i-Ready® Diagnostic
Mathematics

Descriptive Information

Please provide a description of your tool:

Offering a continuum of scale scores from kindergarten through high school, the i-Ready Diagnostic for Mathematics, a web-based adaptive screening assessment, is part of i-Ready’s integrated teaching and learning system. The i-Ready Diagnostic is a valid and reliable tool aligned to rigorous state standards across the following domains: Number and Operations, Algebra and Algebraic Thinking, Measurement and Data, and Geometry, and provides actionable data and reports for each domain. The i-Ready Diagnostic is typically administered three times per academic year, with a recommended 12-18 weeks of instruction between assessments. Each screening takes approximately 45 minutes—which may be broken into multiple sittings—and may be conducted with all students or with specific groups of students who have been identified as at risk of academic failure. i-Ready Diagnostic’s sophisticated adaptive algorithm automatically selects from thousands of technology-enhanced and multiple-choice items to get to the core of each student's strengths and challenges, regardless of the grade level at which he or she is performing. The system automatically analyzes, scores, and provides reports that include student-level and aggregated (e.g., class, school, and district) results. Available as soon as a student completes the assessment, i-Ready Diagnostic’s intuitive reports provide comprehensive information (including developmental analyses) about student performance, group students who struggle with the same concepts, make instructional recommendations to target skill deficiencies, and monitor progress and growth as students follow their individualized instructional paths. Reports include suggested next steps for instruction and PDF Tools for Instruction lesson plans for the teacher to use during individual, small-group, or whole-class instruction. In addition, should educators also purchase the optional i-Ready Personalized Instruction, the system automatically prescribes online lessons that address each student’s identified academic needs.

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):
Measurement & Data

Please describe specific domain, skills or subtests:

The i-Ready Diagnostic for Mathematics assesses four domains (Number and Operations, Algebra and Algebraic Thinking, Measurement and Data, and Geometry). For the domain of Number and Operations, the topics addressed include counting and cardinality; base ten—whole numbers and decimals (place value, compare, add, subtract, multiply, divide); fractions (model, compare, add, subtract, multiply, divide); rational numbers (model, compare, add, subtract, multiply, divide); and real and complex numbers (model, compare, add, subtract, multiply, divide). For the Algebra and Algebraic Thinking domain, the topics addressed include operations and algebraic thinking (fluency, number relationships, properties, solving word problems); expressions and equations (variables, exponents, solving word problems); ratio and proportional relationships (percent, rate, lines, and slope); functions (linear, exponential, quadratic, polynomial, logarithmic, trigonometric, rational interpreting functions); building functions; and systems of equations and inequalities. For the Geometry domain, the topics addressed include two-dimensional shapes; three-dimensional shapes; lines, segments, points, rays, and angles; symmetry and transformations; congruence and similarity; coordinate geometry; Pythagorean theorem; circles; and proofs. For the Measurements and Data domain, the topics addressed include measurement units and tools - customary and metric (time, money, length, capacity, weight, and mass); geometric measurement; area, perimeter, surface area, volume; creating and interpreting graphs; and statistics and probability (randomness, probability distributions, collecting and analyzing data, making inferences and conclusions based on probability and expected values, and correlations).

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

RFPs@cainc.com

Address

153 Rangeway Road, N. Billerica MA 01862

Phone Number

800-225-0248

Website

www.curriculumassociates.com

Initial cost for implementing program:

Cost

$8.00

Unit of cost

student

Replacement cost per unit for subsequent use:

Cost

$8.00

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.

$8.00/student/year for i-Ready Diagnostic for Mathematics; volume and multi-year subscription discounts are available. Annual license fee includes online student access to assessment, plus staff access to management and reporting suite, downloadable lesson plans, and user resources including i-Ready Central support website; account set-up and secure hosting; all program maintenance/ updates/ enhancements during the active license term; unlimited user access to U.S.-based service and support via toll-free phone and email during business hours. Professional development is required and available at an additional cost of $2,300. (Session up to six hours, maximum of 30 participants at any one time. Sessions expire two years from the invoice date.)

Provide information about special accommodations for students with disabilities.

i-Ready Diagnostic for Mathematics is a web-based, vendor-hosted, Software-as-a-Service application. The per-student or site-based license fee includes account set-up and management; unlimited access to i-Ready Diagnostic, management, and reporting functionality; and unlimited access to U.S.-based customer service/technical support and all program maintenance, updates, and enhancements for as long as the license remains active. The license fee also includes hosting, data storage, and data security. Via the i-Ready Diagnostic teacher and administrator dashboards and i-Ready Central support website, educators can access comprehensive user guides and downloadable lesson plans, as well as implementation tips, best practices, video tutorials, and more to supplement our live (either onsite or remotely facilitated), fee-based professional development. These resources are self-paced and available 24/7. At Curriculum Associates, we believe every student has the potential for educational excellence. That’s why we’re dedicated to creating accessible materials that maximize usability for students with disabilities. We strive to ensure that accessibility and accommodation support considerations are incorporated into our product development process from the very beginning, and we’ve developed a continuous improvement approach to accessibility that ensures we’re always improving and learning. In most cases, students who require accommodations will not require additional help to use i-Ready. The design emphasizes making necessary adjustments so that a large percentage of students requiring accommodations will be able to take the test and complete the instruction in a standard manner and the interpretation or the purpose of the test or lesson quiz is not compromised. The goal of Universal Design in such cases is to facilitate the use of the appropriate accommodations and to reduce threats to the validity and comparability of scores. Even though items on universally designed assessments and lessons will be accessible for most students, there will still be some students who continue to need accommodations. In i-Ready Diagnostic, universal accessibility features are available to all students and do not need to be enabled. Additionally, there are processes and tools in i-Ready Diagnostic that are only used to support students who have documented needs, which are usually mandated supports provided as a part of a student’s IEP, 504 plan, or EL plan. IEP teams and other educators determine which accommodations a student receives. Although Curriculum Associates provides guidance on how our products support various accommodations, educators who work with individual students determine which accommodations are needed and how to correctly implement those accommodations. To make i-Ready accessible to the widest population of students, we offer a range of accessibility supports that can also meet the requirements of a number of student accommodations. This accessibility update is designed to provide educators with information about i-Ready’s current accessibility supports, insight into our vision, and plans for future enhancements. For a current list of embedded and non-embedded student supports and accommodations, please refer to the i-Ready Accessibility and Accommodations Update: https://cdn.bfldr.com/LS6J0F7/as/r5zwr5vb7gqtstkxfgszjv/iready-accessibility-and-accommodations-update. This documentation is updated regularly to reflect our most recent i Ready accessibility developments. For more information, please also refer to the i-Ready Diagnostic Universal Accessibility Features and Accommodations Guidance resource: https://cdn.bfldr.com/LS6J0F7/at/34rrjprt98h43bnkj7qjbgf/iready-diagnostic-and-growth-monitoring-universal-accessibility-features-and-accommodations-guidance.pdf. The linked documents and resources are housed on our Accessibility & Accommodations Resource Hub (https://www.curriculumassociates.com/reviews/ireadyaccessibility), along with other helpful accessibility resources such as FAQs, feature overviews, and video demonstrations. We regularly update our documentation and resources with releases to reflect reductions in exceptions and new gains.

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:

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

45

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?

Curriculum Associates recommends giving the i-Ready Diagnostic three times a year. The i-Ready Diagnostic provides benchmark scores in the form of Grade-Level Placements (Three Grades Below, Two Grades Below, One Grade Below, Early on Grade Level, Mid on Grade Level or Above). The i-Ready Diagnostic also provides intervention tiers associated with the Grade-Level Placements.

If yes, for which months are benchmarks available?

The Grade-Level Placement benchmarks are available at the beginning of the year (typically September), middle of the year (typically January or February), and end of the year (typically April or May).

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:

4-8 hours of training

Please describe the minimum qualifications an administrator must possess.

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:

In-person (either onsite or remotely facilitated) professional development is also required and available for an additional cost.

Can users obtain ongoing professional and technical support?

Yes

If Yes, please describe how users can obtain support:

Curriculum Associates provides a dedicated Partner Success Manager plus unlimited access to in-house technical support during business hours to every customer.

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:

i-Ready Diagnostic for Mathematics also provides a Quantile score and criterion-referenced Grade-Level Placement scores.

Does your tool include decision rules?

No

If yes, please describe.

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.

i-Ready Diagnostic scale scores are linear transformations of logit values. Logits are measurement units for logarithmic probability models such as the Rasch model. Logits are used to determine both student ability and item difficulty. Within the Rasch model, if the ability matches the item difficulty, then the person has a .50 chance of answering the item correctly. For i-Ready Diagnostic, student ability and item logit values generally range from around -7 to 6. When the i-Ready vertical scale was updated in August 2016, the equipercentile equating method was applied to the updated logit scale. The appropriate scaling constant and slope were applied to the logit value to convert to scale score values between 100 and 800 (Kolen and Brennan, 2014). This scaling is accomplished by converting the estimated logit values with the following equation: Scale Value = 466.41 + 25.42 × Logit Value Once this conversion is made, floor and ceiling values are imposed to keep the scores within the 100–800 scale range. This is achieved by simply recoding all values below 100 up to 100 and all values above 800 down to 800. The scale score range, mean, and standard deviation on the updated scale are either exactly the same as (range), or very similar (mean and standard deviation) to those from the scale prior to the August 2016 scale update, which generally allows year-over-year comparisons of i-Ready scale scores.

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.

i-Ready Diagnostic’s reports describe student performance in terms of criterion-referenced scores, norm-referenced scores, and scaled scores. 5-Level Placement provides a detailed breakdown of performance for a district, school, grade, class, or student to better target student needs. With these enhanced placements, educators can maintain a consistent perspective on placement throughout the school year and better see movement between placements. Teachers have deep insights into how groups of students are progressing towards proficiency and the level of scaffolding individual students need to be successful. Teachers can see results in terms of these five levels: Level 1: Mid on-grade level or above, Level 2: Early on-grade level, Level 3: One grade level below, Level 4: Two grade levels below, and Level 5: Three or more grade levels below. Cultural and linguistic responsiveness work involves authentically representing various cultural and linguistic backgrounds, while ensuring that students can draw connections between the content and their own cultural and linguistic identities. Much of the industry’s focus and work to date on embedding cultural and linguistic responsiveness into learning tools has been to support curriculum and instruction, not large-scale assessment. We are working to address this. We believe when students see items that reflect their lived experiences, they will perform better. The assessment and research leadership teams have created a theory of action along with guidelines and criteria for what it means to address cultural and linguistic responsiveness in large-scale assessment. We have increasingly sought to support educators’ pursuit of culturally responsive teaching through the cultural and linguistic responsiveness of our products. Through ongoing partnerships with key advisors, we have conducted product reviews, undergone training, and developed guidelines and practices to incorporate cultural and linguistic responsiveness in our assessments. We have made several commitments to ensure cultural and linguistic responsiveness, diversity, and inclusion in our tests; below details this plan. New comprehensive development process: We created a comprehensive process for developing items, passages, and imagery that are representative and affirm the experiences of our student population. This includes authenticity reviews from people who identify as being part of the target group we aim to represent. This process acknowledges student differences in all aspects of the development effort. Diverse author recruiting: We are intentional about recruiting authors from diverse cultural backgrounds to write authentic cultural content. Rigorous training: We invest deeply in training our editorial team and vendors about the history, challenges, and cultural experiences of students from various backgrounds. Training educates and raises awareness so that we intentionally write and edit rich content that taps into students’ funds of knowledge. Feedback from student focus groups: We conduct focus groups with students who belong to marginalized populations to get feedback on our test design and content because the student experience is key to student engagement. New content representation targets: We are intentional about incorporating students’ sociocultural identities into our content. Thus, we develop cultural representation targets (race/ethnicity and disability) for each assessment development cycle so our tests reflect the students we serve. We seek to create content that reflects the full diversity of students’ experiences with all rings of culture, including age, language and language variants, socioeconomic status, family structure, region, and location. Revise, review, and tag legacy item bank: We reviewed assessment items and passages in our legacy bank with a cultural equity lens to ensure that we are attending to bias that unfairly advantages or disadvantages students from various cultures. Further, we have analyzed and collected information on the various representation categories to evaluate our historical item bank. We use this information to intentionally design development plans that increase diversity and are reflective of the students our products reach. Advisory panel consultation: We partner with a panel of industry experts and professors who specialize in equitable assessments to investigate ways to create valid assessments specifically designed to incorporate students’ cultures, languages, and ways of being. Multi-year research commitment: To ensure we do this without sacrificing the validity or reliability of our assessments in measuring student proficiency in reading and mathematics, we built a research agenda which includes expert review, student focus groups, and field testing of items. Curriculum Associates will continue to work diligently to ensure that students see their lived experiences in our assessments. Students who see their lived experiences reflected in their assessments are more likely to stay engaged, which allows for more accurate measurement of what they know and can do. In turn, this keeps teachers informed on the most appropriate instructional pathways for students.

Classification Accuracy & Cross-Validation Summary

Grade	Grade K	Grade 1	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 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.

The criterion measure is the Smarter Balanced Assessment (SBA) Mathematics test for grades 3-8. The SBA is an end-of-year state summative assessment administered in the spring in various states. The percentile scores defined in the Smarter Balanced 2020–2021 Summative Technical Report are used to classify students. Students who scored below the score corresponding to the 10th percentile on the SBA for the given grade were classified as at-risk and students who scored at or above the score corresponding to the 10th percentile were classified as not-at-risk. For grades K-2, the grade 3 or 4 SBA scores were used as the criterion for calculation of predictive classification accuracy, as states do not administer SBA before grade 3. As such, the criterion was administered 1-3 years after the i-Ready Diagnostic administration.

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.

For grades 3-8, the screening measure was administered at three time points in the 2021-2022 academic year: Fall (between 08/01/2021 and 11/15/2021), Winter (between 11/16/2021 and 03/01/2022), and Spring (between 03/02/2022 and 06/15/2022). The criterion measure (SBA) was administered in the Spring of 2022. The Spring i-Ready scores, taken close in time to the SBA, represent concurrent classification accuracy, while the Fall and Winter scores represent predictive classification accuracy. For grade K, the screening measure was administered at three time points in the 2018-2019 academic year: Fall (between 08/01/2018 and 11/30/2018), Winter (between 12/01/2018 and 03/15/2019), and Spring (between 03/16/2019 and 06/15/2019). The criterion measure (SBA) was administered in the Spring of 2022. Due to the difference in time (3 years or more) these analyses represent predictive classification accuracy. For grade 1, the screening measure was administered at two time points in the 2019-2020 academic year: Fall (between 08/01/2019 and 11/30/2019) and Winter (between 12/01/2019 and 03/15/2020); and one time point in the 2018-2019 academic year: Spring (between 03/16/2019 and 06/15/2019). The use of a different academic year for the Spring testing window for grade 1 was due to the disruptions caused by the COVID-19 pandemic in Spring 2020. The criterion measure (SBA) was administered in the Spring of 2022. Due to the difference in time (2 years or more) these analyses represent predictive classification accuracy. For grade 2, the screening measure was administered at one time point in the 2019-2020 academic year: Fall (between 08/01/2019 and 11/30/2019); and two time points in the 2020-2021 academic year: Winter (between 11/16/2020 and 03/01/2021) and Spring (between 03/02/2021 and 06/15/2021). The use of a different academic year for the Fall testing window for grade 2 was due to the disruptions caused by the COVID-19 pandemic in Fall 2020. The criterion measure (SBA) was administered in the Spring of 2022. Due to the difference in time (a year or more) these analyses represent predictive classification accuracy.

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).

Cut points on the criterion measure (SBA) were determined as the scale score corresponding to the 10th percentile defined in the Smarter Balanced 2020–2021 Summative Technical Report for the given subject and grade. This cut point follows the definition of students in need of intensive intervention provided by NCII’s Technical Review Committee. Students who scored below the score corresponding to the 10th percentile on the SBA for the given grade were classified as at-risk and students who scored at or above the score corresponding to the 10th percentile were classified as not-at-risk. Cut points on the screening measure (i-Ready Diagnostic) were empirically identified as scores that best align with SBA’s 10th percentile scores for each subject, grade and testing window. Using these cut scores, students were classified as at-risk if they scored below the cut score in the i-Ready Diagnostic for the given testing window, or not-at-risk if they scored at or above the cut. Classification indices between at-risk/not-at-risk on i-Ready and at-risk/not-at-risk on the SBA assessment are calculated per the formulas in the classification worksheet. For students in grades 3-8, screening scores in the Fall, Winter and Spring of the 2021-22 academic year were used for at-risk classification on the criterion measure administered in Spring 2022 at the same grade level. For students in grade K, screening scores from the 2018-19 academic year were used for at-risk classification on the criterion measure for the same students in grade 3 in Spring 2022. For students in grade 1, Fall and Winter screening scores from the 2019-20 academic year were used for at-risk classification on the criterion measure for the same students in grade 3 in Spring 2022. Additionally, Spring screening scores from the 2018-19 academic year were used for at-risk classification on the criterion measure for the same students in grade 4 in Spring 2022. For students in grade 2, Fall screening scores from the 2019-20 academic year were used for at-risk classification on the criterion measure for the same students in grade 4 in Spring 2022. Additionally, Winter and Spring screening scores from the 2020-21 academic year were used for at-risk classification on the criterion measure for the same students in grade 3 in Spring 2022.

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.

We did not collect information on whether students who were included in this analysis received an intervention in addition to typical classroom instruction between the screening measure and outcome assessment. With that said, based on the criteria for inclusion in this analysis (i.e., students scoring below the 10th percentile in spring) it is likely that students received some form of intervention.

Cross-Validation

Has a cross-validation study been conducted?

Yes

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.

The criterion measure is the Smarter Balanced Assessment (SBA) Mathematics test for grades 3-8. For grades K-2, SBA scores for the same students in 2022 were used as the criterion for calculation of predictive classification accuracy. The SBA is an end-of-year state summative assessment administered in the Spring in various states. The percentile scores defined in the Smarter Balanced 2020–21 Summative Technical Report are used to classify students. Students who scored below the score corresponding to the 10th percentile on the SBA for the given grade were classified as at-risk and students who scored at or above the score corresponding to the 10th percentile were classified as not-at-risk.

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.

For grades 3-8, the screening measure was administered at three time points in the 2021-2022 academic year: Fall (between 08/01/2021 and 11/15/2021), Winter (between 11/16/2021 and 03/01/2022), and Spring (between 03/02/2022 and 06/15/2022). The criterion measure (SBA) was administered in the Spring of 2022. The Spring i-Ready scores, taken close in time to the SBA, represent concurrent classification accuracy, while the Fall and Winter scores represent predictive classification accuracy. For grade K, the screening measure was administered at three time points in the 2018-2019 academic year: Fall (between 08/01/2018 and 11/30/2018), Winter (between 12/01/2018 and 03/15/2019), and Spring (between 03/16/2019 and 06/15/2019). The criterion measure (SBA) was administered in the Spring of 2022. Due to the difference in time (3 years or more) these analyses represent predictive classification accuracy. For grade 1, the screening measure was administered at two time points in the 2019-2020 academic year: Fall (between 08/01/2019 and 11/30/2019) and Winter (between 12/01/2019 and 03/15/2020); and one time point in the 2018-2019 academic year: Spring (between 03/16/2019 and 06/15/2019). The use of a different academic year for the Spring testing window for grade 1 was due to the disruptions caused by the COVID-19 pandemic in Spring 2020. The criterion measure (SBA) was administered in the Spring of 2022. Due to the difference in time (2 years or more) these analyses represent predictive classification accuracy. For grade 2, the screening measure was administered at one time point in the 2019-2020 academic year: Fall (between 08/01/2019 and 11/30/2019); and two time points in the 2020-2021 academic year: Winter (between 11/16/2020 and 03/01/2021) and Spring (between 03/02/2021 and 06/15/2021). The use of a different academic year for the Fall testing window for grade 2 was due to the disruptions caused by the COVID-19 pandemic in Fall 2020. The criterion measure (SBA) was administered in the Spring of 2022. Due to the difference in time (a year or more) these analyses represent predictive classification accuracy.

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).

For the cross-validation study, we used a K-fold cross-validation method by splitting the sample into K=5 parts, using 4 parts (80% of the sample) for the classification accuracy study and 1 part (20% of the sample) for cross-validation. Therefore, the timing of measure administration was the same as the main Classification Accuracy study. In order to validate our results, we used the same cut points as the main Classification Accuracy study for both the criterion measure (SBA) and screening measure (i-Ready Diagnostic) when performing the classification analyses on the cross-validation sample. Cut points on the criterion measure (SBA) were determined as the scale score corresponding to the 10th percentile defined in the Smarter Balanced 2021–22 Summative Technical Report for the given subject and grade. This cut point follows the definition of students in need of intensive intervention provided by NCII’s Technical Review Committee. Students who scored below the score corresponding to the 10th percentile on the SBA test for the given grade were classified as at-risk and students who scored at or above the score corresponding to the 10th percentile were classified as no-risk. Cut points on the screening measure (i-Ready Diagnostic) were the same scores identified as cut-points in the main Classification Accuracy study. Using these cut scores, students were classified as at-risk if they scored below the cut score in the i-Ready Diagnostic for the given testing window, or not-at-risk if they scored at or above the cut. Classification indices between at-risk/not-at-risk on i-Ready Diagnostic and at-risk/not-at-risk on the SBA are calculated per the formulas in the classification worksheet.

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.

We did not collect information on whether students who were included in this analysis received an intervention in addition to typical classroom instruction between the screening measure and outcome assessment. With that said, based on the criteria for inclusion in this analysis (i.e., students scoring below the 10th percentile in spring) it is likely that students received some form of intervention.

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Wisconsin Forward Exam

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.

The criterion measure is the Wisconsin Forward Exam (Forward Exam) Mathematics test for grade 3. The Forward Exam is an end-of-year state summative assessment administered in the spring in Wisconsin. The percentile scores defined in the Forward Exam Scale Score Percentile Table for 2024 published by the Wisconsin Department of Public Instruction at https://dpi.wi.gov/assessment/forward/data#percentile are used to classify students. Students who scored below the score corresponding to the 10th percentile on the Forward Exam for the given grade were classified as at-risk and students who scored at or above the score corresponding to the 10th percentile were classified as not-at-risk. For grade K Winter and Spring and grade 1 Fall, the grade 3 Forward Exam scores were used as the criterion for calculation of predictive classification accuracy, as the Forward Exam is not administered before grade 3. As such, the criterion was administered 2-3 years after the i-Ready Diagnostic administration.

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.

For grade K, the screening measure was administered at two time points in the 2020-21 academic year: Winter (between 11/16/2020 and 03/01/2021), and Spring (between 03/02/2021 and 06/15/2021). The criterion measure (Wisconsin Forward Exam) was administered in the Spring of 2024. Due to the difference in time (3 years or more) these analyses represent predictive classification accuracy. For grade 1, the screening measure was administered in the Fall of the 2021-22 academic year (between 08/01/2021 and 11/15/2021). The criterion measure (Wisconsin Forward Exam) was administered in the Spring of 2024. Due to the difference in time (2 years or more) these analyses represent predictive classification accuracy.

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).

Cut points on the criterion measure (Forward Exam) were determined as the scale score corresponding to the 10th percentile defined in the Forward Exam Scale Score Percentile Table for 2024 for the given subject and grade. This cut point follows the definition of students in need of intensive intervention provided by NCII’s Technical Review Committee. Students who scored below the score corresponding to the 10th percentile on the Forward Exam for the given grade were classified as at-risk and students who scored at or above the score corresponding to the 10th percentile were classified as not-at-risk. Cut points on the screening measure (i-Ready Diagnostic) were empirically identified as scores that best align with Forward Exam’s 10th percentile scores for each subject, grade and testing window. Using these cut scores, students were classified as at-risk if they scored below the cut score in the i-Ready Diagnostic for the given testing window, or not-at-risk if they scored at or above the cut. Classification indices between at-risk/not-at-risk on i-Ready and at-risk/not-at-risk on the Forward Exam are calculated per the formulas in the classification worksheet. For students in grade K, screening scores from the 2020-21 academic year were used for at-risk classification on the criterion measure for the same students in grade 3 in Spring 2024. For students in grade 1, screening scores from the 2021-22 academic year were used for at-risk classification on the criterion measure for the same students in grade 3 in Spring 2024.

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.

We did not collect information on whether students who were included in this analysis received an intervention in addition to typical classroom instruction between the screening measure and outcome assessment. With that said, based on the criteria for inclusion in this analysis (i.e., students scoring below the 10th percentile in spring) it is likely that students received some form of intervention.

Cross-Validation

Has a cross-validation study been conducted?

Yes

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.

The criterion measure is the Wisconsin Forward Exam (Forward Exam) Mathematics test for grade 3. For grades K-1, Forward Exam scores for the same students in 2024 were used as the criterion for calculation of predictive classification accuracy. The Forward Exam is an end-of-year state summative assessment administered in the spring in Wisconsin. The percentile scores defined in the Forward Exam Scale Score Percentile Table for 2024 published by the Wisconsin Department of Public Instruction at https://dpi.wi.gov/assessment/forward/data#percentile are used to classify students. Students who scored below the score corresponding to the 10th percentile on the Forward Exam for the given grade were classified as at-risk and students who scored at or above the score corresponding to the 10th percentile were classified as not-at-risk.

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.

For grade K, the screening measure was administered at two time points in the 2020-2021 academic year: Winter (between 11/16/2020 and 03/01/2021), and Spring (between 03/02/2021 and 06/15/2021). The criterion measure (Wisconsin Forward Exam) was administered in the Spring of 2024. Due to the difference in time (3 years or more) these analyses represent predictive classification accuracy. For grade 1, the screening measure was administered in the Fall of the 2021-2022 academic year (between 08/01/2021 and 11/15/2021). The criterion measure (Wisconsin Forward Exam) was administered in the Spring of 2024. Due to the difference in time (2 years or more) these analyses represent predictive classification accuracy.

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).

For the cross-validation study, we used a K-fold cross-validation method by splitting the sample into K=5 parts, using 4 parts (80% of the sample) for the classification accuracy study and 1 part (20% of the sample) for cross-validation. Therefore, the timing of measure administration was the same as the main Classification Accuracy study. In order to validate our results, we used the same cut points as the main Classification Accuracy study for both the criterion measure (Forward Exam) and screening measure (i-Ready Diagnostic) when performing the classification analyses on the cross-validation sample. Cut points on the criterion measure (Forward Exam) were determined as the scale score corresponding to the 10th percentile defined in the Forward Exam Scale Score Percentile Table for 2024 for the given subject and grade. This cut point follows the definition of students in need of intensive intervention provided by NCII’s Technical Review Committee. Students who scored below the score corresponding to the 10th percentile on the Forward Exam for the given grade were classified as at-risk and students who scored at or above the score corresponding to the 10th percentile were classified as no-risk. Cut points on the screening measure (i-Ready Diagnostic) were the same scores identified as cut-points in the main Classification Accuracy study. Using these cut scores, students were classified as at-risk if they scored below the cut score in the i-Ready Diagnostic for the given testing window, or not-at-risk if they scored at or above the cut. Classification indices between at-risk/not-at-risk on i-Ready Diagnostic and at-risk/not-at-risk on the Forward Exam are calculated per the formulas in the classification worksheet.

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.

We did not collect information on whether students who were included in this analysis received an intervention in addition to typical classroom instruction between the screening measure and outcome assessment. With that said, based on the criteria for inclusion in this analysis (i.e., students scoring below the 10th percentile in spring) it is likely that students received some form of intervention.

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Classification Accuracy - Fall

Evidence	Grade K	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Criterion measure	Smarter Balanced Assessment	Wisconsin Forward Exam	Smarter Balanced Assessment	Smarter Balanced Assessment	Smarter Balanced Assessment	Smarter Balanced Assessment	Smarter Balanced Assessment	Smarter Balanced Assessment	Smarter Balanced Assessment
Cut Points - Percentile rank on criterion measure	10	10	10	10	10	10	10	10	10
Cut Points - Performance score on criterion measure	2292	1479	2334	2292	2334	2351	2348	2362	2360
Cut Points - Corresponding performance score (numeric) on screener measure	333	359	386	394	414	434	445	456	465
Classification Data - True Positive (a)	127	27	795	2074	2206	2312	2343	2429	2204
Classification Data - False Positive (b)	731	114	2605	4376	4403	5076	4505	4545	5119
Classification Data - False Negative (c)	43	6	187	368	370	388	382	495	505
Classification Data - True Negative (d)	2191	523	11667	27127	27877	27775	27018	23667	23995
Area Under the Curve (AUC)	0.83	0.90	0.89	0.93	0.94	0.93	0.94	0.92	0.90
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.80	0.85	0.88	0.93	0.93	0.93	0.93	0.91	0.90
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.86	0.95	0.90	0.94	0.94	0.93	0.94	0.92	0.91

Statistics	Grade K	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Base Rate	0.05	0.05	0.06	0.07	0.07	0.08	0.08	0.09	0.09
Overall Classification Rate	0.75	0.82	0.82	0.86	0.86	0.85	0.86	0.84	0.82
Sensitivity	0.75	0.82	0.81	0.85	0.86	0.86	0.86	0.83	0.81
Specificity	0.75	0.82	0.82	0.86	0.86	0.85	0.86	0.84	0.82
False Positive Rate	0.25	0.18	0.18	0.14	0.14	0.15	0.14	0.16	0.18
False Negative Rate	0.25	0.18	0.19	0.15	0.14	0.14	0.14	0.17	0.19
Positive Predictive Power	0.15	0.19	0.23	0.32	0.33	0.31	0.34	0.35	0.30
Negative Predictive Power	0.98	0.99	0.98	0.99	0.99	0.99	0.99	0.98	0.98

Sample	Grade K	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Date	Spring 2022	Spring 2024	Spring 2022	Spring 2022	Spring 2022	Spring 2022	Spring 2022	Spring 2022	Spring 2022
Sample Size	3092	670	15254	33945	34856	35551	34248	31136	31823
Geographic Representation	New England (CT) Pacific (CA, WA) South Atlantic (DE)	East North Central (WI)	New England (CT) Pacific (CA, WA) South Atlantic (DE)	New England (CT) Pacific (CA, OR, WA) South Atlantic (DE)	New England (CT) Pacific (CA, OR, WA) South Atlantic (DE)	New England (CT) Pacific (CA, OR, WA) South Atlantic (DE)	New England (CT) Pacific (CA, OR, WA) South Atlantic (DE)	New England (CT) Pacific (CA, OR, WA) South Atlantic (DE)	New England (CT) Pacific (CA, OR, WA) South Atlantic (DE)
Male	49.8%	51.9%	50.7%	50.5%	50.7%	50.7%	50.6%	51.4%	51.2%
Female	50.2%	48.1%	49.3%	49.5%	49.3%	49.2%	49.3%	48.5%	48.6%
Other
Gender Unknown			0.0%	0.0%	0.0%	0.0%	0.1%	0.1%	0.1%
White, Non-Hispanic	20.8%	83.9%	28.5%	24.8%	24.7%	25.0%	23.0%	23.3%	23.1%
Black, Non-Hispanic	4.6%	0.6%	5.0%	5.0%	4.9%	5.3%	5.3%	5.5%	5.5%
Hispanic	52.1%	10.0%	40.7%	46.4%	47.1%	47.2%	50.4%	50.1%	51.2%
Asian/Pacific Islander	16.5%	0.7%	14.1%	12.2%	12.0%	11.7%	10.9%	10.2%	10.4%
American Indian/Alaska Native	0.2%		0.2%	0.3%	0.2%	0.2%	0.3%	0.3%	0.2%
Other	4.3%	2.5%	6.4%	6.1%	6.0%	5.9%	5.2%	5.2%	5.1%
Race / Ethnicity Unknown	1.5%	2.2%	5.1%	5.2%	5.1%	4.8%	4.9%	5.5%	4.6%
Low SES
IEP or diagnosed disability
English Language Learner

Classification Accuracy - Winter

Evidence	Grade K	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Criterion measure	Wisconsin Forward Exam	Smarter Balanced Assessment	Smarter Balanced Assessment	Smarter Balanced Assessment	Smarter Balanced Assessment	Smarter Balanced Assessment	Smarter Balanced Assessment	Smarter Balanced Assessment	Smarter Balanced Assessment
Cut Points - Percentile rank on criterion measure	10	10	10	10	10	10	10	10	10
Cut Points - Performance score on criterion measure	1479	2292	2292	2292	2334	2351	2348	2362	2360
Cut Points - Corresponding performance score (numeric) on screener measure	348	376	388	407	426	442	450	461	472
Classification Data - True Positive (a)	10	682	275	2131	2263	2326	2286	2303	2073
Classification Data - False Positive (b)	70	2326	812	3628	4000	4501	3835	4078	4786
Classification Data - False Negative (c)	2	154	50	302	318	369	356	415	419
Classification Data - True Negative (d)	340	10046	4493	27882	28000	27900	26063	22940	22855
Area Under the Curve (AUC)	0.92	0.90	0.92	0.95	0.95	0.94	0.94	0.93	0.91
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.86	0.89	0.91	0.95	0.94	0.93	0.94	0.92	0.91
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.97	0.90	0.93	0.95	0.95	0.94	0.95	0.93	0.91

Statistics	Grade K	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Base Rate	0.03	0.06	0.06	0.07	0.07	0.08	0.08	0.09	0.08
Overall Classification Rate	0.83	0.81	0.85	0.88	0.88	0.86	0.87	0.85	0.83
Sensitivity	0.83	0.82	0.85	0.88	0.88	0.86	0.87	0.85	0.83
Specificity	0.83	0.81	0.85	0.88	0.88	0.86	0.87	0.85	0.83
False Positive Rate	0.17	0.19	0.15	0.12	0.13	0.14	0.13	0.15	0.17
False Negative Rate	0.17	0.18	0.15	0.12	0.12	0.14	0.13	0.15	0.17
Positive Predictive Power	0.13	0.23	0.25	0.37	0.36	0.34	0.37	0.36	0.30
Negative Predictive Power	0.99	0.98	0.99	0.99	0.99	0.99	0.99	0.98	0.98

Sample	Grade K	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Date	Spring 2024	Spring 2022	Spring 2022	Spring 2022	Spring 2022	Spring 2022	Spring 2022	Spring 2022	Spring 2022
Sample Size	422	13208	5630	33943	34581	35096	32540	29736	30133
Geographic Representation	East North Central (WI)	New England (CT) Pacific (CA, WA) South Atlantic (DE)	New England (CT) Pacific (CA, OR, WA) South Atlantic (DE)	New England (CT) Pacific (CA, OR, WA) South Atlantic (DE)	New England (CT) Pacific (CA, OR, WA) South Atlantic (DE)	New England (CT) Pacific (CA, OR, WA) South Atlantic (DE)	New England (CT) Pacific (CA, OR, WA) South Atlantic (DE)	New England (CT) Pacific (CA, OR, WA) South Atlantic (DE)	New England (CT) Pacific (CA, OR, WA) South Atlantic (DE)
Male	52.6%	51.1%	51.9%	50.7%	50.9%	51.0%	50.6%	51.4%	50.9%
Female	47.4%	48.9%	48.1%	49.2%	49.1%	48.9%	49.3%	48.5%	49.0%
Other
Gender Unknown		0.0%	0.0%	0.0%	0.0%	0.0%	0.1%	0.1%	0.1%
White, Non-Hispanic	89.3%	30.9%	38.8%	24.7%	24.8%	25.3%	22.4%	22.5%	22.8%
Black, Non-Hispanic	0.7%	4.9%	3.5%	4.9%	5.0%	5.5%	5.2%	5.3%	5.3%
Hispanic	7.6%	37.3%	39.8%	46.7%	47.0%	47.0%	51.6%	50.9%	51.7%
Asian/Pacific Islander	0.7%	13.7%	9.1%	12.0%	11.8%	11.2%	10.5%	10.5%	10.5%
American Indian/Alaska Native		0.2%	0.2%	0.3%	0.2%	0.2%	0.2%	0.2%	0.2%
Other	1.7%	7.0%	5.3%	6.0%	6.0%	5.9%	4.7%	4.9%	4.8%
Race / Ethnicity Unknown		5.9%	3.2%	5.4%	5.3%	4.9%	5.3%	5.8%	4.8%
Low SES
IEP or diagnosed disability
English Language Learner

Classification Accuracy - Spring

Evidence	Grade K	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Criterion measure	Wisconsin Forward Exam	Smarter Balanced Assessment	Smarter Balanced Assessment	Smarter Balanced Assessment	Smarter Balanced Assessment	Smarter Balanced Assessment	Smarter Balanced Assessment	Smarter Balanced Assessment	Smarter Balanced Assessment
Cut Points - Percentile rank on criterion measure	10	10	10	10	10	10	10	10	10
Cut Points - Performance score on criterion measure	1479	2334	2292	2292	2334	2351	2348	2362	2360
Cut Points - Corresponding performance score (numeric) on screener measure	365	391	396	417	433	447	453	461	471
Classification Data - True Positive (a)	27	453	841	2346	2368	2420	2391	2334	2061
Classification Data - False Positive (b)	109	1398	2036	3430	3654	4348	3814	3807	4314
Classification Data - False Negative (c)	5	105	137	284	336	348	353	393	395
Classification Data - True Negative (d)	432	6122	11524	28945	29178	28755	26998	22560	21889
Area Under the Curve (AUC)	0.86	0.88	0.93	0.96	0.95	0.94	0.95	0.93	0.91
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.80	0.87	0.92	0.96	0.95	0.94	0.94	0.92	0.91
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.92	0.90	0.93	0.96	0.96	0.95	0.95	0.93	0.92

Statistics	Grade K	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Base Rate	0.06	0.07	0.07	0.08	0.08	0.08	0.08	0.09	0.09
Overall Classification Rate	0.80	0.81	0.85	0.89	0.89	0.87	0.88	0.86	0.84
Sensitivity	0.84	0.81	0.86	0.89	0.88	0.87	0.87	0.86	0.84
Specificity	0.80	0.81	0.85	0.89	0.89	0.87	0.88	0.86	0.84
False Positive Rate	0.20	0.19	0.15	0.11	0.11	0.13	0.12	0.14	0.16
False Negative Rate	0.16	0.19	0.14	0.11	0.12	0.13	0.13	0.14	0.16
Positive Predictive Power	0.20	0.24	0.29	0.41	0.39	0.36	0.39	0.38	0.32
Negative Predictive Power	0.99	0.98	0.99	0.99	0.99	0.99	0.99	0.98	0.98

Sample	Grade K	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Date	Spring 2024	Spring 2022	Spring 2022	Spring 2022	Spring 2022	Spring 2022	Spring 2022	Spring 2022	Spring 2022
Sample Size	573	8078	14538	35005	35536	35871	33556	29094	28659
Geographic Representation	East North Central (WI)	New England (CT) Pacific (CA, WA) South Atlantic (DE)	New England (CT) Pacific (CA, OR, WA) South Atlantic (DE)	New England (CT) Pacific (CA, OR, WA) South Atlantic (DE)	New England (CT) Pacific (CA, OR, WA) South Atlantic (DE)	New England (CT) Pacific (CA, OR, WA) South Atlantic (DE)	New England (CT) Pacific (CA, WA) South Atlantic (DE)	New England (CT) Pacific (CA, WA) South Atlantic (DE)	New England (CT) Pacific (CA, WA) South Atlantic (DE)
Male	51.1%	49.9%	50.8%	50.6%	50.8%	50.9%	50.5%	51.0%	51.1%
Female	48.9%	50.1%	49.2%	49.3%	49.1%	49.1%	49.5%	48.8%	48.8%
Other
Gender Unknown			0.0%	0.0%	0.0%	0.0%	0.1%	0.1%	0.1%
White, Non-Hispanic	82.4%	25.6%	33.3%	24.7%	24.9%	25.0%	22.0%	22.7%	23.2%
Black, Non-Hispanic	0.5%	6.4%	4.3%	5.1%	5.1%	5.5%	5.6%	5.9%	6.0%
Hispanic	10.5%	41.6%	42.0%	46.3%	46.5%	46.9%	50.8%	50.3%	50.1%
Asian/Pacific Islander	1.2%	13.8%	9.4%	12.4%	12.2%	11.8%	11.2%	10.1%	10.5%
American Indian/Alaska Native		0.3%	0.3%	0.3%	0.3%	0.2%	0.3%	0.2%	0.2%
Other	2.8%	6.8%	6.4%	6.0%	5.9%	5.8%	5.0%	5.1%	5.3%
Race / Ethnicity Unknown	2.6%	5.5%	4.3%	5.3%	5.2%	4.8%	5.1%	5.6%	4.7%
Low SES
IEP or diagnosed disability
English Language Learner

Cross-Validation - Fall

Evidence	Grade K	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Criterion measure	Smarter Balanced Assessment	Wisconsin Forward Exam	Smarter Balanced Assessment	Smarter Balanced Assessment	Smarter Balanced Assessment	Smarter Balanced Assessment	Smarter Balanced Assessment	Smarter Balanced Assessment	Smarter Balanced Assessment
Cut Points - Percentile rank on criterion measure	10	10	10	10	10	10	10	10	10
Cut Points - Performance score on criterion measure	2292	1479	2334	2292	2334	2351	2348	2362	2360
Cut Points - Corresponding performance score (numeric) on screener measure	333	359	386	394	414	434	445	456	465
Classification Data - True Positive (a)	38	9	189	525	554	530	603	571	554
Classification Data - False Positive (b)	181	31	667	994	1121	1261	1086	1135	1263
Classification Data - False Negative (c)	14	4	41	92	97	92	104	101	127
Classification Data - True Negative (d)	539	179	2916	6875	6941	7004	6768	5977	6011
Area Under the Curve (AUC)	0.81	0.86	0.89	0.94	0.93	0.92	0.93	0.92	0.90
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.76	0.74	0.87	0.93	0.93	0.92	0.93	0.91	0.89
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.87	0.99	0.91	0.95	0.94	0.93	0.94	0.93	0.91

Statistics	Grade K	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Base Rate	0.07	0.06	0.06	0.07	0.07	0.07	0.08	0.09	0.09
Overall Classification Rate	0.75	0.84	0.81	0.87	0.86	0.85	0.86	0.84	0.83
Sensitivity	0.73	0.69	0.82	0.85	0.85	0.85	0.85	0.85	0.81
Specificity	0.75	0.85	0.81	0.87	0.86	0.85	0.86	0.84	0.83
False Positive Rate	0.25	0.15	0.19	0.13	0.14	0.15	0.14	0.16	0.17
False Negative Rate	0.27	0.31	0.18	0.15	0.15	0.15	0.15	0.15	0.19
Positive Predictive Power	0.17	0.23	0.22	0.35	0.33	0.30	0.36	0.33	0.30
Negative Predictive Power	0.97	0.98	0.99	0.99	0.99	0.99	0.98	0.98	0.98

Sample	Grade K	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Date	Spring 2022	Spring 2024	Spring 2022	Spring 2022	Spring 2022	Spring 2022	Spring 2022	Spring 2022	Spring 2022
Sample Size	772	223	3813	8486	8713	8887	8561	7784	7955
Geographic Representation	New England (CT) Pacific (CA, WA) South Atlantic (DE)	East North Central (WI)	New England (CT) Pacific (CA, WA) South Atlantic (DE)	New England (CT) Pacific (CA, OR, WA) South Atlantic (DE)	New England (CT) Pacific (CA, OR, WA) South Atlantic (DE)	New England (CT) Pacific (CA, OR, WA) South Atlantic (DE)	New England (CT) Pacific (CA, OR, WA) South Atlantic (DE)	New England (CT) Pacific (CA, OR, WA) South Atlantic (DE)	New England (CT) Pacific (CA, OR, WA) South Atlantic (DE)
Male	46.0%	50.2%	51.8%	50.1%	51.3%	51.2%	50.2%	51.8%	51.5%
Female	53.9%	49.8%	48.2%	49.8%	48.7%	48.8%	49.6%	48.1%	48.4%
Other
Gender Unknown	0.1%			0.0%	0.1%	0.0%	0.1%	0.1%	0.1%
White, Non-Hispanic	20.5%	81.6%	29.0%	25.5%	24.6%	25.1%	22.2%	22.5%	22.6%
Black, Non-Hispanic	4.4%	1.3%	4.6%	4.9%	5.1%	5.2%	5.3%	5.6%	5.5%
Hispanic	54.5%	12.6%	39.5%	45.6%	47.0%	47.2%	50.6%	50.6%	51.0%
Asian/Pacific Islander	13.5%	1.3%	14.9%	12.5%	12.1%	11.3%	11.5%	10.6%	10.5%
American Indian/Alaska Native	0.5%		0.2%	0.3%	0.3%	0.2%	0.2%	0.2%	0.3%
Other	5.2%	3.1%	6.7%	5.8%	5.6%	5.9%	5.1%	5.0%	5.3%
Race / Ethnicity Unknown	1.4%		5.1%	5.3%	5.2%	4.9%	5.2%	5.5%	4.7%
Low SES
IEP or diagnosed disability
English Language Learner

Cross-Validation - Winter

Evidence	Grade K	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Criterion measure	Wisconsin Forward Exam	Smarter Balanced Assessment	Smarter Balanced Assessment	Smarter Balanced Assessment	Smarter Balanced Assessment	Smarter Balanced Assessment	Smarter Balanced Assessment	Smarter Balanced Assessment	Smarter Balanced Assessment
Cut Points - Percentile rank on criterion measure	10	10	10	10	10	10	10	10	10
Cut Points - Performance score on criterion measure	1479	2292	2292	2292	2334	2351	2348	2362	2360
Cut Points - Corresponding performance score (numeric) on screener measure	348	376	388	407	426	442	450	461	472
Classification Data - True Positive (a)	6	172	70	568	558	532	569	600	579
Classification Data - False Positive (b)	21	539	193	931	998	1154	967	985	1149
Classification Data - False Negative (c)	1	33	14	80	89	98	69	87	109
Classification Data - True Negative (d)	112	2558	1130	6906	7000	6989	6530	5761	5696
Area Under the Curve (AUC)	0.85	0.90	0.92	0.95	0.95	0.93	0.95	0.93	0.92
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.67	0.88	0.90	0.95	0.94	0.92	0.94	0.93	0.91
AUC Estimate’s 95% Confidence Interval: Upper Bound	1.00	0.92	0.95	0.96	0.95	0.94	0.95	0.94	0.93

Statistics	Grade K	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Base Rate	0.05	0.06	0.06	0.08	0.07	0.07	0.08	0.09	0.09
Overall Classification Rate	0.84	0.83	0.85	0.88	0.87	0.86	0.87	0.86	0.83
Sensitivity	0.86	0.84	0.83	0.88	0.86	0.84	0.89	0.87	0.84
Specificity	0.84	0.83	0.85	0.88	0.88	0.86	0.87	0.85	0.83
False Positive Rate	0.16	0.17	0.15	0.12	0.12	0.14	0.13	0.15	0.17
False Negative Rate	0.14	0.16	0.17	0.12	0.14	0.16	0.11	0.13	0.16
Positive Predictive Power	0.22	0.24	0.27	0.38	0.36	0.32	0.37	0.38	0.34
Negative Predictive Power	0.99	0.99	0.99	0.99	0.99	0.99	0.99	0.99	0.98

Sample	Grade K	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Date	Spring 2024	Spring 2022	Spring 2022	Spring 2022	Spring 2022	Spring 2022	Spring 2022	Spring 2022	Spring 2022
Sample Size	140	3302	1407	8485	8645	8773	8135	7433	7533
Geographic Representation	East North Central (WI)	New England (CT) Pacific (CA, WA) South Atlantic (DE)	New England (CT) Pacific (CA, OR, WA) South Atlantic (DE)	New England (CT) Pacific (CA, OR, WA) South Atlantic (DE)	New England (CT) Pacific (CA, OR, WA) South Atlantic (DE)	New England (CT) Pacific (CA, OR, WA) South Atlantic (DE)	New England (CT) Pacific (CA, OR, WA) South Atlantic (DE)	New England (CT) Pacific (CA, OR, WA) South Atlantic (DE)	New England (CT) Pacific (CA, OR, WA) South Atlantic (DE)
Male	52.1%	49.3%	52.5%	49.4%	50.6%	50.0%	50.2%	51.4%	51.7%
Female	47.9%	50.6%	47.5%	50.6%	49.3%	49.9%	49.7%	48.5%	48.2%
Other
Gender Unknown		0.0%		0.0%	0.0%	0.1%	0.1%	0.1%	0.1%
White, Non-Hispanic	90.7%	30.8%	37.4%	24.4%	24.7%	24.0%	21.6%	22.2%	22.1%
Black, Non-Hispanic		5.1%	3.8%	5.3%	5.4%	5.3%	5.4%	5.2%	5.4%
Hispanic	7.9%	36.0%	41.7%	45.8%	46.4%	48.5%	52.1%	52.0%	52.5%
Asian/Pacific Islander		14.4%	9.3%	12.8%	12.5%	11.3%	10.8%	10.1%	10.1%
American Indian/Alaska Native		0.2%	0.2%	0.2%	0.2%	0.2%	0.3%	0.2%	0.3%
Other	1.4%	7.1%	4.3%	6.1%	5.8%	6.0%	4.6%	4.5%	4.8%
Race / Ethnicity Unknown		6.3%	3.3%	5.4%	5.0%	4.7%	5.2%	5.8%	4.8%
Low SES
IEP or diagnosed disability
English Language Learner

Cross-Validation - Spring

Evidence	Grade K	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Criterion measure	Wisconsin Forward Exam	Smarter Balanced Assessment	Smarter Balanced Assessment	Smarter Balanced Assessment	Smarter Balanced Assessment	Smarter Balanced Assessment	Smarter Balanced Assessment	Smarter Balanced Assessment	Smarter Balanced Assessment
Cut Points - Percentile rank on criterion measure	10	10	10	10	10	10	10	10	10
Cut Points - Performance score on criterion measure	1479	2334	2292	2292	2334	2351	2348	2362	2360
Cut Points - Corresponding performance score (numeric) on screener measure	365	391	396	417	433	447	453	461	471
Classification Data - True Positive (a)	8	105	202	545	605	550	624	563	532
Classification Data - False Positive (b)	32	366	539	872	924	1068	908	952	1056
Classification Data - False Negative (c)	1	22	25	88	68	79	72	88	114
Classification Data - True Negative (d)	150	1526	2868	7246	7287	7270	6785	5670	5462
Area Under the Curve (AUC)	0.87	0.89	0.93	0.95	0.96	0.94	0.95	0.93	0.91
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.80	0.86	0.91	0.95	0.95	0.94	0.95	0.92	0.90
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.94	0.92	0.94	0.96	0.96	0.95	0.96	0.93	0.92

Statistics	Grade K	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Base Rate	0.05	0.06	0.06	0.07	0.08	0.07	0.08	0.09	0.09
Overall Classification Rate	0.83	0.81	0.84	0.89	0.89	0.87	0.88	0.86	0.84
Sensitivity	0.89	0.83	0.89	0.86	0.90	0.87	0.90	0.86	0.82
Specificity	0.82	0.81	0.84	0.89	0.89	0.87	0.88	0.86	0.84
False Positive Rate	0.18	0.19	0.16	0.11	0.11	0.13	0.12	0.14	0.16
False Negative Rate	0.11	0.17	0.11	0.14	0.10	0.13	0.10	0.14	0.18
Positive Predictive Power	0.20	0.22	0.27	0.38	0.40	0.34	0.41	0.37	0.34
Negative Predictive Power	0.99	0.99	0.99	0.99	0.99	0.99	0.99	0.98	0.98

Sample	Grade K	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Date	Spring 2024	Spring 2022	Spring 2022	Spring 2022	Spring 2022	Spring 2022	Spring 2022	Spring 2022	Spring 2022
Sample Size	191	2019	3634	8751	8884	8967	8389	7273	7164
Geographic Representation	East North Central (WI)	New England (CT) Pacific (CA, WA) South Atlantic (DE)	New England (CT) Pacific (CA, OR, WA) South Atlantic (DE)	New England (CT) Pacific (CA, OR, WA) South Atlantic (DE)	New England (CT) Pacific (CA, OR, WA) South Atlantic (DE)	New England (CT) Pacific (CA, OR, WA) South Atlantic (DE)	New England (CT) Pacific (CA, WA) South Atlantic (DE)	New England (CT) Pacific (CA, WA) South Atlantic (DE)	New England (CT) Pacific (CA, WA) South Atlantic (DE)
Male	53.9%	51.6%	50.7%	49.4%	50.9%	50.3%	50.6%	52.1%	51.3%
Female	46.1%	48.4%	49.3%	50.6%	49.1%	49.6%	49.3%	47.8%	48.6%
Other
Gender Unknown			0.1%	0.0%		0.1%	0.1%	0.1%	0.1%
White, Non-Hispanic	82.2%	27.2%	35.0%	24.8%	24.6%	24.6%	22.5%	22.8%	22.6%
Black, Non-Hispanic	1.0%	5.7%	4.0%	5.3%	5.0%	5.7%	5.4%	6.3%	6.3%
Hispanic	13.1%	41.7%	40.3%	45.9%	47.1%	46.2%	51.4%	49.7%	50.8%
Asian/Pacific Islander	1.0%	13.3%	9.7%	12.2%	12.0%	12.3%	10.8%	10.3%	10.4%
American Indian/Alaska Native		0.2%	0.3%	0.2%	0.2%	0.3%	0.3%	0.3%	0.3%
Other	1.6%	6.6%	6.6%	6.1%	5.9%	6.4%	4.9%	4.6%	4.9%
Race / Ethnicity Unknown	1.0%	5.3%	4.2%	5.4%	5.2%	4.6%	4.6%	5.9%	4.7%
Low SES
IEP or diagnosed disability
English Language Learner

Reliability

Grade	Grade K	Grade 1	Grade 2	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 i-Ready Diagnostic for Mathematics provides IRT-based reliability measures such as the marginal reliability estimate and standard error of measurement. Marginal Reliability: Given that the i-Ready Diagnostic is a computer-adaptive assessment that does not have a fixed form, some traditional reliability estimates such as Cronbach’s alpha are not an appropriate index for quantifying consistency or inconsistency in student performance. The IRT analogue to classical reliability is called marginal reliability, and operates on the variance of the theta scores and the average of the expected error variance. The marginal reliability uses the classical definition of reliability as proportion of variance in the total observed score due to true score under an IRT model (the i-Ready Diagnostic uses a Rasch model to be specific). Standard Error of Measurement (SEM): In an IRT model, SEMs are affected by factors such as how well the data fit the underlying model, student response consistency, student location on the ability continuum, match of items to student ability, and test length. Given the adaptive nature of i-Ready and the wide difficulty range in the item bank, standard errors are expected to be low and very close to the theoretical minimum for the test of the given length. The theoretical minimum would be reached if each interim estimate of student ability is assessed by an item with difficulty matching perfectly to the student’s ability estimated from previous items. Theoretical minimums are restricted by the number of items served in the assessment—the more items that are served up, the lower the SEM could potentially be. For mathematics, the minimum SEM for overall scores is 6.0.

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

Data for obtaining the marginal reliability and SEM was from the August and September administrations of the i-Ready Diagnostic from 2016 (reported in the 2016 i-Ready Diagnostic technical report). All students tested within the time-frame were included. Sample sizes by grade are presented in the table below.

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

This marginal reliability uses the classical definition of reliability as proportion of variance in the total observed score due to true score. The true score variance is computed as the observed score variance minus the error variance (see equation below). ρ_θ=(σ_(θ-)^2 σ ̅_E^2)/(σ_θ^2 ) where ρθ is the marginal reliability estimate, σ2θ is the observed error variance of the ability estimate, σ ̅_E^2is the observed average conditional error variance. Similar to a classical reliability coefficient, the marginal reliability estimate increases as the standard error decreases; it approaches 1 when the standard error approaches 0. The observed score variance, the error variance, and SEM (the square root of the error variance) are obtained through WINSTEPS calibrations. One separate calibration was conducted for each grade.

*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 K	Grade 1	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.

The following criterion measures are external to the i-Ready screening tool and include widely used assessments of mathematics ability. 1) The Smarter Balanced Assessment (SBA) for Mathematics is a summative, standards-aligned assessment administered in the Spring and used in various states to provide information about students’ Mathematics achievement and to support effective teaching and learning. 2) The Mathematics Alabama Comprehensive Assessment Program (ACAP) summative assessment is a computer-based, criterion-referenced assessment, administered in the Spring and designed to measure student progress on the Alabama Courses of Study Standards in Mathematics. 3) The Massachusetts Comprehensive Assessment System (MCAS) Mathematics is a statewide standards-based summative assessment in English language arts administered in the Spring and developed to help parents, students, educators, and policymakers determine where districts, schools, and students are meeting expectations and where they need additional support. Validity evidence for the i-Ready Diagnostic at grades K-2 also comes from the degree and stability of the relationship of scale scores across extended periods of time, such as across school years. This type of evidence supports the construct validity of i-Ready Diagnostic via the stability of the vertical scale. Therefore, the criterion measure for those analyses are scores in the i-Ready Diagnostic administered to the same sample of students in subsequent academic years.

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

For the concurrent validity analyses, the sample includes students taking both the screening and the criterion measure in the Spring testing window. Sample sizes varied from 2,776 to 14,755 and included students at all performance levels. For predictive validity analyses in grades K-2 using the SBA as the criterion, the sample includes students taking the screening measure between 12 to 36 months prior to the criterion measure. Sample sizes varied from 8,429 to 18,172 and included students at all performance levels. For the predictive validity analyses in grades 3-8, the sample includes students taking both the screening and the criterion measure in different testing windows within the same academic year, with the screening measure being administered in the Fall and the criterion measure in the following Spring. Sample sizes varied from 38,920 to 44,438 and included students at all performance levels. For construct validity evidence in grades K-2, the sample comes from the population of students taking the screening measure across different academic years. Sample sizes varied from 97,301 to 347,380 and included students at all performance levels.

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

For both concurrent and predictive analyses, Pearson correlation coefficients between the screening and criterion measures were calculated and the 95% confidence interval around the Pearson r correlation coefficient was computed using Fisher r-to-z transformation. For grades K-2, scores in the screening measure administered in the Spring were correlated to scores on the criterion measure for the same students in subsequent academic years. Specifically, screening scores in grades K and 2 were correlated to criterion scores in grade 3, and screening scores in grade 1 were correlated to criterion scores in grade 4. For grades 3-8, scores on the screening measure administered in the Fall (predictive) and Spring (concurrent) were correlated to scores on the criterion measure administered in the Spring of the same academic year. For construct validity evidence in grades K-2, evidence comes from the degree and stability of the relationship of scale scores in the screening measure across extended periods of time, such as across school years. This type of evidence supports the construct validity of the i-Ready Diagnostic via the stability of the vertical scale. Scores in grades K on the screening measure administered in Spring were correlated with scores from the same students in the same testing window in grade 2. Scores in grades 1 and 2 on the screening measure administered in Spring were correlated with scores from the same students in the same testing window in grade 3. Fisher r-to-z transformations were conducted to standardize the correlations and the 95% confidence interval around the correlations was computed.

*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.

Predictive validity coefficients for grades K-2 were positive and significant, ranging from .65 to .80. Given that the screening measure for these analyses were administered 12 to 36 months prior to the criterion measure, these correlation coefficients are predictably lower than measures taken closer in time. However, these validity coefficients still exceed the minimum threshold of 0.60, indicating a robust positive relationship between the screening measure and high-stakes statewide assessments, even across an extended period of time. Concurrent and predictive validity coefficients for grades 3–8 were mostly in the .80’s, suggesting a strong relationship between i-Ready Diagnostic and the criterion state summative assessments in the same subject and grade. Construct validity coefficients for grades K–2 for screening scores across different school years ranged from .62 to .79, suggesting a medium to strong positive relationship of i-Ready Diagnostic Mathematics scores across school years. This type of evidence supports the construct validity of the i-Ready Diagnostic via the stability of the vertical scale. Given that grades K-2 scores were correlated with grades 2 and 3 scores in subsequent school years, the increasing correlation coefficients across grades appropriately reflect the effect of time between the predictor and criterion scores, and allows for learning and interventions to have a cumulative impact on student proficiency.

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

No

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

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

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

Manual cites other published reliability studies:

No

Provide citations for additional published studies.

Bias Analysis

Grade	Grade K	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Rating	Provided	Provided	Provided	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:

Differential Item Function (DIF) was investigated using WINSTEPS® by comparing the item difficulty measure for two demographic categories in a pairwise comparison through a combined calibration analysis. The essence of this methodology is to investigate the interaction of the person-groups with each item, while fixing all other item and person measures to those from the combined calibration. The method used to detect DIF is based on the Mantel-Haenszel procedure (MH), and the work of Linacre & Wright (1989) and Linacre (2012). Typically, the group representing test takers in a specific demographic group is referred to as the focal group. The group made up of test takers from outside this group is referred to as the reference group. For example, for gender, Female is the focal group, and Male is the reference group.

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

The latest large-scale DIF analysis included a random sample (10%) of students from the 2015–2016 i-Ready operational data. Given the large size of the 2015–2016 i-Ready student population, it is practical to carry out the calibration analysis with a random sample. The following demographic categories were compared: Female vs. Male; Black or African American and Latino vs. Caucasian; English Learner vs. non–English Learner; Students with Disabilities vs. General Education students; Economically Disadvantaged vs. Not Economically Disadvantaged. In each pairwise comparison, estimates of item difficulty for each category in the comparison were calculated.

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.

Active items in the current item pool for the 2016–2017 school year are included in the DIF analysis. The total numbers of items are 3103 for Mathematics. WINSTEPS (Version 3.92) was used to conduct the calibration for DIF analysis by grade. To help interpret the results, the Educational Testing Service (ETS) criteria using the delta method was used to categorize DIF (Zwick, Thayer, & Lewis, 1999) and is presented. The number and percentage of items exhibiting DIF for each of the demographic categories are reported in the table below. It should be noted that not all students have individual demographic information and the total number of items for two exclusive groups in the categories does not necessarily equal to the total number of items. It is clear that the majority of ELA items show negligible DIF (mostly more than 90 percent), and very few items (less than 6 percent) are showing large DIF (level C) by grade.