Star
Reading

Descriptive Information

Please provide a description of your tool:

Star Reading is a computer-adaptive, interim assessment of reading comprehension and overall reading achievement for students in grades 1 through 12. The tool provides information on student performance in 46 reading skill areas in 11 domains.

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

answers@renaissance.com

Address

Renaissance Learning, PO Box 8036, Wisconsin Rapids, WI 54495

Phone Number

(800) 338-4204

Website

http://www.renaissance.com

Initial cost for implementing program:

Cost

Unit of cost

Replacement cost per unit for subsequent use:

Cost

Unit of cost

Duration of license

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.

There is a one-time setup fee along with a per student subscription fee. Total cost will depend on the number of schools and students. Please contact: answers@renaissance.com or (800) 338-4204 for specific details on pricing for your district. Star Reading is cloud-based and purchase includes the tool, software/technical manual, installation guide, testing instructions, and remote installation and setup.

Provide information about special accommodations for students with disabilities.

Star Reading is a computer-adaptive assessment, and the difficulty of items presented is adjusted automatically to reflect the skill level of the student. Students may use the keyboard or the mouse, accommodating students with limited motor skills. Star Reading offers several accommodations for students with disabilities through the accessibility options built into a computer's operating system. For students with limited vision, the introductory screens of Star Reading respond to the "high contrast" accessibility feature within Windows and the "switch to black and white" accessibility feature in Mac OS. The assessment screens within Star Reading already provide visual contrast through a yellow background and black writing. Furthermore, Star Reading is compatible with Mac’s "zoom in" accessibility feature, which allows users to magnify nearly all Star Reading screens

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:

Group administered

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

18

per (student/group/other unit)

student/group

Additional scoring time:

Time in minutes

per (student/group/other unit)

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?

Unlimited

If yes, for which months are benchmarks available?

All

BEHAVIOR ONLY: Can students be rated concurrently by one administrator?

If yes, how many students can be rated concurrently?

Training & Scoring

Training

Is training for the administrator required?

Yes

Describe the time required for administrator training, if applicable:

Less than 1 hour 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:

Can users obtain ongoing professional and technical support?

Yes

If Yes, please describe how users can obtain support:

Renaissance Technical Support Staff

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:

Instructional Reading Level, scale score

Does your tool include decision rules?

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.

All scores are calculated automatically by the software. The software calculates a maximum likelihood Rasch ability estimate based on the calibrated difficulty of the items that were administered to the student, and the pattern of the student’s right and wrong responses to those items. Star Reading uses a proprietary, Rasch-based, 1-parameter logistic response model to calculate scores. The scaled score is a non-linear, monotonic transformation of the Rasch ability estimate resulting from the adaptive test. From the scaled scores and the student’s current grade placement are derived grade equivalent, percentile, and normal curve equivalent scores. No clusters, composite, or raw scores are reported.

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.

Star Reading is trusted by tens of thousands of schools to efficiently screen and monitoring student progress in general reading achievement from grades 1 through 12. Both criterion- and norm-referenced scores are automatically reported to help teachers make a variety of instructional decisions. The choice to adopt a computer adaptive format was driven by a demand for efficiency and protecting instructional time, standardizing administration and scoring and avoiding threats to reliability, and ability to dynamically adapt the instrument to each student in real time to appropriately match their current level of proficiency. Reports and dashboards guide educators through screening and related decisions. Default risk categories, based on national norms, are provided, although they can be adjusted by local school leaders to best fit local populations. Additionally, projections to state summative and other high stakes tests such as ACT and SAT are provided to inform screening decisions. During Star Reading item development, every effort is made to avoid the use of stereotypes, potentially offensive language or characterizations, and descriptions of people or events that could be construed as being offensive, demeaning, patronizing, or otherwise insensitive. The editing process also includes a strict sensitivity review of all items to attend to issues of gender and ethnic-group balance and fairness. DIF analyses are conducted to ensure the items function equally well for diverse subgroups.

Classification Accuracy & Cross-Validation Summary

Grade	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8	Grade 9	Grade 10	Grade 11
Classification Accuracy Fall
Classification Accuracy Winter
Classification Accuracy Spring

PARCC Reading

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 PARCC assessment is an external outcome measure administered at least 90 days after each student’s Star assessment.

Do the classification accuracy analyses examine concurrent and/or predictive classification?

Concurrent
Predictive

Describe when screening and criterion measures were administered and provide a justification for why the method(s) you chose (concurrent and/or predictive) is/are appropriate for your tool.

Describe how the classification analyses were performed and cut-points determined. Describe how the cut points align with students at-risk. Please indicate which groups were contrasted in your analyses (e.g., low risk students versus high risk students, low risk students versus moderate risk students).

The Star cut-point for students at risk was determined to be at the 25th percentile of Star scores. This point aligns with students in need of intervention, as indicated in Star default benchmarks. Students scoring below the 25th percentile were placed into the at-risk category and students scoring above the 25th percentile were placed into the no-risk category. Consistent with the TRC guidelines, we selected the 20th PR on the outcome measure as the point aligned with students in need of intensive intervention.

Were the children in the study/studies involved in an intervention in addition to typical classroom instruction between the screening measure and outcome assessment?

No

If yes, please describe the intervention, what children received the intervention, and how they were chosen.

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 PARCC ELA assessment is an external outcome measure administered at least 90 days after each student’s Star assessment.

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).

The Star cut-point for students at risk was determined to be at the 25th percentile of Star scores. This point aligns with students in need of intervention, as indicated in Star default benchmarks. Students scoring below the 25th percentile were placed into the at-risk category and students scoring above the 25th percentile were placed into the no-risk category. Consistent with the TRC guidelines, we selected the 20th PR on the outcome measure as the point aligned with students in need of intensive intervention.

Were the children in the study/studies involved in an intervention in addition to typical classroom instruction between the screening measure and outcome assessment?

No

If yes, please describe the intervention, what children received the intervention, and how they were chosen.

---

Smarter Balanced ELA / Literacy

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 Smarter Balanced ELA/Literacy assessment is an external outcome measure administered outside of the Star suite of assessments. The Smarter Balanced ELA/Literacy assessment is an end-of-year summative test administered by states in the spring.

Do the classification accuracy analyses examine concurrent and/or predictive classification?

Concurrent
Predictive

Describe when screening and criterion measures were administered and provide a justification for why the method(s) you chose (concurrent and/or predictive) is/are appropriate for your tool.

Describe how the classification analyses were performed and cut-points determined. Describe how the cut points align with students at-risk. Please indicate which groups were contrasted in your analyses (e.g., low risk students versus high risk students, low risk students versus moderate risk students).

A ROC analysis was used to compare the performance on Star to performance on the criterion measure. Selection of Star cut scores was based on striking an optimal balance between specificity and sensitivity when classifying students as “at risk”. For these analyses, students scoring below the Star cut score are classified as “high risk” and students scoring at or above the Star cut score are classified as “low risk”. Students scoring below the 20th percentile on the criterion measure are considered to be “actually at risk” and students scoring at or above the 20th percentile on the criterion measure are considered to be “actually not at risk”. As a result, students classified as “at risk” on Star and are considered to be “actually at risk” represent a true positive. Students classified as “at risk” on Star, but are “actually not at risk” represent a false positive. Students classified as “not at risk” on Star, but are “actually at risk” represent a false negative. Students classified as “not at risk” on Star and are “actually not at risk” represent a true negative.

Were the children in the study/studies involved in an intervention in addition to typical classroom instruction between the screening measure and outcome assessment?

No

If yes, please describe the intervention, what children received the intervention, and how they were chosen.

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 Smarter Balanced ELA/Literacy assessment is an external outcome measure administered outside of the Star suite of assessments. The Smarter Balanced ELA/Literacy assessment is an end-of-year summative test administered by states in the spring.

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).

For the cross-validation analyses, students scoring below the Star cut score determined from the Classification Accuracy analysis are classified as “high risk” and students scoring at or above the Star cut score are classified as “low risk”. Students scoring below the 20th percentile on the criterion measure are considered to be “actually at risk” and students scoring at or above the 20th percentile on the criterion measure are considered to be “actually not at risk”. As a result, students classified as “at risk” on Star and are considered to be “actually at risk” represent a true positive. Students classified as “at risk” on Star, but are “actually not at risk” represent a false positive. Students classified as “not at risk” on Star, but are “actually at risk” represent a false negative. Students classified as “not at risk” on Star and are “actually not at risk” represent a true negative.

Were the children in the study/studies involved in an intervention in addition to typical classroom instruction between the screening measure and outcome assessment?

No

If yes, please describe the intervention, what children received the intervention, and how they were chosen.

---

SAT Evidence Based Reading and Writing (ERW)

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 SAT Evidence-Based Reading and Writing test (ERW) is an external outcome measure administered outside of the Star suite of assessments. The SAT ERW assessment is a college-and-career readiness assessment of reading, writing, and language developed by the College Board.

Do the classification accuracy analyses examine concurrent and/or predictive classification?

Concurrent
Predictive

Describe when screening and criterion measures were administered and provide a justification for why the method(s) you chose (concurrent and/or predictive) is/are appropriate for your tool.

Describe how the classification analyses were performed and cut-points determined. Describe how the cut points align with students at-risk. Please indicate which groups were contrasted in your analyses (e.g., low risk students versus high risk students, low risk students versus moderate risk students).

A ROC analysis was used to compare the performance on Star to performance on the criterion measure. Selection of Star cut scores was based on striking an optimal balance between specificity and sensitivity when classifying students as “at risk”. For these analyses, students scoring below the Star cut score are classified as “high risk” and students scoring at or above the Star cut score are classified as “low risk”. Students scoring below the 20th percentile on the criterion measure are considered to be “actually at risk” and students scoring at or above the 20th percentile on the criterion measure are considered to be “actually not at risk”. As a result, students classified as “at risk” on Star and are considered to be “actually at risk” represent a true positive. Students classified as “at risk” on Star, but are “actually not at risk” represent a false positive. Students classified as “not at risk” on Star, but are “actually at risk” represent a false negative. Students classified as “not at risk” on Star and are “actually not at risk” represent a true negative.

Were the children in the study/studies involved in an intervention in addition to typical classroom instruction between the screening measure and outcome assessment?

No

If yes, please describe the intervention, what children received the intervention, and how they were chosen.

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 SAT Evidence-Based Reading and Writing test (ERW) is an external outcome measure administered outside of the Star suite of assessments. The SAT ERW assessment is a college-and-career readiness assessment of reading, writing, and language developed by the College Board.

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).

For the cross-validation analyses, students scoring below the Star cut score determined from the Classification Accuracy analysis are classified as “high risk” and students scoring at or above the Star cut score are classified as “low risk”. Students scoring below the 20th percentile on the criterion measure are considered to be “actually at risk” and students scoring at or above the 20th percentile on the criterion measure are considered to be “actually not at risk”. As a result, students classified as “at risk” on Star and are considered to be “actually at risk” represent a true positive. Students classified as “at risk” on Star, but are “actually not at risk” represent a false positive. Students classified as “not at risk” on Star, but are “actually at risk” represent a false negative. Students classified as “not at risk” on Star and are “actually not at risk” represent a true negative.

Were the children in the study/studies involved in an intervention in addition to typical classroom instruction between the screening measure and outcome assessment?

No

If yes, please describe the intervention, what children received the intervention, and how they were chosen.

---

Galileo Early Literacy 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 Galileo Early Literacy assessment is an external measure administered outside of the Star suite of assessments. The Galileo assessments are published by Assessment Technology Incorporated and can be administered at any point throughout the school year as individual districts choose.

Do the classification accuracy analyses examine concurrent and/or predictive classification?

Concurrent
Predictive

Describe when screening and criterion measures were administered and provide a justification for why the method(s) you chose (concurrent and/or predictive) is/are appropriate for your tool.

Describe how the classification analyses were performed and cut-points determined. Describe how the cut points align with students at-risk. Please indicate which groups were contrasted in your analyses (e.g., low risk students versus high risk students, low risk students versus moderate risk students).

A ROC analysis was used to compare the performance on Star to performance on the criterion measure. Selection of Star cut scores was based on striking an optimal balance between specificity and sensitivity when classifying students as “at risk”. For these analyses, students scoring below the Star cut score are classified as “high risk” and students scoring at or above the Star cut score are classified as “low risk”. Students scoring below the 20th percentile on the criterion measure are considered to be “actually at risk” and students scoring at or above the 20th percentile on the criterion measure are considered to be “actually not at risk”. As a result, students classified as “at risk” on Star and are considered to be “actually at risk” represent a true positive. Students classified as “at risk” on Star, but are “actually not at risk” represent a false positive. Students classified as “not at risk” on Star, but are “actually at risk” represent a false negative. Students classified as “not at risk” on Star and are “actually not at risk” represent a true negative.

Were the children in the study/studies involved in an intervention in addition to typical classroom instruction between the screening measure and outcome assessment?

No

If yes, please describe the intervention, what children received the intervention, and how they were chosen.

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 Galileo Early Literacy assessment is an external measure administered outside of the Star suite of assessments. The Galileo assessments can be administered at any point throughout the school year.

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).

A ROC analysis was used to compare the performance on Star to performance on the criterion measure. Selection of Star cut scores was based on striking an optimal balance between specificity and sensitivity when classifying students as “at risk”. For these analyses, students scoring below the Star cut score are classified as “high risk” and students scoring at or above the Star cut score are classified as “low risk”. Students scoring below the 20th percentile on the criterion measure are considered to be “actually at risk” and students scoring at or above the 20th percentile on the criterion measure are considered to be “actually not at risk”. As a result, students classified as “at risk” on Star and are considered to be “actually at risk” represent a true positive. Students classified as “at risk” on Star, but are “actually not at risk” represent a false positive. Students classified as “not at risk” on Star, but are “actually at risk” represent a false negative. Students classified as “not at risk” on Star and are “actually not at risk” represent a true negative.

Were the children in the study/studies involved in an intervention in addition to typical classroom instruction between the screening measure and outcome assessment?

No

If yes, please describe the intervention, what children received the intervention, and how they were chosen.

---

Florida Standards Assessment (FSA) for English language arts

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.

Florida Standards Assessment (FSA) for English language arts is an external outcome measure administered outside of the Star suite of assessments. Florida Standards Assessment (FSA) for English language arts is an end-of-year summative test administered by the Florida DOE in the spring.

Do the classification accuracy analyses examine concurrent and/or predictive classification?

Concurrent
Predictive

Describe when screening and criterion measures were administered and provide a justification for why the method(s) you chose (concurrent and/or predictive) is/are appropriate for your tool.

Describe how the classification analyses were performed and cut-points determined. Describe how the cut points align with students at-risk. Please indicate which groups were contrasted in your analyses (e.g., low risk students versus high risk students, low risk students versus moderate risk students).

A ROC analysis was used to compare the performance on Star to performance on the criterion measure. Selection of Star cut scores was based on striking an optimal balance between specificity and sensitivity when classifying students as “at risk”. For these analyses, students scoring below the Star cut score are classified as “high risk” and students scoring at or above the Star cut score are classified as “low risk”. Students scoring below the 20th percentile on the criterion measure are considered to be “actually at risk” and students scoring at or above the 20th percentile on the criterion measure are considered to be “actually not at risk”. As a result, students classified as “at risk” on Star and are considered to be “actually at risk” represent a true positive. Students classified as “at risk” on Star, but are “actually not at risk” represent a false positive. Students classified as “not at risk” on Star, but are “actually at risk” represent a false negative. Students classified as “not at risk” on Star and are “actually not at risk” represent a true negative.

Were the children in the study/studies involved in an intervention in addition to typical classroom instruction between the screening measure and outcome assessment?

No

If yes, please describe the intervention, what children received the intervention, and how they were chosen.

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.

Florida Standards Assessment (FSA) for English language arts is an external outcome measure administered outside of the Star suite of assessments. Florida Standards Assessment (FSA) for English language arts is an end-of-year summative test administered by the Florida DOE in the spring.

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).

For the cross-validation analyses, students scoring below the Star cut score determined from the Classification Accuracy analysis are classified as “high risk” and students scoring at or above the Star cut score are classified as “low risk”. Students scoring below the 20th percentile on the criterion measure are considered to be “actually at risk” and students scoring at or above the 20th percentile on the criterion measure are considered to be “actually not at risk”. As a result, students classified as “at risk” on Star and are considered to be “actually at risk” represent a true positive. Students classified as “at risk” on Star, but are “actually not at risk” represent a false positive. Students classified as “not at risk” on Star, but are “actually at risk” represent a false negative. Students classified as “not at risk” on Star and are “actually not at risk” represent a true negative.

Were the children in the study/studies involved in an intervention in addition to typical classroom instruction between the screening measure and outcome assessment?

No

If yes, please describe the intervention, what children received the intervention, and how they were chosen.

---

DIBELS ORF

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.

DIBELS is an external outcome measure administered outside of the Star suite of assessments.

Do the classification accuracy analyses examine concurrent and/or predictive classification?

Concurrent
Predictive

Describe when screening and criterion measures were administered and provide a justification for why the method(s) you chose (concurrent and/or predictive) is/are appropriate for your tool.

Describe how the classification analyses were performed and cut-points determined. Describe how the cut points align with students at-risk. Please indicate which groups were contrasted in your analyses (e.g., low risk students versus high risk students, low risk students versus moderate risk students).

A ROC analysis was used to compare the performance on Star to performance on the criterion measure. Selection of Star cut scores was based on striking an optimal balance between specificity and sensitivity when classifying students as “at risk”. For these analyses, students scoring below the Star cut score are classified as “high risk” and students scoring at or above the Star cut score are classified as “low risk”. Students scoring below the 20th percentile on the criterion measure are considered to be “actually at risk” and students scoring at or above the 20th percentile on the criterion measure are considered to be “actually not at risk”. As a result, students classified as “at risk” on Star and are considered to be “actually at risk” represent a true positive. Students classified as “at risk” on Star, but are “actually not at risk” represent a false positive. Students classified as “not at risk” on Star, but are “actually at risk” represent a false negative. Students classified as “not at risk” on Star and are “actually not at risk” represent a true negative.

Were the children in the study/studies involved in an intervention in addition to typical classroom instruction between the screening measure and outcome assessment?

No

If yes, please describe the intervention, what children received the intervention, and how they were chosen.

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.

DIBELS is an external outcome measure administered outside of the Star suite of assessments.

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).

For the cross-validation analyses, students scoring below the Star cut score determined from the Classification Accuracy analysis are classified as “high risk” and students scoring at or above the Star cut score are classified as “low risk”. Students scoring below the 20th percentile on the criterion measure are considered to be “actually at risk” and students scoring at or above the 20th percentile on the criterion measure are considered to be “actually not at risk”. As a result, students classified as “at risk” on Star and are considered to be “actually at risk” represent a true positive. Students classified as “at risk” on Star, but are “actually not at risk” represent a false positive. Students classified as “not at risk” on Star, but are “actually at risk” represent a false negative. Students classified as “not at risk” on Star and are “actually not at risk” represent a true negative.

Were the children in the study/studies involved in an intervention in addition to typical classroom instruction between the screening measure and outcome assessment?

No

If yes, please describe the intervention, what children received the intervention, and how they were chosen.

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Classification Accuracy - Fall

Evidence	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8	Grade 9	Grade 10	Grade 11
Criterion measure	DIBELS ORF	Florida Standards Assessment (FSA) for English language arts	Smarter Balanced ELA / Literacy	Smarter Balanced ELA / Literacy	Smarter Balanced ELA / Literacy	Smarter Balanced ELA / Literacy	Smarter Balanced ELA / Literacy	Smarter Balanced ELA / Literacy	SAT Evidence Based Reading and Writing (ERW)	Florida Standards Assessment (FSA) for English language arts	SAT Evidence Based Reading and Writing (ERW)
Cut Points - Percentile rank on criterion measure	20	20	20	20	20	20	20	20	20	20	20
Cut Points - Performance score on criterion measure
Cut Points - Corresponding performance score (numeric) on screener measure	73	<137	<252	<352	<431	<518	<565	<625	<710	<648	<844
Classification Data - True Positive (a)	12	1198	1201	1205	1212	1191	1219	1225	99	494	360
Classification Data - False Positive (b)	10	1340	1068	952	904	1040	823	923	121	490	410
Classification Data - False Negative (c)	2	306	284	290	292	286	290	311	20	123	87
Classification Data - True Negative (d)	46	5550	4979	5119	5141	4958	5226	5274	496	1991	1736
Area Under the Curve (AUC)	0.89	0.88	0.90	0.91	0.92	0.90	0.92	0.91	0.87	0.89	0.89
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.82	0.88	0.90	0.90	0.91	0.90	0.91	0.90	0.84	0.87	0.87
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.97	0.89	0.91	0.92	0.92	0.91	0.93	0.92	0.90	0.90	0.90

Statistics	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8	Grade 9	Grade 10	Grade 11
Base Rate	0.20	0.18	0.20	0.20	0.20	0.20	0.20	0.20	0.16	0.20	0.17
Overall Classification Rate	0.83	0.80	0.82	0.84	0.84	0.82	0.85	0.84	0.81	0.80	0.81
Sensitivity	0.86	0.80	0.81	0.81	0.81	0.81	0.81	0.80	0.83	0.80	0.81
Specificity	0.82	0.81	0.82	0.84	0.85	0.83	0.86	0.85	0.80	0.80	0.81
False Positive Rate	0.18	0.19	0.18	0.16	0.15	0.17	0.14	0.15	0.20	0.20	0.19
False Negative Rate	0.14	0.20	0.19	0.19	0.19	0.19	0.19	0.20	0.17	0.20	0.19
Positive Predictive Power	0.55	0.47	0.53	0.56	0.57	0.53	0.60	0.57	0.45	0.50	0.47
Negative Predictive Power	0.96	0.95	0.95	0.95	0.95	0.95	0.95	0.94	0.96	0.94	0.95

Sample	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8	Grade 9	Grade 10	Grade 11
Date	Fall 2019	Fall 2016 and Fall 2017	Fall 2014 & Fall 2018	Fall 2014 & Fall 2018	Fall 2014 & Fall 2018	Fall 2014 & Fall 2018	Fall 2014 & Fall 2018	Fall 2014 & Fall 2018	Fall 2013 & 2014	Fall 2017	Fall 2015 & Fall 2016
Sample Size	70	8394	7532	7566	7549	7475	7558	7733	736	3098	2593
Geographic Representation	East South Central (AL)	South Atlantic (FL)	New England (CT) Pacific (CA, OR, WA) West North Central (SD)	New England (CT) Pacific (CA, OR, WA) West North Central (SD)	New England (CT) Pacific (CA, OR, WA) West North Central (SD)	New England (CT) Pacific (CA, OR, WA) West North Central (SD)	New England (CT) Pacific (CA, OR, WA) West North Central (SD)	New England (CT) Pacific (CA, OR, WA) West North Central (SD)	East North Central (IL, MI) Mountain (ID) New England (CT)	South Atlantic (FL)	East North Central (IL, MI) Mountain (ID) New England (CT, ME)
Male	50.0%	50.3%	50.2%	51.3%	51.5%	50.5%	50.3%	51.4%	48.4%	51.2%	48.4%
Female	50.0%	49.7%	49.4%	48.2%	48.8%	49.1%	49.4%	48.1%	47.7%	48.8%	49.8%
Other
Gender Unknown			0.5%	0.5%	0.4%	0.4%	0.3%	0.5%	3.9%		1.7%
White, Non-Hispanic	88.6%	39.7%	41.3%	41.0%	42.2%	39.2%	39.1%	39.8%	31.5%	35.7%	36.7%
Black, Non-Hispanic	5.7%	23.8%	4.7%	4.7%	4.8%	4.6%	5.2%	5.2%	15.2%	26.3%	27.2%
Hispanic		34.2%	33.5%	33.3%	31.0%	30.7%	30.5%	29.9%	37.8%	34.9%	16.3%
Asian/Pacific Islander
American Indian/Alaska Native	1.4%	0.3%	1.1%	1.1%	1.1%	1.0%	1.2%	1.0%	0.4%	1.1%	0.6%
Other			4.2%	3.7%	4.5%	3.5%	3.8%	3.3%		0.3%	4.9%
Race / Ethnicity Unknown			2.8%	3.0%	4.1%	7.4%	7.5%	8.0%	9.2%		10.5%
Low SES
IEP or diagnosed disability
English Language Learner

Classification Accuracy - Winter

Evidence	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8	Grade 9	Grade 10	Grade 11
Criterion measure	PARCC Reading	Smarter Balanced ELA / Literacy	Smarter Balanced ELA / Literacy	Smarter Balanced ELA / Literacy	Smarter Balanced ELA / Literacy	Smarter Balanced ELA / Literacy	Smarter Balanced ELA / Literacy	Smarter Balanced ELA / Literacy	Florida Standards Assessment (FSA) for English language arts	Florida Standards Assessment (FSA) for English language arts	PARCC Reading
Cut Points - Percentile rank on criterion measure	20	20	20	20	20	20	20	20	20	20	20
Cut Points - Performance score on criterion measure
Cut Points - Corresponding performance score (numeric) on screener measure	<88	<208	<306	<398	<462	<533	<597	<666	<602	<658	<769
Classification Data - True Positive (a)	55	945	1218	1219	1224	1093	1094	1078	528	485	33
Classification Data - False Positive (b)	38	917	871	836	778	799	859	1044	538	421	31
Classification Data - False Negative (c)	13	242	293	300	307	238	247	216	130	117	6
Classification Data - True Negative (d)	238	3868	5309	5314	5368	4608	4550	4239	2223	1998	154
Area Under the Curve (AUC)	0.88	0.89	0.92	0.92	0.92	0.92	0.91	0.90	0.89	0.90	0.88
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.84	0.88	0.91	0.91	0.92	0.91	0.90	0.90	0.87	0.88	0.83
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.92	0.90	0.93	0.93	0.93	0.92	0.91	0.91	0.90	0.91	0.93

Statistics	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8	Grade 9	Grade 10	Grade 11
Base Rate	0.20	0.20	0.20	0.20	0.20	0.20	0.20	0.20	0.19	0.20	0.17
Overall Classification Rate	0.85	0.81	0.85	0.85	0.86	0.85	0.84	0.81	0.80	0.82	0.83
Sensitivity	0.81	0.80	0.81	0.80	0.80	0.82	0.82	0.83	0.80	0.81	0.85
Specificity	0.86	0.81	0.86	0.86	0.87	0.85	0.84	0.80	0.81	0.83	0.83
False Positive Rate	0.14	0.19	0.14	0.14	0.13	0.15	0.16	0.20	0.19	0.17	0.17
False Negative Rate	0.19	0.20	0.19	0.20	0.20	0.18	0.18	0.17	0.20	0.19	0.15
Positive Predictive Power	0.59	0.51	0.58	0.59	0.61	0.58	0.56	0.51	0.50	0.54	0.52
Negative Predictive Power	0.95	0.94	0.95	0.95	0.95	0.95	0.95	0.95	0.94	0.94	0.96

Sample	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8	Grade 9	Grade 10	Grade 11
Date	Winter 2012-13	Winter 2013-2014	Winter 2014-15 & Winter 2018-2019	Winter 2014-2015	Winter 2014-2015	Winter 2014-2015 & Winter 2018-2019	Winter 2014-15	Winter 2014-2015 & Winter 2018-2019	Winter 2017-18	Winter 2017-18	Winter 2014-15
Sample Size	344	5972	7691	7669	7677	6738	6750	6577	3419	3021	224
Geographic Representation	East North Central (IL) Mountain (CO, NM) West South Central (AR)	New England (CT) Pacific (CA, OR, WA)	New England (CT) Pacific (CA, OR, WA) West North Central (SD)	New England (CT) Pacific (CA, OR, WA)	New England (CT) Pacific (CA, OR, WA)	New England (CT) Pacific (CA, OR, WA) West North Central (SD)	New England (CT) Pacific (CA, OR, WA)	New England (CT) Pacific (CA, OR, WA) West North Central (SD)	South Atlantic (FL)	South Atlantic (FL)	Middle Atlantic (NJ) Mountain (CO) New England (RI) West South Central (AR)
Male	51.5%	50.4%	50.3%	51.5%	51.5%	51.3%	50.6%	51.6%	52.7%	51.4%	50.0%
Female	48.5%	49.4%	49.4%	48.3%	48.4%	48.5%	49.1%	47.8%	47.3%	48.6%	50.0%
Other
Gender Unknown		0.1%	0.3%	0.2%	0.1%	0.2%	0.3%	0.6%
White, Non-Hispanic	5.8%	40.3%	40.9%	40.9%	42.0%	39.4%	38.8%	39.1%	34.5%	35.4%	36.6%
Black, Non-Hispanic	1.7%	4.9%	5.0%	4.7%	4.9%	5.0%	5.7%	6.0%	24.7%	25.7%	3.1%
Hispanic	1.2%	35.9%	33.9%	33.5%	31.4%	32.7%	32.7%	31.7%	37.6%	35.8%	59.4%
Asian/Pacific Islander
American Indian/Alaska Native		0.4%	1.0%	1.1%	1.1%	1.1%	1.3%	1.1%	1.4%	1.1%
Other		3.3%	4.1%	3.9%	4.5%	3.1%	3.0%	2.5%	0.0%	0.2%	0.4%
Race / Ethnicity Unknown	90.4%	2.6%	2.7%	2.8%	3.8%	7.8%	8.3%	8.8%			0.4%
Low SES
IEP or diagnosed disability
English Language Learner

Classification Accuracy - Spring

Evidence	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8	Grade 9	Grade 10	Grade 11
Criterion measure	Galileo Early Literacy assessment	Smarter Balanced ELA / Literacy	Smarter Balanced ELA / Literacy	Smarter Balanced ELA / Literacy	Smarter Balanced ELA / Literacy	Smarter Balanced ELA / Literacy	Smarter Balanced ELA / Literacy	Smarter Balanced ELA / Literacy	Florida Standards Assessment (FSA) for English language arts	PARCC Reading	PARCC Reading
Cut Points - Percentile rank on criterion measure	20	20	20	20	20	20	20	20	20	20	20
Cut Points - Performance score on criterion measure
Cut Points - Corresponding performance score (numeric) on screener measure	<91	<265	< 350	< 430	< 478	< 566	< 611	< 665	<605	<830	<758
Classification Data - True Positive (a)	71	945	1218	1244	1225	1176	1068	1074	435	30	39
Classification Data - False Positive (b)	65	887	879	786	677	860	696	717	437	8	38
Classification Data - False Negative (c)	14	241	298	292	294	261	236	269	107	5	9
Classification Data - True Negative (d)	284	3935	5300	5382	5416	4897	4600	4751	2129	81	204
Area Under the Curve (AUC)	0.90	0.89	0.92	0.93	0.93	0.92	0.93	0.92	0.90	0.92	0.88
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.86	0.88	0.91	0.92	0.93	0.91	0.92	0.91	0.88	0.87	0.83
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.93	0.90	0.92	0.93	0.94	0.93	0.93	0.93	0.91	0.98	0.93

Statistics	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8	Grade 9	Grade 10	Grade 11
Base Rate	0.20	0.20	0.20	0.20	0.20	0.20	0.20	0.20	0.17	0.28	0.17
Overall Classification Rate	0.82	0.81	0.85	0.86	0.87	0.84	0.86	0.86	0.82	0.90	0.84
Sensitivity	0.84	0.80	0.80	0.81	0.81	0.82	0.82	0.80	0.80	0.86	0.81
Specificity	0.81	0.82	0.86	0.87	0.89	0.85	0.87	0.87	0.83	0.91	0.84
False Positive Rate	0.19	0.18	0.14	0.13	0.11	0.15	0.13	0.13	0.17	0.09	0.16
False Negative Rate	0.16	0.20	0.20	0.19	0.19	0.18	0.18	0.20	0.20	0.14	0.19
Positive Predictive Power	0.52	0.52	0.58	0.61	0.64	0.58	0.61	0.60	0.50	0.79	0.51
Negative Predictive Power	0.95	0.94	0.95	0.95	0.95	0.95	0.95	0.95	0.95	0.94	0.96

Sample	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8	Grade 9	Grade 10	Grade 11
Date	Spring 2017	Spring 2014	Spring 2015 & Spring 2019	Spring 2015 & Spring 2019	Spring 2015 & Spring 2019	Spring 2015 & Spring 2019	Spring 2015 & Spring 2019	Spring 2015 & Spring 2019	Spring 2018	Spring 2014	Spring 2014
Sample Size	434	6008	7695	7704	7612	7194	6600	6811	3108	124	290
Geographic Representation	Mountain (AZ)	New England (CT) Pacific (CA, OR, WA)	New England (CT) Pacific (CA, OR, WA) West North Central (SD)	New England (CT) Pacific (CA, OR, WA) West North Central (SD)	New England (CT) Pacific (CA, OR, WA) West North Central (SD)	New England (CT) Pacific (CA, OR, WA) West North Central (SD)	New England (CT) Pacific (CA, OR, WA) West North Central (SD)	New England (CT) Pacific (CA, OR, WA) West North Central (SD)	South Atlantic (FL)	Mountain (CO) New England (RI)	East North Central (OH) Middle Atlantic (NJ) Mountain (CO, NM) New England (RI)
Male	54.6%	50.0%	50.7%	51.3%	51.1%	50.9%	49.8%	51.3%	52.6%	61.3%	55.5%
Female	45.4%	49.8%	48.9%	48.1%	48.6%	48.6%	49.8%	48.0%	47.4%	38.7%	51.4%
Other
Gender Unknown		0.1%	0.4%	0.5%	0.4%	0.5%	0.4%	0.6%
White, Non-Hispanic	35.0%	40.4%	41.1%	40.8%	42.0%	38.7%	36.6%	36.8%	32.8%	63.7%	29.7%
Black, Non-Hispanic	12.9%	4.8%	4.7%	4.8%	5.0%	4.8%	5.7%	5.4%	22.7%	3.2%	2.8%
Hispanic	45.6%	35.4%	33.7%	33.3%	31.0%	30.0%	32.1%	30.9%	41.2%	30.6%	74.1%
Asian/Pacific Islander
American Indian/Alaska Native	3.0%	0.4%	1.0%	1.1%	1.1%	1.0%	1.0%	0.6%	1.5%
Other		3.4%	4.2%	3.8%	4.4%	3.8%	3.7%	3.4%	0.0%	1.6%	0.3%
Race / Ethnicity Unknown		2.7%	2.8%	3.1%	4.0%	7.9%	8.1%	9.2%
Low SES
IEP or diagnosed disability
English Language Learner

Cross-Validation - Fall

Evidence	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8	Grade 9	Grade 10	Grade 11
Criterion measure	DIBELS ORF	Florida Standards Assessment (FSA) for English language arts	Smarter Balanced ELA / Literacy	Smarter Balanced ELA / Literacy	Smarter Balanced ELA / Literacy	Smarter Balanced ELA / Literacy	Smarter Balanced ELA / Literacy	Smarter Balanced ELA / Literacy	SAT Evidence Based Reading and Writing (ERW)	Florida Standards Assessment (FSA) for English language arts	SAT Evidence Based Reading and Writing (ERW)
Cut Points - Percentile rank on criterion measure	20	20	20	20	20	20	20	20	20	20	20
Cut Points - Performance score on criterion measure
Cut Points - Corresponding performance score (numeric) on screener measure	73	<137	<252	< 352	< 431	< 518	< 565	< 625	<710	<648	<844
Classification Data - True Positive (a)	3	121	136	125	143	137	142	141	9	45	41
Classification Data - False Positive (b)	5	155	119	99	94	122	88	102	6	63	54
Classification Data - False Negative (c)	0	23	34	43	33	23	44	29	2	14	13
Classification Data - True Negative (d)	8	633	547	573	568	548	565	587	64	200	180
Area Under the Curve (AUC)	0.69	0.90	0.90	0.89	0.92	0.92	0.90	0.91	0.96	0.87	0.84
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.43	0.88	0.87	0.87	0.90	0.90	0.88	0.88	0.91	0.82	0.78
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.95	0.93	0.92	0.92	0.94	0.95	0.92	0.93	1.00	0.91	0.89

Statistics	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8	Grade 9	Grade 10	Grade 11
Base Rate	0.19	0.15	0.20	0.20	0.21	0.19	0.22	0.20	0.14	0.18	0.19
Overall Classification Rate	0.69	0.81	0.82	0.83	0.85	0.83	0.84	0.85	0.90	0.76	0.77
Sensitivity	1.00	0.84	0.80	0.74	0.81	0.86	0.76	0.83	0.82	0.76	0.76
Specificity	0.62	0.80	0.82	0.85	0.86	0.82	0.87	0.85	0.91	0.76	0.77
False Positive Rate	0.38	0.20	0.18	0.15	0.14	0.18	0.13	0.15	0.09	0.24	0.23
False Negative Rate	0.00	0.16	0.20	0.26	0.19	0.14	0.24	0.17	0.18	0.24	0.24
Positive Predictive Power	0.38	0.44	0.53	0.56	0.60	0.53	0.62	0.58	0.60	0.42	0.43
Negative Predictive Power	1.00	0.96	0.94	0.93	0.95	0.96	0.93	0.95	0.97	0.93	0.93

Sample	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8	Grade 9	Grade 10	Grade 11
Date	Fall 2019	Fall 2013	Fall 2014 & Fall 2018	Fall 2014 & Fall 2018	Fall 2014 & Fall 2018	Fall 2014 & Fall 2018	Fall 2014 & Fall 2018	Fall 2014 & Fall 2018	Fall 2013 & 2014	Fall 2017	Fall 2015 & Fall 2016
Sample Size	16	932	836	840	838	830	839	859	81	322	288
Geographic Representation	East South Central (AL)	South Atlantic (FL)	New England (CT) Pacific (CA, OR, WA) West North Central (SD)	New England (CT) Pacific (CA, OR, WA) West North Central (SD)	New England (CT) Pacific (CA, OR, WA) West North Central (SD)	New England (CT) Pacific (CA, OR, WA) West North Central (SD)	New England (CT) Pacific (CA, OR, WA) West North Central (SD)	New England (CT) Pacific (CA, OR, WA) West North Central (SD)	East North Central (IL, MI) Mountain (ID) New England (CT)	South Atlantic (FL)	East North Central (IL, MI) Mountain (ID) New England (CT, ME)
Male	62.5%	49.9%	51.1%	48.8%	49.8%	54.3%	48.3%	49.0%	38.3%	51.9%	45.1%
Female	37.5%	50.1%	48.4%	50.5%	50.0%	45.1%	51.5%	50.2%	54.3%	48.1%	53.5%
Other
Gender Unknown			0.5%	0.7%	0.2%	0.6%	0.2%	0.8%	7.4%		1.4%
White, Non-Hispanic	87.5%	38.9%	40.6%	41.3%	42.2%	37.8%	39.3%	38.4%	38.3%	36.3%	34.4%
Black, Non-Hispanic	12.5%	22.4%	6.2%	4.6%	4.9%	4.0%	5.7%	5.7%	18.5%	23.9%	27.4%
Hispanic		36.1%	34.3%	32.0%	32.1%	30.1%	29.6%	29.8%	25.9%	37.3%	18.8%
Asian/Pacific Islander
American Indian/Alaska Native		0.4%	0.4%	1.0%	1.4%	1.2%	1.1%	0.7%	1.2%	0.9%	1.0%
Other			4.3%	5.0%	3.9%	4.3%	3.8%	3.5%		0.6%	4.2%
Race / Ethnicity Unknown			3.0%	3.5%	3.6%	8.3%	8.5%	9.1%	11.1%		10.4%
Low SES
IEP or diagnosed disability
English Language Learner

Cross-Validation - Winter

Evidence	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8	Grade 9	Grade 10
Criterion measure	PARCC Reading	Smarter Balanced ELA / Literacy	Smarter Balanced ELA / Literacy	Smarter Balanced ELA / Literacy	Smarter Balanced ELA / Literacy	Smarter Balanced ELA / Literacy	Smarter Balanced ELA / Literacy	Smarter Balanced ELA / Literacy	Florida Standards Assessment (FSA) for English language arts	Florida Standards Assessment (FSA) for English language arts
Cut Points - Percentile rank on criterion measure	20	20	20	20	20	20	20	20	20	20
Cut Points - Performance score on criterion measure
Cut Points - Corresponding performance score (numeric) on screener measure	<88	<208	<306	<398	< 462	<533	< 597	< 666	<602	<658
Classification Data - True Positive (a)	41	97	158	126	148	96	119	119	52	52
Classification Data - False Positive (b)	81	98	96	97	79	93	96	111	73	56
Classification Data - False Negative (c)	13	31	40	30	39	34	23	18	15	14
Classification Data - True Negative (d)	300	437	560	599	586	525	511	482	246	208
Area Under the Curve (AUC)	0.86	0.89	0.91	0.91	0.92	0.89	0.94	0.91	0.87	0.89
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.81	0.86	0.89	0.89	0.90	0.86	0.91	0.89	0.83	0.85
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.90	0.92	0.93	0.93	0.94	0.92	0.96	0.94	0.92	0.93

Statistics	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8	Grade 9	Grade 10
Base Rate	0.12	0.19	0.23	0.18	0.22	0.17	0.19	0.19	0.17	0.20
Overall Classification Rate	0.78	0.81	0.84	0.85	0.86	0.83	0.84	0.82	0.77	0.79
Sensitivity	0.76	0.76	0.80	0.81	0.79	0.74	0.84	0.87	0.78	0.79
Specificity	0.79	0.82	0.85	0.86	0.88	0.85	0.84	0.81	0.77	0.79
False Positive Rate	0.21	0.18	0.15	0.14	0.12	0.15	0.16	0.19	0.23	0.21
False Negative Rate	0.24	0.24	0.20	0.19	0.21	0.26	0.16	0.13	0.22	0.21
Positive Predictive Power	0.34	0.50	0.62	0.57	0.65	0.51	0.55	0.52	0.42	0.48
Negative Predictive Power	0.96	0.93	0.93	0.95	0.94	0.94	0.96	0.96	0.94	0.94

Sample	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8	Grade 9	Grade 10
Date	Winter 2012-13	Winter 2013-2014	Winter 2014-2015 & Winter 2018-2019	Winter 2014-2015	Winter 2014-2015	Winter 2014-2015	Winter 2014-2015	Winter 2014-2015	Winter 2017-18	Winter 2014-15
Sample Size	435	663	854	852	852	748	749	730	386	330
Geographic Representation	Middle Atlantic (NJ)	New England (CT) Pacific (CA, OR, WA)	New England (CT) Pacific (CA, OR, WA) West North Central (SD)	New England (CT) Pacific (CA, OR, WA)	New England (CT) Pacific (CA, OR, WA)	New England (CT) Pacific (CA, OR, WA)	New England (CT) Pacific (CA, OR, WA)	New England (CT) Pacific (CA, OR, WA)	South Atlantic (FL)	South Atlantic (FL)
Male	48.3%	52.3%	52.8%	49.8%	49.9%	47.9%	47.7%	51.4%	54.9%	52.4%
Female	51.7%	47.7%	47.0%	49.9%	50.0%	51.9%	52.2%	48.1%	45.1%	47.6%
Other
Gender Unknown			0.2%	0.4%	0.1%	0.3%	0.1%	0.5%
White, Non-Hispanic	14.0%	40.9%	42.9%	40.7%	43.0%	37.4%	35.6%	39.7%	32.6%	34.2%
Black, Non-Hispanic	5.7%	4.8%	4.2%	5.9%	4.8%	5.7%	6.3%	5.8%	25.9%	32.1%
Hispanic	46.2%	35.9%	31.3%	32.4%	29.3%	32.2%	33.9%	30.4%	37.0%	31.8%
Asian/Pacific Islander
American Indian/Alaska Native		0.6%	1.1%	0.9%	1.5%	1.6%	1.5%	0.8%	1.0%	0.6%
Other	1.8%	3.5%	4.6%	3.3%	4.2%	2.5%	5.1%	2.7%
Race / Ethnicity Unknown		2.7%	2.7%	3.3%	4.3%	8.8%	8.7%	8.8%
Low SES
IEP or diagnosed disability
English Language Learner

Cross-Validation - Spring

Evidence	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8	Grade 9	Grade 10	Grade 11
Criterion measure	Galileo Early Literacy assessment	Smarter Balanced ELA / Literacy	Smarter Balanced ELA / Literacy	Smarter Balanced ELA / Literacy	Smarter Balanced ELA / Literacy	Smarter Balanced ELA / Literacy	Smarter Balanced ELA / Literacy	Smarter Balanced ELA / Literacy	Florida Standards Assessment (FSA) for English language arts	PARCC Reading	PARCC Reading
Cut Points - Percentile rank on criterion measure	20	20	20	20	20	20	20	20	20	20	20
Cut Points - Performance score on criterion measure
Cut Points - Corresponding performance score (numeric) on screener measure	<91	< 265	< 350	< 430	< 478	< 566	< 611	< 665	<605	<830	<758
Classification Data - True Positive (a)	76	108	115	121	144	109	123	123	48	20	19
Classification Data - False Positive (b)	191	104	103	92	71	92	87	80	43	73	29
Classification Data - False Negative (c)	9	34	22	28	40	22	22	32	8	4	3
Classification Data - True Negative (d)	536	421	614	615	590	576	501	521	234	263	218
Area Under the Curve (AUC)	0.89	0.88	0.92	0.92	0.93	0.93	0.93	0.91	0.91	0.90	0.92
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.86	0.86	0.90	0.89	0.92	0.91	0.91	0.89	0.87	0.84	0.85
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.92	0.91	0.94	0.94	0.95	0.95	0.95	0.94	0.95	0.95	0.98

Statistics	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8	Grade 9	Grade 10	Grade 11
Base Rate	0.10	0.21	0.16	0.17	0.22	0.16	0.20	0.21	0.17	0.07	0.08
Overall Classification Rate	0.75	0.79	0.85	0.86	0.87	0.86	0.85	0.85	0.85	0.79	0.88
Sensitivity	0.89	0.76	0.84	0.81	0.78	0.83	0.85	0.79	0.86	0.83	0.86
Specificity	0.74	0.80	0.86	0.87	0.89	0.86	0.85	0.87	0.84	0.78	0.88
False Positive Rate	0.26	0.20	0.14	0.13	0.11	0.14	0.15	0.13	0.16	0.22	0.12
False Negative Rate	0.11	0.24	0.16	0.19	0.22	0.17	0.15	0.21	0.14	0.17	0.14
Positive Predictive Power	0.28	0.51	0.53	0.57	0.67	0.54	0.59	0.61	0.53	0.22	0.40
Negative Predictive Power	0.98	0.93	0.97	0.96	0.94	0.96	0.96	0.94	0.97	0.99	0.99

Sample	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8	Grade 9	Grade 10	Grade 11
Date	Spring 2016	Spring 2014	Spring 2015 & Spring 2019	Spring 2014	Spring 2015 & Spring 2019	Spring 2015 & Spring 2019	Spring 2015	Spring 2015 & Spring 2019	Spring 2018	Spring 2014	Spring 2014
Sample Size	812	667	854	856	845	799	733	756	333	360	269
Geographic Representation	Mountain (AZ)	New England (CT) Pacific (CA, OR, WA)	New England (CT) Pacific (CA, OR, WA) West North Central (SD)	New England (CT) Pacific (CA, OR, WA) West North Central (SD)	New England (CT) Pacific (CA, OR, WA) West North Central (SD)	New England (CT) Pacific (CA, OR, WA) West North Central (SD)	New England (CT) Pacific (CA, OR, WA)	New England (CT) Pacific (CA, OR, WA) West North Central (SD)	South Atlantic (FL)	Middle Atlantic (NJ)	Middle Atlantic (NJ)
Male	51.5%	52.3%	49.3%	49.6%	53.0%	52.1%	49.5%	52.5%	50.8%	49.4%	44.6%
Female	48.5%	47.7%	50.0%	49.9%	46.6%	47.7%	50.2%	47.1%	49.2%	50.6%	55.4%
Other
Gender Unknown			0.7%	0.5%	0.4%	0.3%	0.3%	0.4%
White, Non-Hispanic	33.6%	39.6%	40.2%	40.3%	40.9%	40.1%	37.9%	35.7%	34.8%	13.6%	13.8%
Black, Non-Hispanic	9.2%	4.3%	6.1%	5.3%	4.1%	3.9%	4.9%	5.6%	24.0%	8.6%	8.2%
Hispanic	51.2%	35.2%	32.6%	32.2%	31.4%	31.3%	30.6%	32.0%	37.5%	42.8%	39.8%
Asian/Pacific Islander
American Indian/Alaska Native		0.6%	0.1%	0.9%	1.4%	1.1%	0.5%	0.3%	0.9%	0.6%	0.7%
Other		3.7%	3.2%	4.1%	5.0%	3.0%	3.1%	4.0%		0.8%	0.7%
Race / Ethnicity Unknown		2.4%	3.7%	3.4%	4.6%	7.6%	8.7%	8.3%
Low SES
IEP or diagnosed disability
English Language Learner

Reliability

Grade	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8	Grade 9	Grade 10	Grade 11
Rating

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

The internal consistency reliability coefficient estimates the proportion of variability within a single administration of a test that is due to inconsistency among the items that comprise the test

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

For each grade, a large sample (n = 100,000) of students completed Star Reading assessments throughout the 2012–2013 school year

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

Reliability was defined as the proportion of test score variance that is attributable to true variation in the trait the test measures. The variance of the test scores was calculated from Scaled Score data. The variance of the errors of measurement was estimated from the conditional standard error of measurement (CSEM) statistics that accompany each of the IRT-based test scores, including the Scaled Scores. The conditional standard error of measurement (CSEM) was calculated along with the IRT ability estimate and Scaled Score. Squaring and summing the CSEM values yielded an estimate of total squared error; dividing by the number of observations yielded an estimate of error variance. Generic reliability was calculated by subtracting the ratio of error variance to Scaled Score variance from 1.

*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:

Yes

Provide citations for additional published studies.

Renaissance Learning (2020). Star Assessments™ for Reading Technical Manual. Wisconsin Rapids, WI: Author. Available by request to research@renaissance.com.

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:

Provide citations for additional published studies.

Validity

Grade	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8	Grade 9	Grade 10	Grade 11
Rating

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

All criterion measures were external to the screening tool system and represent widely used assessments of general reading ability. • CAT-5. The California Achievement Test, is a nationally normed standardized test that measures achievement in reading. • ITBS. The Iowa Tests For Basic Skills are nationally normed standardized tests that offer educators a diagnostic look at how their students are progressing in reading comprehension. • PARCC. The Partnership for Assessment of Readiness for College and Careers end-of-year assessment covers reading and is intended to be used as an indicator of student needs and progress. • SBA. Smarter Balanced assessments are summative tests designed to measure student achievement and growth in reading to support teaching and learning. • ACT. The American College Testing college readiness assessment is a national standardized test for high school achievement and college admissions. • SAT EBRW. The SAT Evidence-based reading and writing assessment is a standardized test widely used for college admissions in the United States.

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

Samples included students who had taken both Star Reading and the criterion measure. The sample sizes varied across criterion and grades, ranging from 105 to 10,800 students.

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

Concurrent and predictive correlations were calculated. A criterion assessment was considered concurrent if it was taken within approximately 30 days of Star Reading. Predictive correlations involve a criterion assessment occurring outside of the concurrent window for Star Reading.

*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:

Yes

Provide citations for additional published studies.

Renaissance Learning (2020). Star Assessments™ for Reading Technical Manual. Wisconsin Rapids, WI: Author. Available by request to research@renaissance.com.

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

The provided data indicate that Star Reading results correspond to other various respected measures of general reading ability

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 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8	Grade 9	Grade 10	Grade 11
Rating	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes

Have you conducted additional analyses related to the extent to which your tool is or is not biased against subgroups (e.g., race/ethnicity, gender, socioeconomic status, students with disabilities, English language learners)? Examples might include Differential Item Functioning (DIF) or invariance testing in multiple-group confirmatory factor models.

Yes

If yes,

a. Describe the method used to determine the presence or absence of bias:

Logistic regression analyses conditional on ability, group membership, and ability by group interaction were conducted to assess the presence of both uniform and non-uniform DIF simultaneously. Additionally, an effect size measure – Nagelkerke R-squared – was computed to quantify the magnitude of DIF where present.

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

DIF analyses were conducted for gender (males and females) and race/ethnicity (Caucasian, African American, American Indian, Asian, and Hispanic subpopulations). Due to insufficient samples sizes on English Language Learner (ELLs) and students with disabilities (SWD), DIF analyses for these two subgroups were not possible at the time of the analyses.

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.

Using a blended criterion that flagged items for uniform/non-uniform DIF if they had a p-value less than 0.01 and Nagelkerke R2 greater than or equal to 0.035, the results indicated that Star Reading is sufficiently bias-free. A total of 153 items (2% of the Star Reading items) were flagged for DIF. Those flagged items were removed from the item banks for review and recalibration.