North East Association for Institutional Research

• define data literacy.
• examine research on the importance of data literacy in practice and professional growth.
• compare and contrast three different approaches to data literacy efforts from one campus.
• reflect on lessons learned from one campus in order to plan for their own data literacy efforts.

• identify the components of RStudio and how they work together;
• describe R objects and how they are written/used in code;
• recall syntax rules for writing functions and code.

• learn to differentiate between the domain-specific and data lifecycle approaches to data literacy.
• draft a mind map of a potential data literacy program.
• be able to identify what makes a strong data literacy program.

• describe how conversational AI can function as a learning partner to support the development of IR technical skills, without replacing professional expertise or judgment.
• practice crafting effective, learning-oriented prompts to use AI for troubleshooting, sense-making, and refining analytical outputs.
• apply AI to explore IR-related workflows, including working with public IPEDS data, making visualization decisions, and supporting analytical reasoning.
• identify key ethical considerations when incorporating AI into institutional research work, including data sensitivity, accuracy, and appropriate use. Intended

• have a general understanding of institutional research workflows (e.g., working with data requests, creating reports, or building dashboards for stakeholders)
• be able to work with data in at least one tool such as Excel, Tableau, Power BI, or a similar platform (e.g., creating basic charts, filtering data, or building simple tables)
• have a basic familiarity with IPEDS and general awareness of Admissions Survey data elements (e.g., applicants, admits, and enrollment counts)
• be comfortable following a live demonstration and optionally replicating steps in their tool of choice

• identify institutional research contexts in which generative AI may be useful for qualitative analysis, such as open-ended survey responses, “other/specify” fields, feedback activities, or other short-form text data;
• be able to apply a reusable prompting framework that specifies context, task, analytic lenses, desired output, and review criteria when using AI to analyze qualitative text; and
• be able to identify how prompt design may affect the usefulness, specificity, and reliability of AI-generated outputs.

• distinguish between tidyverse and base R syntax;
• identify core tidyverse functions useful in institutional research work;
• recall syntax rules for writing tidyverse functions and codes.

• assess strategies to simultaneously protect privacy and answer important research questions;
• examine how comparison across contexts and over time can shape demographic data choices; and
• identify two federal resources they can leverage in planning demographic data collection.

• identify key developmental, professional, and personal factors that contribute to the unique challenges and opportunities of midlife leadership.
• learn evidence-based strategies for managing competing demands related to career advancement, caregiving responsibilities, organizational change, and personal well-being.
• evaluate current leadership priorities and responsibilities through a structured reflection process designed to support resilience, purpose, and long-term effectiveness.
• develop an individualized action plan for navigating the next stage of their leadership journey with greater intention and clarity.

• explore analyses and visualizations drawn directly from the toolkit and will discuss how its structure and methods can be adapted beyond CTE to examine student success across program types, credential levels, and institutional contexts. Whether attendees are building new analytical capacity or looking to reduce duplicative work, they will leave with practical strategies for using the toolkit to generate actionable insights and support data-informed decision-making at their institution or within their system.
• be able to identify the four core diagnostic modules of the SDP CTE Pathways Toolkit and describe the student success questions each module is designed to answer.
• be able to intrpret the types of analyses and visualizations the toolkit produces, including completion rate comparisons, equity gap analyses, and gateway course barriers, and explain what those outputs can reveal about student success at their institution.
• be able to evaluate how the toolkit's analyses, sample code, and guiding questions can be adapted to their institution's data structure, program context, and strategic priorities.

• Query and manipulate student-level datasets, including linking records across enrollment terms and programs of study
• Calculate common postsecondary student outcome metrics such as retention rates, completion rates, and transfer rates from student unit record data
• Interpret crosstabulated data and basic regression or logistic regression output, including the direction and significance of coefficients
• Work with common postsecondary data constructs such as program of study, credential type, gateway courses, and credit accumulation

• identify practical strategies that help new presenters build confidence and engage audiences more effectively during conference presentations; and
• design presentation materials that reduce cognitive overload and communicate information more effectively.

Things to Know Before You Register:
The NEAIR Annual Conference primarily serves attendees from non-profit institutions and organizations. For-profit entities sending multiple attendees to the conference are expected to sponsor the conference at some level. Please see the Sponsor Prospectus for more information about our sponsor levels and benefits.

• A current NEAIR membership is required. You can join NEAIR when completing your conference registration.
• Visit the Conference Website for additional information.
• Please use the same email as your NEAIR membership to register so the system can recognize your record.
• Be sure to also book your hotel room, as reservations and rates are based on availability.
• Print out the confirmation email you receive. It includes your access # should you want to return to the registration form to update any information.
• By registering as a Professional Member, you agree that your participation in the conference is to share and learn about the profession, not to promote or solicit business from other attendees.  Promotion of products and services are restricted to conference sponsors.  You will find information about the benefits of becoming a conference sponsor, including powerful opportunities to showcase company products and to build relationships with NEAIR members, on the Sponsor Page.

If you are a conference sponsor:

• A representative of your organization must first complete the Purchase a 2026 NEAIR Conference Sponsorship Form.  
• Visit the Conference Website for additional information.
• Depending on the level of the conference sponsorship that your organization purchased, complimentary conference registration(s) may be available.
• Select "Sponsor" as the Member Type when completing your conference registration.
• Be sure to also book your hotel room, as reservations and rates are based on availability.
• Print out the confirmation email you receive. It includes your access # should you want to return to the registration form to update any information.

Questions?
Contact NEAIR at neair@neair.org.

                  Welcome to NEAIR Sponsorship!

NEAIR relies on sponsor support of the Annual Conference.  In return, NEAIR works hard to promote our sponsors extensively through a variety of mediums prior to and during our conference.  
  

We recommend purchasing a sponsorship early for best booth location and top choice of presentation time!  Regular Sponsorship ends October 1st.  For complete information about NEAIR Sponsorship, please go to our Sponsor Tab.

Questions? Shirley Wong, shirleywong@mit.edu