Predicting individual differences in early literacy acquisition in German: The role of speech and...

Written Language & Literacy 11:2 (2008), 101–143. doi 10.1075/wll.11.2.02friissn 1387–6732 / e-issn 1570–6001 © John Benjamins Publishing Company

Predicting individual differences in early literacy acquisition in GermanThe role of speech and language processing skills and letter knowledge

Silke Fricke1, Marcin Szczerbinski1, Joy Stackhouse1 and Annette V. Fox-Boyer2

1University of Sheffield, UK / 2University of Applied Sciences Fresenius Hamburg, Germany

International research findings have repeatedly confirmed the significance of speech and language processing skills and letter knowledge for successful literacy acquisition. However, the importance of these skills for early literacy success in German speakers remains uncertain. The present longitudinal study aimed to explore this issue. Sixty-nine German-speaking children were assessed in nursery a few months before starting school (mean age 5;11) and in Grade 1 (mean age 6;11) with tests of phonological awareness, rapid automatized nam-ing, expressive vocabulary, grammar comprehension, letter knowledge, and nonverbal reasoning. Grade 1 assessments also included measures of reading accuracy, speed, comprehension, and spelling. The results confirmed that speech and language processing skills and letter knowledge before and around the time of school enrolment explain individual differences in early literacy development, with letter knowledge and phonological awareness emerging as most impor-tant predictors. No variance in literacy performance was uniquely predicted by nonverbal reasoning.

1. Introduction

Individual differences in reading and writing skills observed during the first three years of formal schooling are relatively stable and reliably predict reading and writing achievements several years later (Cunningham & Stanovich 1997; Scar-borough & Parker 2003; Speece et al. 2004). Furthermore, given that a large part of our learning and communication occurs through the medium of print, early

102 Silke Fricke, Marcin Szczerbinski, Joy Stackhouse and Annette V. Fox-Boyer

literacy difficulties may have serious long-term consequences that extend well be-yond literacy itself, affecting intellectual development, academic success, career prospects, and self-esteem (e.g. Scarborough & Parker 2003; Stanovich 1986).

A large number of correlational and experimental studies has shown that sev-eral cognitive skills (e.g. speech and language processing, letter knowledge, work-ing memory, speed of processing, and some aspects of visual processing) mea-sured in preschool years or at school entry predict later literacy achievement (e.g. Bowey 2005; Lonigan et al. 2000), although the strength of that prediction varies for different literacy components, i.e. reading accuracy, speed, comprehension, and spelling (Muter et al. 2004; Ouellette 2006; Strattman & Hodson 2005). How-ever, the relative importance of the different predictors and the exact role they play (causal prerequisites or non-causal correlates) remain controversial. Clarifying the relationship between various components of literacy and their cognitive predic-tors is interesting, both theoretically (i.e. understanding the cognitive mechanisms of literacy acquisition) and practically (i.e. designing appropriate methods of early detection and remediation of literacy difficulties).

Until recently, the process of learning to read and write has been studied pri-marily in the context of the English language. Nevertheless, our knowledge of literacy acquisition in other languages has grown rapidly over the years (Harris & Hatano 1999; Joshi & Aaron 2006). While the general mechanisms of literacy acquisition, as well as its prerequisites, appear to be language universal, some dif-ferences (especially in the rate of learning) are also documented. These may result from differences in the characteristics of the spoken language (especially phonol-ogy, e.g. syllable structure); in the educational system (e.g. age at which formal education starts, methods of literacy instruction); and, most importantly, in the structure of the orthography (e.g. the linguistic units it represents, its transparency and consistency) (Goulandris 2003).

This paper examines whether certain facets of cognition measured before and after school entry predict individual differences in reading and writing skills among German 1st Graders. The following constructs were chosen for examina-tion: phonological awareness (PA), rapid automatized naming (RAN), expressive vo-cabulary, grammar comprehension, and letter knowledge. They were chosen because a number of studies in several languages have confirmed their role in predicting literacy achievement (e.g. Landerl & Wimmer 2008; Muter et al. 2004; Nation & Snowling 2004; for review see Bowey 2005), and because they can be measured easily by practitioners (e.g. child psychologists, speech and language therapists, teachers) whose task is to ascertain the risk of literacy difficulties and to plan the appropriate intervention.

In the following paragraphs, we will review the literature on the relationships between the selected predictive measures and literacy acquisition, with a particu-

Predicting individual differences in early literacy acquisition in German 103

lar emphasis on German-language findings. The specificity of the German context must be acknowledged. While English and German evolved from the same Proto-Germanic root and thus share a number of similarities in vocabulary, grammar, and phonology (e.g. both languages have a similar syllable structure with relatively frequent consonant clusters), they are also quite different from the point of view of literacy acquisition. Compared to English, German words tend to be longer (poly-syllabic) and change their surface form more due to inflection. Most importantly, German orthography is much more transparent (i.e. directly reflecting phonolo-gy) and consistent in grapheme-phoneme mappings. While the orthographic con-sistency of German is lower in the phoneme-to-grapheme (i.e. spelling) direction (47%) than in the grapheme-to-phoneme (i.e. reading) direction (84%) (see Wim-mer & Mayringer 2002)1, both consistencies are higher than in English (Caravolas 2004; Wimmer & Mayringer 2002). In contrast to most English-language educa-tional systems, German nurseries or kindergartens do not provide any systematic literacy preparation or formal letter knowledge tuition because they focus on chil-dren’s social and general language development. Finally, German children usually start school later (at around 6 years of age) than most of their English-speaking peers (Goulandris 2003; Hulme et al. 2005; Landerl & Thaler 2006). Formal lit-eracy instruction begins at Grade 1 and is mostly based on systematic and explicit phonics.

1.1 Phonological awareness

PA refers to “the ability to reflect on and manipulate the structure of an utterance as distinct from its meaning” (Stackhouse & Wells 1997:53). It is a complex con-struct that can be categorised along at least two dimensions: size of linguistic unit, syllables, onset-rhymes, and phonemes; and level of explicitness, identification, segmentation, blending, and manipulation (Stackhouse & Wells 1997). It is gener-ally assumed that PA starts to develop at preschool age on the level of the largest sublexical unit, the syllable. Syllabic awareness is followed by onset-rhyme and finally phonemic awareness. Less awareness or metalinguistic reflection is needed for more implicit tasks, e.g. identification, and higher levels of awareness for ex-plicit PA tasks, e.g. manipulation (Stackhouse 1997). Research has shown that PA skills are reciprocally linked to literacy experience (Aidinis & Nunes 2001; Catts et al. 2001). PA is found to be a precursor of literacy development across languages and orthographies (e.g. Ziegler & Goswami 2005), and, at the same time, develops further as a result of literacy instruction. This reciprocal relationship gets more pronounced the smaller the linguistic unit and the higher the level of explicitness. Some literature even suggests that children develop phonemic awareness only af-ter they have acquired at least some literacy competence in form of, for example,


letter knowledge (e.g. Castles & Coltheart 2004; Goswami & Bryant 1990; Wimmer et al. 1991). However, recent studies in for example English and Czech (Hulme et al. 2005), Dutch (van Bon & van Leeuwe 2003) and in German (Schneider & Näslund 1997) have shown that, to a certain degree, PA skills at the phonemic level develop independently of literacy competence.

Another controversy concerns the question which the aspect of PA (i.e. the linguistic unit size and the explicitness of operations) is the best predictor of sub-sequent literacy achievement. The answer depends on a child’s level of PA devel-opment, the phase of literacy acquisition, as well as the literacy aspect assessed (Muter et al. 2004; Ziegler & Goswami 2005). The awareness of phonemes tends to be a better predictor of early literacy than the awareness of syllables or onsets and rhymes, in English as well as in more consistent orthographies such as Czech (e.g. Caravolas et al. 2005; Muter et al. 2004). However, the awareness of larger pho-nological units may become more important at stages of later literacy acquisition (e.g. Ziegler & Goswami 2005).

Some seemingly secondary properties of PA tasks, such as the use of pictures as memory prompts and the naming of stimuli by the examiner, may significantly affect the psycholinguistic character of those tasks and consequently their results and interpretation (Pascoe et al. 2006). Working memory capacity is a factor that constrains performance in PA tasks (Oakhill & Kyle 2000). Since using pictures could reduce working memory demands, picture-based PA tasks may be a purer measure of PA itself, less influenced by working memory (Gibbs 2003). Further-more, it is widely acknowledged that successful PA development requires an intact speech processing system. This includes speech perception (input), representa-tions (including knowledge of a word’s phonological form, i.e. phonological rep-resentations), and speech production (output, i.e. the ability to retrieve, rehearse, and utter spoken words) (e.g. Stackhouse & Wells 1997). Only if PA task stimuli are not named by the examiner, do children have to rely on their own stored lexical and phonological representations (e.g. Stackhouse 2006).

In English, PA is most predictive for reading accuracy and spelling (e.g. Loni-gan et al. 2000; Strattman & Hodson 2005), but also for reading speed (e.g. Wag-ner & Torgesen 1987). Furthermore, PA predicts reading comprehension, but the relationship appears to be indirect, via the association of PA with word decoding (e.g. Muter et al. 2004). In contrast, the importance of PA (especially phonemic awareness) for reading accuracy in German is viewed rather marginal and seems restricted to the earliest phase (until the end of Grade 1) (e.g. Landerl & Wimmer 2000; Wimmer et al. 2000). It is more important as a predictor of spelling, however (Landerl & Wimmer 2008). No unique link has been found so far between PA and reading speed in German (Landerl & Wimmer 2008). Awareness of larger units (e.g. rhymes) does not seem to affect the initial, alphabetic stage of learning to read and


write but may become more important at the later, orthographic stage (Wimmer et al. 1994). Generally, mastering literacy in German is suggested to depend only conditionally on PA skills because the high orthographic consistency combined with systematic phonics teaching approaches permits even children with poor preschool PA abilities to master the alphabetic principle relatively quickly (Landerl & Thaler 2006). The conclusion that explicit phonemic awareness develops only as a result of literacy instruction was widely accepted in German-language research on literacy acquisition; and it drew some empirical support from earlier findings (e.g. Wimmer et. al. 1991) that German-speaking school beginners performed at floor level in phoneme manipulation tasks. As a result, most subsequent Austrian and German studies of preschoolers and school entrants did not include explicit phonemic tasks traditionally used in English-language studies (Landerl & Wim-mer 2008; Wimmer et al. 2000). The tasks that were used, for example, onset and/or rhyme oddity detection (e.g. Wimmer 1993; Wimmer et al. 2000), vowel sub-stitution (e.g. Wimmer et al. 1991), phoneme imitation (e.g. Landerl & Wimmer 2008; Wimmer et al. 2000), or phoneme oddity (Näslund & Schneider 1996), may have been insufficient to fully examine the role of explicit PA in literacy acquisition in German. Furthermore, methodological issues like the choice of test stimuli and operations, the lack of pictures, and the naming of the stimuli by the examiners have potentially influenced the psycholinguistic character of the tasks. Thus, the relative contributions of syllabic, onset-rhyme and phonemic awareness towards predicting literacy development in German-speaking children have not yet been investigated systematically. Moreover, findings regarding PA in other consistent orthographies are also inconclusive (Caravolas et al. 2005), and an investigation into the German language may also improve our understanding of the develop-ment and role of PA from a broader, cross-linguistic perspective.

1.2 Rapid automatized naming

RAN is defined as the speed with which an individual names a series of highly familiar visual stimuli, i.e. drawings of common objects, colour patches, letters, or numbers (Denckla & Rudel 1976; Wolf et al. 2000). Several studies have shown that the performance in RAN tasks predicts the mastery of various aspects of lit-eracy, especially reading speed and sometimes spelling (e.g. Meyer et al. 1998a). Independent contributions of RAN to reading comprehension or accuracy are re-ported more rarely (Badian 1993; Cornwall 1992; for review see Wolf et al. 2000). The predictive power of RAN is dependent on the stimuli used. Correlations with literacy tasks are typically stronger for RAN tasks that use alphanumeric stimuli (e.g. letters or digits) and weaker for RAN tasks that use colours or pictures (Car-doso-Martins & Pennington 2004). However, there are also contrasting findings


which state that alphanumeric RAN tasks are less predictive than RAN tasks that employ objects and colours (Meyer et al. 1998b).

The reasons for the existence of the relationship between RAN and literacy remain controversial. Some authors claim that both skills are associated because they require efficient phonological processing (Wagner & Torgesen 1987). Others emphasise the shared demand on high speed of information processing (Catts et al. 2002) or precise temporal coordination of information from various modalities (Wolf et al. 2000).

Highly persistent deficits in reading speed are found to be the main problem of German children with reading difficulties (e.g. Wimmer 1993; Wimmer & May-ringer 2002), in contrast to poor readers of English who struggle with both reading accuracy and speed. Deficient reading fluency in German appears uniquely related to RAN deficits, even at the onset of literacy tuition (e.g. Landerl & Wimmer 2008). However, letter or number RAN tasks cannot be used with German-speaking pre-school children due to the lack of systematic letter and number instruction before school enrolment.

1.3 Vocabulary

Relatively little research has been conducted concerning the role of vocabulary knowledge in acquiring literacy (Nation et al. 2007; Ouellette 2006). However, re-cent studies suggest that vocabulary contributes to literacy development over and above phonological skills (Nation & Snowling 2004). Nation and Snowling (2004) found that vocabulary knowledge was an independent predictor of reading com-prehension and word recognition in English children aged 8;5 to 13;0. Ouellette’s study (2006) of 60 English-speaking Canadian Grade 4 pupils included different vocabulary measures and revealed that pseudoword decoding, word recognition, and reading comprehension were each predicted by at least one of the vocabulary measures. The importance of vocabulary knowledge for reading comprehension is obvious in that children need to know the meaning of words in order to compre-hend what they are reading. The importance of vocabulary for word decoding and recognition may depend on the consistency of the orthography. To become an ac-curate and fast reader in an inconsistent orthography (e.g. English) children may use vocabulary knowledge to semantically bootstrap words which they can only partially decode on a phonological level (Nation & Snowling 2004; Ouellette 2006; Ziegler & Goswami 2005). Therefore, the role of vocabulary for reading accuracy and reading speed may be less in German because of its orthographic consistency.


1.4 Grammatical competence

Even less research has been carried out to investigate the unique influence of gram-matical skills on early literacy development. These may be important in the predic-tion of literacy development because grammatical competence supports children to use contextual cues in word recognition and reading comprehension (e.g. Muter et al. 2004; Rego 1997). Cross-linguistic research suggests that grammatical skills play a more vital role in reading and writing inflected languages (e.g. Greek and German) than in relatively uninflected languages (e.g. English) (e.g. Goulandris 2003). Morphological knowledge in particular might assist children in reading and spelling languages (e.g. English and German) whose orthography incorporates the morphological principle (i.e. keeping the spelling of morphemes constant despite changes in their pronunciation) (Deacon & Kirby 2004; Joshi & Aaron 2006).

1.5 Letter knowledge

By definition, all alphabetic orthographies are based on the alphabetic principle. Thus, a key step in learning to read and write an alphabetic orthography is to understand that letters represent phonemes and to learn grapheme-phoneme-correspondences (Joshi & Aaron 2006). Research on both consistent and incon-sistent orthographies has repeatedly identified letter knowledge as a unique pre-dictor of subsequent reading accuracy and spelling development when measured before or around school enrolment (e.g. Caravolas 2004; Lonigan et al. 2000). In various languages including English and German sizeable correlations between letter knowledge and reading accuracy and spelling have been found (e.g. Muter et al. 1997; Wimmer et al. 1991). Reading comprehension appears only indirectly affected by letter knowledge (via word decoding) (Foulin 2005). Significant correla-tions are also reported between reading speed and letter knowledge in German (e.g. Landerl & Wimmer 2008; Wimmer et al. 1991).

1.6 Aims

Thus, there were two main aims for the present study. The first was to explore the development of preschool cognitive skills mentioned above (with particular emphasis on PA). The second aim was to investigate the power of these preschool skills to predict individual differences in early reading accuracy, reading speed, reading comprehension and spelling measured in Grade 1.


2. Method

We designed a longitudinal group study to investigate these two aims. The present article reports data from the first two waves of data collection (T1 = a few months before children entered school (mean age 5;11); T2 = Grade 1 (mean age 6;11)) and the main subsets of tests given.

2.1 Participants

87 monolingual children from 17 nurseries in Frankfurt, Idstein and Bad Vilbel (in the Federal State of Hessen, Germany) received consent to participate in the investigation. Children had to meet the following criteria to be included in the present study sample:

– Growing up monolingual German– Having no history of speech and/or language difficulties; significant hearing

loss; or developmental, medical or neurological disorders– Attending nurseries in 2005/2006, when first assessed, and enrolling at school

(Grade 1) in autumn 2006– Undergoing all test procedures in nursery (T1) and Grade 1 (T2)

Sixty-nine children (32 girls and 37 boys) met these criteria and, thus, represent the sample presented here. Of the remaining 18 children, 8 were excluded because they showed speech and/or language difficulties, 8 more did not enter Grade 1 of primary school as expected in autumn 2006 (i.e. they either remained in their nurseries or entered reception classes) and two others were not available for Grade 1 (T2) testing because they had moved.

The children included in the study were 5;4–6;8 (mean 5;11) at T1 (last term in nursery) and attended 17 different nurseries. None of them had received any formal literacy or structured letter tuition until then. At T2 (Grade 1), the par-ticipants were spread over 22 primary schools, between one and 14 children per school. They were 6;5–7;9 (mean 6;11) at the first part of T2 (Grade 1: January–May).

2.2 Materials

2.2.1 Speech and language processing measures2.2.1.1 Phonological awareness The PA-tasks (cf. Test für Phonologische Bewuss-theitsfähigkeiten (TPB); Fricke & Schäfer 2008) administered in this study consist-ed of 11 subtests covering the three linguistic units (i.e. syllable, onset-rhyme, and phoneme) and the different explicitness levels (i.e. identification, segmentation,


blending, and manipulation) of the two-dimensional PA-construct (Stackhouse & Wells 1997). Each subtest except for syllable-segmentation consisted of an input and an output version to differentiate between the two sides of the speech process-ing model as recommended by Stackhouse and Wells (1997). The output subtests required spoken answers from the participants, whereas the input versions could be answered by a nonverbal response such as pointing. All PA-subtests contained three practice and 12 test items. Apart from two subtests (Onset-rhyme-blending-output and Sound-blending-output) where pictures were inappropriate for the task structure, stimuli were presented as pictures. Only nouns were used as stimuli. For the input tasks, three possible answers (target and two distracters) were illus-trated for each stimulus word. With the exception of the blending tasks (input and output) the stimuli pictures were not named by the examiner. The pictures were used to reduce memory load. A silent task format (i.e. stimuli were not named by the examiner) was chosen to encourage children to access their own lexical representations when completing the tasks. The sound-identification and sound-deletion tasks contained single consonants as well as consonant clusters as they are assumed to differ in their phonological development (Fox 2005a). The segmenta-tion of a consonant cluster was required in all items asking only for one consonant of a cluster. Such items were incorporated since splitting up consonant clusters is allegedly a special skill (Stackhouse & Wells 1997; 2001) which is also regarded as important for spelling in German (e. g. Schnitzler 2008). For further details of task and stimuli design see Fricke and Schäfer (2008) and Schaefer et al. (submitted). The following tasks were administered:

Syllable tasks

Syllable-segmentation-output (SylSegout)The task required the children to segment depicted nouns into syllables. The ex-aminer introduced three different ways to segment the stimuli words: saying the syllables aloud, saying the syllables aloud accompanied by clapping hands or by knocking on the table. The children were encouraged to choose the type of seg-mentation they felt most familiar with. The stimuli words were 1–4 syllables long, for example: <Buch> / [buːx] ‘book’, <Lampe> / [ˈlam.pə] ‘lamp’, <Telefon> / [ˈteː.lə.foːn] ‘telephone’, <Waschmaschine> / [ˈvaʃ.ma.ʃiː.nə] ‘washing machine’.

Onset-rhyme tasks

Rhyme-production-output (RhymeProdout)The children were asked to look at a picture and say as many rhyming words or nonwords to the depicted stimulus word as possible. A time limit of 15 seconds


was given for each item. Mono- and disyllabic nouns were used, for example: <Hand> / [hant] ‘hand’, <Krone> / [ˈkroː.nə] ‘crown’.

Rhyme-identification-input (RhymeIDin)Each item was presented on a page that showed the stimulus word at the top and the three answer stimuli underneath. The children had to point to the picture at the bottom that rhymed with the stimulus word at the top. Apart from the correct rhyming word, a phonological distracter and a semantic distracter were always presented as answer stimuli (see Table 1 for examples).

Table 1. Example of an item from the RhymeIDin taskStimulus word Rhyme (Target) Phonol. distracter Semantic distracter<Hund> / [hʊnt] ‘dog’ <Mund> / [mʊnt]

‘mouth’<Hand> / [hant] ‘hand’ <Katze> / [ˈkat.sə] ‘cat’

Onset-rhyme-blending-output (OnsetRhymeBlendout)The examiner pronounced an onset and a rhyme with a pause of one second in between. The children were then instructed to say the word that resulted from blending both. All stimuli were monosyllabic nouns, and item onsets were either a single plosive, fricative, nasal or liquid, or a consonant cluster, for example: <T-isch> / [t] + [ɪʃ] ‘table’, <F-uß> / [f] + [uːs] ‘foot’, <Fr-osch> / [fr] + [ɔʃ] ‘frog’.

Onset-rhyme-blending-input (OnsetRhymeBlendin)The examiner pronounced an onset and a rhyme with a pause of one second in between. The children then had to point to the picture that resulted from blending both. Apart from the target word, two distracters (a rhyme distracter and an onset distracter) were presented (see Table 2 for examples). The pictures were not shown to the children until the whole stimulus word had been pronounced to ensure that the children fulfilled a blending task and not, for example, onset detection.

Table 2. Examples of items from the OnsetRyhmeBlendin taskStimulus word Target word Onset distracter Rhyme distracter[m] + [aus] <Maus> / [maus] ‘mouse’ <Mond> / [moːnt]

‘moon’<Haus> / [haus] ‘house’

[t] + [ɔpf] <Topf> / [tɔpf] ‘pot’ <Tür> / [tyːɐ] ‘door’ <Kopf> / [kɔpf] ‘head’[kl] + [aun] <Clown> / [klaun] ‘clown’ <Kleid> / [klait)

‘dress’<Zaun> / [tsaun] ‘fence’


Phoneme tasks

Sound-identification-beginning-output (SoundIDout)For each item, pictures of two nouns were presented to the children. They always shared the same beginning, either a single consonant (C: e.g. <Bus> / [bʊs] ‘bus’ and <Ball> / [bal] ‘ball’), the whole consonant cluster (CC: e.g. <Schlüssel> / [ˈʃlʏsl] ‘key’ and <Schlange> / [ˈʃlaŋ ə] ‘snake’) or the first consonant of a consonant cluster (CC: e.g. <Clown> / [klaun] ‘clown’ and <Knopf> / [knɔpf] ‘button’). The children were asked to say which ‘sound’ the two pictures shared at the beginning.

Sound-identification-beginning-input (SoundIDin)Each item was illustrated on a page containing the stimulus word at the top and the three answer stimuli underneath. The children were instructed to point to the picture at the bottom that began with the same sound as the stimulus word at the top. The stimuli had the same C, CC and CC item structure as in the output task above. Apart from the target word, a phonological distracter and a semantic dis-tracter were presented (see Table 3 for examples).

Table 3. Examples of items from the SoundIDin taskStimulus word Target word Phonol. distracter Semantic dis-

tracterC <Bus> / [bʊs] ‘bus’<Baum> / [baum]

‘tree’<Mond> / [moːnt] ‘moon’

<Auto> / [ˈau.to] ‘car’

CC <Fliege> / [fliː.ɡə] ‘fly’

<Flasche> / [ˈflaʃə] ‘bottle’

<Schlüssel> / [ˈʃlʏsl] ‘key’

<Biene> / [ˈbiː.nə] ‘bee’

CC <Brille> / [ˈbrɪlə] ‘glasses’

<Blume> / [ˈbluː.mə] ‘flower’

<Glocke> / [ˈɡlɔkə] ‘bell’

<Nase> / [ˈnaː.zə] ‘nose’

Sound-blending-output (SoundBlendout)The examiner presented a series of phonemes with a pause of one second between each. The children were then instructed to say the word that resulted from blend-ing these phonemes. Two-, three-, four-, and five-phonemic words were used, for example: <Kuh> / [k] + [uː] ‘cow’, <Maus> / [m] + [au] + [s] ‘mouse’, <Fahne> / [f] + [aː] + [n] + [ə] ‘flag’, <Salat> / [z] + [a] + [l] + [aː] + [t] ‘salad’. Consonant clusters in onset position were not included.

Sound-blending-input (SoundBlendin)The examiner spoke a series of ‘sounds’ with a pause of one second between each. The children were then instructed to point to the picture that resulted from blend-ing these ‘sounds’. Apart from the target word, an onset distracter and a final sound distracter were presented. The stimuli ranged from 2–5 phonemes in length (see Table 4 for examples).


Table 4. Examples of items from the SoundBlendin taskStimulus word Target word Onset distracter Final sound distracter[t] + [eː] <Tee> / [teː] ‘tea’ <Tisch> / [tɪʃ] ‘table’ <Zeh> / [tseː] ‘toe’[h] + [uː] + [t] <Hut> / [huːt] ‘hat’ <Haus> / [haus] ‘house’ <Bett> / [bɛt] ‘bed’[t] + [a] + [s] + [ə] <Tasse> / [ˈtasə] ‘cup’ <Tiger> / [ˈtiː.ɡɐ] ‘tiger’ <Nase> / [ˈnaː.zə] ‘nose’[v] + [ɔ] + [l] + [k] + [ə]

<Wolke> / [ˈvɔl.kə] ‘cloud’

<Wurzel> / [ˈvʊɐ.tsəl] ‘root’

<Suppe> / [ˈzʊpə] ‘soup’

Sound-deletion-output (SoundDelout)The children had to say the nonwords which resulted from deleting the beginning of the illustrated stimulus word. The examiner named only the part that had to be dropped. This was either a single consonant (C), a whole consonant cluster (CC), or the first consonant of a consonant cluster (CC), for example: C: <(N)ase> / [ˈ(n)aː.zə] ‘nose’, CC: <(Fl)öte> / [ˈ(fl)øː.tə] ‘flute’, CC: <(B)latt> / [(b)lat] ‘leaf ’.

Sound-deletion-input (SoundDelin)For this task, the examiner pronounced a whole stimulus word and a part which was supposed to be deleted, without presenting a picture for it. Then, the three an-swer stimuli (target word, phonological and semantic distracter) for each stimulus word were presented to the children on a sheet of paper. The children had to point to the picture that resulted from the deletion. Either an initial single consonant (C), an onset consonant cluster (CC) or a first consonant of a consonant cluster (CC) had to be deleted (see Table 5 for examples).

Table 5. Examples of items from the SoundDelin taskStimulus word Target word Phonol. distracter Semantic dis-

tracterC <‘T’or> / [toːɐ]

‘goal’<Ohr> / [oːɐ] ‘ear’ <Tür> / [tyːɐ]

‘door’<Ball> / [bal] ‘ball’

CC <‘Schn’ur> / [ʃnuːɐ] ‘cord’

<Uhr> / [uːɐ] ‘watch’

<Ohr> / [oːɐ] ‘ear’ <Schlauch> / [ʃlaux] ‘hose’

CC <‘S’turm> / [ʃtʊɐm] ‘storm’

<Turm> / [tʊɐm] ‘tower’

<Stern> / [ʃtɛɐn] ‘star’

<Wolke> / [ˈvɔl.kə] ‘cloud’

Each response was scored as correct (1) or incorrect (0). Non-responses were also scored as incorrect (0).

2.2.1.2 Rapid automatized naming The RAN tasks used here were an adaptation of the procedure originally developed by Denckla and Rudel (1976). The stimuli of


each category (RAN objects, RAN colours, RAN mixed) were presented once on a practice sheet and several times on the test sheet.

RAN of objects

The children were presented with a single sheet of colour drawings of 5 common objects: <Hund> / [hʊnt] ‘dog’, <Baum> / [baum] ‘tree’, <Schuh> / [ʃuː] ‘shoe’, <Auto> / [ˈau.to] ‘car’, and <Käse> / [ˈkɛː.zə] ‘cheese’. The drawings were presented repeatedly in a pseudorandom sequence, each drawing appearing 10 or 11 times (54 drawings in total). The children were asked to name them sequentially as fast as they could. The score was the number of correctly named objects (self-correc-tions allowed) in 15 seconds.

RAN of colours

The task was identical in every aspect to the RAN objects measure apart from the stimuli which were patches of 5 basic colours (<rot> / [ro:t] ‘red’, <gelb> / [ɡɛlp] ‘yellow’, <grün> / [ɡrʏ:n] ‘green’, <blau> / [blau] ‘blue’ and <schwarz> / [ʃvaɐts] ‘black’).

RAN mixed

Stimuli used in the previous two RAN-tasks were intermixed on the same sheet, each stimulus appearing 5–6 times.

2.2.1.3 Expressive vocabulary

Naming pictures (36 nouns and verbs)

The expression part of the test for naming and understanding nouns and verbs by Kauschke (2007) was carried out. The children had to name black and white pic-tures (36 noun items and 36 verb items, each preceded by two practice items). Following Kauschke (2007), the children scored (1) for correct naming and (0) for incorrect naming. The test material is standardised for German children aged 2;6–8;0 (Kauschke 2007; Kauschke & Stan 2004).

Naming the PA picture stimuli (172 nouns)

Prior to the execution of the PA test battery (described in Section 2.2.1.1), the children had to name all 172 nouns that appeared in it. Thus, these pictures were also used as a vocabulary measure and a screening of the children’s articulation and pronunciation skills. The preferred response type was spontaneous naming


(cf. Fox 2005b). In case of naming failure, circumlocutory sentences, alternative questions, and finally imitations were used to elicit the word. If a child still failed to name the picture correctly, the intended word was provided by the examiner. Pictures that were correctly named either spontaneously or following circumlocu-tion were scored as correct (1).

2.2.1.4 Reception of grammar (TROG-D) The Test for the Reception of Grammar-Deutsch (TROG-D) (Fox 2006), the German version of the Test for Reception of Grammar (TROG(-2)) (Bishop 1983; Bishop 2003), was used as a measure of grammatical comprehension. The TROG-D assesses the understanding of 21 gram-matical constructs. For each item, the examiner shows the children a booklet page with four pictures and asks them to select the one that matches his statement. Each item includes grammatical as well as lexical distracters. The test is standardised for German children aged 3;0–9;11.

2.2.2 Basic literacy measureThe children’s basic literacy skills were assessed by testing their letter knowledge.

2.2.2.1 Letter-knowledge Each child was presented with the 26 upper case and 26 lower case letters of the Latin alphabet in random order and they were asked to name the letters they recognised. Responding with either the correct name or the sound of a letter was accepted as correct (1).

2.2.3 Literacy measures2.2.3.1 Early spelling The Hamburger Schreibprobe (HSP) (Hamburg Writing Sample) (May 2002) is a measurement of orthographic knowledge and primary spelling strategies. We used the HSP 1+ version which is suitable for the middle of Grade 1 (i.e. with approximately 6 to 7 year olds). The HSP 1+ requires children to write four words (i.e. <Baum> ‘tree’, <Telefon> ‘telephone’, <Hund> ‘dog’ and <Mäuse> ‘mice’) and a short sentence (i.e. <Die Fliege fliegt auf Uwes Nase> (The fly is flying onto Uwe’s nose’). Then the number of correctly written words and graphemes is counted. This literacy test was administered because it is one of the few German literacy tests with standards available for the middle of Grade 1.

2.2.3.2 Reading accuracy, reading speed and spelling The Salzburger Lese- und Re-chtschreibtest (SLRT) (Salzburg reading and spelling test [Form A]) (Landerl et al. 1997/2001) is a diagnostic tool for developmental reading and writing difficulties in German-speaking 1st to 4th Graders (i.e. approximately 6 to 11 year olds). It measures reading accuracy, reading speed and spelling. The spelling subtest can be used as a group or as an individual test, whereas the reading subtest can only be


administered to individual children. The following reading tasks were adminis-tered between January and May of Grade 1 (first part of T2): 30 frequent words and 24 pseudowords dissimilar to real words. The children were asked to read all items as fast as possible. Reading accuracy (number of words and pseudowords read incorrectly and correctly) and time (in seconds) were measured. The spelling subtest was administered in June of Grade 1 (second part of T2). The children had to fill in gaps with one word in 25 sentences after the examiner had read out the in-dividual sentences and the missing words. Orthographic and phonological spell-ing accuracy was measured mutually exclusively as follows: a) phonological spelling errors were scored if a word was written in a way which is not consistent with its phonological form (for example <Hand> / [hant] ‘hand’ was written <HAT> (i.e. the phoneme [n] was not represented by a letter) or <RANT> (i.e. the phoneme [h] was represented by a wrong letter <R>); b) Orthographic spelling errors were given if a word was written in such a way that it was consistent with its phonologi-cal form but that it was orthographically wrong. For example <Hand> / [hant] was written <HANT> which is possible due to the word’s phonological form. Never-theless, the German orthography requires final <D>.

2.2.3.3 Reading comprehension The Leseverständnistest für Erst- bis Sechstklässler (ELFE 1–6) (Reading comprehension test for 1st to 6th Graders [Form A booklet version]) (Lenhard & Schneider 2006) was carried out to assess reading compre-hension on word, sentence, and text level. In the word level subtest, the children were asked to match one out of four printed words to a picture. The task consisted of 72 pictures with four words each. In the sentence level subtest, the examiner pre-sented sentences to the children, each with one word missing. The children then had to choose this missing word out of five semantically congruent words. The task included 28 sentences. The text level subtest was a multiple choice text com-prehension test (i.e. a text was followed by a question with four answer choices). The text level subtest entailed 20 multiple choice questions. The ELFE 1–6 sub-tests are speeded tasks with a time limit of 3 minutes for the word and sentence level subtests and 7 minutes for the text level measure. The children marked their responses by underlining or ticking their choice. The answers were scored by sum-ming up the number of correctly answered items within each subtest.

2.2.4 Nonverbal reasoning abilityIn order to account for the influence of reasoning skills on the acquisition of lit-eracy, the booklet version of Raven’s Coloured Progressive Matrices (CPM) (Raven et al. 2002) was executed to assess the participants’ nonverbal intellectual abilities. The German version has standards for children aged 3;9–11;8.


2.3 Procedures

All tests were executed by the first author. At T1, the children were assessed in-dividually over two sessions during normal nursery hours in a quiet room at the children’s nursery. T2 data collections took place in a quiet room at the children’s school, after-school-club or at home. In the first part of T2 (Grade 1: January — May) each participant was tested individually in one or two sessions. In the second part of T2 (Grade 1: June) literacy tests were administered either to small groups of children or individually, depending on the number of participants attending a particular institution.

As commonly done in predictive longitudinal studies (e. g. Muter et al. 2004) and due to negligible effects of the order of task execution on performance in a pilot study (Fricke 2007), all tasks were applied in a fixed order. The tasks varied from T1 to T2. Measurements used at T1 assessed the children’s predictor skills: PA, RAN, expressive vocabulary (36 nouns and verbs; PA-vocabulary), grammar comprehension (TROG-D) and letter knowledge. The vocabulary and the grammar comprehension measures as well as the test for nonverbal reasoning (CPM, Raven et al. 2002) were also used as ‘pre-tests’ at T1 to ensure normal speech and language development and reasoning abilities within the normal range. During the first part of T2 (Grade 1: January — May) all assessments used at T1 were repeated except for the test for nonverbal reasoning. In addition to the T1 assessments, the HSP 1+

Literacy: - SLRT spelling - ELFE 1-6

Literacy:- HSP 1+ - SLRT reading

T 1 T 2

Potential predictors: - Phonological awareness - Rapid automatized naming - Expressive vocabulary - Grammar comprehension

(TROG-D) - Letter knowledge

Additional pre-test: CPM

Potential predictors:- Phonological

awareness - Rapid automatized

naming - Expressive

vocabulary - TROG-D - Letter knowledge

Nursery Grade 1

January – May (mean age 5;11)

January – May (mean age 6;11)

June

Figure 1. Overall study design


(May 2002), and subtests of the SLRT reading2 test (Landerl et al. 1997/2001) were executed. In the second part of T2 (Grade 1: June) the SLRT spelling test and the ELFE 1–6 subtests (Lenhard & Schneider 2006) were executed. An overview of the study design is presented in Figure 1.

3. Results

3.1 Background variables

3.1.1 AgeChronological age at T1 did not significantly correlate with performance on any T1 predictor measures (all ps > .05 on Spearman’s test), except for T1 RAN objects (rS = .269). Significant correlations between chronological age at T2 and T2 predic-tor measures were mainly weak and restricted to the following 5 out of 19 assessed skills: T2 Rhyme-production-output (rS = .330), T2 Rhyme-identification-input (rS = .245), T2 Sound-identification-output (rS = .253), T2 RAN mixed (rS = .239) and T2 36 nouns (rS = .239). No T2 literacy measure was significantly correlated with chronological age at T2.

3.1.2 GenderGender differences were examined using Mann-Whitney tests. Although there was a trend for girls to perform better than boys, significant differences were limited to the following tasks: T1 Rhyme-identification-input (U = 444.50, p = .016, r = −.29),3 T1 36 nouns (U = 389.00, p = .019, r = −.28), T1 PA-vocabulary (U = 23.50, p = .048, r = −.24), and T2 36 nouns (U = 414.50, p = .028, r = −.26).

3.1.3 Nonverbal reasoningDescriptive statistics for nonverbal reasoning are summarised in Table 6.

Table 6. Performance on the CPMType of scores M SD Range

Raven’s Coloured Progressive Ma-trices (CPM)

raw scores 21.93 3.62 14–33

percentiles 65.97 19.16 16–100

It can be seen that participants performed well on the nonverbal reasoning task. CPM raw scores correlated significantly (p < .05 on Spearman’s test) with per-formance on the following predictor measures: T2 Rhyme-production-output (rS = .327), T2 Sound-identification-output (rS = .266), T1 RAN objects/colours/


mixed (rS = .340/.253/.278), T2 RAN objects/colours (rS = .294/.312), T2 TROG-D (rS = .271) and T2 36 nouns (rS = .251). Significant correlations were also observ-able between nonverbal reasoning and 6 out of the 12 literacy measures collected at T2: SLRT reading words correctly (rS = .347), SLRT reading time words (rS = −.302), SLRT spelling words correctly (rS = .246) and all three ELFE reading comprehension tasks (words rS = .369, sentences rS = .286, text rS = .315). Correlations with other T1 or T2 variables were not significant. Thus, nonverbal reasoning (i.e. CPM scores) appeared to be a significant (if weak) predictor of literacy.

Given the pattern of relationships outlined above, we decided to statistically control for individual differences in nonverbal reasoning ability in the subsequent analyses. Chronological age and gender were not controlled for, however.

3.2 Performance on measures of potential predictors

Descriptive statistics for the potential predictors at T1 and T2, i.e. speech and language processing measures as well as letter knowledge, are shown in Table 7. The significance of improvement in scores between the two assessments is also indicated (using Wilcoxon Signed Ranks test). If available, standardised scores are given in addition to raw scores.

Table 7 shows that children’s performance on all tasks improved significantly between the first and the second assessment. Concerning PA, this improvement was more pronounced for phoneme (r = .49–.60) and onset-rhyme level (r = .45–.56) tasks than for syllabic (r. = 20) or rhyming (r = .24–.29) tasks. A large variabil-ity in letter knowledge was observed at T1 with some children knowing almost no letters and some knowing all. Not surprisingly, this variance reduced after children entered school.

3.2.1 Reliabilities of predictor measuresSince the reliabilities of most speech and language processing measurements and the letter knowledge measure were unknown, reliability values were calculated for the study sample (see Table 7). The test-retest reliabilities were computed by cor-relating T1 and T2 scores (Spearman’s test (rS)). In addition, internal consisten-cies (Cronbach’s alpha (α)) were calculated for each PA task. Split-half reliabilities (rG) based on children’s scores for upper and lower case letters are given for let-ter knowledge. Reliabilities such as internal consistencies of published tests (i.e. 36 nouns and verbs, TROG-D and all literacy measures) were not computed, as they are reported in the literature. According to Breakwell et al. (2006), reliabilities above 0.70 are necessary if a test is to be used as a research tool, while reliabili-ties above 0.90 are required for diagnostic and job selection purposes. However, a minimum requirement of 0.55 is also often cited as appropriate for assessments


Table 7. Performance on predictor measures at T1 and T2

Tasks Time M SD Range α/rGT1–T2 dif-ferences

rs

PA

SylSeg out (/12)

T1 9.84 2.23 4–12 .75C p = .017, r = .20

.39**T2 10.55 2.26 0–12 .83C

RhymeProdout

T1 39.06 18.59 3–97 .93 p = .001, r = .29

.42**T2 47.00 16.00 6–97 .90

RhymeIDin (/12)

T1 11.61 0.83 9–12 .46C p = .005, r = .24

.35**T2 11.88 0.40 10–12 .32C

OnsetRhymeBlendout (/12)

T1 7.14 3.43 0–12 .85 p<.001, r = .56

.25*T2 11.45 0.92 9–12 .42C

OnsetRhymeBlendin (/12)

T1 10.16 1.66 5–12 .56 p<.001, r = .45

.33**T2 11.36 0.84 9–12 .23C

SoundIDout (/12)

T1 8.39 3.82 0–12 .92 p<.001, r = .49

.07T2 11.67 0.89 8–12 .66C

SoundIDin (/12)

T1 8.01 3.27 2–12 .83 p<.001, r = .56

.35**T2 11.77 0.73 7–12 .63C

SoundBlendout

T1 4.28 3.95 0–12 .90 p<.001, r = .60

.39**T2 11.03 1.62 3–12 .73C

SoundBlendin (/12)

T1 9.43 2.64 3–12 .80 p<.001, r = .51

.42**T2 11.78 0.64 8–12 .52C

SoundDelout (/12)

T1 2.09 3.20 0–12 .89F p<.001, r = .58

.25*T2 8.55 3.13 0–12 .85

SoundDelin (/12)

T1 7.97 2.29 3–12 .54 p<.001, r = .50

.16T2 10.33 1.57 4–12 .50C

RA

N

RAN objectsT1 15.80 2.51 10–22 p<.001,

r = .54.28*

T2 19.39 2.73 12–26

RAN coloursT1 16.75 2.78 6–24 p<.001,

r = .49.53**

T2 19.42 3.22 11–29

RAN mixedT1 12.10 2.50 5–19

p<.001, r = .56

.41**T2 15.54 2.79 10–22T2 48.17 3.32 33–52 .87

Expr

essi

ve

Voca

bula

ry

36 nounsT1 33.10 1.89 28–36 p<.001,

r = .40.44**

T2 34.25 1.26 30–36

36 verbsT1 29.49 2.63 23–36 p<.001,

r = .36.25*

T2 31.20 2.10 25–35

PA-voc (/172)T1 138.42 7.71 112–152 p<.001,

r = .60.61**

T2 148.90 5.96 131–158

TRO

G

TROG-D (/21)T1

13.46 2.10 9–18p<.001, r = .61

.53**T scores 57.68 7.04 43–77TROG-D (/21)

T217.22 2.07 12–21

T scores 63.43 9.85 44–99Letter know-ledge (/52)

T1 24.46 13.72 3–51 .94p<.001, r = .61

.61**

α = Cronbach’s alpha; rG = split-half reliabilities; rS = Spearman’s correlations; C = ceiling effect; F = floor effect


administered in experimental group studies (e.g. Rost 2007). It should be men-tioned that estimates of reliability are themselves unreliable when floor and ceiling effects are present. Thus, reliabilities of measures with observed floor or ceiling effects are flagged.4

The internal consistencies of T1 PA measures (α) were good (> .70) to excel-lent (> .90). The only exception, Rhyme-identification-input, demonstrated a ceil-ing effect. Internal consistencies became generally poorer at T2, however, probably due to ceiling effects. T2 consistencies tended to be higher for PA output tasks (α = .42–.90) than for input tasks (α = .22–.63). Split-half reliabilities (rG) for letter knowledge were very good at T1 (.94) and T2 (.87). The stability (test-retest reli-abilities) of scores was significant for all predictor measures, apart from Sound-identification-output and Sound-deletion-output. It was generally weaker for PA tasks and somewhat stronger for other tasks (RAN, letter knowledge, expressive vocabulary and TROG-D).

3.3 Performance on literacy measures

Table 8 summarises descriptive statistics for literacy measures at T2. In addition to raw scores, percentiles are reported if available. As mentioned in paragraph 2.3 (Procedures), SLRT reading subtests were executed in the first part of T2 (January — May of Grade 1), which made it impossible to compute percentile ranks (as the test is standardised from the end of Grade 1).

Raw scores for reading accuracy (i.e. words and pseudowords correctly) and reading speed (i.e. time words and pseudowords) showed considerable variability. On the SLRT spelling test, children made considerably fewer phonological than or-thographic errors; on average, more than half of the words were misspelled either orthographically or phonologically. The mean percentile rank on this test (19.25) indicates relatively poor orthographic spelling accuracy. In contrast, the children’s mean percentile rank on the HSP 1+ spelling accuracy test was high. Likewise, the children’s performance on the ELFE 1–6 subtests was between the 57th percentile for reading comprehension on sentence and text level and the 62nd percentile for word reading comprehension. Hence, in contrast to poor orthographic spelling on the SLRT spelling test, the participating children appeared to be rather advanced in reading comprehension.

Most correlations (Spearman’s) between the literacy measures were signifi-cant, in the moderate to very strong range (rS = .348 — .872). Only one correlation was weak (rS = .237) (between SLRT reading pseudowords correctly and ELFE text comprehension) and two were not significant (between HSP 1+ graphemes correctly and SLRT reading pseudowords correctly, and between SLRT reading pseudowords correctly and ELFE text comprehension).


3.4 Speech and language processing skills and letter knowledge as predictors of literacy acquisition

To assess the relative power of the speech and language processing measures and letter knowledge for predicting children’s performance on different literacy com-ponents, correlation, multiple regression, and commonality analyses were used. Prior to these analyses, some preparation of the data was necessary. Due to floor and ceiling effects on some measures and the intention to run parametric tests in further analyses, we decided to normalise the distribution of the data. Raw scores on all variables were converted into normalised z-scores using the Blom propor-tion estimation formula. This non-linear transformation results in the distribution of the data being more similar to the normal (bell-shaped) distribution, thus mak-ing it more suitable for parametric statistical analyses. Following this, we produced three literacy composite scores to reduce the number of dependent variables:

– Reading accuracy: the average of normalised SLRT reading words and pseudo-words correctly

Table 8. Descriptives for literacy measures at T2Type of scores

M SD Range

HSP

1+

words correctly (/10) raw scores 5.64 1.98 0–10percentiles 72.34 22.87 0.7–100

graphemes correctly (/40)

raw scores 34.46 3.06 25–40percentiles 77.97 19.14 24–100

SLRT

read

ing words correctly (/30) raw scores 25.53 4.36 13–30

time words (sec.) raw scores 92.68 55.92 13–251pseudowords correctly (/24)

raw scores 19.57 4.95 3–24

time pseudowords (sec.) raw scores 108.99 57.02 33–323

SLRT

spel

ling words correctly (/25) raw scores 11.35 5.68 0–25

orthographic errors (/25)

raw scores 12.06 4.89 0–21percentiles 19.25 23.87 1–80

phonological errors (/25)

raw scores 1.59 2.26 0–13

ELFE

1–6

words (/72) raw scores 20.71 8.18 4–49percentiles 62.27 25.35 4.8–100

sentences (/28) raw scores 7.36 4.84 1–25percentiles 57.18 28.18 10–100

text (/20) raw scores 4.68 3.62 0–19percentiles 57.87 26.59 8.3–100


– Reading speed: the average of normalised SLRT reading time words and pseudo-words

– Reading comprehension: the average of normalised ELFE 1–6 scores for words, sentences and texts

– Spelling: the average of normalised HSP1+ graphemes correctly and SLRT spell-ing words correctly

3.4.1 Longitudinal correlationsPearson’s correlations between T1 predictors and T2 (Grade 1) literacy composites are summarised below and shown in detail in Appendix 1.

The strength of the correlations between PA and literacy varied considerably across tasks. Regarding PA, none of the four literacy composites were predicted by syllabic awareness (however, it must be remembered that only one syllabic task was used). They were predicted weakly to moderately by onset-rhyme awareness, and weakly to strongly by phonemic awareness. RAN objects and RAN colours (but not RAN mixed) predicted reading; none of the RAN tasks predicted spelling. Only one vocabulary task (PA-vocabulary) significantly predicted literacy, and then only one aspect of it (reading accuracy). Grammatical comprehension (TROG-D) was weakly predictive of reading accuracy and spelling. Letter knowledge emerged as a highly significant predictor of all literacy components. As mentioned earlier (cf. 3.1.3.), nonverbal reasoning was also a significant predictor of literacy composites.

Most correlations mentioned here were weak (r < .30) or moderate (r < .50). The only strong correlations (r ≥ .50) occurred between Sound-identification-out-put and –input tasks and reading accuracy, as well as between letter knowledge and reading accuracy, reading speed and reading comprehension.

3.4.2 Concurrent correlationsThe concurrent correlations between predictors at T2 and literacy composites (also measured at T2) are presented in Appendix 2.

The overall pattern was similar to that reported above for the longitudinal correlations. With respect to PA, the strongest correlates of literacy were found among phonemic measures. Onset-rhyme measures continued to correlate mod-erately to strongly with literacy. The only syllabic task that was carried out re-mained essentially unrelated to literacy, with an exception of one weak correlation with reading accuracy. RAN objects (but not RAN pictures or RAN mixed) correlat-ed with reading speed or reading comprehension. The PA-vocabulary measure (but no other measures of vocabulary) correlated with reading accuracy, comprehension and spelling. The same pattern emerged for the TROG-D. Among the correlations mentioned above, the only strong ones were those between Sound-deletion-output


and –input tasks and reading accuracy, as well as between letter knowledge and reading accuracy, speed and comprehension, and spelling.

3.4.3 Regression analysesIn order to establish the contribution of different types of speech and language processing skills and letter knowledge to the development of early literacy, a series of hierarchical multiple regressions and commonality analyses was carried out. Individual predictor variables were grouped into six blocks: PA, RAN, vocabu-lary, TROG-D, letter knowledge and nonverbal reasoning. The order of the entry of blocks was systematically varied, so that each block was entered a) at the first step in one analysis (to measure the percentage of outcome variance it could explain), and b) at the last step in another analysis (to measure the unique percentage of outcome variable it could explain, after controlling for all other blocks). Since the number of individual predictors was too large in relation to sample size, only those tasks that correlated significantly with the outcome variables (i.e. the respective literacy composite) were entered into the analyses (see Appendices 1–2).

3.4.3.1 Reading accuracy Results of regression analyses carried out to predict in-dividual differences in reading accuracy are illustrated in Figure 2 and summarised in Appendix 3.

45.2%

12.9%

2.3%

1.0%

1.0%

35.7%

1.9%

37.0%

0.6%

2.9%

0.7%

2.6%

18.5%

37.7%

Longitudinal predictions (T1 predictor measures)

12.9% of variance in reading accuracy was explained uniquely by PA, that is, by

something that was specific to those PA tasks that were entered into the

analysis and not shared with RAN, vocabulary, grammar, letter knowledge, and

nonverbal reasoning. This contribution was statistically significant. The

remaining five blocks each explained only 0 – 2.3% of reading variance

uniquely. None of these contributions were statistically significant. Additionally,

a large chunk (35.7%) of variance in reading accuracy was accounted for by the

shared contribution of the 6 blocks entered. Hence, this reflects something that

was common to those measures. Altogether, the unique and shared effects of

all T1 predictor skills explained 54.8% of variance in reading accuracy.

Concurrent predictions (T2 predictor measures)

Statistically significant unique variance in reading accuracy was explained by

PA (18.5%) and TROG-D (2.9%), while vocabulary, letter knowledge, and T1

nonverbal reasoning could uniquely account only for non-significant 0.6 – 2.6%

PA RAN LK Voc TROG-D CPM shared unexplained

Figure 2. % of variance in reading accuracy explained by unique and shared contribu-tions of PA, RAN, letter knowledge (LK), vocabulary, grammar (TROG-D), and nonver-bal reasoning (CPM) (left = longitudinal, right = concurrent).

Longitudinal predictions (T1 predictor measures)12.9% of variance in reading accuracy was explained uniquely by PA, that is, by something that was specific to those PA tasks that were entered into the analysis and not shared with RAN, vocabulary, grammar, letter knowledge, and nonverbal


reasoning. This contribution was statistically significant. The remaining five blocks each explained only 0–2.3% of reading variance uniquely. None of these contribu-tions were statistically significant. Additionally, a large chunk (35.7%) of variance in reading accuracy was accounted for by the shared contribution of the 6 blocks entered. Hence, this reflects something that was common to those measures. Alto-gether, the unique and shared effects of all T1 predictor skills explained 54.8% of variance in reading accuracy.

Concurrent predictions (T2 predictor measures)Statistically significant unique variance in reading accuracy was explained by PA (18.5%) and TROG-D (2.9%), while vocabulary, letter knowledge, and T1 nonverbal reasoning could uniquely account only for non-significant 0.6–2.6% of variance in reading accuracy. An additional 37% of reading accuracy variance was explained by the commonality between all five blocks. These concurrent multiple regressions did not include an RAN block because no significant correlations were found be-tween any T2 RAN measure and reading accuracy (cf. Appendix 2). Altogether, T2 predictor measures (with T1 nonverbal reasoning) explained 62.3% of variance in reading accuracy.

3.4.3.2 Reading speed Results of regression analyses for reading speed are shown in Figure 3 and summarised in Appendix 4.

59.9%17.8%

0.1%

11.5%

6.3%4.4%

55.0%

21.9%

7.2%

4.5%

11.4%

Longitudinal predictions (T1 predictor measures)

Altogether, 40.1% of variance in reading speed could be explained by the four

blocks of T1 longitudinal predictors. 17.8% of that reading variance was

accounted for by something shared by all predictors. The following percentages

of variance were uniquely attributable to one block of tasks: 4.4% to PA, 6.3%


Figure 3. % of variance in reading speed explained by unique and shared contributions of PA, RAN, letter knowledge, vocabulary, grammar (TROG-D), and nonverbal reasoning (CPM) (left = longitudinal, right = concurrent)

Longitudinal predictions (T1 predictor measures)Altogether, 40.1% of variance in reading speed could be explained by the four blocks of T1 longitudinal predictors. 17.8% of that reading variance was accounted


for by something shared by all predictors. The following percentages of variance were uniquely attributable to one block of tasks: 4.4% to PA, 6.3% to RAN, 11.5% to letter knowledge and 0.1% to nonverbal reasoning. Of that, only the unique con-tribution of letter knowledge was statistically significant.

Concurrent predictions (T2 predictor measures)Concurrent regressions revealed that 45% of variance in reading speed could be explained by T2 predictors (with T1 nonverbal reasoning). 21.9% of reading vari-ance was attributable to something in common to all entered blocks. Three blocks of predictors, i.e. PA with 11.4%, RAN with 4.5%, and letter knowledge with 7.2%, accounted for a significant unique variance in reading speed. Nonverbal reasoning accounted for no unique variance at all.

3.4.3.3. Reading comprehension

Longitudinal predictions (T1 predictor measures)It can be seen from Figure 4 (and Appendix 5) that of the four blocks of longitudi-nal predictors entered into the analysis, only letter knowledge made a statistically significant contribution to explaining reading comprehension variance, account-ing for 8.3% of it. The unique contributions of PA (4.3%), RAN (4.4%), and CPM (1.5%) were statistically not significant. 22.1% of reading variance was attributable to the factors that were common to all four blocks. Altogether, 40.6% of variance in reading comprehension could be accounted for.

Concurrent predictions (T2 predictor measures)The six blocks of concurrent predictors taken together could explain 50.1% of variance in reading comprehension. 29.6% of this was attributable to something that was common to all six blocks of predictors, 5.7% to the non-shared effect of PA, 0.4% to RAN, 10.9% to letter knowledge, 1.3% to vocabulary, 0.9% to TROG-D, and 1.3% to nonverbal reasoning. Of those non-shared contributions, only that of letter knowledge was statistically significant.


59.4% 22.1%

1.5%

8.3%

4.4%4.3%

49.9%

29.6%

1.3%

0.9%

1.3%

10.9%

0.4%5.7%

3.4.3.4. Spelling

Figure 5 (and Appendix 6) summarise the results of regression analyses

computed to predict variance in spelling performance.

Figure 4: % of variance in spelling explained by unique and shared contributions of PA, RAN, letter knowledge, vocabulary, grammar (TROG-D), and nonverbal reasoning (CPM) (left = longitudinal, right = concurrent)

10.9%

5.6%

0.9%1.4%

20.9%60.3%

21.1%

5.1%

0.1%

2.3%

1.3%

26.6%

43.5%



Figure 4. % of variance in reading comprehension explained by unique and shared con-tributions of PA, RAN, letter knowledge, vocabulary, grammar (TROG-D), and nonver-bal reasoning (CPM) (left = longitudinal, right = concurrent)

3.4.3.4 Spelling Figure 5 (and Appendix 6) summarise the results of regression analyses computed to predict variance in spelling performance.

59.4% 22.1%

1.5%

8.3%

4.4%4.3%

49.9%

29.6%

1.3%

0.9%

1.3%

10.9%

0.4%5.7%

3.4.3.4. Spelling

Figure 5 (and Appendix 6) summarise the results of regression analyses

computed to predict variance in spelling performance.

Figure 4: % of variance in spelling explained by unique and shared contributions of PA, RAN, letter knowledge, vocabulary, grammar (TROG-D), and nonverbal reasoning (CPM) (left = longitudinal, right = concurrent)

10.9%

5.6%

0.9%1.4%

20.9%60.3%

21.1%

5.1%

0.1%

2.3%

1.3%

26.6%

43.5%



Figure 5. % of variance in spelling explained by unique and shared contributions of PA, RAN, letter knowledge, vocabulary, grammar (TROG-D), and nonverbal reasoning (CPM) (left = longitudinal, right = concurrent)

Longitudinal predictions (T1 predictor measures)39.7% of variance in spelling was attributable to the four predictor blocks, whereof 20.9% were explained by something shared by all these T1 measures. 10.9% was uniquely accounted for by PA, 5.6% by letter knowledge, 0.9% by TROG-D, and 1.4% by nonverbal reasoning. Only letter knowledge accounted significantly for variance in spelling over and above all other predictors.


Concurrent predictions (T2 predictor measures)56.5% of variance in spelling could be explained by T2 predictor measures (includ-ing T1 nonverbal reasoning). 26.6% was explained by factors that were common to the five blocks entered. 21.1% was attributable to the unique effect of PA, 0.1% to vocabulary, 2.3% to TROG-D, 5.1% to letter knowledge, and 1.3% to CPM perfor-mance. Among those unique contributions, only those of PA and letter knowledge were significant.

To sum up, the six blocks of predictors used in the regression analyses could predict 39.7–54.8 % of reading and spelling variance when measured approxi-mately one year prior to the assessment of literacy, and 45–62.3 % when measured concurrently with literacy. Thus, it must be acknowledged that a large proportion of individual differences in early literacy skills cannot be accounted for by the predictors measured. Regarding the literacy variance the predictors could account for, its largest chunk by far was attributable to the factors that were common to all blocks of predictors — shared between all six of them, or between their pairs, trip-lets, quadruplets, etc. In contrast, the unique contribution of individual predictor blocks to explaining literacy was usually small. Only PA and letter knowledge ac-counted for a significant unique proportion of literacy variance in several analyses. RAN and TROG-D each made such a unique contribution just once, while vocabu-lary and nonverbal reasoning never did so.

4. Discussion

The study set out (1) to explore the development of selected preschool cognitive skills that appear linked with literacy acquisition in the light of existing evidence (collected mostly in the context of the English language), and (2) to investigate the power of those skills to predict individual differences in literacy acquisition in German speakers at the initial stages of literacy instruction.

4.1 Performance on predictor measures at T1 and T2

As expected, children showed significant improvements of all skills under investi-gation from nursery (T1) to Grade 1 (T2). The greatest gains were observed on PA tasks at phoneme level and letter knowledge.

Given that PA is often assumed to have a reciprocal relationship with litera-cy (e.g. Hulme et al. 2005) and phonemic awareness has been found to develop completely only after literacy tuition has started (Wimmer et al. 1991; Ziegler & Goswami 2005), the findings from the PA tasks will be discussed in more depth. The improvement in PA skills between T1 and T2 was more prominent for pho-


nemic tasks than for syllable or onset-rhyme tasks. Children in this study entered school in the autumn after T1 and, thus, started to receive structured literacy tu-ition from this point in time. The majority of participating primary schools ad-opted a phonics teaching approach that focussed on teaching letter knowledge and grapheme-phoneme-correspondences from the beginning of their literacy instruction. Therefore, it seems reasonable to assume that the teaching approaches focussed specifically on PA at the phoneme level, which may explain the children’s significant improvement on phonemic tasks. In this regard, the start of literacy instruction might have influenced the development of PA in general to a greater extent than other speech and language processing skills because of its widely ac-knowledged reciprocal relationship with literacy (Aidinis & Nunes 2001; Catts et al. 2001). This may account for the fact that individual differences in PA skills were usually less stable than the other speech and language processing measures (cf. test-retest reliabilities, Table 7). Concerning PA skills on phonemic level, the pres-ent study replicates recent findings that phonemic awareness develops to a certain degree before children learn to read and write (Hulme et al. 2005; Schaefer et al. submitted; van Bon & van Leeuwe 2003). While most children did find phonemic tasks hard at T1, their performance was generally not at floor (with an exception of Sound-deletion-output). However, it should be acknowledged that the question whether phonemic awareness develops in children without any letter knowledge (see Castles & Coltheart 2004) cannot be answered with the data presented here.

4.2 Performance on literacy measures at T2

The analyses of the literacy measures administered at T2 revealed that children produced a high proportion of correctly read words (M = 25.53 (/30)) and pseudo-words (M = 19.54 (/24)) on SLRT reading even though the task was administered only after 6–9 months of formal literacy instruction (January — May of Grade 1). This finding confirms that children who learn a relatively transparent and consis-tent orthography like the German one already reach high levels of reading accuracy after a few months of literacy tuition (e.g. Landerl & Wimmer 2008; Wimmer & Hummer 1990). Moreover, the percentile scores of the ELFE 1–6 subtests on word, sentence and text level show that the participating children were rather advanced in all aspects of reading, including reading comprehension at the end of Grade 1.

The results of the spelling assessment show a more complex pattern. In line with earlier German-language findings, children made fewer phonological than orthographic errors on the SLRT spelling measure. This is often interpreted as a sign that German 1st Graders have a good grasp of the phonological components of their writing system, thanks to the consistency of the German orthography and to phonics teaching (Landerl & Thaler 2006). On the other hand, this finding might


partly be an artefact of the spelling assessment administered. The words used in the SLRT spelling subtest are short (1–2 syllables) and have rather simple phonologi-cal structures, thus do not challenge phonological decoding skills. It is likely that the application of a spelling measure more sensitive to phonological errors (e.g. Potsdamer Bilderliste (Potsdam picture list; Scheerer-Neumann et al. 2006)) would have shown a greater number of phonological spelling errors. The high number of orthographic errors on the SLRT suggests that the children were still in the al-phabetic stage of spelling development (e.g. Caravolas 2004). The surprisingly low percentile scores for orthographic spelling (M = 19.25; range: 1–80) may reflect the fact that at the end of Grade 1 most participating schools had only just started to teach orthographic spelling rules (or had not yet done so at all). In contrast, chil-dren obtained high percentile scores on the other early spelling accuracy measure, the HSP 1+ (the mean percentile rank for words written correctly was 72.34 and for graphemes written correctly 77.97). This finding is contradictory to the SLRT spelling results and adds further weight to the existing evidence that the standards of the HSP 1+ overestimate the performance of middle of Grade 1 pupils (Tacke et al. 2001a; Tacke et al. 2001b).

All but two literacy measures correlated significantly with each other. This commonality is likely to reflect alphabetic skills, i.e. efficiency in phonological re-coding. At this early stage of literacy acquisition (Grade 1), it is the phonological recoding that is usually the main constraint on more advanced literacy skills, such as reading comprehension (Gough & Tunmer 1986).

4.3 Predictors of individual differences in literacy skills

Correlation and regression analyses confirmed that early differences in literacy acquisition can be predicted from the mastery of several cognitive skills measured before or around school entry (Lonigan et al. 2000; Muter et al. 2004). The results help to clarify how the strength of these relationships varies across the cognitive skills being measured, and the aspects of literacy being predicted.

As expected, the predictive power of PA skills was dependent on the linguistic unit and level of explicitness assessed (Ziegler & Goswami 2005). In line with stud-ies carried out in the context of English as well as more consistent orthographies, PA on phonemic level was generally more strongly related to early literacy skills than PA on syllable or onset-rhyme level (Caravolas et al. 2005; Muter et al. 1998; Nathan et al. 2004). Regarding RAN, the data suggests that the ability to rapidly name objects may be more predictive of literacy than the ability to rapidly name colours or mixed stimuli (especially if RAN is measured concurrently with literacy skills in Grade 1). The predictive power of vocabulary was moderate at best and depended on the vocabulary measure used, as only one of the three expressive vo-


cabulary measures (i.e. PA-vocabulary) showed significant links with some aspects of literacy. This is consistent with Ouellette’s (2006) findings that the relationship between literacy skills and vocabulary depends on the vocabulary measure.

This relationship mentioned above, albeit statistically significant, was typically not strong. Strong correlations (r > .50) were observed only between two phone-mic PA tasks and reading accuracy, as well as letter knowledge and most aspects of reading. This overall pattern of correlations suggests that letter knowledge and PA are more important for early literacy than the remaining measures. However, to get a more detailed picture of the relationships, we will discuss the findings for reading accuracy, speed, comprehension, and spelling separately in the following paragraphs.

4.3.1 Reading accuracyReading accuracy correlated significantly with PA, vocabulary, TROG-D, letter knowledge, and nonverbal reasoning, both concurrently and longitudinally; it also correlated concurrently with RAN. However, longitudinally, only PA accounted for unique variance in reading accuracy, whereas concurrently, PA and TROG-D were unique predictors of reading accuracy.

The finding that preschool phonemic awareness uniquely predicts reading ac-curacy in Grade 1 is consistent with many English language studies (e.g. Lonigan et al. 2000) and also some German language ones, although the results of the latter are inconsistent. Even if some view PA as rather marginal for reading accuracy (e.g. Landerl & Thaler 2006; Wimmer et al. 2000), previous Austrian/German studies admit that “an early phonemic awareness deficit may cause […] an early problem with the acquisition of phonological recoding in word reading” (Landerl & Wim-mer 2000: 258). The unique (if small) contribution of grammar comprehension (TROG-D) may be attributable to the fact that German is an inflected language (Goulandris 2003; Joshi & Aaron 2006). However, given the early stage of literacy development assessed in the present study, the explanatory power of this hypoth-esis is limited. In contrast, neither letter knowledge nor RAN made a unique con-tribution to the explanation of individual differences in reading accuracy over and above PA — a finding that is at odds with many studies (e.g. Badian 1993; Muter et al. 2004; Wimmer et al. 1991). As suggested by Foulin (2005), the contribution of letter knowledge might lack significance because its influence on reading accuracy is indirect through phonemic awareness. Vocabulary knowledge did not make a unique contribution, either. This accords with the literature since the German or-thography is regarded as a rather consistent one in which semantic bootstrapping of irregular words is less important than, for example, in the English orthography (Ouellette 2006; Ziegler & Goswami 2005).


4.3.2 Reading speedLongitudinally and concurrently, reading speed correlated with PA, RAN, letter knowledge, and nonverbal reasoning. However, only letter knowledge accounted for unique variance in reading speed (over and above the other predictors) on both oc-casions. Concurrently, such unique contribution was also made by PA and RAN.

The unique link between PA and reading speed was rather unexpected for the German language. In contrast to English (e.g. Wagner & Torgesen 1987), no such link has been reported so far in the context of the German language. However, the importance of RAN in the present study appears weaker than previously reported (e.g. Landerl & Wimmer 2008). These findings might be connected to the study design. First, PA was measured more comprehensively (i.e. on different levels of explicitness and all three unit sizes) than in earlier longitudinal studies of German-speaking children. Secondly, potential predictors of literacy were first measured a few months before children entered school, while in the majority of previous Ger-man language studies this was done shortly after school enrolment (e.g. Wimmer & Mayringer 2002; Wimmer et al. 2000). Thirdly, reading speed was assessed near the beginning of children’s reading development, after only 6–9 months of instruc-tion. At this early stage of literacy acquisition, reading speed and accuracy primar-ily reflect the alphabetic skills, which in turn depend on letter knowledge and PA (especially phonemic awareness) (Gough & Tunmer 1986; Wolf et al. 2002). This suggestion is supported by the strong and unique predictive power of letter knowl-edge for reading speed which manifests itself longitudinally and concurrently. The strong correlation between reading accuracy and speed also supports this view.

4.3.3 Reading comprehensionReading comprehension correlated longitudinally with PA, RAN, letter knowledge, and nonverbal reasoning, and concurrently with all predictor measures (i.e. PA, RAN, vocabulary, TROG-D, letter knowledge, and nonverbal reasoning). However, only letter knowledge accounted for unique variance in reading comprehension over and above the remaining measures, both concurrently and longitudinally.

As reading comprehension was measured at an early stage of reading develop-ment, its main constraint may have been alphabetic skills (Gough & Tunmer 1986). Thus, the strong and unique effect of letter knowledge is probably an indirect one: reading comprehension at this age depends critically on accurate and fast decoding which, in turn, depends on letter knowledge, among other things. Although read-ing comprehension was also predicted by semantic and syntactic language skills (i.e. expressive vocabulary and grammar comprehension), their contribution be-came apparent only once they had been measured in Grade 1, concurrently with reading. This finding suggests that oral language skills get more important at the same time as learning to read and write (i.e. concurrently) than at preschool age


(if typically developing children are concerned). Furthermore, contributions of language skills did not stay significant over and above the other measures. This again might be linked to the early assessment of reading comprehension and the effects of decoding.

4.3.4 SpellingLongitudinally apart from RAN and vocabulary, and concurrently apart from RAN, all predictors correlated with the children’s spelling performance. Longitu-dinally, unique contributions were limited to letter knowledge, and concurrently to PA and letter knowledge. These findings are consistent with the results of earlier studies that have also reported the unique contribution of letter knowledge and PA to spelling development, in English as well as in German (Caravolas 2004; Lonigan et al. 2000, Landerl & Wimmer 2008). The spelling of German Grade 1 children was assessed in the middle (HSP 1+) and the end of Grade 1 (SLRT spelling). Spell-ing in German (especially in the early phases) is viewed as more related to PA than reading accuracy is (e.g. Landerl & Wimmer 2008). This is because learning to spell appears more difficult than learning to read (e.g. Caravolas et al. 2001).

4.4 Strengths and limitations of the study

The design of this study makes an important contribution to our understanding of the prediction of literacy acquisition in young German-speaking children. In con-trast to the majority of earlier Austrian/German studies (e.g. Landerl & Wimmer 2008; Wimmer 1993; Wimmer et al. 1991) it measured potential predictors before children enrolled at school. Further, a wider range of predictors (PA, RAN, expres-sive vocabulary, grammar comprehension, and letter knowledge) was investigated in the same sample of children through a systematic assessment procedure based on psycholinguistic principles. Uniquely, PA was assessed comprehensively (i.e. on all three unit sizes and on different levels of explicitness) and the PA tasks were designed to measure PA using tests that are relatively unconfounded for working memory demands (see Fricke & Schäfer 2008). This might be one reason why PA accounted for more variance in literacy performance than in earlier German-language studies. While three RAN tasks were included, no alphanumeric RAN task was used because they appeared inappropriate at T1, due to the lack of for-mal letter knowledge tuition in German nurseries. While the exclusion of alpha-numeric RAN tasks might have decreased the predictive power of RAN because a number of studies has shown that RAN of letters or numbers is more predictive of literacy than RAN tasks with pictures or colours (e.g. Cardoso-Martins & Pen-nington 2004), it was also methodologically sounder: we can be reasonably certain that the RAN tasks used did measure the automaticity of naming and not merely


the knowledge of names (which is a confounding factor when using RAN of digits or letters with preschoolers, see e.g. Bowey 2005). Also, only expressive vocabulary was assessed in the present study. Given that Ouellette (2006) found differences in the relations between literacy skills and both expressive and receptive vocabu-lary, future studies of the German language and orthography may want to include measures of both. Similarly, the importance of grammatical skills needs to be ex-amined in more depth by administering measures of morphological knowledge in addition to syntactic awareness. This may be particularly important for later (orthographic) phases of literacy acquisition in German which is an inflected lan-guage that retain the principle of morpheme consistency. In these later phases, morphological knowledge may play a greater role in reading and spelling than at the initial (alphabetic) stage (Goulandris 2003). Generally, more information is needed about the development of speech and language processing skills and letter knowledge beyond Grade 1, and their predictive power on later stages of literacy development needs further investigation.

It should be acknowledged that the statistical power of the study was rath-er limited, given the sample size and the number of predictors. Also, we did not explore whether speech and language processing skills or letter knowledge con-tributed uniquely to the prediction of literacy skills, once the effects of different aspects of literacy on each other were accounted for (e.g. whether predicting indi-vidual differences in reading comprehension could still be improved by considering speech and language processing skills, once reading accuracy and speed were taken into account). This analysis is planned when the same children are followed up at Grade 2 and the third wave of data is collected. It must also be acknowledged that the results may have been influenced by our choice of data analysis method. The multiple regression analyses we carried out used individual cognitive tests as predictors, only including those that correlated significantly with the outcome (the relevant literacy composite) in the first place; non-significant correlates were excluded (see 3.4.3.). This method maximises the chances of observing strong re-lationships between the predictors and the outcome and assists in identifying indi-vidual tasks that are the most valid predictors of future literacy acquisition. How-ever, it is less effective in studying the relationship between literacy and the latent dimensions of cognition, such as PA or the ability to do RAN tasks in general. Such a different focus and method of analyses might result in different patterns of rela-tionships between cognitive predictors and literacy outcomes and will be the topic of future analyses (Fricke in progress). Finally, it should also be noted that despite the high number of predictors measured, a considerable percentage of variance in literacy skills (nearly a half) remained unexplained. The unexplained variance may be attributed to cognitive skills we did not measure (e.g. visual skills), envi-ronmental factors (e.g. literacy practices at home, quality of teaching, influence of


classroom peers), as well as measurement error (Byrne et al. 2006; Roskos & Neu-man 2001; Samuelsson et al. 2005).

The results have practical applications as they appoint specific skills that need to be measured to identify children at risk of literacy problems, and as they help to design specific tests that can be used to measure these skills. Unfortunately, however, they do not reveal anything about effective methods of literacy instruc-tion or prevention of literacy difficulties. For example, letter knowledge was found to be a highly important predictor of successful literacy acquisition, but this does not imply that the teaching of letter knowledge at preschool in isolation from other skills boosts later literacy development (e.g. Adams 1990). The data is perfectly consistent with the widely held view that teaching letter sounds in combination with PA (especially phoneme analysis and blending skills) is an excellent way to introduce children to literacy (e.g. Roth & Schneider 2002), but such view cannot be in any way proven by the correlational study of the type described above. Well-designed intervention studies are needed to further explore the question of most effective pedagogy.

5. Conclusion

In conclusion, early literacy development is influenced by many factors of which some unique predictors can be found in the speech and language processing and letter knowledge domain. The present findings show that a number of skills has to be assessed in order to predict early literacy development. Furthermore, the data suggest that tasks used in research on literacy predictors should be comprehensive and designed systematically (e.g. for PA tasks see Fricke & Schäfer 2008). Since predictor skills selected for the present study can be measured easily by practitio-ners, the findings provide preliminary guidelines for the identification of children at risk for literacy difficulties.

Acknowledgements

This study was partly supported by a PhD Grant from the Deutscher Akademischer Austausch-dienst (DAAD; German Academic Exchange Service) and the FAZIT-Foundation (Frankfurt; Germany). We thank all children, parents, and staff at nurseries, after-school clubs and schools who participated in and supported this project.


Notes

1. The consistency values reported here are based on body–rime consistency for monosyllabic words and not individual grapheme–phoneme correspondences.

2. Due to logistical reasons, the SLRT reading subtests were given in the first part of T2, thus earlier than stated in the manual.

3. Following the recommendations of Rosenthal (1991:19 in Field 2005:532), r coefficient was computed to express effect sizes of observed differences, with r > .3 and r > .5 deemed to repre-sent medium and large effects, respectively.

4. Within the present study, tasks were interpreted as demonstrating floor effects (F) when more than 50% of children scored 0 or 1 points, and ceiling effects (C) when more than 50% of them achieved the maximum possible score or the maximum score minus 1.

5. The regression tables should be read as follows:

– The two data columns on the left for concurrent and longitudinal regressions, respectively, report data from each model 1, i.e. R2 (change) and significance levels of F change for the regression model that included respectively one block: R2 (change) = amount of variance in the literacy composite accounted for by a given block.

– The two data columns on the right show the results of the final models, i.e. the changes in R2 incl. significance levels that are accounted for by entering respectively the last block of variables: R2 change = unique variance explained by the variables in the block entered last. Significance levels of F change <.05 = contribution of the predictor entered last stays signifi-cant after controlling for all other predictor variables = last block accounts for variance in the literacy composite over and above the other blocks.

– R2 final model (incl. all blocks)’ = amount of variance in the literacy composite explained by all blocks entered together.

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Appendix 1

Correlations between T1 predictors (normalised scores) and T2 literacy composites (based on normalised scores)

reading ac-curacy

reading speed

reading com-prehension

spelling

SylSegout .16 .07 .00 −.03RhymeProdout .18 −.24* .17 .19RhymeIDin .40** −.20 .29* .40**OnsetRhymeBlendout .34** −.16 .22 .27*OnsetRhymeBlendin .19 −.09 .21 .26*SoundIDout .56** −.27* .27* .32**SoundIDin .53** −.26* .32** .39**SoundBlendout .34** −.21 .22 .26*SoundBlendin .10 .03 .15 .21SoundDelout .37** −.31** .35** .32**SoundDelin .25* −.18 .18 .18RAN objects .31** −.31** .34** .23RAN colours .27* −.31** .28* .10RAN mixed .23 −.19 .27* .1736 nouns .23 .03 −.03 .0936 verbs −.12 .09 −.03 −.02PA-vocabulary .44** −.19 .07 .23TROG-D .25* −.06 .07 .27*letter knowledge .54** −.51** .50** .47**CPM .35** −.28* .34** .28*

* Correlation is significant at the 0.05 level (2-tailed) ** Correlation is significant at the 0.01 level (2-tailed)

Appendix 2

Correlations between T2 predictors (normalised scores) and T2 literacy composites (based on normalised scores)

reading ac-curacy

reading speed

reading com-prehension

spelling

SylSegout .27* −.20 .12 .06RhymeProdout .30* −.25* .21 .26*RhymeIDin .17 −.17 .26* .18OnsetRhymeBlendout .32** −.30* .41** .48**OnsetRhymeBlendin −.04 .00 .08 .37**SoundIDout .33** −.29* .23 .25*


SoundIDin .41** −.16 .22 .29*SoundBlendout .13 −.11 .31* .35**SoundBlendin .15 .01 .20 .19SoundDelout .57** −.46** .43** .45**SoundDelin .54** −.42** .37** .44**RAN objects .19 −.35** .29* .16RAN colours .15 −.13 .22 .10RAN mixed .07 −.16 .16 −.0136 nouns .22 .14 .01 .0636 verbs .09 .18 −.16 −.04PA-vocabulary .47** −.20 .25* .43**TROG-D .45** −.19 .28* .36**letter knowledge .54** −.51** .61** .52**

* Correlation is significant at the 0.05 level (2-tailed) ** Correlation is significant at the 0.01 level (2-tailed)

Appendix 3

Multiple regressions of the predictive measures of reading accuracy5

Blocks

Longitudinal Concurrententered first entered last entered first entered lastR2

changeSig. Fchange

R2

changeSig. Fchange

R2

changeSig. Fchange

R2

changeSig. Fchange

PA .432 .000 .129 .048 .519 .000 .185 .002RAN .116 .018 .019 .321LK .289 .000 .023 .102 .294 .000 .026 .053Voc .192 .000 .010 .268 .221 .000 .007 .314TROG-D .061 .043 .000 .903 .202 .000 .029 .043CPM .120 .004 .010 .272 .120 .004 .006 .334R2 final model (incl. all blocks).548 .000 .623 .000


Appendix 4

Multiple regressions of the predictive measures of reading speed

Blocks


changeSig. Fchange

R2

changeSig. Fchange

R2

changeSig. Fchange

R2

changeSig. Fchange

PA .185 .011 .044 .370 .320 .000 .114 .044RAN .133 .010 .063 .052 .125 .003 .045 .031LK .259 .000 .115 .001 .263 .000 .072 .007VocTROG-DCPM .076 .023 .001 .723 .076 .023 .000 .904R2 final model (incl. all blocks)

.401 .000 .450 .000

Appendix 5

Multiple regressions of the predictive measures of reading comprehension

Blocks


changeSig. Fchange

R2

changeSig. Fchange

R2

changeSig. Fchange

R2

changeSig. Fchange

PA .150 .032 .043 .387 .329 .000 .057 .270RAN .141 .019 .044 .233 .086 .014 .004 .520LK .250 .000 .083 .006 .374 .000 .109 .001Voc .065 .035 .013 .233TROG-D .081 .018 .009 .317CPM .117 .004 .015 .233 .117 .004 .013 .228R2 final model (incl. all blocks)

.406 .000 .501 .000


Appendix 6

Multiple regressions of the predictive measures of spelling

Blocks


changeSig. Fchange

R2

changeSig. Fchange

R2

changeSig. Fchange

R2

changeSig. Fchange

PA .306 .002 .109 .195 .447 .000 .211 .004RANLK .225 .000 .056 .025 .266 .000 .051 .014Voc .188 .000 .001 .666TROG-D .074 .025 .009 .359 .132 .002 .023 .096CPM .079 .020 .014 .251 .079 .020 .013 .202R2 final model (incl. all blocks)

.397 .000 .565 .000

Author’s address

Silke FrickeDepartment of Human Communication SciencesUniversity of Sheffield31 Claremont CrescentSheffield S10 2TAUnited Kingdom

[email protected]

mailto:[email protected]

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