Istation’s Indicators of Progress (ISIP)
Early Math

Descriptive Information

Please provide a description of your tool:

ISIP Early Math is a web based computer adaptive formative assessment intended for students in PreKindergarten through Grade 1. The assessment is intended to provide teachers and administrators with student test results to answer two questions: (a) whether students are at risk of failure, and (b) the degree of intensity of instructional support students need to be successful. ISIP Early Math can be administered individually or in group settings. The assessment is administered in English. The assessment is untimed; however, most students complete the assessment within 30 minutes. There is no additional scoring time required for the assessment. Teachers can be trained on ISIP Math through either a webinar or an in-person training session. Training takes between 1 and 4 hours. All training materials are online and are created by Istation. Reports are available for both individual and groups of students indicating single administration results and comparisons of results over time. All reports include student scaled scores and tier levels based on student percentiles.

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

info@istation.com

Address

8150 North Central Expressway, Suite 2000, Dallas, TX, 75206

Phone Number

(866) 883-READ

Website

www.istation.com

Initial cost for implementing program:

Cost

$5.95

Unit of cost

student

Replacement cost per unit for subsequent use:

Cost

$5.95

Unit of cost

student

Duration of license

1 year

Additional cost information:

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

ISIP Early Math is purchased as a yearly subscription. ISIP Early Math assessment packages includes online assessment, data hosting, reporting, teacher resources, online training center, user and manuals. In-person training conducted by a professional development specialist cost is $2800 per specialist per day. Computers and/or tablets are needed to implement this assessment, as well as internet access. ISIP Early can be used on many different technology platforms including desktops, laptops, and tablets.

Provide information about special accommodations for students with disabilities.

Appropriate accommodations are provided during ISIP assessments for students who are receiving support services, including those who have an Individual Education or 504 Plan, or who qualify as English Language learners. These accommodations support students’ access to the content of the assessment by reducing or eliminating the effects of the disability or limitation but do not change the content of the assessment. ISIP assessments provide people with disabilities access that is comparable to access for non-impaired people — with the exception of a totally blind or totally deaf disabled person. Administrators with manager accounts can assign accommodations to students in the Istation report and Management Portal. See attached for more specific accommodations and assistive technology.

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

25

per (student/group/other unit)

student

Additional scoring time:

Time in minutes

0

per (student/group/other unit)

student

ACADEMIC ONLY: What are the discontinue rules?

No discontinue rules provided
Basals
Ceilings
Other

If other, please specify:

Are norms available?

Yes

Are benchmarks available?

Yes

If yes, how many benchmarks per year?

3

If yes, for which months are benchmarks available?

Entire year; January through December

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

If yes, how many students can be rated concurrently?

Training & Scoring

Training

Is training for the administrator required?

Yes

Describe the time required for administrator training, if applicable:

1-4 hrs of training

Please describe the minimum qualifications an administrator must possess.

paraprofessional at minimum

No minimum qualifications

Are training manuals and materials available?

Yes

Are training manuals/materials field-tested?

Yes

Are training manuals/materials included in cost of tools?

Yes

If No, please describe training costs:

Training manuals, users guides, and an online training center are included in the cost of the tool. In-person training conducted by a professional development specialist cost is $2800 per specialist per day.

Can users obtain ongoing professional and technical support?

Yes

If Yes, please describe how users can obtain support:

By email and phone (M-F 7am-6:30pm) CST)

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:

MetaMetrics Quantile 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.

Ability scores are estimated using Bayesian EAP with an informative prior under a 2 PL unidimensional IRT model. Reported scale scores are generated through a linear transformation of the raw IRT-based ability scores. An overall ability is estimated after all of the appropriate sub-contents (number sense, operations, geometry, algebra and algebraic thinking, measurement, data analysis, probability and statistics, personal financial literacy, and mathematical reasoning) are measured based on the responses from all items.

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.

Classification Accuracy & Cross-Validation Summary

Grade	Kindergarten	Grade 1
Classification Accuracy Fall
Classification Accuracy Winter
Classification Accuracy Spring

Test of Early Mathematics Ability (TEMA)

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 Test of Early Mathematics Ability –Third Edition (TEMA) was selected as a criterion assessment for students in K-1 for this study. TEMA is intended to identify children who are significantly behind or ahead of their peers in the development of mathematical thinking (Ginsburg & Baroody, 2003). Mathematical concepts and skills assessed in the TEMA include relative magnitude, counting, calculation, convention, number facts, base 10 concepts, non-verbal production, non-verbal addition and subtraction, part whole concepts, equal partitioning, symbolic additive commutativity, number comparisons, and mental addition and subtraction (Ginsburg & Baroody, 2003).

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).

Classification accuracy analyses were performed to determine if ISIP Math was able to accurately differentiate using TEMA as the criterion assessment. 20th percentile are used as cut points for both TEMA and ISIP MATHS and it is a cut point for intensive need. The data were collected in 2015-16 school year from three districts in the State of Texas.

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 Test of Early Mathematics Ability –Third Edition (TEMA) was selected as a criterion assessment for students in K-1 for this study. TEMA is intended to identify children who are significantly behind or ahead of their peers in the development of mathematical thinking (Ginsburg & Baroody, 2003). Mathematical concepts and skills assessed in the TEMA include relative magnitude, counting, calculation, convention, number facts, base 10 concepts, non-verbal production, non-verbal addition and subtraction, part whole concepts, equal partitioning, symbolic additive commutativity, number comparisons, and mental addition and subtraction (Ginsburg & Baroody, 2003).

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).

Cross-validation analyses were performed to determine if ISIP Math was able to accurately differentiate using TEMA as the criterion assessment. 20th percentile are used as cut points for both TEMA and ISIP MATHS and it is a cut point for intensive need. The data were collected in 2015-16 school year from three districts in the State of Texas.

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.

---

Classification Accuracy - Fall

Evidence	Kindergarten	Grade 1
Criterion measure	Test of Early Mathematics Ability (TEMA)	Test of Early Mathematics Ability (TEMA)
Cut Points - Percentile rank on criterion measure	20	20
Cut Points - Performance score on criterion measure
Cut Points - Corresponding performance score (numeric) on screener measure	1793	1762
Classification Data - True Positive (a)
Classification Data - False Positive (b)
Classification Data - False Negative (c)
Classification Data - True Negative (d)
Area Under the Curve (AUC)	0.71	0.74
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.50	0.66
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.74	0.81

Statistics	Kindergarten	Grade 1
Base Rate
Overall Classification Rate
Sensitivity
Specificity
False Positive Rate
False Negative Rate
Positive Predictive Power
Negative Predictive Power

Sample	Kindergarten	Grade 1
Date	September – October 2015	September – October 2015
Sample Size
Geographic Representation	West South Central (TX)	West South Central (TX)
Male
Female
Other
Gender Unknown
White, Non-Hispanic
Black, Non-Hispanic
Hispanic
Asian/Pacific Islander
American Indian/Alaska Native
Other
Race / Ethnicity Unknown
Low SES
IEP or diagnosed disability
English Language Learner

Classification Accuracy - Winter

Evidence	Kindergarten	Grade 1
Criterion measure	Test of Early Mathematics Ability (TEMA)	Test of Early Mathematics Ability (TEMA)
Cut Points - Percentile rank on criterion measure	20	20
Cut Points - Performance score on criterion measure
Cut Points - Corresponding performance score (numeric) on screener measure	1883	1871
Classification Data - True Positive (a)
Classification Data - False Positive (b)
Classification Data - False Negative (c)
Classification Data - True Negative (d)
Area Under the Curve (AUC)	0.76	0.77
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.58	0.69
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.81	0.84

Statistics	Kindergarten	Grade 1
Base Rate
Overall Classification Rate
Sensitivity
Specificity
False Positive Rate
False Negative Rate
Positive Predictive Power
Negative Predictive Power

Sample	Kindergarten	Grade 1
Date	January – February 2016	January – February 2016
Sample Size
Geographic Representation	West South Central (TX)	West South Central (TX)
Male
Female
Other
Gender Unknown
White, Non-Hispanic
Black, Non-Hispanic
Hispanic
Asian/Pacific Islander
American Indian/Alaska Native
Other
Race / Ethnicity Unknown
Low SES
IEP or diagnosed disability
English Language Learner

Classification Accuracy - Spring

Evidence	Kindergarten	Grade 1
Criterion measure	Test of Early Mathematics Ability (TEMA)	Test of Early Mathematics Ability (TEMA)
Cut Points - Percentile rank on criterion measure	20	20
Cut Points - Performance score on criterion measure
Cut Points - Corresponding performance score (numeric) on screener measure	1984	1965
Classification Data - True Positive (a)
Classification Data - False Positive (b)
Classification Data - False Negative (c)
Classification Data - True Negative (d)
Area Under the Curve (AUC)	0.80	0.74
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.68	0.66
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.91	0.82

Statistics	Kindergarten	Grade 1
Base Rate
Overall Classification Rate
Sensitivity
Specificity
False Positive Rate
False Negative Rate
Positive Predictive Power
Negative Predictive Power

Sample	Kindergarten	Grade 1
Date	May – June 2016	May – June 2016
Sample Size
Geographic Representation	West South Central (TX)	West South Central (TX)
Male
Female
Other
Gender Unknown
White, Non-Hispanic
Black, Non-Hispanic
Hispanic
Asian/Pacific Islander
American Indian/Alaska Native
Other
Race / Ethnicity Unknown
Low SES
IEP or diagnosed disability
English Language Learner

Cross-Validation - Fall

Evidence	Kindergarten	Grade 1
Criterion measure	Test of Early Mathematics Ability (TEMA)	Test of Early Mathematics Ability (TEMA)
Cut Points - Percentile rank on criterion measure	20	20
Cut Points - Performance score on criterion measure
Cut Points - Corresponding performance score (numeric) on screener measure	1793	1762
Classification Data - True Positive (a)
Classification Data - False Positive (b)
Classification Data - False Negative (c)
Classification Data - True Negative (d)
Area Under the Curve (AUC)	0.71	0.74
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.50	0.66
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.74	0.81

Statistics	Kindergarten	Grade 1
Base Rate
Overall Classification Rate
Sensitivity
Specificity
False Positive Rate
False Negative Rate
Positive Predictive Power
Negative Predictive Power

Sample	Kindergarten	Grade 1
Date	September – October 2015	September – October 2015
Sample Size
Geographic Representation	West South Central (TX)	West South Central (TX)
Male
Female
Other
Gender Unknown
White, Non-Hispanic
Black, Non-Hispanic
Hispanic
Asian/Pacific Islander
American Indian/Alaska Native
Other
Race / Ethnicity Unknown
Low SES
IEP or diagnosed disability
English Language Learner

Cross-Validation - Winter

Evidence	Kindergarten	Grade 1
Criterion measure	Test of Early Mathematics Ability (TEMA)	Test of Early Mathematics Ability (TEMA)
Cut Points - Percentile rank on criterion measure	20	20
Cut Points - Performance score on criterion measure
Cut Points - Corresponding performance score (numeric) on screener measure	1883	1871
Classification Data - True Positive (a)
Classification Data - False Positive (b)
Classification Data - False Negative (c)
Classification Data - True Negative (d)
Area Under the Curve (AUC)	0.76	0.77
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.58	0.69
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.81	0.84

Statistics	Kindergarten	Grade 1
Base Rate
Overall Classification Rate
Sensitivity
Specificity
False Positive Rate
False Negative Rate
Positive Predictive Power
Negative Predictive Power

Sample	Kindergarten	Grade 1
Date	January – February 2016	January – February 2016
Sample Size
Geographic Representation	West South Central (TX)	West South Central (TX)
Male
Female
Other
Gender Unknown
White, Non-Hispanic
Black, Non-Hispanic
Hispanic
Asian/Pacific Islander
American Indian/Alaska Native
Other
Race / Ethnicity Unknown
Low SES
IEP or diagnosed disability
English Language Learner

Cross-Validation - Spring

Evidence	Kindergarten	Grade 1
Criterion measure	Test of Early Mathematics Ability (TEMA)	Test of Early Mathematics Ability (TEMA)
Cut Points - Percentile rank on criterion measure	20	20
Cut Points - Performance score on criterion measure
Cut Points - Corresponding performance score (numeric) on screener measure	1984	1965
Classification Data - True Positive (a)
Classification Data - False Positive (b)
Classification Data - False Negative (c)
Classification Data - True Negative (d)
Area Under the Curve (AUC)	0.80	0.74
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.68	0.66
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.91	0.82

Statistics	Kindergarten	Grade 1
Base Rate
Overall Classification Rate
Sensitivity
Specificity
False Positive Rate
False Negative Rate
Positive Predictive Power
Negative Predictive Power

Sample	Kindergarten	Grade 1
Date	May – June 2016	May – June 2016
Sample Size
Geographic Representation	West South Central (TX)	West South Central (TX)
Male
Female
Other
Gender Unknown
White, Non-Hispanic
Black, Non-Hispanic
Hispanic
Asian/Pacific Islander
American Indian/Alaska Native
Other
Race / Ethnicity Unknown
Low SES
IEP or diagnosed disability
English Language Learner

Reliability

Grade	Kindergarten	Grade 1
Rating	d	d

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

Cronbach’s (1951) coefficient alpha is typically used as an indicator of reliability across test items within a testing instance. However, Cronboch’s Alpha is not appropriate for any IRT based measure because alpha assumes that all students in the testing instance respond to a common set of items. Due to its very nature, students taking a CAT-based assessment, such as ISIP Math, will receive a custom set of items based on their initial estimates of ability and response patterns. Thus, students do not respond to a common set of items. The IRT analogue to classical internal consistency is marginal reliability (Bock & Mislevy, 1982) and thus applied to ISIP Math.

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

Samples were obtained from three school districts in Texas during the 2015-2016 school year. There were 171 kindergartners for beginning of the year assessment (BOY/Fall), 170 kindergartners for middle of the year assessment (MOY/Winter), 163 kindergartners for end of the year assessment (EOY/Spring), 217 first graders for beginning of the year assessment (BOY), 230 first graders for middle of the year assessment (MOY), and 221 first graders for end of the year assessment (EOY).

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

IRT based reliability is derived from Classical Test Theory to Item Response Theory as follow. (see attached doc) IRT based reliability in equation (1) is computed at each administration.

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

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

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

Manual cites other published reliability studies:

No

Provide citations for additional published studies.

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

Yes

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

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

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

Additional disaggregated data is available from the Center upon request.

Manual cites other published reliability studies:

No

Provide citations for additional published studies.

Validity

Grade	Kindergarten	Grade 1
Rating	d	d

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

Predictive validity examines the relation between performance on the screener and a criterion of similar content that is administered at some time in the future. On the other hand, Concurrent validity examines the relation between performance on the screener and a criterion of similar content that is administered at the same point in time. Renaissance Learning’s STAR Math is a computerized adaptive test intended for students in Grades 1 through 8. STAR Math is designed to provide teachers and administrators with data that can be used for multiple purposes such as screening, placement, planning instruction, benchmarking, and outcomes measurement. It also provides educators with estimates of students’ instructional math levels relative to national norms. Because STAR Math assesses a similar construct and has a similar use, STAR Math was used to provide concurrent validity for ISIP Math. The Test of Early Mathematics Ability –Third Edition (TEMA-3) was selected to provide concurrent validity for ISIP Math because it assesses a similar construct. TEMA-3 is intended to identify children who are significantly behind or ahead of their peers in the development of mathematical thinking (Ginsburg & Baroody, 2003). The TEMA-3 is intended to be administered at the beginning of the school year as an early indicator of students’ abilities, but can also be administered later in the school year to assess student progress. Mathematical concepts and skills assessed in the TEMA-3 include relative magnitude, counting, calculation, convention, number facts, base 10 concepts, non-verbal production, non-verbal addition and subtraction, part whole concepts, equal partitioning, symbolic additive commutativity, number comparisons, and mental addition and subtraction.

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

Samples are derived from urban school districts in the northeast area of the state of Texas approximately 200 students per each assessment point (Fall/Winter/Spring).

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

Data for this study was obtained from three school districts in Texas during the 2015-2016 school year. There were 178 kindergartners and 239 first graders. The validity was calculated by determining the correlation between the scaled scores of the ISIP Math and the scaled scores of the TEMA-3, and the STAR Math individually, by grade level.

*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.

The results suggest moderate to strong relationships between ISIP Math and STAR Math and TEMA-3. Our findings also show very convincing evidence across all 3 data points (Fall: beginning of the year (BOY), Winter: middle of the year (MOY), and Spring: end of the year (EOY) both K and Grade 1.

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

Yes

If yes, fill in data for each subgroup with disaggregated 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:

Additional disaggregated data is available from the Center upon request.

Manual cites other published reliability studies:

No

Provide citations for additional published studies.

Bias Analysis

Grade	Kindergarten	Grade 1
Rating	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:

Differential Item Functioning (DIF) analysis was conducted by grade level (K - 1) using logistic regression DIF detection analysis by difR package in R software.

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

Four DIF factors were investigated: socioeconomic status, gender, race/ethnicity, and special education students.

c. Describe the results of the bias analyses conducted, including data and interpretative statements. Include magnitude of effect (if available) if bias has been identified.

Using Zumbo & Thomas (ZT) DIF criterion, results showed 97% displayed as A item (negligible or non-significant DIF effect), 2% displayed as B item (slightly to moderate DIF effect), and only 1% displayed as C item (moderate to large DIF effect) across these four DIF factors.