Using an Enhanced MDA Model in study of World Englishes

1. Using an Enhanced MDA Model in study ofWorld Englishes Richard Xiao Richard.Xiao@edgehill.ac.uk

2. Overview of the talk • Biber’s (1988) MF/MD analytical approach • The enhanced multidimensional analysis (MDA) model • Variation across 12 registers in 5 varieties of English in the ICE CRG, Lancaster University

3. Factor analysis • The key to the multidimensional analysis approach • A common data reduction method available in many standard statistics packages such as SPSS • Reducing a large number of variables to a manageable set of underlying factors (“dimensions”) • Extensively used in social sciences to identify clusters of inter-related variables CRG, Lancaster University

4. Biber’s MF/MD framework • Established in Biber (1988): Variation across Speech and Writing (CUP) • Factor analysis of 67 functionally related linguistic features • 481 text samples, amounting to 960,000 running words • LOB • London-Lund • Brown corpus • A collection of professional and personal letters CRG, Lancaster University

5. Biber’s MF/MD approach • Biber’s seven factors / dimensions • Informational vs. involved production • Narrative vs. non-narrative concerns • Explicit vs. situation-dependent reference • Overt expression of persuasion • Abstract vs. non-abstract information • Online informational elaboration • Academic hedging CRG, Lancaster University

6. Biber’s MF/MD approach • Influential and widely used • Synchronic analysis of specific registers / genres and author styles • Diachronic studies describing the evolution of registers • Register studies of non-Western languages and contrastive analyses • Research of University English and materials development • Move analysis and study of discourse structure • …largely confined to grammatical categories CRG, Lancaster University

7. The enhanced MDA model • Enhancing Biber’s MDA by incorporating semantic components with grammatical categories • Wmatrix = CLAWS + USAS • A total of 141 linguistic features investigated • 109 features retained in the final model • Five million words in 2,500 text samples, with one million words in 500 samples for each of the 5 varieties of English • ICE – GB, HK, India, Singapore, the Philippines • 300 spoken + 200 written samples • 12 registers ranging from private conversation to academic writing CRG, Lancaster University

8. ICE registers and proportions CRG, Lancaster University

9. 141 linguistic features covered • A) Nouns: 21 categories, e.g. • nominalisation, other nouns; 19 semantic classes of nouns (e.g. evaluations, speech acts) • B) Verbs: 28 categories, e.g. • Do as pro-verb, be as main verb, tense and aspect markers, modals, passives, 16 semantic categories of verbs • C) Pronouns: 10 categories, e.g. • Person, case, demonstrative • D) Adjectives: 11 categories, e.g. • Attributive vs. predicative use, 9 semantic categories CRG, Lancaster University

10. 141 linguistic features covered • E) Adverbs: 7 categories • F) Prepositions (2 categories) • G) Subordination (3 categories) • H) Coordination (2 categories) • I) WH-questions / clauses (2 categories) • J) Nominal post-modifying clauses (5 categories) • K) THAT-complement clauses (3 categories) • L) Infinitive clauses (3 categories) • M) Participle clauses (2 categories) • N) Reduced forms and dispreferred structures (4 categories) • O) Lexical and structural complexity (3 categories) CRG, Lancaster University

11. 141 Linguistic features covered • P) Quantifiers (4 categories) • Q) Time expressions (11 categories) • R) Degree expressions (8 categories) • S) Negation (2 categories) • T) Power relationship (4 categories) • U) Definiteness (2 categories) • V) Helping/hindrance (2 categories) • X) Linear order (1 category) • Y) Seem / Appear (1 category) • Z) Discourse bin (1 category) CRG, Lancaster University

12. Procedure of data analysis • 1) Data clean-up • 2) Grammatical and semantic tagging with Wmatrix • 3) Extracting the frequencies of 141 linguistic features from 2,500 corpus files • 4) Building a profile of normalised frequencies (per 1,000 words) for each linguistic feature • 5) Factor analysis • Factor extraction (Principal Factor Analysis) • Factor rotation (Pramax) • Optimum structure: 9 factors • 6) Interpreting extracted factors • 7) Computing factor scores • 8) Using the enhanced MDA model in exploration of variation across registers and language varieties CRG, Lancaster University

13. The enhanced MDA model • Nine factors established in the new model • 1) Interactive casual discourse vs. informative elaborate discourse • 2) Elaborative online evaluation • 3) Narrative concern • 4) Human vs. object description • 5) Future projection • 6) Personal impression and judgement • 7) Lack of temporal / locative focus • 8) Concern with degree and quantity • 9) Concern with reported speech • Robustness of the model in register analysis CRG, Lancaster University

14. 1) Interactive casual discourse vs. informative elaborate discourse F=775.86 p<0.0001 R2=77.4% • Private conversation is most interactive and casual • Academic writing is most informative and elaborate • Spoken registers are generally more interactive and less elaborate than written registers CRG, Lancaster University

15. 2) Elaborative online evaluation F=102.20 p<0.0001 R2=31.1% • Public dialogue (e.g. broadcast discussion / interview, parliamentary debate) has the most prominent focus on elaborative online evaluation • Unscripted monologue also involves a high level of elaborative online evaluation • Persuasive writing may relate to elaborative evaluation but is not restricted by real-time production • Private conversation is least elaborative even if the evaluation is made online • Evaluation is not a concern in creative writing CRG, Lancaster University

16. 3) Narrative concern F=134.50 p<0.0001 R2=37.3% • Unscripted monologue (e.g. demonstrations, presentations, commentaries) has a narrative concern • Unsurprisingly, creative writing is also narrative • Not a concern in academic writing, non-professional writing (student essays and exam scripts), and instructional writing CRG, Lancaster University

17. 4) Human vs. object description F=44.03 p<0.0001 R2=16.3% • Private conversation is most likely to have a focus on people • Correspondence (social letters and business letters) also involves human description • Instructional writing tends to give concrete descriptions of objects • Academic and non-academic writings can also be concrete when an object or substance is described CRG, Lancaster University

18. 5) Future projection F=28.10 p<0.0001 R2=11.1% • Persuasive writing (e.g. press editorials, trying to influence people’s future attitudes and actions) has the most prominent focus on future projection • Correspondence and public dialogue also involve future projection to varying extents • Academic writing (timeless truth?) is least concerned with future projection CRG, Lancaster University

19. 6) Personal impression / judgement F=126.22 p<0.0001 R2=35.8% • Factor score of creative writing is by far greater than any other register • Frequent use of possessive and reflective pronouns, as well as adjectives of judgement / appearance • Instructional writing, private conversation, and student essays display low scores • They do not have a focus on personal impression and judgement • Scripted and unscripted monologue, public dialogue and news reportage also tend to avoid expressions of personal impression and judgement CRG, Lancaster University

20. 7) Lack of temporal / locative focus F=89.55 p<0.0001 R2=28.4%) • Student essays and persuasive writing do not have a temporal / locative focus (not concerned with concepts such as when, how long, and where) • Such specific information is of vital importance in correspondence (social and business letters) CRG, Lancaster University

21. 8) Concern with degree / quantity F=19.33 p<0.0001 R2=7.9% • Non-academic popular writing has the greatest concern of degree and quantity • Persuasive writing also displays a high propensity for expressions of degree and quantity • Such expressions tend to be avoided in instructional writing (e.g. administrative documents) and correspondence CRG, Lancaster University

22. 9) Concern with reported speech F=80.02 p<0.0001 R2=26.1% • News reportage has the greatest concern with reported speech (both direct and indirect speech) • Reported speech is also very common in creative writing (fictional dialogue) • Instructional writing and academic prose do not appear to have a concern with reported speech CRG, Lancaster University

23. 12 registers along 9 factors • Factor 1 is the dimension along which the 12 registers demonstrate the sharpest contrasts • Interactive casual discourse vs. informative elaborate discourse: a fundamental aspect of variation across registers • Robustness of the model CRG, Lancaster University

24. 5 English varieties across 9 factors • Both differences and similarities • This general picture may blur many register-based subtleties • Language can vary across registers even more substantially than across language varieties (cf. Biber 1995) CRG, Lancaster University

25. 1) Interactive casual discourse vs. informative elaborate discourse F=9.04, 4 d.f. p<0.001 • Indian English displays the lowest score in nearly all registers - it is less interactive but more elaborate • Sanyal (2007): “clumsy Victorian English [that] hangs like a dead Albatross around each educated Indian’s neck” • Modern BrE appears to be most interactive and least elaborate (e.g. S1A, S1B, W2D) • 3 varieties of English used in East and Southeast Asia are very similar CRG, Lancaster University

26. 2) Elaborative online evaluation F=14.13 4 d.f. p<0.001 • BrE generally shows a higher score than non-native varieties of English (e.g. W2A, W1B, S2B) • Non-native English varieties tend to be very close in most registers CRG, Lancaster University

27. 3) Narrative concern F=7.97 4 d.f. p<0.001 • BrE demonstrates a greater propensity for narrative concern • Most noticeably in news reportage (W2C) and instructional writing (W2D) • Indian English is least concerned with narrative • Esp. in registers like correspondence (W1B), instructional writing (W2D), and unscripted monologue (S2A) CRG, Lancaster University

28. 4) Human vs. object description F=5.92 4 d.f. p<0.001 • Very close in a number of registers (e.g. S2B, W1B, W2E) • Indian English and BrE show similarity in a greater range of registers • HK and Singapore Englishes display great similarity (except W1A) • Creative writing (W2F) is very similar in non-native varieties of English CRG, Lancaster University

29. 5) Future projection F=47.63 4 d.f. p<0.001 • BrE has the highest score in all printed written registers (W2A–W2F) • Indian English shows the lowest score in nearly all registers CRG, Lancaster University

30. 6) Personal impression / judgement F=12.25 4 d.f. p<0.001 • Very similar in many registers…with most noticeable differences in non-printed written registers (W1A, W1B), non-academic writing (W2B), and news reportage (W2C) • HK English displays a distribution pattern similar to Singapore English in spoken registers (S1A–S2B) and unpublished written registers (W1A, W1B), but it is very close to Philippine English in printed writing (W2A–W2F) CRG, Lancaster University

31. 7) Lack of temporal / locative focus F=2.28 4 d.f. p=0.058 • Overall difference is not significant statistically • …but there are noticeable differences in some registers (e.g. W1B, W2D) • Interestingly, Indian English demonstrates a consistently higher score in spoken registers (S1A-S2B) • …but a lower score in unpublished writing (e.g. W1B) CRG, Lancaster University

32. 8) Concern with degree / quantity F=24.32 4 d.f. p<0.001 • BrE generally displays a higher score in nearly all registers • HK English does not appear to be concerned with degree and quantity (e.g. W2D) • Similarly Indian English also lacks a focus on degree and quantity (e.g. W1B) CRG, Lancaster University

33. 9) Concern with reported speech F=1.51 4 d.f. p=0.196 • Overall difference is not significant • …in spite of noticeable difference in news reportage (W2C) • East and Southeast Asian English varieties show a greater propensity for concern with reported speech than BrE and Indian English CRG, Lancaster University

34. Summary and future research • Summary • Seeking to enhance Biber’s MDA model with semantic components • Introducing the new model in research of World Englishes • Directions for future research • More native English varieties from the Inner Circle • A wider and more balanced coverage of geographical regions • Including socio-culturally relevant semantic categories • Making “sense” of corpus finding by combining corpora and more traditional resources in socio-cultural studies and historical research • …adequately descriptive + sufficiently explanatory… CRG, Lancaster University

35. Thank you! Richard.Xiao@edgehill.ac.uk CRG, Lancaster University