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Abstract
Assessments of developmental spelling, also called spelling inventories, are commonly used to understand students’ orthographic knowledge (i.e., knowledge of how written words work) and to determine their stages of spelling and reading development. The information generated by these assessments is used to inform teachers’ grouping practices and instructional priorities. While relatively easy to administer, developmental spelling assessments can be time-consuming to score and are susceptible to human error in both the scoring and the interpretation of results. The purpose of this study is to develop and validate an online version of a commonly used spelling inventory, making the assessment more efficient and accessible and the results more reliable for teachers and scholars alike. Implications for practice and directions for further research are discussed.
For nearly 50 years, researchers have observed that children’s spelling develops in a predictable and consistent manner. Beginning with the work of Charles Read (1971, 1975) and Carol Chomsky (1971), and later Edmund Henderson (Beers & Henderson, 1977; Henderson & Beers, 1980; Henderson, Estes, & Stonecash, 1972), researchers found that students use the three layers of written language (sound, pattern, and meaning) to help them spell unknown words. Their spelling develops sequentially, generally starting with letter-sound correspondences and short vowel patterns in single-syllable words (e.g., pat, ten, and fit), moving to long vowel and other vowel patterns (e.g., cape, throat, fight, and chew), then applying and extending these understandings to words with two or more syllables, including the addition of prefixes and suffixes to base words (e.g., bedrock, carrying, pirate, and rewrite), and later, understanding the influence of word parts, or morphemes, including Greek and Latin roots (e.g., visit, vision, revise, and advisory). This understanding of English orthography develops over time (Chall, 1983; Ehri, 1997, 2005; Henderson, 1981; Morris, Bloodgood, Lomax, & Perney, 2003; Templeton, 2003; Templeton & Bear, 2012/1992, in press; Treiman & Kessler, 2014), and the degree to which students use varying sources of word knowledge (e.g., phonology, orthography, or morphology) changes as their learning progresses along the developmental continuum (Templeton, 2003; Templeton & Gehsmann, 2014b).
Henderson and his colleagues (Henderson, 1981, 1990; Henderson & Beers, 1980; Templeton & Bear, 1992; Templeton & Morris, 2000) were the first to identify five distinct stages of development along this continuum, establishing the foundation for what is known today as developmental spelling (Bear, Invernizzi, Templeton, & Johnston, 2016; Templeton & Gehsmann, 2014a). Other researchers have affirmed the progression of spelling and reading stages or phases in their own research (Ehri, 1975, 1978, 2006; Frith, 1985; Spear-Swerling & Sternberg, 2000, 2001; Zutell, 1992). This developmental progression has been observed across written languages (Bear, Templeton, Helman, & Baren, 2003; He & Wang, 2009; Helman, 2004; Helman & Bear, 2007), among people with learning disabilities (Spear-Swerling & Sternberg, 1997; M. J. Worthy & Invernizzi, 1989), young children (Ouellette & Sénéchal, 2008), and even adults (Massengill, 2006; M. Worthy & Viise, 1996).
Research supports the understanding that orthographic knowledge underlies students’ learning to read, and there is a reciprocal relationship between spelling and reading (see Abbott, Berninger, & Fayol, 2010; Adams, 1990; Cunningham, Nathan, & Schmidt-Rather, 2011; Graham & Hebert, 2011; Graham & Santangelo, 2014; Invernizzi & Hayes, 2004; Rapp & Lipka, 2010; Templeton & Bear, in press; Zutell, 1992, for reviews). In a recent meta-analysis of the relationship between spelling, reading, and writing, Graham and Santangelo (2014) noted medium- to large-weighted effect sizes (ESs), demonstrating that spelling instruction leads to improvements in phonological awareness (ES = .51), correct spelling when writing (ES = .94), and overall reading achievement (ES = .44). The effect of spelling instruction was observed across domains of reading including word reading skills (ES = .40), comprehension (ES = .66), and to a lesser degree, fluency (ES = .36). To understand the magnitude of these ESs, consider the finding that spelling instruction improved phonological awareness by about half a standard deviation; this means spelling instruction would move a child at the 50th percentile to the 70th on tests of phonological awareness. When considering the effect of spelling instruction on overall reading achievement, a student’s reading performance would move from the 50th percentile to the 67th.
These findings extend the earlier work of Abbott, Berninger, and Fayol (2010) who studied the longitudinal relationships between reading and writing in Grades 1–7 and Graham and Hebert (2011) who examined the impact of writing practice and writing instruction on reading. Together, and independently, these findings support theoretical claims of the relationship between orthographic knowledge and reading achievement as well as spelling instruction’s positive influence on reading achievement. Examining students’ spelling provides scholars and educators a window into students’ word knowledge, and hence, their stage of spelling and reading development. As Perfetti observed, “Spelling and reading share the same lexical representation. In fact, spelling is a good test of the quality of the representation” (1992, p. 170).
Knowledge of a student’s stage of development has implications for not only what is taught but how and when (Bear et al., 2016; Gehsmann & Templeton, 2011–2012; Templeton & Gehsmann, 2014a). Understanding the developmental continuum allows teachers to match instruction and instructional materials to students’ “instructional zones” or “zones of proximal development” (Betts, 1946; Vygotsky, 1978). When this happens, learning proceeds more efficiently and yields better results (Betts, 1946; Connor et al., 2011; Ehri, Dreyer, Flugman, & Gross, 2007; Morris, Blanton, Blanton, Nowacek, & Perney, 1995; Morris & Perney, 1984). Providing this kind of targeted, differentiated, and developmentally responsive instruction is essential for all students, particularly those from historically underserved populations. Too often, these students experience one-size-fits-all instructional practices, which only continue to place these students at risk (Allington, 2002, 2012; Gehsmann & Woodside-Jiron, 2005; Woodside-Jiron & Gehsmann, 2009). Easy access to valid and reliable assessment tools that can help teachers develop instruction to better meet the needs of individuals and groups of students, particularly those at risk for reading difficulties, is a critical need.
Method
The present study was conducted in an urban school district in the northeast region of the United States. Of the students in this school district, 81% were eligible for free- or reduced-price lunch (FRPL), and a full third of the students were English learners (ELs), most of them refugees from sub-Saharan Africa and the Middle East. The school’s population was racially diverse with 54% of students identifying as White, 17% Black, 22% Asian, and 6% multiracial. In our sample of 93 students from grades 5 to 8, 25 students were ELs, 11 students were discontinued from receiving EL services but were still being monitored, 21 students were on Individualized Education Plans (IEPs), and 70 received FRPL. Participation in this study was voluntary and was primarily determined by teachers’ availability during the two-day testing schedule. All teachers in grades 5–8 were invited to participate.
Unlike traditional spelling tests, developmental spelling inventories measure students’ word knowledge by examining the orthographic features (e.g., consonants, vowels, inflected endings, syllable juncture, affixes, and Greek and Latin elements) students use when spelling a list of 25–35 intentionally selected words. The list begins with simple short vowel words such as bed, ship, and when and progresses to more difficult words such as confident, civilization, and opposition. Words are selected based on their orthographic features and these features correspond with the early, middle, and late phases of the five stages of spelling development (Bear et al., 2016). The names of these five stages reflect the information spellers primarily use within each stage: emergent, letter-name alphabetic, within-word pattern, syllables and affixes, and derivational relations. These five spelling stages align with five stages of reading development: emergent, beginning, transitional, intermediate, and skillful, though reading generally develops just slightly ahead of spelling (see Bear et al., 2016, or Templeton & Gehsmann, 2014a, for a more thorough explanation).
Students receive feature points for each orthographic feature they include in the spelling of words on the list. Identifying the first set of orthographic features students “use but confuse” (i.e., the place where they miss two or more features in a category) indicates their instructional level or stage of spelling development (Invernizzi, Abouzeid, & Gill, 1994). Points are also awarded for spelling words correctly. The total number of words spelled correctly is known as the power score. Power scores have been described as a way to estimate a student’s stage of development (Bear et al., 2016). The total score reflects the combination of feature points and words spelled correctly and is used, primarily, to track growth between administrations of the assessment.
The assessment used in this study, the Elementary Spelling Inventory (ESI; Bear et al., 2016), has been evaluated by the Center for Research in Education Policy (Sterbinsky, 2007). As reported by Sterbinsky, test–retest data show robust reliability and moderate predictive validity for the ESI. Looking at fifth grade as an example, the test–retest reliability for a group of fifth-grade students not including ELs, students on IEPs, and gifted students was .942; the test–retest reliability for a group that included all fifth-grade students was .959. Sterbinsky also found predictive validity between spring scores on the ESI and students’ scores on the California Standards Test (CST) of English Language Arts (ELA). For example, at the fifth-grade level again, predictive validity for word analysis on the CST was .651, literary response and analysis was .679, and overall ELA was .706, thus affirming the value of the ESI in the literacy curriculum. (All coefficients were significant at the p < .001 level.) The primary purpose of the present study was to determine the reliability and validity of an online version of this same assessment.
Using a test–retest design, participants took two versions of the ESI (Bear et al., 2016): a traditional paper–pencil version and an online version developed by the research team. The two assessments were exactly the same, only differing in the format (print vs. digital). Three questions guided the inquiry:
Is the online assessment reliable? (In other words, does it yield the same results as human scoring?)
Is typing the words (rather than writing words by hand) a valid option for measuring students’ orthographic knowledge? Do these results hold up when comparing different scoring and reporting options (e.g., feature points, words correct/power score, and total score) and across subgroups (i.e., gender, EL, IEP, FRPL, and grade-level groups)?
Is the power score a reliable and valid means for determining a students’ stage of spelling development?
The research proceeded in two phases: one to determine the reliability of the online program’s scoring function, and the second to determine whether having students type the words rather than handwrite them was a valid means for assessing students’ orthographic knowledge, a question that has never been researched. We also compared the feature points scoring method to the power score method to identify which method was more reliable for determining a student’s stage of spelling development, another question that has never been empirically tested.
All participating classes were randomly divided into two groups, one group completed the paper version of the inventory first, followed by the digital version, while the other group started with the digital version and finished with the version taken by hand. The handwritten inventories were administered by the research team following administration guidelines provided by the publisher (Bear et al., 2016). The online program followed the same procedures. Students listened to each word being called by a prerecorded human voice, a sample of which can be heard here (refer audio file in the Online Appendix). The word was used in a sentence to make the meaning of the word clear and then students were prompted to type the word. Consistent with the directions for the administration of the pencil–paper version of the assessment, the program allowed students to listen to the words and/or sentences two additional times, review their work, and make corrections. Students used iPads to interface with the secure online program, which was built using the MongoDB and Mongoose, Expres, Angular JS, and Node.js stack. A sample view of the student interface can be seen in Online Appendix Figure 1. Online Appendix Figure 2 shows an example of the pencil–paper format. The online program generated identical student level and class composite reports as the hand scoring method.
The research team observed students interact with the program with ease. When asked about their preference, all the students reported preferring the online tool as it allowed them to go at their own pace and independently control the playback functionality of words and/or sentences. They also reported liking the headphones and the ability to control the volume, enabling them to hear the words and sentences more clearly.
Findings
Phase 1: Establishing the Reliability of the Online Program
Students’ handwritten responses were entered into the electronic program by members of the research team exactly as they were written by the students. The handwritten inventories were scored, first, by hand and then by the computer program. All discrepancies between the two methods of scoring were investigated and corrected. The vast majority of errors (N = 27) were human errors made by members of the research team (e.g., typing errors, adding errors, or failure to note features that were present in students’ spelling). These errors are typical of those teachers report making when hand scoring the inventories themselves. Three minor glitches were found in the program, all having to do with students adding extra letters to their spelling attempts. These programming errors were corrected and the sample was rescored. After the human and programming errors were corrected, the online program’s scoring function was found to be 100% consistent with the corrected hand scoring of the same inventories, thus affirming the reliability of the online program.
Phase 2: Establishing Validity
After collecting both the handwritten and typewritten spelling samples, the results from the two administrations were compared using Pearson’s r utilizing the Statistics Package for Social Sciences (SPSS) 24.0 (IBM, 2016). Table 1 shows the correlation between the pencil–paper and online administration of the ESI using the feature points scoring method, the power score method, and total points. All correlations were significant at the p < .001 level (two-tailed). These robust results held steady across gender as indicated in the same table. These data affirm there is virtually no difference between the handwritten and typewritten spelling of words, suggesting that word knowledge can, indeed, be measured through students’ typewritten spelling samples, at least with students in the upper elementary and middle school grades. This is promising evidence.
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Table 1. Test–Retest Reliability of Paper and Online Versions of the Elementary Spelling Inventory for All Students.
Robust correlations between pencil–paper and online administrations of the assessment were consistent across subgroups (i.e., EL, IEP, FRPL, and grade-level groups). In Online Appendix Table 2, for example, ELs and non-ELs had nearly identical correlations when comparing feature points (.962 vs. .959), power scores (.962 vs. .963), and total points (.967 vs. .966). Students in the “monitoring” group, ELs who were discontinued from EL services but were still being followed by the program, fared only slightly less well (.845, .834, and .848). However, these lower correlation coefficients were likely affected by a substantially smaller sample size (n = 11). Further disaggregation of the data show strong test–retest reliability between students on IEPs and those not on IEPs (Online Appendix Table 3) and those receiving FRPL and those not receiving FRPL (Online Appendix Table 4). These results were also consistent by grade level (see Online Appendix Table 5). All correlations are significant at p < .001 level (two-tailed). A summary of some of these correlations can be seen in the first row of Tables 2 and 3.
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Table 2. Reliability of Determining Students’ Stage of Spelling Using Feature Point Analysis Across Paper and Online Versions of the Elementary Spelling Inventory.
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Table 3. Reliability of Determining Students’ Stage of Spelling Using Power Score Method Across Paper and Online Versions of the Elementary Spelling Inventory.
Determining stage of development using the feature points method
Developmental spelling inventories are most often used to help teachers identify a student’s stage of spelling and reading development. This is accomplished by identifying the first category (e.g., long vowel patterns or inflected endings) in which students miss two or more orthographic features. For example, students may spell two long vowel words incorrectly (e.g., dreme/dream and fite/fight). Here we see the student is overgeneralizing the consonant–vowel–consonant–silent e long vowel pattern. Using this information, the assessor is able to determine that the student is in need of instruction in long vowel patterns and is therefore a middle within-word pattern speller and transitional reader (Bear et al., 2016; Templeton & Gehsmann, 2014a).
Using this same procedure to determine a student’s stage of development (i.e., finding the first place a student misses two or more features within a category of features), we examined the correlation between the two different administrations of the assessment. Table 2 illustrates this relationship. Using feature points as a proxy for identifying a student’s stage of development was statistically significant across the two versions of the assessment (.846), though not quite as strong as other comparisons. As illustrated in Table 1, students were consistent in the number of words spelled correctly and features points earned between the two administrations, leaving us to believe that they were less consistent in which features they represented correctly across the two administrations, hence the slightly lower correlation when determining stage of development using the feature points method. This was not necessarily surprising, but it is notable. While the correlation is still strong, using feature points to determine a student’s stage of development on the ESI may be less reliable than using other means such as the power score method.
Determining stage of development using the power score method
The power score refers to the number of words spelled correctly on the ESI (Bear et al., 2016). When using the power score method to determine a student’s stage of development (using the procedure described in Bear, Invernizzi, Templeton, & Johnston, 2012), we found a very strong correlation (.966) for all students across the two administrations (see Table 3). This finding suggests that the power score method may indeed be a more reliable method for determining a student’s stage of development, at least with our sample. This finding was unexpected only because the power score is generally considered a means for “estimating” a student’s stage of development (Bear et al., 2016). Its consistency, however, can be easily explained. Power scores represent students’ correct spelling of words rather than the features of words students are “using but confusing” (i.e., their instructional level). By their very nature, instructional zones are places of inconsistent performance, hence the need for instruction and scaffolded practice. Power scores, on the other hand, measure words spelled correctly. With this in mind, power scores may well be measuring students’ independent (mastery) level instead, making the scores more stable.
Comparing the two methods
Members of the research team have long appreciated the importance of examining students’ spelling attempts to better understand the orthographic knowledge students bring to bear when spelling words. We have generally favored using the feature points method for determining students’ stages of development because it helps teachers understand the logic of their students’ spelling attempts, recognizing the predictability of how these attempts change as students acquire greater word knowledge across the developmental continuum. We are somewhat concerned that focusing on words spelled correctly as a means for determining a student’s stage of development may, indeed, send the wrong message about developmental spelling instruction. One of the most important aspects of this approach is teaching students to understand how words work so they can transfer these understandings to a larger corpus of words rather than memorizing the spelling of individual words (Bear et al., 2016; Ganske, 2014; Gehsmann & Bear, 2012, 2014). Overemphasizing the reliability of the power score may undermine the intent of the ESI, which was, in part, to help teachers conceptualize spelling not as a binary (i.e., correct/incorrect) but a developmental process. By shifting our attention to the power score method because it is a slightly more reliable method for determining a student’s stage of development, we may inadvertently undermine the specific intent of spelling inventories and developmental spelling instruction more generally. Replicating this study with a larger sample may, indeed, diminish the statistical difference between to the two methods and should be considered a worthy endeavor.
Conclusions
The current study demonstrates that the online version of the ESI (Bear et al., 2016), when administered to fifth- through eighth-grade students, is a reliable and valid measure of students’ developmental spelling. As evidenced by the high correlation coefficients, results of the pencil–paper and computer-based administrations of the inventory are nearly identical. This is important because the online format increases access, ease, and even reliability in scoring. This makes it much less time-consuming and easier for teachers to administer the inventory, thereby making the results more readily available for planning differentiated, developmentally responsive instruction in spelling and reading. Based on our experiences analyzing the hand-scored samples, we realized human error is quite prevalent and can significantly alter not only the results of the assessment but the instruction students receive. This was perhaps the most unexpected and convincing finding of the study. Based on this finding alone, we would advocate for computer-based administration and scoring of the ESI with upper elementary and middle school students. The online format was also favored by students in our sample. Further research with younger students and students at the earlier stages of development will be necessary to see if these findings hold up across all ages and developmental stages. Replicating this study with a larger sample of students would also be beneficial.
Another unexpected finding was the strong reliability of power scores as a means for determining students’ stages of spelling development between the two administrations (i.e., pencil–paper and online). As compared to students’ feature point scoring method, the constancy of the power score method made it a slightly more reliable measure for determining students’ stages of development across administrations. Importantly, this finding does not suggest the power score method is more valid or even better; it may, indeed, be measuring something slightly different. Future research should examine both the reliability and validity of each of these methods of determining a student’s stage of development.
Given the value and importance of providing developmentally responsive instruction, having an efficient, reliable, and valid means for determining students’ stages of spelling development is essential. Traditionally, pencil–paper administrations of developmental spelling inventories have been the primary means for identifying students’ word knowledge and stages of development. While relatively easy to administer, they can be time-consuming to score and human error is also common. By creating an online assessment of developmental spelling, teachers will be able to administer and get the results of these assessments in less than 15 minute. The ease and efficiency with which the online assessment can be administered could translate into more students receiving differentiated and developmentally responsive instruction, potentially helping teachers more effectively address achievement differences between subgroups of students. Further development of online versions of other developmental spelling inventories such as the Primary and Upper Spelling Inventories (Bear et al., 2016) would add value to this line of inquiry.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
Supplemental Material
Supplementary material is available for this article online.
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Author Biographies
Kristin Gehsmann is an associate professor of education and coordinator of the master’s in literacy program at Saint Michael’s College in Colchester, VT. She can be reached at kgehsmann@smcvt.
Alexandra Spichtig is the chief research officer at Reading Plus in Winooski, VT. She can be reached at alex.
Elias Tousley is a research scientist at Reading Plus in Winooski, VT. He can be reached at elias.
