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NAEP Data Training Workshop

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The Winter 2023 NAEP Data Training Workshop will take place on January 12-14, 2023, in Arlington, VA. This workshop is for quantitative researchers with strong statistical skills who want to learn to conduct data analysis using NAEP data. The training is aimed at advanced graduate students, educational researchers at universities or other research organizations, analysts from state and local education agencies, and those interested in analyzing large-scale assessment data, such as NAEP’s. The training will introduce the unique design features of NAEP and provide guidance and practice in the data analysis strategies required when using NAEP data.

The training will provide participants with hands-on practice in analyzing NAEP data files using the R program. During the training, participants will:

  • get an overview of NAEP operations, from framework and item development to reporting and dissemination;
  • be introduced to the NAEP psychometric models;
  • learn about plausible values and complex sample design, including weights and how to use them in analysis;
  • receive instruction on how to use the NAEP Data Explorer and EdSurvey (an R statistical package);
  • conduct and share statistical analysis projects using NAEP data; and
  • earn about the challenges facing NAEP and opportunities in NAEP operations and research.

A draft agenda for the 2023 data training can be found here.

Financial Support

There is no fee to attend. The National Center for Education Statistics (NCES) will provide the training materials, the computers, software, and data for the hands-on training. NCES will also pay for transportation to Arlington, VA1, three nights of hotel accommodations, and a fixed per diem for meals and incidental expenses during the training. Accepted applicants are expected to be on time and to attend all sessions; financial support will be dependent on their attending and completing the training.

Please note that individuals employed by a holder of a prime or subcontract under the Education Statistics Services Institutes Network (ESSIN) or NAEP Alliance contracts are ineligible to receive lodging, travel, or per diem reimbursement for this workshop.

1 For international applicants only, financial support for transportation is capped at $500.

Application Process

Qualifications

Applicants should have a solid understanding of basic statistics and some topics in advanced statistics, such as the procedures used to analyze complex survey data, and some familiarity with measurement theory, including item response theory. Please note that R will be used during training activities, so participants are expected to have a basic knowledge of using R to perform statistical analysis.

COVID-19 Protocols and Requirements

A mandatory vaccination policy is in effect for participation in this training. Upon acceptance to the training, participants will be required to attest to full vaccination prior to travel arrangements being made. You are considered “fully vaccinated” 2 weeks after the second shot of a two-shot series (e.g., Pfizer or Moderna) or 2 weeks after a single shot (e.g., Johnson & Johnson). It is not necessary to receive a “booster” shot in order to be considered fully vaccinated.

Federal, state, and local public health orders and the CDC's Community Level Tracking rating system will be followed. Masking guidance must be followed by all participants as posted throughout the training facility.

Timeline

Application period closes November 6, 2022.

Notifications to accepted applicants will be sent no later than November 28, 2022.

Application


Application instructions

Provides details on what you need to submit an application. Please read prior to starting an application.

Evaluation

Applicants will be evaluated on the following criteria:

  • Match between their area of research interest and NAEP; applicants are encouraged to explore the NAEP Research Resources website prior to developing their area of research interest statement;
  • Potential to benefit from participation; and
  • Knowledge and skills in statistical analyses