This DA-L1_Readme.txt file was generated on 2022-03-04 by Silvia S‡nchez Calder—n and Raquel Fern‡ndez Fuertes INDEX OF THE DA-L1 DATASET (DATIVE ALTERNATION IN L1 ACQUISITION) 1. GENERAL INFORMATION 1.1. Title of dataset 1.2. Author information 1.2.1. PI and co-PI 1.2.2. Lab 1.3. Objectives 1.4. Funding sources 1.5. Citing information 2. ACCESS INFORMATION 2.1. Licenses or restrictions 2.2. Publications 3. METHODOLOGICAL INFORMATION 3.1. Data selection procedure from CHILDES 3.2. Data extraction procedure 3.3. Data classification procedure: variables 4. DATA 4.1. Database 4.2. Last update 5. RELATED DATASETS 1. GENERAL INFORMATION 1.1. Title of dataset: DA-L1_Dataset 1.2. Author Information 1.2.1. PI and co-PI: Name: Silvia S‡nchez Calder—n Institution: National University of Distance Education Address: Facultad de Filolog’a, Paseo Senda del Rey 7 47012, Madrid (Spain) Email: ssanchez@flog.uned.es Name: Raquel Fern‡ndez Fuertes Institution: University of Valladolid Address: Facultad de Filosof’a y Letras, Paseo del Cauce s/n 47011, Valladolid (Spain) Email: raquelff@uva.es 1.2.2. Lab: Name of the lab: UVALAL (University of Valladolid Language Acquisition Lab) Institution: University of Valladolid Address: https://uvalal.uva.es Email: gir.uvalal@uva.es 1.3. Objectives This study aims to explore how monolingual and bilingual children acquire two types of English and Spanish dative alternation (DA) structures, namely, prepositional and double object constructions. We examine the spontaneous oral production of these children, as available in the CHILDES project (Child Language Data Exchange System; https://childes.talkbank.org/) (MacWhinney 2000) (i.e., English monolingual corpora: Brown, Cruttenden, MacWhinney, Sachs, Suppes and Wells; Spanish monolingual corpora: Linaza, Marrero and Ornat; and English-Spanish bilingual corpora: Deuchar, FerFuLice, PŽrez-Baz‡n and Ticio). In these corpora, the children interact with adults (mainly, parents, but also caregivers or researchers). The analysis of how these monolingual and bilingual children acquire their first language(s), in general, and the constructions at stake, in particular, sheds light on the relationship that is present in the acquisition of process of these constructions across English and Spanish. In this respect, the findings elucidate whether the syntactic derivational relationship (or lack thereof) between prepositional and double object constructions is similar or differs across the two languages under investigation and across the two child groups (monolinguals and bilinguals). 1.4. Funding sources - 2018-2022: Spanish Ministry of Science, Innovation and Universities and European Regional Development Fund (ERDF) [PGC2018-097693-B-I00], Linguistic competence indicators in heritage and non-native languages: linguistic, psycholinguistic and social aspects of English-Spanish bilingualism, PRINCIPAL INVESTIGATOR: R. Fern‡ndez Fuertes (University of Valladolid, Spain) - 2017-2019: Regional Government of Castile and León (Spain) and ERDF [VA009P17], Aspectos de la dimensi—n internacional del contacto de lenguas: diagn—sticos de la competencia lingŸ’stica bilingŸe inglŽs-espa–ol, PRINCIPAL INVESTIGATOR: R. Fern‡ndez Fuertes (University of Valladolid, Spain) - 2013-2015: Women's Institute (Ministry of Health, Social Services and Equality) (Spain) [039/12], La transmisi—n de estereotipos de gŽnero a travŽs de la canci—n y su relaci—n con la violencia de gŽnero, PRINCIPAL INVESTIGATOR: L. Filardo Llamas (University of Valladolid, Spain) 1.5. Citing information Publications using this dataset (or any part of it) should cite this dataset as follows: Fern‡ndez Fuertes, R., S. S‡nchez Calder—n (2021) The status of English dative alternation structures in bilingual and monolingual acquisition data. Linguistic Approaches to Bilingualism 11 (6). https://doi.org/10.1075/lab11.6. 2. ACCESS INFORMATION 2.1. Licenses or restrictions: There are no licenses/restrictions placed on the data from the corpora in CHILDES as they are freely available at the CHILDES project (https://childes.talkbank.org/) (MacWhinney 2000). However, in order to be able to run the CLAN programs (Computerized Language ANalysis) to perform automatic searches and calculations in the data from the following corpora (English monolingual corpora: Brown, Cruttenden, MacWhinney, Sachs, Suppes and Wells; Spanish monolingual corpora: Linaza, Marrero and Ornat; and English-Spanish bilingual corpora: Deuchar, FerFuLice, PŽrez-Baz‡n and Ticio), the CLAN software needs to be downloaded and installed. The CLAN software is freely available in CHILDES and there are Windows, Mac and Unix versions (https://dali.talkbank.org/clan/). 2.2. Publications: A partial or total access to information contained in the database can be found at the UVALAL webpage (publications section, http://uvalal.uva.es/index.php/results/publications-2/). 3. METHODOLOGICAL INFORMATION 3.1. Data selection procedure from CHILDES (https://childes.talkbank.org/) (MacWhinney 2000): name of the corpus; age range of children; language(s) involved; region/state (country); date (if available). - Deuchar (https://childes.talkbank.org/access/Biling/Deuchar.html): 1;03-2;06; Spanish-English; Brighton (UK); 1986-1987 - FerFuLice (https://childes.talkbank.org/access/Biling/FerFuLice.html); 1;01-6;11; English-Spanish; Salamanca (Spain); 1998-2004 - PŽrez-Baz‡n (https://childes.talkbank.org/access/Biling/Perez.html): 1;08-3;03; Spanish-English; Michigan (USA) - Ticio (https://childes.talkbank.org/access/Biling/Ticio.html): 1;06-2;04; Spanish-English; Texas (USA); 2007-2008 - Brown (https://childes.talkbank.org/access/Eng-NA/Brown.html): 1;06-5;01; English; North America (USA); 1962-1966 - Cruttenden (https://phonbank.talkbank.org/access/Eng-UK/Cruttenden.html): 1;05-3;07; English; Manchester (UK) - Lara (https://childes.talkbank.org/access/Eng-UK/Lara.html): 2;07-3;03; English; Nottinghamshire (UK) - Sachs (https://childes.talkbank.org/access/Eng-NA/Sachs.html): 1;11-5;01; English; North America (USA) - Wells (https://childes.talkbank.org/access/Eng-UK/Wells.html): 1;06-5;00; English; England (UK) - MacWhinney (https://childes.talkbank.org/access/Eng-NA/MacWhinney.html): 0;06-8;00; English; North America (USA) - Suppes (https://childes.talkbank.org/access/Eng-NA/Suppes.html): 1;11-3;03; English; North America (USA); 1972-1973. - AguadoOrea/Pine (https://childes.talkbank.org/access/Spanish/OreaPine.html): 1;10-2;02; Spanish; Madrid (Spain); 2000-2003 - Linaza (https://childes.talkbank.org/access/Spanish/Linaza.html): 2;00-4;00; Spanish; Madrid (Spain); 1992 - LlinasOjea (https://childes.talkbank.org/access/Romance/Catalan/MireiaEvaPascual.html); 0;11-3;02; Spanish; Barcelona (Spain) - Marrero (https://childes.talkbank.org/access/Spanish/Marrero.html): 1;08-8;00; Spanish; the Canary Islands (Spain) - Montes (https://childes.talkbank.org/access/Spanish/Montes.html): 1;07-2;11; Spanish; Patzcuaro (Mexico) - Nieva (https://childes.talkbank.org/access/Spanish/Nieva.html): 1;08-2;03; Madrid (Spain); 2006-2007 - Ornat (https://childes.talkbank.org/access/Spanish/Ornat.html): 1;07-4;00; Spanish; Madrid (Spain); 1988-1991 - Vila (https://childes.talkbank.org/access/Spanish/Vila.html): 0;11-4;08; Spanish; Barcelona (Spain) 3.2. Data extraction procedure: The search for English and Spanish DA cases combines manual extraction with automatic extraction via one of the CLAN programs, KWAL (Key Word And Line). While manual extraction involves those corpora that do not display a morphology dependent tier (%mor) in their transcript data (namely, AguadoOrea/Pine, FerFuLice, Lara, LlinasOjea, Montes, Nieva, PŽrez-Baz‡n, Ticio, Vila), as available in CHILDES, automatic searches include those corpora that display a %mor tier (namely, Brown, Cruttenden, Linaza, MacWhinney, Marrero, Ornat, Sachs, Suppes, Wells). Both types of searches are carried out for child and for child-directed speech data transcribed in CHAT (Codes for the Human Analysis of Transcripts) written format. DA cases are compiled in the following .csv documents: - DA-L1_English.csv - DA-L1_Spanish.csv 3.3. Data classification procedure: variables - Identifying variables: file name; dative alternation (DA) instance; DA number; corpus; data type (monolingual / bilingual) - Demographic variables: age of the child (years; months; days); MLUw (per session); participant's name, participants' biological gender - Linguistic variables for the English data: VOICE Active voice Passive voice TYPE OF STRUCTURE IN THE ACTIVE VOICE Monotransitive Monotransitive+ for-dative Dativizable for-dative Dativizable DOC = Dativizable Double Object Construction Non-dativizable for-dative Non-dativizable DOC Ditransitive Dativizable to-dative Dativizable DOC Non-dativizable to-dative Non-dativizable DOC Syntactically ambiguous Od+P complement (Oi) (to/for-omission) = Direct object (Od) followed by a prepositional complement (indirect object, Oi) for which the prepositions "to" and "for" are omitted Null Od+P complement (Oi) (to/for-omission) = null direct object followed by a prepositional complement (indirect object) for which the prepositions "to" and "for" are omitted P complement (Oi) (to/for-omission)+Od+P+DP = prepositional complement (indirect object) for which the prepositions "to" and "for" are omitted, followed by a direct object and a prepositional phrase (preposition (P) + determiner phrase (DP)) Null Od+realized to/for-dative = null direct object followed by a realized prepositional phrase headed by the prepositions "to" or "for" Null P complement+Od = null prepositional complement followed by a direct object TYPE OF STRUCTURE IN PASSIVE VOICE Derived from monotransitive Derived from monotransitive+for-dative Dativizable for-dative (Od passivizes) = Dativizable for-dative construction in which the direct object passivizes Dativizable DOC (P complement passivizes) = Dativizable double object construction in which the prepositional complement passivizes Non-dativizable for-dative (Od passivizes) = Non-dativizable for-dative construction in which the direct object passivizes Non-dativizable DOC (P complement passivizes) = Non-dativizable double object construction in which the prepositional complement passivizes Derived from ditransitive Dativizable to-dative (Od passivizes) = dativizable to-dative construction in which the direct object passivizes Dativizable DOC (P complement passivizes) = dativizable double object construction in which the prepositional complement passivizes Non-dativizable to-dative (Od passivizes) = non-dativizable to-dative construction in which the direct object passivizes Non-dativizable DOC (P complement passivizes) = non-dativizable double object construction in which the prepositional complement passivizes Syntactically ambiguous P complement (Oi) (to/for-omission) = passive construction in which the direct object occupies a subject position and the prepositional complement omits the prepositions "to" or "for" in the indirect object constituent Null Od = passive construction in which the indirect object occupies a subject position and the direct object is null - Linguistic variables for the Spanish data: VOICE Active voice Passive voice TYPE OF STRUCTURE IN THE ACTIVE VOICE Monotransitive Prepositional construction/Non-Dative Clitic Doubling (NDCLD) Canonical word order Non-canonical word order Double object construction (DOC)/Dative Clitic Doubling (DCLD) Canonical word order Non-canonical word order TYPE OF STRUCTURE IN THE PASSIVE VOICE Derived from monotransitive Periphrastic Reflexive with "se" Derived from ditransitive Derived from NDCLD Periphrastic Reflexive with "se" Derived from DCLD Periphrastic Reflexive with "se" 4. DATA 4.1 Database The database contains the raw data with all the information related to the dataset, organized according to 3 different types of variables: identifying variables, demographic variables and linguistic variables (see section 3.3). The DA cases compiled were analyzed in the following csv files: -DA-L1_English.csv; number of variables = 8; number of rows = 54,091. -DA-L1_Spanish.csv; number of variables = 8; number of rows = 38,321. 4.2. Last update: 2018 5. RELATED DATASETS - Bilingual acquisition data: longitudinal corpus_FerFuLice dataset (https://uvadoc.uva.es/handle/10324/50964)