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Reading Research Quarterly • 43(3) • pp. 206–226 • dx.doi.org/10.1598/RRQ.43.3.1 • © 2008 International Reading Association

The Development of Children’sOrthographic Knowledge: A Microgenetic PerspectiveAnn C. SharpBrigham Young University, Provo, UT, USA

Gale M. Sinatra, Ralph E. ReynoldsUniversity of Nevada, Las Vegas, USA

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Developmental psychologists have debated fordecades whether children’s cognitive develop-ment proceeds in stages mediated by innate ten-

dencies or in incremental degrees mediated byexperience (Bjorklund, 2000). These different perspec-tives have been discussed within and across various do-mains including the development of spelling ability. Athird, more recent alternative provides yet another viewof children’s cognitive development. According to Siegler(1996), cognitive development can be more accuratelycharacterized by a pattern of overlapping waves that rep-resent developmental trajectories of adaptive strategy use.

In addition to claiming that cognitive developmentoccurs gradually over time, Siegler (1996) posited thatthis development is adaptive and responds to children’schoices regarding the strategies and behaviors that theyuse to accomplish a particular task. Siegler (1996) notedthat rather than moving directly from less to more so-phisticated strategy use over time, children tend to holdon to established ways of performing tasks, even though

they may have advanced in their thinking. Early unso-phisticated strategies gradually decline in use, while moresophisticated strategies tend to increase in use. Siegler(1996) viewed children’s use of strategies as adaptive; thatis, over time, children adapt their strategy use to the de-mands of the task. The more difficult the task, the morestrategies children choose to use to accomplish the task.

Rather than viewing development as flowing incre-mentally with the help of a more experienced other, how-ever, Siegler (1996) viewed change as mediated bychildren’s goal-driven motivation for accomplishing a tasksuccessfully, a motivation that leads to adaptive strategyuse. Thus, Siegler (1996) viewed the adaptation of strate-gy use as driving children’s cognitive development.

Phases of Spelling DevelopmentCurrent views of spelling development reflect those ofdevelopment in general; that is, spelling development isusually characterized as proceeding incrementally or in

Theoretical perspectives on spelling characterize development as a progression through qualitatively different phases oras a process of more or less continuous growth. This study investigated the potential utility of a different perspective, theoverlapping-wave model, for characterizing spelling development (Rittle-Johnson & Siegler, 1999). In addition, the studyexamined the relationship between students’ developing use of spelling strategies and their spelling errors. We employeda microgenetic, mixed-design approach with multiple forms of data for triangulation. We administered six spellinginventories to 31 first-grade students individually over a period of five months in a trial-by-trial investigation. Ourfindings showed evidence of the three requirements for the overlapping-wave model: (1) variability, (2) gradual change,and (3) adaptive choice. Our findings also suggested a reciprocal relationship between spelling strategy use anddeveloping orthographic knowledge. Additionally, our data showed evidence of a Matthew effect. The results of thisstudy extend prior research by demonstrating a more complex view of spelling development and the need for additionalstudies employing a fine-grained analysis of developmental spelling.

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phases. Researchers who share the latter perspectivecharacterize phases of growth by the type of decoding orencoding a child displays (e.g., Ehri, 1991; Frith, 1985;Henderson, 1980, 1981). As children encounter moreand more words, their immature decoding and encod-ing systems adjust to meet the increasing demands ofthe writing system. Children move into subsequent phas-es of their spelling development as they increase theirphonological awareness and develop an intent to encodeall letters in a word (Gough & Hillinger, 1980).

Using error feature analysis, literacy theorists havenoticed that children’s misspellings could be used tocharacterize particular phases of spelling growth. Read(1986) was among the first to demonstrate the interestingcharacteristics of children’s invented misspellings. For in-stance, he discovered that children spell words using let-ter names (e.g., “EGL” for eagle) before they spell wordsin ways that represent spelling patterns. Read’s work pro-vided evidence that young children’s attempts at spellingare not a result of rote memorization (Spache, 1940) butinstead reveal their developing deduction of the alpha-betic system.

Developmental models can also be seen in the workof Henderson (1980, 1981, 1992), Frith (1985), and Ehri(1987, 1989, 1992), all of whom looked for and inter-preted errors in young children’s spelling in a mannersimilar to Read (1986). These literacy scholars used chil-dren’s invented spelling to infer the evolution of chil-dren’s developing understanding of letter–soundcorrespondences. According to Bear, Invernizzi,Templeton, and Johnston (1996), development of under-standing is revealed in the orthographic features children“use but confuse” (p. 42). Henderson (1981), Frith(1985), and Ehri (1991) independently proposed modelsidentifying distinctive phases of spelling development.Although they disagreed on the exact number and namesof these phases, all of these researchers generally recog-nized three early phases of spelling development: (1) anonphonetic phase where children demonstrate a lackof understanding that letters represent sounds, (2) a pho-netic phase where children directly attempt to mapphonemes and graphemes, and (3) an orthographicphase where children analyze words into larger ortho-graphic units.

According to this phase view, spelling developmentcan be characterized as a hierarchical progression of qual-itatively different ways of thinking that build progressive-ly upon one another.1 However, unlike stage theorystrictly interpreted, the phases of spelling developmentare not viewed as discrete, nonoverlapping stages.Rather, these phases are characterized by a speller’s use ofa particular spelling feature more frequently during onephase of development than another.

Although the conceptualization of spelling develop-ment as occurring in phases has resulted in a number of

critically important insights, Ehri (1992) has called formore fine-grained analyses that plot individual children’sspelling development in more depth. Specifically, shecalled for studies that mark when spelling features appearin development. Ehri (1992) felt more work was neededto explain how orthographic features come to be repre-sented consistently in children’s spellings and how thesuccessful acquisition of one feature (such as short vow-els) impacts the development of children’s consistentuse of other features (such as long vowels). Ehri’s (1992)call for more fine-grained analyses of children’s transitionfrom one phase of spelling development to another pro-vided one of the motivations for the present study, asdid insights from research grounded in the continuityview of spelling development.

Continuity in Spelling DevelopmentIn contrast to the phase view of literacy development, thecontinuity perspective hypothesizes that children ad-vance gradually in reading and spelling as the quality ofthe spelling patterns represented in their memories im-proves (Perfetti, 1992, 1997). Perfetti (1992) explainedthat the gradualness of children’s spelling developmentwas due to two ways in which the quality of spelling pat-terns improves within their lexicon. First, the specificityof letters that are represented in memory increases. Forexample, beginning readers would only partially repre-sent the word black with “bl” in their memory. Overmany successful attempts to read black, the children’smemories would begin to recognize with increasing dis-tinctiveness all of the letters in their correct positions.Perfetti (1992, 1997) referred to this as specificity or pre-cision. Second, the matching of specific letter–sound cor-respondences (graphemes to phonemes) becomesredundant as the correspondences are connected toword-specific orthographic forms. For instance, readersgeneralize that the vowel e says its name when thegrapheme is at the end of specific words (e.g., me, he, andshe). With more experience, readers learn that the samegrapheme–phoneme connection applies at the syllablelevel (e.g., neon and cement) and, finally, at the mor-pheme level (e.g., derail and prepare). This layering at thephoneme, syllable, and morpheme levels produces a size-able quantity of useful redundancy, which increases thequality of representation. According to the continuityview, a reader moves from sounding out words to auto-matically retrieving words when a high-quality pattern ofspelling representation develops.

The results from a longitudinal study by Rieben andSaada-Robert (1997) were consistent with a continuityview of spelling development. Rieben and Saada-Robertobserved 11 kindergarten and 10 first-grade students,all of whom were initially nonreaders, as the children’s

The Development of Children’s Orthographic Knowledge: A Microgenetic Perspective 207

teacher shared a story with them and then asked the chil-dren to dictate it back to her. The teacher wrote the stu-dents’ contributions on chart paper and mounted thepaper on the classroom walls. The children were thenasked to draw a picture about something related to thestory and to write about it using words displayed on thecharts. Using the wall charts, children searched andcopied words that they needed to describe their pictures.The tasks required active problem-solving strategiesbased on clues from the words displayed on the walls,as well as retrieval strategies to access the representa-tions of words stored in memory.

Rieben and Saada-Robert (1997) identified sevenstrategies that the children used for word searching andseven strategies that they used for word copying. In addi-tion, Rieben and Saada-Robert’s analysis found evidencefor incremental development; that is, their data showedstrategies appearing together at different levels of devel-opment with new strategies continuously increasing inprecision and redundancy. Rieben and Saada-Robert alsoidentified four general phases of development that cor-respond with those found in the literature: (1) the pre-reading phase (which was recognized by Ehri, 1991, andHenderson, 1981, but not included in our previous dis-cussion of phases) where a string of words is copied bymerely guessing, (2) the nonphonetic phase where visu-al cues relating to the length or other salient features ofthe word are used with no evidence that letters repre-sent sounds, (3) the phonetic phase where grapheme–phoneme correspondences are used, and (4) the ortho-graphic phase where direct and deliberate identificationof a word is made without sounding it out. Moreover,they found that the children made no exclusive use ofany single type of strategy during any given period ofobservation; rather, the children’s strategy use varied. Inaddition, the children demonstrated flexibility in usingboth less and more sophisticated strategies from theirrepertoire concurrently. The children’s strategy useseemed to reflect their efforts to determine the most effec-tive strategies for specific circumstances. In contrast to re-searchers who have concentrated on analyzing patternsof spelling errors that children make (Bear, Invernizzi,Templeton, & Johnston, 2004; Henderson, 1981; Read,1986; Schlagal, 1992), Rieben and Saada-Robert concen-trated on analyzing patterns of behavioral strategies thatchildren choose. As a result, they argued that strategy usewas not simply “background noise” but a psychologicalphenomenon in need of further examination (Rieben &Saada-Robert, 1997, p. 313).

Overlapping WavesThe overlapping-wave perspective suggests that incre-mental spelling growth occurs as children make adap-

tive choices among a variety of strategies that they use tospell words (Rittle-Johnson & Siegler, 1999). Spellingperformance changes as children adapt their strategy useto the goal of accurately representing the words that theyspell. Over time, children choose more effective strategiesmore often such that these strategies become predomi-nate as less effective strategies fade. This point of viewcharacterizes spelling development as a pattern of in-creasing and declining trajectories of spelling strategyuse, resulting in the overlapping-wave phenomenon (seeFigure 1).

Siegler (1996) indicated adaptability of children’s useof strategies in general by categorizing these strategies aseither retrieval or backup strategies. According to Siegler(1996), retrieval was used as a strategy when childrenretrieved a word faster than when executing a backupstrategy but slower than when automatically retrieving.In addition, using retrieval as a strategy required a level ofsophistication such that, given the children’s experience,they were capable of consulting with their memory, andalthough children consciously selected retrieval as a strat-egy, these strategies were not always accurate. In con-trast, backup strategies were executed more slowly andtended to be more accurate than retrieval strategies.Siegler (1996) determined adaptive choice by examin-ing the correlation between the difficulty of the problemand the number of backup strategies that children at-tempted to use.

Other researchers have also characterized spelling asa more thoughtful problem-solving endeavor as Siegler(1996) suggested. Nunes, Bryant, and Bindman (1997)characterized morphological spelling attempts as strate-gies, and they used these strategies to describe the de-velopmental stages that children go through inrepresenting inflected verb endings (specifically -ed).Nunes et al. also described how the development of suf-ficient morphological knowledge and explicit awarenessof grammatical categories is necessary for children toadopt a specific spelling strategy. They noted that dur-ing development “children may use basic strategies atfirst and add other more sophisticated ones later,” sup-porting the view that as children add various spellingstrategies to their repertoires, they use these strategiesadaptively (Nunes et al., 1997, p. 637).

Steffler (2001) characterized this shifting perspec-tive on spelling development as follows:

As was popular during the 1970s and 1980s, developmentalresearchers considered spelling development to occur instage-like progressions. More recently, spelling researchershave provided considerable evidence of the complexity ofchildren’s knowledge of the spelling system. Spellers use avariety of approaches during spelling and such variabilitydemonstrates considerable flexibility when approaching aspelling task. (p. 194)

Reading Research Quarterly • 43(3) 208

Rittle-Johnson and Siegler (1999) conducted a lon-gitudinal study to determine whether the variety andflexibility described by Steffler (2001) was evident in theapproaches that children used during spelling over atwo-year period. They followed first-grade children intothe second grade, examining the strategies that the chil-dren used to spell 30 words. These words were takenfrom two lists created using classroom spelling books.Each list was composed of words that were introducedduring the beginning, middle, and end of the school yearand were counterbalanced for similar patterns.

In order to determine adaptation, Rittle-Johnson andSiegler (1999) had the children spell words under twoconditions: (1) a prohibited condition where the re-searcher encouraged retrieval and (2) an allowed condi-tion where the researcher encouraged the use of backupstrategies (e.g., sounding out a word or using a rule). Theindex of word difficulty was determined by the correla-tion between the percentage of words spelled correctly inthe prohibited condition and the number of backupstrategies employed in the allowed condition.

During the allowed condition, the researcher askedthe children immediately after they had spelled a word,“How did you figure out how to spell _____?” If the chil-dren did not know, the researcher probed with the fol-lowing kinds of questions: “Did you just know how tospell it? Sound it out? Use another word to help you spellit? Use a rule? Do anything else?” (Rittle-Johnson &

Siegler, 1999, p. 336). In the prohibited condition, theresearcher interrupted children who were using a backupstrategy and asked them to begin writing the letters. Allspelling sessions were videotaped, and the solution re-sponse times were recorded using the videotape.

Support is growing for research that relies on chil-dren’s self-reports regarding their strategy use as a sourceof valuable insights. A recent study by Critten, Pine, andSteffler (2007) characterized children’s self-reports asexplanations and affirmed this method of research, not-ing, “The use of children’s explanations…provides a dif-ferent method of assessment that is not solely associatedwith accuracy; explanation of errors is just as informa-tive as explanation of correct choices” (p. 215).

Rittle-Johnson and Siegler’s (1999) analysis of stu-dents’ explanations of their strategy choices indicatedthat the students used multiple strategies to spell.Moreover, the majority of students reported having usedmultiple strategies to spell a single word on at least oneoccasion. Rittle-Johnson and Siegler argued that the chil-dren were adaptive in their selection of strategies becausethey used more strategies on more difficult words, eventhough these spelling strategies did not always yield cor-rect spellings. In addition, the overall pattern of changerevealed a gradual progression toward more effectivestrategy use over time.

Rittle-Johnson and Siegler’s (1999) success in findingvaried strategy use, adaptive strategy use, and gradual


Figure 1. Overlapping Waves Depiction of Cognitive Development

Note. By permission of Oxford University Press, Inc., from Siegler, R.S. (1996). Strategic development: Trudging up the staircase or swimming with the tide? InEmerging minds: The process of change in children’s thinking (Figure 4.4, p. 89). New York: Oxford University Press.

Younger OlderAge

More

Less

Perc

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f use

Strategy 1

Strategy 2

Strategy 4

Strategy 5

Strategy 3

change in strategy use provided evidence of spelling de-velopment occurring in overlapping waves. However,their results were obtained exclusively through strategyobservation combined with children’s immediate retro-spective self-report. To extend this work, we combinedRittle-Johnson and Siegler’s methodology with the classicerror feature analysis method initiated by Henderson(1980) and used effectively by others (e.g., Bear et al.,2004; Schlagal, 1989). By combining these methods, ourgoal was both to heed Ehri’s (1992) call for a more fine-grained analysis of individual children’s spelling develop-ment and to examine how strategy use and knowledgeof orthographic features develop reciprocally.

To obtain the more precise information needed touncover fine-grained details of the spelling developmentprocess, we used the microgenetic method (Siegler,1995). Typically, researchers use other kinds of meth-ods that look at change by following the same childrenover a long period of time (a longitudinal design) or bycomparing different populations of children at differentages (a cross-sectional design). These methods are verysuccessful in yielding information about the stability ofindividual differences, as well as about age-related dif-ferences. However, longitudinal and cross-sectional de-signs lack observational density (Ellis, 1997; Siegler,1995) and, thus, are unable to provide a sufficient num-ber of trials over a concentrated period of time. The datafrom studies using these designs demonstrate what hap-pens before and after the change but require inferencesabout the process of change itself. In contrast, the mi-crogenetic method makes it possible for researchers toobserve change as it occurs, thereby yielding informationof a finer grain size (Siegler, 1995).

Use of the microgenetic method has been called for inspelling research. Specifically, Steffler (2001) noted that“microgenetic studies…that investigate change as it oc-curs will give further insight into the developmental na-ture of knowledge representation” and that the use of“qualitative and quantitative studies can inform us of theconditions that facilitate change” (p. 197).

The microgenetic method has three defining charac-teristics: (1) repeated observations during a period of rap-id change, (2) trial-by-trial analysis using both qualitativeand quantitative techniques, and (3) documentation ofchange as it occurs (Siegler, 1995). Successful data col-lection using this method requires the analysis of chil-dren’s explanations of their actions, thereby enablingresearchers to have access to the children’s thinking andto validate their overt behavior. The current study usedthe microgenetic method to provide a fine-grained analy-sis of children’s spelling development. Our purpose wasto extend prior work (Rittle-Johnson & Siegler, 1999)by combining analysis of strategy use with analysis ofspelling errors and to determine what the implicationswere of this combined analysis for views of spelling de-

velopment. The following two research questions guid-ed our investigation: (1) Does the overlapping-wave per-spective characterize spelling development? and (2)What are the relationships between spelling strategy useand observed orthographic error patterns?

MethodsParticipantsParticipants were 31 first-grade students (8 females and23 males) who were at risk for reading failure drawnfrom three elementary schools in a large school district inthe southwestern United States. The participants camefrom three classrooms, one from each school, that hadthree characteristics in common: (1) a reduced classroomsize of 10 to 15 students, (2) a student population thathad been screened by the school for potential readingfailure using a screening instrument and/or teacher rec-ommendations, and (3) a strong phonics-based spellingprogram with daily instruction. All participating schoolsserved student populations that were of medium- tohigh-socioeconomic status as established by the percent-age of students receiving free and reduced-price lunch(26% for the first school, 21.3% for the second, and18.9% for the third). We hypothesized that the midyearwould provide rich opportunities for catching strugglingfirst graders’ development changes in the moment atwhich these changes occur (a requirement for a micro-genetic study). Midyear is generally an optimal period forresearchers to observe the transition that first-grade stu-dents make from using rudimentary letter–sound knowl-edge to developing an array of strategies for spellingwords. Because first graders at midyear have likely hadonly a moderate number of opportunities to use spellingstrategies, they should still be drawing on multiple strate-gies of varying sophistication (Siegler, 1996).

Students in the present study had received fourmonths of formal spelling instruction before we adminis-tered a preliminary baseline measure (the PrimarySpelling Inventory; Bear et al., 2004). The mean spelling-accuracy score on this measure was 0.49 words spelledcorrectly out of 15, indicating that our subjects’ initial per-formance was relatively low as the expected performanceon this list for first graders would be 4–6 words spelledcorrectly. Although the three participating teachers ap-proached spelling instruction differently (e.g., using ornot using word walls), all conducted daily 20-minute les-sons that consisted of explicit teaching of commonspelling patterns (e.g., word families) and spelling features(e.g., r-controlled vowels). All students practiced spellingwords throughout the week using game formats, wordsorts, and multiple copying. At the end of each week, thechildren were tested on accuracy. In addition, all three


teachers made an effort to match spelling instruction ob-jectives with reading activities (i.e., teaching particularspelling patterns when the children were learning to readwords that contained those patterns).

MeasuresSpelling InventoriesThis study employed six spelling inventories. The first,the Primary Spelling Inventory, was used as a pretest to establish baseline performance (Pretest BaselineMeasure). Four other inventories were used to measureadaptability, Spelling Inventories I and II (Bear,Invernizzi, Templeton, & Johnston, 2000) and Rittle-Johnson and Siegler’s (1999) Spelling Lists 1 and 2. Inaddition, Spelling Inventories I and II were reused to de-termine spelling growth over time. A sixth inventory,Spelling Inventory III, was created by the authors of thepresent study to mirror Spelling Inventories I and II; thisinventory was also used to measure growth. All invento-ries consisted of 15 words sequenced in gradual grada-tions of orthographic complexity.

Participants were randomly assigned to two condi-tions (described below), and word lists were counterbal-anced by condition and over sessions to eliminate thepossibility of a test effect due to repeated administration.Additionally, to assure equivalence in word difficultyacross Inventories I, II, and III, spelling words forInventory III were carefully matched in terms of spellingpatterns and features (i.e., word length, number of consonant-vowel-consonant patterns, long-vowel pat-terns, suffixes, percentage of high frequency words, etc.).For example, when comparing the three 15-word inven-tories with Dolch’s Basic Sight Vocabulary (Dolch, 1936),the inventories each contained one or two high-frequencywords (between 0.06% and 0.13%). (See the Appendixfor all spelling inventories and lists.)

Automaticity Baseline MeasureThe automaticity baseline was a mean score derived fromthe solution response times for 10 words that werespecifically selected by our participants’ teachers to meas-ure spellings that would be familiar to the students (seethe Appendix). We administered the measure during thefirst week of the study. Spellings were timed post hocusing a video recording of the event. Only accuratespellings were used in determining the automaticity base-line mean for each child. The measure was calibrated inseconds per letter. The automaticity baseline provided away to determine if words from the six spelling invento-ries and lists were being retrieved from memory more au-tomatically or more strategically. By comparing thesolution response times for the words from the spellinginventories and lists with the automaticity baseline mean,

we inferred that slower times suggested intentional con-sultation with memory.

Solution Response TimesSolution response times were determined by the amountof time it took a child to spell a word and were calibrat-ed in seconds per word as has been done in previous re-search (Rittle-Johnson & Siegler, 1999). Response timeswere derived post hoc using video recordings. Timingbegan when children placed their pencil on the paper tobegin and ended when they lifted their pencil from thepaper. The purpose of recording solution response timeswas to avoid ambiguity and to verify children’s self-reportswhen the children reported using strategies that the re-searcher did not observe. If a solution response time wassimilar to the slower solution response times generally re-quired by children for a strategy, then we assumed theself-report to be accurate. This is consistent with Rittle-Johnson and Siegler’s method of resolving discrepanciesbetween observations and self-reports.

Research DesignBased on the key components of a microgenetic design,we used both quantitative and qualitative analysis and col-lected trial-by-trial data (i.e., spelling word by spellingword). Table 1 presents each data source, the purpose andtype of analysis to which the data contributed, and thetiming of the administrations. The complex mixed de-sign incorporated several different analyses. To deter-mine whether the data showed evidence of theoverlapping-wave theory, we analyzed children’s (a) vari-ance in strategy use, (b) adaptability in strategy use, and(c) rate of growth (Siegler, 1996; see Table 1). The quali-tative analyses were used to determine the relationshipsbetween orthographic features and spelling strategies.These analyses served to substantiate the quantitative data(Johnson & Onwuegbuzie, 2004). Data consisted of ob-servations of children’s overt spelling behavior, their im-mediate retrospective self-report, their solution responsetimes used to validate the self-report, and their spelling re-sponses. We combined quantitative and qualitative analy-ses based on the fundamental principle of mixed research,which stipulates that the analyses should be combinedwhen the results complement both methods’ strengthsand do not have overlapping weaknesses (Johnson &Turner, 2003).

ConditionsRestricted and UnrestrictedIn keeping with previous research (e.g., Rittle-Johnson &Siegler, 1999; Siegler, 1996), we included two spellingconditions to determine whether children were exhibit-ing adaptive spelling behavior in their spelling strategy


use, a requirement for evaluating the viability of the overlapping-wave theory. In the unrestricted condition,students were given ample time to spell the words onthe inventory. We encouraged them to do whatever theyneeded to spell the word correctly and invited them touse any strategy at their disposal.

The restricted condition limited the students’ oppor-tunity to use multiple strategies to spell words by reduc-ing the amount of time that they were allowed to producea spelling. In the restricted condition, children were en-couraged to spell a word however it came to their mindsand as quickly as possible. These conditions providedan opportunity to compare students’ spelling error ratewhen retrieval was encouraged (in the time-restricted

condition) to their rate when strategy use was encour-aged (in the untimed, unrestricted condition). These stip-ulations were a part of Session 1 (weeks 3–9) during theadministration of Spelling Inventories I or II and SpellingLists 1 & 2; however, the restricted condition was not apart of the administration of the measures duringSessions 2 (weeks 14–16) and 3 (weeks 17–19).

ProceduresThe first author collected all of the data. The PrimarySpelling Inventory was administered as a whole-groupactivity, and the automaticity baseline measure was ad-ministered individually. Spelling Inventories I, II, and


Table 1. Microgenetic, Mixed-Research Design

Purpose of analysis Type of analysis Data source Sessions

Quantitative

Validate children’s self-report One-way ANOVA Automaticity baseline 1st weekand establish strategy classification Solution response times 1, 2, 3

Researcher observation Children’s self-report Inventory I, II, III

Evidence of overlapping-wave modelVariance in strategy use Descriptive statistics Inventory I, II, III 1, 2, 3

Repeated measures of variance Researcher observationChildren’s self-report

Adaptability Correlation coefficient Inventory I or II 1Spelling Lists 1 & 2Restricted conditions

Rate of growth Hierarchical linear modeling Primary Inventory 1st weekInventory I, II, III 1, 2, 3

Qualitative

Determined performance level Error feature analysis Inventories I, II, III 1, 2, 3of spelling feature accuracy

Display matrixesDetermined relationships of features Checklist matrix Error feature analysis 1, 2, 3and strategies Inventory I, II, IIIVerified developmental level Researcher observation

Children’s self-report

Examined strategy use over time in Time-ordered matrix Checklist matrix 1, 2, 3relationship to the spelling features

Componential analysesCompared individual progression with First Time-ordered matrix 1, 2, 3spelling stages, revealing growth-rate patternsCompared patterns of growth rate to Second Checklist matrix 1, 2, 3number of incidents of strategies used Time-ordered matrixat each session, revealing increase or 1st componential analysisdecrease of strategiesCompared patterns of growth rate to Third 2nd componential analysis 1, 2, 3strategy choice, revealing preferred strategy use at each stage

Note. Session 1 = weeks 3–9; Session 2 = weeks 14–16; Session 3 = weeks 17–19.

III and Spelling Lists 1 and 2 were administered individ-ually as well. We applied the restricted and unrestrictedconditions during Session 1 by randomly assigning chil-dren into one of the two conditions. Spelling Inventory Ior II and Spelling List 1 or 2 were presented equally oftenwith half of the children participating in the unrestrictedcondition first and half of them participating in the re-stricted condition first.

In the restricted condition, if the first author ob-served children trying to use a strategy, she interruptedand encouraged the children to write as fast as possible.Like Rittle-Johnson and Siegler (1999), we did not as-sume that children would suppress all spelling strategieswhen restricted but reasoned that adequate decrease inthe use of strategies would occur if discouraged.

Children’s self-reports were gathered in the unre-stricted condition and during administration ofInventories I, II, and III in Sessions 1, 2, and 3. Afterparticipants took as much time as they needed to spelleach word, the first author asked them one of two ques-tions: “How did you spell ____?” or “What did you doto decide on those letters?” On those occasions when thechildren did not know how they spelled a word, the re-searcher probed them for a response with the followingkinds of questions: “Did you sound it out?” “Use a rule?”“Did you just know how to spell it?”

We used the number of correct spellings fromSpelling Lists 1 and 2 to analyze adaptability, reliability,and validity of strategy classification; however, becausethe spelling words that made up these two lists were notequal in difficulty to the words found in Inventories I,II, and III, we did not use Spelling Lists 1 and 2 in thegrowth curve analysis or the other analyses. Childrenwho received Inventory I during Session 1 were adminis-tered Inventory II during Session 2, while children whoreceived Inventory II during Session 1 were administeredInventory I during Session 2. Finally, we administeredInventory III during Session 3.

ResultsReliability and Validity of Strategy ClassificationsTo determine specific spelling strategies that childrenused, we began with a two-part preliminary analysis.First, the analysis identified two types of spelling behav-iors: (1) accessing memory and (2) using overt spellingstrategies. Accessing memory refers to direct retrieval of aspelling pattern from memory with no overt strategy per-ceived. An overt strategy was a strategy directly observed(e.g., sounding out, using a rule, or visual checking).Rittle-Johnson and Siegler (1999) referred to direct re-trieval of spellings from memory as a strategy, and we

retained this terminology for two reasons: (1) We basedour research on theirs, and (2) when compared with theautomaticity baseline measure, the results of which aredescribed later, the time that it took our participants tospell certain words suggested that they were not automat-ically retrieving these spellings from memory, even whenthey reported doing so.

Next, we coded data using observations and children’sself-reports. If the first author’s observation and the child’sself-report of a spelling strategy matched, the trial wasconsidered unambiguous. If the observation and self-report did not match, the trial was treated as ambiguous.The unambiguous and ambiguous data types establisheda foundation for four classifications of strategy behavior.

The two unambiguous classifications were (1) “mem-ory access observed and reported,” meaning that thechild’s accessing of memory was observed and was consis-tent with the child’s self-report, and (2) “strategy use ob-served and reported,” indicating that the use of a spellingstrategy was observed and was consistent with the child’sself-report. The two ambiguous classifications were (1)“strategy use observed but not reported,” meaning that theuse of a spelling strategy was observed but the child re-ported accessing memory, and (2) “strategy use not ob-served but reported,” indicating that the child reportedusing a spelling strategy but it was not observed. Theseclassifications and analysis conditions were consistentwith those used by Rittle-Johnson and Siegler (1999). Anindependent rater who had been trained by the first au-thor observed the spelling trials on videotape and agreedwith the first author on 86% of all spelling-trial classifi-cations. The two raters resolved their differences, eventu-ally achieving a 100% interrater reliability. (The solutionsresponse times for each analysis condition, which are ex-plained later in this section, are reported in Table 2.)

Table 3 shows a comparison among solution re-sponse times for each classification of strategy behaviorand the automaticity baseline measures in seconds perletter. Significant differences in solution response timeswere found for each of the four classifications when com-pared with the solution response times for the wordsspelled to obtain the automaticity baseline. Each compar-ison revealed that students’ response time for spelling thewords in these conditions was significantly longer thanthe time taken to spell words automatically, suggestingmemory consultation (see Table 3).

Using the four classifications of strategy behavior asthe grouping variable, we conducted a one-way ANOVAto determine whether there were significant differencesamong the four classifications. We found significant dif-ferences between the conditions on accuracy (wordsspelled correctly), F (3, 926) = 16.57, p < .005, andspelling solution response time (the amount of time chil-dren took to spell a word), F (3, 926) = 4.32, p < .005(see Table 2).


Once we established differences among the classifica-tions, we conducted a follow-up Bonferroni analysis todetermine the nature of these differences. Findings indi-cated that solution response times were significantlyfaster when memory access was both observed and re-ported than when strategy use was observed and report-ed (M difference = 2.04, SE = 0.61, p < .005). Findingswere similar for accuracy. Accessing memory was moreaccurate than using a strategy (M difference = 0.30, SE =0.04, p < .001). The two unambiguous classificationsmade up 78% of all the spelling trials; these results wereconsistent with Rittle-Johnson and Siegler’s (1999) find-ings. The accuracy rates and solution response times forthe two unambiguous conditions were used as baselinemeasures for the two ambiguous conditions.

Only 8% of all spelling trials were classified as “strat-egy use observed but not reported.” The follow-upBonferroni analysis showed that children who claimedto be accessing their memory but who were observedusing a strategy were less accurate in their spellings thanthose who both reported and were observed accessingtheir memory (M difference = 0.18, SE = 0.06, p < .05).In addition, solution response times for trials classified as“strategy use observed but not reported” were longerthan the response times for trials classified “as memoryaccess observed and reported” (M difference = 2.29, SE= 0.87, p < .05). These results suggest that children wereusing a strategy despite their self-report to the contrary.

Of the total number of spelling trials, 14% were clas-sified as “strategy use not observed but reported.” Weassumed that if children used a strategy covertly, then thesolution response times and accuracy rates would re-semble those for the overt-strategy spelling trials. Thiswas the case. The solution response times for trials classi-fied as “strategy use not observed but reported” (M =9.11) were consistent with the response times for trials inwhich strategy use was both observed and reported (M= 9.94). Additionally, the accuracy rate for trials classifiedas “strategy use not observed but reported” (27%) wascloser to the accuracy rate for the unambiguous overt-strategy condition (18%) than that for the unambiguousaccessing-memory condition (48%). These comparisonsof solution response time means and accuracy rates vali-dated children’s self-report in the “strategy use not observed but reported” condition. The follow-upBonferroni analysis provided evidence that the children’sself-reports were consistent with the researcher’s observa-tions on 92% of all spelling trials. For the remaining 8%,researcher observation alone was used for the refining ofclassifications into categories of strategies for the analysesinvestigating the overlapping-wave model and for thequalitative analyses.

To refine the four classifications of strategy behav-ior, we subdivided those spelling trials classified as“memory access observed and reported” into two cate-gories of strategies: (1) complete retrieval (observation


Table 2. Mean Proportion of Correct Spellings and Solution Response Times by Classification of Spelling-Strategy Behavior

Classification % of trial Proportion correct SD Solution response timea SD

Memory access observed and reported 11% .48 0.50 7.90 6.08Strategy use observed and reported 67% .18 0.38 9.94 5.71Strategy use observed but not reported 8% .30 0.46 10.20 6.72Strategy use not observed but reported 14% .27 0.44 9.11 4.92

a Solution response time given in seconds per word.

Table 3. Mean Solution Response Time for Words Spelled on Automaticity Baseline Measure Compared to SolutionResponse Times for Classifications of Spelling-Strategy Behavior

Solution Automaticity Classification response timea SD baselineb SD N t-values df

Memory access observed and reported 2.10 0.90 1.45 0.59 17 2.98* 16Strategy use observed and reported 2.49 0.59 1.48 0.52 31 8.31* 30Strategy use observed but not reported 2.33 0.76 1.48 0.52 18 5.85* 17Strategy use not observed but reported 2.25 0.56 1.55 0.50 23 4.99* 22

a Solution response time given in seconds per letter.b Automaticity baseline given in seconds per letter.* p < .001.

of words spelled accurately from memory) and (2) partialretrieval (observation of words spelled inaccurately frommemory). The remaining spelling trials, which had beenclassified as either “strategy use observed and reported,”“strategy use observed but not reported,” or “strategyuse not observed but reported,” were also subdivided, re-sulting in the following seven categories of spelling strate-gies: (1) guessing, (2) sounding out, (3) rule use, (4)analogy, (5) visual checking, (6) copying, and (7) chunk-ing. The names of all nine strategy categories were chosento suggest their content in a straightforward way, andthe use of these categories was validated by the literature(Bear et al., 2004; Gaskins, Ehri, Cress, O’Hara, &Donnelly, 1996/1997; Rittle-Johnson & Siegler, 1999).Using these nine categories, an independent rater trainedby the first author categorized 25% of the spelling trialsas a reliability check. The independent rater and the firstauthor agreed on 92% of the trials and resolved all dif-ferences, achieving 100% interrater reliability. The ninecategories of strategies were used to code all of thespelling trials as follows:

1. Complete retrieval was coded when a child ac-cessed spelling features from memory to spell aword correctly. The researcher observed no overtuse of a spelling strategy, and the child’s self-reportindicated a knowledge of the word.

2. Partial retrieval was coded when a child accessedpartial spelling features from memory to spell aword incorrectly. The researcher observed noovert use of a spelling strategy, and the child’s self-report claimed a knowledge of the word.

3. Guessing was coded when a child did not knowwhat letters to choose. There was little or nophonological logic or morphological awareness in-

volved in the child’s choices (e.g., “MELP” for floator “MOXCK” for marched).

4. Sounding out was coded when a child audiblyused phoneme–grapheme matching, segmentingthe sounds of a word and matching each soundwith a letter or digraph. Sounding out was fre-quently self-reported.

5. Rule use was coded when the child used ortho-graphic knowledge to spell a word. The use of thisstrategy was determined through the child’s self-report and the generated spelling. The followingare examples of the rules that children used: con-sideration of silent e, the knowledge that every syl-lable must contain a vowel, and the spelling ofsuffixes.

6. Analogy was coded when a child used anotherword to help spell the current word. For example,one child spelled the word clown and then self-reported using the word down to help her. Thisstrategy was determined solely through the child’sself-report and was validated by the child’sspelling.

7. Visual checking was coded when a child was ob-served rechecking the just-spelled word to see if it“looked right” and then changing the originalspelling, whether accurately or inaccurately.

8. Copying was coded when a child consulted a visi-ble source such as the word wall in a classroom.

9. Chunking was coded when a child used commonletter patterns to help spell unfamiliar words. Forinstance, many children successfully used the -ingchunk or reported, “I knew the -et chunk.”

Table 4 summarizes all nine strategies.


Table 4. Spelling-Strategy Descriptions

Strategy name Definition Source of identification

Complete retrieval Accessing spelling features from memory to correctly No overt behavior and self-report indicated knowledge spell words of the word

Partial retrieval Accessing partial spelling features from memory No overt behavior and self-report claimed knowledge to spell words incompletely of the word

Guessing Choosing a string of letters with no phonological Child’s writing and self-reportor morphological logic

Sounding out Using phoneme to grapheme matching Observation and self-report

Rule use Applying orthographic rules to help spell words Child’s writing and self-report

Analogy Using another word to help spell a current word Self-report

Visual checking Perceptually checking a word to see if it looks right Observation

Copying Visually consulting a visible source Observation

Chunking Using common letter patterns to help spell Child’s writing, observation, and self-reportunfamiliar words

Evidence of the Overlapping-Wave ModelOnce our analysis had established reliability of children’sself-report for the four general classifications and we hadidentified and defined spelling strategies that we validatedusing previous literature, we examined the data in relationto the overlapping-wave model. According to Siegler(1996), evidence of the following three characteristicsmust be present in children’s developing behavior in or-der to support the overlapping-wave model: (1) variabili-ty, (2) adaptive choice, and (3) gradual rate of growth. Weused statistical methods to investigate whether childrendemonstrated each of these characteristics.

Variability of Spelling Strategy UseTo determine the degree of variability in children’s strat-egy use, we created frequency counts to calculate the per-centage of children using each of the nine identifiedspelling strategies, as well as the percentage of overall useof each strategy. We also calculated the number of strate-gies used per word and per individual. Findings showedthat every child used multiple strategies during each ses-sion. Overall, students used two to seven of the ninestrategies examined (M = 5.14). Individuals used morethan one strategy to spell a single word 89% of the time,with a range of one to four strategies per word. For exam-ple, a participant was recorded using three strategieswhile attempting to generate the word spoil. He demon-strated using a vowel-pattern rule (“SPIOLD”) and ex-plained to the first author, “Some [letters] I knew andsome I was sounding out in my head.” Importantly,strategies were not linked systematically to word charac-teristics. The most common strategy used was soundingout. During each session and over many types of words,all of the participants used sounding out at least 38% ofthe time. Rule use for the short-vowel word bed wasdemonstrated when one participant said, “I knew it need-ed an e, ’cause every word has a vowel.” Yet for the short-vowel word ship, this same participant was observedsounding the word out, and when asked how he spelledthe word, he simply stated, “Sounded out.” The leastcommon strategy was copying. Only 3% to 12% of stu-dents used this strategy during any one session, and itwas never used more than once. We eliminated the copy-ing strategy from further analysis because of its infre-quent use.

To further investigate variability, we used a repeatedmeasures analysis of variance to examine the frequency ofuse of particular strategies across the 15 spelling trialsthat took place during each session. A post-hoc Tukeytest identified significant differences among the threepoints in time for the following strategies: complete re-trieval, guessing, and rule use. Figure 2 shows the in-crease and decrease in frequency of use of these threestrategies across the three sessions. Complete retrieval

significantly increased over time, F (2, 60) = 26.57, p <.005, partial eta squared = .47. The post-hoc Tukey testsshowed significant increases in the frequency of use ofcomplete retrieval from Session 1 (M = 1.26, SD = 1.55)to Session 2 (M = 3.03, SD = 1.85) and from Session 1to Session 3 (M = 3.42, SD = 1.98) but not from Session2 to Session 3. Frequency of use of guessing significant-ly decreased. The multivariate results were used becausesphericity was violated, Wilks’ lambda = .72, F (2, 29) =5.53, p < .01, partial eta squared = .28. The post-hocTukey tests showed significant decreases in the frequen-cy of guessing from Session 1 (M = 2.03, SD = 2.92) toSession 2 (M = 0.42, SD = 0.85) and from Session 1 toSession 3 (M = 0.35, SD = 0.55) but not from Session 2 toSession 3. Due to the number of hypotheses, a Bonferronicorrection controlled the error rate and adjusted the pvalue for each comparison (p < .005). As with completeretrieval, the frequency of rule use also increased signifi-cantly over time, F (2, 60) = 32.91, p < .005, partial etasquared = .52). Post-hoc Tukey tests showed significantincreases in the frequency of rule use from Session 1 (M= 2.94, SD = 2.97) to Session 2 (M = 7.71, SD = 4.61) andfrom Session 1 to Session 3 (M = 8.10, SD = 4.10) but notfrom Session 2 to Session 3.

Adaptive ChoiceTo determine if children were adaptively choosing strate-gies to help them spell words, we used a correlation coef-ficient to compare misspellings of each word in therestricted condition with the percentage of strategies thatwe observed children using to spell each word in the un-restricted condition. We hypothesized that the more dif-ficult the word was, the more often participants wouldmisspell it and the more they would adapt by choosing touse multiple strategies in order to help them spell it(Rittle-Johnson & Siegler, 1999).

The correlation between word difficulty and strategyuse showed that fewer strategies were used to spell diffi-cult words than were used to spell easier words. Theamount of variance shared between the two measureswas within the meaningful range (Tabachnick & Fidell,2001) but low (r = -.37, p < .05). This finding was con-trary to Rittle-Johnson and Siegler’s (1999) observationthat the relationship between strategy use and problemdifficulty showed a high correlation among high-performing spellers (r = .85), demonstrating substantialevidence of adaptive behavior on difficult words.However, it is important to note that on average the par-ticipants in the Rittle-Johnson and Siegler study werehigh achievers (B. Rittle-Johnson, personal communica-tion, May 23, 2005). In contrast, the at-risk, strugglingspellers in our study were demonstrating little adaptive-ness in their performance on difficult spelling words andless adaptiveness overall than the high achievers in theprevious study. One possible explanation for this differ-


ence between the two studies is that our participants like-ly found the easier words challenging enough to evokegreater strategy use and the more difficult words too chal-lenging to attempt.

Rate of GrowthUnconditional and conditional growth models were es-timated as a means for measuring and assessing spellinggrowth trajectories. An unconditional two-level growthmodel was first run in order to estimate a spelling growthtrajectory for each student. Second, a conditional two-level model was used to regress the estimated growth-trajectory components (i.e., final status and growth) onthe hypothesized individual-level predictors of spellingdevelopment. In both models, Level 1 was composed ofa longitudinal growth model that fit a linear regressionfunction to each individual student’s spelling scores overthe three sessions. In the conditional model, relationshipsbetween pretest achievement status (which was the be-ginning performance accuracy scores calculated using thePrimary Spelling Inventory) and classroom were esti-mated. Equation 1 specifies the unconditional model,

Yti = π0i + π1i (time) + eti (1)

π0i = β00 +r0i (2a)

π1i = β10 + r1i’ (2b)

where Yti is the outcome (spelling achievement) for indi-vidual i at time t, π0i is the intercept or status parameterfor individual i at the final developmental time point, π1i

is the linear growth rate over the three sessions for indi-vidual i, and eti is the residual term representing unex-plained variation from the latent growth trajectory. Ascan be seen in Equations 2a and 2b, final status andgrowth were modeled only as a function of the mean sta-tus (β00) at time three and the mean growth (β10) of stu-dents over the duration of the study. The r0i and r1i arethe student-level residual terms.

At Level 2, the analysis modeled variation in the fi-nal status (π0i) and growth rate (π1i) of students in termsof the mean status at time three and the growth of student-level and individual-level residual terms. In theconditional model, pretest achievement status and thedummy coded classroom indicators were added to theequation. The following equations specify the condition-al Level 2 model:

π0i = β00 + β01 (CORRECT) + β02 (CLASS1) + β03 (CLASS2) + r0i (3a)

π1i = β10 + β11 (CORRECT) + β12 (CLASS1) + β13 (CLASS2) + r1i (3b)


Figure 2. Percentage of Times Students Used Spelling Strategies During Each Session

Strategy Change Over Sessions

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Unconditional Two-Level Model for Spelling AchievementThe descriptive statistics showed that children spelled4.15 words correctly out of 15 on the final assessment (N = 31 students; SD = 2.36). Calculation of 95% confi-dence intervals for the achievement level (3.11, 4.51) andgrowth estimate (0.92, 1.50) indicated that both param-eter estimates were different from zero. For fixed effects,the mean achievement status (β00 = 3.81) was significant(t = 11.16, p < .001). The mean growth rate (β10 = 1.21)was significant (t = 8.51, p < .001). Individual achieve-ment effect (r0i = 1.21) was significant (χ2 = 163.67, p < .001). Individual growth rate (r1i = 0.30) was signifi-cant (χ2 = 47.05, p < .05). The chi-square tests related tothe significance of the variability of the student-levelresiduals and indicated that achievement levels andgrowth rates varied across students. A test for skewnessand kurtosis was conducted and showed that both valueswere less than the absolute value of 1, providing evidencethat skewness was not an issue (skewness = .82, SE = .42;kurtosis = .28, SE = .82). Classroom 1 had .32 of the par-ticipants in proportion to the entire sample, whileClassroom 2 and Classroom 3 had .26 and .42, respec-tively (see Table 5).

Conditional Two-Level Model for Spelling AchievementTable 6 presents the results of the two-level conditionalmodel. The fixed effects presented at the top of Table 6indicate that the pretest achievement status and class-room differences were related to final status levels. Thenumber of words spelled correctly on the pretest waspositively related to the number of words spelled correct-ly at final status (β01 = 0.48). The coefficient indicates thatfor every one-unit change in pretest achievement status,final status (number of words spelled correctly at the lastsession) increased by 0.48 words. The coefficient associ-ated with Classroom 1 (β02 = 1.47) indicated that the stu-dents in this classroom achieved higher spelling scoresat final status relative to students in Classroom 3.

A similar pattern emerged with student spellinggrowth rates in that the students’ pretest achievement sta-tus was a positive predictor of their rate of spellinggrowth. The coefficient relating pretest achievement sta-tus to growth (β11 = 0.09) indicated that for every one-unit change in the students’ pretest achievement status(scale scores), their spelling growth increased on aver-age by 0.09 scale-score points per session. The resultwas that the initial gap between higher and lower achiev-ers widened over the course of the developmental peri-od studied. In addition, students’ spelling growth inClassroom 1 outpaced that of students in Classroom 3 byone word per session (β12 = 0.96), while the spellinggrowth of students in Classroom 2 outpaced that of stu-dents in Classroom 3 by a quarter of a word per session(β13 = 0.25).

Conditional variance estimates of students’ spellingachievement level and growth are presented at the bot-tom of Table 6. Chi-square tests demonstrated that ad-justments for students’ pretest achievement status and


Table 5. Descriptive Statistics for Unconditional Two-LevelModel for Mean Spelling Achievement

Variable name N M SD

Classroom 1 10 0.32 0.48Classroom 2 9 0.26 0.44

Note. Final status, M = 4.15, SD = 2.36, and range = 0–15.

Table 6. Two-Level Conditional Model Relating Feedback, Pretest Achievement Status, and Classroom Differences to Spelling Achievement

Fixed effects Estimates SE t

Mean accuracy achievement, β00 3.81 .25 15.51***Pretest achievement status, β01 0.48 .09 5.57***Classroom 1 accuracy, β02 1.47 .61 2.39*Classroom 2 accuracy, β03 0.30 .60 0.51Mean growth rate, β10 1.21 .11 10.64***Pretest achievement with growth status, β11 0.09 .03 3.16**Classroom 1 growth rate, β12 0.96 .28 3.46**Classroom 2 growth rate, β13 0.25 .27 0.92

Random effects Variance component dƒ χ2

Individual achievement, r0i 1.66 26 94.18***Individual growth, r1i 0.21 26 33.44

Note. β00 = final status; β01 = growth rate.* p < .05. ** p < .01. *** p < .001.

classroom differences did not completely explain the dif-ference among students in spelling achievement at thefinal developmental time point but did explain all theestimated variance in rate of growth. A comparison of un-conditional and conditional variance estimates revealedthat Level 2 predictor variables accounted for 28% ofthe variance in student growth and 46% of the variancein students’ final achievement levels.

The results of the conditional two-level model indi-cated that the initial gap between higher and lower achiev-ers widened over the course of the 19 weeks of this study.Children with higher initial status grew at a faster rate thanthose with lower initial status (see Figure 3), leading to afantail pattern. However, we note that there were limita-tions to the hierarchical level modeling (HLM) analysisused in that the chi-square test may have been underpow-ered by our small sample size. In addition, with only threetime points, we were limited to a linear function, whichunfortunately gave us a far more simplistic view of growththan four or more time points might have done.

Relationships Between OrthographicFeatures and Spelling StrategiesOur second research question was “What are the rela-tionships between spelling strategy use and observed or-

thographic error patterns?” We used a qualitative fine-grained analysis of each spelling trial to examine thisquestion. Due to the extensive nature of this analysis,we limited our sample to the students in the highest-performing classroom (10 children). We assumed that alllevels of performance (high as well as low) were reflect-ed within the performance of these students and that,given this, their performance was more likely to representthe performance of first-grade students in general. Thequalitative analyses were conducted in three phases: (1)analysis of children’s spelling errors (Bear et al., 2000),(2) construction of data display matrixes (Miles &Huberman, 1994), and (3) componential analysis (adapt-ed from Spradley, 1980).

Phase 1: Error Feature AnalysisThe error feature analysis created by Bear et al. (2000)classified students’ spelling errors on a continuum of de-veloping spelling ability defined by six spelling stages(Henderson, 1981; Schlagal, 1989). Each of the three setsof 15 words used on Spelling Inventories I, II, and III,respectively, represent a range of spelling growth fromthe first to the fifth developmental spelling stage. Duringthe first developmental stage, known as the emergentstage, children exhibit partial alphabetic knowledge. By


Figure 3. Fitted Trajectories Displaying Participants’ Initial Performance Gap Widening

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the fifth developmental stage, called the syllable junc-ture stage, children have mastered the basic patterns inone-syllable words and are able to look at spelling pat-terns across syllables. In addition, children in the fifth de-velopmental stage are beginning to understand roots,complex prefixes (e.g., addition and community), and suf-fixes (e.g., easiest and admitted). Classifying our partici-pants using the Bear et al. (2000) error feature analysishelped us to determine which specific orthographic fea-tures the students knew and which they still confused.This information allowed us to place children within adevelopmental spelling stage.

Phase 2: The Display MatrixesData display matrixes (Bernard, 1988) were used to ex-plore strategy use in relation to the orthographic featuresof the spelling words. We employed a checklist matrixas a technique for revealing those relationships (see Miles& Huberman, 1994). A checklist matrix was developedfor each child for Sessions 1, 2, and 3. This allowed acomprehensive look at individual participants and theiruse of spelling features and spelling strategies, and weused this information to determine both the spellingstage and the frequency of strategy use for each child.Each checklist matrix displayed the 15 spelling wordsthat appeared on the inventory used during that partic-ular session. Columns were used to record the strategiesthat the child drew upon to spell each word accordingto our multiple data sources (researcher observation, stu-dent self-report, and the generated spelling). The remain-ing columns displayed the feature types discerned fromthe error feature analysis (e.g., initial letter, final letter,and short vowel). By looking across the rows of each stu-dent’s matrix, we were able to determine the number andtype of strategies used on each word and the spellingfeatures that were represented. We were also able to seewhether these features were used conventionally.

The second display was a time-ordered matrix (Miles& Huberman, 1994). The purpose of the time-orderedmatrix was to examine change over time in children’s useof spelling strategies in relation to orthographic features.This matrix had columns displaying the three sessions.The rows showed spelling features, strategies used, andthe child’s developmental stage at the time of each ses-sion. By looking across a row, we could determine whenspecific features were added and when features becamephonetically and/or conventionally correct. We couldalso see which strategies were added or deleted over ses-sions and on what level of development the child wasfunctioning. The matrixes allowed for a visual display ofthe data that was examined for patterns across students,trials, conditions, and time.

The results of the Phase 2 matrixes revealed at leastthree important relationships between strategy use andfeature use. The checklist matrixes revealed that some

strategies such as sounding out and partial retrieval wereused consistently and frequently across all feature typesand that other strategies such as visual checking andguessing occurred less frequently and inconsistentlyacross feature types. These findings provided converg-ing evidence of the variability in strategy use.

The checklist matrixes also showed that as ortho-graphic features became more complex, a wider array ofstrategies was used; however, once the words became toodifficult for the child to attempt effectively, variety instrategy use began to decline. For example, a participantspelled the first four words of Spelling Inventory III,which were the easiest, using one strategy and thenspelled the next seven words, which were more difficult,using 2–4 strategies. However, when spelling the lastthree words, which were the most difficult, the partici-pant again used only 1–2 strategies. In addition, the typesof strategies that children used to spell the list of wordsvaried as they moved from easier to more difficult words.When the children were spelling the first words on thelist, the strategy of sounding out or retrieval (completeor partial) was predominantly used. For the next set ofwords, the children added rule use to the set of strate-gies that they used, along with chunking; however, forthe final set of words, the children once again relied onsounding out as their sole strategy. This pattern appearedoften among our participants.

This finding regarding the variability in the num-ber and types of spelling strategies used by our partici-pants reflects the adaptability exhibited by childrenwho are in the early phases of knowledge acquisition(Siegler, 1996). Siegler (1996) hypothesized that theamount of experience that children have with a taskinfluences the number of strategies that they use to per-form the task. Inexperienced children with low knowl-edge of strategies would then use fewer strategies. Astheir experience with a task increased, burgeoningknowledge would allow children to draw upon a largerarray of strategies. Children with a great deal of experi-ence would need only one or two strategies that theycould use consistently and effectively. As the task be-came more difficult, however, these children wouldagain use multiple strategies. The participants in ourstudy did, indeed, exhibit this kind of adaptability intheir strategy use when the spelling features were with-in the range of their experience.

The checklist matrixes revealed a more complex pic-ture of adaptability than was seen through the correla-tional analysis. By focusing on individual words,displaying the degree of each word’s complexity, andshowing the number and type of strategies used to spelleach word, these matrixes provided a visual picture thatrevealed a more nuanced pattern of adaptability than wasindicated by the correlation coefficient.


The time-ordered matrixes revealed a third importantrelationship between strategy use and spelling features.As we investigated how the relationships between strate-gy use and spelling features emerged over time, we sawthat less effective strategies diminished in use and moreeffective strategies increased in use at the same time thatspelling features were represented in increasing degreesof accuracy. For example, one of our participants demon-strated an overall decline in the use of guessing (from fivecases in Session 1 to one case in Session 3), added twonew strategies in Session 2 (chunking and rule use), andremained stable in the number of strategies used inSession 3. In addition, the arrangement of data in thetime-ordered matrix allowed us to determine that thisparticipant was in the semiphonetic stage during Session1 and had moved to the phonetic stage by Session 2. Wewere also able to see that the participant remained in thephonetic stage during Session 3.

Overall, the checklist matrix and the time-orderedmatrix revealed three characteristics of children’s spellingdevelopment: variability, adaptability, and a develop-mental relationship between increased awareness ofspelling features and the frequency of use of particularspelling strategies.

Phase 3: Componential AnalysesThree componential analyses were conducted in Phase 3.While the data matrixes used in Phase 2 allowed us to ex-amine the relationship between feature representationand strategy use for individual children, the purpose ofthe componential analyses was to examine the patterns ofspelling development across all 10 children and to de-scribe the rate of growth for spelling features. The firstanalysis compared the 10 participants’ individual pro-gression through the spelling stages, revealing five pathsof development. Because we relied on the orthographicstages that Bear et al. (2004) established to represent de-veloping feature knowledge, we also were able to use thisfirst analysis to determine the rate of growth for spellingfeatures.

To conduct the componential analyses, we created amatrix with 10 columns and 8 rows. We displayed eachof the developmental stages and substages, as describedby Bear et al. (2004), in the rows of the matrix (i.e., latesemiphonetic; early, middle, and late phonetic; etc.) andshowed each of the participants by number across thecolumns. Within the cells of the matrix, we placed thesession number (1, 2, or 3) at which the child was func-tioning at each developmental stage and substage.Looking down the columns, we were able to see a path ofdevelopmental progression for each participant. We ob-served five patterns of growth when we looked across thecolumns for all 10 students.

These patterns of growth were distinguished by thechildren’s initial and final status and their rate of progres-

sion through the stages. The five patterns are as follows:(1) low-performing spellers with little progress, (2) low-performing spellers with steady progress, (3) average-performing spellers with no progress, (4) average-performingspellers with steady progress, and (5) high-performingspellers with rapid progress. These patterns representedthe orthographic understanding that children were de-veloping and the rate at which they were progressingthrough the developmental stages and substages. The pat-terns revealed a great deal of diversity of developmentaltrajectories for these emerging spellers.

The second componential analysis examined the pro-gression of the 10 children’s strategy use over time in re-lation to the patterns of spelling feature growth. Thismatrix had rows displaying the five patterns of stage de-velopment that occurred over the three sessions. Thecolumns recorded the specific strategies observed (i.e.,complete retrieval, analogy, chunking, etc.). This analysisrevealed children’s developing sophistication in strategyuse in relation to their progress in understanding the or-thographic system. For example, we examined the num-ber of incidents of complete retrieval during each session.The analysis revealed that children designated as high-performing spellers with rapid progress increased theiruse of complete retrieval by four incidents per sessioncompared to children designated as low-performingspellers with steady progress who increased their use ofcomplete retrieval by only one incident per session.Thus, those children with a stronger performance inspelling during the early part of the study had a greaterrate of increased competence over time.

The third componential analysis contrasted the fivepatterns of growth with the range of spelling strategiesthat the participants used over the three sessions. In thismatrix, the columns recorded the particular strategiesused, as well as how often each strategy was used. Therows recorded the patterns of stage development dis-cerned from the second componential analysis. This ma-trix revealed that low-performing spellers with steadyprogress used only three different types of strategies dur-ing Session 1, but by Session 3, they were using six typesof strategies. The high-performing spellers with rapidprogress used five different types of strategies duringSession 1 and seven different types of strategies duringSession 3. At first glance, it appears as though these twogrowth patterns are quite similar. However, the numberof different strategies used does not tell the whole story.By examining which strategies were used, we were able todiscern that these two groups progressed differently. Thelow-performing spellers used less sophisticated strategiessuch as guessing during each of the three sessions,whereas the high-performing spellers rarely guessed andcontinued to add more sophisticated strategies (analogy,chunking, and visual checking) to their repertoires overtime.


Qualitative FindingsOverall, the qualitative analyses provided evidence con-sistent with the three requirements of the overlapping-wave model for strategy use development: variability,adaptability, and gradual change. Consistent withSiegler’s (1996) description of the development of strat-egy use, overlapping waves of growth and decline ofstrategy use were evident in our data as illustrated inFigure 4. The interaction of strategy use developmentand the emergence of knowledge of orthographic featuresis also demonstrated in Figure 4. For example, guessingwas a prominent strategy for late-semiphonetic spellers,but when these spellers emerged as early-within-wordspellers, guessing had markedly declined. In contrast,sounding out remained a predominate strategy through-out all stages represented by our spellers. Rule use fol-lowed yet another pattern: the use of this strategy wasbarely evident in the late-semiphonetic spellers, but bythe time these students had reached the early syllablejuncture phrase, this strategy had become predominate.Complete retrieval followed a path parallel to that of ruleuse; that is, as rule use became a more preferred strate-gy, complete retrieval became more frequent. The moresophisticated strategies (chunking, visual checking, and

analogy) did not emerge until the children had reachedthe early phonetic stage at least, indicating that sufficientknowledge of the alphabetic principle was needed tobenefit from these strategies. In sum, spelling strategiesdeveloped in wave-like progressions as orthographic fea-ture knowledge developed in phases.

The qualitative analyses also revealed, as evidenced inthe different trajectories of spelling development, thatthose spellers who started out with greater spelling acu-men showed a steeper trajectory of growth, both in strat-egy use and in accuracy of orthographic featurerepresentation in their spelling attempts. This findingwas also evident in the growth curve trajectories thatemerged from the HLM analysis (see Figure 3).

DiscussionIn this study, we provided a fine-grained, microgeneticanalysis of developmental spelling. To do so, we repli-cated and extended Rittle-Johnson and Siegler’s (1999)work by combining their methodology of self-reportedstrategy use (confirmed with observations and timingdata) with the method of error feature analysis used suc-


Figure 4. Mapping Strategy Development Onto Orthographic Feature Development

Note. LSP = late semiphonetic; EP = early phonetic; MP = middle phonetic; LP = late phoentic; EWW = early within word; MWW = middle within word; LWW = late within word; ESJ = early syllable juncture.

18

16

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10

8

6

4

2

0

–2LSP EP MP LP EWW MWW LWW ESJ

Phases of orthographic knowledge

Freq

uenc

y of

str

ateg

y us

e

Sounding Out

Guessing

Partial Retrieval

Rule Use

Complete Retrieval

Visual Checking

Chunking

Analogy

cessfully by phase theorists (e.g., Bear et al., 2004). Ourstudy addressed two research questions. The first ques-tion asked if Siegler’s (1996) overlapping-wave theoryprovided a viable explanation of spelling development.Our second question used a qualitative approach to ex-amine the relationship between spelling strategy use andorthographic features analysis as sources of evidence fordevelopmental growth.

To answer the first question, we examined our datafor the hallmarks of the overlapping-wave model in thedevelopmental progression of children’s spelling abilities:variability, adaptability, and gradual growth (Rittle-Johnson & Siegler, 1999; Siegler, 1996). Findingsshowed evidence of all three characteristics. Variabilitywas apparent when children who were not influencedby word characteristics used less and more sophisticatedstrategies at the same time point. Moreover, the use of avariety of strategies occurred not only all along the devel-opmental continuum but across 89% of the individualspelling trials.

This percentage is substantially higher than that re-ported by Rittle-Johnson and Siegler (1999). Only 8%of the spelling trials in their study involved the use ofmultiple strategies on single words. There are severalpossible explanations for this discrepancy. First, we ob-served a greater number of strategies in our study (e.g.,guessing and chunking), and we counted these strate-gies individually, whereas Rittle-Johnson and Siegler(1999) defined “retrieving part of the word’s spelling andsounding out other parts” (p. 338) as one strategy. Theynoted that 5% of their first graders used this strategy,whereas we saw this combination far more frequentlyamong our participants. In addition, we subdivided re-trieval into two strategies (complete and partial) in con-trast to Rittle-Johnson and Siegler who counted these twosubstrategies as one strategy. These differences couldhave contributed to the discrepancy between our find-ings and those of our colleagues in the field. More impor-tantly, we do not think that these divergent percentages(89% and 8%) imply anything uncharacteristic about theoverlapping-wave theory, despite their being vastly dif-ferent. In both studies, children were demonstrating vari-ability. However, our participants demonstrated it overa larger percentage of words.

Our findings demonstrated gradual growth across thequalitative matrixes and in the HLM analysis. Growth tra-jectories showed that overall the rate of growth was 0.25words every three to four weeks over a five-month peri-od. The growth trajectories also revealed that participantsgrew in spelling ability at different rates. Higher-performingstudents grew at a faster rate than their lower-performingpeers, causing the differences between these two groupsto increase.

With regard to adaptability, we found that, contraryto our expectations, participants used fewer strategies

on more difficult words. At first, these results appeared toindicate a lack of adaptability. However, the qualitativechecklist matrixes revealed that children used morestrategies with difficult words that were within theirrange of competence and fewer strategies with words thatwere outside this range.

We concluded that adaptability is likely dependenton the choices available to children within the range oftheir capabilities (Siegler, 1996). We observed an in-crease in our participants’ adaptive use of strategies thatwas tied directly to their growing knowledge of ortho-graphic features. In other words, we found that childrenadaptively used greater numbers of strategies as their or-thographic knowledge increased.

Thus, with regard to our first research question, theoverall results could be interpreted as consistent withoverlapping-wave theory. Our findings are in keepingwith a view of spelling development in which over -lapping waves of strategy use are superimposed ontogradually expanding levels of orthographic featureknowledge. We interpret these results as suggesting thatspelling development proceeds as children develop theability to adaptively apply their growing knowledge tothe problem of representing English orthography.

Other interpretations of these data are possible, ofcourse. For example, our findings are also consistent withtheories of memory, attention, and learning that suggestthat individuals become less strategic and more auto-matic over time as repeated encounters with stimuli buildup the quality of the representations of familiar stimuli inmemory (see, for example, instance theory, Logan, 1988,2002). It is precisely this tradeoff between strategicchoice and automatic retrieval that could provide themechanistic explanation for the pattern of overlappingwaves observed in our data. Indeed, the process of learn-ing to spell features of words is characteristically similarto the process of learning to recognize any stimuli towhich an individual is exposed multiple times.

Our findings also do not contradict those of eitherthe phase theorists or the incrementalists; however, we dothink that our findings extend these perspectives. Ourdata show that both of these perspectives are reflective ofthe development of spelling ability as being mutuallyphase-like and incremental-like. We saw evidence of apreferred strategy becoming predominant and then fad-ing, mirroring the observations of dozens of studies em-ploying feature error analysis (e.g., Bear & Barone, 1989;Brown, Sinatra, & Wagstaff, 1996; Schlagal, 1989). In ad-dition, we observed that progress in development oc-curred incrementally as students gained more experienceand presumably developed richer representations of or-thographic patterns in memory. Indeed, we believe thatboth the phase-theory and the incremental perspectivescontribute key insights on the process of spelling develop-ment and that our findings indicate that spelling proceeds


both in phases and in degrees concomitantly. We alsothink that the concept of overlapping waves provides aperspective from which to think about how spelling de-velopment can proceed both in phases and in degrees.This perspective suggests that students may not neces-sarily think in a qualitatively different fashion from onephase to the next, so much as adapt the various ways thatthey think and reason to the current task. This perspectivesuggests that although progress is incremental, it is hard-ly steady. Rather, more and less sophisticated strategiesare in use at the same time, indicating that progress inspelling development is a complex nonlinear progression.

Our second research question explored the relation-ship between spelling strategy use and orthographic fea-tures. In examining the data related to this question, weobserved that growth begets growth in spelling develop-ment (Stanovich, 1986). That is, we interpret the resultsof the feature error analysis as indicating that knowledgeof orthographic features and strategy use developed re-ciprocally: Children’s developing orthographic under-standing supported an increase in more sophisticatedstrategy use, and more sophisticated strategy use allowedfeature knowledge to become more efficient. Greater pre-cision and specificity of knowledge of orthographic fea-tures seemed to lead to greater proficiency in strategy useand vice versa. We interpret these findings as suggestingthat orthographic knowledge and strategy use had inter-active effects in children’s developing spelling ability.

Such effects were present in the results of both thequalitative analysis and the growth-curve trajectories.Together, they revealed that the children who startedout with greater spelling acumen progressed at a greaterrate of growth, exhibiting what Stanovich (1986) calledthe Matthew effect. Indeed, our patterns of findings in-dicate that the initial gap between higher and lowerachievers widened markedly over the 19 weeks of thestudy. Although it could be argued that five months is in-sufficient to establish definitively the presence of theMatthew effect, the data certainly point in that direction.

Any study has limitations, and ours is no exception.More subjects and points of measurement would haveprovided greater support for the conclusions that wedrew from the growth-curve analysis. In addition, ob-servations over a developmental period longer than fivemonths would have given stronger support for our evi-dence suggesting a Matthew effect. And, as with any psy-chological study employing self-report, we must becautious in our interpretations. Given the limitations ofa single study, we call upon other researchers to also ex-amine spelling using a fine-grained, microgeneticmethod.

The contribution of our study, we believe, is the sug-gestion that more fine-grained analyses can and do reveala more complex picture of spelling development thanwe have previously seen. Before any definitive conclu-

sions can be drawn, additional studies at this grain sizeare needed to fully understand the complexities ofspelling development. However, what we have learnedfrom this study reminds us of the parable of the blindmen and the elephant. That is, whether you look for evi-dence of stages of spelling development or evidence ofcontinuous growth, you are likely to find what you lookfor since both of these kinds of evidence are reflective ofaspects of spelling development. Indeed, we view de-scriptions of the nature of spelling development as de-pendent on the type of data examined and how thosedata are analyzed. Only when our observational densityhas been increased by greater numbers of studies willwe be able to obtain the precision needed to develop agreater understanding of children’s spelling develop-ment, a surprisingly complex phenomenon.

Notes1 Our thanks to an anonymous reviewer for this phraseology.

A previous version of this paper was presented at the AmericanEducational Research Association’s annual meeting, San Francisco,2006.

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Submitted February 28, 2007Final revision received February 22, 2008

Accepted February 26, 2008

Ann C. Sharp works in the Dean’s Office of the McKaySchool of Education, Brigham Young University, Provo, Utah,USA; email [email protected]. Prior to joining the staff atBrigham Young University, Dr. Sharp was at the University ofNevada, Las Vegas, USA, where she earned her Ph.D. ineducational psychology.

Gale M. Sinatra is a Professor in the Department ofEducational Psychology, University of Nevada, Las Vegas,USA; e-mail [email protected].

Ralph E. Reynolds is a Professor in the Department ofEducational Psychology and Director of the Center forEvaluation and Assessment, University of Nevada, Las Vegas,USA; e-mail [email protected].


Automaticity Primary baseline Spelling Inventory Inventory I Inventory IIa Inventory III List 1 List 2

Teacher (Bear et al., Created by (Rittle-Johnson & Siegler, suggested 2004, p. 301) (Bear et al., 2000, pp. 288, 292) researcher 1999, p. 346)

first name fan bed net red hat batat pet ship trip drip bug rugthe dig when then bend dog pophe rob lump dump hump red bedcat hope float soap boat kite dimemy wait train chain brain stop dropto gum place trace grace fish dishsee sled drive crime shine candy babycan stick bright fright slight boat goatmom shine shopping popping stopping moon soon

dream spoil broil foil chair stairblade serving jerking perching girl birdcoach chewed brewed threw letter betterfright carries bunnies worries frown clownchewing marched parched charmed feet leaf

Appendix

Spelling Word Lists


a The word list from Inventory II was modified by the first author to more specifically mirror across inventories the structure of the words (i.e., word length,number of consonant-vowel-consonant patterns, long-vowel patterns, suffixes, percentage of high frequency words, etc.).

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.

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