decode, s/he can still engage with a story book or a pictorial non-fiction book in a meaningful way, making connections with the images and symbols of the book, perhaps even assigning sequence to a pictorial narrative. S/he will also make connections with previous books that have been encountered. Figure 1.3 Tale of a tadpole (Wallace, 2009) Take, for example, the book shown in figure 1.3. Without being able to recognise or decode the word ‘Tadpole’ or even without knowing that text carries meaning, a child can engage with this book; can know that this book is about frogs and tadpoles: can share this book with a friend or adult; can reinforce or learn something new about tadpoles; can satisfy an interest in non-fiction books; can navigate through the book, knowing where to start and in which direction to continue; and can take enjoyment from engaging with the book. The decoding of the text is thus demonstrated to be one facet of reading, and not even, chronologically, the first facet that a child develops. One might therefore argue that if a child is to want read this book, an interest in tadpoles must precede an interest in phoneme/grapheme correspondences. Meek (1994) observed that “everything that children eat, wear, play with or pass in the streets has a sign or symbol” (p.2), and so the act of reading is not confined to the interpretation of books, or even to organised texts. Children learn to read through
As such, it is clear that the emergent reader of English has not only to grapple with inconsistency of grapheme-phoneme correspondence, but also with a range and variability of structure even within the syllables of the words s/he is learning to read. Take, for example, the monosyllabic homophones ‘poor’, ‘paw’, ‘pore’ and ‘pour’. Within these examples there is to be found no consistent grapheme/phoneme correlation in the vowel sound. It is true that the onset (glossary, p.245) of each is consistent, but the vowel grapheme is different in each (<oor, aw, ore, our>), while the vowel phoneme is the same, namely /ɔː/ (in many accents in England, that is). These words have no spoken coda (glossary, p.245). Similarly the words ‘scam’ and ‘skim’ share an ‘initial consonant cluster – short medial vowel – single consonant coda’ structure, yet the onset clusters, though audibly identical, are different graphically, because the phoneme /k/ is spelt differently (though according to rule: /k/ is mostly spelt <c> before <e, i, y>, <k> elsewhere). 2.7 The vocabulary of the field of phonics At this point it is necessary to define terms, particularly the word ‘blend’. In current documentation (EYFS guidance, DCSF, 2008; Primary Framework, DfES, 2006a; Letters and Sounds, DfES, 2007b) this word is used only as a verb, which describes the process, within synthetic phonics for reading, once the constituent parts (graphemes) of a word have been recognised and sounded out, of pushing them together to read the word as a whole. This use of the word is perceived as unproblematic:
1999: 46). The participants in my study had already entered Year 1 via an Early Years setting of course, but their reading and phonological awareness would still have been developing, and this study contributed to that process. 2.11 The notion of transfer All of this hinges on this notion of ‘transfer’: one skill being able to impact upon another (potentially related) skill. This is contested ground, because the development of one skill (or body of knowledge, or area of understanding) causally enabling the development of another has yet to be demonstrated in any discipline. “Despite a century’s worth of research, arguments surrounding the question of whether transfer occurs have made little progress toward resolution” (Barnett and Ceci, 2002: 612). Dettermann and Sternberg (1993) are nothing short of dismissive: First, most studies fail to find transfer. Second, those studies claiming transfer can only be said to have found transfer by the most generous of criteria and would not meet the classical definition of transfer, defined by Detterman [1993: 4] as "the degree to which a behaviour will be repeated in a new situation". ... In short, from studies that claim to show transfer and that don't show transfer, there is no evidence to contradict Thorndike's general conclusions: Transfer is rare, and its likelihood of occurrence is directly related to the similarity between two situations. (Dettermann and Sternberg, 1993: 15) It is of interest that Detterman and Sternberg refer to ‘behaviour’, rather than learning, or knowledge. This reference to similarity of situation is a recurring theme which shall be returned to and explored throughout this literature review’s section on transfer.
and Ceci) six contextual variables alone, each with degrees of distance. I have mentioned Barnett and Ceci because for my research model I was building on Gromko (2005), who made analysis of children’s reading scores after a series of lessons in the reading of music. Gromko refers to Barnett and Ceci’s (2002) “near transfer hypothesis” (Gromko, 2005: 201), by which she means that the contexts of ‘when, how and where’ the two modes of reading are conducted were very similar. With regard to my own research, there are contextual variants which had to be taken into account methodologically. To this end, I adapted Gromko’s method, the changes from which I will explain, analyse and justify in a later section. But for now, were I to apply Barnett and Ceci’s taxonomy for transfer to my research design, in order to demonstrate the ‘nearness of transfer’, the table would appear as follows. Table 2.4: Analysis of the distance of transfer contained in this research design Context: When and where transferred from and to Near Far Knowledge domain Interpretation of symbols, albeit in different formats Physical context Different room at school Temporal context During the same period Functional context Both academic Social context Both individual, with the use of small and large groups Modality Both written, different format
Source: Adapted from Hansen et al. (2007: 149) Analyses of assessment opportunities and intentions offer a means of clarifying what a teacher is trying to achieve – indeed experienced teachers often begin by determining what is to be assessed, and how, before designing teaching plans and learning environments to facilitate the fair demonstration of the learning. Hansen et al.’s (2007) categorisation of assessment demonstrates effectively aspects of which might usefully be developed through the teaching of music reading, and a useful comparison can be made between this categorisation and current Early Years and Primary documentation. 2.16.1 Auditory skills (i): Auditory association Both the Early Years Foundation Stage (DCSF, 2007a) and strand 5 of the Primary Framework for the teaching of mathematics and literacy (DfES, 2006a) require children between the ages of 40 and 60 months to be taught to “Link sounds to letters, naming and sounding the letters of the alphabet”, and to “Use a pencil and hold it effectively to form recognisable letters” (DfES, 2006a: 48). This parallels Hansen et al.’s ‘auditory association’, being a kinaesthetic response to an auditory stimulus. The EYFS (DCSF, 2007a) suggests that phonic teaching should be in order to capture [children’s] interests, sustain motivation and reinforce learning” (p.53). sensory
2.16.2 Auditory skills (ii): Auditory blending Similarly, the vocabulary of ‘auditory blending’ speaks of the identification and synthesis of separate sounds, which Hansen considers an essential component of learning to read music, and is also a central tenet of the systematic synthetic phonics pedagogy found in ‘Letters and Sounds’ (DfES, 2007a). “Beginner readers should be taught to apply the highly important skill of blending (synthesising) phonemes in the order in which they occur” (DfES, 2007a: 10). This skill of blending is prevalent in the guidance for the teaching of ‘Letters and Sounds’ (DfES, 2007a), indeed the word ‘blending’ appears 28 times in the 28-page document. Children are encouraged to participate in “sound talk. For example, [children are encouraged to] sound talk a word in an instruction (e.g. Give yourselves a p-a-t on the back” (p.55). Often robotic chopping arm movements are employed to reinforce the separation, or segmentation, of a word’s component sounds, in an attempt to make the experience multi-sensory. 2.16.3 Auditory skills (iii): Auditory discrimination Hansen et al.’s (2007) ‘auditory discrimination’ relates to the identification of features of specific sounds (high/low, soft/loud, long/short), and this echoes the studies by Butzlaff (2000) and by Lamb et al. (1993), mentioned earlier. The Primary Framework (DfES, 2006a, strand 5) invites children to “explore and experiment with sounds, words and texts” (p.48). The first three aspects of phase one of the Letters and Sounds (DfES, 2007b) documentation are listed as follows: Aspect 1: General sound discrimination – environmental sounds Aspect 2: General sound discrimination – instrumental sounds
In my study, the pairs of standard deviations which were sufficiently different to warrant the use of the more complicated formula occurred in single-letter recognition (s.d’s of 2.26 and 1.46); and cluster and digraph recognition (2.97 and 2.74). The smaller post-test standard deviations of the other three categories fell within 5% of the larger value. However, to be consistent, all effect sizes were calculated using the more complex formula. 4.4.3 Analyses of variance “Where one [or more] groups of participants are studied at different time points ... a two-way mixed analysis of variance will be particularly appropriate” (Howitt and Cramer, 2008: 196 & 197). This variant of anova (also known in older texts as analysis of variance with repeated measures on one factor’) was used here. Appendices 21-25 contain the anova results produced by SPSS from the RCT data. The anova results of interest were not those for the main factor, time, but for the interaction of time and group membership – this was to investigate whether or not one of the two groups made significantly more progress than the other. 4.4.4 t-tests of post-test differences These were used to investigate whether the two groups’ mean scores differed significantly at the post-test stage (appendices 21-25). Here, because there was a hypothesis identifying the likely direction of difference (in favour of the intervention group), one-tailed p-values were identified and presented. 4.4.5 Confidence intervals around effect sizes
<0.001), but that the interaction of time and group was not statistically significant (F(1,48) = 2.465, p = 0.123). The test of the mean post-mean scores was also (t = 0.074; df = 48; one-tailed p = 0.471). Gromko (2005) found the effect size of her intervention on letter-naming fluency to be to be d = 0.09 (see chapter 2, 2.19.3), which was a much smaller effect than those in the other two categories of her study. Because of the ceiling, in my study the effect of the intervention on children’s ability to identify single graphemes is much more pronounced even than in Gromko’s, to the extent that it has produced a negative result. A calculated effect size of d = -0.45 for single-letter grapheme recognition, demonstrates that the control group made significantly greater progress than the intervention group. An explanation for this can be found in the data of two children (one from each class) who each scored only eleven marks in the pre-test, all of which were gained exclusively from recognition of single-letter graphemes (the focus of this analysis), but who had both been randomly allocated to the control group by the casting of the die. However, the single-letter grapheme recognition scores of these two children improved greatly during the period of the intervention (post-test scores of 16 and 19 respectively), a level of improvement which was mathematically impossible for the other children, because their knowledge of this area at pre-test was very close to the maximum. The ‘catch-up’ that these two children enjoyed explains the greater average progress of the control group. Because their scores produced a
Although the analysis of children’s recognition of clusters and digraphs has been combined (in order for comparison with Gromko’s (2005) findings to be made), it is interesting that the interpretation and decoding of clusters was clearly more problematic than that of digraphs for these participating children. Appendices 13 and 14 (showing the children’s correct and incorrect answers) reveal that in the pre-test, 33 of the 50 children were unable to recognise and sound any of the four clusters. This number was reduced to fifteen children in the post-test. Of the eighteen children who moved from a score of 0 at pre-test to >0 at post-test, twelve were in the intervention group. This imbalance of progression contributed to a positive effect size larger than any other category in this research programme. By Wiliam’s (2008: 254) reckoning (“0.3 standard deviations is approximately the average progress in achievement made by a pupil in a year”), the effect size of 0.49 represents about 1.5 year’s progress, an astonishing amount of progress over a six-week period. 4.12 Nonsense word recognition mean scores Table 4.6: Nonsense word recognition mean scores Group n Pre-test Post-test Gain Effect size mean (s.d.) mean (s.d.) Intervention 25 7.12 (10.07) 15.64 (14.43) 8.52 d = 0.37 Control 25 8.00 (11.09) 11.44 (13.73) 3.44 Figure 4.7: Nonsense word recognition mean scores
By Cohen et al.’s (2007: 521) categorisation, this effect size is “modest”, but again, is stronger than that for total scores. The anova results (appendix 25) show the effect of time (factor 1) was highly significant (F(1,48) = 25.495, p <0.001), and, as with the result of the tests of overall scores excluding single-letter grapheme recognition fluency, the interaction of time and group was statistically significant (F(1,48) = 4.600, p = 0.037). The test of the mean post-mean scores was (t = 1.055; df = 48; one-tailed p = 0.149). Further analysis of the nonsense-word recognition data revealed some interesting additional patterns. Table 4.7: Further analysis of Nonsense word recognition scores Pre-test Post test Children unable to recognise any nonsense word (Intervention) / Control 16 (9) / 7 9 (3) / 6 Children recognising 1-9 nonsense words 22 (9) / 13 19 (8) / 11 Children recognising 10-19 nonsense words 6 (5) / 1 5 (4) / 1 Children recognising 20-29 nonsense words 3 / 2 8 (4) / 4 Children recognising 30 or more nonsense words 3 / 2 9 (6) / 3 Total 50 (25) / 25 50 (25) / 25 The most noticeable difference between the two groups was the pre-test / post-test reduction in the number of children unable to recognise a single nonsense word. Of
size mean (s.d.) mean (s.d.) Intervention 55 4.40 0.94 5.42 (1.13) 1.02 d = 0.14 Control 53 4.26 (0.96) 5.11 (1.34) 0.85 The anova results (appendix 28) show the effect of time (factor 1) was very highly significant (F(1,106) = 208.798; p <0.001), but that the interaction of time and group was not statistically significant (F(1,106) = 3.113; p = 1.713). The test of the mean post-mean scores was also non-significant (t = 1.279; df = 106; one-tailed p = 0.102). By Cohen et al.’s (2007: 521) categorisation, this effect size is “weak”. However, I return to Wiliam’s (2008: 254) observation that “0.3 standard deviations is approximately the average progress in achievement made by a pupil in a year”, ... “and [referring to an educational setting] an effect size of 0.1 or even 0.05 [can be] substantively useful”. This places the effect size of these parallel cohorts into a positive light. Once again, the result is statistically non-significant, but an effect is shown. Table 4.16: 95% confidence interval of effect size, phase 3 Lower Actual Higher -0.13 0.14 0.63 Table 4.16 identifies the upper and lower ranges of the 95% confidence interval, using ClinTools (2012). These mirrored closely those of the RCT. The values of the confidence interval straddle 0, and so, as in phase 1, it was not possible to be 95% certain that, were my study to be repeated, an effect would be present.
text. No formal pre- or post-testing of music reading was undertaken, because during my initial presentation of the programme to the children, where the three music symbols ( ) were shown but not explained, no child could suggest what they might be. From an ethical point of view, it seemed counterproductive to try to test children in an area where they apparently had no prior experience – such tests could easily discourage children from participation and have a detrimental effect on self-esteem. Instead, three of the research assistants were interviewed in a semi-structured way once the intervention was complete; to ascertain whether there was evidence that progress in music reading had been made. Five questions formed the basic structure of the interviews, and these, together with a summary of responses, are presented as follows. (J, E & G = Research Assistants 1,2 &3 respectively: I = interviewer) 4.30 Question 1, Think back to the very first lessons that the children received. What were your initial impressions of the children’s level of recognition of the three music symbols (crotchets, quavers and rests) during the first lessons? J: I don’t think any of them could. E: Maybe one or two said something like they had seen a recorder before, and might have done it at home, but, I don’t know, not really. G: Yeah, I can’t remember any one in particular being able to. J: No, not the symbols, no. I didn’t get the impression that any of them knew any of the symbols before they started.
were asked to read words such as ‘red’ and ‘egg’, in order to identify those children who recognised high-frequency words by sight, but who could not yet freely blend and segment nonsense words. Perhaps the nonsense words being assessed could have been interspersed amongst un-assessed common words of identical syllabic complexity and orthographic depth (Seymour et al., 2003). This might have helped those (few) children who, having taken one look at the nonsense words, announced that they could or would not read them because “They are not real words.” It is clear from this immediate reaction that whole-word recognition is a common way for children to interpret and blend multiple graphemes, since such children must have ‘read’ or engaged with the word in some way in order to be able to pronounce judgement upon it as ‘not being real’. This research is focused on decoding skills, not whole-word recognition, but any research that involves itself with early reading development must acknowledge that whole-word recognition is a legitimate method used by many children to read words; ‘tricky words’ (DfES, 2007b: 194); or those words which have a passing physical resemblance to the object they represent (such as ‘aeroplane’). However, methodologically, the inclusion of recognisable high-frequency words before or during the reading of the nonsense words might have served to put children at their ease, and thus perform more comfortably in the tests. This might have produced a more ‘bell-like’ curve on the frequency diagrams. Similarly, when asking children to decode the clusters it would have been useful to determine whether or not the letters that made up those clusters (dr, fl) were letters known to those children who struggled on the single-grapheme recognition section of
References Adams, D. (1979) The Hitchhiker’s Guide to the Galaxy Basingstoke and Oxford, England: Heinemann Atterbury, B. W. (1985) ‘Musical differences in learning-disabled and readers, aged seven, eight and nine’ Psychology of Music, 13, 114-123 DOI: 10.1177/0305735685132005 Barnett, S. M., and Ceci, S.J. (2002) ‘When and Where Do We Apply What We Learn? A Taxonomy for Far Transfer’ Psychological Bulletin Vol 128, No 4, DOI: Barr, R., Kamil, M.L., Mosenthal, P.B. and Pearson, P.D. (1996) Handbook of Reading Research, Volume II Mahwah, New Jersey: Routledge Barrs, M. (1991) The Reading Book London, England: CLEP Barton, D., Hamilton, M., and Ivanic, R. (2000) (Eds.), Situated literacies: Reading and writing in context New York: Routledge. Baumrind, D. (1964) ‘Some thoughts on ethics of research: After reading Milgram's "Behavioral Study of Obedience."’ American Psychologist, Vol 19(6), pp421-423. DOI: Beach, K. (2004) ‘Consequential Transitions: A Sociocultural Expedition beyond Transfer in Education’ Review of Research in Education Vol 24 pp101-139 Retrieved from http://www.jstor.org/stable/1167268 Bentley, A. (1966) Measures of Musical Ability London, England: Harrap Besson M., and Friederici A. (2005) ‘Part II: Language and Music—A Comparison’ Annals of the New York Acadamy of Sciences DOI: 10.1196/annals.1360.061 Bolduc, J. (2008) The Effects of Music Instruction on Emergent Literacy Capacities among Preschool Children: A Literature Review Early Childhood Research and Practice Vol 10 No 1, no page numbers available. Retrieved from Bradley, L. and Bryant, P. E. (1978) ‘Difficulties in auditory organisation as a possible cause for reading backwardness’ Nature 271, 746-747
Clay, M.M. (1985) The Early Detection of Reading Difficulties Auckland, New Zealand: Heinemann ClinTools Software(2012) Effect Size Generator Retrieved from Coe, R. (2002) ‘It's the Effect Size, Stupid: What effect size is and why it is important’ Paper presented at the Annual Conference of the British Educational Research Association, University of Exeter, England, September 2002 Coffey, A. and Atkinson, P. (1996) Making sense of Qualitative Data Analysis: Complementary Strategies Thousand Oaks, CA: Sage Cohen, J. (1988) Statistical Power Analysis for the Behavioural Sciences (2nd Ed) Hillsdale, New Jersey: Lawrence Erlbaum Associates: Cohen, J. (1969) Statistical Power Analysis for the Behavioural Sciences Hillsdale, New Jersey: Lawrence Erlbaum Associates Cohen, L., Manion, L., and Morrison, K. (2007) Research methods in Education (6th Edition) London, England and New York: RouteledgeFalmer Coldwell, M., Shipton, L., Stevens, A., Stiell, B., Willis B., and Wolstenholme C. (2011) Process evaluation of the Year 1 Phonics Screening Check Pilot Sheffield, England: Centre for Education and Inclusion Research Retrieved from https://www.education.gov.uk/publications/eOrderingDownload/DFE-RB159.pdf Copple, C., Bredekamp, S., and Neuman, S. (2000) Learning to read and write: Developmentally appropriate practices for young children Washington DC; National Association for the Education of Young Children Corriveau, K.H., and Goswami, U. (2009) ‘Rhythmic motor entrainment in children with speech and language impairments: Tapping to the beat’ Cortex 45 (1) 119-130 DOI:10.1016/j.cortex.2007.09.008 Cousin, G. (2010) Global Citizenship as a Graduate Attribute Keynote lecture, Metropolitan University Learning and Teaching conference, 8th July 2010, London, England Davies, H. and Crombie, I. (2009) What are confidence intervals and p-values? (2nd Edition) Dundee, Scotland: Hayward Medical Communications DCSF (2009) Independent Review of the Primary Curriculum: Final Report London, England: HMSO
DCSF (2008) Practice Guidance for the Early Years Foundation Stage London, England: HMSO DCSF (2007a) The Early Years Foundation Stage London, England: HMSO DCSF (2007b) What works for pupils with literacy problems? The effectiveness of intervention schemes London, England: HMSO Dehaene, S. (2009) Reading in the Brain New York: Viking Penguin DES (1985) Curriculum Matters 4: Music from 5 to 16 London, England: HMSO Detterman, D. L. (1993) The case for the prosecution: Transfer as epiphenomenon, in D. K. Detterman and R. J. Sternberg (1993) (Eds.), Transfer on trial: Intelligence, cognition, and instruction. Norwood, New Jersey: Ablex. Detterman, D. K., and Sternberg, R. J. (Eds.). (1993). Transfer on trial: Intelligence, cognition, and instruction. Norwood, New Jersey: Ablex. Dewey, J. (1916) Democracy and Education. An introduction to the philosophy of education (1966 edn.), New York: Free Press DfE (2011a) Music Education in England London, England: HMSO DfE (2011b) Statistical First Release, Schools, Pupils, and their Characteristics, SFR 12 National Statistics Retrieved from DfE (2010) ‘The Importance of Teaching - The Schools White Paper’ London, England: HMSO DfEE (1999) The National Curriculum: Handbook for Primary Teachers in England and Wales. London, England: DfEE Publications DfEE (1998) The National Literacy Strategy London, England: DfEE Publications DfES (2007a) Letters and Sounds: Principles and Practice of High Quality Phonics – notes of guidance for practitioners and teachers London, England: DfES Publications DfES (2007b) Letters and Sounds: Principles and Practice of High Quality Phonics – six-phase teaching programme. London, England: DfES Publications DfES (2006a) Primary Framework for literacy and mathematics London, England: DfES Publications
Group 3 Hig, reb, tov ost, arn, ent shib, thun, quop fluz, grad, scug hibe, vome, yake c-v-c v-c-c Digraph-v-c Cluster-v-c Split digraph Group 4 nugfim, fetzum, jumdop fiss, gudd, wobb leab, waig, joam tremp, drint, sculp chish, shoth, phash c-v-c- x2 Double final consonants Medial vowel digraphs Cluster, beginning and end Digraphs, beginning and end Group 5 remar, disfug, prejox zining, yogful, hitate luttle, gimming, vesser ginkly, fornter, duntness Buntingham, slantering, preflopation c-v-c with prefix c-v-c with suffix Double medial consonants two syllables, separate medial consonants Multi-syllable Appendix 2: Nonsense words (post-test)
………………………………………………………………. Child’s name Appendix 8: Calculation of imputed data for children 29 and 30 Child 29 Total grapheme Digraph recognition Cluster recognition Nonsense word recognition group 3 Nonsense word recognition group 4 Nonsense word recognition group 5 Post score 68 24 5 4 12 12 11
children made progress in their ability to read music. So if you think back to the very first lessons the children had, perhaps the very first one, um, your initial impressions of the children’s ability to read music before we started – could any of them read music at all? E: Barely G: Yeah, I can’t remember any one in particular being able to E: Maybe one or two said something like they had seen a recorder before, and might have done it at home, but, I don’t know, not really G: No E: I don’t remember, when we showed them the notes, I don’t remember them M: Because I showed them the notes when we did that initial visit, I showed them what the notes were, but I was careful not to say ‘this is a rest’, or ‘this is a quaver’. There wasn’t, there wasn’t a, I’m trying not to feed questions to you, there wasn’t a, you weren’t aware of children who could say ‘that’s a crotchet, that’s a rest, I’ve seen this before’ G: No, I didn’t. In any of the groups I did there was no one who was really confident, no one who was evidently better at it than anyone else. M: Right. And, right at the end, was it clear to you that children were reading music, or were they ... what they were asked to play was in some way an approximation of what was on the paper, or were they still floundering? E: Mmmm ... both. Some and some, do you think? G: There were some children that just blew (laughs) E: anything G: into their recorder weren’t there? E: Yeah G: and in terms of, like, rhythmically, I think they grasped that a lot more than, like , note reading, in my opinion I think E: Yeah, and the idea of rests. I think the crotchets and stuff they could get M: Ok. So let’s explore that a bit more. So if we take away the recorders for a second, just the clapping of the rests, the quavers and the crotchets, so at the very beginning they couldn’t do that at all
Appendix 15: Summary of pre- and post-test endpoints Appendix 16: RCT, pre-test t-test, overall scores From SPSS outputs Paired Samples Statistics Mean N Std. Deviation Std. Error Mean Pair 1 Total1 33.96 25 13.474 2.695 Group1 .00 25 .000 .000 Pair 2 Total1 33.64 25 14.634 2.927 Group2 ,00 25 ,000 ,000 Independent Samples Test Levene's Test for Equality of Variances t-test for Equality of Means F Sig. t df Sig. Mean Difference Std. Error Difference 95% Confidence Interval of the Difference Lower Upper TotalPretest Equal variances assumed .051 .822 .080 48 .936 .320 3.978 -7.679 8.319
Independent Samples Test Levene's Test for Equality of Variances t-test for Equality of Means F Sig. t df Sig. Mean Difference Std. Error Difference 95% Confidence Interval of the Difference Lower Upper SingleLetter1 Equal variances assumed .930 .340 1.086 48 .283 .880 .810 -.749 2.509 Equal variances not assumed 1.086 34.499 .285 .880 .810 -.766 2.526 Appendix 18: RCT, pre-test t-test, overall scores less single-letter grapheme recognition From SPSS outputs Paired Samples Statistics Mean N Std. Deviation Std. Error Mean Pair 1 TotalLessSing1 10.60 25 12.560 2.512 Group1 .00 25 .000 .000 Pair 2 TotalLessSing1 11.12 25 12.991 2.598 Group2 ,00 25 ,000 ,000 Paired Samples Test Paired Differences t df Sig. Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference Lower Upper Pair 1 TotalLessSing1 - Group1 10.600 12.560 2.512 5.416 15.784 4.220 24 .000 Pair 2 TotalLessSing1 - Group2 11.120 12.991 2.598 5.757 16.483 4.280 24 .000 Independent Samples Test
F Sig. t df Sig. Mean Std. Error 95% Confidence Interval of the Difference Lower Upper Equal variances assumed 2.097 .154 -.259 48 .797 -.200 .774 -1.756 1.356 Equal variances not assumed -.259 46.125 .797 -.200 .774 -1.757 1.357 Paired Samples Test Paired Differences t df Sig. Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference Lower Upper Pair 1 ClustDigraph1 - Group1 3.160 2.444 .489 2.151 4.169 6.465 24 .000 Pair 2 ClustDigraph1 - Group2 3.360 2.998 .600 2.122 4.598 5.603 24 .000 Appendix 20: RCT, pre-test t-test, nonsense words From SPSS outputs Paired Samples Statistics Mean N Std. Deviation Std. Error Mean Pair 1 NonsenseWord1 7.12 25 10.072 2.014 Group1 .00 25 .000 .000 Pair 2 NonsenseWord1 8.00 25 11.094 2.219 Group2 ,00 25 ,000 ,000 Paired Samples Test Paired Differences t df Sig. Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference
Lower Upper Pair 1 NonsenseWord1 - Group1 7.120 10.072 2.014 2.963 11.277 3.535 24 .002 Pair 2 NonsenseWord1 - Group2 8.000 11.094 2.219 3.421 12.579 3.605 24 .001 Independent Samples Test Levene's Test for Equality of Variances t-test for Equality of Means F Sig. t df Sig. Mean Difference Std. Error Difference 95% Confidence Interval of the Difference Lower Upper Equal variances assumed .165 .686 -.294 48 .770 -.880 2.997 -6.906 5.146 Equal variances not assumed -.294 47.558 .770 -.880 2.997 -6.907 5.147 Appendix 21: RCT anova and unrelated t-tests, overall scores Descriptive Statistics Group Mean Std. Deviation N Total1 dimension1 1 33.96 13.474 25 2 33.64 14.634 25 Total 33.80 13.923 50 Total2 dimension1 1 45.20 16.593 25 2 40.12 17.256 25 Total 42.66 16.950 50 Independent Samples Test Levene's Test for Equality of Variances t-test for Equality of Means F Sig. t df Sig. Mean Std. Error 95% Confidence Interval of the Difference
Equal variances not assumed .074 41.102 .941 .471 .040 .539 -1.047 1.127 Appendix 23: RCT anova and unrelated t-tests, overall scores minus single-letter graphemes Descriptive Statistics Group Mean Std. Deviation N TotalLessSing1 Intervention 10.60 12.560 25 Control 11.12 12.991 25 Total 10.86 12.649 50 TotalLessSing2 Intervention 21.04 16.306 25 Control 16.00 16.432 25 Total 18.52 16.400 50 Independent Samples Test Levene's Test for Equality of Variances t-test for Equality of Means F Sig. t df Sig. Sig. Mean Difference Std. Error Difference 95% Confidence Interval of the Difference Lower Upper TotalLessSing2 Equal variances assumed .000 .987 1.089 48 .282 .181 5.040 4.630 -4.269 14.349 Equal variances not assumed 1.089 47.997 .282 .181 5.040 4.630 -4.269 14.349 Multivariate Testsb Effect Value F Hypothesis df Error df Sig. Partial Eta Squared factor1 Pillai's Trace .409 33.159a 1.000 48.000 .000 .409 Wilks' Lambda .591 33.159a 1.000 48.000 .000 .409 Hotelling's Trace .691 33.159a 1.000 48.000 .000 .409
Roy's Largest Root .691 33.159a 1.000 48.000 .000 .409 factor1 * Group Pillai's Trace .083 4.368a 1.000 48.000 .042 .083 Wilks' Lambda .917 4.368a 1.000 48.000 .042 .083 Hotelling's Trace .091 4.368a 1.000 48.000 .042 .083 Roy's Largest Root .091 4.368a 1.000 48.000 .042 .083 a. Exact statistic b. Design: Intercept + Group Within Subjects Design: factor1 Appendix 24: RCT anova and unrelated t-tests, Clusters and Digraphs Descriptive Statistics Group Mean Std. Deviation N ClustDi1 Intervention 3.16 2.444 25 Control 3.36 2.998 25 Total 3.26 2.709 50 ClustDi2 Intervention 5.52 2.740 25 Control 4.36 2.970 25 Total 4.94 2.888 50 Independent Samples Test Levene's Test for Equality of Variances t-test for Equality of Means F Sig. t df Sig. Sig. Mean Difference Std. Error Difference 95% Confidence Interval of the Difference Lower Upper ClustDi2 Equal variances assumed .361 .551 1.435 48 .158 .079 1.160 .808 -.465 2.785 Equal variances not assumed 1.435 47.692 .158 .079 1.160 .808 -.465 2.785 Multivariate Testsb Effect Value F Hypothesis df Error df Sig. Partial Eta Squared factor1 Pillai's Trace .302 20.794a 1.000 48.000 .000 .302
Equal variances not assumed .459 .136 .183 -.227 .498 Appendix 28: Phase 3 SPSS outputs Descriptive Statistics Group Mean Std. Deviation N pretest dimension1 1 4.40 .935 55 2 4.26 .964 53 Total 4.33 .947 108 posttest dimension1 1 5.42 1.134 55 2 5.11 1.340 53 Total 5.27 1.243 108 Independent Samples Test Levene's Test for Equality of Variances t-test for Equality of Means F Sig. t df Sig. Sig. Mean Difference Std. Error Difference 95% Confidence Interval of the Difference Lower Upper posttest Equal variances assumed .044 .835 1.279 106 .204 .102 .305 .238 -.168 .778 Equal variances not assumed 1.275 101.828 .205 .102 .305 .239 -.170 .779 Multivariate Testsb Effect Value F Hypothesis df Error df Sig. Partial Eta Squared factor1 Pillai's Trace .663 208.798a 1.000 106.000 .000 .663 Wilks' Lambda .337 208.798a 1.000 106.000 .000 .663 Hotelling's Trace 1.970 208.798a 1.000 106.000 .000 .663 Roy's Largest Root 1.970 208.798a 1.000 106.000 .000 .663