Acadience Math
Composite

Descriptive Information

Please provide a description of your tool:

Acadience Math is a screening and progress monitoring assessment used to measure math skills. The Acadience Math screening measures are efficient indicators of math skills. For each grade and time of year, the Acadience Math component measures that correlate highly with later outcomes are combined to form a Math Composite Score. The component measures used in the Composite Score depend upon grade and time of year. The Composite Score is the best overall predictor of later outcomes and conveys that all of the aspects of math proficiency are critical.

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@acadiencelearning.org; customerservice@voyagersopris.com

Address

Acadience Learning Inc. 4710 Village Plaza Loop, Suite 210, Eugene OR 97401; Voyager Sopris Learning 17855 Dallas Parkway, Suite 400 Dallas, TX 75287

Phone Number

Acadience Learning: (541)431-6931, (888)943-1240; Voyager Sopris Learning: (800)547-6747

Website

https://acadiencelearning.org/; https://www.voyagersopris.com/

Initial cost for implementing program:

Cost

$0.00

Unit of cost

Replacement cost per unit for subsequent use:

Cost

$0.00

Unit of cost

Duration of license

Unlimited

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.

Included in Cost: All materials are available for free download at https://acadiencelearning.org, including progress monitoring worksheets for each grade, assessor scoring booklets and keys for each grade, the Acadience Math Assessment Manual, and the Acadience Math Technical Adequacy Brief. ADDITIONAL INFO WHERE TOOL CAN BE OBTAINED: Acadience Learning Inc. (Free download version in black and white) Voyager Sopris Learning (print materials in color) Website: http://voyagersopris.com Address: 17855 Dallas Parkway, Suite 400, Dallas, TX 75287-6816 Telephone number: (800) 547-6747 ADDITIONAL INFO COST INFO: Print materials: Voyager Sopris Learning (Published print version in color) Website: http://voyagersopris.com Address: 17855 Dallas Parkway, Suite 400, Dallas, TX 75287-6816 Telephone number: (800) 547-6747. Classroom Kit for K = $80.00, $3.20 per student Classroom Kit for 1 = $117.00, $4.68 per student Classroom Kits for 2, 3, 4, 5, 6 = $161.00, $6.44 per student Data management via Acadience Learning Online: Manual Entry Licenses for K-6 = $2.50 per student, per year

Provide information about special accommodations for students with disabilities.

Approved accommodations are any accommodations that will not alter the standardization of the assessment. Specific approved accommodations include, but are not limited to: 1. The use of colored overlays, filters, or lighting adjustments for students with visual impairments. 2. The use of student materials that have been enlarged or with larger print for students with visual impairments. 3. The use of assistive technology, such as hearing aids and assistive listening devices (ALDs), for students with hearing impairments. 4. The use of a marker or ruler to focus student attention on the materials for students who are not able to demonstrate their skills adequately without one. Unapproved accommodations are accommodations that are likely to change how the assessment functions (such as modifying the timing rules or reading Concepts and Applications items to students). Scores from measures administered with unapproved accommodations should not be treated or reported as official Acadience Math scores and cannot be compared to other Acadience Math scores or benchmark goals but can be used to measure individual growth for a student. An unapproved accommodation may be used when (a) a student cannot be tested accurately using the standardized rules or approved accommodations, but the school would still like to measure progress for that student; or (b) a student’s Individualized Education Plan (IEP) requires testing with an unapproved accommodation. For more information about accommodations please see the Acadience Math Assessment Manual.

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?

No

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:

The administration time varies depending on the grade level and the assessment. The time ranges from 2 - 16 minutes per worksheet.

What is the administration time?

Time in minutes

2

per (student/group/other unit)

worksheet

Additional scoring time:

Time in minutes

1

per (student/group/other unit)

worksheet

ACADEMIC ONLY: What are the discontinue rules?

No discontinue rules provided
Basals
Ceilings
Other

If other, please specify:

Are norms available?

Yes

Are benchmarks available?

Yes

If yes, how many benchmarks per year?

3

If yes, for which months are benchmarks available?

Beginning of year (months 1 - 3 of school year), middle of year (months 4 - 6 of the school year), and end of year (months 7 - 9 of the school year).

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:

One to three hours of training to cover foundations of Acadience Math as well as administration and scoring of the measures.

Please describe the minimum qualifications an administrator must possess.

Administrator must have adequate training on the administration and scoring of the assessments.

No minimum qualifications

Are training manuals and materials available?

Yes

Are training manuals/materials field-tested?

Yes

Are training manuals/materials included in cost of tools?

No

If No, please describe training costs:

The Acadience Math K-6 Assessment Manual is available for free download along with the test materials. In addition to administrator support provided within the Acadience Math K-6 Assessment Manual, Acadience Learning offers a variety of training options to meet different needs and at different price points. Training options include asynchronous training, and synchronous training via live online training, and onsite training (hiring a trainer to come out to the school or district). Acadience Learning staff can work with schools, LEAs, regional agencies, and SEAs to develop customized training plans to meet their unique needs. We also have an Acadience Mentor program, where individual participants or small groups of participants can become Acadience Math K-6 Mentors. Mentors receive access to our official training materials, which they can use to train others in their school or district. For an individual teacher subscription to the online Acadience Math K-6 Essential Workshop, the cost is $129. Please note: Other training options may cost more or less depending on the circumstances and the number of attendees.

Can users obtain ongoing professional and technical support?

Yes

If Yes, please describe how users can obtain support:

Acadience Learning provides customer support for all Acadience Math assessments, as well as support for the data management and reporting system, Acadience Learning Online. Staff are available by phone and email on weekdays from 7am to 5pm Pacific Time, for no additional cost. The majority of customer support requests are resolved in less than one business day.

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?

Yes

If yes, please describe.

The Acadience Math benchmark goals provide targeted levels of skill that students need to achieve by specific points in time in order to be considered to be making adequate progress. The Stanford Achievement Test Series, Tenth Edition–Total Math score (SAT10; Pearson, 2003), a high-quality, nationally norm-referenced assessment, was used as an external criterion in the validity study. In the validity study, the 40th percentile at or above the SAT10 Total Math Raw Score was used as one approximation of adequate math skill. The intent is to develop generalizable benchmark goals and cut points that are relevant and appropriate for a wide variety of math outcomes, across a wide variety of states and regions, and for diverse groups of students. The principle vision for Acadience Math is a step-by-step vision. Student skills at or above benchmark at the beginning of the year put the odds in favor of the student achieving the middle-of-year benchmark goal. In turn, students with skills at or above benchmark in the middle of the year have the odds in favor of achieving the end-of-year benchmark goal. Finally, students with skills at or above benchmark at the end of the year have odds in favor of having adequate math skills on a wide variety of external measures of math proficiency. The fundamental logic for developing the benchmark goals and cut points for risk was to begin with the external outcome goal and work backward in that step-by- step system. We first obtained an external criterion measure (the SAT10 Total Math Raw Score) at the end of the year with a level of performance that would represent adequate math skills (the SAT10 Total Math Raw Score at the 40th percentile rank). Next, we specified the benchmark goal and cut point for risk for end-of-year Composite Score with respect to the end-of-year external criterion. Then, using the Composite Score end-of-year goal as an internal criterion, we established the benchmark goals and cut points for risk for middle-of-year Composite Score. Finally, we established the benchmark goals and cut points for risk for beginning-of-year Composite Score using the middle-of-year Composite Score goal as an internal criterion (see the Acadience Math Benchmark Goals Document). The same standard setting methodology used for the Acadience Math Composite was also used for each individual Acadience Math component measure.

Can you provide evidence in support of multiple decision rules?

Yes

If yes, please describe.

Research evidence supporting the use of Acadience Math measures for benchmark assessment three times per year is found in the Acadience Math Technical Adequacy Brief, Acadience Math Assessment Manual, and the Acadience Math Benchmark Goals document.

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.

In second through sixth grade, the Computation and Concepts and Applications component assessments are combined into an overall Math Composite Score. Depending on the grade level and time of year, different weights may be given to the component assessments. Math Composite Scores can be calculated manually or automatically. If calculating manually, Math Composite Score worksheets are provided.

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.

The Acadience Math measures were developed to provide teachers with information they need to make decisions about instruction. The authors advocate a data-based decision-making model referred to as the Outcomes-Driven Model, because the data are used to make decisions to improve student outcomes by matching the amount and type of instructional support with the needs of the individual students. These steps of the model repeat each trimester (i.e., beginning of year, middle of year, and end of year) as a student progresses through the grades. At the beginning of the trimester, the first step is to identify students who may need additional support. At the end of the trimester, the final step is to review outcomes, which also facilitates identifying students who need additional support for the next trimester. In this manner, educators can ensure that students who are on track to become proficient at math continue to make adequate progress, and that those students who are not on track receive the support they need to become proficient at math. Step 1: Identify need for support early. This process occurs during benchmark assessment, and is also referred to as universal screening. The purpose is to identify those students who may need additional instructional support to achieve benchmark goals. The benchmark assessment also provides information regarding the performance of all students in the school with respect to benchmark goals. All students within a school or grade are tested three times per year on grade-level material. The testing occurs at the beginning, middle, and end of the school year. Step 2: Validate need for support. The purpose of this step is to be reasonably confident that the student needs or does not need additional instructional support. Before making individual student decisions, it is important to consider additional information beyond the initial data obtained during benchmark testing. Teachers can always use additional assessment information and knowledge about a student to validate a score before making decisions about instructional support. If there is a discrepancy in the student’s performance relative to other information available about the student, or if there is a question about the accuracy of a score, the score can be validated by retesting the student using alternate forms of the Acadience Math measures or additional diagnostic assessments as necessary. Step 3: Plan and implement support. In general, for students who are meeting the benchmark goals, a good, research-based core classroom curriculum should meet their instructional needs, and they will continue to receive benchmark assessment three times per year to ensure they remain on track. Students who are identified as needing support are likely to require additional instruction or intervention in the skill areas where they are having difficulties. Step 4: Evaluate and modify support as needed. Students who are receiving additional support should be progress monitored more frequently to ensure that the instructional support being provided is helping them get back on track. Students should be monitored on the measures that test the skill areas where they are having difficulties and receiving additional instructional support. Monitoring may occur once per month, once every two weeks, or as often as once per week. In general, students who need the most intensive instruction are progress monitored most frequently. Step 5: Review outcomes. By looking at the benchmark assessment data for all students, schools can ensure that their instructional supports—both core curriculum and additional interventions—are working for all students. If a school identifies areas of instructional support that are not working as desired, the school can use the data to help make decisions on how to improve. The use of Acadience Math measures within the Outcomes-Driven Model is consistent with the most recent reauthorization of the Individuals with Disabilities Education Improvement Act (IDEA), which allows the use of a Response to Intervention (RtI) approach to identify children with learning disabilities. In an RtI approach to identification, early intervention is provided to students who are at risk for the development of learning difficulties. Data are gathered to determine which students are responsive to the intervention provided and which students need more intensive support (Fuchs & Fuchs, 2006). The Outcomes-Driven Model is based on foundational work with a problem-solving model (see Deno, 1989; Shinn, 1995; Tilly, 2008) and the initial application of the problem-solving model to early literacy skills (Kaminski & Good, 1998). The general questions addressed by a problem-solving model include: What is the problem? Why is it happening? What should be done about it? Did it work? (Tilly, 2008). The Outcomes-Driven Model was developed to address these questions, but within a prevention-oriented framework designed to preempt early math difficulty and ensure step-by-step progress toward outcomes that will result in established, adequate math achievement.

Classification Accuracy & Cross-Validation Summary

Grade	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6
Classification Accuracy Fall
Classification Accuracy Winter
Classification Accuracy Spring

SAT 10 Math

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.

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).

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.

---

Classification Accuracy - Fall

Evidence	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6
Criterion measure	SAT 10 Math	SAT 10 Math	SAT 10 Math	SAT 10 Math	SAT 10 Math
Cut Points - Percentile rank on criterion measure	20	20	20	20	20
Cut Points - Performance score on criterion measure
Cut Points - Corresponding performance score (numeric) on screener measure
Classification Data - True Positive (a)	11	18	5	11	7
Classification Data - False Positive (b)	5	7	4	8	3
Classification Data - False Negative (c)	6	12	4	14	5
Classification Data - True Negative (d)	106	73	55	86	55
Area Under the Curve (AUC)	0.92	0.86	0.95	0.82	0.90
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.86	0.78	0.89	0.74	0.83
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.97	0.93	0.99	0.90	0.97

Statistics	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6
Base Rate	0.13	0.27	0.13	0.21	0.17
Overall Classification Rate	0.91	0.83	0.88	0.82	0.89
Sensitivity	0.65	0.60	0.56	0.44	0.58
Specificity	0.95	0.91	0.93	0.91	0.95
False Positive Rate	0.05	0.09	0.07	0.09	0.05
False Negative Rate	0.35	0.40	0.44	0.56	0.42
Positive Predictive Power	0.69	0.72	0.56	0.58	0.70
Negative Predictive Power	0.95	0.86	0.93	0.86	0.92

Sample	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6
Date
Sample Size	128	110	68	119	70
Geographic Representation	Pacific (CA, OR) West North Central (KS)	Pacific (CA, OR) West North Central (KS)	Pacific (CA, OR) West North Central (KS)	Pacific (CA, OR) West North Central (KS)	Pacific (OR) West North Central (KS, MO)
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	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6
Criterion measure	SAT 10 Math	SAT 10 Math	SAT 10 Math	SAT 10 Math	SAT 10 Math
Cut Points - Percentile rank on criterion measure	20	20	20	20	20
Cut Points - Performance score on criterion measure
Cut Points - Corresponding performance score (numeric) on screener measure
Classification Data - True Positive (a)	10	19	6	18	9
Classification Data - False Positive (b)	7	6	1	6	2
Classification Data - False Negative (c)	6	12	2	8	5
Classification Data - True Negative (d)	106	75	59	87	58
Area Under the Curve (AUC)	0.89	0.88	0.94	0.90	0.90
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.83	0.82	0.88	0.84	0.83
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.96	0.94	0.99	0.96	0.96

Statistics	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6
Base Rate	0.12	0.28	0.12	0.22	0.19
Overall Classification Rate	0.90	0.84	0.96	0.88	0.91
Sensitivity	0.63	0.61	0.75	0.69	0.64
Specificity	0.94	0.93	0.98	0.94	0.97
False Positive Rate	0.06	0.07	0.02	0.06	0.03
False Negative Rate	0.38	0.39	0.25	0.31	0.36
Positive Predictive Power	0.59	0.76	0.86	0.75	0.82
Negative Predictive Power	0.95	0.86	0.97	0.92	0.92

Sample	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6
Date
Sample Size	129	112	68	119	74
Geographic Representation	Pacific (CA, OR) West North Central (KS)	Pacific (CA, OR) West North Central (KS)	Pacific (CA, OR) West North Central (KS)	Pacific (CA, OR) West North Central (KS)	Pacific (OR) West North Central (KS, MO)
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	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6
Criterion measure	SAT 10 Math	SAT 10 Math	SAT 10 Math	SAT 10 Math	SAT 10 Math
Cut Points - Percentile rank on criterion measure	20	20	20	20	20
Cut Points - Performance score on criterion measure
Cut Points - Corresponding performance score (numeric) on screener measure
Classification Data - True Positive (a)	12	19	5	23	9
Classification Data - False Positive (b)	4	9	2	4	2
Classification Data - False Negative (c)	6	12	4	5	4
Classification Data - True Negative (d)	110	73	58	90	56
Area Under the Curve (AUC)	0.90	0.88	0.94	0.91	0.93
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.84	0.81	0.87	0.86	0.87
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.97	0.94	0.99	0.96	0.99

Statistics	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6
Base Rate	0.14	0.27	0.13	0.23	0.18
Overall Classification Rate	0.92	0.81	0.91	0.93	0.92
Sensitivity	0.67	0.61	0.56	0.82	0.69
Specificity	0.96	0.89	0.97	0.96	0.97
False Positive Rate	0.04	0.11	0.03	0.04	0.03
False Negative Rate	0.33	0.39	0.44	0.18	0.31
Positive Predictive Power	0.75	0.68	0.71	0.85	0.82
Negative Predictive Power	0.95	0.86	0.94	0.95	0.93

Sample	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6
Date
Sample Size	132	113	69	122	71
Geographic Representation	Pacific (OR)	Pacific (OR)	Pacific (CA, OR) West North Central (KS)	Pacific (CA, OR) West North Central (KS)	Pacific (OR) West North Central (KS, MO)
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	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6
Rating

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

Reliability refers to the relative stability with which a test measures the same skills across minor differences in conditions. Four types of reliability are reported in the table below, alternate-form reliability, alpha, and inter-rater reliability. Alternate-form reliability is the correlation between different forms of the Computation measure. High alternate-form reliability coefficients suggest that these multiple forms are measuring the same construct. Coefficient alpha is a measure of reliability that is widely used in education research and represents the proportion of true score to total variance. Alpha incorporates information about the average inter-test correlation as well as the number of tests. Omega is another measure of internal consistent, but one that does not make the same essential tau-equivalence assumptions that alpha does, which means the estimate is likely a truer estimate of reliability.

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

Alternate form and test-retest reliability was obtained from a study designed to specifically examine reliability in grades 3 and 6. The total data involves 498 students from five schools. Alpha and omega were calculated using the national dataset for Acadience Math, which totaled 542,407 students from K-6, from 1,518 schools.

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

Alternate-form reliability is reported as the correlation between two alternate forms of the Computation measures. Coefficient alpha examined the internal consistency of the Math Composite Score by examining the correlation between two computation forms, and concepts and applications. Alpha was calculated using the average correlation among these three, and the number of tests involved, which is three. Coefficient omega for each grade was based on a different time of year as was used for alpha, but also consisted of the correlations among the two computation forms as well as concepts and applications.

*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	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6
Rating

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

The Stanford Achievement Test Series, Tenth Edition–Total Math score (SAT10; Pearson, 2003) was used as the external criterion. The SAT10 is a widely used, timed, group-administered, norm-referenced achievement test appropriate for children in kindergarten through grade 12. In second through sixth grade, the SAT10 Total Math score includes scores from the subtests of Mathematics Problem Solving and Mathematics Procedures. Students are given 80 minutes in total to complete both subtests. The SAT10 Total Math score was compared to all Acadience Math measures given during the year, providing both predictive criterion-related validity correlations for beginning- and middle-of-year measures and concurrent criterion-related validity data for end-of-year measures.

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

Validity data were collected during the 2017–2018 school for second through sixth grade. This sample included 537 students across five schools in four districts in four US states. Demographic information is not available for this sample.

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

Predictive validity is the correlation between the Math Composite Score, which includes the Computation and Concepts and Applications component measures, at the beginning of the year and the SAT 10 score at the end of the school year. This coefficient represents the extent to which Math Composite Score can predict later math outcomes. Concurrent validity is the correlation between the Math Composite Score and the SAT 10 measure both at the end of the year. This coefficient represents the extent to which the Math Composite Score is related to important math outcomes.

*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:

Yes

Provide citations for additional published studies.

Gray, J. S., Warnock, A. N., Dewey, E. N., Latimer, R., & Wheeler, C. E. (2019) Acadience™ Math Technical Adequacy Brief. Eugene, OR: Acadience Learning Inc.

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

Both the concurrent and predictive correlation are generally high. These strong correlations suggest that the Acadience Math Composite Score is assessing skills relevant to math outcomes. Given the wide range of skills assessed on the SAT10, these data support the conclusion that the Math Composite Score is an excellent indicator of math proficiency.

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	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6
Rating	No	No	No	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.