easyCBM
Proficient Reading (formerly Reading Comprehension)

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 17 in reading) to be used to monitor progress. All measures have been developed with reference to specific content in reading (National Reading Panel) 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

District: inquiry@riversideinsights.com; Individual: support@easycbm.com

Address

District Users: Riverside Insights, Attention: Customer Service One Pierce Place, Suite 900W, Itasca, Illinois 60143

Phone Number

District: 800/323.9540 Individual: 541/346.3535

Website

District Accounts: http://www.riversideinsights.com/solutions/easyCBM; Individual teacher users: 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 must have access to an Internet-connected computer (Mac or PC). 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. However, districts are directed to develop their own practices for 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?

September/January/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:

1-4 hrs 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

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 PRF 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.

Answer keys are available for all selected-response benchmark screening measures (reading comprehension) Each subtest has its own score; these individual scores are not converted to a total composite score. The subtest total 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 reading, the authors used the National Reading Panel report (NRP) to develop a full complement of tasks across the grade levels. easyCBM reading measures include phonemic awareness (letter names and phoneme segmentation), phonics (letter sounds, and word reading fluency), fluency (passage reading fluency), vocabulary (word meaning synonyms), and comprehension (multiple-choice narrative stories that have associated questions addressing literal, inferential, and evaluative understanding). b. From a scaling perspective, the authors designed alternate forms for most measures (passage reading fluency is the only exception) 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 (e.g., letter names, letter sounds, words in a list for word reading fluency, vocabulary, and comprehension), approximately 250 students responded to multiple item sets, and each test form contained 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. They then placed the items into final alternate forms so that each form included items with similar levels of difficulty. They 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. For passage fluency, original narrative stories were written and Flesh Kinkaid estimates of ability were calculated for each paragraph; where readability exceeded beyond a half grade level for that grade, words were exchanged from the word list (which had measure estimates for each word) by inserting more difficult words (for passages deemed too easy) or less difficult (for passages deemed too difficult). N.B. The authors used IRT to equate the forms but not to make scales for reporting purposes; rather, all outcomes are based on raw scores. See technical reports. Tasks are grade-level referenced. For all computer-based tests, the student administration is compatible with popular browsers (PC: Internet Explorer, 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, we conduct DIF analyses to provide evidence that the items function equivalently across different student populations.

Classification Accuracy & Cross-Validation Summary

Grade	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Classification Accuracy Fall
Classification Accuracy Winter
Classification Accuracy Spring

Smarter Balanced English Language Arts Assessment

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 English Language Arts Assessment as our criterion measure. This measure is completely independent from the screening measure. 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 (performance on other parts of the literacy benchmark assessment such as passage reading fluency and reading comprehension, success of prior years’ interventions, whether they also had identified mathematics needs) and resources available at the schools. Interventions ranged from one-on-one daily instruction on reading comprehension, focusing on literal, inferential, and evaluative comprehension, to small group (2-6 students) twice-weekly supplemental instruction focused on improving comprehension of both narrative fiction and non-fiction informational text, to after-school reading clubs focused on exposure to a wide variety of reading materials. A number of students concurrently received several of these interventions (typically only those students whose mathematics performance did not indicate a need for mathematics intervention as well because those students who also needed mathematics 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 English Language Arts Assessment	Smarter Balanced English Language Arts Assessment	Smarter Balanced English Language Arts Assessment	Smarter Balanced English Language Arts Assessment	Smarter Balanced English Language Arts Assessment	Smarter Balanced English Language Arts Assessment
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)	164	244	316	268	201	213
Classification Data - False Positive (b)	24	31	47	18	20	17
Classification Data - False Negative (c)	487	490	432	526	478	427
Classification Data - True Negative (d)	558	670	728	682	717	809
Area Under the Curve (AUC)	0.84	0.83	0.79	0.80	0.80	0.81
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.82	0.81	0.76	0.78	0.78	0.79
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.86	0.85	0.81	0.82	0.82	0.83

Statistics	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Base Rate	0.53	0.51	0.49	0.53	0.48	0.44
Overall Classification Rate	0.59	0.64	0.69	0.64	0.65	0.70
Sensitivity	0.25	0.33	0.42	0.34	0.30	0.33
Specificity	0.96	0.96	0.94	0.97	0.97	0.98
False Positive Rate	0.04	0.04	0.06	0.03	0.03	0.02
False Negative Rate	0.75	0.67	0.58	0.66	0.70	0.67
Positive Predictive Power	0.87	0.89	0.87	0.94	0.91	0.93
Negative Predictive Power	0.53	0.58	0.63	0.56	0.60	0.65

Sample	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Date	SY2014-15	SY2014-15	SY2014-15	SY2014-15	SY2014-15	SY2014-15
Sample Size	1233	1435	1523	1494	1416	1466
Geographic Representation	Pacific (OR, WA)	Pacific (OR, WA)	Pacific (OR, WA)	Pacific (OR, WA)	Pacific (OR, WA)	Pacific (OR, WA)
Male	1,027.3%	843.2%	821.9%	811.0%	834.5%	940.2%
Female	930.0%	822.3%	766.1%	764.2%	786.2%	908.4%
Other
Gender Unknown	171.7%	464.6%	413.7%	419.5%	446.1%	487.7%
White, Non-Hispanic	455.6%	340.3%	368.8%	305.7%	373.1%	496.8%
Black, Non-Hispanic
Hispanic
Asian/Pacific Islander
American Indian/Alaska Native
Other	1,673.4%	1,789.8%	1,632.8%	1,689.0%	1,693.7%	1,839.5%
Race / Ethnicity Unknown
Low SES	659.6%	573.7%	520.9%	555.6%	524.9%	526.4%
IEP or diagnosed disability	217.6%	192.8%	167.4%	171.8%	161.2%	187.6%
English Language Learner	219.0%	171.9%	148.9%	119.3%	134.2%	113.7%

Classification Accuracy - Winter

Evidence	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Criterion measure	Smarter Balanced English Language Arts Assessment	Smarter Balanced English Language Arts Assessment	Smarter Balanced English Language Arts Assessment	Smarter Balanced English Language Arts Assessment	Smarter Balanced English Language Arts Assessment	Smarter Balanced English Language Arts Assessment
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)	216	208	315	205	175	208
Classification Data - False Positive (b)	21	17	23	21	19	40
Classification Data - False Negative (c)	463	558	452	629	528	471
Classification Data - True Negative (d)	575	698	776	693	739	803
Area Under the Curve (AUC)	0.82	0.84	0.75	0.78	0.76	0.83
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.79	0.82	0.72	0.75	0.74	0.81
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.84	0.86	0.77	0.80	0.79	0.85

Statistics	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Base Rate	0.53	0.52	0.49	0.54	0.48	0.45
Overall Classification Rate	0.62	0.61	0.70	0.58	0.63	0.66
Sensitivity	0.32	0.27	0.41	0.25	0.25	0.31
Specificity	0.96	0.98	0.97	0.97	0.97	0.95
False Positive Rate	0.04	0.02	0.03	0.03	0.03	0.05
False Negative Rate	0.68	0.73	0.59	0.75	0.75	0.69
Positive Predictive Power	0.91	0.92	0.93	0.91	0.90	0.84
Negative Predictive Power	0.55	0.56	0.63	0.52	0.58	0.63

Sample	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Date	SY2014-15	SY2014-15	SY2014-15	SY2014-15	SY2014-15	SY2014-15
Sample Size	1275	1481	1566	1548	1461	1522
Geographic Representation	Pacific (OR, WA)	Pacific (OR, WA)	Pacific (OR, WA)	Pacific (OR, WA)	Pacific (OR, WA)	Pacific (OR, WA)
Male	993.5%	817.0%	799.3%	782.8%	808.8%	905.6%
Female	899.4%	796.8%	745.0%	737.5%	762.0%	875.0%
Other
Gender Unknown	166.0%	450.2%	402.3%	404.8%	432.4%	469.8%
White, Non-Hispanic	440.5%	329.7%	358.7%	295.0%	361.6%	478.5%
Black, Non-Hispanic
Hispanic
Asian/Pacific Islander
American Indian/Alaska Native
Other	1,618.3%	1,734.2%	1,587.9%	1,630.0%	1,641.5%	1,771.8%
Race / Ethnicity Unknown
Low SES	637.9%	555.9%	506.6%	536.2%	508.8%	507.0%
IEP or diagnosed disability	210.4%	186.8%	162.8%	165.8%	156.3%	180.7%
English Language Learner	211.8%	166.6%	144.8%	115.2%	130.0%	109.5%

Classification Accuracy - Spring

Evidence	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Criterion measure	Smarter Balanced English Language Arts Assessment	Smarter Balanced English Language Arts Assessment	Smarter Balanced English Language Arts Assessment	Smarter Balanced English Language Arts Assessment	Smarter Balanced English Language Arts Assessment	Smarter Balanced English Language Arts Assessment
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)	177	148	205	145	190	201
Classification Data - False Positive (b)	8	8	7	7	30	35
Classification Data - False Negative (c)	511	492	386	514	373	348
Classification Data - True Negative (d)	589	657	733	633	616	664
Area Under the Curve (AUC)	0.80	0.82	0.80	0.81	0.80	0.85
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.77	0.79	0.78	0.79	0.78	0.83
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.82	0.84	0.82	0.83	0.83	0.87

Statistics	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Base Rate	0.54	0.49	0.44	0.51	0.47	0.44
Overall Classification Rate	0.60	0.62	0.70	0.60	0.67	0.69
Sensitivity	0.26	0.23	0.35	0.22	0.34	0.37
Specificity	0.99	0.99	0.99	0.99	0.95	0.95
False Positive Rate	0.01	0.01	0.01	0.01	0.05	0.05
False Negative Rate	0.74	0.77	0.65	0.78	0.66	0.63
Positive Predictive Power	0.96	0.95	0.97	0.95	0.86	0.85
Negative Predictive Power	0.54	0.57	0.66	0.55	0.62	0.66

Sample	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Date	SY2014-15	SY2014-15	SY2014-15	SY2014-15	SY2014-15	SY2014-15
Sample Size	1285	1305	1331	1299	1209	1248
Geographic Representation	Pacific (OR, WA)	Pacific (OR, WA)	Pacific (OR, WA)	Pacific (OR, WA)	Pacific (OR, WA)	Pacific (OR, WA)
Male	985.8%	927.2%	940.4%	932.8%	977.4%	1,104.4%
Female	892.4%	904.2%	876.6%	878.9%	920.8%	1,067.1%
Other
Gender Unknown	164.7%	510.9%	473.3%	482.4%	522.5%	572.9%
White, Non-Hispanic	437.1%	374.2%	422.0%	351.6%	437.0%	583.6%
Black, Non-Hispanic
Hispanic
Asian/Pacific Islander
American Indian/Alaska Native
Other	1,605.7%	1,968.0%	1,868.3%	1,942.5%	1,983.7%	2,160.8%
Race / Ethnicity Unknown
Low SES	632.9%	630.9%	596.0%	639.0%	614.8%	618.3%
IEP or diagnosed disability	208.8%	212.0%	191.6%	197.6%	188.8%	220.4%
English Language Learner	210.1%	189.0%	170.3%	137.3%	157.2%	133.6%

Reliability

Grade	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Rating	d	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 MCRC measures. Because the easyCBM MCRC measures are often administered for a set period of time (typically 30-45 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 Vocabulary benchmark assessments. The fall benchmark was taken by 20,252 grade 2 students; 23,694 grade 3 students; 17,850 grade 4 students; 26,978 grade 5 students; 17,222 grade 6 students; 12,798 grade 7 students; and 8,965 grade 8 students. The winter benchmark was taken by 19,158 grade 2 students; 21,807 grade 3 students; 15,031 grade 4 students; 23,146 grade 5 students; 15,575 grade 6 students; 11,506 grade 7 students; and 9,464 grade 8 students. 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 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.

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

Prior to analysis, students who had not responded to any items on a particular MCRC measure were removed from the dataset. 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:

Additional disaggregated data is available from the Center upon request.

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	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.

We used the Smarter Balanced English Language Arts Assessment as our criterion measure. This measure is completely independent from the screening measure. SBAS is a large-scale assessment in wide use across the United States as a state accountability measure. Because it is used by so many states for their accountability measure, school districts are quite interested in the relation between SBAS and easyCBM MCRC.

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

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. Known 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.

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

We analyzed the data using bivariate correlations and linear regression using 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 these validity studies support the concurrent and predictive validity of the tool. Correlations between the easyCBM MCRC measures and an external measure of English Language Arts that includes reading comprehension as a tested construct suggest that the easyCBM MCRC assessments are, indeed, capturing important information about students’ ability to make sense of what they are reading. The easyCBM MCRC measures consistently predict student performance on other measures of English Language Arts.

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	Grade 7	Grade 8
Rating	Yes	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 easyCBM Reading Comprehension 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 easyCBM Vocabulary measures are not biased against gender, disability status, or race/ethnicity. At all grade levels, and all seasons, the easyCBM Vocabulary items received a grade of “A” in the DIF analyses in all three of the groupings examined.