languages bind to each other and perhaps the historical depth of their connection (though placing dates on proto-languages is generally not possible without external historical documentation)” [Johnson 2008: 182].
ЗАДАНИЕ 4. Найдите более подробную информацию о количественных и экспериментальных методах, используемых в различных областях лингвистики. Сделайте сообщение.
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PNRPU
3. МЕТОДЫ СБОРА, ОБРАБОТКИ И АНАЛИЗА ЛИНГВИСТИЧЕСКИХ ДАННЫХ
ЗАДАНИЕ 5. Ознакомьтесь с выдержками /16/–/23/ и ответьте на следующие вопросы:
1.Что оказалось в центре внимания исследователей, использовавших нижеприведенные методы?
2.Что обозначают использованные исследователями базовые понятия?
3.Каковы достоинства и недостатки перечисленных методов?
4.Какие другие методы могли бы быть использованы исследователями?
/16/ “There are hundreds of different inferential statistical tests than can be used in quantitative analyses. The choice of which test to use depends primarily on the kind and number variables in your data set, and the sorts of relationships that exist between the variables you consider. <…>
Different statistical tests are used depending on whether the variables you are examining (both independent and dependent) are continuous or categorical. <…> Statistical tests exist for examining continuous independent variables (e.g. correlation analyses) or for examining a combination of continuous and categorical independent variables (e.g. Generalized Linear Models, Linear Mixed Models); <…> Again, tests exist for examining multiple independent and dependent variables (e.g. ANOVAs, MANOVAs, Linear Regressions – see Bryman and Gramer, 2008), but these tests require a more advanced explanation than we can provide here. <…> When the dependent variable is categorical, the statistical test we use is called a chi-square test (sometimes abbreviated as x2). When the dependent variable is continuous, the statistical test we use is called a t-test” [Levon 2010: 72-73].
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/17/ “In a Likert scale (LS) task, participants are given a numerical scale with the endpoints defined as acceptable or unacceptable, and asked to rate each sentence along the scale. The most commonly used scales, as in Figure 3.3, usually consist of an odd number of points (such as 1–5 or 1–7) because odd numbers contain a precise middle point; however, if the research goals require it, a preference can be forced by choosing an even number of points. One of the primary benefits of LS is that it is both numerical and intuitive” [Schütze, Sprouse 2013: 33].
/18/ “In the magnitude estimation (ME) task, participants are given a reference sentence and told that the acceptability of the reference sentence is a specific numerical value (e.g., 100). The reference sentence is called the standard and the value it is assigned is called the modulus. Participants are then asked to rate additional sentences as a proportion of the value of the standard, as in Figure 3.4. For example, a sentence that is twice as acceptable as the standard would be rated 200.
ME was developed by Stevens (1957) explicitly to overcome the problem of potentially non-uniform, and therefore non-meaningful, intervals in the LS task (in the domain of psychophysics). In the ME task, the standard is meant to act as a unit of measure for all of the other sentences in the experiment. In this way, the intervals between sentences can be expressed as proportions of the standard (the unit of measure). <…> As a numerical task, an ME experiment requires the same design properties as an LS task (see Section 3). The choice of the standard can affect the amount of the number line that is available for ratings: a highly acceptable standard set at a modulus of 100 means that nearly all ratings will be between 0 and 100, whereas a relatively unacceptable standard means that nearly all ratings will be above 100. For this reason, and in order to prevent certain types of response strategies, it is normal practice to employ
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a standard that it is in the middle range of acceptability” [Schütze, Sprouse 2013: 33].
/19/ “Linguists, across the subdisciplines of the field, use sound recordings for a great many purposes – as data, stimuli, and a medium for recording notes. For example, phoneticians often record speech under controlled laboratory conditions to infer information about the production and comprehension of speech in subsequent acoustic and perception studies, respectively. In addition to analyzing acoustic data, phoneticians may employ articulatory methods to observe more directly how speech is produced. By contrast, sociolinguists often record unscripted speech outside of a university environment, such as a speaker’s home. Sometimes these recordings themselves constitute the data (e.g., for sociophonetic analysis), while other times they may be transcribed at varying levels of detail (see Chapter 12), with the resultant text serving as the data (e.g., for the analysis of lexical or morphosyntactic variation and discourse analysis)” [Podesva, Zsiga 2013: 169].
/20/ “Forensic speaker recognition (FSR) is a relatively young type of forensic science. The majority of casework in this field is performed using a so-called “auditory-acoustic” method, in which detailed analytic listening by a human expert is combined with acoustic measurements (Cambier-Langeveld, 2007; Gold & French, 2011). The auditory-acoustic approach is targeted towards analysis and documentation of separable features contained within the speech signal (French & Stevens, 2013). This method is also used at the Netherlands Forensic Institute (NFI) and described briefly below.
Casework in FSR generally consists of questioned audio materials, containing speech which has been attributed to a particular suspect but which the suspect denies having produced. In the casework performed at the NFI, questioned samples most often come from wire-tapped telephone
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conversations or covert recordings. These questioned samples are compared to reference material (i.e., non-disputed audio samples from the suspect). The first hypothesis to be tested is that the questioned samples and the reference material were produced by the same speaker.
The expert (forensic phonetician) will analyse the materials, keeping in mind a certain population of speakers with the same background as the suspect who could also have produced the questioned material. This population serves to test the alternative hypothesis: that the questioned material and the reference material were produced by different speakers. Similarities and differences between the questioned material and the reference material will always be encountered. For the similarities the expert must consider the discriminating power, i.e. the extent to which the similarity sets a speaker in the relevant population. For the differences the expert must consider whether they fall within the variability to be expected within the speech of one person (intra-speaker variability) or whether they involve the kind of variability to be expected when the speech materials are produced by different persons (inter-speaker variability)” [Cambier-Langeveld, Rossum, Vermeulen 2014: 14-15].
/21/ “Another area where on-line methods have made important contributions has to do with the way in which the human language processing system accesses and makes use of different kinds of linguistic information, such as syntactic vs semantic information. A sentence like “The witness examines by the lawyer turned out to be unreliable” is syntactically temporarily ambiguous, because when only the first few words are available (“The witness examined …”), a comprehender might be tempted to interpret “the witness” as the agentive subject of the verb “examined” – an interpretation that is subsequently shown to be false. This kind of situation, where the parser builds a syntactic structure that is later shown to be
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