fs 2016 class 3: comparability and variablesa2622b46-ab20-4… · domain of investigation,...

Linguistic typology

FS 2016

Class 3: Comparability and variables

Formulate research question

Decide what and how to

measure

Collect data

Summarize data Analyze

data

Interpret results

1

2

3

4

5

6

Report results

7

Comparability and variables

The research cycle


Substeps for 1 and 2 1. Define domain (+subdomain) 2. Come up with potential research questions 3. Define variables + possible values 4. Fix hypothesis/research question

Today: theoretical background Next week: applied to linguistic topic


Section Name Contents Class

1 Introduction Define/describe Phenomenon, domain of investigation, hypothesis, variables

2-4

2 Sample Describe and evaluate the sample 5

3 Data analysis Describe, explore, test data statistically

6-9

4 Discussion Are there patterns in the data, what explanations could be offered (tentatively)

10

5 Outlook What would you need to give a better answer in 4?

10

(6) Abbreviations 11

(7) References 11


Domain: the limits and nature of the phenomenon that you want to investigate Variables: those attributes that can vary between individuals or objects in your research set-up.


Suppose you want to know which concepts are generally expressed by adjectives. You start with English (monomorphemic) adjectives, and ask yourself the question: what is an adjective in English? e.g. tall, white, nice, big, etc. Three characteristics that they have in common 1. They can come in-between an article and a noun 2. They form comparatives/superlatives with -er/-est 3. They appear without article in X is ... copula constructions


1. They can come in-between an article and a noun 2. They form comparatives/superlatives with -er/-est 3. They appear without article in X is ... copula constructions

Now try if you can apply those criteria to another language you know (preferably non-Germanic)


Korean [KOREANIC], Song (2005: 210) mek-nun-ta eat-NONPAST-DECL ‘eats’

musep-ta scary-NONPAST.DECL ‘is scary’


Puinave [ISOLATE], Giron (2008: 296-297) i-kai ATTR-rot ‘rotten’

i-dam ATTR-be.bland ‘bland’

i-pik

ATTR-be.black ‘black’


language data from language X

language data from language Y

language data from language Z


Comparative concepts

1. Determine the particular semantic(-pragmatic) structure or situation type that

one is interested in studying. 2. Examine the morphosyntactic construction(s) or strategies used to encode that

situation type. 3. Search for dependencies between the construction(s) use for that situation and

other linguistic factors: other structural features, other external functions expressed by the constructions, or both.


Comparative concepts

Domain: the limits and nature of the phenomenon that you want to investigate Variables: those attributes that can vary between individuals or objects in your research set-up. Dependent variables: those variables Independent variables


Comparative concepts are concepts created by comparative linguists for the

specific purpose of cross-linguistic comparison. Unlike descriptive categories, they

are not part of particular language systems and are not needed by descriptive

linguists or by speakers. They are not psychologically real, and they cannot be right

or wrong. They can only be more or less well- suited to the task of permitting cross-

linguistic comparison (...) Comparative concepts are universally applicable, and

they are defined on the basis of other universally applicable concepts: universal

conceptual-semantic concepts, general formal concepts, and other comparative

concepts.


“A dative case is a morphological marker that has among its functions the coding of the recipient argument of a physical transfer verb (such as ‘give’, ‘lend’ ‘sell’, ‘hand’), when this is coded differently from the theme argument”.


German [Indo-European, Germanic]

Er gab dem Mann den Schlüssel

he gave the.DAT.MASC man the.ACC.MASC key ‘He gave the man the key.’

Latin [Indo-European, Italic]

dedit viro clavem

gave.3SG man.DAT key.ACC

‘He gave the man the key.’

(Thanks, Hans)


Multi-variate typology 1. Collect all relevant issues that relate to the phenomena you want to study in the

individual languages of your sample and the existing typological and theoretical literature

2. Treat all these issues as different variables, for which you need to come up with an answer as to how they function in other languages of the sample (e.g. in terms of ’yes’ versus ’no’ answers to detailed questions).

3. Determine the variation space for the phenomenon at issue. 4. Explore the resulting data structure (e.g. by look for meaningful clusters and

correlations (also with non-linguistic variables), directly comparing specific subaspects from one language to another, etc.


Canonical typology

In a canonical approach, we take definitions to their logical end point and build theoretical spaces of possibilities. Only then do we ask how this space is populated.


Multi-variate typology vs. canonical typology Continua

«Pure» agreement: Person marker and Controller must both be present «Mixed» agreement: Person marker must be present, Controller may be present «Almost» agreement: Bound person marker alternates with Controller No agreement: only juxtaposition of NP and verb, no additional marking scheme

Pure agreement

No agreement




1. Let’s look at the relevant criteria that describe agreement in language X 2. Now let’s look how language Y scores on those criteria, and add the criteria that

are important in language Y (and go back to language X to score it for those criteria)

3. ...etc



Canonical agreement


1. What would be the most agreement-like contstruction type imaginable (i.e. where absolutely everyone would agree, the nec plus ultra), let’s call that (theoretical) point «canonical agreement».

2. Now let’s look in what ways actual language structures deviate from that point.


“multivariate typology does not force one to squeeze a construction into a predefine set and instead allows one to capture all relevant properties.” MT allows for this diachronic perspective to be taken into account with more precision, perhaps even testing diachronic claims or hypotheses they are very labor-intensive and time-consuming. It can furthermore be a problem to find all the relevant information for the languages of your sample, because not all grammatical descriptions are equally detailed. the approach does not discharge you of the necessity to define a domain


Variables and measurement in

statistics


Variable: attribute of an “object” that can vary between “objects”

Mr. Brown

Sex Male

Occupation Student

Age (years) 24

Weight (kg) 75


Variable: attribute of an “object” that can vary between “objects”

Mr. Brown

Sex Male

Occupation Student

Age (years) 24

Weight (kg) 75

Object

Variables Values


Types of variables Qualitative (categorical) variables have values that are names or labels, there is no natural sense of ordering them. Quantitative variables can be measured on a numerical scale.

Mr. Brown

Sex Male

Occupation Student

Age (years) 24

Weight (kg) 75


Types of variables Quantitative variables can be discrete (the number of values they can take on is limited) or continuous (they can take on any number in a particular range).

Mr. Brown

Sex Male

Occupation Student

Age (years) 24

Weight (kg) 75


Identify which of the following represent quantitative variables and which

qualitative variables. 1. Age of a language consultant

2. Alignment type of case marking of nouns in a language

3. Presence of subject agreement on the verb in a language

4. The maximal number of affixes a verb can take in a language

5. Phoneme inventory size (in phonemes) of a language

6. Possibility of verb reduplication in a language

7. Number of speakers of a language 8. Number of uncommon consonants present in a language 9. Marking of predicative possession in a language (e.g. with a locative pos-

sessive, with a dative possessive, a genitive possessive)

Nominal: data are categorized into mutually exclusive categories

Ordinal: like nominal, but data can be ranked -> WALS


Scales of measurement (on what type of scale can the values of a

variable be classified?)

Interval: like ordinal, but the steps from one measurement to the other are identical Ratio: like interval, but with an absolute zero point

http://wals.info/feature/12A2/19.3/152.8

Nominal Ordinal Interval Ratio


Scales of measurement (on what type of scale can the values of a

variable be classified?)

- Informative +


Identify the scale of measurement applied in the following WALS chapters

Para-linguistic usages of clicks

Remoteness distinctions in the past tense

Productivity of the antipassive construction

Verbal person marking

http://wals.info/chapter/142














Which of these variables can modified, so that a different measurement scale is used.

The information content of the modified variable does not have to be identical. For

instance, you may chose to ignore some of the distinctions made or consider finer

distinctions.

• Para-linguistic usages of clicks

Alternative scale of measurement (if possible): Possible values: • Remoteness distinctions in the past tense

Alternative scale of measurement (if possible):

Possible values: • Productivity of the antipassive construction


Possible values: • Verbal person marking


Possible values:

fs 2016 class 3: comparability and variablesa2622b46-ab20-4… · domain of investigation,...

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