Acadience Math
Computation

Descriptive Information

Please provide a description of your tool:

Computation is a standardized measure designed to assess students’ progress in the basic skills of math computation. It can be administered individually or to groups. Students write their answers to basic computation problems under standardized conditions and time limits. The time limit depends on the grade level the student is in and varies from two minutes to six minutes. The total score is based on the number of correct digits in the final answer of each problem. An optional response pattern analysis can also be completed to give additional instructional information by analyzing the student's response patterns.

Is your tool designed to measure progress towards an end-of-year goal (e.g., oral reading fluency) or progress towards a short-term skill (e.g., letter naming fluency)?

End-year goal
Short-term skill

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 dimensions does the tool assess?

Reading

Global Indicator of Reading Competence
Listening Comprehension
Vocabulary
Phonemic Awareness
Decoding
Passage Reading
Word Identification
Comprehension

Spelling & Written Expression

Global Indicator of Spelling Competence
Global Indicator of Writting Expression Competence

Mathematics

Global Indicator of Mathematics Comprehension
Early Numeracy
Mathematics Concepts
Mathematics Computation
Mathematics Application
Fractions
Algebra

Other

Please describe specific domain, skills or subtests:

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

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

Acquisition and Cost Information

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:

BEHAVIOR ONLY: What is the administration format?

Direct observation
Rating scale
Checklist
Performance measure
Other

If other, please specify:

BEHAVIOR ONLY: What is the administration setting?

General education classroom
Special education classroom
School office
Recess
Lunchroom
Home
Other

If other, please specify:

Does the program require technology?

No

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

Computer or tablet
Internet connection
Other technology (please specify)

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 ranges from 2 - 6 minutes per worksheet.

ACADEMIC ONLY: What are the discontinue rules?

No discontinue rules provided
Basals
Ceilings
Other

If other, please specify:

Training & Scoring

Training

Is training for the administrator required?

Yes

Describe the time required for administrator training, if applicable:

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

Please describe the minimum qualifications an administrator must possess.

Paraprofessional-level training and adequate training on administration and scoring of Computation.

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?

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:

Customer support is available from 8:00am to 5:00pm PT, Monday through Friday by phone, email, or through Acadience Learning's website.

Scoring

BEHAVIOR ONLY: What types of scores result from the administration of the assessment?

Score

Observation	Behavior Rating
Frequency Duration Interval Latency	Raw score

Conversion

Observation	Behavior Rating
Rate Percent	Standard score Subscale/ Subtest Composite Stanine Percentile ranks Normal curve equivalents IRT based scores

Interpretation

Observation	Behavior Rating
Error analysis Peer comparison Rate of change	Dev. benchmarks Age-Grade equivalent

How are scores calculated?

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

If other, please specify:

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:

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.

The Computation total score is based on the number of correct digits in the final answer of each of the problems completed within the time limit. Each correct digit is associated with a specific number of points, as indicated by a legend on the teacher key. There are between 16 and 25 problems on each worksheet arranged in rows and columns. The number of items on each worksheet is as follows: Grade 2: 20 problems; Grade 3: 25 problems; Grade 4: 25 problems; Grade 5: 16 problems; Grade 6: 16 problems. For each problem that the student completed or attempted, the number of points he or she received are written next to that problem. The points are added across each row, written in the margin, and then summed to calculate the student’s total score, which is recorded at the top of the page in the space provided. The final score for a progress monitoring assessment is the score from one student worksheet.

Do you provide basis for calculating slope (e.g., amount of improvement per unit in time)?

No

ACADEMIC ONLY: Do you provide benchmarks for the slopes?

No

ACADEMIC ONLY: Do you provide percentile ranks for the slopes?

No

What is the basis for calculating slope and percentile scores?

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

Describe the tool’s approach to progress monitoring, behavior samples, test format, and/or scoring practices, 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 designed to be economical and efficient indicators of a student's progress toward achieving a general outcome such as math, and to be used for both benchmark assessment and progress monitoring. Progress monitoring refers to the more frequent testing of students who may be at risk for future math difficulty on the skill areas in which they are receiving instruction, to ensure that they are making adequate progress. Progress monitoring can be conducted using grade-level or out-of-grade materials, depending on the student's needs. Decisions about the skill areas and levels to monitor are made at the individual student level. Students who are receiving additional support should be monitored for progress more frequently to ensure that the instructional support being provided is helping them get back on track. 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 monitored for progress most frequently. Progress monitoring materials contain alternate forms of the same measures administered during benchmark assessment. Each alternate form is of equivalent difficulty. Not all students will need progress monitoring. Progress monitoring materials are organized by measure, since students who need progress monitoring will typically be monitored on specific measures related to the instruction they are receiving, rather than on every measure for that grade. Material selected for progress monitoring must be sensitive to growth, yet still represent an ambitious goal. The standardized procedures for administering an Acadience Math measure may apply when using Acadience Math for progress monitoring. Progress monitoring data should be graphed and readily available to those who teach the student. An aimline should be drawn from the student's current skill level (which may be the most recent benchmark assessment score) to the goal. Progress monitoring scores can then be plotted over time and examined to determine whether they indicate that the student is making adequate progress (i.e., fall above or below the aimline). The Acadience Math assessments were designed to support students of varied backgrounds. Acadience Math's Computation measures are appropriate for most students for whom an instructional goal is to learn to read in English. Approved accommodations are allowed for any student and consist of accommodations that will not alter the standardization of the assessment.

Rates of Improvement and End of Year Benchmarks

Is minimum acceptable growth (slope of improvement or average weekly increase in score by grade level) specified in your manual or published materials?

Yes

If yes, specify the growth standards:

Based on the student's initial level of performance on the Acadience Math measures, growth standards are set so that students scoring in the well-below benchmark range will be scoring in at least the below benchmark range by the next benchmark period, students scoring in the below-benchmark range will be scoring at or above benchmark by the next benchmark period, and students scoring in the at or above benchmark range will remain in that range at the next benchmark period.

Are benchmarks for minimum acceptable end-of-year performance specified in your manual or published materials?

Yes

If yes, specify the end-of-year performance standards:

Three primary end-of-year performance standards are specified: Well Below Benchmark, Below Benchmark, and At or Above Benchmark. These standards are used to indicate increasing odds of achieving At or Above Benchmark status at the next benchmark administration or goals on external measures of math achievement. End-of-year benchmarks goals and cut points for risk: Grade 2 Benchmark goal: 15, Cut point: 10; Grade 3 Benchmark goal: 29, Cut point: 21; Grade 4 Benchmark goal: 46, Cut point: 33; Grade 5 Benchmark goal: 56, Cut point: 38; Grade 6 Benchmark goal: 66, Cut point: 47.

What is the basis for specifying minimum acceptable growth and end of year benchmarks?

Norm-referenced
Criterion-referenced
Other

If other, please specify:

False

National representation (check all that apply):

Northeast:

New England

Middle Atlantic

Midwest:

East North Central

West North Central

South:

South Atlantic

East South Central

West South Central

West:

Mountain

Pacific

Local representation (please describe, including number of states)

The percentile ranks for the Acadience Math national norms are based on a large national sample of school children across the United States. The Acadience Math data in this sample were collected and entered into Acadience Data Management during the 2016-2017 school year. The final sample included approximately 229,000 students from 1,094 schools within 421 school districts in 49 states, representing every census region in the United States. Seventeen percent of schools were located in cities, 38% were located in suburbs, 14% were located in towns, and 31% were located in rural areas.

Date

2019

Size

228,779

Gender (Percent)

Male

51%

Female

49%

Unknown

0%

SES indicators (Percent)

Eligible for free or reduced-price lunch

57%

Other SES Indicators

Race/Ethnicity (Percent)

White, Non-Hispanic

70.11%

Black, Non-Hispanic

7.64%

Hispanic

13.70%

American Indian/Alaska Native

1.32%

Asian/Pacific Islander

2.61%

Other

4.63%

Unknown

0%

Disability classification (Please describe)

First language (Please describe)

Language proficiency status (Please describe)

Do you provide, in your user’s manual, norms which are disaggregated by race or ethnicity? If so, for which race/ethnicity?

White, Non-Hispanic
Black, Non-Hispanic
Hispanic
American Indian/Alaska Native
Asian/Pacific Islander
Other
Unknown

If criterion-referenced, describe procedure for specifying criterion for adequate growth and benchmarks for end-of-year performance levels.

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 Computation with respect to the end-of-year external criterion. Then, using the Computation end-of-year goal as an internal criterion, we established the benchmark goals and cut points for risk for middle-of-year Computation. Finally, we established the benchmark goals and cut points for risk for beginning-of-year Computation using the middle-of-year Computation goal as an internal criterion (see the Acadience Math Benchmark Goals Document).

Describe any other procedures for specifying adequate growth and minimum acceptable end of year performance.

Reliability

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

Legend

Convincing evidence

Partially convincing evidence

Unconvincing evidence

Data unavailable

^dDisaggregated data available

*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. Three 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. Inter-rater reliability indicates the extent to which results generalize across assessors scoring the measure.

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

Alternate-form reliability and alpha were calculated from data collected during the 2018-2019 school year and entered in Acadience Data Management. There were 146,297 students in this sample from 1,339 schools in 606 districts in 46 states. Demographic information is not available for this sample. Inter-rater reliability was calculated from two samples. Data was collected for second, fourth, and fifth grade Computation during the 2012-2013 school year. There were 188 students in this sample from four schools in four districts in four US states. Inter-rater reliability for 227 students in third and sixth grade was collected as part of a larger reliability study during the 2014–2015, 2015–2016, 2016–2017, and 2018-2019 school years. This study had a sample size of 1,810, and participants were from 17 schools in 14 districts in 10 US states. Demographic information is not available for this sample.

*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 treats each of the three tests as separate indicators and is calculated using the alternate-form reliability, where the number of tests is equal to two. To calculate inter-rater reliability, photocopies were made of unscored student worksheets. The two copies (original and photocopy) were scored separately and independently. The inter-rater reliability coefficient is the correlation between the total scores from these two independently scored assessments.

*In the table(s) below, report the results of the reliability analyses described above (e.g., model-based evidence, internal consistency or inter-rater reliability coefficients). Include detail about the type of reliability data, statistic generated, and sample size and demographic information.

Results from other forms of reliability 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.

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.

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

Legend

Convincing evidence

Partially convincing evidence

Unconvincing evidence

Data unavailable

^dDisaggregated data available

*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 year for second through sixth grade. This sample included 537 students across five schools in four districts in four states in the Pacific West and West North Central Midwest regions of the United States. Demographic data for four of the five participating schools were gathered at the school level from the NCES website (http://nces.ed.gov/). One school, a private Catholic school in the Midwest, was not listed on NCES. Across the four schools which reported demographic information, 10% of the student population was reported as American Indian or Alaska Native, 8% as Asian or Native Hawaiian/Pacific Islander, 2% as Black or African American, 39% as Hispanic, 37% as White, and 4% as Two or More Races . Forty-six percent of the student population was female, and 39% of all students were eligible for free/reduced lunch. Research sites were recruited from schools that were actively using the measures during the 2018-2019 school year and planned on collecting data at three benchmark periods: fall, winter, and spring, as per school/district practice, and entering that data into Acadience Data Management (ADM). Recruitment targeted schools that had average scores for each grade level within the upper 1/3 and lower 1/3 of all schools that enter their data in ADM. The purpose of this recruitment strategy was to include participants who represented a full range of student performance. Schools also needed to have a sufficient sample size of students within their 2nd, 3rd, 4th, 5th, and/or 6th grade classrooms with the minimum goal of having 50 students per grade participate. Students in general education classrooms in 2nd, 3rd, 4th, 5th, and/or 6th grades in participating schools who were receiving mathematics instruction were invited to participate, including students with disabilities provided the students had the response capabilities to participate. Both students who were struggling in mathematics and those who were typically achieving were included in this study, provided their parents provided consent.

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

Predictive validity is the correlation between Computation at the beginning of the year and the SAT10 score at the end of the school year. This coefficient represents the extent to which Computation can predict later math outcomes. Concurrent validity is the correlation between the Computation score and the SAT10 measure both at the end of the year. This coefficient represents the extent to which the Computation 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.

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.

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 Computation measure is assessing skills relevant to math outcomes. Given the wide range of skills assessed on the SAT10, these data support the conclusion that the Computation measure 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.

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

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:

Bias was conceptualized as different classification accuracy between different groups. This was assessed using a Cleary model, with the outcome being performance on the Group Mathematics Assessment and Diagnostic Evaluation (GMADE; Williams, 2004), where the Computation score, subgroup , and the interaction between the two were used as predictors. The GMADE is a group-administered, norm-referenced test appropriate for children in kindergarten through grade 12. In second through fifth grade, the GMADE includes scores from the subtests of Concepts and Communications, Operations and Computation, and Process and Applications. If a model with the subgroup and interaction term do not add significantly to model fit, there is evidence that the Computation measure is not biased. The effect size for bias was assessed using the model change in R-squared. This procedure was conducted in grades 2, 3, 4, and 5 (data were unavailable at grade 6). These models were built for each time of year at each grade under consideration. This resulted in 3 (times of year) X 2 (subgroup comparisons) X 4 (number of grades) = 24 total models being compared.

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

Bias was assessed among white and non-white students and students who do and do not qualify for free/reduced lunch.

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.

Of the 12 models assessing racial bias, 2 were statistically significant (grade 2 middle of year and grade 5 middle of year). The largest change in R-squared was .025. Of the 12 models assessing bias in free/reduced lunch, again 2 were statistically significant (grade 2 beginning and middle of year). The largest R-squared here was .043. Overall, the rate of significant models is not much higher than the nominal Type I error rate of .05, and the change in R-squared values indicates that if bias exists, the extent is small.

Sensitivity: Reliability of Slope

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

Legend

Convincing evidence

Partially convincing evidence

Unconvincing evidence

Data unavailable

^dDisaggregated data available

Describe the sample, including size and characteristics. Please provide documentation showing that the sample was composed of students in need of intensive intervention. A sample of students with intensive needs should satisfy one of the following criteria: (1) all students scored below the 30th percentile on a local or national norm, or the sample mean on a local or national test fell below the 25th percentile; (2) students had an IEP with goals consistent with the construct measured by the tool; or (3) students were non-responsive to Tier 2 instruction. Evidence based on an unknown sample, or a sample that does not meet these specifications, may not be considered.

The sample consisted of students who were identified as being "Well Below Benchmark" using the benchmark assessment of Acadience Math at the beginning of the school year. Being Well Below Benchmark corresponds to being below the 14th, 26th, 22nd, 26th, and 24th percentiles for second, third, fourth, fifth, and sixth grades, respectively. Students were only selected if they had a minimum of 15 observations.

Describe the frequency of measurement (for each student in the sample, report how often data were collected and over what span of time).

Progress monitoring data were collected throughout the school year at the discretion of the administering school, but not more frequently than once per week. Any student who had fewer than fifteen progress monitoring assessments was excluded from the analysis.

Describe the analysis procedures.

Reliability of slope was calculated as the ratio of true score variance to observed total variance. The true score variance estimate came from a hierarchical linear model based estimate of the variance in progress monitoring slopes (using the R package lme4), the observed score variance was calculated as the variance of the ordinary least squares slopes created for each student that met the aforementioned inclusion criteria. Confidence intervals were calculated using bootstrap estimation.

In the table below, report reliability of the slope (e.g., ratio of true slope variance to total slope variance) by grade level (if relevant).

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 of the slope data that is disaggregated by subgroups (e.g., race/ethnicity, gender, socioeconomic status, students with disabilities, English language learners)?

No

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

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.

Sensitivity: Validity of Slope

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

Legend

Convincing evidence

Partially convincing evidence

Unconvincing evidence

Data unavailable

^dDisaggregated data available

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

For Acadience Math Computation, the outcome considered for validity of slope was the students' Acadience Math Concepts and Applications scores at the end of the following year. For instance, to calculate the validity of grade 2 progress monitoring slopes, we used the Concepts and Applications score at the end of grade 3. While not an external criterion, we believe that using both an alternative measure of math skills (Computation vs. Concepts and Applications) and the length of time between the end of progress monitoring and the criterion (an entire year between the last progress motioning occasion and the criterion) provides a sufficiently powerful examination of the validity of slope.

Describe the sample(s), including size and characteristics. Please provide documentation showing that the sample was composed of students in need of intensive intervention. A sample of students with intensive needs should satisfy one of the following criteria: (1) all students scored below the 30th percentile on a local or national norm, or the sample mean on a local or national test fell below the 25th percentile; (2) students had an IEP with goals consistent with the construct measured by the tool; or (3) students were non-responsive to Tier 2 instruction. Evidence based on an unknown sample, or a sample that does not meet these specifications, may not be considered.

The sample consisted of students who were identified as being "Well Below Benchmark" using the benchmark assessment of Acadience Math at the beginning of the year. Being Well Below Benchmark corresponds to being below the 14th, 26th, 22nd, 26th, and 24th percentiles for second, third, fourth, fifth, and sixth grades, respectively. Students were only selected if they had a minimum of 15 observations.

Describe the frequency of measurement (for each student in the sample, report how often data were collected and over what span of time).

Progress monitoring data were collected throughout the school year at the discretion of the administering school, but not more frequently than once per week. Any student who had fewer than fifteen progress monitoring assessments was excluded from the analysis.

Describe the analysis procedures for each reported type of validity.

Validity of slope was assessed using the partial correlations between the students' ordinary least squares slope and the criterion, while controlling for the students' ordinary least squares intercept.

In the table below, report predictive validity of the slope (correlation between the slope and achievement outcome) by grade level (if relevant).
NOTE: The TRC suggests controlling for initial level when the correlation for slope without such control is not adequate.

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

Manual cites other published validity studies:

No

Provide citations for additional published studies.

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

The moderate to strong partial correlations the OLS slopes have with a criterion that is both separated by an entire year and an alternative assessment of math skills indicates that this tool has strong evidence for validity.

Do you have validity of the slope data that is disaggregated by subgroups (e.g., race/ethnicity, gender, socioeconomic status, students with disabilities, English language learners)?

No

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

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

Manual cites other published validity studies:

No

Provide citations for additional published studies.

Alternate Forms

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

Legend

Convincing evidence

Partially convincing evidence

Unconvincing evidence

Data unavailable

^dDisaggregated data available

Describe the sample for these analyses, including size and characteristics:

What is the number of alternate forms of equal and controlled difficulty?

If IRT based, provide evidence of item or ability invariance

If computer administered, how many items are in the item bank for each grade level?

If your tool is computer administered, please note how the test forms are derived instead of providing alternate forms:

Decision Rules: Setting & Revising Goals

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

Legend

Convincing evidence

Partially convincing evidence

Unconvincing evidence

Data unavailable

^dDisaggregated data available

In your manual or published materials, do you specify validated decision rules for how to set and revise goals?

If yes, specify the decision rules:

What is the evidentiary basis for these decision rules?
NOTE: The TRC expects evidence for this standard to include an empirical study that compares a treatment group to a control and evaluates whether student outcomes increase when decision rules are in place.

Decision Rules: Changing Instruction

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

Legend

Convincing evidence

Partially convincing evidence

Unconvincing evidence

Data unavailable

^dDisaggregated data available

In your manual or published materials, do you specify validated decision rules for when changes to instruction need to be made?

If yes, specify the decision rules:

What is the evidentiary basis for these decision rules?
NOTE: The TRC expects evidence for this standard to include an empirical study that compares a treatment group to a control and evaluates whether student outcomes increase when decision rules are in place.