AERA Institute on Statistical Analysis
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AERA Institute on Statistical Analysis
 
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AERA Institute on Statistical Analysis:
Development of Mathematics Competencies in Early Childhood


Call for Applications
 

February 23-27, 2020

Laguna Beach, California

 

Deadline for Applications:  Wednesday, December 18, 2019

With support from the National Science Foundation (NSF), the American Educational Research Association (AERA) Grants Program announces a special AERA Institute on Statistical Analysis focused on identifying factors that promote the developmental trajectories of mathematics skills in early childhood using large diverse publicly available datasets. The Institute’s goal is to build the capacity of the U.S. education research community, who have a background or demonstrable interest in mathematics or mathematics education research, to undertake scientific studies on mathematics competencies in early childhood using large-scale national data sets such as those from the National Institutes of Health (NIH), National Center for Education Statistics (NCES), NSF, or databases from other federal or state agencies (e.g., Head Start). 
 

Institute Description

The Institute has two substantive emphases: (1) development of mathematics skills in early childhood (0-8 years of age) and (2) causal inference. During early childhood, children acquire basic mathematics skills and relatively complex math ideas. Mathematics skills at entry to kindergarten are one of the best predictions of subsequent academic achievement; therefore, identification of factors that promote the development of these skills is a priority for early childhood education. It is generally accepted that both family and early care and education (ECE) play an important role in promoting children’s mathematics skills, but most studies have not allowed for inferring causality.

The second emphasis of the Institute is on causal inference using large-scale diverse early childhood datasets that give attention to mathematics education and learning. Causal inference is a central issue in education research. This includes inferring causality from the design of true randomized experiments, as well as drawing inferences from quasi-experimental non-randomized studies. Causal analysis includes propensity scores matching, fixed effect methods, instrumental variable approaches, path analysis, and structural equation models. During the Institute, participants will engage in hands-on working sessions and engage in implementing selected methods with national and state data on early childhood education.


Eligibility and Review Criteria

Advanced doctoral students and early career researchers (7 years post doctorate and pre-tenure) are eligible to apply. Applicants must have a background in mathematics or mathematics education in addition to research experience related to early childhood education, development theories, and training in statistics. They must have completed at least one year of statistics courses at the doctoral level and have familiarity with multiple regression methods.  Review criteria include:

  • course work in early childhood development, ideally with a focus on mathematics skills and competencies,
  • statistical background to at least the intermediate level of multiple regression,
  • computer literacy with knowledge of at least one statistical software package,
  • experience using a large-scale dataset,
  • substantive policy or practice interest in early childhood, and
  • Institute’s fit with career goals and research agenda.

Applications from historically underrepresented racial and ethnic minority groups are strongly encouraged to apply. Applicants may be US citizens, US permanent residents, or non US citizens working or studying at a US Institution. 


Participant Support

A select group of scholars will be chosen to participate in the Institute. Those selected for participation will receive support covering the Institute fees, transportation, lodging, and group meals for the days of the Institute. Participants must bring their own computer loaded with the statistical software of their choice.


Dates and Location

February 23-27, 2020; Laguna Beach, California
 

Institute Directors

Margaret Burchinal, University of North Carolina

Deborah Lowe Vandell, University of California, Irvine
 

Institute Faculty

Douglas Clements, University of Denver

Nancy C. Jordan, University of Delaware

Barbara Schneider, Michigan State University
 

Application Requirements

All applications should include:

  1. Background information, entered via the online submission portal, including contact information for two (2) references familiar with the applicant’s work. No letters of recommendation are required for this application, although references may be contacted during the review process.
  2. Statement of Interest in PDF (maximum two pages single-spaced), describing the applicant's background, career goals, and how the applicant would benefit from the Institute.
  3. Summarized Curriculum Vita in PDF (maximum one page), listing education, research, and relevant employment history, relevant graduate courses, and publications that are relevant to the Institute.

Submit the Statement of Interest and the Summarized Curriculum Vita in one PDF document and upload to the submission system by the deadline.
 

Application Submission

The deadline for applications is 11:59 pm Pacific Time on Wednesday, December 18, 2019. Applications must be submitted to AERA via the online submission portal. Incomplete submissions will not be considered. Applicants will be informed of decisions in early January 2020. If you have any questions, please contact George L. Wimberly, Co-Principal Investigator AERA Grants Program at grantsprogram@aera.net or 202-238-3200.

Click here to apply for the AERA Institute on Statistical Analysis: Development of Mathematics Competencies in Early Childhood

 
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