Abstract:Identifying clear and unequivocal psycholinguistic effects for lexical retrieval tasks has been the aim of a significant proportion of recent research activity. Debates have erupted concerning the existence or otherwise of particular effects on particular lexical tasks. Here, it is suggested that the reason for these debates is that researchers exercise choice in what variables they consider in their analysis. It is further illustrated that methods that have been employed for comparing the size of these effect… Show more
“…The relationships within the proposed model can be discussed in terms of their causality, but the cause-effect relationships must be backed up by relevant theory-that is, the proposed set of relationships in a model of L2 aptitude and L2 learning must stem from hypotheses and theories on L2 aptitude and L2 learning. As explained by Lewis and Vladeanu (2006):…”
Section: Using Structural Equation Modeling To Investigate Second Lanmentioning
confidence: 98%
“…The relationships within the proposed model can be discussed in terms of their causality, but the cause–effect relationships must be backed up by relevant theory—that is, the proposed set of relationships in a model of L2 aptitude and L2 learning must stem from hypotheses and theories on L2 aptitude and L2 learning. As explained by Lewis and Vladeanu (): While there are similarities between structural equation modeling and multiple regression (e.g., they are based on analysis of intercorrelations) there are fundamental differences. Multiple regression provides simple definitive results about which independent variables produce significant effects on which dependent variables.…”
Section: Using Structural Equation Modeling To Investigate Second Lanmentioning
In this study I examine the construct of aptitude in learning Chinese as a second language (L2) to an advanced level. I test 2 hypotheses: first, that L2 aptitude comprises 4 components—working memory, rote memory, grammatical sensitivity, and phonemic coding ability—and second, that L2 aptitude affects learning both directly and indirectly (mediated by strategy use and motivation). Native speakers of English (n = 96) studying advanced Chinese took the Modern Language Aptitude Test and a phonological working memory test and responded to motivation and strategy use questionnaires. Using end‐of‐course listening, reading, and speaking proficiency test results as measures of Chinese learning, I constructed a structural equation model to test the hypotheses. The model fit the observed data. Of the 4 components foreseen to comprise L2 aptitude, rote memory contributed the most and working memory the least. Aptitude, strategy use, and motivation had about the same impact on learning but varied in how well they predicted the individual skills of listening, reading, and speaking. The results shed light on L2 aptitude in the particular context of an advanced L2 Chinese course.
“…The relationships within the proposed model can be discussed in terms of their causality, but the cause-effect relationships must be backed up by relevant theory-that is, the proposed set of relationships in a model of L2 aptitude and L2 learning must stem from hypotheses and theories on L2 aptitude and L2 learning. As explained by Lewis and Vladeanu (2006):…”
Section: Using Structural Equation Modeling To Investigate Second Lanmentioning
confidence: 98%
“…The relationships within the proposed model can be discussed in terms of their causality, but the cause–effect relationships must be backed up by relevant theory—that is, the proposed set of relationships in a model of L2 aptitude and L2 learning must stem from hypotheses and theories on L2 aptitude and L2 learning. As explained by Lewis and Vladeanu (): While there are similarities between structural equation modeling and multiple regression (e.g., they are based on analysis of intercorrelations) there are fundamental differences. Multiple regression provides simple definitive results about which independent variables produce significant effects on which dependent variables.…”
Section: Using Structural Equation Modeling To Investigate Second Lanmentioning
In this study I examine the construct of aptitude in learning Chinese as a second language (L2) to an advanced level. I test 2 hypotheses: first, that L2 aptitude comprises 4 components—working memory, rote memory, grammatical sensitivity, and phonemic coding ability—and second, that L2 aptitude affects learning both directly and indirectly (mediated by strategy use and motivation). Native speakers of English (n = 96) studying advanced Chinese took the Modern Language Aptitude Test and a phonological working memory test and responded to motivation and strategy use questionnaires. Using end‐of‐course listening, reading, and speaking proficiency test results as measures of Chinese learning, I constructed a structural equation model to test the hypotheses. The model fit the observed data. Of the 4 components foreseen to comprise L2 aptitude, rote memory contributed the most and working memory the least. Aptitude, strategy use, and motivation had about the same impact on learning but varied in how well they predicted the individual skills of listening, reading, and speaking. The results shed light on L2 aptitude in the particular context of an advanced L2 Chinese course.
“…Word features are characteristics inherent to words. Therefore, you cannot manipulate them at will (Lewis & Vladeanu, 2006). All you can do is correlate them with processing times.…”
Section: Introductionmentioning
confidence: 99%
“…All you can do is correlate them with processing times. As a result, multiple regression analysis (and to a lesser extent, structural equation modelling) has become an essential part of psycholinguistic research, in addition to factorial designs where small-scale samples of stimuli are selected and matched on a series of control variables (Baayen, Feldman, & Schreuder, 2006; Balota, Cortese, Sergent-Marshall, Spieler, & Yap, 2004; Lewis & Vladeanu, 2006; Liben-Nowell, Strand, Sharp, Wexler, & Woods, 2019).…”
We present a new database of Dutch word recognition times for a total of 54 thousand words, called the Dutch Crowdsourcing Project. The data were collected with an internet vocabulary test. The database is limited to native Dutch speakers. Participants were asked to indicate which words they knew. Their response times were registered, even though the participants were not asked to respond as fast as possible. Still, the response times correlate around .7 with the response times of the Dutch Lexicon Projects for shared words. Also results of virtual experiments indicate that the new response times are a valid addition to the Dutch Lexicon Projects. This not only means that we have useful response times for some 20 thousand extra words, but we now also have data on differences in response latencies as a function of education and age. The new data correspond better to word use in the Netherlands.
“…Authors realise that the traditional small-scale factorial experiments are not the best approach because they lack power (Keuleers, Diependaele, & Brysbaert, 2010), do not give information about the full range of variables (Kuperman, Estes, Brysbaert, & Warriner, in press), and are open to experimenter bias in stimulus selection (Forster, 2000;Kuperman, in press). A better approach is to treat word recognition studies not as experiments in which word features can be manipulated but as correlational studies in which covariations between word features and word processing performance can be assessed (Baayen, Feldman, & Schreuder, 2006;Balota, Cortese, Sergent-Marshall, Spieler, & Yap, 2004;Lewis & Vladeanu, 2006). As a result, researchers have collected word processing times for thousands of words in so-called lexicon projects.…”
a b s t r a c t a r t i c l e i n f oWord processing studies increasingly make use of regression analyses based on large numbers of stimuli (the socalled megastudy approach) rather than experimental designs based on small factorial designs. This requires the availability of word features for many words. Following similar studies in English, we present and validate ratings of age of acquisition and concreteness for 30,000 Dutch words. These include nearly all lemmas language researchers are likely to be interested in. The ratings are freely available for research purposes.
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