NotesFAQContact Us
Collection
Advanced
Search Tips
Peer reviewed Peer reviewed
PDF on ERIC Download full text
ERIC Number: ED551291
Record Type: Non-Journal
Publication Date: 2014-Sep
Pages: 14
Abstractor: ERIC
Reference Count: 2
ISBN: N/A
ISSN: N/A
The College Readiness Data Catalog Tool: User Guide. REL 2014-042
Rodriguez, Sheila M.; Estacion, Angela
Regional Educational Laboratory Northeast & Islands
As the name indicates, the College Readiness Data Catalog Tool focuses on identifying data that can indicate a student's college readiness. While college readiness indicators may also signal career readiness, many states, districts, and other entities, including the U.S. Virgin Islands (USVI), do not systematically collect career readiness indicators. Although the College Readiness Data Catalog Tool was developed with the needs of the USVI Alliance in mind, it is flexible enough to be used by other interested parties. This tool allows states, districts, and other entities to create data catalogs to assess the availability of college readiness indicators and identify gaps that may present challenges for indicator systems. Researchers may also use data catalog tools as they work with states, districts, and other entities to determine the feasibility of studies on college readiness, including, for example, identifying the strongest indicators in a given context. The tool is meant to be used only after a research question has been identified.The College Readiness Data Catalog Tool is a flexible-use Excel workbook that provides a shell for organizing and tracking student data relevant for measuring college readiness. The shell organizes data at three levels: (1) Constructs: Concepts identified during the literature scan that are of interest for measuring. Broad and indirectly measurable, constructs identify important ideas that should be measured to answer each research question and meaningfully group the information in the catalog. College readiness constructs are overarching categories that are highly relevant for predicting success in college. (2) Indicators: Concepts that provide greater specificity than constructs. Not all constructs have associated indicators, and some have more than one. (3) Data elements: Specific data sources that describe specific and measurable concepts. Data elements include traditional sources such as measures from assessments, performance evaluations, and formal observations, as well as documents such as emails, agendas, and meeting notes. The tool also provides a list of key data element characteristics, such as years of available data, information about linking, changes to data elements across years, and specific fields or values. All three data levels (construct, indicator, and data element) and their characteristics are important for informing decisions about using college readiness data. The following are appended: (1) Sample data catalog summary report; and (2) Template for a data catalog summary report.
Regional Educational Laboratory Northeast & Islands. Available from: Institute of Education Sciences. 555 New Jersey Avenue NW, Washington, DC 20208. Tel: 800-872-5327; Web site: http://ies.ed.gov/ncee/edlabs/
Publication Type: Guides - Non-Classroom
Education Level: High Schools; Secondary Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: Regional Educational Laboratory Northeast & Islands (ED); National Center for Education Evaluation and Regional Assistance (ED); Education Development Center, Inc. (EDC)
Identifiers - Location: Virgin Islands
IES Funded: Yes
Grant or Contract Numbers: ED-IES-12-C-009