Abstract
A problem that tends to be ignored in the statistical analysis of experimental data in the language sciences is that responses often constitute time series, which raises the problem of autocorrelated errors. If the errors indeed show autocorrelational structure, evaluation of the significance of predictors in the model becomes problematic due to potential anti-conservatism of p-values.
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Notes
- 1.
The parametric coefficients suggest that regularity is irrelevant as predictor of naming times, that singulars are named faster than plurals, that words with voiced initial segments have longer naming times, as do words with a large number of words at Hamming distance 1 at the initial segment. Words with a greater Shannon entropy calculated over the probability distribution of their inflectional variants elicited shorter response times. A thin plate regression spline for log-transformed word frequency suggests a roughly U-shaped effect (not shown) for this predictor.
- 2.
For this to work properly, it is necessary to use treatment contrasts for ordinal factors, in R: options(contrasts = c("contr.treatment", "contr.treatment")).
- 3.
The details of the coefficients in the present model differ from those obtained in the analysis of Baayen [1]. Thanks to the factor smooths for subject and compound and the inclusion of a thin plate regression spline for word frequency, the present model provides a better fit (aic 177077.4 versus 187308), suggesting the present reanalysis may provide a more accurate window on sex-specific realizations of compounds’ pitch.
- 4.
Data points with an absolute amplitude exceeding 15 μV, approximately 2.6% of the data points, were removed to obtain an approximately Gaussian response variable.
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Baayen, R.H., van Rij, J., de Cat, C., Wood, S. (2018). Autocorrelated Errors in Experimental Data in the Language Sciences: Some Solutions Offered by Generalized Additive Mixed Models. In: Speelman, D., Heylen, K., Geeraerts, D. (eds) Mixed-Effects Regression Models in Linguistics. Quantitative Methods in the Humanities and Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-69830-4_4
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