FastBridge
earlyReading Composite

Descriptive Information

Please provide a description of your tool:

FastBridge earlyReading is a brief assessment for universal screening up to five times per year and weekly progress monitoring used with students in pre-Kindergarten through Grade 1. The earlyReading composite is comprised of four very brief assessments (about 1-2 minutes each). The set of assessments used in the composite varies somewhat across seasons within a grade to best reflect the typical developmental reading progress of students and assess risk. Each individual assessment measures a specific foundational reading skill that provides data for instructional planning and that research indicates is predictive of future reading success.

The tool is intended for use with the following grade(s).

Preschool / Pre - kindergarten
Kindergarten
First grade
Second grade
Third grade
Fourth grade
Fifth grade
Sixth grade
Seventh grade
Eighth grade
Ninth grade
Tenth grade
Eleventh grade
Twelfth grade

The tool is intended for use with the following age(s).

0-4 years old
5 years old
6 years old
7 years old
8 years old
9 years old
10 years old
11 years old
12 years old
13 years old
14 years old
15 years old
16 years old
17 years old
18 years old

The tool is intended for use with the following student populations.

Students in general education
Students with disabilities
English language learners

ACADEMIC ONLY: What skills does the tool screen?

Reading

Phonological processing:

RAN
Memory
Awareness
Letter sound correspondence
Phonics
Structural analysis

Word ID

Accuracy
Speed

Nonword

Accuracy
Speed

Spelling

Accuracy
Speed

Passage

Accuracy
Speed

Reading comprehension:

Multiple choice questions
Cloze
Constructed Response
Retell
Maze
Sentence verification
Other (please describe):

Listening comprehension:

Multiple choice questions
Cloze
Constructed Response
Retell
Maze
Sentence verification
Vocabulary
Expressive
Receptive

Mathematics

Global Indicator of Math Competence

Accuracy
Speed
Multiple Choice
Constructed Response

Early Numeracy

Accuracy
Speed
Multiple Choice
Constructed Response

Mathematics Concepts

Accuracy
Speed
Multiple Choice
Constructed Response

Mathematics Computation

Accuracy
Speed
Multiple Choice
Constructed Response

Mathematic Application

Accuracy
Speed
Multiple Choice
Constructed Response

Fractions/Decimals

Accuracy
Speed
Multiple Choice
Constructed Response

Algebra

Accuracy
Speed
Multiple Choice
Constructed Response

Geometry

Accuracy
Speed
Multiple Choice
Constructed Response

Other (please describe):

Please describe specific domain, skills or subtests:

BEHAVIOR ONLY: Which category of behaviors does your tool target?

Internalizing
Externalizing
Internalizing and Externalizing

BEHAVIOR ONLY: Please identify which broad domain(s)/construct(s) are measured by your tool and define each sub-domain or sub-construct.

Acquisition and Cost Information

Where to obtain:

Email Address

answers@renaissance.com

Address

Renaissance Learning, PO Box 8036, Wisconsin Rapids, WI 54495

Phone Number

(800) 338-4204

Website

http://www.renaissance.com

Initial cost for implementing program:

Cost

Unit of cost

Replacement cost per unit for subsequent use:

Cost

Unit of cost

Duration of license

Additional cost information:

Describe basic pricing plan and structure of the tool. Provide information on what is included in the published tool, as well as what is not included but required for implementation.

FastBridge assessments are accessed through an annual subscription using a per-student assessed pricing model with no additional fixed costs. The all-inclusive subscription provides access to all FastBridge reading and math assessments for universal screening, progress monitoring, and diagnostic purposes, including both Computer Adaptive Tests (CATs) and Curriculum-Based Measurements (CBMs). The subscription also includes social-emotional behavior assessment tools (SAEBRS, mySAEBRS), developmental milestones assessments, and both English and Spanish assessments at no extra cost. The fully cloud-based data management and reporting system includes screening, progress monitoring, diagnostic, and impact reports, along with digital record forms for assessment administration. All training manuals and materials are included in the subscription cost, along with embedded online system training modules, access to professional development technicians, ongoing technical support, and Renaissance How-to Webinars available via recording. Administration documents, parent information letters, and training resources are accessible through the software's Training & Resources tab. Optional onsite training packages are available in one-day, two-day, or three-day formats, determined by implementation size and which FastBridge assessments a district intends to use. Any onsite training purchase includes a complimentary online Admin/Manager training session for District Managers and School Managers. Additionally, web-based consultation and training delivered by certified FastBridge trainers is available on an hourly basis. The subscription model allows students to take multiple assessments with no per-assessment fees, includes all updates and new features during the subscription period, and provides year-long technical support with unlimited access to training resources.

Provide information about special accommodations for students with disabilities.

FastBridge provides the following accommodations within the assessment for students with and without disabilities. These accommodations are allowed for both screening and progress monitoring purposes: Magnification (enlarged materials); Sound amplification; Extra breaks as needed; Preferential seating and use of quiet space; Proxy responses; Students with different needs or abilities may use scratch paper FastBridge is committed to supporting student accessibility. The FastBridge student experience has been evaluated against recognized accessibility standards, including WCAG 2.0 and 2.1, and a Voluntary Product Accessibility Template (VPAT®) is available that documents current levels of support. Accessibility considerations continue to be incorporated into ongoing product development as we work to improve access for all learners.

Administration

BEHAVIOR ONLY: What type of administrator is your tool designed for?

General education teacher
Special education teacher
Parent
Child
External observer
Other

If other, please specify:

What is the administration setting?

Direct observation
Rating scale
Checklist
Performance measure
Questionnaire
Direct: Computerized
One-to-one
Other

If other, please specify:

Does the tool require technology?

Yes

If yes, what technology is required to implement your tool? (Select all that apply)

Computer or tablet
Internet connection
Other technology (please specify)

If your program requires additional technology not listed above, please describe the required technology and the extent to which it is combined with teacher small-group instruction/intervention:

What is the administration context?

Individual
Small group   If small group, n=
Large group   If large group, n=
Computer-administered
Other

If other, please specify:

What is the administration time?

Time in minutes

7

per (student/group/other unit)

student

Additional scoring time:

Time in minutes

per (student/group/other unit)

ACADEMIC ONLY: What are the discontinue rules?

No discontinue rules provided
Basals
Ceilings
Other

If other, please specify:

Some subtests are timed. These subtests include Letter Names, Letter Sounds, Sight Words, Decodable Words, and Nonsense Words

Are norms available?

Yes

Are benchmarks available?

Yes

If yes, how many benchmarks per year?

3

If yes, for which months are benchmarks available?

August - November, December - mid-March, Mid-March - July

BEHAVIOR ONLY: Can students be rated concurrently by one administrator?

If yes, how many students can be rated concurrently?

Training & Scoring

Training

Is training for the administrator required?

Yes

Describe the time required for administrator training, if applicable:

Less than 1 hour of training

Please describe the minimum qualifications an administrator must possess.

No minimum qualifications

Are training manuals and materials available?

Yes

Are training manuals/materials field-tested?

No

Are training manuals/materials included in cost of tools?

Yes

If No, please describe training costs:

Can users obtain ongoing professional and technical support?

Yes

If Yes, please describe how users can obtain support:

Renaissance Technical Support Staff

Scoring

How are scores calculated?

Manually (by hand)
Automatically (computer-scored)
Other

If other, please specify:

Do you provide basis for calculating performance level scores?

Yes

What is the basis for calculating performance level and percentile scores?

Age norms
Grade norms
Classwide norms
Schoolwide norms
Stanines
Normal curve equivalents

What types of performance level scores are available?

Raw score
Standard score
Percentile score
Grade equivalents
IRT-based score
Age equivalents
Stanines
Normal curve equivalents
Developmental benchmarks
Developmental cut points
Equated
Probability
Lexile score
Error analysis
Composite scores
Subscale/subtest scores
Other

If other, please specify:

Words read correct per minute

Does your tool include decision rules?

If yes, please describe.

Risk benchmarks

Can you provide evidence in support of multiple decision rules?

No

If yes, please describe.

Please describe the scoring structure. Provide relevant details such as the scoring format, the number of items overall, the number of items per subscale, what the cluster/composite score comprises, and how raw scores are calculated.

For screening, students complete four brief assessments. Each earlyReading assessment produces a total correct or corrects per minute score. These scores are combined using a two-stage weighting procedure to generate the earlyReading composite score, the primary screening score. The weighting procedure was designed to optimize prediction of end of grade broad reading achievement, in Kindergarten and Grade 1. The weighting also results in a scale that enables direct comparison of composite scores across season within grade. The composite score in the fall of Kindergarten includes: Concepts of Print, Onset Sounds, Letter Sounds, and Letter Naming. The composite score for winter includes Onset Sounds, Letter Sounds, Word Segmenting and Nonsense Words. The composite score for spring includes: Letter Sounds, Word Segmenting, Nonsense Words, and Sight Words (50). The Decodable Words assessment score may be used in place of Nonsense Words for computing any of the composite scores specified. The composite score in the fall of Grade 1 includes: Word Segmenting, Nonsense Words, Sight Words (150), and Sentence Reading. The composite score for winter includes: Word Segmenting, Nonsense Words, Sight Words (150), and CBMreading. The composite score for spring includes: Word Segmenting, Nonsense Words, Sight Words (150), and CBMreading. The Decodable Words assessment score may be used in place of Nonsense Words for computing any of the composite scores specified.

Describe the tool’s approach to screening, samples (if applicable), and/or test format, including steps taken to ensure that it is appropriate for use with culturally and linguistically diverse populations and students with disabilities.

FastBridge earlyReading is an assessment suite used to screen and monitor students’ progress in both a unified and skill-based approach in prekindergarten – Grade 1. The earlyReading composite score (as described above) is a combination of scores from four individual assessments. The combination of assessments varies somewhat across seasons and grades. The overall composite score is the primary indicator of reading competence and is compared to empirically derived performance benchmarks. These benchmarks result in four performance levels: high risk, some risk, low risk, and advanced. For students at risk, the individual assessments provide input for additional risk assessment and instructional planning in four domains: concepts of print, phonemic awareness, phonics, and decoding. The set of earlyReading assessments that form the composite also provide input for personalized and classroom instruction plans described in the Screening-to-Intervention (S2i) report. Additional earlyReading assessments not contained in the composite are available for further diagnostic evaluation.

Classification Accuracy & Cross-Validation Summary

Grade	Kindergarten	Grade 1
Classification Accuracy Fall
Classification Accuracy Winter
Classification Accuracy Spring

GRADE (Group Reading Assessment and Diagnostic Evaluation)

Classification Accuracy

Select time of year

Fall

Winter

Spring

Describe the criterion (outcome) measure(s) including the degree to which it/they is/are independent from the screening measure.

The GRADE (Group Reading Assessment and Diagnostic Evaluation) is a diagnostic reading test that that determines what developmental skills PreK-12 students have mastered and where students need instruction or intervention. The GRADE is a paper and pencil test that can take 50-90 minutes to complete. The GRADE comprises two levels with 10 parallel forms per level. Grade-based norms are provided fall and spring.

Do the classification accuracy analyses examine concurrent and/or predictive classification?

Concurrent
Predictive

Describe when screening and criterion measures were administered and provide a justification for why the method(s) you chose (concurrent and/or predictive) is/are appropriate for your tool.

Describe how the classification analyses were performed and cut-points determined. Describe how the cut points align with students at-risk. Please indicate which groups were contrasted in your analyses (e.g., low risk students versus high risk students, low risk students versus moderate risk students).

Cut points were selected by optimizing sensitivity, and then balancing sensitivity with specificity using methods presented in Silberglitt and Hintze (2005). The cut points were derived for the 20th percentile

Were the children in the study/studies involved in an intervention in addition to typical classroom instruction between the screening measure and outcome assessment?

No

If yes, please describe the intervention, what children received the intervention, and how they were chosen.

Cross-Validation

Has a cross-validation study been conducted?

No

If yes,

Select time of year.

Fall

Winter

Spring

Describe the criterion (outcome) measure(s) including the degree to which it/they is/are independent from the screening measure.

Do the cross-validation analyses examine concurrent and/or predictive classification?

Concurrent
Predictive

Describe when screening and criterion measures were administered and provide a justification for why the method(s) you chose (concurrent and/or predictive) is/are appropriate for your tool.

Describe how the cross-validation analyses were performed and cut-points determined. Describe how the cut points align with students at-risk. Please indicate which groups were contrasted in your analyses (e.g., low risk students versus high risk students, low risk students versus moderate risk students).

Were the children in the study/studies involved in an intervention in addition to typical classroom instruction between the screening measure and outcome assessment?

If yes, please describe the intervention, what children received the intervention, and how they were chosen.

---

Star Early Literacy

Classification Accuracy

Select time of year

Fall

Winter

Spring

Describe the criterion (outcome) measure(s) including the degree to which it/they is/are independent from the screening measure.

Renaissance Star Early Literacy is a comprehensive computer adaptive assessment for PreK-3 students that helps teachers identify child levels of literacy development. The assessment measures a child’s command of 41 different sets of skills in ten key literacy sub-domains. Each assessment takes about 15 minutes. Star Early Literacy provides teachers with the immediate feedback needed to monitor skill development, focus teaching or identify children who need special attention. Star Early Literacy was developed completely, independent from Fastbridge earlyReading; Fastbridge was developed at the University of Minnesota and Star was developed at Renaissance Learning. Star Early Literacy serves as a good criterion measure because, like Fastbridge earlyReading, it assesses broad reading achievement using content aligned to state and national learning standards and has been highly rated by NCII as a reliable and valid measure of reading achievement.

Do the classification accuracy analyses examine concurrent and/or predictive classification?

Concurrent
Predictive

Describe when screening and criterion measures were administered and provide a justification for why the method(s) you chose (concurrent and/or predictive) is/are appropriate for your tool.

Describe how the classification analyses were performed and cut-points determined. Describe how the cut points align with students at-risk. Please indicate which groups were contrasted in your analyses (e.g., low risk students versus high risk students, low risk students versus moderate risk students).

Cut points were selected by optimizing sensitivity, and then balancing sensitivity with specificity using methods presented in Silberglitt and Hintze (2005). The 11th national percentile is the criterion measure cut point for grade 1, while the 17th percentile is the cut point for kindergarten. The cut points between the 10th and 20th national percentile contrasts high risk students against some and low risk students from the sample.

Were the children in the study/studies involved in an intervention in addition to typical classroom instruction between the screening measure and outcome assessment?

If yes, please describe the intervention, what children received the intervention, and how they were chosen.

Cross-Validation

Has a cross-validation study been conducted?

No

If yes,

Select time of year.

Fall

Winter

Spring

Describe the criterion (outcome) measure(s) including the degree to which it/they is/are independent from the screening measure.

Do the cross-validation analyses examine concurrent and/or predictive classification?

Concurrent
Predictive

Describe when screening and criterion measures were administered and provide a justification for why the method(s) you chose (concurrent and/or predictive) is/are appropriate for your tool.

Describe how the cross-validation analyses were performed and cut-points determined. Describe how the cut points align with students at-risk. Please indicate which groups were contrasted in your analyses (e.g., low risk students versus high risk students, low risk students versus moderate risk students).

Were the children in the study/studies involved in an intervention in addition to typical classroom instruction between the screening measure and outcome assessment?

If yes, please describe the intervention, what children received the intervention, and how they were chosen.

---

Classification Accuracy - Fall

Evidence	Kindergarten	Grade 1
Criterion measure	Star Early Literacy	Star Early Literacy
Cut Points - Percentile rank on criterion measure	17	11
Cut Points - Performance score on criterion measure
Cut Points - Corresponding performance score (numeric) on screener measure	30	26
Classification Data - True Positive (a)	33	103
Classification Data - False Positive (b)	18	89
Classification Data - False Negative (c)	9	13
Classification Data - True Negative (d)	58	217
Area Under the Curve (AUC)	0.84	0.86
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.77	0.82
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.91	0.90

Statistics	Kindergarten	Grade 1
Base Rate	0.36	0.27
Overall Classification Rate	0.77	0.76
Sensitivity	0.79	0.89
Specificity	0.76	0.71
False Positive Rate	0.24	0.29
False Negative Rate	0.21	0.11
Positive Predictive Power	0.65	0.54
Negative Predictive Power	0.87	0.94

Sample	Kindergarten	Grade 1
Date	2021-22	2021-22
Sample Size	118	422
Geographic Representation	Pacific (CA) West North Central (MN)	Pacific (CA) West North Central (MN)
Male
Female
Other
Gender Unknown
White, Non-Hispanic
Black, Non-Hispanic
Hispanic
Asian/Pacific Islander
American Indian/Alaska Native
Other
Race / Ethnicity Unknown
Low SES
IEP or diagnosed disability
English Language Learner

Classification Accuracy - Winter

Evidence	Kindergarten	Grade 1
Criterion measure	Star Early Literacy	Star Early Literacy
Cut Points - Percentile rank on criterion measure	17	11
Cut Points - Performance score on criterion measure
Cut Points - Corresponding performance score (numeric) on screener measure	44.5	34
Classification Data - True Positive (a)	30	96
Classification Data - False Positive (b)	33	45
Classification Data - False Negative (c)	5	28
Classification Data - True Negative (d)	59	200
Area Under the Curve (AUC)	0.79	0.85
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.70	0.81
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.88	0.90

Statistics	Kindergarten	Grade 1
Base Rate	0.28	0.34
Overall Classification Rate	0.70	0.80
Sensitivity	0.86	0.77
Specificity	0.64	0.82
False Positive Rate	0.36	0.18
False Negative Rate	0.14	0.23
Positive Predictive Power	0.48	0.68
Negative Predictive Power	0.92	0.88

Sample	Kindergarten	Grade 1
Date	2021-22	2021-22
Sample Size	127	369
Geographic Representation	Pacific (CA) West North Central (MN)	Pacific (CA) West North Central (MN)
Male
Female
Other
Gender Unknown
White, Non-Hispanic
Black, Non-Hispanic
Hispanic
Asian/Pacific Islander
American Indian/Alaska Native
Other
Race / Ethnicity Unknown
Low SES
IEP or diagnosed disability
English Language Learner

Classification Accuracy - Spring

Evidence	Kindergarten	Grade 1
Criterion measure	GRADE (Group Reading Assessment and Diagnostic Evaluation)	GRADE (Group Reading Assessment and Diagnostic Evaluation)
Cut Points - Percentile rank on criterion measure	15	15
Cut Points - Performance score on criterion measure	52.00	45.00
Cut Points - Corresponding performance score (numeric) on screener measure
Classification Data - True Positive (a)	14	8
Classification Data - False Positive (b)	24	11
Classification Data - False Negative (c)	2	1
Classification Data - True Negative (d)	172	104
Area Under the Curve (AUC)	0.95	0.99
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.93	0.99
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.97	1.00

Statistics	Kindergarten	Grade 1
Base Rate	0.08	0.07
Overall Classification Rate	0.88	0.90
Sensitivity	0.88	0.89
Specificity	0.88	0.90
False Positive Rate	0.12	0.10
False Negative Rate	0.13	0.11
Positive Predictive Power	0.37	0.42
Negative Predictive Power	0.99	0.99

Sample	Kindergarten	Grade 1
Date	2012-13	2012-13
Sample Size	212	124
Geographic Representation	West North Central (MN)	West North Central (MN)
Male
Female
Other
Gender Unknown
White, Non-Hispanic
Black, Non-Hispanic
Hispanic
Asian/Pacific Islander
American Indian/Alaska Native
Other
Race / Ethnicity Unknown
Low SES
IEP or diagnosed disability
English Language Learner

Reliability

Grade	Kindergarten	Grade 1
Rating

*Offer a justification for each type of reliability reported, given the type and purpose of the tool.

Because the FastBridge earlyReading composite score is comprised of four separates assessments some of which are timed and some untimed, a confirmatory factor model-based approach produces an estimate of stability of the composite score from a single administration. This reliability coefficient is analogous to the tau equivalent measurement model.

*Describe the sample(s), including size and characteristics, for each reliability analysis conducted.

The model-based reliability was derived from a national sample of students screened using the FastBridge earlyReading composite in the fall of 2018. The sample consisted of more than 94,000 students in Kindergarten and more than 72,000 in Grade 1 with 51% male, 71% white, 6% African American, 12% Hispanic, 5% Asian and 5% other race/ethnicity.

*Describe the analysis procedures for each reported type of reliability.

Model-based reliability coefficients were derived from factor loadings using a confirmatory factor model in which the four subtests were regressed onto a single factor model. Reliability is ratio of the sum of the squared factor loadings (true score variance) to the total variance (sum of squared loadings plus error variance).

*In the table(s) below, report the results of the reliability analyses described above (e.g., internal consistency or inter-rater reliability coefficients).

Type of	Subgroup	Informant	Age / Grade	Test or Criterion	n	Median Coefficient	95% Confidence Interval Lower Bound	95% Confidence Interval Upper Bound

Results from other forms of reliability analysis not compatible with above table format:

Manual cites other published reliability studies:

No

Provide citations for additional published studies.

Do you have reliability data that are disaggregated by gender, race/ethnicity, or other subgroups (e.g., English language learners, students with disabilities)?

No

If yes, fill in data for each subgroup with disaggregated reliability data.

Type of	Subgroup	Informant	Age / Grade	Test or Criterion	n	Median Coefficient	95% Confidence Interval Lower Bound	95% Confidence Interval Upper Bound

Results from other forms of reliability analysis not compatible with above table format:

Manual cites other published reliability studies:

No

Provide citations for additional published studies.

Validity

Grade	Kindergarten	Grade 1
Rating

*Describe each criterion measure used and explain why each measure is appropriate, given the type and purpose of the tool.

The criterion measure for predictive validity analyzed in Kindergarten is the GRADE. The GRADE is a diagnostic reading test that that determines what developmental skills PreK-12 students have mastered and where students need instruction or intervention. The GRADE is a paper and pencil test that can take 50-90 minutes to complete. The GRADE comprises two levels with 10 parallel forms per level. Grade-based norms are provided fall and spring. The criterion measure for concurrent validity analyzed in Kindergarten is Renaissance Star Early Literacy. Renaissance Star Early Literacy is a comprehensive computer adaptive assessment for PreK-3 students that helps teachers identify child levels of literacy development. The assessment measures a child’s command of 41 different sets of skills in ten key literacy sub-domains. Each assessment takes about 15 minutes. Star Early Literacy provides teachers with the immediate feedback needed to monitor skill development, focus teaching or identify children who need special attention. Star Early Literacy was developed completely, independent from Fastbridge earlyReading; Fastbridge was developed at the University of Minnesota and Star was developed at Renaissance Learning. Star Early Literacy serves as a good criterion measure because, like Fastbridge earlyReading, it assesses broad reading achievement using content aligned to state and national learning standards and has been highly rated by NCII as a reliable and valid measure of reading achievement. The criterion measure for both types of validity analyzes (concurrent and predictive) in Grade 1 is the NWEA MAP Growth K-2 reading assessment. MAP Growth K-2 is a computer adaptive test that indicates a student's general reading ability.

*Describe the sample(s), including size and characteristics, for each validity analysis conducted.

Predictive analyses in Kindergarten with GRADE were conducted on a sample of 173 students from Minnesota. Concurrent validity for Kindergarten scores with Star Early Literacy was conducted on a sample of students from Minnesota and California. There were 112 Kindergarten students from a single school in California and six Kindergarten students from a single school in Minnesota. Concurrent and predictive analyses in Grade 1 with MAP Growth K-2 were conducted on a sample of 463 and 167 students respectively from four states: MI, WI, MN, and MO.

*Describe the analysis procedures for each reported type of validity.

Validity coefficients were calculated by computing Pearson product moment correlations between FastBridge earlyReading Composite and the criterion measure. Confidence intervals represent 95% confidence intervals.

*In the table below, report the results of the validity analyses described above (e.g., concurrent or predictive validity, evidence based on response processes, evidence based on internal structure, evidence based on relations to other variables, and/or evidence based on consequences of testing), and the criterion measures.

Type of	Subgroup	Informant	Age / Grade	Test or Criterion	n	Median Coefficient	95% Confidence Interval Lower Bound	95% Confidence Interval Upper Bound

Results from other forms of validity analysis not compatible with above table format:

Manual cites other published reliability studies:

No

Provide citations for additional published studies.

Describe the degree to which the provided data support the validity of the tool.

Do you have validity data that are disaggregated by gender, race/ethnicity, or other subgroups (e.g., English language learners, students with disabilities)?

No

If yes, fill in data for each subgroup with disaggregated validity data.

Type of	Subgroup	Informant	Age / Grade	Test or Criterion	n	Median Coefficient	95% Confidence Interval Lower Bound	95% Confidence Interval Upper Bound

Results from other forms of validity analysis not compatible with above table format:

Manual cites other published reliability studies:

No

Provide citations for additional published studies.

Bias Analysis

Grade	Kindergarten	Grade 1
Rating	Provided	Provided

Have you conducted additional analyses related to the extent to which your tool is or is not biased against subgroups (e.g., race/ethnicity, gender, socioeconomic status, students with disabilities, English language learners)? Examples might include Differential Item Functioning (DIF) or invariance testing in multiple-group confirmatory factor models.

Yes

If yes,

a. Describe the method used to determine the presence or absence of bias:

Statistical bias was assessed by grade level (K-1) using data from about 65,000 administrations from the 2024-25 school year. An effect size measure was used to evaluate the practical difference between subgroup level AUC scores. This approach was selected over a test of statistical signifance (e.g., DeLong’s Test, chi-squared test), because many of these tests are sensitive to group imbalances (i.e., different counts between risk groups) and large sample sizes, which can lead to increased Type I error rates. Instead, a difference between AUC scores (∆AUC ) was used as an effect size measure to assess practical differences between subgroup AUC scores. A common rule-of-thumb approach to gauge ∆AUC is: negligible (ES <=0.02), small (ES 0.03-0.05), small (ES 0.06-0.10), and large (ES >0.10).

b. Describe the subgroups for which bias analyses were conducted:

The data set was sufficient to examine bias in relation to race/ethnicity by grade, K - 1. The race/ethnicity group comparisons examined were White versus African American, White versus Hispanic, White versus Asian, and White versus Other.

c. Describe the results of the bias analyses conducted, including data and interpretative statements. Include magnitude of effect (if available) if bias has been identified.

Overall ∆AUC never exceeded 0.03 when comparing White vs Black/African American, White vs Hispanic, White vs Asian, or White vs Other students across all grades indicating that the difference in misclassification across subgroups was either small or negligible and bias was minimal. Comparisons between White and Black/African American students are as follows: KG = 0.012; G1 = 0.002. White and Hispanic/Latino students are as follows: KG = 0.006; G1 = 0.006. White and Asian students are as follows: KG = 0.020; G1 = 0.024. White and Other students are as follows: KG = 0.028; G1 = 0.011.