Domain 1: Data-Based Decision Making
School psychologists understand and utilize assessment methods for identifying strengths and needs; developing effective interventions, services, and programs; and measuring progress and outcomes within a multi-tiered system of supports. School psychologists use a problem-solving framework as the basis for all professional activities. School psychologists systematically collect data from multiple sources as a foundation for decision-making at the individual, group, and systems levels, and they consider ecological factors (e.g., classroom, family, and community characteristics) as a context for assessment and intervention, (National Association of School Psychologists, 2020).
Data-based decision making at the school, classroom, and student level is essential in ensuring instructional match and facilitating student growth. School psychologists can support data-based decision making by analyzing academic data, such as universal screening for reading and mathematics, as well as behavioral data, such as universal behavioral screenings and review of office discipline referral (ODR) trends. Data should drive decision making to facilitate the match between evidence-based instructional practices and interventions to skill(s) in need of further development with stages of learning (i.e., acquisition, fluency, mastery / generalization).
Often, school psychologists are involved with data-based decision making at the individual student level; however, school psychologists are thoroughly trained on the gathering, interpreting, and sharing of data for groups of students, and even systems. Educational decisions made for individual students and districts as a whole should always be supported by data. School psychologists can use their knowledge of data collection and interpretation to promote data-based decision making at a systemic level.
Effective Data Systems Include data that are available, easy- to -understand, and linked to action planning. Using this data, school psychologists can:
- Evaluate
- Identify problems with precision
- Plan
- Establish goals and develop solutions
- Implement
- Implement solutions with fidelity and integrity monitor outcomes and compare to goal
- Reassess and Revise solutions as needed and repeat the process
Common Data Sources
When making systems-level changes, it is important to develop core questions that drive your evaluation and help you determine what kind of data to collect. These questions act as a framework for data-based decision making:
- Reach: Who is part of the systems-level change?
- Common data sources: Program/school counts, counts of organizations, students educated in program and schools
- Process: What is happening with systems-level change?
- Common data sources: Action plans, professional development plans, professional development evaluations, self-reports, and coaching logs
- Capacity: What is the ability to implement and sustain systems-level change?
- Common data sources: Fidelity measures, resource maps, budget reviews, human capital, coaching logs, staff turn-over/ ongoing PD needs, and change in policies
- Fidelity: Is the systems-level change implemented well?
- Common data sources: Fidelity measures, coaching logs, initial line of inquiry data, and referrals/access to mental health services
- Outcomes: Are the desired outcomes achieved?
- Common data sources: Academic performance levels/growth, proportion of students needing advanced tiers of support, exclusionary disciplinary placements, (dis)proportionality data, attendance/ chronic absenteeism, and school climate. Outcome data sources are all tied to fidelity data.