Integrating Big Data into Evaluation: R Code for Topic Identification and Modeling

Autor/inn/en	Cintron, Dakota W.; Montrosse-Moorhead, Bianca
Titel	Integrating Big Data into Evaluation: R Code for Topic Identification and Modeling
Quelle	In: American Journal of Evaluation, 43 (2022) 3, S.412-436 (25 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Cintron, Dakota W.) ORCID (Montrosse-Moorhead, Bianca)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1098-2140
DOI	10.1177/10982140211031640
Schlagwörter	Evaluation Research; Evaluation Problems; Evaluation Methods; Models; Data Analysis; Data Use; Research Methodology; Data Interpretation + Suchen Sie Ihr Suchwort? Evaluationsforschung; Analogiemodell; Auswertung; Research method; Forschungsmethode; Data evaluation; Datenauswertung
Abstract	Despite the rising popularity of big data, there is speculation that evaluators have been slow adopters of these new statistical approaches. Several possible reasons have been offered for why this is the case: ethical concerns, institutional capacity, and evaluator capacity and values. In this method note, we address one of these barriers and aim to build evaluator capacity to integrate big data analytics into their studies. We focus our efforts on a specific topic modeling technique referred to as latent Dirichlet allocation (LDA) because of the ubiquitousness of qualitative textual data in evaluation. Given current equity debates, both within evaluation and the communities in which we practice, we analyze 1,796 tweets that use the hashtag "#equity" with the R packages "topicmodels" and "ldatuning" to illustrate the use of LDA. Furthermore, a freely available workbook for implementing LDA topic modeling is provided as Supplemental Material Online. (As Provided).
Anmerkungen	SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01