2008
DOI: 10.1017/cbo9780511801686
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Analyzing Linguistic Data

Abstract: Statistical analysis is a useful skill for linguists and psycholinguists, allowing them to understand the quantitative structure of their data. This textbook provides a straightforward introduction to the statistical analysis of language. Designed for linguists with a non-mathematical background, it clearly introduces the basic principles and methods of statistical analysis, using 'R', the leading computational statistics programme. The reader is guided step-by-step through a range of real data sets, allowing … Show more

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Cited by 2,765 publications
(599 citation statements)
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“…Following Kulik, Muniz, Mundry, and Widdig (2012), to control for this nonindependence, we used a repeated random selection of all events. We used a generalized linear mixed model (GLMM; Baayen, 2008) with binomial error structure and logit link function ('lmer'; Bates, Maechler, & Bolker, 2012). To establish the significance of the full model we used a likelihood ratio test, comparing the deviance with that of the null model comprising only the intercept and random effects; the full and null model were compared using the R-function anova (R Development Core Team, 2012).…”
Section: Resultsmentioning
confidence: 99%
“…Following Kulik, Muniz, Mundry, and Widdig (2012), to control for this nonindependence, we used a repeated random selection of all events. We used a generalized linear mixed model (GLMM; Baayen, 2008) with binomial error structure and logit link function ('lmer'; Bates, Maechler, & Bolker, 2012). To establish the significance of the full model we used a likelihood ratio test, comparing the deviance with that of the null model comprising only the intercept and random effects; the full and null model were compared using the R-function anova (R Development Core Team, 2012).…”
Section: Resultsmentioning
confidence: 99%
“…LME models were calculated to estimate the fixed Model fitting was performed in a step-wise fashion, starting with the most complex model that included the full factorial set of random effects (random slope-adjustment for Remnant Congruency, Context and the Interaction for both random effects Participant and Item). During model fitting, the complex models were trimmed down in a step-wise fashion using log-likelihood tests for model comparisons (see Baayen, 2008;Baayen, Davidson, & Bates, 2008). Slope-adjustments were kept in the models if the models fitted the data better than the less complex models.…”
Section: Resultsmentioning
confidence: 99%
“…Since these points are likely to have an undue influence on the model fits, points with residuals more than 3 SD from the origin were excluded (voiced: 28 points; voiceless: 35 points) (Baayen 2008). The models were then refitted to the trimmed datasets, with the result that the residual distributions were brought closer to normality.…”
Section: Procedures and Diagnosticsmentioning
confidence: 99%
“…The condition number of the model matrix was 6.8 for voiced stops and 7.3 for voiceless stops, indicating a low level of collinearity between predictors, unlikely to affect model results (Belsley et al 1980;Baayen 2008). 8 The (Pearson) correlation between fitted values and log(VOT) was r ¼ 0.693 for the voiced model and r ¼ 0.733 for the voiceless model (r 2 ¼ 0.480, 0.537).…”
Section: Procedures and Diagnosticsmentioning
confidence: 99%