<table><caption>Table 37: Causative MAKE constructions in the Textbook Conversation and Spoken BNC2014 sample</caption><thead><tr><th>Construction</th><th>Example</th><th>Freq in<br>Textbook<br>Conversation</th><th>Freq in<br>Spoken<br>BNC2014</th></tr></thead><tbody><tr><td>[X MAKE Y<br>AdjP]</td><td>The atmosphere is so oppressive and the<br>characters are so insane, it just made<br>me really uncomfortable.<br>&lt;TEC: New Bridges 3e&gt;</td><td>41.28 % (71)</td><td>36.06 % (75)</td></tr><tr><td>[X MAKE (Y)<br>NP]</td><td>So friendly, and they don't make you<br>feel like you're just another tourist.<br>They made it a fantastic experience for<br>me. &lt;TEC: Green Line New 3&gt;</td><td>8.14 % (14)</td><td>9.62 % (20)</td></tr><tr><td>[X MAKE Y V<sub>inf</sub>]</td><td>I didn't actually steal any money. I<br>wanted to make people understand the<br>dangers of cybercrime.<br>&lt;TEC: Solutions intermediate plus&gt;</td><td>50.58 % (87)</td><td>53.85 % (112)</td></tr><tr><td>[X MAKE Y V<sub>pp</sub>]</td><td>I can't remember exactly what it was<br>that he said or what would be said but<br>he would make it known that he was<br>disappointed in the fact that I wasn't<br>sticking up &lt;BNC2014: SKPP&gt;</td><td>0 % (0)</td><td>0.48 % (1)</td></tr></tbody></table>

In the following, we turn to the most frequent collexemes associated with these causative MAKE constructions: for [X MAKE Y V<sub>inf</sub>] constructions, this means the non-finite verb, e.g., in the example from Table 37, *understand*; for [X MAKE Y AdjP] constructions, the adjective, e.g., *uncomfortable*; and for the rarer nominal constructions, the lemma of the head of the nominal phrase, e.g., *experience*. Note that verbal MAKE constructions associated with more than one verb were counted as one separate causative construction per non-finite verb slot: e.g., in (173), one instance of MAKE + STOP and one instance of MAKE + THINK were accounted for. This also corresponds to the procedure followed by Gilquin (2012: 11 fn).

(173) The sign *makes you stop and think*, doesn't it? &lt;TEC: Green Line New 3&gt;

Since there are considerably more causative MAKE occurrences in the Spoken BNC2014 sample than in the Textbook Conversation, Fig. 27 displays the relative frequencies of the most frequent collexemes as a percentage of all the causative MAKE occurrences within each sample. The plot can be interpreted similarly to Fig. 24. The only difference is that the colours correspond to the type of causative construction associated with each collexeme. Note that due to the Zipfian distributions of the collexemes it also features logarithmic scales.
<figure><img src="image_31.png" /><figcaption>Fig. 27: The most frequent collexemes of the causative MAKE constructions of Textbook Conversation and the Spoken BNC2014 sample</figcaption></figure>

Fig. 27 shows that, in both samples, *feel* is by far the collexeme most strongly associated with MAKE causative constructions. However, it is more frequently observed in the natural conversation sample (n = 22; 10.57% of all causative MAKE occurrences in the sample) than in the textbook dialogues (n = 15; 8.72%). Strikingly, both Liu & Shaw (2001) and Gilquin (2012; 2016a) report that FEEL is used significantly more frequently by EFL than ENL users in essay writing. The present results suggest that this effect may be indicative of EFL learners' limited awareness of lexical differences between different modalities and registers, rather than an overrepresentation of [X MAKE Y *feel*] in pedagogical materials.

Whilst also frequent in both datasets, Fig. 27 shows that *look* is more frequent in the Spoken BNC2014 sample than in Textbook Conversation. In the case-study chapter exploring the progressive in Textbook English, it was observed that *look* in its prototypical sense of visual perception was over-represented in Textbook Conversation (see 4.3.4.1). Here, however, [X MAKE Y *look*] constructions fulfil the
same semantic function in both datasets; it depicts appearance and, as such, *look* is
most frequently followed by *like*:

(174) cos it's such a big bouffanty coat and it's not tailored it's not nicely
shaped
right

it makes me look like a marshmallow <BNC2014: S575>

(175) Sarah! You're making us look like tourists.
So...?
Well stop it, it's embarrassing! I'm a New Yorker [...]<TEC: Green Line New 3>

As they are printed to the left of the regression line in Fig. 27, *do*, *go* and *happen* also appear to be more frequent in naturally occurring conversation than in the conversation-like texts featured in school EFL textbooks. A qualitative examination of [X MAKE Y go] concordances from the Spoken BNC2014 suggests that, in many cases, *go* in the non-finite verb slot of causative constructions is used as a quotative verb, e.g.:

(176) I know it's very difficult but I just don't know if like mum said more
violence or retaliation against them doesn't **make them go** alright we'll
stop then it makes them worse <BNC2014: SAVN>

Quotatives are very frequent in natural conversation. Indeed, Fig. 27 also shows that
*say*, just like *go*, was also not observed in causative MAKE constructions in the
Textbook Conversation. Qualitative analyses of the corresponding concordance lines
suggest that it, too, frequently functions in this quotative sense in MAKE causatives
in natural conversation, e.g.:

(177) well I think it's only fair that you pick something I don't wanna do but
actually it will benefit my life massively and **make me say** oh I'm so
glad you did that cos that's what's gonna happen when you watch Game of
Thrones you're gonna say oh thank you for making me watch that this is
amazing <BNC2014: SAG4>

By contrast, EFL textbooks traditionally neglect direct speech, usually focusing
exclusively on the prescriptive rules of reported speech with person- and tense-shifting
(see Barbieri & Eckhardt 2007). However, reading examples (176) and (177) also
makes clear that intonation is crucial to making these quotatives intelligible. It could
therefore be argued that this makes quotatives inherently unsuitable for printed
dialogues; however, direct speech is not found in any of the audio and video materials
of the textbooks included in the TEC either. Instead, reported speech, as it is taught
in the textbooks' grammar sections is regularly modelled, even when the result is
clearly not register-appropriate, e.g.:

(178) PROF: When Ruby went to school for the first time, in New Orleans, **she** said that **she** heard people shouting abuse. **She** added that **she** saw them throwing things. TOMMY: How did the other kids react?
## 5.4 Conclusions

This concluding section attempts to sketch out answers to the five research questions formulated at the beginning of this chapter (see 5.1). In evaluating these results, it also reflects on the limitations of the methodology.

The first research question asked whether the verb MAKE was significantly over- or underrepresented in Textbook Conversation and Textbook Fiction as compared to ENL corpora of the same registers. It was shown in 5.3.1 that, when factoring out the overall considerably lower verb/noun ratios in Textbook English, MAKE is marginally overrepresented in Textbook Conversation as compared to the Spoken BNC2014, whilst it is seemingly underrepresented in Textbook Fiction as compared to the Youth Fiction corpus. This latter finding, in particular, was contrary to expectations. Indeed, the novels that make up the Youth Fiction corpus have a much higher lexical diversity than can be expected in the narrative texts of school EFL textbooks; yet, MAKE is the 12th most frequent verb in the Youth Fiction corpus, whereas it is ranked 16th in the Textbook Fiction subcorpus. The corresponding verb frequency lists suggest that LOOK and WANT are overrepresented in Textbook Fiction, whereas MAKE and KNOW are underrepresented.

MAKE being highly polysemous, it was deemed necessary to look at the distribution of its various meanings in order to further explore these differences at the semantic level. It was originally hypothesised that the prototypical 'produce' sense of MAKE would be overrepresented in the textbook registers, but this hypothesis was refuted (see 5.3.2). Textbook Conversation was shown to significantly underrepresent causative and phrasal uses of the verb. Idioms with MAKE were also found to be overrepresented although, as explained in 5.3.2, this is a topic-effect due to the prominence of the one idiomatic phrase: *to MAKE friends*. Textbook Fiction, on the other hand, was seen to feature considerably fewer delexical MAKE forms and, as in Textbook Conversation, phrasal verbs were also grossly underrepresented.

Sections 5.3.3 and 5.3.4 examined the noun object collocates of MAKE in the prototypical 'produce' sense and its 'delexical' usage in an attempt to answer the third research question concerning the collocates and semantic fields attributed to these collocates typically associated with MAKE in Textbook Conversation and Textbook Fiction. MAKE collocations relating to food are very prominent across all four corpora examined but may be slightly overrepresented in the reference Spoken BNC2014 corpus as a result of the convenience sampling method that perhaps favoured meal-time conversations over other conversational settings. In addition to
such topic-effects, task-effects were also identified in the textbook data, e.g., in the overrepresentation of MAKE ‘produce’ collocations pertaining to film and video.

The data is too sparse to provide an exhaustive quantitative evaluation of the representation of phrasal verbs with MAKE in Textbook Conversation and Textbook Fiction. However, section 5.3.5 made clear that the selection of phrasal verbs featured in school EFL textbooks is evidently not based on any of the widely accepted pedagogical criteria for selecting the lexis to be acquired, such as frequency, range, or learnability. Phrasal verbs involving MAKE seem to be strongly underrepresented in Textbook English and, even within a textbook series, no apparent effort is made to prioritise the acquisition of any specific multi-word verbs. Indeed, no lexical consistency could be detected across the 43 textbooks volumes or, even, at the publication series level. These results echo Koprowski’s (2005) conclusion who found that less than 1% of lexical phrases were shared across three EGP textbooks, as well as the results of Gouverneur’s (2008a: 240) study on phraseological patterns of MAKE in textbook exercises, which revealed that less than a fifth of the collocations featured were common to all textbooks of the same level (see 2.2.1.2).

Similarly, no systematic approach to introducing the most frequent and communicatively meaningful delexical MAKE collocations could be identified, even though such structures are known to be “particularly treacherous” for learners of English (Altenberg & Granger 2001: 189; see also Nesselhauf 2004). Thus, although the absolute number of delexical MAKE concordance lines examined here was relatively low, the comparative analysis (see 5.3.4) suggested that by far the most frequent delexical MAKE collocation in Spoken English, *to MAKE sense*, is vastly underrepresented in Textbook Conversation, whilst the collocation *MAKE + mistake* is considerably more frequent in Textbook English than in the reference corpora queried. Whilst it is to be expected that school-related and textbook task-relevant collocations, e.g., *MAKE + dialogue/film/list/sentences* etc., appear more frequently in the textbooks than in the ENL reference corpora, textbooks’ strong focus on *MAKING mistakes* in dialogues and narrative texts, rather than, say, on *MAKING sense* or *an effort*, seems rather at odds with modern language teaching principles anchored in communicative approaches.

Moreover, and this may shed some light on the fourth and fifth research questions, it was found that delexical MAKE collocates associated with ‘speech/communication’ actions, e.g., *MAKE + argument/assumption/complaint/small talk*, are either entirely absent, or woefully underrepresented in the two textbook registers under study. Learner corpus studies had previously identified these collocations as underused by learners of English (Altenberg & Granger 2001: 179–180). Whilst they account for nearly a quarter of all delexical MAKE constructions in the two ENL reference corpora, their proportion was found to be significantly lower in the two textbook subcorpora.
It was argued in 5.3.4 that since many of these collocations are both frequent and fulfil particularly useful communicative functions, they ought to feature more prominently in school EFL textbooks. The same can be said of quotative MAKE causative constructions such as those found to be very rare in Textbook Conversation in 5.3.6.

In answer to the final research question some aspects of the present results suggest that textbook-based language input may indeed deprive learners of valuable exposure to frequent forms. This appears to concern especially delexical, phrasal and causative uses of MAKE which have been shown to be particularly problematic for learners of English and whose representations in Textbook English seemingly do not follow any evidence-based selection criteria. Thus, a number of constructions with particularly useful communicative functions, such as ‘speech/communication’ delexical MAKE collocations and quotative MAKE causative constructions, have been highlighted as conspicuously underrepresented in Textbook English (see 5.3.4 and 5.3.6).
# 6 A multi-dimensional description of
Textbook English

Let's get started! We're on our way.
We're learning English! Let's go! Hooray!
<TEC: Green Line 1>

## 6.1 Introduction

In the literature review (Chapter 2) we saw that previous research on Textbook English has tended to focus on individual lexico-grammatical features. Collectively, these studies have provided us with a vast patchwork of (corpus-based) evidence demonstrating how individual linguistic features are (frequently mis-)represented in ESL/EFL textbooks as compared to various interpretations of what is often termed “real”, “natural”, or “authentic” English. However, the review concluded that such individual-feature studies cannot account for relevant interactions between features. In addition, we saw that potential differences between the various registers featured in English textbooks have yet to be adequately explored.

This chapter<fnref n="34" /> and the following chapter aim to overcome these limitations by using a multi-variable statistical analysis aimed at reducing a large set of potentially relevant grammatical, lexical, and semantic features to a parsimonious set of meaningful factors of linguistic variation. Thus, the objective is to provide a more comprehensive view of the defining characteristics of Textbook English and of the linguistic variation found within school EFL textbooks. Bearing this in mind, this chapter will seek to answer the following research questions:

1. What is the extent of the linguistic variation across the major registers of Textbook English? How are the different textbook registers characterised linguistically? To what extent do the proficiency levels of textbooks interact with register-based variation? Do some textbook series show significantly more or less register-based variation?

2. To what extent do Textbook English registers differ from situationally similar, naturally occurring registers? To what extent are (some of) the observed patterns moderated by textbook series, their country of use, and/or the proficiency level of individual textbook volumes?

3. What are the defining linguistic features that characterise Textbook English registers as compared to these target language registers?

To answer these questions, Biber's (1984; 1988) multi-feature/multi-dimensional analytical framework of register variation is applied to the study of Textbook English.

<footnote n="34">³⁴ Selected results from this chapter have been published as: Le Foll, Elen. 2021. Register Variation in School EFL Textbooks. *Register Studies* 3(2). https://doi.org/10.1075/rs.20009.lef.</footnote>
Before detailing the two possible approaches to the multi-dimensional analysis (hereafter MDA) of textbook language, 6.1.1 explains the principles behind MDA. For reasons of space, this chapter only provides a brief outline of the method. It is, however, described in detail in a number of book-length publications (e.g. Biber 1984; 1988: chaps. 5–6; Biber et al. 2004: sec. 4.4-4.5; Biber & Conrad 2019: chap. 2; Fríngital & Hardy 2014). Next, 6.1.2.1 and 6.1.2.2 describe how MDA has already been successfully applied to the exploration of textbook language in English for Academic Purposes (EAP) and English L1 contexts: first, in a ‘full MDA’ involving biology and history university textbooks (Conrad 1996a; 2013) and, second, in an ‘additive MDA’ (Berber Sardinha et al. 2019) exploring US-American elementary school textbooks (Reppen 1994a; 2013). Section 6.2 then outlines how additive MDA is applied in the context of the present study.

### 6.1.1 Multi-feature/multi-dimensional analysis (MDA)

The MDA framework was pioneered by Douglas Biber (1984; 1988; 1995) to capture the underlying dimensions of variation across different registers of natural languages. It is based on the theoretical assumption that “differences in registers include patterns of co-occurring lexico-grammatical features” (Halliday 1988: 162), which result from texts having register-specific contexts of use and communicative goals (Biber & Conrad 2001; cf. Hymes 1984). MDA is used to reduce these large matrices of linguistic co-occurrence patterns to a few core functional dimensions of systemic, situational variation. Thus, it allows for the conceptualisation of register variation as a continuous phenomenon, which varies along multiple fundamental dimensions. It has been successfully applied to tease out register differences at different levels of granularity, e.g., between a broad range of registers as different as face-to-face conversation and official documents (e.g., Biber 1988), but also between academic writing across different disciplines (e.g., Gray 2015), or student essay writing across different levels of proficiency (e.g., Fríngital & Weigle 2014).

MDA is an exploratory method and therefore makes no a priori assumptions about how the registers explored may differ from one another. As in any corpus-based analysis, in conducting an MDA, the first step consists in selecting, collecting and sampling the texts to be analysed in conjunction with the relevant metadata. The corpus ought to be representative of the variety and full range of the registers to be explored. In parallel, potentially relevant linguistic features need to be determined. At this stage, the aim is to be as inclusive as possible, so as not to omit any inconspicuously relevant features that may not have been identified in previous studies (Egbert & Staples 2019: 132). Biber’s (1988) original study of spoken and written registers of English included 67 lexical, grammatical and semantic features,
ranging from first-person pronouns<fnref n="35" /> to verbal contractions and downtoners (see Table 38 for full list). Due to the large number of features and texts involved, the features chosen are best operationalised such that they can be automatically identified and counted. This inevitably limits the types of linguistic features that can be entered in an MDA, but modern taggers are very powerful, so that the benefits of being able to count a wide range of features across very many texts largely outweigh the drawbacks (but see 6.5 for limitations).

<table><caption>Table 38: Linguistic features used in Biber's (1988) MDA of general English (as categorised and listed in Conrad & Biber 2013: 18-19)</caption><thead><tr><th>Tense and aspect markers</th><th>Prepositional phrases, adjectives, and adverbs</th></tr></thead><tbody><tr><td>1. past tense</td><td>39. total prepositional phrases</td></tr><tr><td>2. perfect aspect</td><td>40. attributive adjectives (e.g., <i>the small room</i>)</td></tr><tr><td>3. present tense</td><td>41. predicative adjectives (e.g., <i>the room is small</i>)</td></tr><tr><td><strong>Place and time adverbials</strong></td><td>42. total adverbs (except those in any other category)</td></tr><tr><td>4. place adverbials (e.g., <i>behind, downstairs, locally</i>)</td><td><strong>Lexical specificity</strong></td></tr><tr><td>5. time adverbials (e.g., <i>eventually, immediately</i>)</td><td>43. type/token ratio (in the first 400 words of each text)</td></tr><tr><td><strong>Pronouns and pro-verbs</strong></td><td>44. mean word length</td></tr><tr><td>6. first-person pronouns (e.g., <i>I, me, my, mine, we, our</i>)</td><td><strong>Lexical classes</strong></td></tr><tr><td>7. second-person pronouns (e.g., <i>you, your, yours</i>)</td><td>45. conjuncts (e.g., <i>alternatively, therefore</i>)</td></tr><tr><td>8. third-person pronouns (excluding <i>it</i>)</td><td>46. downtoners (e.g., <i>mildly, partially, somewhat</i>)</td></tr><tr><td>9. pronoun <i>it</i></td><td>47. hedges (e.g., <i>almost, maybe, sort of</i>)</td></tr><tr><td>10. demonstrative pronouns (<i>this, those</i> as pronouns)</td><td>48. amplifiers (e.g., <i>completely, totally, utterly</i>)</td></tr><tr><td>11. indefinite pronouns (e.g., <i>anyone, everybody</i>)</td><td>49. emphatics (e.g., <i>a lot, for sure, really</i>)</td></tr><tr><td>12. pro-verb <i>DO</i> (but the algorithm listed in Biber 1988: Appendix II actually identifies <i>DO</i> as a main verb!)</td><td>50. discourse particles (e.g., sentence initial <i>anyhow, now, well</i>)</td></tr><tr><td><strong>Questions</strong></td><td>51. demonstratives</td></tr><tr><td>13. direct WH-questions</td><td><strong>Modals</strong></td></tr><tr><td><strong>Nominal forms</strong></td><td>52. possibility modals (<i>can, could, may, might</i>)</td></tr><tr><td>14. nominalizations (all nouns ending in <i>-tion, -ment, -ness, -ity</i>)</td><td>53. necessity modals (<i>must, ought, should</i>)</td></tr><tr><td>15. gerunds (participial forms functioning as nouns)</td><td>54. predictive modals (<i>shall, will, would</i>)</td></tr><tr><td>16. total other nouns</td><td><strong>Specialised verb classes</strong></td></tr><tr><td><strong>Passives</strong></td><td>55. public verbs (e.g., <i>COMPLAIN, EXPLAIN, PROMISE</i>)</td></tr><tr><td>17. agentless passives</td><td>56. private verbs (e.g., <i>BELIEVE, THINK, KNOW</i>)</td></tr><tr><td>18. <i>by</i>-passives</td><td>57. suasive verbs (e.g., <i>COMMAND, PROPOSE, RECOMMEND</i>)</td></tr><tr><td><strong>Stative forms</strong></td><td>58. <i>SEEM</i> and <i>APPEAR</i></td></tr><tr><td>19. <i>BE</i> as main verb</td><td></td></tr><tr><td>20. existential <i>there</i></td><td></td></tr><tr><td><strong>Subordination features</strong></td><td></td></tr></tbody></table>

<footnote n="35">³⁵ This chapter uses Biber's (1988) terminology when referring to the linguistic features of the first MDAs. This means that, for example, the category of 'first-person pronouns' includes reflexive pronouns and possessive determiners (see Table 38 for details).</footnote>
<table><caption>Table 38: Linguistic features used in Biber's (1988) MDA of general English (as categorised and listed in Conrad & Biber 2013: 18–19)</caption><tr><td>21. that verb complements (e.g., We felt that we needed a financial base.)</td></tr><tr><td>22. that adjective complements (e.g., It's quite obvious that...)</td></tr><tr><td>23. WH-clauses (e.g., I wondered what to do.)</td></tr><tr><td>24. infinitives</td></tr><tr><td>25. present participial adverbial clauses (e.g., Screaming with rage)</td></tr><tr><td>26. past participial adverbial clauses (e.g., Given these characteristics)</td></tr><tr><td>27. past participial postnominal clauses (e.g., the exhaust air volume required by the grid)</td></tr><tr><td>28. present participial postnominal clauses (e.g., the currents of dissent swirling...)</td></tr><tr><td>29. that-relative clauses on subject position (e.g., the papers that are on the table)</td></tr><tr><td>30. that-relative clauses on object position (e.g., the papers that she thought...)</td></tr><tr><td>31. WH-relatives on subject position (e.g., people who know him)</td></tr><tr><td>32. WH-relatives on object position (e.g., people who he knows)</td></tr><tr><td>33. pied-piping relative clauses (e.g., the way in which food is digested)</td></tr><tr><td>34. sentence relatives (e.g., We waited for six hours, which was ridiculous.)</td></tr><tr><td>35. causative adverbial subordinator (because)</td></tr><tr><td>36. concessive adverbial subordinators (although, though)</td></tr><tr><td>37. conditional adverbial subordinators (if, unless)</td></tr><tr><td>38. other adverbial subordinators (e.g., insomuch as, such that, while)</td></tr><tr><td><strong>Reduced forms and dispreferred structures</strong></td><td><strong>Coordination</strong></td></tr><tr><td>59. contractions</td><td>64. phrasal coordination (NOUN and NOUN; ADJ and ADJ; V and V; ADV and ADV)</td></tr><tr><td>60. complementizer that deletion (e.g., I think [Ø] he's gone already.)</td><td>65. independent clause coordination (clause initial and)</td></tr><tr><td>61. stranded prepositions (e.g., the person that I was talking to)</td><td><strong>Negation</strong></td></tr><tr><td>62. split infinitives (e.g., I want to completely convince you that...)</td><td>66. synthetic negation (e.g., No evidence was found...)</td></tr><tr><td>63. split auxiliaries (e.g., They have apparently sold it.)</td><td>67. analytic negation (e.g., That's not true.)</td></tr></table>

Once the texts have been automatically tagged (or partially automatically tagged, cf. Le Foll 2021a: 28–29) for the chosen linguistic features, the total number of occurrences of all the features selected are counted in each text of the corpus. These raw counts are then normalised to a common denominator (e.g., 1,000 words) to enable comparisons across texts of different lengths, as illustrated in Table 39. Excerpts of the texts (179)–(181) in which an example selection of eight features were counted can be found below.
<table><caption>Table 39: Selected normalised feature counts (per 100 words) in three texts (see excerpts (179)-(181) below)</caption><thead><tr><th></th><th>Text (179)</th><th>Text (180)</th><th>Text (181)</th></tr></thead><tbody><tr><td>Attributive adjectives</td><td>4.20</td><td>7.92</td><td>7.41</td></tr><tr><td><i>because</i></td><td>0.66</td><td>0.21</td><td>0.13</td></tr><tr><td>Contractions</td><td>5.97</td><td>0.00</td><td>3.84</td></tr><tr><td>1<sup>st</sup> person pronouns</td><td>5.34</td><td>0.00</td><td>7.41</td></tr><tr><td>Negation</td><td>2.12</td><td>0.43</td><td>1.85</td></tr><tr><td>Nominalisations</td><td>0.54</td><td>2.78</td><td>0.00</td></tr><tr><td>Prepositions</td><td>5.04</td><td>13.49</td><td>6.35</td></tr><tr><td>2<sup>nd</sup> person pronouns</td><td>4.15</td><td>0.00</td><td>3.57</td></tr></tbody></table>

<figure><img src="image_32.png" /><figcaption>Fig. 28: Correlation matrix of the normalised counts in Table 39</figcaption></figure>

A correlation matrix of all the normalised feature counts is then computed (see Fig. 28, in which the strength of the correlation between any two features is represented by the size of the circle, whilst its colour indicates the sign of the correlation). Since language features are not randomly distributed but rather according to contextual usage and communicative aims, we expect to observe many significant correlations. Indeed, Fig. 28 shows that a text with many occurrences of first and second-person pronouns is also likely to feature more negated verbs, contracted verb forms, and causative adverbial subordinators (e.g., *because*). Such positive correlations, marked in blue in Fig. 28, are frequently found in involved, spontaneous spoken conversations, as illustrated in (179), in which these features have been highlighted.

(179) I just did these well I just did these staid really laboured monologues which you'd get from textbooks and yeah yeah yeah and it was cringe cringeworthy John what I taught and I'm thinking why
didn't I think? but that's that's but that's because you're not
because that's the problem you're not encouraged to think the teaching language teaching industry doesn't encourage you to think it encourages you to use textbooks textbooks
it no but yeah because I was a newly qualified TEFL teacher I was obsessed with sticking to the plan
yeah
and the techniques that I'd been taught but they didn't teach me to use
my knowledge and to say what do people really say in English you know?
<BNC2014: SHJJ>

By contrast, high normalised frequencies of nominal forms ending in -tion, -ment, -ness and -ity tend to correlate negatively with the features highlighted in (179), but correlate positively with prepositions, attributive adjectives and high type/token ratios (see Fig. 28). Such clusters of features are typical of edited, information-dense texts, as illustrated in (180).

(180) Ionesco, Eugène özhēn´ yōněs´kō, 1912-94, French playwright, b. Romania. Settling in France in 1938, he contributed to Cahiers du Sud and began writing avant-garde plays. His works stress the absurdity both of bourgeois values and of the way of life that they dictate. They express the futility of human endeavor in a universe ruled by chance. His play La Cantatrice chauve (1950; tr. The Bald Soprano, 1965) was suggested by the idiotic phrases in an English language textbook; it has become an enormously popular classic of the theater of the absurd.
<Info Teens: factmonster.com>

Naturally, the normalised counts of the linguistic features mentioned above can also be calculated for an excerpt from a textbook dialogue (181) to compare these frequencies to those counted in “real-life English” excerpts (179) and (180). Thus, in this toy example, we can see that, although excerpt (181) purports to be spoken interaction, it features almost as many attributive adjectives as an informative text from the Info Teens (see reported frequencies for Text (180) and Text (181) in Table 39). The textbook dialogue (Text (181)) also features fewer causative subordinators, verbal contractions, negated verbs, and second-person pronouns than the conversation transcript from the Spoken BNC2014 (Text (179)). MDA facilitates these kinds of comparisons across large numbers of texts and variables.

(181) Jennifer: Hi Grandpa!
Grandpa: Good morning, honey!
Jennifer: What are you doing?
Grandpa: I'm looking at my old fairy tale book ...
Jennifer: It's beautiful!
Grandpa: What's your favourite tale?
Jennifer: I think the funniest tale is The three little pigs.
Grandpa: I agree with you! The Big Bad Wolf is so ridiculous!
Jennifer: Yes, it is. I like Sleeping Beauty too. It's the most romantic story and Prince Charming is so handsome! <TEC: Piece of Cake 6>e>
More precisely, MDA is applied to tease out the quantitative relationships – in
statistical parlance referred to as the ‘shared variance’ – between linguistic features
 variables) across a large corpus of texts. This is achieved on the basis of a correlation
matrix of normalised variable counts, similar to that presented in Fig. 28, albeit much
larger. The statistical method used to this effect is called exploratory factor analysis.
It extracts factors that correspond to clusters of frequently co-occurring linguistic
features. By definition, a factor analysis can continue to extract factors until all of
the shared variance has been accounted for; indeed, once the first factor has been
determined, the second factor accounts for the maximum amount of shared variance
remaining, as does the third, etc. However, beyond the first few factors, additional
factors are unlikely to account for more than nontrivial amounts of shared variance
and may therefore be disregarded. It is up to the researcher to determine how many
factors account for a sufficient amount of shared variance and can meaningfully be
interpreted. In his original MDA of general English, Biber (1988) extracted seven
factors, which, together, account for 51.9% of the total shared variance.

Several linguistic features contribute to, or load on, each of the extracted factors. The
strengths of their relationship to a factor are captured by the factor loadings. Factor
loadings thus reflect the amount of variance a feature has in common with the total
pool of shared variance accounted for by any one factor. Features with a factor loading
above a certain cut-off point are considered relevant contributors to the factor. Biber
(1988: 87) included all features with an absolute factor loading of > 0.35 in his final
model. This resulted in a final factor solution involving 60 (out of the original 67)
linguistic features loading onto seven factors. This solution is summarised in Table
40, which lists the salient co-occurring features that constitute the seven factors along
with their factor loadings. Note that the positive and negative signs of the loadings
on any one factor serve to identify features that occur in a complementary pattern.
Thus, as observed in excerpts (179) and (180), when a factor’s features with positive
loadings frequently co-occur within a text, those with negative loadings are, on
average, also markedly less frequent (or even entirely absent), and vice versa. Features
listed in brackets on Table 40 were not included in Biber’s (1988) final model because
they have a higher loading on a different factor and, in order “to assure the
experimental independence of the factor scores” (Biber 1988: 93), each feature was
only included in the computation of a single factor score.
<table><caption>Table 40: Features with a minimum factor loading of ±0.35 that make up Biber's (1988) seven-factor solution</caption><thead><tr><th>Factor 1</th><th>Loading</th></tr></thead><tbody><tr><td>Private verbs</td><td>.96</td></tr><tr><td><i>that</i>-deletion</td><td>.91</td></tr><tr><td>Contractions</td><td>.90</td></tr><tr><td>Present tense verbs</td><td>.86</td></tr><tr><td>Second-person pronouns</td><td>.86</td></tr><tr><td>DO as pro-verb</td><td>.82</td></tr><tr><td>Analytic negation</td><td>.78</td></tr><tr><td>Demonstrative pronouns</td><td>.76</td></tr><tr><td>General emphatics</td><td>.74</td></tr><tr><td>First-person pronouns</td><td>.74</td></tr><tr><td>pronoun <i>it</i></td><td>.71</td></tr><tr><td>BE as main verb</td><td>.71</td></tr><tr><td>Causative subordination</td><td>.66</td></tr><tr><td>Discourse particles</td><td>.66</td></tr><tr><td>Indefinite pronouns</td><td>.62</td></tr><tr><td>General hedges</td><td>.58</td></tr><tr><td>Amplifiers</td><td>.56</td></tr><tr><td>Sentence relatives</td><td>.55</td></tr><tr><td>WH-questions</td><td>.52</td></tr><tr><td>Possibility modals</td><td>.50</td></tr><tr><td>Non-phrasal coordination</td><td>.48</td></tr><tr><td>WH-clauses</td><td>.47</td></tr><tr><td>Final prepositions<br>(Adverbs)</td><td>(.42)</td></tr><tr><td>Nouns</td><td>-.80</td></tr><tr><td>Word length</td><td>-.58</td></tr><tr><td>Prepositions</td><td>-.54</td></tr><tr><td>Type/ token ratio</td><td>-.54</td></tr><tr><td>Attributive adjectives<br>(Place adverbials)</td><td>(-.42)</td></tr><tr><td>(Agentless passives)</td><td>(-.39)</td></tr><tr><td>(Past participle WHIZ deletions)</td><td>(-.38)</td></tr></tbody></table>

<table><thead><tr><th>Factor 6</th><th>Loading</th></tr></thead><tbody><tr><td><i>that</i>-clauses as verb complements</td><td>.56</td></tr><tr><td>Demonstratives</td><td>.55</td></tr><tr><td><i>that</i>-relative clause on object positions</td><td>.46</td></tr><tr><td><i>that</i>-clauses as adjective complements</td><td>.36</td></tr></tbody></table>

<table><thead><tr><th>Factor 2</th><th>Loading</th></tr></thead><tbody><tr><td>Past tense verbs</td><td>.90</td></tr><tr><td>Third-person pronouns</td><td>.73</td></tr><tr><td>Perfect aspect verbs</td><td>.48</td></tr><tr><td>Public verbs</td><td>.43</td></tr><tr><td>Synthetic negation</td><td>.40</td></tr><tr><td>Present participial clauses</td><td>.39</td></tr><tr><td>(Present tense verbs)</td><td>(-.47)</td></tr><tr><td>(Attributive adjectives)</td><td>(-.41)</td></tr></tbody></table>

<table><thead><tr><th>Factor 3</th><th>Loading</th></tr></thead><tbody><tr><td>WH-rel. clauses on object positions</td><td>.63</td></tr><tr><td>Pied piping constructions</td><td>.61</td></tr><tr><td>WH-rel. clauses on subject positions</td><td>.45</td></tr><tr><td>Phrasal coordination</td><td>.36</td></tr><tr><td>Nominalizations</td><td>.36</td></tr><tr><td>Time adverbials</td><td>-.60</td></tr><tr><td>Place adverbials</td><td>-.49</td></tr><tr><td>General adverbs</td><td>-.46</td></tr></tbody></table>

<table><thead><tr><th>Factor 4</th><th>Loading</th></tr></thead><tbody><tr><td>Infinitives</td><td>.76</td></tr><tr><td>Prediction modals</td><td>.54</td></tr><tr><td>Suasive verbs</td><td>.49</td></tr><tr><td>Conditional subordination</td><td>.47</td></tr><tr><td>Necessity modals</td><td>.46</td></tr><tr><td>Split auxiliaries</td><td>.44</td></tr><tr><td>(Possibility modals)</td><td>(.37)</td></tr></tbody></table>

<table><thead><tr><th>Factor 5</th><th>Loading</th></tr></thead><tbody><tr><td>Conjuncts</td><td>.48</td></tr><tr><td>Agentless passives</td><td>.43</td></tr><tr><td>Past participial clauses</td><td>.42</td></tr><tr><td><i>by</i>-passives</td><td>.41</td></tr><tr><td>Past participial WHIZ deletions</td><td>.40</td></tr><tr><td>Other adverbial subordinators</td><td>.39</td></tr></tbody></table>

<table><thead><tr><th>Factor 7</th><th>Loading</th></tr></thead><tbody><tr><td>SEEM/APPEAR</td><td>0.35</td></tr></tbody></table>

The next step in an MDA involves the functional interpretation of each factor, with its co-occurrence patterns of features and their loadings, as an underlying dimension of variation. To this end, a functional micro-analysis of the individual features is conducted, seeking the shared function(s) of the clusters of features loading on each factor. Functionally interpreted factors are then referred to as ‘dimensions’.
Table 43 summarises the functional interpretation of Biber's (1988) six dimensions of general English and their associated linguistic features.<fnref n="36" /> It was derived from the linguistic features that load on each factor listed in Table 40. For instance, features with positive factor loadings on the first factor include those identified as particularly frequent in excerpt (179): first and second-person pronouns, negated verbs, and contractions. In terms of a functional interpretation, it can be said that these features are "associated with an involved, non-informational focus, related to a primarily interactive or affective purpose and on-line production circumstances" (Conrad & Biber 2013: 24). Thus, texts with a high proportion of nouns, prepositions, and attributive adjectives, as well as long words and a high type/token ratio are typical of highly informational texts with precise lexical choices, as illustrated in (180). Consequently, Biber (1988) interpreted the first factor as the 'Involved vs. Informational Discourse Dimension', whereby positive Dimension 1 scores correspond to involved texts and negative Dimension 1 scores to informational discourse.

Finally, for each text in the corpus, dimension scores for each of the dimensions identified may be computed. Before doing so, however, the normalised counts are standardised to a mean of zero and a standard deviation of one (resulting in z-scores) to prevent particularly frequent features from having a disproportionate impact on the computed dimension scores. This can be illustrated with a simplified example, in which a dimension has six features, with present tense, discourse particles, negation and *because* loading positively, whereas nouns and type/token ratios (TTR) load negatively. As shown in Table 41, if we simply added the normalised frequencies of the positively loading features and subtracted the negative ones to calculate the dimension scores of these three texts, we would conclude that Texts (179) and (181) are very similar to each other on this dimension. However, a closer look at the normalised frequencies presented in Table 41 reveals that, across all six features, Texts (179) and (180) are, in fact, much more similar to each other than Texts (179) and (181) are. However, because nouns are overall much more frequent than the other five features, any small relative differences in the noun counts will unduly influence the dimension scores if normalised, rather than standardised, frequencies are used.

<footnote n="36">³⁶ Although Biber (1988) originally extracted a seven-factor solution, he did not attempt to interpret the seventh factor because it only has one feature that loads above the pre-determined threshold.</footnote>
<table><caption>Table 41: The computation of dimension scores on the basis of normalised frequencies</caption><thead><tr><th></th><th>Present tense</th><th>Discourse particles</th><th>Negation</th><th><em>because</em></th><th>Nouns</th><th>TTR</th><th>Dimension score</th></tr></thead><tbody><tr><td>Text (179)</td><td>4.93</td><td>0.68</td><td>1.53</td><td>0.17</td><td>37.91</td><td>0.53</td><td>-31.13</td></tr><tr><td>Text (180)</td><td>4.80</td><td>0.64</td><td>1.60</td><td>0.16</td><td>41.00</td><td>0.53</td><td>-34.33</td></tr><tr><td>Text (181)</td><td>5.67</td><td>0.81</td><td>3.24</td><td>0.81</td><td>41.31</td><td>0.34</td><td>-31.12</td></tr><tr><td><em>Mean</em></td><td>5.13</td><td>0.71</td><td>2.12</td><td>0.38</td><td>40.07</td><td>0.47</td><td></td></tr><tr><td><em>SD</em></td><td>0.47</td><td>0.09</td><td>0.97</td><td>0.37</td><td>1.88</td><td>0.11</td><td></td></tr></tbody></table>

In Table 42, by contrast, the dimension scores are based on standardised frequencies (z-scores). These have been calculated on the basis of the mean and standard deviation of the normalised frequencies of each feature (see Table 41). The dimension scores thus computed in Table 42 make evident that Text 1 is, indeed, more like Text 2 than Text 3. Hence, with standardised frequencies, the features with the highest relative frequencies, here nouns, no longer exert undue influence on the dimension scores. In other words, standardised frequencies “give each feature a weight in terms of the range of its variation, rather than in terms of its absolute frequency” (Biber 1988: 95).

<table><caption>Table 42: The computation of dimension scores on the basis of standardised frequencies (z-scores)</caption><thead><tr><th></th><th>Present tense</th><th>Discourse particles</th><th>Negation</th><th><em>because</em></th><th>Nouns</th><th>TTR</th><th>Dimension score</th></tr></thead><tbody><tr><td>Text (179)</td><td>-0.43</td><td>-0.34</td><td>-0.61</td><td>-0.56</td><td>-1.15</td><td>0.58</td><td>-1.37</td></tr><tr><td>Text (180)</td><td>-0.71</td><td>-0.79</td><td>-0.54</td><td>-0.59</td><td>0.49</td><td>0.58</td><td>-3.70</td></tr><tr><td>Text (181)</td><td>1.14</td><td>1.13</td><td>1.15</td><td>1.15</td><td>0.66</td><td>-1.15</td><td>5.07</td></tr><tr><td><em>Mean</em></td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td></td></tr><tr><td><em>SD</em></td><td>1.00</td><td>1.00</td><td>1.00</td><td>1.00</td><td>1.00</td><td>1.00</td><td></td></tr></tbody></table>
<table><caption>Table 43: Summary of Biber's six dimensions of English (1988)</caption><thead><tr><td>Dimension</td><td>Description</td><td>Features</td></tr></thead><tbody><tr><td>1. Involved vs. Informational Discourse</td><td>Low scores indicate informationally dense discourse, e.g., official documents and academic writing, whereas high scores indicate that the text is affective and interactional, e.g., face-to-face and telephone conversations.</td><td>Involved production features: private verbs, that-deletions, contractions, present tenses, second-person pronouns, DO as pro-verb, analytic negations, demonstrative pronouns, emphatics, first-person pronouns, it, BE as main verb, causative subordinations, discourse particles, indefinite pronouns, hedges, amplifiers, sentence relatives, WH-questions, possibility modals, non-phrasal coordination, WH-clauses, stranded prepositions. Informational production features: nouns, longer words, prepositions, higher type/token ratio, attributive adjectives.</td></tr><tr><td>2. Narrative vs. Non-Narrative Concerns</td><td>Works of fiction score high on this dimension, whereas official documents, academic prose and broadcasts score lowest.</td><td>Narrative concerns features: past tense, third-person pronouns, perfect aspect, public verbs, synthetic negations, present participial clauses.</td></tr><tr><td>3. Explicit vs. Situation-Dependent Reference</td><td>Low scores indicate dependence on the context, as is the case in sport broadcasts and conversations, whereas high scores indicate independence from context, e.g., academic prose and official documents.</td><td>Explicit Reference features: WH-relative clauses on object position, pied-piping relatives, WH-relative clauses on subject position, phrasal coordination, nominalisations.</td></tr><tr><td>4. Overt Expression of Persuasion</td><td>Texts with high scores explicitly mark the author's point of view and attempt to persuade, e.g., professional letters and editorials, as opposed to factual broadcasts and press reviews, which score low.</td><td>Overt expression of persuasion features: infinitives, prediction modals, suasive verbs, conditional subordinations, necessity modals, split auxiliaries.</td></tr><tr><td>5. Abstract vs. Non-Abstract Information</td><td>The higher the score on this Dimension the higher the degree of technical and abstract information, as for example in scientific discourse.</td><td>Abstract information features: conjuncts, agentless passives, past participial clauses, by-passives, past participial WHIZ deletion relatives, other adverbial subordinators.</td></tr><tr><td>6. On-Line Informational Elaboration</td><td>High scores on this Dimension indicate that the information expressed is produced under certain time constraints, as for example in speeches.</td><td>On-line informational elaboration features: that clauses as verb complements, demonstratives, that relative clauses on object position, that clauses as adjective complements.</td></tr></tbody></table>
Once dimension scores have been computed for each text, these can be compared to explore register-based linguistic variation across a corpus. Fig. 29 plots the mean Dimension 1 scores of the registers included in Biber’s (1988) analysis. We see that on Biber’s first ‘Involved vs. Informational Discourse dimension’, highly involved, spontaneously produced texts, such as telephone and face-to-face conversations, score very high and information-dense official documents and academic writing obtain low negative scores, whilst fiction scores around zero.

<figure><img src="image_33.png" /><figcaption>Fig. 29: Mean scores of general spoken and written registers of English on Biber’s (1988) Dimension 1 (as summarised in Biber & Conrad 2019: 292)</figcaption></figure>
Biber's first and the many subsequent MDAs have shown that text registers cluster in different configurations along different dimensions, thereby revealing the truly multi-dimensional nature of registers, which are characterised by several groups of linguistic features (Thompson et al. 2017: 155).

### 6.1.2 MDA and textbook language

Post-1988, two approaches to register variation studies applying MDA have emerged. One approach involves comparing one or more new or more specialised registers relative to the dimensions of an earlier analysis of registers (most commonly Biber's original 1988 analysis): this is referred to as 'additive MDA' (Berber Sardinha et al. 2019). The second approach consists in conducting a new, 'full MDA' following the steps outlined in 6.1.1. for an entire (new) set of registers (cf. Fríngeral & Hardy 2014; Egbert & Staples 2019). Given enough data, researchers can choose between these two approaches to analyse textbook language using MDA. In the following, Conrad's (1996a; 2013) investigation of biology and history university textbooks will illustrate the use of additive MDA to explore textbook language, whilst Reppen's (1994b; 2013) study of Elementary School English will serve to point to the potential of a full MDA.

#### 6.1.2.1 Exploring Textbook English using Additive MDA

Biber's (1988) original MDA study led to the elaboration of a model of language variation in spoken and written English that can now be used for predictive purposes. With a detailed, empirical validation of its generalisation to new texts using the Brown corpus, Nini (2014; 2019) demonstrated its robustness (though see Lee 2000 for issues in replicating the six dimensions on new data). Thus, in theory at least, this means that:

> it is possible to determine how a text, corpus, or even register behaves linguistically in comparison to other registers of English. In essence, the [Biber's 1988] model represents a base-rate knowledge of English that allows the description or evaluation of other texts or registers (Nini 2019: 70).

Compared to conducting a full MDA, additive MDA approaches have the advantage of requiring considerably smaller datasets. Indeed, when conducting a full MDA, large and internally well stratified corpora are essential to be able to extract meaningful register dimensions. Where obtaining such data is not feasible, Nini (2019: 70) claims that "plotting the input corpus onto Biber's model of English can be a reasonable approximation to running a new [MDA]".

Despite this potential, relatively few studies have applied Biber's or other subsequent MDA-derived models to describe or evaluate new registers and/or varieties of English (Berber Sardinha et al. 2019). Thus far, two registers have been the focus of most additive MDAs: television registers (Quaglio 2009; Al-Surmi 2012; Forchini 2012;
Berber Sardinha 2014; Berber Sardinha and Veirano Pinto 2017) and academic registers (Atkinson 1996, Conrad 1996, 2001, 2014; Biber et al. 2002). All of these studies relied on Biber's (1988) model as their baseline.

Conrad (1996a; 2013) applied Biber's (1988) model of variation in general English to research articles and university-level textbooks in the fields of ecology and American history. She uses Biber's (1988) dimensions to compare linguistic variation between a) the two disciplines, b) professional academic writing and the pedagogical writing of textbooks, and b) all the disciplinary texts of her corpus and the English registers investigated by Biber in his original study. In the following, the focus will be on the second comparison, which concerns the specificities of Textbook English as a language variety.

Conrad's corpus comprised a total of eighty 800-word samples from 20 ecology and 20 American history research articles and ten 500-word samples from each textbook in a corpus of nine textbooks per discipline (Conrad 1996a: chap. 4 and Appendix A). She calculated the dimension scores on Biber's (1988) first five dimensions for each sample and reported mean scores for each text register (research article or textbook) and discipline (ecology or history). On Biber's (1988) first dimension, the text scores cluster at the negative end of the scale around -20 (Conrad 2001: 97). When comparing them to Biber's (1988) registers of general English, these disciplinary texts clearly form a distinct cluster. However, the additive MDA also highlights notable differences between the two academic registers. In both disciplines, the research articles feature more nouns, prepositions, and attributive adjectives and tend to have longer words, thus conveying information that is more densely packed than the textbooks. Understandably, textbooks have a more novice audience than research articles and thus include more explanations and examples, leading to less dense informational content.

In a preliminary study with a smaller corpus focusing only on biology texts, Conrad (1996b) triangulated her results with interviews with a professor of ecology who taught with the textbooks included in the corpus. Strikingly, although the professor reported that he preferred to use textbooks rather than research articles in his courses "because they were less dense and more engaging for the students" (Conrad 1996b: 314), the students were expected to develop the academic writing skills necessary to write research papers. Conrad (1996b: 302) points to a number of studies which concluded that biology students often struggle to manipulate language as their biology professors expect them to. Thus, such comparative studies of an 'input register' – here, the textbooks – and a 'target register' – in this case, the research articles – can contribute to a better understanding of the linguistic difficulties students may face and lead to more effective materials development. Conrad (1996b: 320) claims that if "the texts that [students] have been exposed to are primarily textbooks, writing an
experimental report is likely to require new ways of using language”, for which
students may not have been exposed to adequate models. Pursuing a similar,
pedagogical aim, Zuppardo (2013) performed an additive MDA comparing the
language of aircraft manuals to Biber’s (1988) model and concluded that additive
MDAs can support teachers and textbook authors in developing tailored materials for
ESP or EAP classes by providing salient linguistic information about specialised
registers.

6.1.2.2 Exploring Textbook English by conducting a full MDA

Within the MDA framework, the alternative to situating textbook language relative
to other written or spoken registers of English is to conduct a new, full MDA with a
corpus of textbooks or a corpus that includes textbook materials. This second
methodological approach was adopted by Reppen (1994b; 2013) in her extensive study
of (English L1) elementary student speech and writing. She compiled a corpus of
spoken and written texts either produced or consumed by fifth-graders in the USA
that included, among other texts, 10,000 words from elementary school science and
social studies textbooks and 5,000 words from basal readers commonly used at that
level in Arizona. Following the procedure described in 6.1.1, Reppen extracted five
factors and functionally interpreted the linguistic features associated with each factor
and their factor loadings to arrive at a lexico-grammatical description of the variation
of registers in Elementary Student English.

Reppen was then able to both compare the registers of Elementary Student English
to one another and, in a second step, to compare the dimensions of ‘Elementary
Student English’ to those of ‘Adult English’ (Biber 1988). Her results show that
textbooks share linguistic features typical of edited informational and non-personal
uninvolved discourse (Reppen 1994a; 2013). Biber’s (1988) adult model and Reppen’s
(1994, 2001) elementary student model share many characteristics. In both models,
the first and strongest dimension depicts an oral-written continuum, reflecting
production circumstances and the density of informational content. The two models
also feature a second dimension pertaining to narrative vs. non-narrative discourse.
There are, however, some notable differences. For instance, Reppen (2013: 196)
observes that many of these reflect developmental processes because, although fifth-
graders’ communicative goals largely match adults’, ten- to eleven-year-olds rely on
a more limited set of linguistic resources to pursue the same objectives. Students
presumably acquire the necessary linguistic resources to construct subtle arguments
and persuade at a later stage because features associated with these communicative
aims are largely absent from Reppen’s dimensions of elementary school English.
Moreover, Reppen (2013: 197–198) notes that some of the tasks elementary students
are asked to complete at school, e.g., describing hypothetical scenarios, involve
clusters of lexico-grammatical features that are not found to co-occur in adult
registers, thus – at least from a linguistic point of view – the pedagogical relevance of
such school tasks may be called into question.

## 6.2 Method

Although, to the present author's best knowledge, MDA has yet to be applied to EFL
textbook registers, both Conrad's (1996a; 2013) additive MDA involving university
textbooks and Reppen's (1994b; 2013) full MDA of elementary school language have
showcased the considerable potential of MDA in the context of textbook language
studies. In the following, this chapter presents an additive MDA of six major
Textbook English registers identified in 3.3.1.4, as compared to Biber's 1988
dimensions of spoken and written English (see 6.1.1). A full MDA of Textbook English
and relevant target language registers is conducted in Chapter 7. The results of the
two MDAs corroborate one another and are discussed in Chapter 8, together with the
results of the case studies presented in Chapters 4 and 5.

It is hypothesised that an MDA of Textbook English will reveal which textbook
registers are closest to the register they intend to represent, as well as which specific
linguistic features contribute to the most striking differences between textbook
registers and their intended target register. Note that this chapter and the next seek
to answer the research questions outlined in 6.1 in a descriptive manner. Chapter 8
will take a more evaluative stance by discussing both the potential pedagogical
reasonings behind and implications of the idiosyncrasies of Textbook English registers
identified in Chapters 4 to 7.

### 6.2.1 Choosing a baseline MDA study

Evidently, to perform an additive MDA, the first step involves selecting an existing
study whose MDA dimensions will serve as the baseline (Berber Sardinha et al. 2019:
169–170). For the present study, Biber’s (1988) original MDA of spoken and written
English was deemed most appropriate for five reasons.

First, it covers a broad range of registers, whereas follow-up MDAs have tended to
focus on more specialised or restricted ranges of registers (e.g., University Language
[Biber 2006]; academic English [Gray 2015]; blogs [Grieve et al. 2010]; online registers
[Biber & Egbert 2018], etc.). In addition, Biber’s 1988 model is the only MDA model
whose reliability has been formally evaluated (see, e.g., Biber 1990; Biber 1992; Biber
1993b; though its validity has been more difficult to ascertain, see Lee 2000; Le Foll
2021a). Third, although Biber’s dimensions have *de facto* served as a baseline for
variation in all varieties of English, they were derived from British English texts (from
the LOB and London-Lund corpora, except for the professional and personal letter
registers, see Biber 1988: 66) and, as explained in 3.3.2.1, British English was chosen
as the comparison baseline for the language of European school EFL textbooks. The
fourth reason is that relying on Biber's 1988 study allows for comparisons with other relevant MDA studies that have also applied these same dimensions (e.g., Al-Surmi 2012; Biber et al. 2002; Berber Sardinha et al. 2019; Berber Sardinha & Veirano Pinto 2017; Quaglio 2009). Finally, from a practical point of view, the availability of the Multidimensional Analysis Tagger (MAT, Nini 2014), an open-source computer programme that automatically tags and counts all the lexico-grammatical features used in Biber's (1988) analysis, eases the procedure considerably.

Biber (1988) originally extracted seven factors, though the sixth and seventh dimensions have so few features (see Table 40) that they are very difficult to interpret. Hence, in practice, only the first five or six dimensions are usually referred to in studies applying Biber's (1988) dimensions to additional registers (Conrad & Biber 2013: 39). The first six dimensions from Biber's 1988 MDA are of interest to the present investigation (see Table 43 for a summary of the dimensions and their features). The full list of the linguistic features and their loadings on each dimension can be found in Biber (1988: 102-103; Appendix II). These are the features for which each text in the corpora under study was tagged.

### 6.2.2 Defining text units in the TEC

The first requirement for any MDA study is to compile a text corpus representing the text categories under investigation. The design of the Textbook English Corpus (TEC) and of the three target language reference corpora, the Spoken BNC2014, Youth Fiction and Info Teens, was already described in 3.3.2.

In text-linguistic research designs, as traditionally adopted in MDA studies, the units of analysis are the individual texts within a corpus, with each text representing one observation (Biber et al. 2016: 357). Since the TEC consists of one large file per textbook volume, with each file manually annotated for register and text using a simple XML structure (see 3.3.1.3), it was possible to use a simple script to extract individual texts and remove the annotation tags from these textbook files. This process, however, resulted in very many extremely short text files (in the case of instructional texts, often just a single sentence), for which meaningful normalised feature counts cannot be computed. Linguists attempting to apply MDA to social media texts face a similar problem. To solve this issue in their multi-dimensional analysis of Twitter data, Clarke & Grieve (2017: 2) opted for binary feature frequencies (i.e., whether a feature is present or absent within a tweet) rather than relative frequencies. If, as Clarke & Grieve did, one considers a single tweet (as opposed to a thread of tweets) as a single text, this approach is very sensible because single tweets have, by corpus linguistic standards, a very small maximum character limit (currently 280 characters) and as a result, relative frequencies would largely depend on tweet length. The case of textbook texts, however, is much more complex: whilst many textbook texts are as short as a tweet (e.g., task instructions, short
rhymes), countless others run well over 1,000 words (e.g., short stories, news articles, transcript of a dialogue). Indeed, defining text units in school EFL textbooks is a particularly challenging task. Numerous possibilities arise. Up until now, entire textbook volumes have often been conceived as single texts. However, as mentioned earlier, such an approach entirely ignores the variety of text registers encountered within a single textbook volume. A second approach might consider all the texts of one register found within a chapter or unit of a textbook volume to constitute one text. In some cases, this may be justified because texts within a textbook unit will often be thematically related and may therefore form a coherent whole; however, this will depend on the textbook series and is not always consistent across an entire textbook series, either (cf. Le Foll 2020c).

In addition to the problem of defining text units, the great variety of text lengths encountered in school EFL textbooks must also be considered. Short texts may not present enough opportunities for many linguistic features to occur. In other words, even if a feature is not particularly rare, a text may simply happen to be too short to feature it. In many corpus-linguistic studies, it is often tacitly assumed that normalising counts of occurrences somehow solves this problem (see 7.2.4). In the case of zero counts, however, it evidently does not. This is easily illustrated by imagining a short informative textbook text totalling 100 words that might feature 20 nouns and six present tense verbs but not a single adverb or relative clause. If we normalise these counts to 1,000 words, we are implying that a longer version of this informative text would feature 200 nouns, 60 present tense verbs and still zero adverbs and zero relative clauses! As this example makes clear, the minimum text length must therefore be determined on the basis of the frequency of the least frequent linguistic feature to be counted in an MDA. In order to carry out an additive MDA based on Biber's (1988) model, however, the type/token ratio variable must be calculated on the basis of the first 400 words of any text<fnref n="37" /> (Biber 1988: 238-239); thus it made sense to take this number as the minimum text length for the present additive MDA.

In light of both the great variety of text lengths encountered in school EFL textbooks and the fact that the majority are under 400 words, shorter texts within each textbook volume and register were collated into longer text files. This means that, for example, a number of short, consecutive instructional texts from any one textbook volume were combined until a total word count of at least 400 words was reached. This is standard practice in many MDA studies. On this Lee (2000: 226) writes: "[...] in cases where texts are relatively homogeneous within the genre, joining together several texts to

<footnote n="37">³⁷ It has long been established that type/token ratios must be calculated on the basis of text samples of equal text length as this lexical diversity measure is highly sensitive to text length (e.g., Brezina 2018: 58). Biber (1988) chose to calculate this type/token ratio of the first 400 words of each text of his corpus and, if using Biber (1988) as the base rate model in an additive MDA, this procedure should be adhered to in order for the results to be comparable.</footnote>
form one large ‘text’ may be justified.” For the texts of the TEC, concatenation was
performed sequentially within each textbook volume so that short files from within a
chapter/unit or across directly adjacent chapters/units were grouped together. Hence,
the collated text files also correspond to the progression that the learners are expected
to make. This process resulted in the exclusion of Poetry & rhyme texts from thirteen
volumes, Fiction texts from seven volumes, and Informative texts from two volumes
because the texts of these registers within these volumes did not total to at least 400
words. Following these data preparation steps, 1,949 textbook text files were created.
Table 44 shows the dispersion of these texts across the six selected textbook registers.
They are collectively referred to as the texts of the TEC in the rest of this chapter.

<table><caption>Table 44: Distribution of Textbook English Corpus (TEC) texts processed in this chapter</caption><thead><tr><th>Textbook registers</th><th>Number of texts</th><th>Number of words<sup>38</sup></th></tr></thead><tbody><tr><td>Conversation</td><td>529</td><td>512,587</td></tr><tr><td>Fiction</td><td>285</td><td>241,512</td></tr><tr><td>Informative texts</td><td>364</td><td>304,695</td></tr><tr><td>Instructional texts</td><td>647</td><td>585,049</td></tr><tr><td>Personal correspondence</td><td>88</td><td>69,570</td></tr><tr><td>Poetry & rhyme</td><td>37</td><td>26,445</td></tr><tr><td><strong>Total</strong></td><td><strong>1,949</strong></td><td><strong>1,739,858</strong></td></tr></tbody></table>

### 6.2.3 Tagging and counting features

A marginally modified cross-platform version of the MAT (Nini 2014)<fnref n="39" />, an open-source programme that aims to replicate the Biber Tagger, was used to automatically tag and count all the linguistic features included in Biber’s (1988) final six dimensions of general spoken and written English. The validity and reliability of the MAT as compared to the Biber Tagger has been demonstrated in Nini (2014; 2019). The programme builds on the grammatical analysis and part-of-speech tagging of the Stanford Tagger (Toutanova & Manning 2000; Toutanova et al. 2003) with a series of regular expressions designed to match the feature descriptions from Biber’s (1988: Appendix II) original publication. The MAT not only computes the normalised counts for all of Biber’s features (and several additional features, though they are not used in this additive MDA), it also automatically standardises them based on the mean

<footnote n="38">³⁸ As counted by a slightly modified version of the MAT (Nini 2014).</footnote>

<footnote n="39">³⁹ Many thanks to Stephanie Evert for improving the efficiency of the MAT and to Peter Uhrig for subsequently modifying the script for it to call on an updated version of the Stanford tagger (v. 3.9.2 with bidirectional model).</footnote>
and standard deviations from Biber (1988: 77), and ultimately computes all six dimension scores for each text.

The MAT outputs three tab-separated files: 1) normed counts per 100 words for each feature per text, 2) z-scores based on Biber (1988) for each feature in each text, and 3) the six computed dimension scores for each text. Thus, for the purposes of a full MDA using the same linguistic features as Biber, the first table may be used, whereas the third table is of most interest to carry out an additive MDA. However, the first and second table contain valuable additional information to interpret the results of an additive MDA.

### 6.2.4 Computing the mean dimension scores for the new registers

We have seen in 6.1.1 that, to compute dimension scores, normalised counts must be standardised to avoid frequent features from having a disproportionate influence on the model (see Tables 41 and 42). Standardisation involves scaling the normalised counts as standard deviation units. Thus, z-scores for each variable in each text are calculated as follows:

(182) (Normalised frequency in text - mean normalised frequency of corpus) / standard deviation of corpus

Consequently, texts whose normalised count for any one variable is equal to the variable mean have a z-score of 0. Positive z-scores indicate that a feature occurs more than on average across the corpus, and negative z-scores indicate below average normalised counts. In an additive MDA, however, the mean z-scores and standard deviations of each feature are not taken from the corpus under study, but rather from the reference corpus from which the original model was derived. Thus, the second table that MAT outputs is based on the means and standard deviation values reported in Biber (1988: 77).

### 6.2.5 Computing dimension scores for additional reference corpora

In theory, conducting an additive MDA makes it possible to compare “new” registers to Biber’s (1988) “old” general English registers without resorting to any additional reference corpora. However, in order to better answer RQ2 and RQ3, three target language reference corpora are also mapped onto Biber’s (1988) dimensions for comparison with the registers of the TEC. Both theoretical and methodological reasons justify this additional step.

Starting with the methodological rationale, whilst Nini (2014; 2019) demonstrated the overall reliability of the MAT Tagger, his analyses pointed to small differences in the feature counts as compared to the original 1988 version of the Biber Tagger. In addition, the present study makes use of a slightly modified version of the MAT that relies on a more up-to-date version of the Stanford Tagger (Toutanova & Manning
2000; Toutanova et al. 2003) as the first layer of part-of-speech tagging (see 6.2.3), for which no such reliability tests were conducted. It goes without saying that the results of comparisons are more likely to be robust if exactly the same tools and methods are used to identify and count the features of both the Textbook English Corpus and any other corpora with which comparisons are to be made.

From a pedagogical and linguistic perspective, although the registers included in Biber's (1988) model can provide useful comparison points for school EFL textbook registers, any differences observed, say between Biber's (1988) fiction registers and EFL textbook fiction, could potentially be due to different target readerships. Indeed, the fiction subcorpora of the Lancaster-Oslo-Bergen Corpus of British English (LOB) predominantly contain samples from literature aimed at an adult readership, rather than children or teenagers. Additionally, the corpora from which Biber's (1988) model was derived consist of transcripts of spoken material recorded between 1953 and 1987 (London-Lund; Svartvik & Quirk 1980) and texts published in 1961 (LOB; Johansson, Leech & Goodluck 1978). Modern EFL textbooks, however, can reasonably be expected to reflect more recent language change, especially in informal, spoken language. Consequently, in the second half of the present results section (6.3.2), the dimension scores of the texts extracted from the three target language reference corpora (the Spoken BNC2014, the Youth Fiction and the Info Teens, see 3.3.2) are processed with the same modified version of the MAT for comparison with the Conversation, Fiction, and Informative texts of the TEC respectively. This 'comparative additive MDA' focuses on Biber's (1988) first three dimensions, which are identified in the analysis of intra-textbook linguistic variation (6.3.1) as the most relevant to the study of the language of school EFL textbooks.

### 6.2.6 Comparing dimension scores

To compare different registers on any one of Biber's (1988) dimensions, the mean dimension scores of all the texts of a register are usually compared to each other. Such comparisons have typically been tested and quantified using ANOVAs and coefficients of determination (e.g., Biber 1988: 95; Biber et al. 2004: 64; Gray 2015: 216; Berber Sardinha & Veirano Pinto 2019: 6), or with nonparametric Kruskal Wallis ANOVAs (e.g., Shakir 2020). More recently, the use of predictive Discriminant Function Analysis (DFA) as a post-hoc analysis method has also been proposed to verify the robustness of dimensions as predictors of register (e.g., Crossley, Allen & McNamara 2014; Crossley, Kyle & Römer 2019; Veirano Pinto 2019). However, a crucial assumption of both ANOVAs and DFAs is that the data points be independent of each other (cf. Gries 2015b; Winter 2019: chaps. 14–15; on the consequences of using DFA on non-independent data, cf. Mundry & Sommer 2007). In the context of the present additive MDAs, and, indeed, in many, if not most, corpus linguistic studies, however, this assumption is not met. In fact, in the case of the TEC, each textbook series has largely been written by the same group of authors, following the
same publisher guidelines. They are thus not truly independent. Similarly, the Youth Fiction and the Info Teens consist of several samples from any one book or web domain (see 3.3.2.2–3.3.2.3) which means that not all of these texts can be said to be truly independent data points.

Consequently, linear mixed-effects models were computed using the R package *lme4* (Bates et al. 2015). Following Barr et al. (2013) and many others, maximal random-effect structure models that included by-series/by-source varying intercepts and slopes were originally computed; however, for the models of the intra-textbook linguistic variation (6.3.1), this resulted in singular model fits. Singularity is typically either due to random effects having (near) zero estimated variance or to some random effects being (almost) perfectly correlated with one another. Variance-covariance matrices of the random effects were therefore computed and matrices of correlations among the estimated random effects were plotted to examine these possible causes (e.g., Fig. 30 for Dimension 2 where each point on each scatterplot represents a textbook series). These showed that these maximal models result in some almost perfect correlations between specific textbook registers and the by-series random effects. For instance, as seen in Fig. 30, in the maximal model used to predict Dimension 2 scores, the by-series random effects are almost perfectly negatively correlated with those of the Instructional register (-0.98). This means that textbook series that have a higher random intercept also have a lower random slope for the Instructional register. Since the reference group for the registers is Conversation, the intercept represents the estimated Dimension 2 scores for Conversation texts of the TEC: in other words, textbook series with higher estimated Dimension 2 scores for their dialogues also tend to feature instructional texts with lower estimated Dimension 2 scores.
<figure><img src="image_34.png" /><figcaption>Fig. 30: Matrix of correlations among the estimated random effects of the maximal model: lmer(Dim2 ~ Register + Level + Level*Register + (Register|Series))</figcaption></figure>

It thus becomes clear that some of these random slopes for the different textbook registers provide redundant information: if the random effect for one register is almost perfectly correlated with another, there is arguably no need to include them both. Whilst it is entirely feasible that some of these random effects are indeed almost perfectly correlated (near negative perfect correlations, in particular, may simply point to generally better register distinctions in some series than in others), this redundancy in the models makes it difficult to estimate the effects reliably. Several solutions have been proposed to tackle singularity in mixed-effects models (cf. Bolker 2020). Having compared these, the most satisfactory solution for the purposes of this study was to simplify the mixed-effects structures to include by-series intercepts only.

To estimate the relationships between textbook registers and the dimension scores on any one dimension, a baseline model was first fitted with a random effect structure consisting of by-series varying intercepts to account for the non-independence of texts from within one textbook series. In all models reported on in this chapter, the dimension scores are the outcome variable. Textbook register and textbook level are modelled as fixed level predictors. In addition, their two-way interaction term is also fitted, since it can be hypothesised that, as the proficiency of learners increases, the dimension scores of textbook texts within a register may move closer to their target
language equivalents. For instance, upper-intermediate fictional texts from textbooks may be more like teenage or young adult fiction than a short story printed in a beginner textbook. If this were true, we would expect dimension scores for some registers to increase as learners are expected to become more proficient, whilst they may decrease for other registers. Thus, on Biber's (1988) first dimension, advanced textbook dialogues may score higher than beginner ones, thereby more resembling naturally occurring conversation, whereas we may hypothesise that informative texts in advanced textbooks are likely to score lower on Dimension 1 than those targeted at beginner or intermediate learners. For data sparsity reasons, a subset of the data that excluded the textbook register Poetry & rhyme was entered in these mixed-effect models since several textbook volumes did not include any poems or songs longer than 400 words that could therefore be entered in the MDAs (see 6.2.2), and thus models involving this register failed to converge.

Using the *anova* function in base R, the baseline model (a) consisting only of the random by-series intercepts was compared to models with the same random effect structure and register as a fixed effect (b), textbook proficiency level as a fixed effect (c), both register and level as fixed effects (d), and with both variables as fixed effects and their two-way interaction (e). These model fits were compared by means of likelihood ratio tests by comparing the full models against the corresponding models without the effects of interest using likelihood ratio tests (cf. Baayen 2012: 675–676).

(a) lmer(DimScores ~ (1|Series))

(b) lmer(DimScores ~ Register + (1|Series))

(c) lmer(DimScores ~ Level + (1|Series))

(d) lmer(DimScores ~ Register + Level + (1|Series))

(e) lmer(DimScores ~ Register + Level + Register*Level + (1|Series))

To compare the dimension scores of Textbook Conversation, Fiction, and Informative texts with the three corresponding target language reference corpora in 6.3.2, a second type of linear mixed-effects model was computed. In these models, the random effect structure consists of varying by-Source intercepts, where ‘Source’ corresponds to a factor variable with nine textbook levels corresponding to each textbook series for the TEC, 300 book levels for the Youth Fiction, 14 web domain levels for the Teens Info corpus, and one level for the Spoken BNC2014. These levels have been chosen as the best-available proxies to capture the variation inherent to each (group of) author(s)/editor(s). In these models ((f)–(j)), the fixed effects are Corpus type (Textbook vs. Target Language Reference), Register (Conversation, Fiction, and Informative texts) and their two-way interactions.

(f) lmer(DimScores ~ (1|Source))

(g) lmer(DimScores ~ Register + (1|Source))

(h) lmer(DimScores ~ Corpus + (1|Source))

(i) lmer(DimScores ~ Register + Corpus + (1|Source))

(j) lmer(DimScores ~ Register + Corpus + Register*Corpus + (1|Source))
Model diagnostic plots were inspected to check the assumptions of linearity, homogeneity of variance, and the normal distribution of residuals of the model (i.e., the differences between the observed and fitted values). In addition, observed and estimated values were plotted for additional visual checks of the final model fits. Note that, for reasons of space, most of these model comparisons and visualisations are not printed but they may be reproduced using the data and code provided in the *Online Appendix 6.2–6.3*.

In all model summaries reported in this chapter and the next, the confidence interval (CI) ranges reported are 95% confidence intervals. The $R^2_{marginal}$-values summarise the predictive power of the fixed effects only, whilst $R^2_{conditional}$-values summarise those of both the fixed and random effects. The latter were computed using the R package *sjPlot* (Lüdecke 2020) on the basis of the procedure outlined in Nakagawa et al. (2017). The degrees-of-freedom (df), which represent the number of independent observations in each group and are necessary to estimate these *p*-values, were estimated using the Kenward-Roger method (as recommended in Luke 2017). The estimators of relative contrast effects between each register under study were computed using the default parameters of the *emmeans* package (Lenth 2020). Hence, here, too, degrees-of-freedom (df) were calculated using the Kenward-Roger method (Luke 2017). Further, *p*-value adjustment followed the Tukey method.<fnref n="40" /> For these, too, the confidence level reported is 0.95.

## 6.3 Results

Section 6.3.1 attempts to answer the first research question (RQ1) formulated at the beginning of the chapter, namely: *What is the extent of the linguistic variation across the major registers of Textbook English?* In the following, this is referred to as ‘intra-textbook variation’. To do so, the scores of the textbook registers on Biber’s (1988) dimensions are first compared to each other. Large within-register dispersions in these dimension scores are further explored and examples of salient features that contribute to strikingly low or high scores are discussed in context. This section also seeks to find out whether some textbook series show significantly more or less register-based variation and whether the different proficiency levels of the textbooks of the TEC (significantly) interact with register-based variation. Hence, this section begins with an exploration of intra-textbook linguistic variation.

<footnote n="40">⁴⁰ Tukey’s HSD (honestly significant difference) test was chosen for these post-hoc analyses because it does not make any strong distributional assumptions and operates on relatively conservative estimates for unequal sample sizes. It computes pairwise comparisons of all the means and calculates the smallest significant difference between them taking into account the cumulated Type I error level. It therefore does not result in a loss of test power in spite of the multiple comparisons it makes (Rasch et al. 2014: 29–30).</footnote>
This is followed, in 6.3.2, by a more fine-grained comparison of the dimension scores of three key textbook registers (Conversation, Fiction and Informative texts) to those of three comparable target language corpora with the aim of investigating RQ2: *To what extent do textbook registers differ from situationally similar registers encountered outside the classroom?* Finally, the results of this additive MDA also provide first answers to RQ3 which seeks to pinpoint the key linguistic features which most contribute to these differences. This last research question, however, is explored more comprehensively in Chapter 7.

### 6.3.1 Intra-textbook linguistic variation

#### 6.3.1.1 Intra-textbook variation on Biber's Dimension 1

Fig. 31 displays the scores of the six textbook registers on Biber's (1988) first, 'Involved vs. Informational Discourse', dimension. In Biber's original model of general spoken and written English, 84.3% of the variation in Dimension 1 scores of the individual texts from his corpus is explained by their register membership (Biber 1988: 126–127). This dimension is thus a very powerful predictor of register variation in general English. Moreover, its involved/oral/verbal versus informational/literate/nominal opposition has almost universally emerged as the strongest and most stable predictor of variation in many subsequent MDAs carried out on a range of languages and domains (Biber 2014). As was to be expected, on this 'Involved vs. Informational' dimension, Conversation scores highest ($\bar{x}$ = 15.75, SD = 7.89), followed by Personal correspondence ($\bar{x}$ = 9.62, SD = 6.81) and Fiction ($\bar{x}$ = 5.03, SD = 8.29). The lowest scores are found in the Informative ($\bar{x}$ = -5.26, SD = 7.53) and Instructional ($\bar{x}$ = -4.69, SD = 4.60) registers (see Fig. 31). However, no significant difference between these latter two textbook registers could be ascertained on this first dimension (see Table 67).

Fig. 31 shows that the TEC scores on Dimension 1 are fairly normally distributed within each textbook register, except for Poetry & rhyme, which is due to the fact that only 37 texts from this register could be entered into the MDA (see Table 44). As a result, all statistical analyses presented in the following sections exclude this register (see 6.2.6).
<figure><img src="image_35.png" /><figcaption>Fig. 31: Distribution of texts of the TEC on Biber's (1988) Dimension 1</figcaption></figure>

As illustrated in Fig. 31, textbook register is evidently a strong predictor of Dimension 1 scores. A simple model featuring only register as a fixed effect and by-series varying intercepts already accounts for some 66% of the variance in Dimension 1 scores ($R^2_{marginal} = 0.63$, $R^2_{conditional} = 0.66$). Although model comparisons revealed that the proficiency level of textbooks is also a significant predictor of Dimension 1 scores ($\chi^2(4) = 52.27$, $p < 0.001$, as compared to the baseline model), its predicting power is very weak ($R^2_{marginal} = 0.03$, $R^2_{conditional} = 0.08$).

Table 66 summarises the final linear mixed-effects model computed for Biber's (1988) Dimension 1 scores with register, level and their two-way interaction as fixed effects, and random by-series intercepts. The reference levels are Conversation for register and A for the proficiency level of the textbooks (corresponding to the first year of secondary school, see Table 3). Thus, the coefficient estimate for the intercept (16.34) represents the estimate mean Dimension 1 score for Textbook Conversation texts in beginner (level A) textbooks. All other mean estimates on Table 66 (and all other subsequent model summary tables) represent the values that need to be added (or subtracted) from the intercept mean value to obtain the score estimates for the other textbook registers and proficiency levels. For example, the estimated Dimension 1 score for Textbook Informative texts from level A textbooks is equal to -2.61 which is the result of the sum of 16.34 (intercept) and -18.95 (Informative register). This value is very close to the actual observed mean value of -2.83 (SD = 7.52). Since all the mixed-effects models in the present chapter and the next also include Register*Level interactions, these also need to be accounted for when calculating estimated dimension scores. Thus, to find out the estimated Dimension 1 score of Level E Fiction textbook texts, it is necessary to calculate the sum of four coefficients:
16.34 (intercept), -6.96 (Fiction register), -1.27 (Level E) and -5.64 (Fiction*E interaction) which totals to 2.47 and is also close to the observed value of 3.06 (SD = 8.51).

<table><caption>Table 45: Summary of the model: lmer(Dim1 ~ Register + Level + Level*Register + (1|Series))</caption><thead><tr><th>Predictors</th><th>Estimates</th><th>CI</th><th>p-value</th></tr></thead><tbody><tr><td>(Intercept) [Conversation, Level A]</td><td>16.34</td><td>14.41 - 18.26</td><td>&lt;0.001</td></tr><tr><td>Register [Fiction]</td><td>-6.96</td><td>-9.44 - -4.47</td><td>&lt;0.001</td></tr><tr><td>Register [Informative]</td><td>-18.95</td><td>-21.54 - -16.36</td><td>&lt;0.001</td></tr><tr><td>Register [Instructional]</td><td>-21.42</td><td>-23.23 - -19.62</td><td>&lt;0.001</td></tr><tr><td>Register [Personal]</td><td>-5.29</td><td>-9.03 - -1.56</td><td>0.006</td></tr><tr><td>Level [B]</td><td>0.62</td><td>-1.12 - 2.36</td><td>0.485</td></tr><tr><td>Level [C]</td><td>-0.28</td><td>-2.01 - 1.45</td><td>0.753</td></tr><tr><td>Level [D]</td><td>-2.19</td><td>-3.94 - -0.44</td><td>0.014</td></tr><tr><td>Level [E]</td><td>-1.27</td><td>-3.25 - 0.71</td><td>0.208</td></tr><tr><td>Register [Fiction] * Level [B]</td><td>-4.09</td><td>-7.30 - -0.88</td><td>0.013</td></tr><tr><td>Register [Informative] * Level [B]</td><td>-1.80</td><td>-5.07 - 1.46</td><td>0.279</td></tr><tr><td>Register [Instructional] * Level [B]</td><td>0.21</td><td>-2.19 - 2.60</td><td>0.866</td></tr><tr><td>Register [Personal] * Level [B]</td><td>-2.82</td><td>-7.56 - 1.91</td><td>0.243</td></tr><tr><td>Register [Fiction] * Level [C]</td><td>-4.09</td><td>-7.31 - -0.86</td><td>0.013</td></tr><tr><td>Register [Informative] * Level [C]</td><td>-0.61</td><td>-3.74 - 2.52</td><td>0.703</td></tr><tr><td>Register [Instructional] * Level [C]</td><td>1.20</td><td>-1.17 - 3.57</td><td>0.321</td></tr><tr><td>Register [Personal] * Level [C]</td><td>-1.53</td><td>-6.34 - 3.29</td><td>0.534</td></tr><tr><td>Register [Fiction] * Level [D]</td><td>-6.06</td><td>-9.19 - -2.93</td><td>&lt;0.001</td></tr><tr><td>Register [Informative] * Level [D]</td><td>-1.37</td><td>-4.48 - 1.73</td><td>0.386</td></tr><tr><td>Register [Instructional] * Level [D]</td><td>2.21</td><td>-0.17 - 4.60</td><td>0.069</td></tr><tr><td>Register [Personal] * Level [D]</td><td>-0.46</td><td>-5.49 - 4.56</td><td>0.856</td></tr><tr><td>Register [Fiction] * Level [E]</td><td>-5.64</td><td>-8.87 - -2.40</td><td>0.001</td></tr><tr><td>Register [Informative] * Level [E]</td><td>-4.45</td><td>-7.71 - -1.18</td><td>0.008</td></tr><tr><td>Register [Instructional] * Level [E]</td><td>-0.24</td><td>-2.83 - 2.35</td><td>0.857</td></tr><tr><td>Register [Personal] * Level [E]</td><td>-0.53</td><td>-5.58 - 4.52</td><td>0.836</td></tr><tr><td colspan="4"><strong>Random Effects</strong></td></tr><tr><td>σ<sup>2</sup></td><td colspan="3">41.29</td></tr><tr><td>τ<sub>00 Series</sub></td><td colspan="3">4.60</td></tr><tr><td>ICC</td><td colspan="3">0.10</td></tr><tr><td>N<sub>Series</sub></td><td colspan="3">9</td></tr><tr><td>Observations</td><td colspan="3">1912</td></tr><tr><td>Marginal R<sup>2</sup> / Conditional R<sup>2</sup></td><td colspan="3">0.647 / 0.682</td></tr></tbody></table>

Fig. 32 presents a visualisation of the model summarised in Table 66: Dimension 1 scores as predicted by the model are plotted as red triangles, whilst the actual, observed scores for each text are represented as grey dots. In addition, Fig. 32 also
serves as a reminder of categories for which there is only sparse or no data; for
instance, the textbook series *Piece of Cake* (POC) and *Solutions* do not have a Level
E textbook in their series, and some textbook series contain very few or no fictional
texts at certain levels (see 6.2.2). These aspects need to be taken into consideration
when evaluating the results of the following analyses.
<figure><img src="image_36.png" /><figcaption>Fig. 32: Observed (grey circles) and predicted (red triangle) Dimension 1 scores across textbook register, proficiency level and textbook series. Predicted values as computed by the model summarised in Table 66. (Le Foll 2021. Zenodo. Retrieved on 7 May 2021. http://doi.org/10.5281/zenodo.4732323).</figcaption></figure>
Even at this earlier stage, however, the model estimates in Table 66 confirm the need to examine school EFL textbook language under the lens of register. Indeed, register appears to have a much larger impact on the choice and frequencies of the linguistic features that constitute Dimension 1 (see Table 40) than the proficiency level of the textbook, or the linguistic idiosyncrasies of its authors (as, admittedly imperfectly, captured in the random by-textbook-series intercepts). The model estimates in Table 66 also reaffirm that textbook level only has a minimal influence on the Dimension 1 scores of textbook texts. In fact, as will be discussed further, the significant effect of textbook proficiency level on Dimension 1 scores is entirely driven by its interactions with Fiction, which tends towards very marginally lower Dimension 1 scores as the proficiency level of the textbooks increases. In addition to the small effect sizes (as shown by the estimate coefficients), this finding must be approached with caution because Fiction is known to be rather poorly captured by Biber's (1988) first dimension. Indeed, fiction consists of (frequently alternating) dialogues and narrative passages so that novels with a high proportion of dialogues will inevitably score high, whilst those with longer descriptive passages will score lower (cf. Biber & Finegan 1994; Egbert & Mahlberg 2020). Whilst it may be tempting to conclude that beginner textbooks feature more dialogue-heavy fictional writing than more advanced textbooks, it is also worth remembering that the data for the textbook Fiction register is rather sparse for several textbook series (see Fig. 32). A far more relevant factor is likely to be the fact that fictional texts printed in beginner textbooks feature many more present tense verbs simply because the past tense has yet to have been introduced.

Thus, whilst all five interactions between the Fiction register and the textbook proficiency levels are significant, the effect of textbook level on Dimension 1 scores remains very small (see Table 66). This is why the model's estimated mean values for the Dimension 1 scores of the TEC texts displayed in Table 46 have been averaged across the five textbook levels. Echoing the results displayed in Fig. 31, Table 67 confirms that the estimated register means for Dimension 1 are all significantly different from each other ($p < .001$), except for the Informative-Instructional contrast. The relatively large standard errors associated with the Personal register is due to the fact that, as also illustrated in Fig. 32, there are relatively few Personal correspondence texts in the TEC. This small sample size is also reflected in the relatively large confidence intervals of the mean Dimension 1 score for the Personal correspondence register (see Table 46).
<table><caption>Table 46: Estimated register means of Dimension 1 scores (averaged across all textbook levels)</caption><thead><tr><th>Register</th><th>mean</th><th>SE</th><th>lower CL</th><th>upper CL</th></tr></thead><tbody><tr><td>Conversation</td><td>15.71</td><td>0.81</td><td>12.7</td><td>13.94</td></tr><tr><td>Fiction</td><td>4.78</td><td>0.87</td><td>16.4</td><td>2.94</td></tr><tr><td>Informative</td><td>-4.89</td><td>0.85</td><td>15.0</td><td>-6.70</td></tr><tr><td>Instructional</td><td>-5.04</td><td>0.81</td><td>11.9</td><td>-6.79</td></tr><tr><td>Personal</td><td>9.35</td><td>1.0</td><td>35.3</td><td>7.24</td></tr></tbody></table>

Given these results, we can, at first blush, conclude that textbook authors do make different, register-based linguistic choices when crafting the texts featured in school EFL textbooks. Further, the distribution of scores on Biber's (1988) original Dimension 1 – with Textbook Conversation scoring highest and Textbook Informative writing at the bottom of the scale – conforms to our expectations based on previous MDA studies.

<table><caption>Table 47: Estimated differences between mean Dimension 1 scores for each TEC register pair (averaged across all textbook levels)</caption><thead><tr><th>Contrast</th><th>Estimate</th><th>SE</th><th>df</th><th>p.value</th></tr></thead><tbody><tr><td>Conversation - Fiction</td><td>10.93</td><td>0.505</td><td>1936</td><td>&lt;.0001</td></tr><tr><td>Conversation - Informative</td><td>20.6</td><td>0.478</td><td>1933</td><td>&lt;.0001</td></tr><tr><td>Conversation - Instructional</td><td>20.75</td><td>0.394</td><td>1933</td><td>&lt;.0001</td></tr><tr><td>Conversation - Personal correspondence</td><td>6.36</td><td>0.763</td><td>1930</td><td>&lt;.0001</td></tr><tr><td>Fiction - Informative</td><td>9.67</td><td>0.564</td><td>1936</td><td>&lt;.0001</td></tr><tr><td>Fiction - Instructional</td><td>9.82</td><td>0.487</td><td>1936</td><td>&lt;.0001</td></tr><tr><td>Fiction - Personal correspondence</td><td>-4.57</td><td>0.812</td><td>1930</td><td>&lt;.0001</td></tr><tr><td>Informative - Instructional</td><td>0.15</td><td>0.452</td><td>1929</td><td>0.9974</td></tr><tr><td>Informative - Personal correspondence</td><td>-14.23</td><td>0.795</td><td>1928</td><td>&lt;.0001</td></tr><tr><td>Instructional - Personal correspondence</td><td>-14.38</td><td>0.748</td><td>1928</td><td>&lt;.0001</td></tr></tbody></table>

Table 48 helps shed light on the linguistic features which contribute most to the textbook register variation captured on this first dimension. It displays the mean z-scores for all the features that load on Biber's (1988) Dimension 1. Features that contribute to positive dimension scores are visualised in blue, whilst those that have negative loadings are listed in red. Z-scores above 1 or below -1 have been highlighted as particularly salient: they correspond to features whose mean values are at least one standard deviation above or below Biber's (1988) general English corpus mean. Individual z-scores highlighted in blue contribute to higher Dimension 1 scores, while those in red contribute to lower scores. Table 48 shows that several factors contributed to Textbook Conversation texts obtaining comparatively high scores on this dimension, including high relative frequencies of contractions, first- and second-
person pronouns, *it* pronouns, discourse particles<sup>41</sup>, general emphatics<sup>42</sup>, negation, and WH-questions.

<table><caption>Table 48: Mean z-scores of the features that load on Biber's (1988) Dimension 1</caption><thead><tr><th>Dim 1 feature</th><th>Conversation</th><th>Fiction</th><th>Informative</th><th>Instructional</th><th>Personal</th><th>Poetry</th></tr></thead><tbody><tr><td>Private verbs</td><td>0.19</td><td>0.23</td><td>-0.39</td><td>0.58</td><td>0.06</td><td>0.00</td></tr><tr><td>that-deletion</td><td>0.30</td><td>0.00</td><td>-0.30</td><td>-0.20</td><td>0.10</td><td>0.10</td></tr><tr><td>Contractions</td><td>1.56</td><td>0.47</td><td>-0.19</td><td>-0.46</td><td>1.13</td><td>1.23</td></tr><tr><td>Present tense verbs</td><td>0.26</td><td>-0.66</td><td>-0.66</td><td>-0.81</td><td>-0.15</td><td>0.10</td></tr><tr><td>2<sup>nd</sup> person pronouns</td><td>1.52</td><td>0.35</td><td>0.05</td><td>2.55</td><td>0.94</td><td>1.19</td></tr><tr><td>DO as a pro-verb</td><td>0.12</td><td>-0.29</td><td>-0.35</td><td>0.20</td><td>-0.09</td><td>-0.29</td></tr><tr><td>Analytic negation</td><td>1.15</td><td>0.61</td><td>-0.20</td><td>-0.69</td><td>0.79</td><td>0.55</td></tr><tr><td>Demonstratives</td><td>0.63</td><td>-0.03</td><td>-0.33</td><td>-0.63</td><td>-0.15</td><td>-0.06</td></tr><tr><td>General emphatics</td><td>1.22</td><td>0.23</td><td>0.63</td><td>-0.57</td><td>1.18</td><td>0.32</td></tr><tr><td>1<sup>st</sup> person pronouns</td><td>1.25</td><td>0.56</td><td>-0.49</td><td>-0.91</td><td>2.02</td><td>1.84</td></tr><tr><td>pronoun it</td><td>1.02</td><td>0.52</td><td>0.10</td><td>-0.63</td><td>0.90</td><td>0.42</td></tr><tr><td>BE as a main verb</td><td>0.47</td><td>-0.58</td><td>-0.88</td><td>-1.73</td><td>0.06</td><td>-0.04</td></tr><tr><td>Causative sub.</td><td>0.16</td><td>0.12</td><td>0.29</td><td>-0.53</td><td>0.46</td><td>-0.39</td></tr><tr><td>Discourse particles</td><td>1.04</td><td>0.11</td><td>-0.36</td><td>0.01</td><td>-0.11</td><td>-0.39</td></tr><tr><td>Indefinite pronouns</td><td>-0.47</td><td>-0.29</td><td>-0.54</td><td>-0.68</td><td>-0.46</td><td>-0.23</td></tr><tr><td>General hedges</td><td>0.37</td><td>0.17</td><td>-0.31</td><td>-0.36</td><td>0.13</td><td>-0.05</td></tr><tr><td>Amplifiers</td><td>0.14</td><td>-0.13</td><td>0.08</td><td>-0.89</td><td>0.19</td><td>-0.79</td></tr><tr><td>Sentence relatives</td><td>0.54</td><td>0.34</td><td>2.04</td><td>0.58</td><td>0.54</td><td>0.13</td></tr><tr><td>WH-questions</td><td>1.66</td><td>1.52</td><td>-0.15</td><td>1.35</td><td>-0.01</td><td>-0.03</td></tr><tr><td>Possibility modals</td><td>0.92</td><td>0.48</td><td>0.22</td><td>0.39</td><td>0.30</td><td>0.75</td></tr><tr><td>Non-phrasal co-ord.</td><td>0.68</td><td>0.84</td><td>0.48</td><td>-0.13</td><td>0.24</td><td>0.34</td></tr><tr><td>WH-clauses</td><td>0.59</td><td>0.88</td><td>0.27</td><td>4.96</td><td>0.66</td><td>0.42</td></tr><tr><td>Final prepositions</td><td>0.15</td><td>0.18</td><td>0.45</td><td>-.07</td><td>-0.12</td><td>-0.20</td></tr><tr><td>Nouns</td><td>1.78</td><td>1.16</td><td>2.78</td><td>2.40</td><td>1.24</td><td>1.51</td></tr><tr><td>Average word length</td><td>-1.39</td><td>-1.16</td><td>0.03</td><td>-0.24</td><td>-1.34</td><td>-1.61</td></tr><tr><td>Prepositions</td><td>-1.76</td><td>-1.16</td><td>-0.54</td><td>-0.69</td><td>-1.42</td><td>-1.75</td></tr><tr><td>Type/token ratio</td><td>-1.00</td><td>-0.25</td><td>0.60</td><td>-1.31</td><td>.01</td><td>-0.88</td></tr><tr><td>Attributive adj.</td><td>-1.09</td><td>-0.89</td><td>0.02</td><td>-0.68</td><td>-0.80</td><td>-0.98</td></tr></tbody></table>

<footnote n="41"><sup>41</sup> The discourse particles included in this variable are *well*, *now*, *anyhow*, *anyway* and *anyways*. For full details on the operationalisation of all the linguistic variables used in this additive MDA, see Nini (2014).</footnote>

<footnote n="42"><sup>42</sup> General emphatics are operationalised as any occurrences of *just*, *really*, *most*, *more*, *real* + ADJ, *so* + ADJ, *DO* + V, *for sure*, *a lot* and *such a* (see Nini 2014).</footnote>
In addition, Textbook Conversation features low standardised frequencies of prepositions and attributive adjectives, as well as shorter than average words and low type/token ratios. Since these are features with significant negative loadings, these negative z-scores contributed to high scores. Table 48 further reveals that Conversation is the only textbook register with a positive mean z-score for demonstrative pronouns. Textbook Conversation also features comparatively more occurrences of *that* deletion, present tense verbs, DO and BE as main verbs, hedges<fnref n="43" /> and possibility modals than the other five textbook registers.

Textbook Informative texts are characterised by comparatively few occurrences of present tense verbs, pronouns, questions, and negation, which all contribute to this register's low Dimension 1 scores ($\bar{x}$ = -5.26, SD = 7.53). Additionally, texts in this register feature the highest average word length among textbook registers and the highest type/token ratio. It is also the most nominal of textbook registers with a mean noun frequency of 27.9 per 100 words (SD = 4.80).

Although instructional texts feature very many second-person pronouns and WH-questions and have the lowest type MOTOR ratio of all registers, all of which contribute to higher scores on Biber's Dimension 1, on average, dimension scores for the Instructional register are also characteristically low ($\bar{x}$ = -4.69). Compared to the other five textbook registers under study, they display the least intra-register variation (SD = 4.60). The low scores for Instructional texts are due to an absence of first-person pronouns, very few other pronouns aside from second-person pronouns, few contractions, negated verbs, and few occurrences of BE as a main verb. Among the features with negative loadings, the relatively high proportion of nouns also contributes to these low scores.

### 6.3.1.2 Intra-textbook variation on Biber's Dimension 2

As shown in Fig. 33, the distinctions between the different textbook registers on Biber's (1988) Dimension 2 are considerably less pronounced than on the first. Nevertheless, as expected on this narrative dimension, Textbook Fiction scores highest ($\bar{x}$ = 2.53, SD = 2.83), whilst Textbook Conversation scores lowest ($\bar{x}$ = -3.17, SD = 2.05).

Fig. 33 excludes a clear outlier in the Poetry register (Dimension 2 score = 16.66). This data point corresponds to the lyrics of three songs from the textbook Solutions Elementary which were collated to reach the 400-word threshold: Nina Simone's *Ain't Got No, I've Got Life*, Alicia Key's *Fallin' and Take the last train to Clarksville* by The Monkees. The remarkably high score on this second dimension is the result of

<footnote n="43">⁴³ Hedges are *maybe, at about, something like, more or less, sort of / kind of + DT/QAN/CD/JJ/PRP$/WH* (see Nini 2014).</footnote>
the repeated phrase *Ain't got no* in the first song, in which all 50 instances of *got* were erroneously tagged by the MAT (see 6.2.3) as past tense verbs and of 18 occurrences of *no* which were (correctly) identified as synthetic negation. Both of these features load positively on Biber's (1988) narrative dimension.

<figure><img src="image_37.png" /><figcaption>Fig. 33: Distribution of the texts of the TEC on Biber's Dimension 2 (excluding one outlier text from the Poetry register)</figcaption></figure>

As explained in 6.2.6, Poetry & rhyme texts were excluded from all statistical analyses, so that this outlier has not unduly influenced the model summarised in Table 49. Although considerably less intra-textbook register-based variation is observed on Biber's (1988) second dimension than on the first, the analysis of the pair contrasts shows that the differences in Dimension 2 means are significant for all the contrasts involving Conversation and Fiction (*p* < .001). A comparison of the R² marginal and R² conditional also makes very clear that the textbook series does not contribute to any significant differences in Dimension 2 scores.

<table><caption>Table 49: Summary of the model: lmer(Dim2 ~ Register + Level + Level*Register + (1|Series))</caption><thead><tr><th>Predictors</th><th>Estimates</th><th>CI</th><th>p-value</th></tr></thead><tbody><tr><td>(Intercept) [Conversation, Level A]</td><td>-4.09</td><td>-4.52 - -3.65</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Register [Fiction]</td><td>3.95</td><td>3.14 - 4.76</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Register [Informative]</td><td>0.71</td><td>-0.13 - 1.56</td><td>0.097</td></tr><tr><td>Register [Instructional]</td><td>1.36</td><td>0.77 - 1.95</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Register [Personal]</td><td>0.57</td><td>-0.64 - 1.79</td><td>0.357</td></tr><tr><td>Level [B]</td><td>0.27</td><td>-0.30 - 0.83</td><td>0.357</td></tr><tr><td>Level [C]</td><td>0.90</td><td>0.33 - 1.46</td><td>0.002</td></tr><tr><td>Level [D]</td><td>1.74</td><td>1.17 - 2.31</td><td><strong>&lt;0.001</strong></td></tr></tbody></table>
<table><thead><tr><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td>Level [E]</td><td>1.78</td><td>1.14 – 2.41</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Register [Fiction] * Level [B]</td><td>2.79</td><td>1.74 – 3.84</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Register [Informative] * Level [B]</td><td>0.69</td><td>-0.37 – 1.76</td><td>0.203</td></tr><tr><td>Register [Instructional] * Level [B]</td><td>-0.19</td><td>-0.97 – 0.59</td><td>0.629</td></tr><tr><td>Register [Personal] * Level [B]</td><td>-0.21</td><td>-1.75 – 1.34</td><td>0.790</td></tr><tr><td>Register [Fiction] * Level [C]</td><td>2.71</td><td>1.66 – 3.77</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Register [Informative] * Level [C]</td><td>0.77</td><td>-0.25 – 1.79</td><td>0.140</td></tr><tr><td>Register [Instructional] * Level [C]</td><td>-0.46</td><td>-1.23 – 0.32</td><td>0.246</td></tr><tr><td>Register [Personal] * Level [C]</td><td>0.65</td><td>-0.92 – 2.22</td><td>0.415</td></tr><tr><td>Register [Fiction] * Level [D]</td><td>1.25</td><td>0.23 – 2.27</td><td>0.016</td></tr><tr><td>Register [Informative] * Level [D]</td><td>0.69</td><td>-0.32 – 1.70</td><td>0.183</td></tr><tr><td>Register [Instructional] * Level [D]</td><td>-1.05</td><td>-1.83 – -0.28</td><td><strong>0.008</strong></td></tr><tr><td>Register [Personal] * Level [D]</td><td>0.26</td><td>-1.37 – 1.90</td><td>0.753</td></tr><tr><td>Register [Fiction] * Level [E]</td><td>0.97</td><td>-0.09 – 2.02</td><td>0.072</td></tr><tr><td>Register [Informative] * Level [E]</td><td>0.84</td><td>-0.22 – 1.91</td><td>0.120</td></tr><tr><td>Register [Instructional] * Level [E]</td><td>-0.68</td><td>-1.53 – 0.16</td><td>0.113</td></tr><tr><td>Register [Personal] * Level [E]</td><td>0.67</td><td>-0.97 – 2.32</td><td>0.423</td></tr><tr><td colspan="4"><strong>Random Effects</strong></td></tr><tr><td>σ<sup>2</sup></td><td>4.40</td><td></td><td></td></tr><tr><td>τ<sub>00 Series</sub></td><td>0.01</td><td></td><td></td></tr><tr><td>ICC</td><td>0.00</td><td></td><td></td></tr><tr><td>N<sub>Series</sub></td><td>9</td><td></td><td></td></tr><tr><td>Observations</td><td>1912</td><td></td><td></td></tr><tr><td>Marginal R<sup>2</sup> / Conditional R<sup>2</sup></td><td>0.472 / 0.474</td><td></td><td></td></tr></tbody></table>

As illustrated in Fig. 33, the Dimension 2 scores of Textbook Fiction and Informative texts are much more dispersed than those of the other textbook registers. Textbook Fiction and Informative texts at the higher end of the scale tend to feature short sentences with many verbs, the vast majority of which are in the past form and/or perfect aspect, and used with third-person pronouns – three of the six features with positive loadings on Biber’s narrative dimension, e.g., (183)–(184). In terms of semantics, many of these verbs belong to Biber’s public verb category (e.g., DISCUSS, SAY, WRITE). In addition, we find higher-than-average occurrences of present participial clauses and synthetic negation (e.g., *her mother had had *no idea*), e.g., (183)–(184).

(183) Cal **climbed** over the sheet of rusted metal, **sniffing** the air. He could detect ammonia and sulphur. But there **was** also something else, something special. He **called** out to **his** sister and immediately **she was** there, **jumping** down from a blackened metal beam overhead. Her green eyes **stared** at **him** out of **her** scarred and blistered face. 'Keep watch.'

**She nodded** and **turned**, **looking** out over a nightmarish landscape of twisted metal and smashed vehicles. Cal **dropped** to all fours and **crawled** towards a hole in the metal canopy. He **leaned** over the edge and **peered** into the darkness. First **he had smelt** them, now **he could** see them: pale
blue shapes **glimmering** in the darkness. They were mushrooms. They were food! <TEC: Achievers B1+>

(184) The very first cell phone novel **was written** in 2003 by a man in Tokyo who called **himself** Yoshi. It **became** so popular, mainly through word of mouth, that it **was** later **published** as a paperback. The book version **sold** 2.6 million copies and a television series, a comic book and a film **were** **made** of the story. [...] Although the idea **originated** in Japan, cell phone novels have also **sprung** up in the rest of East Asia, Europe and Africa. Many are **written** by high school or university students who are very familiar with the topics that teenagers are interested in. Twenty-one-year-old Rin **said** that **she** **started her** novel during her final year at high school and **explained** that it **was** the tragic love story of two childhood friends. [...] Rin said that her mother **had had** no idea that she **had been writing** a novel and **was** therefore very surprised when she **saw** a book with **her** daughter's name on it. <TEC: Solutions Intermediate>

Since only linguistic features with positive loadings contribute to Biber's (1988) 'Narrative vs. Non-Narrative Concerns' dimension (see Table 40), at the lower end of the Dimension 2 scale, we find texts with particularly low counts of the features mentioned above. The majority of these texts were extracted from beginner and lower-intermediate textbooks, thus largely before the past tense and perfect aspect are introduced to English learners. It is worth noting that many of the features of Dimension 2 can be considered opposites to those with positive loadings on Dimension 1: present vs. past tense, first- and second-person vs. third-person pronouns, and private vs. public verbs. Thus, it comes as no surprise that Textbook Conversation and Informative texts with high scores on Dimension 1 also tend to score low on Dimension 2. For instance, the text from which example (185) was extracted scores 21.02 on Dimension 1 and -6.76 on Dimension 2. In extract (185), features with positive loadings on Dimension 1 are highlighted. In this extract, there are zero occurrences of any of the six features that load on Dimension 2.

(185) Lucy: Hi, Sam, Justin. **What's up?**
Sam: Hi Lucy, hi Maya. Look, Justin **has** a video camera and **we want** to make a film.
Justin: **What ... we want** to make a film?
Lucy: **That's** a good idea! But **what's** the film **about?**
Sam: **It's** an action film. A kung fu film! **Do you** want to help?
Justin: An action film? Kung fu? But ...
Lucy: Sorry, Sam, but **I don't like** action films.
Sam: **Well, do you** have any other ideas?
Justin: Sorry, but **I want** to make a video about Plymouth for **my** dad.
<TEC: Access G 1>

The model summary in Table 49 also highlights a number of significant interactions between three textbook registers and three textbook proficiency levels. Since the magnitude of these effects is not immediately obvious from the model summary, the Dimension 2 estimates are plotted per register and level in Fig. 34. The mean Dimension 2 estimates are also listed in
Table 50. The scatterplots in Fig. 34 make clear that, as expected, for most registers, beginner textbooks feature very few of the features that constitute Dimension 2, especially those that have yet to be introduced to learners at this early stage of language acquisition, e.g., the past tense, perfect aspect, and present participial clauses. The difference across the different proficiency levels is greatest for the Fiction register, which reaches a peak in Dimension 2 scores in the fictional texts of level C textbooks, corresponding with the year in which the textbooks series tend to have a strong focus on the past tense and the perfect aspect, and then does not see any significant changes across the other two levels. Personal correspondence, Conversation and Informative texts all see a gradual increase in Dimension 2 scores with increasing proficiency levels, though as reported in Table 49, many of these differences are not significant and, indeed,

Table 50 shows many overlapping confidence intervals across textbook levels.

<figure><img src="image_38.png" /><figcaption>Fig. 34: Visualisation of the effects of Register*Level interactions on the estimated Dimension 2 values of the TEC texts (as calculated with the model summarised in Table 49)</figcaption></figure>
<table><caption>Table 50: Estimated Dimension 2 means and 95% confidence intervals (CI)</caption><thead><tr><th>Register</th><th>Level</th><th>mean</th><th>SE</th><th>df</th><th>lower.CL</th><th>upper.CL</th></tr></thead><tbody><tr><td>Conversation</td><td>A</td><td>-4.09</td><td>0.22</td><td>870</td><td>-4.52</td><td>-3.65</td></tr><tr><td>Conversation</td><td>B</td><td>-3.82</td><td>0.20</td><td>680</td><td>-4.21</td><td>-3.43</td></tr><tr><td>Conversation</td><td>C</td><td>-3.19</td><td>0.20</td><td>633</td><td>-3.58</td><td>-2.80</td></tr><tr><td>Conversation</td><td>D</td><td>-2.34</td><td>0.20</td><td>626</td><td>-2.74</td><td>-1.95</td></tr><tr><td>Conversation</td><td>E</td><td>-2.31</td><td>0.25</td><td>982</td><td>-2.80</td><td>-1.82</td></tr><tr><td>Fiction</td><td>A</td><td>-0.14</td><td>0.36</td><td>1547</td><td>-0.84</td><td>0.56</td></tr><tr><td>Fiction</td><td>B</td><td>2.92</td><td>0.29</td><td>1172</td><td>2.35</td><td>3.49</td></tr><tr><td>Fiction</td><td>C</td><td>3.48</td><td>0.30</td><td>1341</td><td>2.90</td><td>4.05</td></tr><tr><td>Fiction</td><td>D</td><td>2.86</td><td>0.26</td><td>1154</td><td>2.35</td><td>3.37</td></tr><tr><td>Fiction</td><td>E</td><td>2.61</td><td>0.25</td><td>1116</td><td>2.11</td><td>3.10</td></tr><tr><td>Informative</td><td>A</td><td>-3.37</td><td>0.38</td><td>1722</td><td>-4.11</td><td>-2.63</td></tr><tr><td>Informative</td><td>B</td><td>-2.41</td><td>0.28</td><td>1335</td><td>-2.96</td><td>-1.87</td></tr><tr><td>Informative</td><td>C</td><td>-1.71</td><td>0.23</td><td>927</td><td>-2.16</td><td>-1.25</td></tr><tr><td>Informative</td><td>D</td><td>-0.94</td><td>0.22</td><td>893</td><td>-1.37</td><td>-0.51</td></tr><tr><td>Informative</td><td>E</td><td>-0.75</td><td>0.23</td><td>954</td><td>-1.21</td><td>-0.30</td></tr><tr><td>Instructional</td><td>A</td><td>-2.72</td><td>0.21</td><td>899</td><td>-3.14</td><td>-2.31</td></tr><tr><td>Instructional</td><td>B</td><td>-2.65</td><td>0.19</td><td>649</td><td>-3.02</td><td>-2.28</td></tr><tr><td>Instructional</td><td>C</td><td>-2.28</td><td>0.18</td><td>589</td><td>-2.64</td><td>-1.93</td></tr><tr><td>Instructional</td><td>D</td><td>-2.03</td><td>0.18</td><td>580</td><td>-2.39</td><td>-1.68</td></tr><tr><td>Instructional</td><td>E</td><td>-1.63</td><td>0.20</td><td>660</td><td>-2.02</td><td>-1.24</td></tr><tr><td>Personal correspondence</td><td>A</td><td>-3.51</td><td>0.59</td><td>1918</td><td>-4.67</td><td>-2.36</td></tr><tr><td>Personal correspondence</td><td>B</td><td>-3.46</td><td>0.45</td><td>1841</td><td>-4.35</td><td>-2.57</td></tr><tr><td>Personal correspondence</td><td>C</td><td>-1.96</td><td>0.47</td><td>1888</td><td>-2.89</td><td>-1.03</td></tr><tr><td>Personal correspondence</td><td>D</td><td>-1.51</td><td>0.53</td><td>1906</td><td>-2.55</td><td>-0.47</td></tr><tr><td>Personal correspondence</td><td>E</td><td>-1.07</td><td>0.51</td><td>1890</td><td>-2.08</td><td>-0.06</td></tr></tbody></table>

### 6.3.1.3 Intra-textbook variation on Biber's Dimension 3

On Biber's (1988) third dimension, textbook registers exhibit few marked differences in their scores. The distribution of Dimension 3 scores on Fig. 35 and comparisons for relative contrast effects suggest two clusters of textbook registers on this dimension. On the upper end of the range, we find the Instructional ($\bar{x}$ = 3.52, SD = 2.82) and Informative texts ($\bar{x}$ = 3.12, SD = 3.07) and, on the lower end, Personal correspondence ($\bar{x}$ = -0.83, SD = 2.96), Conversation ($\bar{x}$ = -0.45, SD = 2.37), and Fiction texts ($\bar{x}$ = -0.65, SD = 2.74). The differences in scores are significant across the clusters ($p$ < .001), but there are no significant differences between the scores of the registers within each cluster.
<figure><img src="image_39.png" /><figcaption>Fig. 35: Distribution of the texts of the TEC on Biber's (1988) Dimension 3</figcaption></figure>

<table><caption>Table 51: Summary of the model: lmer(Dim3 ~ Register + Level + Level*Register + (1|Series))</caption><thead><tr><th>Predictors</th><th>Estimates</th><th>CI</th><th>p-value</th></tr></thead><tbody><tr><td>(Intercept) [Conversation, Level A]</td><td>-0.18</td><td>-1.13 - 0.77</td><td>0.715</td></tr><tr><td>Register [Fiction]</td><td>-0.15</td><td>-1.10 - 0.81</td><td>0.764</td></tr><tr><td>Register [Informative]</td><td>3.03</td><td>2.04 - 4.03</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Register [Instructional]</td><td>5.16</td><td>4.46 - 5.85</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Register [Personal]</td><td>-0.04</td><td>-1.47 - 1.39</td><td>0.957</td></tr><tr><td>Level [B]</td><td>-0.29</td><td>-0.96 - 0.37</td><td>0.389</td></tr><tr><td>Level [C]</td><td>-0.24</td><td>-0.91 - 0.42</td><td>0.471</td></tr><tr><td>Level [D]</td><td>0.52</td><td>-0.15 - 1.19</td><td>0.128</td></tr><tr><td>Level [E]</td><td>-0.08</td><td>-0.84 - 0.67</td><td>0.829</td></tr><tr><td>Register [Fiction] * Level [B]</td><td>0.35</td><td>-0.89 - 1.58</td><td>0.582</td></tr><tr><td>Register [Informative] * Level [B]</td><td>0.48</td><td>-0.77 - 1.74</td><td>0.45</td></tr><tr><td>Register [Instructional] * Level [B]</td><td>-0.89</td><td>-1.81 - 0.03</td><td>0.058</td></tr><tr><td>Register [Personal] * Level [B]</td><td>0.18</td><td>-1.64 - 2.00</td><td>0.844</td></tr><tr><td>Register [Fiction] * Level [C]</td><td>-0.15</td><td>-1.39 - 1.09</td><td>0.809</td></tr><tr><td>Register [Informative] * Level [C]</td><td>1.08</td><td>-0.12 - 2.28</td><td>0.077</td></tr><tr><td>Register [Instructional] * Level [C]</td><td>-1.52</td><td>-2.43 - -0.61</td><td><strong>0.001</strong></td></tr><tr><td>Register [Personal] * Level [C]</td><td>-0.56</td><td>-2.41 - 1.28</td><td>0.55</td></tr><tr><td>Register [Fiction] * Level [D]</td><td>-0.05</td><td>-1.25 - 1.15</td><td>0.934</td></tr><tr><td>Register [Informative] * Level [D]</td><td>-0.14</td><td>-1.33 - 1.06</td><td>0.821</td></tr><tr><td>Register [Instructional] * Level [D]</td><td>-2.13</td><td>-3.04 - -1.21</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Register [Personal] * Level [D]</td><td>-2.10</td><td>-4.03 - -0.17</td><td><strong>0.033</strong></td></tr><tr><td>Register [Fiction] * Level [E]</td><td>-0.52</td><td>-1.76 - 0.72</td><td>0.411</td></tr><tr><td>Register [Informative] * Level [E]</td><td>1.07</td><td>-0.18 - 2.32</td><td>0.094</td></tr><tr><td>Register [Instructional] * Level [E]</td><td>-1.08</td><td>-2.07 - -0.08</td><td><strong>0.033</strong></td></tr></tbody></table>
<table><thead><tr><th></th><th></th><th></th></tr></thead><tbody><tr><td>Register [Personal] * Level [E]</td><td>0.18</td><td>-1.75 - 2.12</td><td>0.852</td></tr><tr><td colspan="4" style="text-align: center; font-style: italic;">Random Effects</td></tr><tr><td>σ<sup>2</sup></td><td>6.08</td><td></td><td></td></tr><tr><td>τ<sub>00</sub> Series</td><td>1.51</td><td></td><td></td></tr><tr><td>ICC</td><td>0.2</td><td></td><td></td></tr><tr><td>N<sub>Series</sub></td><td>9</td><td></td><td></td></tr><tr><td>Observations</td><td>1912</td><td></td><td></td></tr><tr><td>Marginal R<sup>2</sup> / Conditional R<sup>2</sup></td><td>0.353 / 0.482</td><td></td><td></td></tr></tbody></table>

According to Biber's interpretation of this third dimension, textbook registers are characteristically situation-dependent, thus more like broadcasts and conversation, rather than explicit, as in official documents and professional letters. However, it should be noted that most of the features with positive loadings on this dimension, WH-relative clauses, pied-piping constructions, and nominalisations, are comparatively rare across all textbook registers. That said, these are fairly rare features in general English and the textbook texts being relatively short, the median count for WH-relative clauses on object and subject positions and pied-piping constructions is zero, thus very low z-scores are observed across all registers for this feature. According to the counts generated by the MAT (see 6.2.3), nominalisations are more frequent in instructional language than in any other textbook register. This is due to how nominalisations are operationalised in Biber's original MDA: all noun lemmas ending in -tion, -ment, -ness, and -ity are counted as instances of nominalisations (Biber 1988: 227). As a result, this variable is highly inflated by a number of lemmas that are very frequent in textbooks' instructions and explanations: foremost activity, followed by statement, document, comment, argument and element (186)-(188), even though many of these nouns are not necessarily foremost derived from a verb, e.g., document and element.

(186) In pairs, read out your **statements** and guess if your partner's **statements** are true or false. <TEC: Achievers A1>

(187) Team **activity**: Impressions of New York
a) In groups of four, write each of these **statements** about New York on a piece of paper: <TEC: Green Line 4>

(188) What sport is the **document** about? b. Find important qualities related to this **activity**. <TEC: Hi There 5>

For both the Instructional and Informative registers, the variables which contribute most to their high scores on Dimension 3 are phrasal coordination, with high mean z-scores (which have a positive loading on this dimension), as well as adverbs (with a negative loading), for which the corresponding z-scores are very low. For the Biber Tagger, and correspondingly also for the MAT, phrasal coordination refers to two adjectives, adverbs, verbs, or nouns separated by *and*. This is a pattern which occurs more frequently across all textbook registers than on average in Biber's (1988) general English corpus, but which is particularly frequent in instructional and informative
texts of the TEC. Verb + *and* + verb trigrams are particularly frequent in textbook instructions, e.g., (189)–(190). Though it is worth noting that the lemma list of these verb combinations spans more than 300 different combinations, the most frequent ones are: *ask and answer* (*n* = 283), *listen and check* (*n* = 251), *read and listen* (*n* = 76), *listen and repeat* (*n* = 66), *check and repeat* (*n* = 53), *listen and write* (*n* = 25), *listen and say* (*n* = 25), *listen and find* (*n* = 21), *check and correct* (*n* = 18), *look and speak* (*n* = 17), *listen and match* (*n* = 15), *listen and answer* (*n* = 15), *like and do* (*n* = 15), *listen and complete* (*n* = 13), *stop and think* (*n* = 11), *listen and identify* (*n* = 11) and *read and complete* (*n* = 10).

(189) Pronunciation (Different stress in German and English words)
a) Write down the German and English words. Underline the stressed vowel in each word.
b) Listen and check. True and false friends. <TEC: Access G 3>

(190) Jamie's school dinners. a. Read and learn about Jamie's campaign. <TEC: Hi There 6e>

Although noun + *and* + noun combinations are the most frequent type of phrasal coordination in both registers, these are considerably less formulaic than the verb + *and* + verb collocations listed above. In instructions, the most frequent nominal phrasal coordination trigrams are *words and phrases* (*n* = 121), *words and expressions* (*n* = 55), *A and B* (*n* = 49), *question(s) and answer(s)* (*n* = 47), *pros and cons* (*n* = 27), and *advantages and disadvantages* (*n* = 25). Adjective + *and* + adjective and adverb + *and* + adverb trigrams are more frequent in informative texts than in instructions but, apart from the trigrams *more and more* (*n* = 29) and *again and again* (*n* = 8), these also display considerably less formulaicity and thus the Textbook Informative subcorpus features almost as many trigram types as tokens, e.g., (192)–(193). In the Instructional register, the most frequent adjectival trigrams are *positive and negative* (*n* = 21) and *good and bad* (*n* = 12), as well as several phrases specific to language learning, e.g., (194)–(195).

(191) TV is so 20th century! **More and more** young people are using online social networks like my Space and facebook.com, popular in many different countries. <TEC: English in Mind 3>

(192) The fresh sea air and the warm sun are nice. But the beaches are not always warm and sandy. Sometimes they are cold, **windy and rocky**. <TEC: Green Line 1>

(193) Gumbo is a dish from Louisiana, a kind of stew with many ingredients. There are different ways of making it, but it usually has vegetables, meat, fish, **herbs and spices** in it. [...] It is eaten by **young and old**, **black and white**, **rich and poor**, both every day and on important occasions. <TEC: Access G 4>

(194) Finish the dialogue. Use **positive and negative** verbs. <TEC: Access G 1>

(195) YOU MUST: use the past tense (**regular and irregular** verbs), use negatives. <TEC: Piece of Cake 5e>
As compared with the previous two dimensions, Dimension 3 scores display the greatest influence due to textbook series-based idiosyncrasies: the mixed-effects model that predicts Biber's (1988) Dimension 3 scores without the by-series random effect structure explains 35% of the total variance among the texts of the TEC (see R²<sub>marginal</sub> on Table 51) whilst, by adding by-series intercepts, its predictive power reaches 48% (see R²<sub>marginal</sub> on Table 51). This effect is visualised in Fig. 36

<figure><img src="image_40.png" /></figure>

on which the three French textbook series, *Hi There* (HT), *Join the Team* (JTT) and *Piece of Cake* (POC), stand out as having markedly higher Dimension 3 scores across all registers. Two sets of features contribute to this effect. First, phrasal coordination, which, as discussed above, is generally very prominent in Textbook English, appears to be even more frequent in French textbooks. Second, three adverbial features which contribute negatively to Dimension 3 scores, time adverbials, place adverbials and
general adverbs<fnref n="44" /> are comparatively rare in these three French series as compared to the German and Spanish series of the TEC.

<figure><img src="image_41.png" /><figcaption>Fig. 36: Estimated scores on Biber's (1988) Dimension 3 subdivided by register and textbook series</figcaption></figure>

<footnote n="44">⁴⁴ Biber’s ‘general adverbs’ is a broad category that includes prototypical adverbs such as *easily*, *remarkably*, *yet* and *never*, question words, the particles of some phrasal verbs such as *go back* and *turn around* and, following Biber’s [1988] tagging rules, also encompasses many words that frequently function as discourse markers, e.g., *so*, *right*, *yes*, *anyway*, whilst excluding adverbs counted in more specific categories such as time and place adverbials, hedges, amplifiers and downtoners. This general adverbs category is problematic both linguistically, because it somewhat arbitrarily agglomerates adverbs with very different functions, and, technically, since the MAT is known to inflate z-scores for this category (Nini 2014: 13) – the cause of which has yet to be identified (Nini 2019: 77 and personal communication).</footnote>
6.3.1.4 Intra-textbook variation on Biber's Dimension 4

Fig. 37 shows that the mean textbook register scores on Biber’s (1988) fourth dimension are all negative. The original raincloud plot revealed an outlier in the Informative register which was removed from the plot printed below. It corresponds to a text from *Join the Team 5e* which gives advice about how “princesses” should respond if “a guy” asks them out on a date, an extract of which is printed below (196). Its primary function is to persuade, so it comes as no surprise that it should score so high on this ‘Overt Persuasion’ dimension. Indeed, it features singularly high normalised counts for five of the six features that make positive contributions to this dimension: infinitives (e.g., *go*, *prepare*), predictive modals (*will*, *would*), suasive verbs (e.g., *ASK*, *ALLOW*), conditional subordination (*if*) and necessity modals (*must*).

(196)

**If HE asks YOU out...**

You lucky girl! He **asked!** He finally **asked!** [...] Be enthusiastic, but be cool.

**If** you are like me, and your father, the prince of a small European country, **won't allow** you to **go** out with a boy he hasn't met, you **must** confess this IMMEDIATELY to any boy who **asks** you out. [...] He needs time to **prepare** mentally [...].

**If** you have to **check** with your parents before accepting a date, say « Oh, I'd love to **go** to the planetarium with you on Saturday, but I have to check with my mom first. May I call you back when I know for sure?

Then be sure to **call** him back promptly.

<TEC: Join the Team 5e, ellipses in the original>

<figure><img src="image_42.png" /><figcaption>Fig. 37: Distribution of the texts of the TEC on Biber’s (1988) Dimension 4 (with one outlier from the Informative register removed)</figcaption></figure>
In order for it to not unduly influence the model, this outlier text was also removed from the dataset used to model Dimension 4 scores. As with Dimensions 2 and 3, this 'Overt Persuasion' dimension is also susceptible to significant Register*Level effects (see Table 52). These are visualised in Fig. 38, which shows that all six linguistic features that load on Dimension 4 are least frequent in Level A textbooks, and slightly more frequent, though still less than average, in Level B textbooks. The differences between the three more advanced levels are not significant. Whilst it was to be expected that *if*-sentences, and consequently conditional subordinators (*if*, *unless*) and, to a lesser extent, prediction modals (*will*, *would* and *shall*), as well as split auxiliaries are not introduced until more advanced textbooks, the most notable difference between beginner and the more advanced textbooks is found in the comparatively low relative frequencies of *to*-infinitives in the lower-level textbooks. The most frequent lexico-grammatical patterns associated with *to*-infinitives, *WANT to*, *BE going to*, *HAVE to*, *LIKE to*, *TRY to*, are largely the same across the five textbook levels but they are much more frequent in the pedagogical materials designed for EFL students in their third, fourth and fifth year of secondary school than in the first two years of tuition. It is also worth bearing in mind that some textbook authors do not introduce the *BE going to* future construction in the first volume of their studies (e.g., *Access G* and *English in Mind*) – a choice that will inevitably also influence these Dimension 3 scores.

<table><caption>Table 52: Summary of the model: lmer(Dim4 ~ Register + Level + Level*Register + (1|Series)) (excluding the outlier discussed above)</caption><thead><tr><th>Predictors</th><th>Estimates</th><th>CI</th><th>p-value</th></tr></thead><tbody><tr><td>(Intercept) [Conversation, Level A]</td><td>-5.74</td><td>-6.56 - -4.91</td><td>&lt;0.001</td></tr><tr><td>Register [Fiction]</td><td>1.01</td><td>-0.23 - 2.24</td><td>0.11</td></tr><tr><td>Register [Informative]</td><td>0.98</td><td>-0.30 - 2.27</td><td>0.134</td></tr><tr><td>Register [Instructional]</td><td>1.75</td><td>0.85 - 2.64</td><td>&lt;0.001</td></tr><tr><td>Register [Personal]</td><td>0.49</td><td>-1.37 - 2.34</td><td>0.608</td></tr><tr><td>Level [B]</td><td>3.83</td><td>2.97 - 4.70</td><td>&lt;0.001</td></tr><tr><td>Level [C]</td><td>5.96</td><td>5.11 - 6.82</td><td>&lt;0.001</td></tr><tr><td>Level [D]</td><td>5.69</td><td>4.82 - 6.56</td><td>&lt;0.001</td></tr><tr><td>Level [E]</td><td>6.38</td><td>5.39 - 7.36</td><td>&lt;0.001</td></tr><tr><td>Register [Fiction] * Level [B]</td><td>-1.59</td><td>-3.19 - 0.00</td><td>0.05</td></tr><tr><td>Register [Informative] * Level [B]</td><td>-2.43</td><td>-4.06 - -0.80</td><td>0.003</td></tr><tr><td>Register [Instructional] * Level [B]</td><td>-2.49</td><td>-3.67 - -1.30</td><td>&lt;0.001</td></tr><tr><td>Register [Personal] * Level [B]</td><td>-0.11</td><td>-2.46 - 2.24</td><td>0.928</td></tr><tr><td>Register [Fiction] * Level [C]</td><td>-1.82</td><td>-3.42 - -0.22</td><td>0.026</td></tr><tr><td>Register [Informative] * Level [C]</td><td>-2.89</td><td>-4.44 - -1.33</td><td>&lt;0.001</td></tr><tr><td>Register [Instructional] * Level [C]</td><td>-3.94</td><td>-5.12 - -2.76</td><td>&lt;0.001</td></tr><tr><td>Register [Personal] * Level [C]</td><td>-0.32</td><td>-2.71 - 2.07</td><td>0.793</td></tr><tr><td>Register [Fiction] * Level [D]</td><td>-1.21</td><td>-2.76 - 0.35</td><td>0.128</td></tr><tr><td>Register [Informative] * Level [D]</td><td>-2.13</td><td>-3.67 - -0.59</td><td>0.007</td></tr><tr><td>Register [Instructional] * Level [D]</td><td>-3.59</td><td>-4.77 - -2.40</td><td>&lt;0.001</td></tr></tbody></table>
<table><tbody><tr><td>Register [Personal] * Level [D]</td><td>2.76</td><td>0.27 – 5.26</td><td><strong>0.030</strong></td></tr><tr><td>Register [Fiction] * Level [E]</td><td>-2.2</td><td>-3.81 – -0.59</td><td><strong>0.007</strong></td></tr><tr><td>Register [Informative] * Level [E]</td><td>-2.39</td><td>-4.02 – -0.77</td><td><strong>0.004</strong></td></tr><tr><td>Register [Instructional] * Level [E]</td><td>-2.85</td><td>-4.14 – -1.56</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Register [Personal] * Level [E]</td><td>-0.12</td><td>-2.63 – 2.38</td><td>0.923</td></tr><tr><td colspan="4"><strong>Random Effects</strong></td></tr><tr><td>σ<sup>2</sup></td><td colspan="3">10.18</td></tr><tr><td>τ<sub>00 Series</sub></td><td colspan="3">0.59</td></tr><tr><td>ICC</td><td colspan="3">0.06</td></tr><tr><td>N<sub>Series</sub></td><td colspan="3">9</td></tr><tr><td>Observations</td><td colspan="3">1911</td></tr><tr><td>Marginal R<sup>2</sup> / Conditional R<sup>2</sup></td><td colspan="3">0.213 / 0.256</td></tr></tbody></table>

<figure><img src="image_43.png" /><figcaption>Fig. 38: Estimated mean Dimension 4 scores subdivided per textbook register and proficiency level</figcaption></figure>

### 6.3.1.5 Intra-textbook variation on Biber's Dimension 5

The scores on Biber's (1988) fifth and sixth dimensions are only marginally useful for the exploration of Textbook English registers because they are made up of just six and four positive-loading features respectively (see Table 40) – all of which are relatively rare in Textbook English (see Table 43). As a result, for many textbook
texts, the normalised counts for these features are equal to zero. This leads to floor effects, as seen in the corresponding rainplots (see Fig. 39 and 40). For Dimension 5, the floor level is -3.92: the score obtained for 265 out of the 1949 textbook texts processed and analysed for this additive MDA (14%) (see Fig. 39).

<figure><img src="image_44.png" /><figcaption>Fig. 39: Distribution of the texts of the TEC on Biber's (1988) Dimension 5</figcaption></figure>

The mean textbook register scores on Biber's fifth 'Abstract vs. Non-Abstract Information' dimension are all negative. This corresponds to Biber's (1988) results since only academic prose and official documents – two registers largely absent from school EFL textbooks – registered positive scores on this dimension (around 5), whilst newspaper writing and popular lore were situated around zero and all other registers scored negatively. Newspaper writing is the closest register to the Informative textbook register and this is the only register with a significantly different mean Dimension 5 score from all other textbook registers ($\bar{x} = -0.11, SD = 2.43$). However, it should also be noted that the features that make positive contributions to Dimension 5 (conjuncts, agentless passives, past participial clauses, *by*-passives, past participial WHIZ deletion relatives<fnref n="45" />, other adverbial subordinators) are not typically introduced until after the first few years of EFL tuition. Indeed, the Dimension 5 scores reported in Table 53 show that, as students are gradually introduced to these features, the scores of the Textbook Informative texts progressively rise from negative values to above zero. It is worth noting that the Level E mean score for Textbook Informative writing roughly corresponds to Biber's (1988) Dimension 5 scores for his

<footnote n="45">⁴⁵ In the TEC, this feature corresponds to constructions such as “Match two words to make collocations *used* in the DVD clips” <TEC: Solutions Intermediate Plus> and “[P]eople sing Auld Lang Syne (The Good Old Days), a song *written* by Robert Burns in the 1780s to honour old and new friends.” <TEC: Hi There 5⁶>.</footnote>
press reportage, editorials, and reviews subcorpora, though the increasingly large standard deviation values also show that the informative texts featured in the more advanced textbooks vary substantially in their use of the six features that load on Biber's (1988) Dimension 5 (see Table 40).

<table><caption>Table 53: Mean Dimension 5 scores for Informative texts grouped by textbook level</caption><thead><tr><th>Textbook Level</th><th>Mean score (SD)</th></tr></thead><tbody><tr><td>A</td><td>-2.52 (0.99)</td></tr><tr><td>B</td><td>-1.18 (1.94)</td></tr><tr><td>C</td><td>0.08 (2.39)</td></tr><tr><td>D</td><td>0.46 (2.28)</td></tr><tr><td>E</td><td>0.68 (2.54)</td></tr></tbody></table>

Since the few features that load on Dimensions 5 and 6 are largely absent in beginner textbooks, these two dimensions were only modelled for the texts of intermediate to advanced textbooks (Levels C to E). For Dimension 5, this filtering of the textbook data resulted in just 16 texts scoring the minimum dimension score corresponding to zero occurrences of all six features. However, in spite of this filtering, the distributions of the Dimension 5 and 6 scores entered in the models remain skewed. This leads to violations of the normal distribution of residuals so that the results of the linear mixed-effects models summarised in Tables 54 and 55, in particular the reported *p*-values and confidence intervals, must be interpreted with caution. For both these models, the reference levels are the Conversation for the Register variable and Level C for the proficiency level variable.

<table><caption>Table 54: Summary of the model: lmer(Dim5 ~ Register + Level + Level*Register + (1|Series)) for Levels C to E textbook texts only</caption><thead><tr><th>Predictors</th><th>Estimates</th><th>CI</th><th>p-value</th></tr></thead><tbody><tr><td>(Intercept) [Conversation, Level C]</td><td>-2.07</td><td>-2.58 - -1.55</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Register [Fiction]</td><td>0.57</td><td>-0.05 - 1.20</td><td>0.071</td></tr><tr><td>Register [Informative]</td><td>1.78</td><td>1.25 - 2.31</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Register [Personal]</td><td>1.02</td><td>0.12 - 1.92</td><td><strong>0.026</strong></td></tr><tr><td>Level [D]</td><td>0.51</td><td>0.03 - 0.99</td><td><strong>0.036</strong></td></tr><tr><td>Level [E]</td><td>0.48</td><td>-0.09 - 1.04</td><td>0.096</td></tr><tr><td>Register [Fiction] * Level [D]</td><td>0.13</td><td>-0.70 - 0.97</td><td>0.752</td></tr><tr><td>Register [Informative] * Level [D]</td><td>0.05</td><td>-0.68 - 0.78</td><td>0.886</td></tr><tr><td>Register [Personal] * Level [D]</td><td>-0.96</td><td>-2.29 - 0.38</td><td>0.160</td></tr><tr><td>Register [Fiction] * Level [E]</td><td>-0.39</td><td>-1.26 - 0.48</td><td>0.375</td></tr><tr><td>Register [Informative] * Level [E]</td><td>0.67</td><td>-0.12 - 1.46</td><td>0.095</td></tr><tr><td>Register [Personal] * Level [E]</td><td>-0.62</td><td>-1.97 - 0.72</td><td>0.363</td></tr><tr><td colspan="4"><strong>Random Effects</strong></td></tr><tr><td>σ<sup>2</sup></td><td>3.57</td><td></td><td></td></tr><tr><td>τ<sub>00 Series</sub></td><td>0.34</td><td></td><td></td></tr></tbody></table>
<table><tr><td>ICC</td><td>0.09</td></tr><tr><td>N<sub>Series</sub></td><td>9</td></tr><tr><td>Observations</td><td>833</td></tr><tr><td>Marginal R<sup>2</sup> / Conditional R<sup>2</sup></td><td>0.184 / 0.255</td></tr></table>

The model designed to predict the scores of the intermediate and advanced textbooks on Biber's fifth 'Abstract vs. Non-Abstract Information' dimension explains just 25% of the variance (see Table 54). As also illustrated in Fig. 39, relatively little register-based variation can be observed on this dimension. The main discernible effect consists in the higher mean score for Informative texts, which, following Biber's interpretation of Dimension 5, may be considered more abstract than the other four textbook registers examined.

### 6.3.1.6 Intra-textbook variation on Biber's Dimension 6

Biber's (1988) sixth dimension is calculated on the basis of just four features: *that*-clauses as verb complements, *that*-relative clauses on object position, *that*-clauses as adjective complements and demonstratives, all which make positive contributions to this dimension. Since the first three of these are relatively low frequency features in general English, many textbook texts have a count of zero, thus the corresponding rainplot (see Fig. 40) features overwhelmingly negative scores: 66/1949 texts (3%) have the lowest value possible of -3.495 which corresponds to an absence of all four features in these texts. As hinted at in Fig. 40, these minimum scores are fairly evenly distributed across the six registers.

<figure><img src="image_45.png" /><figcaption>Fig. 40: Distribution of the texts of the TEC on Biber's (1988) Dimension 6</figcaption></figure>
Fig. 40 also features two outliers in the Conversation and Poetry registers. The former corresponds to a dialogue set during a guided tour of a touristic site (197). As all of the interlocutors are in the same environment, demonstrative pronouns are frequently employed and the tour guide's reporting of the legend results in an unusually high density of *that* clauses as verbal complements of verbs such as *BELIEVE*, *THINK* and *SEE*.

(197) OK now we are in the centre of Tintagel Castle. **This** part is called the Island Courtyard. It was once a great hall for celebrations and feasts and meetings.

Now let's go through **this** gate. Is everybody here?

OK, here's a question for all of you: Why did Prince Richard build a castle here? [...]

Maybe **this** is what he thought: "Many people here believe **that** King Arthur lived at Tintagel. If I build a castle here, maybe they will think **that** it was once King Arthur's castle. Then if I live in this castle, they will think **that** King Arthur and I are from the same family.

Yeah, but couldn't people see **that** it was a new castle, and not King Arthur's castle? <TEC: Access G 2>

The second outlier corresponds to the poem *The Lost Generation* by Jonathan Reed (198), which can be read line-by-line both from top to bottom and from bottom to top. In order to achieve this, the poet makes extensive use of *that*-clauses as verb complements, which results in the unusually high score of nearly 6 on Biber's 'On-Line Informational Elaboration' dimension.

(198) I am part of a lost generation
and I refuse to believe **that**
I can change the world
I realize this may be a shock but
"Happiness comes from within"
is a lie, and
"Money will make me happy."
So in 30 years I will tell my children
they are not the most important thing in my life
My employer will know **that**
I have my priorities straight because
work
is more important than
family
I tell you this
I will live in a country of my own making
In the future
Environmental destruction will be the norm
No longer can it be said **that**
My peers and I care about this earth
It will be evident **that**
My generation is apathetic and lethargic
It is foolish to presume **that**
There is hope. <TEC: Solutions Intermediate>
Aside from these two outliers, however, no noticeable register differences on Biber's (1988) Dimension 6 can be discerned. This is not surprising given the generally very low frequencies of the four features that load on this dimension and is confirmed by the model summarised in Table 55, which, as explained in 6.3.1.5, only covers textbook levels C to E. As compared to Biber's (1988) first five dimensions, the model for Dimension 6 explains the least amount of variance (11%).

<table><caption>Table 55: Summary of the model: lmer(Dim6 ~ Register + Level + Level*Register + (1|Series)) for levels C to E textbooks only</caption><thead><tr><th>Predictors</th><th>Estimates</th><th>CI</th><th>p-value</th></tr></thead><tbody><tr><td>(Intercept) [Conversation, Level C]</td><td>-1.67</td><td>-1.98 - -1.36</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Register [Fiction]</td><td>0.39</td><td>-0.01 - 0.78</td><td>0.055</td></tr><tr><td>Register [Informative]</td><td>0.17</td><td>-0.16 - 0.51</td><td>0.308</td></tr><tr><td>Register [Personal]</td><td>-0.05</td><td>-0.62 - 0.53</td><td>0.876</td></tr><tr><td>Level [D]</td><td>0.52</td><td>0.21 - 0.82</td><td><strong>0.001</strong></td></tr><tr><td>Level [E]</td><td>0.94</td><td>0.58 - 1.29</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Register [Fiction] * Level [D]</td><td>-0.67</td><td>-1.20 - -0.14</td><td><strong>0.013</strong></td></tr><tr><td>Register [Informative] * Level [D]</td><td>-0.27</td><td>-0.74 - 0.19</td><td>0.248</td></tr><tr><td>Register [Personal] * Level [D]</td><td>-0.35</td><td>-1.20 - 0.50</td><td>0.416</td></tr><tr><td>Register [Fiction] * Level [E]</td><td>-1.10</td><td>-1.65 - -0.54</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Register [Informative] * Level [E]</td><td>-0.82</td><td>-1.32 - -0.32</td><td><strong>0.001</strong></td></tr><tr><td>Register [Personal] * Level [E]</td><td>-0.14</td><td>-0.99 - 0.71</td><td>0.747</td></tr><tr><td colspan="4"><strong>Random Effects</strong></td></tr><tr><td>σ<sup>2</sup></td><td>1.44</td><td></td><td></td></tr><tr><td>τ<sub>00</sub> Series</td><td>0.11</td><td></td><td></td></tr><tr><td>ICC</td><td>0.07</td><td></td><td></td></tr><tr><td>N<sub>Series</sub></td><td>9</td><td></td><td></td></tr><tr><td>Observations</td><td>833</td><td></td><td></td></tr><tr><td>Marginal R<sup>2</sup> / Conditional R<sup>2</sup></td><td>0.042 / 0.112</td><td></td><td></td></tr></tbody></table>

In spite of these very low R² values and the relatively small differences in the coefficients listed in Table 55, the model summary nevertheless invites further exploration of the Register*Level interactions, which the model returns as significant. These effects are plotted in Fig. 41.
<figure><img src="image_46.png" /><figcaption>Fig. 41: Estimated scores on Biber's (1988) Dimension 6 across levels C to E textbooks</figcaption></figure>

As seen in Fig. 41, the most noticeable textbook level effect arises in the Conversation register with a very moderate increase in Dimension 6 mean scores as learners are expected to become more proficient users of English. Extract (199) corresponds to an audio transcript from a level E textbook with a high Dimension 6 score (2.24). This is due to a relatively high density of complex sentence structures with *that*-clauses complementing verbs and adjectives, relative clauses, as well as its frequent use of demonstrative pronouns.

(199) Ben: [...] Australia Day should be about celebrating the present and the future, not worrying about things **that** happened 250 years ago.

Newsreader: Alice, can I bring you in at **this** point?

Alice: Obviously I disagree. Australia Day celebrates the arrival of the British on January the 26th 1788. A lot of people feel **this** is not something to be celebrated. Look at any group of protesters - it's not just black faces in the crowd. A very wide range of Australian people feel **that** the British colonisation of our land is not something that anybody should be particularly proud of. Many of us prefer to call it 'Invasion Day'.

Ben: Yeah, but don't you think it's right **that** people want to celebrate their Australian identity? Millions of people enjoy Australia Day. You really want to stop **that**?

Alice: That's not what we're saying, and I think you know **that** really,

Ben. <TEC: Green Line New 5>
### 6.3.2 Comparative additive MDA

Having examined the extent of register variation *within* school EFL textbooks, in the following, three major textbook registers, Conversation, Fiction and Informative writing, are compared with the three corresponding target language reference corpora on Biber's (1988) first three dimensions. In other words, the three reference corpora detailed in 3.3.2 are superimposed onto the additive MDA of the Textbook English Corpus (TEC) conducted in the previous section (6.3.1).

#### 6.3.2.1 The specificities of Textbook English on Biber's (1988) Dimension 1

The distribution of the three TEC registers on Biber's (1988) Dimension 1 as compared to that of the three reference corpora is visualised in Fig. 42.

<figure><img src="image_47.png" /><figcaption>Fig. 42: Comparison of the Conversation, Fiction and Informative texts from the TEC with the three corresponding target language reference corpora on Biber's (1988) Dimension 1 (as calculated by the MAT, see below)</figcaption></figure>

Although Textbook Conversation scores highest among the textbook registers, the Spoken BNC2014 displays considerably higher scores than Textbook Conversation ($\bar{x}$ = 15.75, SD = 7.89 vs. $\bar{x}$ = 26.02, SD = 4.04). Crucially, this difference is, in fact, even greater because most Spoken BNC2014 plotted on Fig. 42 are artificially deflated: this is caused by the absence of punctuation marks in the Spoken BNC2014. Indeed, the Biber Tagger and, as its faithful “copy”, also the MAT requires the presence of punctuation marks to identify five of the 22 features with positive loadings on Biber’s Dimension 1: stranded prepositions, discourse particles, non-phrasal clause coordination, sentence relatives and direct WH-questions. As a result, the MAT
returns counts of zero for sentence relatives and direct WH-questions in the Spoken BNC2014 because these features rely on commas and full stops, both of which are entirely absent from the corpus (Love, Hawtin & Hardie 2018: 37–38). Similarly, the discourse particle variable has an incredibly low mean normalised count of 0.05 (as compared to 0.36 in Textbook Conversation). Following Biber’s (1988) operationalisation of this variable, only discourse markers preceded by a punctuation mark are counted as such by the MAT; thus, this low value only accounts for discourse markers occurring immediately after a question mark, as this is the only punctuation sign to occur in the Spoken BNC2014 (see example of the MAT output in (200)). Incidentally, this highly restrictive operationalisation of discourse markers also excludes many relevant features in Textbook Conversation, e.g., in (201) *well* is erroneously tagged as a noun. The same issue occurs with stranded prepositions, which are operationalised as prepositions immediately followed by a punctuation mark. Finally, the counts for non-phrasal clause coordination are also considerably deflated as its operationalisation also partly depends on punctuation.

(200) oh_UH is_VPRT [BEMA] he_TPP3 rapping_VBG ?_.
well_DPAR what_WP ‘s_VPRT [CONT] [BEMA] he_TPP3 doing_VBG [PROD] ?_.
<BNC2014: SQWN, as tagged by the MAT>

(201) Presenter_NN Oh_UH ?_. Why_RB ‘s_VPRT [CONT] that_DEMP ?_.
Gloria_NN Well_NN ,_, I_FPP1 ‘m_VPRT [CONT] [BEMA] from_PIN South_NN
America_NN ,_, but_CC New_NN York_NN is_VPRT [BEMA] my_FPP1 new_JJ
home_NN ._.
<TEC: Solutions Elementary, as tagged by the MAT>

Consequently, the five aforementioned features that rely on punctuation were excluded from the Dimension 1 scores of the Spoken BNC2014 and Textbook Conversation. New adjusted scores were calculated by adding the z-scores of the remaining features with positive loadings and subtracting those with negative loadings. Thus, the model summarised in Table 71 takes these adjusted comparable Dimension 1 scores as the outcome variable for the Textbook Conversation and the Spoken BNC2014 corpora. In Table 71 and the following model summaries, the reference levels are Textbook English for the Corpus variable and Conversation for the Register variable. In other words, the estimated Dimension 1 mean score for the Spoken BNC2014 (the reference corpus for Conversation) is 30.66, which corresponds to 16.01 plus the mean estimated for Textbook Conversation (14.65, the intercept). The estimated mean for the Info Teens is -12.20 which is the sum of the following coefficient estimates: 14.65 (intercept), 16.01 (Reference corpus), -20.72 (Informative register) and -22.15 (Reference*Informative interaction).
<table><caption>Table 56: Summary of the model: lmer(Dim1 adjusted ~ 1 + Corpus + Register + Corpus*Register + (Register|Source))</caption><thead><tr><th>Predictors</th><th>Estimates</th><th>CI</th><th>p-value</th></tr></thead><tbody><tr><td>(Intercept) [Textbook Conversation]</td><td>14.65</td><td>12.29 - 17.02</td><td>&lt;0.001</td></tr><tr><td>Corpus [Reference]</td><td>16.01</td><td>8.72 - 23.30</td><td>&lt;0.001</td></tr><tr><td>Register [Fiction]</td><td>-10.75</td><td>-12.56 - -8.93</td><td>&lt;0.001</td></tr><tr><td>Register [Informative]</td><td>-20.72</td><td>-22.70 - -18.74</td><td>&lt;0.001</td></tr><tr><td>Corpus [Reference] * Register [Fiction]</td><td>-15.48</td><td>-22.64 - -8.33</td><td>&lt;0.001</td></tr><tr><td>Corpus [Reference] * Register [Informative]</td><td>-22.15</td><td>-29.71 - -14.59</td><td>&lt;0.001</td></tr><tr><td colspan="4"><strong>Random Effects</strong></td></tr><tr><td>σ<sup>2</sup></td><td>35.32</td><td></td><td></td></tr><tr><td>τ<sub>00</sub> Source</td><td>12.34</td><td></td><td></td></tr><tr><td>τ<sub>11</sub> Sourceanthor</td><td>4.87</td><td></td><td></td></tr><tr><td>τ<sub>11</sub> Sourceanthor</td><td>7.32</td><td></td><td></td></tr><tr><td>ρ<sub>01</sub></td><td>0.40</td><td></td><td></td></tr><tr><td>ρ<sub>01</sub></td><td>0.12</td><td></td><td></td></tr><tr><td>ICC</td><td>0.35</td><td></td><td></td></tr><tr><td>N Source</td><td>325</td><td></td><td></td></tr><tr><td>Observations</td><td>5033</td><td></td><td></td></tr><tr><td>Marginal R<sup>2</sup> / Conditional R<sup>2</sup></td><td>0.829 / 0.889</td><td></td><td></td></tr></tbody></table>

As shown on Fig. 43, the exclusion of the features that rely on punctuation for their operationalisation further widens the gap between naturally occurring conversation and textbook dialogues ($\bar{x}$ = 30.70, SD = 4.61 vs. $\bar{x}$ = 14.80, SD = 8.09, $\bar{x}$ estimated difference = -16.01, SE = 3.72, $p$ < .0001).

<figure><img src="image_48.png" /><figcaption>Fig. 43: Comparison of scores on Biber's (1988) Dimension 1 of the Spoken BNC2014 and Textbook Conversation, recalculated so as to exclude the features that rely on punctuation marks for their operationalisation</figcaption></figure>
Table 57 sheds light on the linguistic features which most contribute to these characteristically low Dimension 1 scores for Textbook Conversation. Significance testing was performed with independent two-tailed Wilcoxon tests (asterisks denote *p*-values of < .001 after Holm correction). All the features listed in Table 57 except amplifiers, possibility modals, second-person pronouns and indefinite pronouns, contribute to textbook dialogues obtaining lower scores on this dimension.

<table><caption>Table 57: Normalised counts for the features loading on Biber's (1988) Dimension 1, except those that rely on punctuation for their operationalisations (features with positive loadings in red and bold, with negative loadings in blue)</caption><thead><tr><th rowspan="2">Features that load on Biber's (1988) Dimension 1</th><th colspan="2">Textbook Conversation</th><th colspan="2">Spoken BNC2014</th><th rowspan="2">Comparison<br>mean %<br>difference</th></tr><tr><th>mean</th><th>SD</th><th>mean</th><th>SD</th></tr></thead><tbody><tr><td><strong>Hedges</strong></td><td>0.11</td><td><strong>0.15</strong></td><td>0.26</td><td><strong>0.18</strong></td><td>-0.81*</td></tr><tr><td><strong>That-deletions</strong></td><td>0.43</td><td><strong>0.35</strong></td><td>0.91</td><td><strong>0.26</strong></td><td>-0.72*</td></tr><tr><td><strong>WH-clauses</strong></td><td>0.12</td><td><strong>0.14</strong></td><td>0.22</td><td><strong>0.09</strong></td><td>-0.59*</td></tr><tr><td><strong>Pronoun it</strong></td><td>1.76</td><td><strong>0.80</strong></td><td>3.21</td><td><strong>0.66</strong></td><td>-0.58*</td></tr><tr><td><strong>Nouns</strong></td><td>24.40</td><td><strong>4.34</strong></td><td>14.44</td><td><strong>1.73</strong></td><td>0.51*</td></tr><tr><td><strong>Causative subordination</strong></td><td>0.14</td><td><strong>0.17</strong></td><td>0.23</td><td><strong>0.16</strong></td><td>-0.49*</td></tr><tr><td><strong>DO as a pro-verb</strong></td><td>0.34</td><td><strong>0.29</strong></td><td>0.55</td><td><strong>0.21</strong></td><td>-0.47*</td></tr><tr><td><strong>Emphatics</strong></td><td>1.14</td><td><strong>0.61</strong></td><td>1.71</td><td><strong>0.51</strong></td><td>-0.40*</td></tr><tr><td><strong>Analytic negation</strong></td><td>1.55</td><td><strong>0.75</strong></td><td>2.19</td><td><strong>0.48</strong></td><td>-0.34*</td></tr><tr><td><strong>Contractions</strong></td><td>4.24</td><td><strong>1.53</strong></td><td>5.79</td><td><strong>0.86</strong></td><td>-0.31*</td></tr><tr><td><strong>Amplifiers</strong></td><td>0.31</td><td><strong>0.30</strong></td><td>0.23</td><td><strong>0.15</strong></td><td>0.30*</td></tr><tr><td><strong>Demonstrative pronouns</strong></td><td>0.76</td><td><strong>0.43</strong></td><td>1.02</td><td><strong>0.30</strong></td><td>-0.29*</td></tr><tr><td><strong>Private verbs</strong></td><td>2.00</td><td><strong>0.86</strong></td><td>2.56</td><td><strong>0.59</strong></td><td>-0.25*</td></tr><tr><td><strong>Prepositions</strong></td><td>6.58</td><td><strong>1.62</strong></td><td>5.40</td><td><strong>0.74</strong></td><td>0.20*</td></tr><tr><td><strong>Type/token ratio</strong></td><td>0.46</td><td><strong>0.05</strong></td><td>0.40</td><td><strong>0.03</strong></td><td>0.14*</td></tr><tr><td><strong>Attributive adjectives</strong></td><td>4.02</td><td><strong>1.33</strong></td><td>3.60</td><td><strong>0.60</strong></td><td>0.11*</td></tr><tr><td><strong>Average word length</strong></td><td>3.94</td><td><strong>0.26</strong></td><td>3.65</td><td><strong>0.10</strong></td><td>0.08*</td></tr><tr><td><strong>Present tense</strong></td><td>8.66</td><td><strong>2.27</strong></td><td>9.41</td><td><strong>1.22</strong></td><td>-0.08*</td></tr><tr><td><strong>First-person pronouns</strong></td><td>5.98</td><td><strong>2.14</strong></td><td>5.56</td><td><strong>1.19</strong></td><td>0.07*</td></tr><tr><td><strong>Possibility modals</strong></td><td>0.90</td><td><strong>0.53</strong></td><td>0.85</td><td><strong>0.28</strong></td><td>0.06</td></tr><tr><td><strong>BE as a main verb</strong></td><td>3.28</td><td><strong>1.07</strong></td><td>3.31</td><td><strong>0.45</strong></td><td>-0.01</td></tr><tr><td><strong>Second-person pronouns</strong></td><td>3.09</td><td><strong>1.41</strong></td><td>3.10</td><td><strong>0.84</strong></td><td>&lt;0.00</td></tr><tr><td><strong>Indefinite pronouns</strong></td><td>0.05</td><td><strong>0.10</strong></td><td>0.05</td><td><strong>0.04</strong></td><td>&lt;0.00</td></tr></tbody></table>

As compared to the Spoken BNC2014, the greatest underuses in Textbook Conversation are observed in the frequency of hedges (e.g., *sort of*), *that*-deletion (marked [THATD] in the example (202)) and the use of the pronoun *it*. Furthermore, WH-clauses (e.g., *do you know what I mean*), causatives (e.g., *because, cos*), DO as a pro-verb, emphatics (e.g., *just, really*), analytic negation, contractions, demonstrative pronouns and private verbs (e.g., *THINK, KNOW, BELIEVE, SEE, MEAN*) are also
considerably more frequent in naturally occurring conversation (202) than in textbook representations thereof (203).

(202) **it's** the term whatever you call it greenfly
yes **it's**ers that **sort of** greenflies
yes **it's** its term something from the greenflies I **think** rather than **it's** not the tree itself **it's**
the fact that **it's** the aphids term producing something
Do you **think** they drink too much of **this** and **it** makes them ill?
I **think** [THATD] they go they go too too mad on the one on the sap and **it** just produces all **this** sticky goo
oh gosh I **didn't know** <BNC2014: SRWD>

Nouns, on the other hand, appear to be considerably overrepresented in pedagogical dialogues (203). These high noun counts correlate positively with high frequencies of prepositional phrases, attributive adjectives, higher type/token ratios and longer words – all of which weigh negatively on this dimension. These features, together with relatively low frequencies of the features with positive loadings discussed above, frequently make textbook dialogues sound like rather unlikely transcripts of real-life conversations, e.g., (203).

(203) Man: Is that your **favourite British dish**?
Woman: Well, I like **roast beef** a lot. But my **real favourite** is waking up in the **morning** to the **smell** of a full **English breakfast**. Or **Welsh breakfast**, or the **full Irish breakfast**. Or the **Ulster fry**. Or the **Scottish breakfast**. **Eggs, bacon** and lots of other **tasty things**. It's more or less the **same** wherever you go in the **British Isles**. It's just the **name** that changes.
Man: Is that what you have for **breakfast** every **day**?
Woman: Well, not every **day**, but sometime at **weekends**. And of course, at **hotels** you can usually have the **full cooked breakfast** if you like.
Tastes great with a **nice cup of tea**. By the way, did you know that **people** in the **British Isles** drink around three **kilos** of **tea** every **year**.
Man: Three **kilos**?
Woman: Yes, that's over ten **times** as much **tea** as **people** in **Germany** drink. Can you pass the **milk** and **sugar**, please? <TEC: Access G 3>

By contrast, textbook conversations with comparatively high Dimension 1 scores feature more verbal features, such as present tense forms, contractions, negation, first and second-person and *it* pronouns, as well as higher normalised counts of discourse markers, amplifiers, hedges, direct WH-questions and stranded prepositions than the majority of textbook dialogues (204).

(204) Jack: Lily, there's no way I'm going to **recognise** a model, **it doesn't** matter how famous she **is**. But I tell you **what** - I **bet it isn't** her.
What's a famous model going to **be doing** in a shopping mall in **our** town?
Lily: **I think it is** her, you know! And she's **going** into that shop. Come on - **let's** go in too.
Jack: **No way.** Even if it is her - leave her alone, she **just wants** to do some shopping. And anyway, what **are you going** to do - ask her for her autograph or something?

Lily: **I don't know.** Maybe I'll just go up and say hello. What do you reckon?

<TEC: English in Mind 4>

The model summarised in Table 71 does not lend support to the hypothesis that the dialogues featured in more advanced textbooks have higher, hence more authentic-like, Dimension 1 scores. In fact, some of the Level A textbook dialogues score comparatively high on Dimension 1 owing to their restricted vocabulary, shorter utterances and frequent turns leading to lower type/token and higher verb/noun ratios (205). By contrast, many of the texts intended to represent spoken interactions in the more advanced textbooks of the TEC are characterised by a much more nominal style with high informational density, thus featuring high type/token ratios, many prepositions and longer words (206).

(205)
Lucy: **Hey, watch out!**
Sam: **Oh, sorry! Hey, you're** at Plymstock School.
Lucy: **So?**
Sam: **I'm** at Plymstock school too.
Lucy: **You aren't** from Plymouth!
Sam: **No, I'm not. I'm** new here. I'm from London.
Lucy: **OK.**
Sam: **I'm** in Year 7 in class 7EB. What about **you**?
Lucy: **I'm** in 7EB too.
Sam: **Hey, that's** cool. <TEC: Access G 1>

(206)
P: Thanks **for** your **input**, and **good luck**! Now, let's ask someone else.
Hello, can I ask you what you think **of** the **American Dream**?
B: Hello! Well, my **ancestors** moved **to** the **United States** long ago, **in**
1846, **during** the **Irish potato famine**. They were **in dire straits** and
wanted to escape **poverty**. They had to take care **of** themselves. They
worked hard, and slowly they got richer and managed to build a **new life**.
They saw the **US** as a **land of freedom** and **opportunity**, where everyone
could work hard and be successful. <TEC: Piece of Cake 3>

Textbook series only account for 8.4% of the large dispersion in Dimension 1 scores observed among Textbook Conversation texts. Nonetheless, the textbook dialogues of *English in Mind* (EIM) and *New Green Line* (NGL) tend to score marginally higher than the average textbook dialogue of the TEC. By contrast, the spoken representations of two of the three French textbook series, *Piece of Cake* and *Hi There*, score lowest on this modified version of Biber's (1988) Dimension 1. This is illustrated in Fig. 44, which shows how the effect ranges for estimated Textbook Conversation scores on this modified dimension compare (simulated using the R function merTools::REsim; Knowles & Frederick 2020).
<figure><img src="image_49.png" /><figcaption>Fig. 44: Estimated varying effects of textbook series on modified Dimension 1 scores for Textbook Conversation (simulated using the R function merTools::REsim; Knowles & Frederick 2020)</figcaption></figure>

Contrary to Textbook Conversation, which appears to be considerably less “oral-like” than the Spoken BNC2014 data, Informative texts in EFL textbooks tend to score higher on Biber’s (1988) first dimension than the Info Teens corpus ($\bar{x}$ estimated difference = 6.14, *p* = 0.002). The features which most contribute to this mean difference are first and second-person pronouns, DO as a pro-verb, contractions, and amplifiers. The prevalence of these features reflects the often informal, “chatty” tone of the informative texts featured in school EFL textbooks (207).

(207) So how can **you** help yourself to remember things better in the long term?
Well, there are several things **you** can **do**. One of them is to make sure
**you** pay attention and take in the information properly in the first
place. Others are to **do** with the effort you make to remember it
afterwards. [...] **Don't** wait to revise until exam time - by then **it's** too
late!
Although the human brain is **amazingly** powerful, most people only use a
tiny amount of its power. The brain is like a muscle. If **you** don't
exercise it, it loses its strength and deteriorates. If **you** want to
develop and improve **your** mind and make the most of it, **you** need to do
regular mental exercises. In spite of all our potential brain power, **we**
can easily forget 80% of what **we** learn in hours unless **we** make a special
attempt to remember it. <TEC: Achievers B2>

The text from which (207) was extracted corresponds to the mean Dimension 1 score
of the Textbook Informative subcorpus. By way of comparison, extract (208) scores
around the mean score of the Info Teens corpus. The latter is characterised by more nouns, prepositions, attributive adjectives and longer words.

(208) **Ayanna Pressley** has won her **election**, making her the first **black woman** to represent **Massachusetts** in the **House of Representatives**, **Boston.com** reports. She ran unopposed in **Massachusetts's** 7th district.

**Before** the **polls** closed on **election day**, she urged **people** on **Twitter** to vote. "Today, we are powerful. There are only a **few hours** left to get out the **vote**. Go #vote for **progressive candidates** who will fight for **equity** and **justice**," she tweeted. "Vote for **activist leaders** who will work in and with **community**. Vote, because this is your **democracy** and your **voice** matters." <Info Teens: teenvogue.com>

In both the TEC and Info Teens, informative texts that score lowest on Dimension 1 tend to include bullet point lists and thus feature a high proportion of nominal sentences, as well as many attributive adjectives, a high type/token ratio and longer words, e.g., (209) and Fig. 45.

(209) **Borders:** Ireland is an island in the North Atlantic Ocean. The Republic of Ireland takes up most of the island with Northern Ireland (which is part of the United Kingdom) taking up a northern section.
**Total Size:** 70,280 square km
**Size Comparison:** slightly larger than West Virginia
**General Terrain:** mostly level to rolling interior plain surrounded by rugged hills and low mountains; sea cliffs on west coast
**Geographical Low Point:** Atlantic Ocean 0 m
**Geographical High Point:** Carrauntoohil 1,041 m
**Climate:** temperate maritime; modified by North Atlantic Current; mild winters, cool summers; consistently humid; overcast about half the time
<Info Teens: ducksters.com>

<figure><img src="image_50.png" /><figcaption>Fig. 45: Informative textbook text with a low Dimension 1 score <TEC: Join the Team 4º></figcaption></figure>

Contrary to the two textbook registers discussed above, the difference in mean Dimension 1 scores between Textbook Fiction and the reference Youth Fiction is not significant (x̄ estimated difference -0.53, SE = 1.71, *p* = 0.78). Fiction usually consists of alternating narration and fictional speech. Thus, novels with a high
proportion of dialogues inevitably score high on Biber's first dimension, whilst those
with longer descriptive passages score lower. Indeed, additive MDAs of 19th century
novels have shown large significant differences on Biber's Dimension 1 between
narrative passages, which are more associated with features corresponding to the
informational end of the scale, and fictional speech, which is more associated with
features characteristic of involvement and interaction (η² = .83, p<0.001; Egbert &
Mahlberg 2020: 85; cf. Biber & Finegan 1994). These findings imply that this
dimension is not best suited to examine the potentially defining characteristics of
Textbook Fiction. That said, the non-significant difference in mean Dimension 1
scores for Textbook Fiction and the Youth Fiction does suggest that they feature
similar proportions of narration to fictional speech.

In addition, the model estimates for the mean Dimension 1 scores of the TEC registers
listed in Table 71 make clear that the small but significant effect of textbook level on
Dimension 1 scores is driven by its interactions with the Fiction register: Textbook
Fiction tends towards marginally lower Dimension 1 scores as the proficiency level of
the textbooks increases. As mentioned in 6.3.1.1, this finding must be approached
with caution: not only are the effect sizes very small, Fig. 32 also showed that some
series feature no or few Fiction texts at certain levels. Nonetheless, we can observe
that beginner textbooks tend to feature more dialogue-heavy fictional writing, leading
to a greater use of first and second-personal pronouns, contractions, negation and
demonstrative pronouns (210), than more advanced teaching materials, which often
feature many more prepositions, nouns and attributive adjectives (211). Moreover,
beginner textbooks that have yet to introduce past tense forms rely on present-tense
narration, which also contributes to these higher Dimension 1 scores (210).

(210) 'Very funny,' Lucy says. 'I think this is just a silly trick. I don't believe a word.'

'A silly trick?' the Time Lord laughs. 'Ha, ha, ha, just look at this, you silly girl!'

The lights in the Planetarium flicker again, and on the huge screen, Lucy, Sandy and Asim can see pictures of Greenwich - and it already looks very different. There aren't many old people any more, and children are looking down at clothes that are too big for them.

Then they hear the scary voice again.

'So, children. The future of the human race lies in your hands. See this hourglass here? When the sand is through, your time will be up. [...]'

<TEC: Green Line New 1>

(211) The mountains stretched into infinity: exquisite shades of green, grey and brown against a deep azure, cloudless sky. Along the wall, here and there, were small groups of tourists basking in the wonder of their surroundings. But the strangest sight of all was a table and four plastic chairs beneath a huge red parasol, and a man selling bottled water and cans of chilled drinks from an icebox. <TEC: Achievers B2>
### 6.3.2.2 The specificities of Textbook English on Biber's (1988)
Dimension 2

Biber's second 'Narrative vs. Non-narrative Concerns' dimension is also of particular interest to the analysis of Textbook English as many textbook texts can be said to have a narrative function. Fig. 46 compares the Conversation, Fiction and Informative texts of the TEC with the three reference corpora: it immediately makes apparent that the texts of Info Teens display the greatest variation in their degree of narrativeness. In fact, although their mean scores are very similar ($\bar{x}$ = -1.76 vs. -1.95), the Info Teens and the Spoken BNC2014 evidently follow very different distributions on this dimension. Textbook Fiction scores highest among the textbook registers but, on average, the texts of the Youth Fiction corpus score considerably higher ($\bar{x}$ = 2.53, SD = 2.83 vs. $\bar{x}$ = 4.91, SD = 2.08).

<figure><img src="image_51.png" /><figcaption>Fig. 46: Comparison of the Conversation, Fiction and Informative texts from the TEC with the three corresponding target language reference corpora on Biber's (1988) Dimension 2</figcaption></figure>

Table 58 summarises the model computed to predict scores on Biber's (1988) Dimension 2 for the three TEC subcorpora and the three corresponding reference corpora. As in Table 71, the reference levels are Corpus: Textbook and Register: Conversation.
<table><caption>Table 58: Summary of the model: lmer(Dim2 ~ 1 + Corpus + Register + Corpus*Register + (Register|Source))</caption><thead><tr><th>Predictors</th><th>Estimates</th><th>CI</th><th>p-value</th></tr></thead><tbody><tr><td>(Intercept) [Textbook Conversation]</td><td>-3.10</td><td>-3.47 - -2.74</td><td>&lt;0.001</td></tr><tr><td>Corpus [Reference]</td><td>1.15</td><td>0.14 - 2.17</td><td>0.026</td></tr><tr><td>Register [Fiction]</td><td>5.75</td><td>4.51 - 6.98</td><td>&lt;0.001</td></tr><tr><td>Register [Informative]</td><td>1.37</td><td>0.68 - 2.06</td><td>&lt;0.001</td></tr><tr><td>Corpus [Reference] * Register [Fiction]</td><td>1.10</td><td>-0.47 - 2.68</td><td>0.169</td></tr><tr><td>Corpus [Reference] * Register [Informative]</td><td>-1.15</td><td>-2.45 - 0.16</td><td>0.084</td></tr><tr><td colspan="4"><strong>Random Effects</strong></td></tr><tr><td>σ<sup>2</sup></td><td>3.67</td><td></td><td></td></tr><tr><td>τ<sub>00</sub> Source</td><td>0.23</td><td></td><td></td></tr><tr><td>τ<sub>11</sub> Sourceanthor</td><td>3.23</td><td></td><td></td></tr><tr><td>τ<sub>11</sub> Sourceanthor</td><td>0.92</td><td></td><td></td></tr><tr><td>ρ<sub>01</sub></td><td>-0.58</td><td></td><td></td></tr><tr><td></td><td>0.1</td><td></td><td></td></tr><tr><td>ICC</td><td>0.25</td><td></td><td></td></tr><tr><td>N Source</td><td>325</td><td></td><td></td></tr><tr><td>Observations</td><td>5033</td><td></td><td></td></tr><tr><td>Marginal R<sup>2</sup> / Conditional R<sup>2</sup></td><td>0.649 / 0.738</td><td></td><td></td></tr></tbody></table>

Table 46 lists the estimated means for each of the three TEC and reference registers. Of these three comparisons, the Conversation ($p = .03$) and Fiction ($p < .001$) contrasts are significant. The Textbook Informative and ITCC texts, on the other hand, have remarkably similar estimated mean Dimension 2 scores.

<table><caption>Table 59: Estimated Dimension 2 means (degrees-of-freedom method: asymptotic)</caption><thead><tr><th></th><th>mean</th><th>SE</th><th>lower CL</th><th>upper CL</th></tr></thead><tbody><tr><td>Textbook Conversation</td><td>-3.10</td><td>0.19</td><td>-3.47</td><td>-2.74</td></tr><tr><td>Spoken BNC2014</td><td>-1.95</td><td>0.49</td><td>-2.90</td><td>-1.00</td></tr><tr><td>Textbook Fiction</td><td>2.65</td><td>0.55</td><td>1.56</td><td>3.73</td></tr><tr><td>Youth Fiction</td><td>4.90</td><td>0.11</td><td>4.69</td><td>5.11</td></tr><tr><td>Textbook Informative</td><td>-1.73</td><td>0.39</td><td>-2.50</td><td>-0.97</td></tr><tr><td>Info Teens</td><td>-1.73</td><td>0.30</td><td>-2.31</td><td>-1.16</td></tr></tbody></table>

The intra-textbook analysis in 6.3.1 demonstrated that this dimension is subject to strong textbook level effects because it comprises grammatical features, such as the past tense and perfect aspect, which are usually not taught until the second or third year of English instruction at secondary school. A comparison of the present results with

Table 50, which summarises these textbook level effects, shows that, on average, the dialogues of the more advanced textbooks score very similarly to the Spoken BNC2014 on this narrative dimension.
However, although Dimension 2 means for Textbook Fiction do dramatically increase from -0.15 to 2.92 between Level A and Level B textbooks to reach a peak in Level C textbooks (3.48), even that latter score remains significantly lower than the Youth Fiction estimated mean (4.90). Fig. 47 explores the reason behind this lower average score for Textbook Fiction as compared to the Youth Fiction reference data by comparing the relative frequencies of all six features that load on Dimension 2. As already explained in 6.3.1.2, four of these features are largely absent from beginner textbooks; however, the boxplots for past tense, perfect aspect verbs, and present participial clauses (see Fig. 47) suggest that the median frequencies at the more advanced levels are close to the Youth Fiction ones. It is also worth noting that for particularly rare features such as synthetic negation and present participial clauses, the difference in means may well be largely attributable to text length only: indeed, textbook fictional texts are, on average, considerably shorter than the text samples of the Youth Fiction and this reason alone may account for the apparent absence of these features in Textbook Fiction texts. By contrast, the trend for public verbs across textbook levels counters expectations: whilst Level A and B fictional texts feature above-average relative frequencies of public verbs, foremost *SAY*, *WRITE*, *EXPLAIN*, *REPLY*, *REPORT* and *EXPLAIN*, Level D and E texts tend to feature fewer than in the Youth Fiction reference corpus. This confirms the finding that beginner Textbook Fiction texts feature more dialogues than narrative passages.
<figure><img src="image_52.png" /><figcaption>Fig. 47: Normalised frequencies of features loading on Biber's (1988) Dimension 2 for Textbook Fiction (subdivided by proficiency level A to E) and Youth Fiction</figcaption></figure>

### 6.3.2.3 The specificities of Textbook English on Biber's (1988) Dimension 3

On Biber's (1988) third dimension, the Spoken BNC2014 and Youth Fiction texts largely score negative values and follow very tight distributions ($\bar{x}$ = -1.18, SD = 1.29 and $\bar{x}$ = -0.985, SD = 1.62), whereas the mean score for the Info Teens is positive and the texts show much greater variation ($\bar{x}$ = 4.18, SD = 3.82).
<figure><img src="image_53.png" /><figcaption>Fig. 48: Comparison of the Conversation, Fiction and Informative texts from the TEC with the three corresponding target language reference corpora on Biber's (1988) Dimension 3</figcaption></figure>

As models with both random intercepts and slopes failed to converge, Dimension 3 scores were modelled with a simplified random effect structure with random by-source intercepts only (see 6.2.6). As shown in Table 60, only one significant effect can be observed on this dimension.

<table><caption>Table 60: Summary of the model: lmer(Dim3 ~ 1 + Corpus + Register + Corpus*Register + (1|Source))</caption><thead><tr><th>Predictors</th><th>Estimates</th><th>CI</th><th>p-value</th></tr></thead><tbody><tr><td>(Intercept) [Textbook Conversation]</td><td>-0.40</td><td>-1.09 - 0.28</td><td>0.248</td></tr><tr><td>Corpus [Reference]</td><td>-0.78</td><td>-2.85 - 1.29</td><td>0.462</td></tr><tr><td>Register [Fiction]</td><td>-0.06</td><td>-0.43 - 0.30</td><td>0.733</td></tr><tr><td>Register [Informative]</td><td>3.74</td><td>3.41 - 4.08</td><td>&lt;0.001</td></tr><tr><td>Corpus [Reference] * Register [Fiction]</td><td>0.26</td><td>-1.73 - 2.25</td><td>0.799</td></tr><tr><td>Corpus [Reference] * Register [Informative]</td><td>1.61</td><td>-0.44 - 3.66</td><td>0.123</td></tr><tr><td colspan="4"><strong>Random Effects</strong></td></tr><tr><td>σ<sup>2</sup></td><td>5.65</td><td></td><td></td></tr><tr><td>τ<sub>00</sub> Source</td><td>0.99</td><td></td><td></td></tr><tr><td>ICC</td><td>0.15</td><td></td><td></td></tr><tr><td>N Source</td><td>325</td><td></td><td></td></tr><tr><td>Observations</td><td>5033</td><td></td><td></td></tr><tr><td>Marginal R<sup>2</sup> / Conditional R<sup>2</sup></td><td>0.459 / 0.539</td><td></td><td></td></tr></tbody></table>

Table 61 lists the estimated Dimension 3 means allowing for a direct comparison between the Conversation, Fiction, and Informative texts of the TEC and the three
corresponding reference corpora. In addition, pairwise comparisons show that none of these reported mean differences between the three TEC and reference registers are statistically different. Thus, on this dimension, the three TEC registers can be said to largely match their target language counterparts. Note, however, that, as with the other dimensions, register differences appear to be less marked within textbooks than across the three target language reference corpora. Indeed, even though the differences are not significant, Table 61 suggests that, on this dimension, the conversations of the Spoken BNC2014 tend to score lower than Textbook Conversation, whereas the texts of the Info Teens usually score higher than Textbook Informative texts.

<table><caption>Table 61: Estimated mean Dimension 3 scores (degrees-of-freedom method: asymptotic)</caption><thead><tr><th></th><th>mean</th><th>SE</th><th>lower CL</th><th>upper CL</th></tr></thead><tbody><tr><td>Textbook Conversation</td><td>-0.40</td><td>0.35</td><td>-1.09</td><td>0.28</td></tr><tr><td>Spoken BNC2014</td><td>-1.18</td><td>1.00</td><td>-3.13</td><td>0.77</td></tr><tr><td>Textbook Fiction</td><td>-0.47</td><td>0.36</td><td>-1.18</td><td>0.25</td></tr><tr><td>Youth Fiction</td><td>-0.99</td><td>0.09</td><td>-1.16</td><td>-0.81</td></tr><tr><td>Textbook Informative</td><td>3.34</td><td>0.36</td><td>2.64</td><td>4.04</td></tr><tr><td>Info Teens</td><td>4.17</td><td>0.27</td><td>3.65</td><td>4.70</td></tr></tbody></table>

## 6.4 Discussion

This chapter presented the results of two additive MDAs using Biber's (1988) model of general spoken and written English as the baseline model. It demonstrated that, with some caveats, additive MDA can be successfully used to explore linguistic variation within school EFL textbooks. In the first half of the results section (6.3.1), the major registers of the TEC were compared to each other along each of Biber's (1988) six dimensions.

In answer to this chapter's RQ1, these additive MDAs have shown that Textbook English does vary considerably across different text registers. This was most striking on Dimension 1, where register explains 63.0% of the variance observed in dimension scores across the five major registers of the TEC (Conversation, Fiction, Personal correspondence, Instructional and Informative texts). By contrast, on this same dimension, textbook level and textbook series only explain 2.4% and 5.3%, respectively. This is an important finding, which confirms the absolute necessity to consider register as a key factor when investigating and/or evaluating the language of EFL textbooks – an aspect which, as shown in the literature review (Chapter 2), has largely been ignored to date. However, the results made clear that not all of Biber's (1988) dimensions are equally useful for exploring linguistic variation in EFL textbooks. Whilst register also accounts for 40.6% of variance on Dimension 2, 33.7% on Dimension 3, and 21.7% on Dimension 5, it explains less than 2% along Dimensions 4 and 6. In addition, Dimensions 4, 5 and 6 comprise some relatively rare lexico-grammatical features, which are difficult to reliably account for in the often
relatively short texts of the TEC – an issue which persisted even after having
concatenated texts totalling fewer than 400 words, as explained in 6.2.2.

Moreover, the six models computed as part of the intra-textbook analysis (see 6.3.1) confirmed that some of Biber's (1988) dimensions are more subject to textbook level effects than others. In particular, the Dimensions 1 and 3 scores of the TEC texts are relatively stable across the five different textbook levels, whereas some salient proficiency level effects were observed along Dimensions 2, 4, and 5, accounting for 6.5%, 14.7%, and 14.6% of variance among these dimension scores, respectively. In many instances, significant interactions between textbook register and textbook level were identified.

Overall, the mixed-effects models showed that Textbook English, as a language variety, is relatively homogenous across the nine different textbook series and three countries of use represented in the Textbook English corpus. The largest, though still very modest, differences between the textbook series and countries of use were observed on Dimensions 1 and 3, on which textbook series accounted for 5.3% and 13.9% of variance, respectively. In both instances, it was French textbook series that stood out: on Biber's (1988) Dimension 1, the dialogues of the series *Piece of Cake* and *Hi There* were found to score significantly lower than the Textbook Conversation average, and, on Biber's (1988) Dimension 3, all three French series scored higher across all textbook registers, thus suggesting that their texts are more explicit and less situation-dependent than those of their Spanish and German counterparts.

Whilst Section 6.3.1 showed considerable intra-textbook register variation (which largely followed the expected patterns along Biber's (1988) Dimensions 1, 2, 3 and 5), it would nevertheless appear that register-based linguistic differences are far less pronounced within textbooks than across the different text registers entered in Biber's (1988) model. This trend was confirmed in the comparative MDA analysis conducted in 6.3.2. which addressed RQ2 and RQ3: *To what extent do Textbook English registers differ from target language registers and what are the defining linguistic features that characterise Textbook English registers as compared to these target language registers?* To this end, the three TEC registers with, on average, the longest texts, Fiction, Informative and Conversation, were compared to three target language reference corpora (see 3.3.2). For instance, on Biber's (1988) Dimension 1, Textbook Conversation scored considerably lower than the Spoken BNC2014, whilst Textbook Informative texts, on average, scored higher than the texts of the Info Teens. Thus, register-based linguistic variation in Textbook English appears to be more constrained than in situationally similar, naturally occurring registers.

When accounting for textbook level effects, Textbook Fiction resembles most closely
its corresponding reference corpus of Youth Fiction novels. Two reasons were
proposed as to why fictional texts from beginner textbooks score higher than Youth
Fiction on Biber's (1988) Dimension 1. First, it may be the result of a higher
proportion of direct speech to narration in texts targeted at beginner learners.
However, it is also certainly due to the fact that, before the past tense is introduced
to learners, fictional narration is necessarily conducted in the present tense. This
second cause is also clearly observable on Biber's (1988) second, 'Narrative vs. Non-
narrative Concerns', dimension, on which beginner Textbook Fiction scores lower
than Youth Fiction. Encouragingly, however, as soon as the past tense and perfect
aspect have been introduced, few of the originally observed differences between
Textbook Fiction and the Youth Fiction remain significant.

The informative texts of the TEC, on the other hand, were shown to differ more
substantially from their counterparts of the Info Teens. On Biber's (1988)
Dimension 1, they were found to be more interactional and spoken-like than the texts
featured on informative websites targeted at English-speaking teenagers, i.e., they
feature considerably more present tense verbs, contractions, and first and second-
personal pronouns. In addition, textbook authors, understandably and likely
consciously, do not employ many of the lexico-grammatical features that characterise
informative texts (e.g., the feature that load on Dimension 4: passives, past participial
clauses, conjuncts, and adverbial subordinators) until learners are expected to have
mastered simpler structures. As a result, on several dimensions, the informative texts
of beginner textbooks are significantly less like the corresponding target language
reference corpus than those printed in the more advanced textbooks.

The most striking TEC vs. reference corpora differences were observed in the
Conversation register. On Biber's (1988) Dimension 1, in particular, Textbook
Conversation scores markedly lower than the Spoken BNC2014. This is largely due
to the much more nominal style of textbook dialogues, which tend to feature longer
speaker turns, longer words and higher type/token ratios. Thus, textbook dialogues
appear to primarily function as reinforcers of previously acquired vocabulary, rather
than as models of realistic spontaneous spoken interactions. Excluding the features
that rely on punctuation for their operationalisation, the most underrepresented
Dimension 1 features in Textbook Conversation were found to be hedges, *that*-
deletions, WH-clauses and *it* pronouns.

In spite of some limitations (see 6.5), this chapter has shown the potential of additive
MDA to both explore linguistic variation within textbooks and compare individual
textbook registers with comparable target language registers along major functional
dimensions of variation. It also has the advantage of yielding results that are
potentially comparable to many other additive MDAs based on Biber's (1988) model
of general English. That said, the main advantage of the method presented in this
chapter undoubtedly lies in the relative simplicity of conducting additive MDAs based
on Biber's (1988) model thanks to the availability of the MAT (Nini 2014) that largely automates the process (see 6.2.3). Indeed, the MAT also includes a graphical user interface (for Windows only), thus genuinely making the method accessible beyond academia. It is therefore hoped that the method may be of interest to textbook authors, editors, publishers, and representatives of educational authorities who may want to consider applying additive MDA using the MAT as part of a wide range of methods for textbook evaluation and revision purposes.

Though it is by no means claimed that additive MDA could or should be used as a unique solution, this chapter has shown that some of Biber's (1988) dimensions can provide a valuable synthesis of the relative frequencies of relevant linguistic features that can help to distinguish particularly unnatural-sounding textbook texts from more natural-sounding ones. In particular, we saw that Biber's (1988) Dimension 1 lends itself particularly well to the examination of representations of spoken language as it captures functional variation along an involved/oral vs. informational/literate continuum. Thus, a high score on Biber's Dimension 1, such as that scored by the dialogue quoted in excerpt (212) (Dim 1 = 38.19 as calculated by the MAT; items that contributed to this high score are highlighted in bold), points to a pedagogical text that is likely to paint a more authentic picture of natural conversation than one with a much lower score, e.g. (213).

(212)
Amy: Hi, Nick.
Nick: Hi, Amy. Amy, is this your backpack on the floor?
Amy: That's right.
Nick: Well, could you perhaps put it somewhere else? It's kind of in the way.
Amy: No, it's not. It's where I always leave it.
Nick: Yes, I know you always leave it there. And it's always in the way.
This is a pretty small place, Amy. So perhaps just for once you could put your backpack somewhere where it isn't in the way, hmm?
Amy: You don't own this place, Nick. So don't try and tell me what to do. I came in early to get some things done. I put my backpack on the floor. You deal with it! <TEC: English in Mind 4>

(213)
Journalist: This is Sally Gordon here in Leicester Square, London. I'm right in the middle of sports fans. Excuse me, Sir. Who is your favourite sports hero?
Dwayne: Definitely, Chris Hoy, the British track cyclist - won two gold medals. He represents strength and courage, he never gave up.
Journalist: What about you? Who is the best representative of your country?
Donna: Kobe Bryant for sure. I'm American and we are very patriotic when it comes to sport. He has shown the world we remain the dominant leaders in basketball, no doubt. And Michael Phelps of course.
Journalist: Why?
Donna: Why? He has just won four golds and two silver medals and he is a record holder. The dream came true. Incredible. That's why he is nicknamed "the Baltimore Bullet". He symbolises determination,