Making choices in Russian: pros and cons of statistical methods for rival forms

Baayen, R. Harald; Endresen, Anna; Janda, Laura A.; Makarova, Anastasia; Nesset, Tore

doi:10.1007/s11185-013-9118-6

Cited by 85 publications

(50 citation statements)

References 21 publications

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“…• Classification and Regression Trees is a new method that is quickly gaining popularity in genetics, medicine (Strobl et al 2009: 324), social sciences, and linguistics (Tagliamonte &Baayen 2012 andBaayen et al 2013). …”

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confidence: 99%

Five statistical models for Likert-type experimental data on acceptability judgments

Endresen

Janda

2017

JRDS

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This paper contributes to the ongoing debate over Likert scale experiments, in particular the issues of how to treat acceptability judgment data (as ordinal or interval) and what statistical model is appropriate to apply. We analyze empirical data on native speakers’ intuitions regarding marginal change-of state verbs in Russian (e.g. ukonkretit’ ‘concretize’, ovnešnit’ ‘externalize’) and compare the outcomes of five statistical models (parametric and non-parametric tests): (1) ANOVA; (2) Ordinal Logistic Regression Model; (3) Mixed-Effects Regression Model for Ordinal data; (4) Regression Tree and Random Forests Model; and (5) Classification Tree and Random Forests Model. We make four claims: (1) all five models are appropriate for this data to a greater or lesser degree; (2) overall, the outcomes of parametric and non-parametric tests applied to this data provide comparable results; (3) Classification Tree and Random Forests Model is the most appropriate, informative, and user-friendly regarding this data; and (4) the use of a culturally entrenched grading scale is an advantage.

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confidence: 99%

Five statistical models for Likert-type experimental data on acceptability judgments

Endresen

Janda

2017

JRDS

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“…Baayen et al (2013), for example, compare the performance of logistic regression against the models of 'tree and forest' and 'naive discriminative learning' (NDL) on the basis of four datasets concerning rival forms in Russian. Their basic finding is that "the three models generally provide converging analyses, with complementary advantages" (Baayen et al 2013: 253).…”

Section: What Falls Outside the Scope Of This Review Article?mentioning

confidence: 99%

“…Gries 2003, Bresnan 2007, Bresnan & Ford 2010, Kendall et al 2011, Klavan 2012, morphology (e.g. Antić 2012, Baayen et al 2013, phonetics and phonology (e.g. Erker & Guy 2012, Raymond & Brown 2012 and in areas as diverse as sociolinguistics (e.g.…”

Section: Introductionmentioning

confidence: 99%

The cognitive plausibility of statistical classification models: Comparing textual and behavioral evidence

Klavan

Divjak

2016

Folia Linguistica

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(100-200 words)Usage-based linguistics abounds with studies that use statistical classification models to analyse either textual corpus data or behavioral experimental data. Yet, before we can draw conclusions from statistical models of empirical data that we can feed back into cognitive linguistic theory, we need to assess whether the text-based models are cognitively plausible and whether the behavior-based models are linguistically accurate. In this paper, we review four case studies that evaluate statistical classification models of richly annotated linguistic data by explicitly comparing the performance of a corpus-based model to the behavior of native speakers. The data come from four different languages (Arabic, English, Estonian, and Russian) and pertain to both lexical as well as syntactic near-synonymy. We show that behavioral evidence is needed in order to fine-tune and improve statistical models built on data from a corpus. We argue that methodological pluralism and triangulation are the keys for a cognitively realistic linguistic theory.

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“…The second data set shows similar trends but the difference between the regions is so small as to be insignificant: Another way to explore our forced-choice data is through the use of c l a s s i f i c a t i o n t r e e s . As Baayen et al (2013) demonstrated, this method of analysis can be an effective complement to linear regression, as it often shows more clearly how choices emerge in variant systems. A classification tree graphically distributes significant factors in choices according to their influence.…”

Section: Personal Data and Forced Choicesmentioning

confidence: 99%

Morphological variation and sensitivity to frequency of forms among native speakers of Czech

2015

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This is a prepublication draft of an article published in Russian Linguistics 2015 (3). Please cite from the published version, which differs from this one in some respects.Abstract: This article looks at inter-speaker variation in two environments: the genitive and locative singular cases of masculine "hard inanimate" nouns in юzechз using a largescale survey of native speakers that tested their preferences for certain forms and their choices. Our hypothesis that such variation exists was upheld, but only within limited parameters. Most biographical data (age, gender, education) played no role in respondents' choices or preferencesй Their region of origin played a small but significant role, although not the one expected. Relating the two types of tasks to each other, we found that respondents' use of the ratings scale did not correlate to their choice of formsз but their overall strength of preference for one form over another did correlate with their choices. Inter-speaker variation does thus go some way to explaining the persistent diversity in this paradigm and arguably may contribute to its maintenance.

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Making choices in Russian: pros and cons of statistical methods for rival forms

Cited by 85 publications

References 21 publications

Five statistical models for Likert-type experimental data on acceptability judgments

Five statistical models for Likert-type experimental data on acceptability judgments

The cognitive plausibility of statistical classification models: Comparing textual and behavioral evidence

Morphological variation and sensitivity to frequency of forms among native speakers of Czech

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