easyCBM
Proficient Math (formerly CCSS Math)

Descriptive Information

Please provide a description of your tool:

easyCBM® is a web-based district assessment system that includes both benchmarking and progress monitoring assessments combined with a comprehensive array of reports. The assessments in easyCBM are curriculum-based general outcome measures, or CBMs, which are standardized measures that sample from a year’s worth of curriculum to assess the degree to which students have mastered the skills and knowledge deemed critical at each grade level. easyCBM, available for Grades K–8, provides three forms of a screening measure to be used locally for establishing benchmarks and multiple forms (generally 10 per strand in math) to be used to monitor progress. All measures have been developed with reference to specific content in math (Common Core State Standards) and developed using Item Response Theory (IRT)

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

inquiry@riversideinsights.com or support@easycbm.com

Address

District Users: Riverside Insights, Attention: Customer Service One Pierce Place, Suite 900W, Itasca, Illinois 60143 Individual Users: BRT, University of Oregon, Eugene, OR 97403

Phone Number

District: 800/323.9540 or Individual: 541/346.3535

Website

District: http://www.riversideinsights.com/solutions/easyCBM; teachers: easyCBM.com

Initial cost for implementing program:

Cost

$5.35

Unit of cost

student

Replacement cost per unit for subsequent use:

Cost

$5.35

Unit of cost

student

Duration of license

1 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.

easyCBM is available through Riverside Insights on an annual subscription license for districts. Price is $5.35/student/year, which gives teachers access to all measures. The price includes manuals and use of the assessments. Training webinars are provided through the Riverside Training Academy; prices range from $500-$1700 depending on the number of teachers in the district. It is also available directly through the University of Oregon for individual classroom teacher use (limited to one teacher per building, maximum of 200 students). This teacher subscription includes the online training that is part of the system. As easyCBM is computer-administered, students should have access to an Internet-connected computer (Mac or PC). However, they can also take the tests in paper/pencil versions, with responses being manually added to the online system for scoring. Teachers need Internet-connected computers to access the manual, score reports, training videos, etc.

Provide information about special accommodations for students with disabilities.

All measures were developed following Universal Design for Assessment guidelines to reduce the need for accommodations. All mathematics items include a built-in read aloud option for every question and response requiring reading. Districts are directed to follow their standard practices for providing additional accommodations as needed

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:

Educators can also use printers to print reports and PDF versions of the measures, if they wish

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

30

per (student/group/other unit)

student

Additional scoring time:

Time in minutes

0

per (student/group/other unit)

student

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?

August/September, December/January, and May/June

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 hr of training

Please describe the minimum qualifications an administrator must possess.

paraprofessional level

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:

Training is provided through the Riverside Training Academy for an annual cost of $500–$1700, depending on the number of educators in the district.

Can users obtain ongoing professional and technical support?

Yes

If Yes, please describe how users can obtain support:

Help Desk via email and phone, as well as through the online FAQ/help page

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.

Students are identified as “low risk”, “some risk”, or “high risk” based on their performance on the CCSS Mathematics Measures relative to grade-level peers in the national norm group. Individual Districts set the range of percentile ranks for such classifications following training provided by Riverside Insights on the system and its uses. The Benchmark/Screener reports provide suggested progress monitoring measures to use as follow-up for students identified as “high risk”. Trainings on the system provide guidance to teachers to log and provide interventions to students identified as “some” or “high risk” following their district’s policies.

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.

The CCSS Mathematics composite score is simply the total of all items correct

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 authors have approached screening from two perspectives with respect to (a) goal level sampling from nationally framed standards and (b) scaling. Test format focuses on principles of universal design with either individually administered tasks (for early reading skills and fluency) or computer-based testing for group-administered tests in vocabulary, comprehension, and all mathematics tests. Scoring practices emphasize objectivity with diagnostic information for teachers and immediate feedback for students. a. In mathematics, the authors used the Common Core State Standards (CCSS) to direct item content for the CCSS Mathematics assessments. Grade-level teachers with expertise in Special Education and Mathematics were hired to write the items, with further review by content and assessment experts at the University of Oregon. b. From a scaling perspective, the authors designed alternate forms for the math measures to be comparable using item response theory (IRT). A common-person, common-item equating design was used to scale all items. Within specific skill areas, approximately 250 students responded to multiple item sets, and each test form contains items common across forms. The equated item scale scores and model fit statistics were used to (a) identify items of similar difficulty, (b) estimate student equated scores, and (c) remove/revise items of poor psychometric quality. The authors then placed the items into final alternate forms so that each form included items with similar levels of difficulty. The authors generally placed easier items and interspersed common items near the beginning of the form, as Benchmark screening measures are timed, and students would then be assured of a sensitive measure for estimating their ability. Tasks are grade-level referenced. For all computer-based tests, the student administration is compatible with popular browsers (PC: Firefox and Chrome, Mac: Safari, Firefox, and Chrome). Furthermore, the computer presentation is optimized for a clear presentation of the item, with large-option buttons to facilitate option selection, and “next” buttons to assure easy navigation in moving forward or backward across problems. Test items and test forms underwent bias review to ensure that they are appropriate for diverse populations, with a special emphasis on culturally and linguistically diverse student populations and students with learning disabilities. In addition, the authors conduct DIF analyses to provide evidence that the items function equivalently across different student populations.

Classification Accuracy & Cross-Validation Summary

Grade	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Classification Accuracy Fall
Classification Accuracy Winter
Classification Accuracy Spring

Smarter Balanced Mathematics Assessment (SBAS 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.

We used the Smarter Balanced Mathematics Assessment as our criterion measure. This measure is completely independent from the screening measure, although both SBAS and easyCBM are aligned to the Common Core State Standards in Mathematics. SBAS is a large-scale assessment in wide use across the United States as a state accountability 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).

We used R statistical package to perform the classification analyses. The cut point of the score associated with the 20th percentile from the easyCBM National Norms was selected, as prior studies and wide-spread district policy suggests this is an appropriate cut-point for identifying students with intensive need.

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.

Students who scored below the cut-point 20th percentile were assigned a variety of interventions, depending on specific pattern of need (types of assessment items with which they struggled, success of prior years’ interventions, whether they also had identified literacy needs) and resources available at the schools. Interventions ranged from one-on-one daily instruction on mathematics to small group (2-6 students) twice-weekly supplemental mathematics instruction, to after-school mentoring with a focus on mathematics, to “flex-Friday” one-hour preview/review sessions with cross-grade-level groupings based on identified content need. A number of students concurrently received several of these interventions (typically only those students whose ELA performance did not indicate a need for literacy intervention as well because those students who also needed literacy intervention simply did not have sufficient time in the school day to receive all the instructional interventions they needed). Interventions were delivered by a variety of personnel (depending on school/district resources): Special Education teachers, general education teachers during their “intervention block”, instructional assistants, and student mentors (some adult, some older children).

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 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Criterion measure	Smarter Balanced Mathematics Assessment (SBAS math)	Smarter Balanced Mathematics Assessment (SBAS math)	Smarter Balanced Mathematics Assessment (SBAS math)	Smarter Balanced Mathematics Assessment (SBAS math)	Smarter Balanced Mathematics Assessment (SBAS math)	Smarter Balanced Mathematics Assessment (SBAS math)
Cut Points - Percentile rank on criterion measure	20	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)	141	228	310	334	233	270
Classification Data - False Positive (b)	32	12	22	13	13	25
Classification Data - False Negative (c)	413	555	588	595	543	547
Classification Data - True Negative (d)	648	730	600	547	631	524
Area Under the Curve (AUC)	0.84	0.87	0.85	0.88	0.89	0.87
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.82	0.85	0.83	0.86	0.87	0.85
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.86	0.89	0.87	0.89	0.91	0.89

Statistics	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Base Rate	0.45	0.51	0.59	0.62	0.55	0.60
Overall Classification Rate	0.64	0.63	0.60	0.59	0.61	0.58
Sensitivity	0.25	0.29	0.35	0.36	0.30	0.33
Specificity	0.95	0.98	0.96	0.98	0.98	0.95
False Positive Rate	0.05	0.02	0.04	0.02	0.02	0.05
False Negative Rate	0.75	0.71	0.65	0.64	0.70	0.67
Positive Predictive Power	0.82	0.95	0.93	0.96	0.95	0.92
Negative Predictive Power	0.61	0.57	0.51	0.48	0.54	0.49

Sample	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Date	2014-15 SY	2014-15 SY	2014-15 SY	2014-15 SY	2014-15 SY	2014-15 SY
Sample Size	1234	1525	1520	1489	1420	1366
Geographic Representation	Pacific (OR, WA)	Pacific (OR, WA)	Pacific (OR, WA)	Pacific (OR, WA)	Pacific (OR, WA)	Pacific (OR, WA)
Male	1,026.5%	793.4%	823.5%	813.8%	832.2%	1,009.0%
Female	929.3%	773.8%	767.6%	766.8%	784.0%	974.9%
Other
Gender Unknown	171.6%	437.2%	414.5%	420.9%	444.9%	523.4%
White, Non-Hispanic	455.2%	320.2%	369.5%	306.7%	372.0%	533.2%
Black, Non-Hispanic
Hispanic
Asian/Pacific Islander
American Indian/Alaska Native
Other	1,672.0%	1,684.1%	1,636.0%	1,694.6%	1,688.9%	1,974.2%
Race / Ethnicity Unknown
Low SES	659.1%	539.9%	521.9%	557.4%	523.5%	564.9%
IEP or diagnosed disability	217.4%	181.4%	167.8%	172.4%	160.8%	201.3%
English Language Learner	218.8%	161.8%	149.1%	119.7%	133.8%	122.0%

Classification Accuracy - Winter

Evidence	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Criterion measure	Smarter Balanced Mathematics Assessment (SBAS math)	Smarter Balanced Mathematics Assessment (SBAS math)	Smarter Balanced Mathematics Assessment (SBAS math)	Smarter Balanced Mathematics Assessment (SBAS math)	Smarter Balanced Mathematics Assessment (SBAS math)	Smarter Balanced Mathematics Assessment (SBAS math)
Cut Points - Percentile rank on criterion measure	20	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)	192	194	276	213	212	266
Classification Data - False Positive (b)	12	9	0	1	0	6
Classification Data - False Negative (c)	396	631	652	755	588	581
Classification Data - True Negative (d)	684	745	632	579	661	632
Area Under the Curve (AUC)	0.88	0.86	0.91	0.91	0.92	0.93
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.87	0.84	0.89	0.90	0.91	0.92
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.90	0.87	0.92	0.93	0.94	0.94

Statistics	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Base Rate	0.46	0.52	0.59	0.63	0.55	0.57
Overall Classification Rate	0.68	0.59	0.58	0.51	0.60	0.60
Sensitivity	0.33	0.24	0.30	0.22	0.27	0.31
Specificity	0.98	0.99	1.00	1.00	1.00	0.99
False Positive Rate	0.02	0.01	0.00	0.00	0.00	0.01
False Negative Rate	0.67	0.76	0.70	0.78	0.74	0.69
Positive Predictive Power	0.94	0.96	1.00	1.00	1.00	0.98
Negative Predictive Power	0.63	0.54	0.49	0.43	0.53	0.52

Sample	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Date	2014-15 SY	2014-15 SY	2014-15 SY	2014-15 SY	2014-15 SY	2014-15 SY
Sample Size	1284	1579	1560	1548	1461	1485
Geographic Representation	Pacific (OR, WA)	Pacific (OR, WA)	Pacific (OR, WA)	Pacific (OR, WA)	Pacific (OR, WA)	Pacific (OR, WA)
Male	986.5%	766.3%	802.4%	782.8%	808.8%	928.1%
Female	893.1%	747.3%	747.9%	737.5%	762.0%	896.8%
Other
Gender Unknown	164.9%	422.2%	403.8%	404.8%	432.4%	481.5%
White, Non-Hispanic	437.5%	309.2%	360.1%	295.0%	361.6%	490.4%
Black, Non-Hispanic
Hispanic
Asian/Pacific Islander
American Indian/Alaska Native
Other	1,606.9%	1,626.5%	1,594.0%	1,630.0%	1,641.5%	1,816.0%
Race / Ethnicity Unknown
Low SES	633.4%	521.4%	508.5%	536.2%	508.8%	519.7%
IEP or diagnosed disability	209.0%	175.2%	163.5%	165.8%	156.3%	185.2%
English Language Learner	210.3%	156.2%	145.3%	115.2%	130.0%	112.3%

Classification Accuracy - Spring

Evidence	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Criterion measure	Smarter Balanced Mathematics Assessment (SBAS math)	Smarter Balanced Mathematics Assessment (SBAS math)	Smarter Balanced Mathematics Assessment (SBAS math)	Smarter Balanced Mathematics Assessment (SBAS math)	Smarter Balanced Mathematics Assessment (SBAS math)	Smarter Balanced Mathematics Assessment (SBAS math)
Cut Points - Percentile rank on criterion measure	20	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)	180	180	281	234	219	317
Classification Data - False Positive (b)	11	5	3	4	9	8
Classification Data - False Negative (c)	421	658	595	725	607	523
Classification Data - True Negative (d)	689	757	619	577	655	621
Area Under the Curve (AUC)	0.88	0.86	0.91	0.91	0.92	0.93
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.87	0.85	0.89	0.90	0.91	0.92
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.90	0.88	0.92	0.93	0.94	0.94

Statistics	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Base Rate	0.46	0.52	0.58	0.62	0.55	0.57
Overall Classification Rate	0.67	0.59	0.60	0.53	0.59	0.64
Sensitivity	0.30	0.21	0.32	0.24	0.27	0.38
Specificity	0.98	0.99	1.00	0.99	0.99	0.99
False Positive Rate	0.02	0.01	0.00	0.01	0.01	0.01
False Negative Rate	0.70	0.79	0.68	0.76	0.73	0.62
Positive Predictive Power	0.94	0.97	0.99	0.98	0.96	0.98
Negative Predictive Power	0.62	0.53	0.51	0.44	0.52	0.54

Sample	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Date	2014-15 SY	2014-15 SY	2014-15 SY	2014-15 SY	2014-15 SY	2014-15 SY
Sample Size	1301	1600	1498	1540	1490	1469
Geographic Representation	Pacific (OR, WA)	Pacific (OR, WA)	Pacific (OR, WA)	Pacific (OR, WA)	Pacific (OR, WA)	Pacific (OR, WA)
Male	973.6%	756.3%	835.6%	786.8%	793.1%	938.3%
Female	881.4%	737.5%	778.8%	741.4%	747.2%	906.5%
Other
Gender Unknown	162.7%	416.7%	420.6%	406.9%	424.0%	486.7%
White, Non-Hispanic	431.7%	305.2%	375.0%	296.6%	354.6%	495.8%
Black, Non-Hispanic
Hispanic
Asian/Pacific Islander
American Indian/Alaska Native
Other	1,585.9%	1,605.2%	1,660.0%	1,638.5%	1,609.6%	1,835.7%
Race / Ethnicity Unknown
Low SES	625.1%	514.6%	529.6%	539.0%	498.9%	525.3%
IEP or diagnosed disability	206.2%	172.9%	170.2%	166.7%	153.2%	187.2%
English Language Learner	207.5%	154.2%	151.3%	115.8%	127.5%	113.5%

Reliability

Grade	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Rating	d	d	d	d	d	d

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

Split-half reliability and Cronbach’s Alpha are both estimates of the internal consistency of the CCSS mathematics measures. Because the easyCBM CCSS mathematics measures are often administered for a set period of time (typically 30 minutes), not all students will complete all items. Having an internally-consistent measure, where scores on two split halves of the assessment are correlated with one another, provides some reassurance that scores obtained when students complete only some of the items (for instance, when they “time out” after responding to only half of the possible items on the assessment) reflect the distribution of scores that would be obtained were the entire test completed.

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

Demographic information for the convenience sample used for both the Split-half and Cronbach’s Alpha analyses are presented below. The study was conducted using values from the fall and winter 2013‐2014 CCSS math benchmark assessments. The fall benchmark was taken by 9,360 kindergarteners; 18,911 grade 1 students; 20,910 grade 2 students; 18,356 grade 3 students; 18,229 grade 4 students; 18,652 grade 5 students; 11,860 grade 6 students; 10,384 grade 7 students; and 8,961 grade 8 students for a total of 135,893 participants. The winter benchmark was taken by 14,158 kindergarteners; 20,448 grade 1 students; 22,441 grade 2 students; 20,807 grade 3 students; 20,031 grade 4 students; 19,446 grade 5 students; 11,919 grade 6 students; 10,116 grade 7 students; and 9,254 grade 8 students for a total of 148,640 participants. Students of American Indian or Alaskan Native descent comprised 1‐4% of the sample, and Asian students made up 2‐3% of the sample across all grades. Black or African American students made up 10‐19% of the sample in grade K‐2 and 3‐5% of the sample in grade 3‐8. Native Hawaiian or other Pacific Islander students made up 0‐1% and students identified as Two or more Races constituted 1‐2% of the sample across all grades. Lastly, White students made up 44‐55% of the sample, and those classified as Unknown ethnicity made up 29‐47% of the sample across all grades. Similarly, students identified as Hispanic/Latino made up 8‐16% of the sample and students identified as Not Hispanic/Latino made up 48‐70% of the sample, varying by grade level. The percentage of ELL students in the sample had a range of 16‐33%. Students identified by their districts as disabled constituted 17‐31% of the sample. Males made up 49‐53% of the sample. Further demographic data are presented in Tables 1, 2, and 3.

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

Prior to analysis, students who had not responded to any items on a particular math measure were removed from the dataset. Next, the data were examined for scoring irregularities, and any out‐of‐range values were removed. One 5th grade student from the winter benchmark was removed due to a score being not within the possible range for the measure. The measures were analyzed for internal consistency using Cronbach’s Alpha and Split‐half reliability (first half/second half). For the split‐half reliability, the measures were analyzed using the first half to the median compared to the second half.

*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)?

Yes

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 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Rating

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

Two separate studies are described here. STUDY 1: We analyzed the relation between the easyCBM CCSS Mathematics Assessment (Grades 3-8) and the Mathematics section of the Smarter Balanced Assessment (SBAS). The widespread use of SBAS across the United States makes it an appropriate measure to use for analyzing criterion validity (both predictive and concurrent). It is external to the easyCBM system and is considered a high-stakes assessment, as it is the measure many states are using for their statewide large-scale assessment system. STUDY 2: We analyzed the relation between the easyCBM CCSS Mathematics Winter Benchmark Assessment (Grades 6-8) and the Stanford Achievement Test, 10th Edition (SAT-10), given one week later. We selected the SAT-10 based on its documented validity evidence (Pearson, 2004) and because both tests were designed to measure a similar construct (mathematics).

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

STUDY 1: Data for this study came from a convenience sample provided by two school districts in the Pacific Northwest. All students enrolled in school and present during the three-week easyCBM Benchmark Assessment windows in the fall (September 2014), winter (January 2015) and spring (May 2015) were administered the easyCBM assessments. All enrolled students were likewise administered the Smarter Balanced assessments during the testing window provided by the state in the spring of 2015. The data set provided by the districts included easyCBM CCSS Math, Passage Reading Fluency, Vocabulary, and Multiple Choice Reading Comprehension (MCRC) as well as Smarter Balanced Math and English Language Arts total scores for students enrolled in grades 3-8. District 1 provided data for Grades 3-8, while District 2 provided data for Grades 4-8. In addition, District 1 provided demographic information, while District 2 (approximately ¼ the size of the first district) did not. Demographics of the sample are provided in Table 1. Because of the missing demographics from a large proportion of the sample, the percentages for each of the demographic variables are calculated based on the students in the sample whose data included full-resolution demographic information. During data cleaning, data from students who were administered the Alternate Assessment rather than the General Education assessment were removed from the dataset prior to further analyses. In all, six students each from Grades 4, 6, and 7 and three students from Grade 5 were removed from the dataset in this step. Data from all additional students were retained. STUDY 2: This convenience sample came from a random sample of students per grade (grades 6, 7, and 8) within one school in the Pacific Northwest. The 6th grade sample (n = 67) included 33 girls, 8 students receiving special education services (SPED classification included: 1 identified as having an intellectual disability, 1 communication disorder, 1 other health impairment, and 6 learning disability). Of the 67 students, 6 were identified as English Language Learners, and 42 received free (n=36) or reduced-price (n=6) meals. In all, 56 students in this sample were identified as white, 7 as Hispanic, 1 as Asian, and 3 as “other”. The 7th grade sample (n = 63) included 24 girls, 7 students receiving special education services (SPED classification included: 3 identified as having an intellectual disability, 1 other health impairment, and 3 learning disability). Of the 63 students, 3 were identified as English Language Learners, and 36 received free (n=31) or reduced-price (n=5) meals. In all, 49 students in this sample were identified as white, 7 as Hispanic, 2 as Black/African American, and 5 as “other”. The 8th grade sample (n = 64) included 38 girls, 6 students receiving special education services (SPED classification included: 1 Autism spectrum disorder, 1 other health impairment, and 4 learning disability). Of the 64 students, 0 were identified as English Language Learners, and 29 received free (n=22) or reduced-price (n=7) meals. In all, 51 students in this sample were identified as white, 10 as Hispanic, and 2 as “other”.

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

In both studies, we analyzed the data using bivariate correlations and linear regression using the SPSS software.

*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.

Data from both studies support the concurrent and predictive validity of the tool. Correlations between the easyCBM CCSS Mathematics measures and two very different external measures of mathematics suggest that the easyCBM CCSS Math assessments are, indeed, capturing important information about students’ knowledge of mathematics. The easyCBM CCSS Mathematics measures consistently predict student performance on other measures of mathematics

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 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Rating	Yes	Yes	Yes	Yes	Yes	Yes

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:

We ran DIF analyses for all our CCSS Math measures using the Mantel-Haenszel (MH) procedure with an iterative purification process. Items were evaluated by the delta MH statistic, and assigned letter grades (A, B, or C) based on the recommendation of Holland and Thayer (1988).

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

We conducted DIF analyses for the following sub-groups: gender, disability status, and race/ethnicity.

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.

Results indicated the CCSS math measures are not biased against gender, disability status, or race/ethnicity. At all grade levels, and all seasons, the CCSS math measures received a grade of “A” in the DIF analyses in all three of the groupings examined.