NotesFAQContact Us
Collection
Advanced
Search Tips
Showing all 8 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
Aarnes Gudmestad; Thomas A. Metzger – Language Learning, 2025
In this Methods Showcase Article, we illustrate mixed-effects modeling with a multinomial dependent variable as a means of explaining complexities in language. We model data on future-time reference in second language Spanish, which consists of a nominal dependent variable that has three levels, measured over 73 participants. We offer step-by-step…
Descriptors: Second Language Learning, Spanish, Applied Linguistics, Predictor Variables
Peer reviewed Peer reviewed
Direct linkDirect link
Hancock, Gregory R.; Schoonen, Rob – Language Learning, 2015
Although classical statistical techniques have been a valuable tool in second language (L2) research, L2 research questions have started to grow beyond those techniques' capabilities, and indeed are often limited by them. Questions about how complex constructs relate to each other or to constituent subskills, about longitudinal development in…
Descriptors: Structural Equation Models, Language Research, Second Language Learning, Statistical Analysis
Peer reviewed Peer reviewed
Direct linkDirect link
Linck, Jared A.; Cunnings, Ian – Language Learning, 2015
Second language acquisition researchers often face particular challenges when attempting to generalize study findings to the wider learner population. For example, language learners constitute a heterogeneous group, and it is not always clear how a study's findings may generalize to other individuals who may differ in terms of language background…
Descriptors: Research Methodology, Language Research, Language Proficiency, Second Language Learning
Peer reviewed Peer reviewed
Direct linkDirect link
Blythe, Richard A.; Croft, William A. – Language Learning, 2009
Language is a complex adaptive system: Speakers are agents who interact with each other, and their past and current interactions feed into speakers' future behavior in complex ways. In this article, we describe the social cognitive linguistic basis for this analysis of language and a mathematical model developed in collaboration between…
Descriptors: Mathematical Models, Interaction, Interpersonal Communication, Social Cognition
Peer reviewed Peer reviewed
Direct linkDirect link
Carota, Francesca; Sirigu, Angela – Language Learning, 2008
Real-time estimation of what we will do next is a crucial prerequisite of purposive behavior. During the planning of goal-oriented actions, for instance, the temporal and causal organization of upcoming subsequent moves needs to be predicted based on our knowledge of events. A forward computation of sequential structure is also essential for…
Descriptors: Language Patterns, Brain, Language Processing, Time Perspective
Peer reviewed Peer reviewed
Direct linkDirect link
Evans, Vyvyan – Language Learning, 2008
Recent work addressing the phenomenon of perceptual simulation offers new and exciting avenues of investigating how to model knowledge representation. From the perspective of language, the simulation approach has given rise to new impetus to work on models of language understanding (e.g., Zwaan, 2004, and references therein), and provides a way of…
Descriptors: Semantics, Language Role, Knowledge Representation, Language Processing
Peer reviewed Peer reviewed
Direct linkDirect link
Toni, Ivan – Language Learning, 2008
The article by Carota and Sirigu addresses a fundamental issue, namely the domain specificity of people's ability to learn and implement sequential structures of events. The authors review theoretical positions and empirical findings related to this issue, providing a useful summary of representative models of sequential event structures, and a…
Descriptors: Linguistics, Prediction, Models, Behavior
Peer reviewed Peer reviewed
Bley-Vroman, Robert – Language Learning, 1986
Answers to theoretical questions about the place of input in a formal second language acquisition model are dependent on a distinction between two kinds of learner hypotheses. Type-N hypotheses require "negative evidence" for testing, while Type-P hypotheses are tested on the basis of "positive data" alone. (Author/CB)
Descriptors: Comparative Analysis, Error Patterns, Hypothesis Testing, Interlanguage