FastBridge
earlyMath Composite

Descriptive Information

Please provide a description of your tool:

FAST™ earlyMath is an evidence-based assessment used for universal screening in grades PreK-1 with the option to do so up to five times per year, or for frequent progress monitoring at any grade. Each assessment is designed to be highly efficient and inform instruction. The FAST™ earlyMath assessments are comprised of 14 sub-tests. Of those sub-tests, FastBridge Learning provides recommendations for specific combinations of up to four sub-tests to be given per benchmark period. This composite varies from fall, winter, or spring per grade level to best match math skill development and reliably assess performance. The composite is typically completed in 5-10 minutes per student. The remaining assessments may be used as needed to further evaluate skill deficits. Results help identify student risk while informing instruction.

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

info@fastbridge.org

Address

150 South Fifth Street Suite 600 Minneapolis, MN 55402

Phone Number

6122542534

Website

www.fastbridge.org

Initial cost for implementing program:

Cost

$7.50

Unit of cost

student

Replacement cost per unit for subsequent use:

Cost

$7.50

Unit of cost

student

Duration of license

year

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.

The FastBridge system is provided to customers through an annual subscription that is priced on a per student basis. The subscription rate for school year 2020–21 is $7.50 per student. There are no additional fixed costs. FastBridge subscriptions are all inclusive providing access to all FastBridge reading, math, and behavior assessments, the data management and reporting system, embedded online training, basic implementation, and client support. The academic assessment suite includes adaptive assessments for reading and math screening as well as curriculum-based measurement for universal screening, progress monitoring and skills diagnostics for K – 12. The behavior suite includes screeners completed by both teachers (K-12) and students (2-12) and a direct behavior rating system for progress monitoring for K – 12. In addition to the online training modules embedded within the FastBridge system, Illuminate Education offers both onsite and online training options. All day-long sessions include 6 hours of content. All sessions are capped at a maximum of 30 participants in order to provide a high-quality learning experience. Concurrent sessions of 30 participants are available. The costs are as follows: 1 Day Onsite is $3000 with a 30-participant maximum; 1 Day Online (three, 2-hour sessions) is $1500 with a 30-participant maximum; 2-hour Online Module is $500 with a 30-participant maximum. Training packages include beginner and advanced levels as well as individual online sessions on specific topics. FastBridge recommends that all new users purchase the 2-day FAST Essentials beginner package. This option includes one day on universal screening and a second day on progress monitoring. Advanced options for FastBridge training include day-long FAST Focus sessions, provided either onsite or online and cover topics such as leader reports, data-based decision making, and using FastBridge with special populations. Finally, single-topic sessions selected from the FastBridge module library are available for 2-hour online webinars. FastBridge trainings are provided by highly trained veteran educators who are expert with not only FastBridge features but also how to use the data to support student leaning.

Provide information about special accommodations for students with disabilities.

The FastBridge system is a fully cloud-based system, and therefore computer and Internet access are required for full use of the application. Some of the assessments are computer-administered and others are teacher-administered. A paraprofessional can administer the assessment as a Group Proctor in the FastBridge system. There are embedded online training courses included in the platform and these include certification tests. The courses require between 15 and 30 minutes each to complete. The number of assessments that a teacher needs to administer varies by grade level and so total training time will vary. The system allows for the following accommodations to support accessibility for students with documented disabilities: • Text magnification • Sound amplification • Enlarged and printed paper materials are available upon request • Extended time on selected assessments (FastBridge adaptive assessments are untimed, thus, students take as much time as needed) • Extra breaks (the adaptive assessments can be paused for breaks) • Preferential seating • Small group or individual sessions • Proxy responses • Scratch paper on selected subtests

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.

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:

Users have access to professional development technicians, as well as ongoing technical support.

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:

Does your tool include decision rules?

No

If yes, please describe.

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 three brief assessments. Each FastBridge earlyMath assessment produces a total correct or corrects per minute score. These scores are combined using a two-stage weighting procedure to generate the FastBridge earlyMath composite score, the primary screening score. The weighting procedure was designed to optimize prediction of end of grade broad math 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: Match Quantity, Number Sequence, and Numeral ID. The composite score for winter includes: Decomposing, Number Sequence, and Numeral ID. The composite score for spring includes: Decomposing, Number Sequence, and Numeral ID. The composite score in the fall of Grade 1 includes: Decomposing, Number Sequence, and Numeral ID. The composite score for winter includes: Decomposing, Number Sequence, and Place Value. The composite score for spring includes: Decomposing, Place Value, and Story Problems. NOTE: Decomposing, Number Sequence, and Numeral ID in Grade 1 use larger numbers than the Kindergarten version to reflect grade level expectations.

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 earlyMath is an evidence-based assessment suite used to screen and monitor students’ progress in both a unified and skill-based approach in prekindergarten – Grade 1. The earlyMath composite score (as described above) is a combination of scores from three individual assessments. The combination of assessments varies somewhat across seasons and grades. The overall composite score is the primary indicator of math competence and is compared to research-based, 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 FastBridge earlyMath assessments that form the composite also provide input for personalized and classroom instruction plans described in the Screening-to-Intervention (S2i) report. Additional FastBridge earlyMath 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

GMADE (Group Mathematics 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 GMADE (Group Mathematics Assessment and Diagnostic Evaluation) is a diagnostic math test that that determines what developmental skills PreK-12 students have mastered and where students need instruction or intervention. The GMADE is a paper and pencil test that can take 50-90 minutes to complete. The GMADE 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.

---

NWEA MAP Growth

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 NWEA MAP Growth assessment in mathematics is a computer-adaptive test administered in the fall, winter, and spring for universal screening. The content includes the following domains with 6 - 8 items per domain: operations and algebraic thinking, number and operations, measurement and data, and geometry. Students complete 43 items.

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.

Both screening and criterion measures were administered concurrently in the fall, winter, and spring.

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).

The 15th national percentile on MAP Growth was used to divide the sample into a high risk and a moderate to low risk group. The earlyMath composite cut-points were derived by optimizing specificity and sensitivity using Youden’s J index. The analyses were repeated for each grade and season.

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

Yes

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

The data were derived from universal screening at each grade level and season in districts implementing MTSS. Although, the information regarding the specific intervention was not available for these analyses, most students scoring in the high risk range were assigned to some form of intensive intervention.

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	NWEA MAP Growth	NWEA MAP Growth
Cut Points - Percentile rank on criterion measure	15	15
Cut Points - Performance score on criterion measure
Cut Points - Corresponding performance score (numeric) on screener measure	30	37
Classification Data - True Positive (a)	17	76
Classification Data - False Positive (b)	66	72
Classification Data - False Negative (c)	6	25
Classification Data - True Negative (d)	170	195
Area Under the Curve (AUC)	0.80	0.83
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.68	0.79
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.91	0.88

Statistics	Kindergarten	Grade 1
Base Rate	0.09	0.27
Overall Classification Rate	0.72	0.74
Sensitivity	0.74	0.75
Specificity	0.72	0.73
False Positive Rate	0.28	0.27
False Negative Rate	0.26	0.25
Positive Predictive Power	0.20	0.51
Negative Predictive Power	0.97	0.89

Sample	Kindergarten	Grade 1
Date	2018-19	2018-19
Sample Size	259	368
Geographic Representation	East North Central (MI)	East North Central (MI)
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	NWEA MAP Growth	NWEA MAP Growth
Cut Points - Percentile rank on criterion measure	15	15
Cut Points - Performance score on criterion measure
Cut Points - Corresponding performance score (numeric) on screener measure	52	49
Classification Data - True Positive (a)	29	36
Classification Data - False Positive (b)	54	39
Classification Data - False Negative (c)	9	12
Classification Data - True Negative (d)	174	227
Area Under the Curve (AUC)	0.87	0.89
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.81	0.83
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.93	0.94

Statistics	Kindergarten	Grade 1
Base Rate	0.14	0.15
Overall Classification Rate	0.76	0.84
Sensitivity	0.76	0.75
Specificity	0.76	0.85
False Positive Rate	0.24	0.15
False Negative Rate	0.24	0.25
Positive Predictive Power	0.35	0.48
Negative Predictive Power	0.95	0.95

Sample	Kindergarten	Grade 1
Date	2018-19	2018-19
Sample Size	266	314
Geographic Representation	East North Central (MI)	East North Central (MI)
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	NWEA MAP Growth	NWEA MAP Growth
Cut Points - Percentile rank on criterion measure	15	15
Cut Points - Performance score on criterion measure
Cut Points - Corresponding performance score (numeric) on screener measure	62	61
Classification Data - True Positive (a)	21	39
Classification Data - False Positive (b)	27	64
Classification Data - False Negative (c)	5	14
Classification Data - True Negative (d)	194	207
Area Under the Curve (AUC)	0.88	0.86
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.80	0.80
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.96	0.91

Statistics	Kindergarten	Grade 1
Base Rate	0.11	0.16
Overall Classification Rate	0.87	0.76
Sensitivity	0.81	0.74
Specificity	0.88	0.76
False Positive Rate	0.12	0.24
False Negative Rate	0.19	0.26
Positive Predictive Power	0.44	0.38
Negative Predictive Power	0.97	0.94

Sample	Kindergarten	Grade 1
Date	2018-19	2018-19
Sample Size	247	324
Geographic Representation	East North Central (MI)	East North Central (MI)
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.

Test-retest reliability assess the consistency of scores from repeated administrations of the same test form as can happen when a test administration is spoiled, and the same form needs to be readministered. Because the FastBridge earlyMath 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.

Test-retest reliability was based on students who completed the FastBridge earlyReading composite twice within 60 days. The sample was distributed across 20 states. The model-based reliability was derived from a national sample of students screened using the FastBridge earlyMath composite in the fall of 2017. The sample consisted of more than 46,000 students in Kindergarten and more than 43,000 in Grade 1 with 52% male, 66% white, 6% African American, 12% Hispanic, 9% Asian and 7% other race/ethnicity.

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

Test-retest reliability coefficients were estimated from the Pearson correlation between two administrations of the same earlyMath composite test forms. 95% confidence intervals were computed using the z-transformation method. 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). 95% confidence intervals were computed using the z-transformation method.

*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)?

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:

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 NWEA MAP Growth K-2 assessment in mathematics is a computer-adaptive test administered in the fall, winter, and spring for universal screening. The content includes the following domains with 6 - 8 items per domain: operations and algebraic thinking, number and operations, measurement and data, and geometry. Students complete 43 items.

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

The sample was comprised of students from several school districts in Michigan. For both grades a total of about 300 students completed both assessments.

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

Validity coefficients were calculated by computing Pearson product moment correlations between FastBridge earlyMath Composite scale score and MAP Growth RIT score. Predictive validity was based on winter earlyMath scores and spring MAP Growth scores. 95% confidence intervals were computed using the z-transformation method.

*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	No	No

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.

No

If yes,

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

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

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.