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Using an enhanced MDA model in study of World Englishes

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1 Using an enhanced MDA model in study of World Englishes
Richard Xiao University of Central Lancashire

2 Overview of the talk Biber’s (1988) MF/MD analytical framework
The enhanced multidimensional analysis (MDA) model An MDA analysis of five varieties of English in the ICE

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 or dimensions Extensively used in social sciences to identify clusters of variables

4 Biber’s MF/MD approach
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

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

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

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 for each of the 5 varieties of English ICE – GB, HK, India, Singapore, the Philippines 300 spoken written samples 12 registers ranging from private conversation to academic writing

8 ICE registers and proportions
Spoken – Private S1B (16%) Spoken – Public S2A (14%) Spoken – Monologue – Unscripted S2B (10%) Spoken – Monologue – Scripted W1A (4%) Written – Non-printed – Non-professional writing W1B (6%) Written – Non-printed – Correspondence W2A (8%) Written – Printed – Academic writing W2B (8%) Written – Printed – Non-academic writing W2C (4%) Written – Printed – Reportage W2D (4%) Written – Printed – Instructional writing W2E (2%) Written – Printed – Persuasive writing W2F (4%) Written – Printed – Creative writing

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

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)

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)

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

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

14 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)

15 1) Interactive casual discourse vs. informative elaborate discourse
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

16 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 similar in most registers

17 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)

18 4) Human vs. object description
F=5.92 4 d.f. p<0.001 Very close in a number of registers Indian English and BrE show similarity in a greater range of registers HK and Singapore Englishes display great similarity

19 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

20 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)

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

22 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)

23 9) Concern with reported speech
F=1.51 4 d.f. p=0.196 Overall difference is not significant 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

24 Summary and future research
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 Combining corpora and more traditional resources in socio-cultural studies and historical research …adequately descriptive + sufficiently explanatory…

25 Thank you!

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