Productivity and accuracy counts

From the spreadsheets, the number of correct and attempted words could be calculated for each child. An additional measure of accuracy and productivity was calculated from the number of correct and attempted graphemes.

Word error count

The number of rows was counted for each child and gave the total number of words attempted per child. Incorrectly-spelt words were defined as incorrect representations of the word in its sentence context. This included grammatical as well as lexical and segmentation spelling errors, that-is-to-say problems with the inflectional word ending, with the word root, and with the word boundary. Capitalisation and punctuation errors were not considered, in order to make the free writing and word dictation tasks comparable. When there was an incorrectly-spelt word, the correct spelling was written in the following cell of the row and a word error was counted. Spellings were checked against the Oxford English Dictionary online (2017) in English, and the online dictionary “Le Robert” (2017) in French. The number of words incorrectly-spelt was deducted from the total number of words attempted and a proportion of words correctly-spelt was calculated using the following formula:

number of words attempted − number of words incorrectly spelt number of words attempted

Grapheme error count

In order to account for word length, a grapheme error count was conducted as well as the word error count (Daigle, Costerg, Plisson, Ruberto, & Varin, 2016). Three types of graphemes were considered: a) phonographs: graphemes that represent a sound (e.g. <ss> in <class> represents the /s/ sound); b) morphographs: graphemes that represent a morpheme (e.g. <s> in <hours>, which marks the plural); c) visuographs: graphemes with no phonological or morphological function (silent letters, e.g. <h> in <hour>).

Most graphemes represented one phoneme (e.g. <but> for /bʌt/), although sometimes phoneme and grapheme counts did not match perfectly (e.g. in the case of silent letters, the letter <x>-/ks/ or /gz/, or multigraphs such as <ur>, <er>, <ir> -/ə/ in English or <in>-/ɛ̃/, <oin>-/wɛ̃/ in French).

When there was any doubt about grapheme segmentation, the Oxford English Dictionary was used for a phonetic transcription of the word analysed, and the corresponding graphemes were checked against the list provided by Brooks (2015). Both those references account for the Received Pronunciation (RP) of English. In French, the French Dictionary “Le Robert” was similarly used to obtain a phonetic transcription. It was also decided that an incorrect spacing or hyphen would be counted as one grapheme error (whether it was missing or in excess). Similarly, an error with the use of an apostrophe (e.g. <Im> for <I’m>) was counted as one grapheme error. The proportion of correctly-spelt graphemes was calculated using the following formula (Daigle et al., 2016):

number of graphemes attempted − number of incorrect graphemes number of graphemes attempted

6.5.3. Qualitative analysis of spelling errors

The spelling errors obtained in both tasks were further analysed qualitatively. The coding scheme for analysing spelling errors was adapted from the multilinguistic framework of analysis developed by Apel and Masterson (2001) and used by McCarthy, Hogan, and Catts (2012). Adaptations were made to this scheme following a first round of double-coding, with the support of two independent coders, native speakers of the language coded. Both coders had extensive experience of coding spelling errors in atypical populations. Initially, samples were independently coded by myself and the two independent coders. The samples represented 10% of all texts. A Cohen’s Kappa of .17 (30% agreement) was obtained in English and .59 (69% agreement) in French. Where recurrent overlaps were found, categories were collapsed. The terminology used and definition of the different categories was also

clarified at this stage, this round of coding serving both as a training and piloting of the coding scheme.

The adapted coding comprised four categories, depending on whether the child produced:  A phonological error, i.e. an error to do with the representation of the sounds in

the words

 An orthographic error, i.e. an error to do with the orthographic rules, regularities and word-specific knowledge of the orthographic system

 A morphological error, i.e. an error to do with misrepresentations of the morphemes within the word

 Semantic errors, i.e. errors to do with the segmentation and meaning representation (homophones).

It is important to stress that these four categories were exclusive: for example, for an error to be classified as morphological, it could not affect the phonology of the word (e.g. il a

manger for il a mangé in French would be classified as a morphological error, given that the

change of inflection cannot be heard, but he say for he says in English would not be classified as morphological as the inflection is heard). For those errors at the overlap between more than one error type, a fifth category was created. This mixed category comprised errors which included more than one of the above representations (phonological and orthographic, morphological and phonological, orthographic and morphological, semantic and phonological, morphological and semantic). Additionally, because morphological errors are largely audible in English and mostly silent in French, errors at the overlap between morphology and phonology are reported in turn in the morphological and in the mixed category in the results section. Table 6-15 details the categories considered with examples from both French and English.

Following adaptations, a second round of coding was conducted jointly by myself and each of the two coders to ensure familiarity with the adapted scheme. Finally, each coder independently coded a further 10% of the samples. Cohen’s Kappa reached .82 (88%

agreement) in English and .76 (81% agreement) in French following this third round of blind coding.

Table 6-15: Multilinguistic framework for coding spelling errors (adapted from Apel & Masterson, 2001 and McCarthy, Hogan, & Catts, 2012).

Overall category Fine-grained coding Definition Example (EN) Target (EN) Example (FR) Target (FR)

PHON - Errors where the child did not represent the phonological

skeleton of the word

PHON-OM-vow Omission of a stressed vowel *destintion destination *frpé frappé

PHON-OM-cons Omission of an obligatory consonant *chool school *tabeau tableau

PHON-SUB-vow Substitution of a stressed vowel *dack duck *lou les

PHON-SUB-cons Substitution of a consonant *den then *pardi parti

PHON-ADD Addition of a phoneme *minunts minutes *lavai avait

ORTH - Errors where the child did not call

on relevant

orthographic

knowledge in his/her production

ORTH-IRR-silent Omission of an unpredictable silent letter *climed climbed *plafon plafond ORTH-IRR-cons Substitution of an ambiguous consonant

spelling

*squeesing squeezing *cand quand

ORTH-IRR-vow Substitution of an inconsistent long vowel grapheme *laiter *hed later head *ancre *copin encre copain ORTH-IRR-vow Substitution or omission of an unstressed

vowel grapheme *apon *favrite upon favourite N/A N/A

ORTH-IRR-accent Error on an accent N/A N/A *embêter embéter

ORTH-IRR-MGR Error of letter inversion *beacuse because *avce avec

ORTH-REG Error on a regular spelling pattern *sista sister *journé journée

ORTH-RUL Error on a taught spelling rule or an illegal letter sequence *recieve *annd receive and *grinpa grimpa MOR - Errors where

the child did not call

on relevant

morphological knowledge in his/her production

MOR-INF-gender Error on gender inflection N/A N/A rempli remplie

MOR-INF-tense Error on tense inflection *happend happened demander demandé

MOR-INF- Person Error on person marking *comse comes avais avait

MOR-INF-Number Error on number marking way’s ways copain copains

MOR-INF-Poss Error on possessive marking teachers teacher’s N/A N/A

MOR-DER-base Error on the base of a complex word ment meant gran grand

MOR-DER-Pre Error on the prefix of a complex word *extrordinary extraordinary

MOR-DER-Suff Error on the suffix of a complex word assemble assembly *maîtrèsse maîtresse

Overall category Fine-grained coding Definition Example (EN) Target (EN) Example (FR) Target (FR) SEM - Errors on the

meaning of the word attempted

SEM-SEG Segmentation errors (related or not related to a “liaison”)

*some thing something *les cole *on n’a

l’école

on a

(liaison) SEM-HOMO Homophone errors (within the same

grammatical category)

peace piece poing point

MIX - Errors affecting more than one aspect of spelling

PHON-ORTH Error with orthographically-constrained graphemes affecting phonology

*tims *techer times teacher *amourese *gour amoureuse jour PHON-MOR Error with a morphological marker affecting

phonology head (verb) goal headed goals grand le grande les PHON-SEM Wrong word choice: use of another word,

affecting semantics and phonology

were wear j’ai j’aime

MOR-ORTH Error with rule-constrained inflections and derivations *realy *blammed really blamed *obligait obligeait

MOR-SEM Use of a grammatical homophone their

your there you’re et à est a