Multi-Dimensional Analysis, 25 years on : A tribute to Douglas Biber.

Multi-Dimensional Analysis, 25 years on -- Editorial page -- Title page -- LCC data -- Dedication page -- Table of contents -- List of contributors -- Preface -- Acknowledgments -- Introduction -- References -- Opening -- Multi-Dimensional Analysis -- References -- Part 1. Multi-Dimensional analysis and synchronic analyses -- A Multi-Dimensional analysis of regional variation in American English -- 1. Introduction -- 2. Corpus -- 3. Variable selection -- 4. Local spatial autocorrelation analysis -- 5. Factor analysis -- 5.1 Factor 1 -- 5.2 Factor 2 -- 5.3 Factor 3 -- 6. Discussion -- References -- Appendix -- Dimensions of register variation in Brazilian Portuguese -- 1. Introduction -- 2. Methods -- 3. Interpretation of the factors -- 4. Discussion and conclusion -- Acknowledgments -- References -- Appendix -- 25 years later: Comparing Internet and pre-Internet registers -- 1. Introduction -- 2. Goals, methods, and research questions -- 3. Adding the Internet registers to the pre-Internet register dimensions -- 3.1 Dimension 1 -- 3.2 Dimension 2 -- 3.3 Dimension 3 -- 3.4 Dimension 4 -- 3.5 Dimension 5 -- 4. Comparing Internet and pre-Internet registers -- 5. Conclusions -- Acknowledgments -- References -- Appendix -- Part 2. Multi-Dimensional analysisand diachronic analyses -- Dimensions of variation in North American movies -- 1. Introduction -- 2. Method and corpus -- 2.1 The North American Movie Corpus (NAMC) -- 2.2 Situational variables -- 2.3 MD analysis: An overview -- 3. Findings -- 3.1 Dimension 1: Focus on the expression of stance vs. focus on the expression of information -- 3.2 Dimension 2: Spontaneous vs. non-spontaneous discourse -- 3.3 Dimension 3: Focus on the expression arguments vs. focus on the reporting of events -- 3.4 Dimension 4: Focus on attitudinal discourse -- 3.5 Dimension 5: Situational vs. interpersonal focus.

3.6 Dimension 6: Focus on the expression of persuasion -- 3.7 Dimension 7: Focus on the overt expression of characters' nature -- 4. Conclusion -- Acknowledgements -- References -- Appendix 1 -- App endix 2 -- Dimension scores -- Multi-Dimensional analysis of pop songs -- 1. Introduction -- 2. Goals and method -- 3. Dimensions of variation in pop songs -- 3.1 Lexico-grammatical dimensions -- 3.1.1 Lexico-grammatical dimension 1: Persuasion -- 3.1.2 Lexico-grammatical dimension 2: Interaction -- 3.1.3 Lexico-grammatical dimension 3: Narrative concerns -- 3.2 Semantic dimensions -- 3.2.1 Semantic dimension 1: Personal action -- 3.2.2 Semantic dimension 2: Emotion and society -- 3.2.3 Semantic dimension 3: Musical reference -- 4. Conclusion -- Acknowledgments -- References -- Appendix 2 -- Dimensions of variation in TIME magazine -- 1. Introduction -- 2. Method -- 3. Adding TIME to the 1988 dimensions -- 4. TIME magazine's own dimensions -- 5. Variation associated with external circumstances -- 6. Text types -- 7. Summary and conclusion -- Acknowledgments -- References -- Part 3. Multi-Dimensional Analysis and language learning -- A Multi-Dimensional analysis of essay writing -- 1. Introduction -- 1.1 Essay writing -- 1.2 Writing development -- 1.3 Essay quality -- 2. Method -- Anchor 156 -- 2.2 Parameters -- 2.2.1 Essay scoring -- 2.2.2 Prompt -- 2.2.3 Grade level -- 2.2.4 Geographic area -- 2.2.5 Production type -- 2.2.6 Timing -- Anchor 164 -- 2.3.1 Basic text properties -- 2.3.2 Basic lexical types -- 2.3.3 Lexical overlap -- 2.3.4 Semantic overlap -- 2.3.5 Givenness -- 2.3.6 Causality -- 2.3.7 Connectives -- Anchor 172 -- 2.3.9 Anaphoric reference -- 2.3.10 Spatial cohesion -- 2.3.11 Temporal cohesion -- 2.3.12 Contextual cohesion indices -- 2.3.13 Rhetorical indices -- 2.3.14 Syntactic complexity -- 2.3.15 Syntactic similarity -- Anchor 180.

2.3.17 Syntactic categories -- 2.3.18 Readability formulas -- 2.3.19 Easability scores -- 2.3.20 Minimal edit distance -- 2.3.21 Word frequency -- 2.3.22 N-gram indices -- 2.3.23 N-gram accuracy -- Anchor 188 -- 2.3.25 N-gram proportion -- 2.3.26 Lexical diversity -- 2.3.27 Word property indices -- 2.3.28 Hypernymy -- 2.3.29 Polysemy -- 2.4 Statistical analysis -- 3. Results -- Anchor 196 -- 3.2 Dimension analysis -- 3.2.1 Dimension 1 (Essay Quality: Interval Scale) -- 3.2.2 Dimension 2 (Essay Quality: Low, Middle, High) -- 3.2.3 Dimension 3 (Prompt and Assignment) -- 3.2.4 Dimension 4 (Low vs. High Grade Level) -- 4. Discussion -- 4.1 Essay quality -- Anchor 204 -- 4.3 Grade level -- 5. Conclusion -- Acknowledgments -- References -- Appendix 1 -- Appendix 2 -- A Multi-Dimensional analysis of advanced written L2 Spanish -- 1. Introduction -- 1.1 Multi-Dimensional analysis and English academic writing -- 1.2 Multi-Dimensional analysis and Spanish academic writing -- 2. Methodology -- 2.1 Corpus description -- 2.2 Procedures: Tagging, searching, and norming -- 2.3 actor analysis -- 3. Results -- 3.1 Dimension 1: Expository prose -- 3.2 Dimension 2: Reporting past events -- 3.3 Dimension 3: Defining/explaining -- 3.4 Dimension 4: Hypothesizing about instructional practices -- 3.5 Dimension 5: Speculating about past actions -- 3.6 Dimension 6: Expressing causal relations in instructional settings (causal focus) -- 4. Discussion and conclusions -- References -- Appendix -- Part 4. Multi-Dimensional Analysis: Exploring the methodology -- Expanding Multi-Dimensional Analysis with qualitative research techniques -- 1. Introduction -- 2. The study -- 2.1 Background: Why study civil engineering? -- 2.2 The corpus -- 2.3 Analysis: Applying Biber's (1988) dimensions of variation in English -- 2.4 The use of interviews -- 3. Findings: The MD analysis.

3.1 Dimension 3: Elaborated vs. Situation-Dependent reference -- 3.2 Dimension 5: Abstract vs. non-abstract style -- 3.3 Problems with student writing: The MD analysis perspective -- 4. Findings: The interviews' contribution -- 4.1 Students: 'It looks better if it's longer.' -- 4.2 Students: 'The tech writing thing of no I, no we, no you.' -- 4.3 Practitioners: 'Unambiguous writing is one way to prevent unintentional liability.' -- 5. Conclusion: Integrating MD analysis and qualitative techniques -- Acknowledgements -- References -- Appendix -- MD Scores for Civil Engineering Registers -- Conducting Multi-Dimensional analysis using SPSS -- 1. Introduction -- 2. A step-by-step guide -- 2.1 SPSS v.18 and SPSS tutorials -- 3. Obtaining a dataset for FA -- 3.1 Sample size and multivariate assumptions -- 3.2 Overview of FA steps in SPSS -- 4. Running FA -- 4.1 SPSS commands to produce the FA dialog box -- 4.2 Interpreting your output -- 3.3 Rotation -- 5. Interpreting factors (from factors to dimensions) -- 5.1 Computing dimension scores -- 6. Summary -- References -- Appendix -- Author index -- Subject index.

This research describes a case study from engineering education that explores the potential for integrating Multi-Dimensional (MD) analysis into mixed methods research so that MD analyses can address more specific educational problems. The study investigates a specific problem in civil engineering: the mismatch between students' writing skills and the demands of writing in the workplace. An MD analysis compares a small number of registers of student and practitioner writing, applying the dimensions of variation in English from Biber (1988). Data from interviews with students, faculty, and practitioners are used to interpret the MD findings more fully, assess the importance of the findings, and refine the teaching applications. The two methods together result in a stronger study than either would alone.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.