Full-text resources of CEJSH and other databases are now available in the new Library of Science.
Visit https://bibliotekanauki.pl

Results found: 4

first rewind previous Page / 1 next fast forward last

Search results

help Sort By:

help Limit search:
first rewind previous Page / 1 next fast forward last
EN
I discuss three common practices that obfuscate or invalidate the statistical analysis of randomized controlled interventions in applied linguistics. These are (a) checking whether randomization produced groups that are balanced on a number of possibly relevant covariates, (b) using repeated measures ANOVA to analyze pretest-posttest designs, and (c) using traditional significance tests to analyze interventions in which whole groups were assigned to the conditions (cluster randomization). The first practice is labeled superfluous, and taking full advantage of important covariates regardless of balance is recommended. The second is needlessly complicated, and analysis of covariance is recommended as a more powerful alternative. The third produces dramatic inferential errors, which are largely, though not entirely, avoided when mixed-effects modeling is used. This discussion is geared towards applied linguists who need to design, analyze, or assess intervention studies or other randomized controlled trials. Statistical formalism is kept to a minimum throughout.
EN
Whereas Standard Dutch only distinguishes between two adnominal grammatical genders, substandard varieties of Belgian Dutch distinguish between three such genders. German, too, distinguishes between three genders. Nevertheless, when assigning gender to German nouns with Dutch cognates, speakers of Belgian Dutch are strongly influenced by Standard Dutch gender but to a much lesser degree (if at all) by substandard gender. On the hypothesis that a lack of metalinguistic knowledge about L1 substandard gender decreases its use as a source for transfer, I experimentally manipulated the metalinguistic knowledge about L1 substandard gender of 45 speakers of substandard Belgian Dutch varieties. I then assessed how strongly this manipulation affected the participants’ reliance on substandard gender distinctions when they assigned gender to L2 German nouns with Dutch cognates. Results confirm the strong influence of Standard Dutch, hint at a weak influence of substandard Dutch, and show no appreciable effect of the experimental manipulation.
EN
I discuss three common practices that obfuscate or invalidate the statistical analysis of randomized controlled interventions in applied linguistics. These are (a) checking whether randomization produced groups that are balanced on a number of possibly relevant covariates, (b) using repeated measures ANOVA to analyze pretest-posttest designs, and (c) using traditional significance tests to analyze interventions in which whole groups were assigned to the conditions (cluster randomization). The first practice is labeled superfluous, and taking full advantage of important covariates regardless of balance is recommended. The second is needlessly complicated, and analysis of covariance is recommended as a more powerful alternative. The third produces dramatic inferential errors, which are largely, though not entirely, avoided when mixed-effects modeling is used. This discussion is geared towards applied linguists who need to design, analyze, or assess intervention studies or other randomized controlled trials. Statistical formalism is kept to a minimum throughout.
first rewind previous Page / 1 next fast forward last
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.