Using an enhanced MDA model in study of World Englishes

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Using an enhanced MDA model in study of World
Englishes

• Richard Xiao
• University of Central Lancashire
• RXiao_at_uclan.ac.uk

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Overview of the talk

• Bibers (1988) MF/MD analytical framework
• The enhanced multidimensional analysis (MDA)
  model
• An MDA analysis of five varieties of English in
  the ICE

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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

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Bibers 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

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Bibers MF/MD approach

• Bibers 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

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Bibers 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

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The enhanced MDA model

• Enhancing Bibers 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 200 written samples
• 12 registers ranging from private conversation to
  academic writing

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ICE registers and proportions
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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

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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)

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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)

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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

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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

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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)

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1) Interactive casual discourse vs. informative
elaborate discourse
F9.04, 4 d.f. plt0.001

• 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
  Indians 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

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2) Elaborative online evaluation
F14.13 4 d.f. plt0.001

• 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

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3) Narrative concern
F7.97 4 d.f. plt0.001

• 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)

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4) Human vs. object description
F5.92 4 d.f. plt0.001

• 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

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5) Future projection
F47.63 4 d.f. plt0.001

• BrE has the highest score in all printed written
  registers (W2AW2F)
• Indian English shows the lowest score in nearly
  all registers

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6) Personal impression / judgement
F12.25 4 d.f. plt0.001

• Very similar in many registerswith 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
  (S1AS2B) and unpublished written registers (W1A,
  W1B), but it is very close to Philippine English
  in printed writing (W2AW2F)

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7) Lack of temporal / locative focus
F2.28 4 d.f. p0.058

• 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)

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8) Concern with degree / quantity
F24.32 4 d.f. plt0.001

• 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)

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9) Concern with reported speech
F1.51 4 d.f. p0.196

• 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

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Summary and future research

• Summary
• Seeking to enhance Bibers 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

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• Thank you!