utterances with a low error count will be very high and will decrease dramatically as the number of errors per utterance increases.
A bimodal distribution is like a combination of two normal distributions – there are two peaks. If you find that your data fall in a bimodal distribution you might consider whether the data actually represent two separate populations of measurements. <…>” [Johnson 2008: 13–15].
ЗАДАНИЕ 2. Откройте словарь английского языка на любой странице. Посчитайте количество слов, состоящих из одного, двух, трех, четырех слогов. Какой это тип распределения?
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2. ОБЛАСТИ ПРИМЕНЕНИЯ КОЛИЧЕСТВЕННЫХ (СТАТИСТИЧЕСКИХ) И ЭКСПЕРИМЕНТАЛЬНЫХ МЕТОДОВ ЛИНГВИСТИЧЕСКОГО АНАЛИЗА
ЗАДАНИЕ 3. Ознакомьтесь с выдержками /9/–/15/ и ответьте на следующие вопросы:
1.В каких областях лингвистики используются количественные методы исследования данных?
2.Какие методы упоминаются в указанных высказываниях?
3.Какие ещё количественные (статистические) и экспериментальные методы используются в области психолингвистики, социолингвистики и исторической лингвистики?
4.Какие методы задействованы в нескольких областях лингвистики?
/9/ “Increasingly, linguists handle quantitative data in their research. Phoneticians, sociolinguists, psycholinguists, and computational linguists deal in numbers and have for decades. Now also, phonologists, syntacticians, and historical linguists are finding linguistic research to involve quantitative methods. For example, Keller (2003) measured sentence acceptability using a psychophysical technique called magnitude estimation. Also, Boersma and Hayes (2001) employed probabilistic reasoning in a constraint reranking algorithm for optimality theory” [Johnson 2008: 1].
/10/ “Phoneticians have a long tradition of quantifying their observations of pronunciation and hearing. In this history both hypothesis testing and regression techniques have been extensively used. Obviously the full range of methods used in the quantitative analysis of phonetic data cannot be covered in a short chapter on the topic, however I do hope to extend the discussion of t-test and regression in interesting ways here, and to introduce factor analysis” [Johnson 2008: 70].
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/11/ “With respect to phonetics, researchers also have available to them a range of methods, ranging from the more naturalistic approach of having fieldworkers phonetically transcribe responses to questionnaire items (an approach that is widely used to investigate language variation; see Chapter 6), to the more controlled approaches involving the acoustic analysis of speech (see Thomas 2011 for a recent review; see also Chapters 4, 9, and 17). In experimental elicitation, participants may be asked to read words in isolation or in connected prose, with the goal of conducting an acoustic analysis of intonation, word stress, tone, and various features of consonants and vowels (Ladefoged 2003). There is also a range of imaging techniques to investigate the vocal tract during the production of speech, such as X-rays, computed tomography, MRIs, and ultrasound (see Stone 2010 for an overview of these and other laboratory techniques). In addition to informing debates on phonology and phonetics, these analyses may be used in sociolinguistic studies on language variation, first language studies on the development of pronunciation, and second language studies on the phonological development of non-native speakers” [Schütze, Sprouse 2013: 127].
/12/ “In psycholinguistics experiments, research factors such as word frequency, syntactic construction, or linguistic context are manipulated in a set of materials and then participants provide responses that can be scored on a continuous scale – such as reaction time, preference, or accuracy. The gold standard approach to the quantitative analysis of this type of data (continuous response measure, factorial experimental conditions) testing hypotheses about experimental factors is the analysis of variance (ANOVA)” [Johnson 2008: 104].
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/13/ Combining corpus and experimental data is also common in the field of psycholinguistics (Gilcuin and Gries 2009). For example, a researcher may investigate the frequency of a particular syntactic construction in a corpus and then collect experimental data using one or more of the many procedures available to psycholinguistics, including lexical decision tasks, eye-tracking, priming, sentence completion, moving window experiments, and acceptability judgment tasks (see Fernández and Cairns 2011 for a recent overview of these techniques). Psycholinguists also make use of a variety of neuroimaging techniques to determine which areas of the brain are active during different types of language processing, including electroencephalography (EEG), which measures electrical activity in the brain, and functional magnetic resonance imaging (fMRI), which measures blood flow levels in the brain (see Fernández and Cairns 2011)” [Schütze, Sprouse 2013: 126].
/14/ “The main data that we study in sociolinguistics are counts of the number of realizations of sociolinguistic variables. For example, a phonological variable might span the different realizations of a vowel. In some words, like pen, I say [I] so that pen rhymes with pin, while other speakers say [ε]. The data that go into a quantitative analysis of this phonological variable are the categorical judgments of the researcher – did the talker say [I] or [ε]? Each word of interest gets scored for the different possible pronunciations of /ε/ and several factors that might influence the choice of variant are also noted. For example, my choice of [I] in pen is probably influenced by my native dialect of English and the fact that this /ε/ occurs with a following /n/ in the syllable coda. Perhaps, also, the likelihood that I will say [I] is influenced by my age, socioeconomic status, gender, current peer group, etc.
Other sociolinguistic variables have to do with other domains of language. For example, we can count how many
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times a person uses a particular verb inflection and try to predict this morphological usage as a function of syntactic environment, social group, etc. Or we could count how many times a person uses a particular syntactic construction, and try to model this aspect of language behavior by noting relevant linguistic and social aspects of the performance.
The key difference between these data and the data that we typically deal with in phonetics and psycholinguistics is that the critical variable – the dependent measure – is nominal. We aren’t measuring a property like formant frequency or reaction time on a continuous scale, but instead are noting which of a limited number of possible categorical variants was produced by the speaker” [Johnson 2008: 144].
/15/ “Though there are many opportunities to use quantitative methods in historical linguistics, this chapter will focus on how historical linguists take linguistic data and produce tree diagrams showing the historical “family” relationships among languages. For example, Figure 6.1 shows a phylogenetic tree of Indo-European languages (after figure 6 of Nakhleh, Ringe, & Warnow, 2005). This tree embodies many assertions about the history of these languages. For example, the lines connecting Old Persian and Avestan assert that these two languages share a common ancestor language and that earlier proto-Persian/Avestan split in about 1200 BC. As another example, consider the top of the tree. Here the assertion is that proto Indo-European, the hypothesized common ancestor of all of these languages, split in 4000 BC in two languages – one that would eventually develop into Hittite, Luvian and Lycian, and another ancient language that was the progenitor of Latin, Vedic, Greek and all of the other Indo-European languages in this sample. The existence of historical proto-languages is that asserted by the presence of lines connecting languages or groups of languages to each other, and the relative height of the connections between branches shows how tightly the
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