Методы лингвистического анализа

incorrect, is called garden-pathing. However, if the comprehension system makes immediate use of semantic animacy cues, a sentence like “The evidence examined by the lawyer turned out to be unreliable” should not result in a garden path: since “the evidence” is inanimate, it cannot be the subject of “examine.” This brings up the question of modularity, a key theme in psycholinguistics: does the language processing system use both syntactic and semantic cues (as well as other cues) when parsing a sentence (an interactive system), or is the system modular – in particular, do early stages of processing only make use of syntactic information? A range of on-line methods have been used to investigate this question (e.g., Ferreira and Clifton 1986; Truswell, Tanenhaus, and Gamsey 1994; Clifton et al. 2003), which has significant implications for our understanding of the architecture of the language processing system” [Kaiser 2013: 136].

/22/ “When we have a small amount of data, we can avoid statistics completely. In such cases, we can inspect and discuss each and every observation or data point. For example, if we measured the fundamental frequencies (F0) of three siblings’ speech, we might observe that Betty’s voice was 25 Hz lower than Sue’s, but 100 Hz higher than Frank’s. It would probably be uninteresting to report a statistic like the average pitch of the family. With a larger dataset, like F0 measurements taken from 1,000 men and 1,000 women, the situation is reversed. It is no longer possible to discuss each data point individually, and while it can still be useful to make graphs that display every observation, we will usually be less interested in individual points and more interested in the patterns or trends formed by groups of points.

This is where descriptive statistics come in. Descriptive statistics generally constitute the second step in a quantitative analysis. The first step is to display the data in a tabular or

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graphical format, using a histogram, bar chart, scatterplot, cross-tabulation, or other method. This will reveal any peculiarities of the data that will shape further analysis. For example, a severely skewed dataset may motivate a transformation, or the use of non-parametric statistics. The second step is the descriptive statistics themselves, which distill the complexities of the data down to a small, manageable set of numbers, abstracting away from details (and noise) in order to describe the basic overall properties of the data. This process can suggest the answers to existing questions or inspire new hypotheses to be tested” [Johnson 2013: 288].

/23/ “Multivariate analysis deals with observations made on many variables simultaneously. Datasets with such observations arise across many areas of linguistic inquiry. For instance, Jurafsky et al. (2001) provide an overview of the many factors that co-determine a word’s acoustic duration (including its neighboring words, syntactic and lexical structure, and frequency). The importance of these factors is determined with the help of multiple regression modeling of data extracted from speech corpora. Koesling et al. (in press) used multivariate analysis to study the pitch contours of English tri-constituent compounds, with as predictors not only time and compound structure, but also speaker, word, a word’s frequency of occurrence, and the speaker’s sex. In morphology, the choice between two rival affixes can depend on a wide range of factors, as shown for various Russian affix pairs by Baayen et al. (in press). F. Jaeger (2010) showed that whether the complementizer that is present in an English sentence depends on more than fifteen different factors. Gries (2003) and Bresnan et al. (2007) clarified the many factors that join in determining the choice of particle placement and dative constructions, respectively. In psycholinguistics, multivariate methods are becoming increasingly important (see, e.g., Kuperman et al. 2009, for eye-tracking research),

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especially with the advent of so-called megastudies (Balota et al. 2004). Multivariate methods have a long history of use in sociolinguistics (Sankoff 1987), and play an important role in present-day dialectometry (Wieling 2013). What is common across all these studies is that they address linguistic phenomena for which monocausal explanations fail. Many phenomena can only be understood properly when a great many explananda are considered jointly. This is where multivariate statistics come into play” [Baayen 2013: 337].

ЗАДАНИЕ 6. Найдите более подробную информацию о перечисленных методах лингвистических исследований. Сделайте сообщение, используя примеры.

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СПИСОК ИСПОЛЬЗОВАННОЙ ЛИТЕРАТУРЫ

1.Baayen R. H. Multivariate statistics // Research methods in linguistics / еd. by R.J. Podesva and D. Sharma. – Cambridge University Press, 2013. – 526 p. – P. 337–372.

2.Cambier-Langeveld T., Rossum M. van, Vermeulen J. Whose voice is that? Challenges in forensic phonetics // Above and beyond the segments. Experimental linguistics and phonetics / еd. by Johanneke Caspers, Yiya Chen, Willemijn Heeren, Jos Pacilly, Niels O. Schiller and Ellen van Zanten. – Amsterdam; Philadelphia: John Benjamins Publishing Company, 2014. – P. 14–27.

3.Johnson D.E. Descriptive statistics // Research methods in linguistics / еd. by R.J. Podesva and D. Sharma. – Cambridge University Press, 2013. – 526 p. – P. 288–315.

4.Johnson K. Quantitative methods in linguistics. – Blackwell Publishing, 2008. – 277 p.

5.Kaiser E. Experimental paradigms in psycholinguistics // Research methods in linguistics / еd. by R.J. Podesva and D. Sharma. – Cambridge University Press, 2013. – 526 p. – P. 135–168.

6.Levon E. Organizing and processing your data: the nuts and bolts of quantitative analyses // Research methods in linguistics / еd. by Lia Litosseliti. – London; New York: Continuum International Publishing Group, 2010. – P. 68–92.

7.Podesva R. J., Zsiga E. Sound recordings: acoustic and articula-

tory data // Research methods in linguistics / еd. by R.J. Podesva and

D.Sharma. – Cambridge University Press, 2013. – 526 p. – P. 169–194.

8.Rasinger S.M. Quantitative methods: concepts, frameworks and issues // Research methods in linguistics / еd. by Lia Litosseliti. – London; New York: Continuum International Publishing Group, 2010. – P. 49–67.

9.Schütze C.T., Sprouse J. Judgement data // Research methods in linguistics / еd. by R.J. Podesva and D. Sharma. – Cambridge University Press, 2013. – 526 p. – P. 27–50.

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