Synthesizing the theory behind and methodology for conducting grammaticality judgment tests, Using Judgments in Second Language Acquisition Research aims to clarify the issues surrounding this method and to provide best practices in its use. The text is grounded on a balanced and comprehensive background of the usage of judgment data in the past up through its present-day applications. SLA researchers and graduate students will find useful a chapter serving as a "how-to" guide for a variety of situations to conduct research using judgments, including ways to optimize task design as well as examples from successful studies. Lucid and practical, Using Judgments in Second Language Acquisition Research offers guidance on a method widely used by SLA researchers, both old and new to the field.
weitere Ausgaben werden ermittelt
Patti Spinner is an Associate Professor at Michigan State University. Her work focuses on the acquisition of grammar by second language learners. She has published numerous articles in journals such as Studies in Second Language Acquisition, Language Learning, Linguistic Approaches to Bilingualism, Applied Linguistics, and more.
Susan Gass is University Distinguished Professor at Michigan State University. She has published widely in the field of Second Language Acquisition, including the textbook, co-authored with Jennifer Behney and Luke Plonsky (Second Language Acquisition: An Introductory Course, published by Routledge). She co-edits (with Alison Mackey) the Routledge series on Second Language Acquisition Research.
Table of Contents
Chapter 1: Judgment Data in Linguistic Research
2. Judgment Data in Linguistics
2.1 Terminology and underlying constructs: Grammaticality and acceptability.
2.2. Usefulness of judgment data.
2.5. Rigor in research methods.
2.6. In defense of acceptability judgments.
2.7. Gradience of linguistic data.
2.8. Who are judgments collected from?
2.9. Acceptability judgments as performance: Confounding variables.
Chapter 2: Judgment data in L2 research: Historical and theoretical perspectives
2. Judgment Data in Second Language Research
2.1. Grammaticality versus acceptability judgments.
2.2. L2 versus L1 Knowledge.
3. L2 Judgment Data: A Brief history
3.1. The value of judgment data.
3.3. The comparative fallacy.
3.4. Use as sole measure or one of many.
3.5. Judgment data and empirical research.
4. What Knowledge is Being Measured?
4.1. Measuring knowledge of form.
4.2. Implicit and Explicit Knowledge.
5. What are Judgment Tasks Used for?
6. Intervening Variables
Chapter 3: Uses of judgments in L2 research
2.1. Formal approaches.
2.2. Usage-based Approaches.
2.3. Skill acquisition theory.
2.4. Input processing/processing instruction.
2.5. Processability theory.
2.6. Interactionist approaches.
2.7. Sociocultural theory.
3. Knowledge Types
3.1. Implicit and explicit knowledge.
3.2. Procedural/declarative knowledge.
4. Specific Constructs
4.1. Critical/sensitive period.
4.2. Working memory.
5. Additional Research Areas
5.1. Neurolinguistic processing.
5.2. Neurocognitive disorders.
6. What languages have been used?
7. Proficiency levels
Chapter 4: A guide to using judgment tasks in L2 research
2. Design Features
2.1. Total number of sentences to be judged.
2.2. Number of grammatical and ungrammatical tokens per grammatical form/structure.
2.3. Target and non-target stimuli.
2.4. Instructions and practice items.
2.5. Constructing grammatical and ungrammatical pairs.
2.7. Ratings and scales.
2.8. Gradience of judgments: Alternative approaches.
2.8.1. Using lines.
2.8.2. Magnitude estimation.
2.9. Confidence ratings and source ratings.
2.10. Identifying and correcting errors.
2.12. Time limits.
2.14. Sentences in context.
- Other Considerations
3.3 Proficiency level.
4. Data Sharing
Chapter 5: Variations on judgment tasks
- Interpretation Tasks
3. Pragmatic Tasks
4. Preference Tasks
5. Error Correction Tasks
6. Multiple-Choice Tasks
7. Judgment Tasks in Combination with Psycholinguistic and Neurolinguistic Measures
8. Many Task Types in One
Chapter 6: Analyzing judgment Data
- Cleaning the data
3. Scoring responses to binary and scalar judgments
3.1. Binary judgments
3.2. Scalar responses
- Scoring corrections
5. Basic inferential statistics with judgment data
- Descriptive statistics
5.2 Comparisons between groups: t-tests and ANOVAs
5.3. Effect sizes
5.5 Mixed-effects models
6. Reporting individual results
8. Analyzing Likert scale data: z-scores
9. Binary judgments: d-prime scores
10. Analyzing magnitude estimation scores
11. Using response time data
12. Using judgment data results in conjunction with other measures
Dewey Decimal Classfication (DDC)