, the performance on Orthographic Choice was used as a singleindicator of orthographic processing. Finally, we calculated a composite score for word reading fluencyby averaging the z scores of word reading efficiency and phonemic decoding efficiency.

The order of the variables entered in the regression equation was as follows. First, each of the RANcomponents was entered in the regression equation alone to estimate its effect on word and text read-ing fluency. Second, each of the RAN components was entered interchangeably in the regression equa-tion at Step 2 after controlling for the effects of speed of processing (entered at Step 1). Third, the RANcomponents were entered in the regression equation at Step 3 following speed of processing (enteredat Step 1) and phonological awareness or orthographic processing (entered interchangeably at Step 2).Fourth, the contribution of the RAN components was estimated after controlling for speed of process-ing, phonological awareness, and orthographic processing (entered as a block at Step 1). Finally, weexamined the effect of RAN components (entered interchangeably at Step 2) on reading fluency aftercontrolling for the effects of the autoregressor (reading achievement at an earlier point of develop-ment). R2 changes and level of significance are presented in each table.

Does the predictive value of RAN components, when assessed relatively early (Grade 2), increase or decreaseacross time?

Table 4 shows first that, when entered at Step 1, both RAN Digits components explained a significantamount of variance in reading fluency across time (R2 values ranged from .44 to .59 for articulation timeand from .29 to .48 for pause time). Both RAN components accounted for more variance in word readingfluency than in text reading fluency. Second, both components continued to explain unique variance in

3 The calculation of the composite scores was theoretically driven and statistically supported. The correlations between the twomeasures of phonological awareness were .54 in Grades 2 and 4 and .51 in Grade 6. The correlations between the two measures oforthographic processing were .63 in Grade 4 and .74 in Grade 6. Finally, the correlations between the two speed of processingmeasures were .72 in Grade 2, .65 in Grade 4, and .74 in Grade 6.


reading fluency even after controlling for speed of processing, phonological awareness, and ortho-graphic processing. The unique contribution of articulation and pause time to word reading fluency in-creased slightly across time (articulation time: from 20% in Grade 2 to 23% in Grade 6; pause time: from17% in Grade 2 to 23% in Grade 6). In contrast, when text reading fluency was the dependent variable,the unique contribution of both components decreased slightly across time (articulation time: from15% in Grade 2 to 10% in Grade 6; pause time: from 11% in Grade 2 to 8% in Grade 6).

To examine whether the changes in the unique contribution of the RAN components across timewere statistically significant, we calculated the 95% confidence intervals of the unstandardized betacoefficients of the RAN components’ contribution to reading fluency (when RAN components were en-tered at Step 2). The confidence intervals overlapped with each other, indicating that none of the ob-served R2 changes was statistically significant.

The contribution of RAN Objects components to reading fluency was weak (see Table 4). After con-trolling for speed of processing, phonological awareness, and orthographic processing, articulationtime accounted for 2 to 3% of unique variance and pause time for 3% to 6% of unique variance in wordreading fluency. When text reading fluency was the dependent variable, articulation time accountedfor 3% to 5% of unique variance and pause time for 2% to 4% of unique variance. The differences in theR2 changes across time were not statistically significant.

The last analyses reported in Table 4 indicate further that the reading fluency scores were highlystable across measurement points. When the autoregressive effect was controlled, both RAN Digitscomponents accounted for a small (1–3%), but still significant, amount of variance in reading fluency(with one exception). The contribution of the RAN Objects components was nonsignificant.

Does the concurrent relationship between RAN components and reading fluency change across time (Grades2, 4, and 6)?

Next, we examined the contribution of the RAN components to reading fluency concurrently. Aftercontrolling for all cognitive processing skills, the contribution of both RAN Digits components to wordand text reading fluency decreased across time (see Table 5). For example, articulation time accountedfor 20% of unique variance in word reading fluency in Grade 2 (from Table 4), 17% in Grade 4, and 11%in Grade 6 (see Table 5). RAN Digits pause time accounted for 17% of unique variance in Grade 2 (fromTable 4), 17% in Grade 4, and 12% in Grade 6 (see Table 5). In contrast to RAN Digits, there was an in-crease in the unique contribution of the RAN Objects components from Grade 2 to Grade 4 (more sub-stantial for articulation time) followed by a decrease from Grade 4 to Grade 6 (see Table 5). However,only the R2 change from Grade 2 to Grade 6 in the contribution of RAN Digits articulation time to textreading fluency was significant (Grade 2: 95% CI = 0.603–1.240; Grade 6: 95% CI = 0.055–0.592).

The regression model with all processing skills entered at Step 1 and RAN Digits components en-tered at Step 2 accounted for 76% of the variance in word reading fluency in Grade 2, 78% in Grade4, and 75% in Grade 6. The corresponding amount of variance explained when text reading fluencywas the dependent variable was 73% in Grade 2, 69% in Grade 4, and 77% in Grade 6. In turn, theregression model with the RAN Objects components in the place of RAN Digits components accountedfor 58% of the variance in word reading fluency in Grade 2, 66% in Grade 4, and 73% in Grade 6. Whentext reading fluency was the dependent variable, the model accounted for 62% of the variance in Grade2, 68% in Grade 4, and 78% in Grade 6.

How much of RAN components’ predictive variance in reading fluency is shared with speed of processing,phonological awareness, and orthographic processing?

The results (see Tables 4 and 5) indicated, first, that a significant amount (more than half) of RANcomponents’ predictive variance was shared with speed of processing and that this amount increasedacross time. The amount of predictive variance shared between RAN Objects components and speed ofprocessing was consistently higher than that shared between RAN Digits components and speed of pro-cessing. Second, RAN components shared some of the remaining predictive variance with phonologicalawareness and orthographic processing. In the case of phonological awareness, the amount of sharedpredictive variance decreased across time. Finally, speed of processing, phonological awareness, andorthographic processing jointly explained a large amount of RAN components’ predictive variance inword and text reading fluency. For example, in Grade 6 (see Table 5), all cognitive processing skills


explained 79% [(.52–.11) � (100/.52)] of RAN Digits articulation time’s predictive variance in word read-ing fluency and 94% [(.35–.02) � (100/.35)] of RAN Digits articulation time’s predictive variance in textreading fluency. The corresponding amount of variance for RAN Digits pause time was 74% [(.47–.12) � (100/.47)] in word reading fluency and 90% [(.31–.03) � (100/.31)] in text reading fluency.

Discussion

The purpose of this study was to examine how RAN components—articulation time and pausetime—predict word and text reading fluency in an orthographically consistent language (Greek).The results indicated, first, that articulation time and pause time correlated significantly with thereading fluency measures. This replicates our previous findings in English (Georgiou, Parrila, Kirby,et al., 2008; Georgiou et al., 2009). However, articulation time in this study correlated equally wellwith the reading fluency measures as pause time already by Grade 2. In fact, in some instances, thecorrelations were even higher than the corresponding ones with pause time. The strong relationshipbetween articulation time and reading fluency in Greek (also found in Georgiou, Parrila, & Liao, 2008)could be attributed to the greater variability of the articulation times that is due to the relatively longnames of digits and objects in Greek.

The results of the regression analyses indicated further that the contribution of the RAN compo-nents varied according to the reading outcome and the type of RAN task. The RAN Digits componentscontinued to predict reading fluency even after controlling for the effects of speed of processing, pho-nological awareness, and orthographic processing, and their contribution did not diminish across time.Both RAN Digits components accounted for more variance in word reading fluency than in text readingfluency. The higher contribution of RAN to word reading fluency than to text reading fluency has beenfound in previous studies in English (e.g., Georgiou, Parrila, Kirby, et al., 2008; Katzir, Kim, Wolf, Mor-ris, & Lovett, 2008) and suggests that text reading fluency likely involves processes (e.g., semantic andsyntactic processing) that go beyond speeded word recognition and are unrelated to individual differ-ences in RAN. In contrast to RAN Digits, the unique contribution of the RAN Objects components wasweak and in some instances nonsignificant (see Neuhaus et al., 2001, for a similar finding).

When the contribution of RAN components to reading fluency was examined concurrently, the re-sults revealed that the RAN Digits components made their greatest impact on reading fluency in Grade2, which was followed by a decrease thereafter (albeit a nonsignificant one). Taken together, the find-ings of this study suggest that the best time to assess RAN is during the early grades in elementaryschool because it does not lose its predictive value longitudinally (see Kirby et al., 2003, for a similarfinding) and accounts for more unique variance in reading fluency than when assessed in Grades 4 and6. This last finding is somewhat surprising and warrants further examination.

Including an autoregressor in the regression equation affected the usefulness of both RAN compo-nents as predictors of reading fluency. The inclusion of an autoregressor changes the question fromone of predicting growth in general to one of predicting further growth or ‘‘unexpected’’ growth thatcannot be accounted for by the skill itself at an earlier time. De Jong and van der Leij (2002) and Wolfand Bowers (1999) pointed out that the absence of an additional direct effect does not by itself allowthe conclusion that the predictors are no longer important. In other words, the effects of RAN may con-tinue over time, affecting later reading ability in a manner indistinguishable from the effects on earlierreading. According to de Jong and van der Leij (2002), additional effects are observed only when twoconditions are met. The first condition is that individual differences in the target skill are not entirelystable over time, allowing for unexpected growth. In our study, word reading fluency in Grade 2 cor-related .89 with word reading fluency in Grade 4, which in turn correlated .92 with word reading flu-ency in Grade 6. This level of stability leaves very little variance for the RAN components to accountfor. The second condition is that any predictor variable should be relatively more strongly related tothe criterion variable at a later point in time. In our study, the longitudinal correlations between theRAN components and the reading outcomes were not systematically higher than the concurrentcorrelations.

It is worth noting that when word and text reading fluency at an earlier point in time were usedto predict RAN components at a later point in time, they accounted for 3 to 5% of unique varianceafter the effects of the autoregressor were controlled. This suggests that there is likely a reciprocal


relationship between RAN components and reading fluency (see Compton, 2003, for similar findings inEnglish). Taken together with the finding that both RAN and reading measures were highly stable, wesuggest that development in both RAN and reading fluency is primarily driven and constrained by thesame processes.

To further examine the process that may underlie the RAN–reading relationship, we estimated howmuch of the predictive variance of the RAN components was shared with speed of processing, phono-logical awareness, and orthographic processing. Importantly, both RAN components shared more thanhalf of their predictive variance with speed of processing and this amount increased across time. Theassociation with speed of processing was expected given that RAN and reading fluency requirespeeded responses. However, it was larger than what has been reported in previous studies (e.g.,Bowey et al., 2005; Georgiou et al., 2009; van den Bos et al., 2003) and increased across time. Thiswould suggest that the more automatized the RAN tasks become, the more commonalities they havewith speed of processing measures.

Beyond speed of processing, RAN components shared part of their predictive variance with phono-logical awareness. However, the amount of shared variance with phonological awareness decreasedacross time. This replicates our earlier findings in English (Georgiou, Parrila, Kirby, et al., 2008; Geor-giou et al., 2009) and supports the argument put forward by Parrila and colleagues (2004) that ‘‘whatis unique to these tasks [RAN and phonological awareness] is more important in terms of prediction ofreading variance than what they share’’ (p. 16).

The decreasing amount of shared variance with phonological awareness possibly reflects the shiftin the unit of analysis employed by readers in consistent orthographies when decoding words. InGrades 1 and 2, phonological recoding is the dominant approach used in reading (e.g., Cuetos & Suár-ez-Coalla, 2009; Goswami, Porpodas, & Wheelwright, 1997). In contrast, in later grades, readers relyon lexical knowledge to recognize words (e.g., Cuetos & Suárez-Coalla, 2009; Orsolini et al., 2006).Subsequently, RAN components should share more predictive variance with orthographic processingin later grades because orthographic processing is fundamental in sight word recognition and readingfluency (e.g., Barker, Torgesen, & Wagner, 1992; Georgiou et al., 2009). The results of this study weremixed. There was indeed an increase in the amount of shared variance between RAN Digits pause timeand orthographic processing from Grade 2 to Grade 4. However, for the rest of the components, eitherthe amount of shared variance decreased across time (see RAN Digits articulation time) or it was neg-ligible (see RAN Objects articulation time).

An explanation for this unexpected finding could be the increased relationship between ortho-graphic processing and speed of processing (Grade 2: r = .53; Grade 4: r = .63; Grade 6: r = .72). Be-cause speed of processing accounted for more variance in reading fluency in later grades and theeffect of speed of processing was controlled before estimating the contribution of orthographic pro-cessing, this may have lowered the contribution of orthographic processing to reading and the sharedvariance with RAN components. An alternative explanation could be the nature of Greek orthography.Although Greek is not fully consistent in the direction of phonemes to graphemes, it is not as incon-sistent as English (Protopapas & Vlahou, 2009). The inconsistency is primarily due to the spelling ofsome vowels (e.g., the phoneme /i/ can be written in five different ways [g, i, t, ei, and oi], the pho-neme /o/ in two different ways [o and x], and the phoneme /e/ in two different ways [e and ai]) andthe spelling of some consonant clusters (e.g., lp, /b/, ms, /d/). The few inconsistencies are rule-gov-erned and are taught in school (e.g., if the word is a verb and ends in /o/, it is spelled with x; if theword is a noun and ends in /o/, it is spelled with o). Subsequently, Greek children could potentiallyspell some words (particularly words that do not include any of the aforementioned vowels or conso-nant clusters) without word-specific orthographic knowledge (e.g., darja� ka; teacher). If RAN isinvolved in the construction of orthographic representations of words (Bowers & Wolf, 1993), itshould have a very limited contribution, namely only in the case of words with one or more inconsis-tencies.4 This interpretation is in line with Papadopoulos and colleagues’ (2009) findings in Greek thatchildren with a single RAN deficit did not experience spelling difficulties.

4 Perhaps more sensitive to individual differences in RAN could be orthographic processing response times rather thanorthographic processing accuracy. Unfortunately, response times were not available in this study.


Taken together, the findings of the current study suggest that RAN is related to reading fluency inGreek because of the unique contributions of articulation and pause time and of what these two com-ponents share with speed of processing, phonological awareness, and orthographic processing. Theunique contribution of articulation time would be expected on the basis that oral reading and rapidnaming require articulation (Wolf & Bowers, 1999) and that no other measures of articulation ratewere considered in the current study. In contrast, the unique contribution of pause time calls for inter-pretations that go beyond the role of speed of processing, phonological awareness, and orthographicprocessing. It is possible that what is left unaccounted for in the relationship between pause time andreading fluency is the serial format of the RAN tasks that forces the children to shift attention from onestimulus to another in a short time (encapsulated in pause time). Recently, de Jong (2011) andGeorgiou, Parrila, Papadopoulos, and Scarborough (2010) demonstrated that serial naming producedsignificantly higher correlations with reading fluency than discrete naming.

Some limitations of this study are worth mentioning. First, RAN was assessed with a set of some-what different tasks in various grades. Participants in Grade 2 were asked to name as fast as possiblesix recurring numbers and objects arranged semirandomly in four rows of nine. In Grades 4 and 6, theywere asked to name five recurring items that were arranged semirandomly in five rows of ten.Although it would have been preferable to use the same tasks across time, research has shown thatit is not the length of the task that matters but rather the number of different symbols that mustbe retrieved from memory and named (e.g., Georgiou et al., 2010; Scarborough & Domgaard, 1998).Third, we assessed general intelligence only once (in Grade 4), and for this reason we did not includeit as a control variable in the regression analyses. Fourth, it is possible that if we had assessed phono-logical awareness earlier (Grade 1), its effect on the RAN–reading relationship could have been larger(e.g., de Jong & van der Leij, 1999; Papadopoulos et al., 2009; Porpodas, 1991). Fifth, we shouldacknowledge the whole-word nature of the orthographic processing tasks used in our study. Grainsizes bigger than graphemes, but smaller than words, should also be tested. A task such as Wordlike-ness (Siegel, Share, & Geva, 1995), in which individuals are asked to select the letter string that lookslike a real word from a pair of pronounceable nonwords (e.g., filk–filv), could be used in future studies.Finally, our sample size was modest and did not allow us to run more sophisticated statisticalanalyses.

Overall, the results of this study indicate that articulation time and pause time correlate with read-ing fluency and that this relationship remains strong across time. Both articulation time and pausetime (particularly in RAN Digits) shared a significant amount of predictive variance with speed of pro-cessing, phonological awareness, and orthographic processing but also accounted for unique variancein reading fluency beyond the effects of the three processing skills. This finding challenges the prom-inent RAN–reading theoretical accounts (Bowers & Wolf, 1993; Kail et al., 1999; Torgesen et al., 1997).Future studies should examine the RAN–reading relationship across languages varying in orthographicconsistency because, as we showed in this study, the importance of the underlying cognitive mecha-nisms may differ across languages and across time.

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