Before you plan your battery, account for what already exists. Every category below is either informative or documentably not applicable. A thorough review here protects against over-testing, surfaces patterns before you test, and — on a REED — supports your scope decisions. "Not relevant" is a clinical judgment, not a skip.
Academic performance
Grades & Report Cards
Look for: trend over time, specific subject weaknesses, discrepancy between effort and output, teacher comments. One bad quarter ≠ pattern; sustained decline or subject-specific failure matters more.
What it shows / why not applicable
STAAR / State Assessment Data
Look for: approaching/did not meet patterns, specific reporting category weaknesses (e.g., Foundational Language Skills, Literary Text), multi-year trend. Note any testing accommodations used.
What it shows / why not applicable
Intervention & MTSS history
RTI / MTSS Tier History & Progress Monitoring
Look for: duration and intensity of intervention, progress monitoring data with slope/ROI, fidelity documentation, whether tiers were implemented with adequate time and consistency. Lack of response to quality intervention is meaningful; lack of intervention is a gap to document.
What it shows / why not applicable
Prior evaluation data
Prior FIE / REED / Outside Evaluations
Look for: prior eligibility determinations, previous cognitive and achievement scores, domains previously assessed, eligibility history, any outside psychological or medical evaluations. On a REED: prior scores are existing data — you may not need to re-administer every domain.
What it shows / why not applicable
Student work samples
Work Samples
Look for: error patterns in reading (decoding vs. comprehension), writing (mechanics, fluency, ideation), and math (computation vs. application). Work samples are direct evidence — use the Error Analyzer to classify patterns before planning your battery.
What it shows / why not applicable
Health & background records
Attendance & Health Records
Look for: chronic absenteeism (10%+ of school days) that could explain academic gaps, vision/hearing screening results, medical diagnoses relevant to learning (ADHD, seizure disorder, chronic illness), medication history. These are exclusionary factor checks, not primary data sources.
What it shows / why not applicable
Input from key informants
Teacher Input
Look for: specific behavioral and academic observations, accommodations already in place and their effect, consistency across settings (one teacher vs. all teachers), and whether concerns align with the referral reason. Teacher Input Form — FIE is the standard collection tool.
What it shows / why not applicable
Parent / Family Input
Look for: developmental history, home language and literacy environment, parent observations of learning differences, family history of learning disabilities, and any outside services (tutoring, private therapy). Parent Input Form — FIE is the standard collection tool.
What it shows / why not applicable
Student Input
Look for: student's own awareness of difficulty, what helps and what doesn't, emotional impact of academic struggles, self-concept around learning. Especially important for older students. Student Interview Form is the standard collection tool.
What it shows / why not applicable
Language proficiency data
Language Proficiency Data (TELPAS / LPAC / CLI)
Look for: TELPAS composite and domain ratings, LPAC designation and home language, WJ-IV OL or CLI results for language dominance determination, years in U.S. schools, and prior language services. This data drives who does the eval. Use the EB Eligibility Screener if dominance is unclear.
What it shows / why not applicable
Behavioral & disciplinary records
Discipline Records
Look for: frequency and type of behavioral incidents, settings where behavior occurs (academic demand vs. transitions vs. unstructured), whether behavior is function-based (avoidance of academic tasks), and any prior FBA or BIP. Most relevant for OHI/ADHD, ED, and AU referrals.
What it shows / why not applicable
🔍 Working hypothesis before assessment
Based on all existing data reviewed above — what pattern are you seeing? What eligibility are you thinking toward? What domains still need direct assessment? This is a clinical thinking prompt, not a commitment.