easyCBM
Passage Reading Fluency

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@service.riversideinsights.com; Individual: support@easycbm.com

Address

District Users: Riverside Insights, Attention: Customer Service One Pierce Place, Suite 101C, Itasca, Illinois 60143 Individual Users: BRT, University of Oregon, Eugene, OR 97403

Phone Number

District: 800/323.9540

Website

District accounts: https://www.riversideinsights.com/easy_cbm; Teacher accounts: easyCBM.com

Initial cost for implementing program:

Cost

$7.50

Unit of cost

student

Replacement cost per unit for subsequent use:

Cost

$7.50

Unit of cost

student

Duration of license

year

Additional cost information:

Describe basic pricing plan and structure of the tool. Provide information on what is included in the published tool, as well as what is not included but required for implementation.

easyCBM is available through Riverside Insights on an annual subscription license for districts. Price is $7.50/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. 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

1

per (student/group/other unit)

student

Additional scoring time:

Time in minutes

1

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 hours 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, otherwise the defaults are set at 0-10% as “high risk” and 11-24% as “some risk.” In addition, when students take the PRF measure and the Vocabulary and Proficient Reading measures in the same benchmark window, they receive a Reading Composite Score (percentile score) that can be used to determine risk levels for reading. If students score at or below the 20th percentile on the Composite Score, they are considered “at risk” for reading. Trainings on the system provide guidance to teachers to log and provide interventions to students identified as “some risk” or “high risk” following their district’s policies. The benchmark/screener reports provide suggested progress monitoring measures to use as follow-up for students identified as “high risk.”

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 individually-administered PRF measures include Administrator and Student test forms. The Administrator test form is used to mark any errors in student responses (any area in which the student’s response differs from the exact items presented on the test protocols). At the end of the test, the Assessor should fill in the spaces indicating Total Words Read, Errors, and Total Correct Words. This is automatically calculated when testing online. Each PRF measure has its own score based on the number of words in the passage; the total score is simply the total of all items correct. For Passage Reading Fluency (PRF) measures, the score is Correct Words Read per Minute. In addition, when students take the PRF measure and the Vocabulary and Proficient Reading measures, a Reading Composite Score is reported. This percentile score is designed to help teachers determine students' overall reading risk levels. The Composite Score is based on mathematical calculations that takes the students’ total raw scores for the three measures and converts to a standard scores (z-scores), then averaging the z-scores, and finally transforming the averaged z-scores back into percentile ranks. If students score at or below the 20th percentile on the Composite Score, they are considered “at risk” for reading.

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 for Passage Reading Fluency, original narrative stories were written and Flesch-Kincaid 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 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.

The screening measures were administered in the fall and winter (predictive) and spring (concurrent) of the 2023-24 school year, and the criterion measure was administered in the spring of the same 2023-24 school year, making the methods we chose for concurrent and predictive classification accuracy are appropriate for our 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 conducted a receiver operating characteristics (ROC) analysis for each grade, season, and measure to determine the optimal cut score (defined as the score associated with the highest sum of sensitivity and specificity) for each measure to classify students at or below the 20th percentile on the Smarter Balance Assessment System reading/math. The cut point of the score associated with the 20th percentile aligns with prior studies and wide-spread district policy that suggests this is an appropriate cut-point for identifying students with intensive need, contrasting high risk students with students not at high risk. Analyses were conducted and created in the R programming environment (R Core Team, 2024) with the cutpointr R package (Theile & Hirschfeld, 2021).

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 vocabulary 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 phonics to small group (2-6 students) twice-weekly supplemental fluency instruction, to after-school mentoring with a focus on oral reading fluency. 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	2342	2378	2411	2432	2456	2468
Cut Points - Corresponding performance score (numeric) on screener measure	66	90	129	134	151	148
Classification Data - True Positive (a)	819	862	844	411	367	472
Classification Data - False Positive (b)	63	63	65	42	35	49
Classification Data - False Negative (c)	244	202	237	102	222	113
Classification Data - True Negative (d)	250	163	175	102	115	73
Area Under the Curve (AUC)	0.85	0.84	0.81	0.82	0.77	0.75
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.85	0.84	0.81	0.82	0.77	0.75
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.85	0.84	0.82	0.82	0.77	0.75

Statistics	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Base Rate	0.77	0.82	0.82	0.78	0.80	0.83
Overall Classification Rate	0.78	0.79	0.77	0.78	0.65	0.77
Sensitivity	0.77	0.81	0.78	0.80	0.62	0.81
Specificity	0.80	0.72	0.73	0.71	0.77	0.60
False Positive Rate	0.20	0.28	0.27	0.29	0.23	0.40
False Negative Rate	0.23	0.19	0.22	0.20	0.38	0.19
Positive Predictive Power	0.93	0.93	0.93	0.91	0.91	0.91
Negative Predictive Power	0.51	0.45	0.42	0.50	0.34	0.39

Sample	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Date	Fall 2023	Fall 2023	Fall 2023	Fall 2023	Fall 2023	Fall 2023
Sample Size	1376	1290	1321	657	739	707
Geographic Representation	New England (CT) Pacific (OR, WA)	New England (CT) Pacific (OR, WA)	New England (CT) Pacific (OR, WA)	New England (CT) Pacific (OR, WA)	New England (CT) Pacific (OR, WA)	New England (CT) Pacific (OR, WA)
Male	52.0%	51.9%	51.3%	53.3%	54.4%	54.9%
Female	48.0%	48.1%	48.4%	46.6%	45.5%	44.4%
Other
Gender Unknown	1.5%	2.6%	1.6%	0.2%	0.1%	0.7%
White, Non-Hispanic	63.4%	60.6%	62.2%	64.4%	62.9%	60.5%
Black, Non-Hispanic	7.3%	7.8%	8.9%	7.9%	8.8%	9.1%
Hispanic	20.3%	22.4%	21.7%	21.2%	21.8%	21.6%
Asian/Pacific Islander	9.3%	7.6%	9.2%	9.6%	10.1%	12.4%
American Indian/Alaska Native	1.3%	2.2%	1.2%	1.1%	1.2%	1.4%
Other	17.2%	19.1%	17.0%	17.0%	16.9%	16.5%
Race / Ethnicity Unknown
Low SES
IEP or diagnosed disability	21.3%	17.3%	16.9%	14.6%	14.3%	17.5%
English Language Learner	7.7%	6.4%	5.1%	5.8%	5.4%	4.2%

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	2342	2378	2411	2432	2456	2468
Cut Points - Corresponding performance score (numeric) on screener measure	92	122	150	156	169	134
Classification Data - True Positive (a)	807	768	723	452	400	537
Classification Data - False Positive (b)	68	35	38	53	38	50
Classification Data - False Negative (c)	170	211	390	177	229	130
Classification Data - True Negative (d)	192	150	219	127	119	72
Area Under the Curve (AUC)	0.85	0.86	0.82	0.78	0.77	0.77
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.85	0.86	0.82	0.78	0.77	0.77
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.86	0.86	0.82	0.78	0.77	0.77

Statistics	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Base Rate	0.79	0.84	0.81	0.78	0.80	0.85
Overall Classification Rate	0.81	0.79	0.69	0.72	0.66	0.77
Sensitivity	0.83	0.78	0.65	0.72	0.64	0.81
Specificity	0.74	0.81	0.85	0.71	0.76	0.59
False Positive Rate	0.26	0.19	0.15	0.29	0.24	0.41
False Negative Rate	0.17	0.22	0.35	0.28	0.36	0.19
Positive Predictive Power	0.92	0.96	0.95	0.90	0.91	0.91
Negative Predictive Power	0.53	0.42	0.36	0.42	0.34	0.36

Sample	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Date	Winter 2024	Winter 2024	Winter 2024	Winter 2024	Winter 2024	Winter 2024
Sample Size	1237	1164	1370	809	786	789
Geographic Representation	New England (CT) Pacific (OR, WA)	New England (CT) Pacific (OR, WA)	New England (CT) Pacific (OR, WA)	New England (CT) Pacific (OR, WA)	New England (CT) Pacific (OR, WA)	New England (CT) Pacific (OR, WA)
Male	52.1%	52.1%	51.2%	53.5%	53.1%	54.2%
Female	47.9%	47.9%	48.5%	46.4%	46.7%	45.0%
Other
Gender Unknown			1.5%	0.1%	0.3%	0.8%
White, Non-Hispanic	60.2%	58.2%	61.9%	61.6%	61.7%	60.5%
Black, Non-Hispanic	8.3%	9.4%	9.2%	8.0%	8.7%	7.4%
Hispanic	21.9%	22.9%	22.0%	20.5%	21.2%	21.7%
Asian/Pacific Islander	10.7%	8.7%	9.1%	10.3%	9.5%	11.9%
American Indian/Alaska Native	1.5%	2.7%	1.2%	0.7%	1.7%	1.8%
Other	19.3%	21.0%	17.2%	19.4%	18.4%	18.5%
Race / Ethnicity Unknown
Low SES
IEP or diagnosed disability	19.4%	17.1%	17.3%	12.5%	14.6%	15.0%
English Language Learner	8.4%	6.6%	5.0%	4.7%	5.2%	3.8%

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	2342	2378	2411	2432	2456	2468
Cut Points - Corresponding performance score (numeric) on screener measure	99	110	147	146	144	156
Classification Data - True Positive (a)	783	924	871	237	285	390
Classification Data - False Positive (b)	57	68	64	21	32	19
Classification Data - False Negative (c)	321	197	257	59	71	180
Classification Data - True Negative (d)	289	179	195	57	77	85
Area Under the Curve (AUC)	0.84	0.83	0.82	0.83	0.81	0.80
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.84	0.83	0.82	0.83	0.81	0.79
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.84	0.84	0.82	0.83	0.81	0.80

Statistics	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Base Rate	0.76	0.82	0.81	0.79	0.77	0.85
Overall Classification Rate	0.74	0.81	0.77	0.79	0.78	0.70
Sensitivity	0.71	0.82	0.77	0.80	0.80	0.68
Specificity	0.84	0.72	0.75	0.73	0.71	0.82
False Positive Rate	0.16	0.28	0.25	0.27	0.29	0.18
False Negative Rate	0.29	0.18	0.23	0.20	0.20	0.32
Positive Predictive Power	0.93	0.93	0.93	0.92	0.90	0.95
Negative Predictive Power	0.47	0.48	0.43	0.49	0.52	0.32

Sample	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Date	Spring 2024	Spring 2024	Spring 2024	Spring 2024	Spring 2024	Spring 2024
Sample Size	1450	1368	1387	374	465	674
Geographic Representation	New England (CT) Pacific (OR, WA)	New England (CT) Pacific (OR, WA)	New England (CT) Pacific (OR, WA)	New England (CT) Pacific (OR, WA)	New England (CT) Pacific (OR, WA)	New England (CT) Pacific (OR, WA)
Male	52.2%	51.3%	51.3%	52.1%	54.0%	55.2%
Female	47.7%	48.7%	48.5%	47.9%	45.4%	43.6%
Other
Gender Unknown	1.6%	2.5%	1.5%		0.6%	1.2%
White, Non-Hispanic	62.8%	60.2%	61.8%	74.6%	69.5%	62.2%
Black, Non-Hispanic	7.5%	8.4%	9.4%	6.1%	7.3%	9.3%
Hispanic	20.9%	22.4%	22.2%	18.4%	18.9%	22.3%
Asian/Pacific Islander	9.4%	7.7%	8.8%	6.4%	7.1%	11.6%
American Indian/Alaska Native	1.4%	2.3%	1.2%	0.8%	1.9%	1.6%
Other	17.4%	18.9%	17.2%	12.0%	14.2%	16.6%
Race / Ethnicity Unknown
Low SES
IEP or diagnosed disability	21.3%	18.1%	16.6%	18.2%	18.9%	15.3%
English Language Learner	7.7%	6.4%	5.4%	5.6%	4.3%	3.7%

Reliability

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

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

The PRF measures provide an efficient and easy-to-administer assessment of students’ oral reading fluency. For the results to be most interpretable, however, it is important that alternate forms of the measure be of equivalent difficulty/return equivalent results in the absence of changes in students’ underlying oral reading fluency proficiency. Alternate form reliability provides an estimate of the consistency of scores were different test forms to be administered. This type of reliability gives us information about how consistent results might be if the winter measure was used in place of the fall measure. This consistency in performance across forms is important when evaluating the trustworthiness of screening results. The G-theory studies extend on the alternate form reliability analyses, further examining the degree to which variation in score can be attributed to alternate forms and/or alternate testing occasions.

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

Students from three public elementary schools in the Pacific Northwest participated in alternate form reliability studies, with sample size varying by grade. In grade 1, 41 students participated. In grade 2, 48 students participated. In grade 3, 50 students participated. In grade 4, 55 students participated. In grade 5, 50 students participated. A sub-sample of 38 grade 1, 34 grade 2, 38 grade 3, 39 grade 4, and 18 grade 5 students also participated in G-theory studies. No demographic information was collected in this study (see Tables 1a and b for descriptive statistics); however, on average, the participating schools comprised of 53% male students, 2% American Indian/Alaskan, 2% Asian/Pacific Islander, less than 1% of Black, 23% Hispanic, 67% White, and 8% two or more races students. 70% of the students are eligible for Free and Reduced Lunch programs. The district consists of 6% English Language Learners and 17% of students with Individualized Education Program (IEP).

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

For our generalizability theory study (G-Study) we calculated the variances associated with persons and two facets: forms and occasions. We then conducted decision studies (D-Studies) to help determine the necessary conditions for reliable measurement. Data for this study were analyzed in a two-facet fully crossed design (i.e., all students in the analysis were included in both testing occasions and administered the same test forms). The test forms were often administered in a different order on the separate occasions to mitigate order effects. The forms themselves remained constant across occasions in all analyses. For each grade level, we conducted 4 different G-theory analyses for passage reading fluency (PRF) to investigate 8 different test forms. The first facet in the analysis, form, was generally counterbalanced across the second facet, occasions.

*In the table(s) below, report the results of the reliability analyses described above (e.g., internal consistency or inter-rater reliability coefficients).

Type of	Subgroup	Informant	Age / Grade	Test or Criterion	n	Median Coefficient	95% Confidence Interval Lower Bound	95% Confidence Interval Upper Bound

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

Manual cites other published reliability studies:

No

Provide citations for additional published studies.

Do you have reliability data that are disaggregated by gender, race/ethnicity, or other subgroups (e.g., English language learners, students with disabilities)?

No

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

Type of	Subgroup	Informant	Age / Grade	Test or Criterion	n	Median Coefficient	95% Confidence Interval Lower Bound	95% Confidence Interval Upper Bound

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

Manual cites other published reliability studies:

No

Provide citations for additional published studies.

Validity

Grade	Grade 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.

For STUDY 1, we used the Smarter Balanced Assessment System (SBAS) 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 PRF. For STUDY 2, we used the DIBELs ORF measure to gather construct-related validity evidence. DIBELs ORF is a well-established measure for estimating students’ oral reading fluency with a long history of published validity evidence. Like SBAS, DIBELs is external to the easyCBM system. Unlike SBAS, however, the DIBELs ORF and the easyCBM PRF are designed to measure the exact same construct: Oral Reading Fluency. Thus, higher correlations between easyCBM and DIBELs ORF than between easyCBM and SBAS ELA provide strong evidence in support of the PRF measuring the intended construct (oral reading fluency). For STUDY 3, we also used the Smarter Balanced English Language Arts Assessment as our criterion measure.

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

STUDY 1: Data for the study examining the relation between the easyCBM PRF and the Smarter Balanced English Language Arts assessment came from a convenience sample of students 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 Proficient Math, Passage Reading Fluency, Vocabulary, and Proficient Reading 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: For the study examining the relation between the easyCBM PRF and the DIBELs ORF measures, Data came from a convenience sample of students from ten schools in an Oregon school district that uses easyCBM® reading measures as part of its Response to Intervention (RTI) model. This study was conducted in January 2013, with the initial duration of the study extended from one month to 1.5 months, due to an unexpected severe flu season, which caused a high absenteeism rate. At the beginning of the study, a total of 1017 students from grade 2 (n=240), grade 3 (n=311), grade 4 (n=247), and grade 5 (n=219) were recruited. As a result of the high absenteeism rate, the final sample consisted of 204 2nd-grade students, 288 3rd-grade students, 184 4th-grade students, and 206 5th-grade students. No demographic information was collected in this study, however, data came from participating schools with 53% male students, 2% American Indian/Alaskan, 2% Asian/Pacific Islander, less than 1% of Black, 23% Hispanic, 67% White, and 8% two or more races students. 70% of the students are eligible for Free and Reduced Lunch programs. The district consists of 6% English Language Learners and 17% of students with Individualized Education Program (IEP). STUDY 3: Data came from a sample of students in four districts who took the Smarter Balanced Assessment in the spring of 2023 and who were also assessed with an easyCBM fall, winter, or spring benchmark measure for Proficient Math, Passage Reading Fluency, Vocabulary, and Proficient Reading in the 2022-2023 school year. The Smarter Balanced Math and English Language Arts total scores for students enrolled in Grades 3-8 were used for comparison. One of the four districts provided data for Grades 3-5 for Smarter Balanced ELA only. Demographics of the sample are provided in the Technical Report 2401 (available from the Center upon request).

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

STUDY 1: We used linear regression to analyze the predictive validity of the easyCBM PRF measures to the Smarter Balanced English Language Arts assessment. STUDY 2: We used bivariate correlations to analyze concurrent validity for easyCBM PRF to DIBELs ORF measures. STUDY 3: We used Pearson bivariate correlations using the rstatix package (Kassambara, 2023) in the R programming environment (R Core Team, 2024). Kassambara, A. (2023). rstatix: Pipe-Friendly Framework for Basic Statistical Tests. R package version 0.7.2.

*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.

STUDY 1: The provided data indicate a moderate positive relation between the easyCBM PRF measures and the large-scale Smarter Balanced English Language Arts assessment at all tested grades and seasons. STUDY 2: The provided data indicate a very strong positive relation between the easyCBM PRF measures and the DIBELs ORF measures at all tested grades. These findings, taken in concert with one another, provide strong evidence of the easyCBM PRF measure as an appropriate assessment of students’ oral reading fluency. The correlations between the easyCBM PRF measures and the DIBELs ORF measures suggest they are measuring the same construct (as intended). STUDY 3: The provided data indicate a moderate positive relation between the easyCBM PRF measures and the large-scale Smarter Balanced English Language Arts assessment at tested grades and seasons. Because oral reading fluency has consistently been shown to predict other reading outcomes, such as direct measures of comprehension (e.g., the SBAS ELA assessment), coefficients ranging from .57 to .68 support the validity of including the easyCBM PRF measures as part of an assessment battery for screening students at risk for not meeting end-of-year performance expectations. The PRF measures are one of three different measures that together comprise the easyCBM Benchmark Assessments in reading.

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	Provided	Provided	Provided	Provided	Provided	Provided

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:

A Differential Test Functioning approach was used to determine the presence or absence of bias. A series of logistic regressions was conducted in which risk on an year-end outcome measure was predicted by risk-status as determined by the screening tool, membership in a selected demographic group, and an interaction term between the two variables. The year-end outcome was the Smarter Balanced Assessment English Language Arts score where risk-status was a score at or below the 20th percentile, and risk-status on the easyCBM Passage Reading Fluency score was determined by the cut score produced by receiver operating characteristics (ROC) analysis for each grade and season. Models were fit for each time of year (3), for each grade (6), and for each subroup comparisons (4) for a total of 72 models. Model results with a statistically significant interaction term would suggest differential accuracy in predicting year-end performance existed for different groups of students based on the risk status determined by the screening assessment (Linn, 1982).

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

Bias was assessed among: male and female students; students with disabilities and students without disabilities; English Learners and non English Learners; and non-White students and White students.

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 18 models assessing gender bias, 0 interaction terms were statistically significant. Of the 18 models assessing disability bias, 2 interaction terms were statistically significant (Grade 8 winter and spring). Of the 18 models assessing English language bias, 5 interaction terms were statistically significant (Grade 3 spring, Grade 4 winter & spring, and Grade 5 fall & spring). Of the 18 models assessing race bias, 3 interaction terms were statistically significant (Grade 6 fall, and Grade 8 fall and winter). Overall, the rate of significant interaction terms across all models (10/72) indicates that if bias exists, the extent is small. More details are available from the Center upon request.