Istation's Indicators of Progress (ISIP)
Reading

Descriptive Information

Please provide a description of your tool:

ISIP Reading is a web based computer adaptive assessment intended for students in preK through Grade 8 and can be administered simultaneously to an entire classroom in approximately 30 minutes. There is no additional scoring time required for the assessment. Teachers can be trained on ISIP 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

sales@istation.com

Address

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

Phone Number

(866)883-READ

Website

https://info.istation.com/contact-sales

Initial cost for implementing program:

Cost

$6.75

Unit of cost

student

Replacement cost per unit for subsequent use:

Cost

$6.75

Unit of cost

student

Duration of license

year

Additional cost information:

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

ISIP is purchased as a yearly subscription. ISIP assessment packages includes online assessment, data hosting, reporting, teacher resources, online training center, user guides and manuals. Computers and/or tablets are needed to implement this assessment, as well as internet access. ISIP can be used on many different technology platforms including desktops, laptops, and tablets. Additionally, in-person training conducted by a professional development specialist cost is $2750 per specialist per half day. Virtual training starts at $1950. Istation also creates custom PD services to support customer or district learning outcomes with project management services.

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. Istation provides guidance for accommodations for students with various needs. We have universal features that are available for all students. Designated features are options available to a student with a documented need. Both universal and designated features may be embedded (available within Istation) or non-embedded (provided at the local level). Administrators with manager accounts can assign accommodations to students in the Istation report and Management Portal.

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

30

per (student/group/other unit)

student

Additional scoring time:

Time in minutes

30

per (student/group/other unit)

group

ACADEMIC ONLY: What are the discontinue rules?

No discontinue rules provided
Basals
Ceilings
Other

If other, please specify:

Adaptive test stopping criteria based on minimized standard error of measurement.

Are norms available?

Yes

Are benchmarks available?

Yes

If yes, how many benchmarks per year?

12

If yes, for which months are benchmarks available?

Norms with benchmark cut scores are available for each month of the year. Schools may choose which months they would like to consider their official benchmarks.

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

If yes, how many students can be rated concurrently?

Training & Scoring

Training

Is training for the administrator required?

Yes

Describe the time required for administrator training, if applicable:

1-4 hours of training

Please describe the minimum qualifications an administrator must possess.

Paraprofessional 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:

Can users obtain ongoing professional and technical support?

Yes

If Yes, please describe how users can obtain support:

Scoring

How are scores calculated?

Manually (by hand)
Automatically (computer-scored)
Other

If other, please specify:

Do you provide basis for calculating performance level scores?

Yes

What is the basis for calculating performance level and percentile scores?

Age norms
Grade norms
Classwide norms
Schoolwide norms
Stanines
Normal curve equivalents

What types of performance level scores are available?

Raw score
Standard score
Percentile score
Grade equivalents
IRT-based score
Age equivalents
Stanines
Normal curve equivalents
Developmental benchmarks
Developmental cut points
Equated
Probability
Lexile score
Error analysis
Composite scores
Subscale/subtest scores
Other

If other, please specify:

Does your tool include decision rules?

No

If yes, please describe.

Can you provide evidence in support of multiple decision rules?

No

If yes, please describe.

Please describe the scoring structure. Provide relevant details such as the scoring format, the number of items overall, the number of items per subscale, what the cluster/composite score comprises, and how raw scores are calculated.

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. Abilities for each of the subskills (phonemic awareness, letter knowledge, alphabetic decoding, spelling, vocabulary, and comprehension) are estimated separately based on examinee response patterns to the items adaptively administered. An overall ability is estimated after all of the appropriate subtests are given 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.

Ability scale scores are compared to cut-points determined from nationally representative norming sample to classify students into one of three instructional tiers. Ongoing reviews of item parameters, score scaling, and the setting of cut-points is practiced for ISIP. The data used for the item calibration was based on an ethnically diverse regional sample, including urban and suburban students of varied ability and backgrounds.

Classification Accuracy & Cross-Validation Summary

Grade	Kindergarten	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Classification Accuracy Fall
Classification Accuracy Winter
Classification Accuracy Spring

MAP 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 criterion measure is NWEA MAP Reading which is a computer adaptive assessment of reading ability. Its computer adaptive nature is similar to ISIP in that the items a student receives are dependent on a student’s ability level. Both ISIP and NWEA MAP reading measure similar constructs as in overall reading ability. The criterion is 100% independent of ISIP Assessments. They are published by different organizations.

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).

Students were classified as “at risk” and requiring intensive intervention if their MAP Growth scores were below the 20th percentile based on the MAP Growth norming study conducted in 2020 by NWEA. Cut points on the screening measure (ISIP) were empirically identified as scores that best align with NWEA MAP 20th percentile scores for each grade and administration period. Using these cut scores, students were classified as at-risk if they scored below the cut score in the ISIP for the given administration period, or not-at-risk if they scored at or above the cut. A logistic regression model was run to obtain the ROC and classification accuracy indices that are reported.

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?

No

If yes,

Select time of year.

Fall

Winter

Spring

Describe the criterion (outcome) measure(s) including the degree to which it/they is/are independent from the screening measure.

Do the cross-validation analyses examine concurrent and/or predictive classification?

Concurrent
Predictive

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

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

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

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

---

Classification Accuracy - Fall

Evidence	Kindergarten	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Criterion measure	MAP Reading	MAP Reading	MAP Reading	MAP Reading	MAP Reading	MAP Reading	MAP Reading	MAP Reading	MAP Reading
Cut Points - Percentile rank on criterion measure	20	20	20	15	20	20	20	20	20
Cut Points - Performance score on criterion measure
Cut Points - Corresponding performance score (numeric) on screener measure
Classification Data - True Positive (a)	345	408	771	600	637	554	380	372	303
Classification Data - False Positive (b)	514	395	380	253	355	327	210	220	164
Classification Data - False Negative (c)	94	78	135	122	129	111	72	80	54
Classification Data - True Negative (d)	1613	1832	1924	1760	1694	1463	898	941	849
Area Under the Curve (AUC)	0.77	0.83	0.84	0.85	0.83	0.83	0.83	0.82	0.84
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.75	0.81	0.83	0.84	0.81	0.81	0.81	0.80	0.82
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.79	0.85	0.86	0.87	0.84	0.84	0.85	0.84	0.87

Statistics	Kindergarten	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Base Rate	0.17	0.18	0.28	0.26	0.27	0.27	0.29	0.28	0.26
Overall Classification Rate	0.76	0.83	0.84	0.86	0.83	0.82	0.82	0.81	0.84
Sensitivity	0.79	0.84	0.85	0.83	0.83	0.83	0.84	0.82	0.85
Specificity	0.76	0.82	0.84	0.87	0.83	0.82	0.81	0.81	0.84
False Positive Rate	0.24	0.18	0.16	0.13	0.17	0.18	0.19	0.19	0.16
False Negative Rate	0.21	0.16	0.15	0.17	0.17	0.17	0.16	0.18	0.15
Positive Predictive Power	0.40	0.51	0.67	0.70	0.64	0.63	0.64	0.63	0.65
Negative Predictive Power	0.94	0.96	0.93	0.94	0.93	0.93	0.93	0.92	0.94

Sample	Kindergarten	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Date	2021/22	2021/22	2021/22	2021/22	2021/22	2021/22	2021/22	2021/22	2021/22
Sample Size	2566	2713	3210	2735	2815	2455	1560	1613	1370
Geographic Representation	Mountain (NM) Pacific (CA) West South Central (TX)	Mountain (NM) Pacific (CA) West South Central (TX)	Mountain (NM) Pacific (CA) West South Central (TX)	Mountain (NM) Pacific (CA) West South Central (TX)	Pacific (CA) West South Central (TX)	Pacific (CA) West South Central (TX)	Pacific (CA) West South Central (TX)	West South Central (TX)	West South Central (TX)
Male	53.3%	52.3%	52.0%	50.2%	49.5%	50.8%	51.0%	50.1%	48.7%
Female	46.7%	47.7%	48.0%	49.8%	50.5%	49.2%	49.0%	49.9%	51.3%
Other
Gender Unknown
White, Non-Hispanic	22.4%	21.3%	19.6%	14.9%	17.9%	15.5%	14.7%	15.4%	17.5%
Black, Non-Hispanic	2.8%	3.1%	2.5%	3.7%	5.5%	4.7%	4.0%	4.3%	4.0%
Hispanic	68.0%	69.7%	73.2%	78.0%	70.8%	75.6%	77.4%	76.8%	75.4%
Asian/Pacific Islander	1.3%	1.1%	0.9%	0.7%	2.8%	2.0%	1.5%	1.0%	1.2%
American Indian/Alaska Native
Other	5.5%	4.9%	3.8%	2.7%	3.0%	2.2%	2.4%	2.4%	1.9%
Race / Ethnicity Unknown
Low SES
IEP or diagnosed disability
English Language Learner

Classification Accuracy - Winter

Evidence	Kindergarten	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Criterion measure	MAP Reading	MAP Reading	MAP Reading	MAP Reading	MAP Reading	MAP Reading	MAP Reading	MAP Reading	MAP Reading
Cut Points - Percentile rank on criterion measure	20	10	10	20	20	20	20	20	20
Cut Points - Performance score on criterion measure
Cut Points - Corresponding performance score (numeric) on screener measure
Classification Data - True Positive (a)	282	592	818	639	693	619	445	452	348
Classification Data - False Positive (b)	459	394	362	251	396	307	245	233	256
Classification Data - False Negative (c)	58	135	122	146	141	99	95	84	75
Classification Data - True Negative (d)	1961	1755	1886	1542	1706	1579	1050	1017	1106
Area Under the Curve (AUC)	0.82	0.81	0.84	0.82	0.82	0.85	0.82	0.83	0.82
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.80	0.80	0.83	0.81	0.81	0.83	0.80	0.81	0.80
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.84	0.83	0.85	0.84	0.84	0.86	0.84	0.85	0.84

Statistics	Kindergarten	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Base Rate	0.12	0.25	0.29	0.30	0.28	0.28	0.29	0.30	0.24
Overall Classification Rate	0.81	0.82	0.85	0.85	0.82	0.84	0.81	0.82	0.81
Sensitivity	0.83	0.81	0.87	0.81	0.83	0.86	0.82	0.84	0.82
Specificity	0.81	0.82	0.84	0.86	0.81	0.84	0.81	0.81	0.81
False Positive Rate	0.19	0.18	0.16	0.14	0.19	0.16	0.19	0.19	0.19
False Negative Rate	0.17	0.19	0.13	0.19	0.17	0.14	0.18	0.16	0.18
Positive Predictive Power	0.38	0.60	0.69	0.72	0.64	0.67	0.64	0.66	0.58
Negative Predictive Power	0.97	0.93	0.94	0.91	0.92	0.94	0.92	0.92	0.94

Sample	Kindergarten	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Date	2021/22	2021/22	2021/22	2021/22	2021/22	2021/22	2021/22	2021/22	2021/22
Sample Size	2760	2876	3188	2578	2936	2604	1835	1786	1785
Geographic Representation	Mountain (NM) Pacific (CA) West South Central (TX)	Mountain (NM) Pacific (CA) West South Central (TX)	Mountain (NM) Pacific (CA) West South Central (TX)	Mountain (NM) Pacific (CA) West South Central (TX)	Pacific (CA) West South Central (TX)	Pacific (CA) West South Central (TX)	Pacific (CA) West South Central (TX)	West South Central (TX)	West South Central (TX)
Male	52.9%	53.8%	53.7%	49.6%	48.9%	50.4%	51.4%	51.2%	49.4%
Female	47.1%	49.4%	48.9%	50.1%	51.1%	49.6%	48.6%	48.8%	50.6%
Other
Gender Unknown				0.3%
White, Non-Hispanic	21.9%	21.0%	20.1%	14.5%	17.1%	16.3%	15.5%	16.2%	20.2%
Black, Non-Hispanic	2.9%	3.2%	3.0%	3.8%	5.8%	5.2%	5.2%	6.2%	5.3%
Hispanic	68.7%	72.8%	74.7%	78.0%	71.1%	73.2%	73.8%	71.9%	69.0%
Asian/Pacific Islander	1.3%	1.1%	0.9%	0.7%	3.1%	2.6%	1.8%	2.6%	2.6%
American Indian/Alaska Native				0.3%
Other	5.3%	5.1%	3.9%	2.4%	2.9%	2.7%	3.6%	3.1%	2.9%
Race / Ethnicity Unknown				0.3%
Low SES
IEP or diagnosed disability
English Language Learner

Classification Accuracy - Spring

Evidence	Kindergarten	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Criterion measure	MAP Reading	MAP Reading	MAP Reading	MAP Reading	MAP Reading	MAP Reading	MAP Reading	MAP Reading	MAP Reading
Cut Points - Percentile rank on criterion measure	20	20	15	20	20	20	20	15	20
Cut Points - Performance score on criterion measure
Cut Points - Corresponding performance score (numeric) on screener measure
Classification Data - True Positive (a)	424	636	844	639	507	460	331	336	247
Classification Data - False Positive (b)	411	455	533	326	420	308	198	377	199
Classification Data - False Negative (c)	82	138	106	120	111	97	72	42	43
Classification Data - True Negative (d)	1964	1800	1902	1504	1698	1504	870	1002	869
Area Under the Curve (AUC)	0.83	0.81	0.83	0.83	0.81	0.83	0.82	0.81	0.83
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.81	0.80	0.82	0.82	0.80	0.81	0.80	0.79	0.81
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.85	0.83	0.85	0.85	0.83	0.85	0.84	0.83	0.86

Statistics	Kindergarten	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Base Rate	0.18	0.26	0.28	0.29	0.23	0.24	0.27	0.22	0.21
Overall Classification Rate	0.83	0.80	0.81	0.83	0.81	0.83	0.82	0.76	0.82
Sensitivity	0.84	0.82	0.89	0.84	0.82	0.83	0.82	0.89	0.85
Specificity	0.83	0.80	0.78	0.82	0.80	0.83	0.81	0.73	0.81
False Positive Rate	0.17	0.20	0.22	0.18	0.20	0.17	0.19	0.27	0.19
False Negative Rate	0.16	0.18	0.11	0.16	0.18	0.17	0.18	0.11	0.15
Positive Predictive Power	0.51	0.58	0.61	0.66	0.55	0.60	0.63	0.47	0.55
Negative Predictive Power	0.96	0.93	0.95	0.93	0.94	0.94	0.92	0.96	0.95

Sample	Kindergarten	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Date	2021/22	2021/22	2021/22	2021/22	2021/22	2021/22	2021/22	2021/22	2021/22
Sample Size	2881	3029	3385	2589	2736	2369	1471	1757	1358
Geographic Representation	Mountain (NM) Pacific (CA) West South Central (TX)	Mountain (NM) Pacific (CA) West South Central (TX)	Mountain (NM) Pacific (CA) West South Central (TX)	Mountain (NM) Pacific (CA) West South Central (TX)	Pacific (CA) West South Central (TX)	Pacific (CA) West South Central (TX)	Pacific (CA) West South Central (TX)	West South Central (TX)	West South Central (TX)
Male	53.6%	52.7%	52.4%	50.4%	49.6%	51.1%	51.3%	45.4%	48.6%
Female	46.4%	47.3%	47.6%	49.6%	50.4%	48.9%	48.7%	45.3%	51.4%
Other
Gender Unknown
White, Non-Hispanic	22.5%	20.9%	20.0%	16.1%	18.2%	16.5%	15.4%	14.1%	17.7%
Black, Non-Hispanic	2.8%	2.6%	2.8%	3.2%	6.1%	5.3%	4.2%	3.9%	4.1%
Hispanic	67.6%	69.9%	72.0%	76.9%	69.4%	73.5%	76.5%	69.7%	74.9%
Asian/Pacific Islander	1.2%	1.2%	0.9%	0.7%	3.2%	2.1%	1.5%	0.9%	1.3%
American Indian/Alaska Native
Other	5.8%	5.4%	4.3%	3.2%	3.1%	2.6%	2.4%	2.0%	2.0%
Race / Ethnicity Unknown
Low SES
IEP or diagnosed disability
English Language Learner

Reliability

Grade	Kindergarten	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Rating	d	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, Cronbach’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 Reading, 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). Marginal reliability is a method of combining the variability in estimating abilities at different points on the ability scale into a single index. Like Cronbach’s alpha, marginal reliability is a unitless measure bounded by 0 and 1, and it can be used with Cronbach’s alpha to directly compare the internal consistencies of classical test data to IRT-based test data. ISIP Reading has stopping criteria based on minimizing the standard error of the ability estimate. As such, the lower limit of the marginal reliability of the data for any testing instance of ISIP Reading will always be approximately 0.90. Test-retest Reliability: Students can take ISIP reading multiple times a year. This type of evidence allows to examine how consistently students respond to the assessment over different occasions. Evidence of test-retest stability was obtained for a subset of the normed sample. Students who tested twice within a time interval of 2-21 days in the beginning, middle, and end of the year were selected in this study.

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

Samples were derived from the total population of students using the ISIP Reading assessment throughout the 2018-2019 school year. Large sample sizes ranged from 111,308-169,623 students in K-grade 2 across the United States to estimate IRT based reliability. For test-retest a subset of the normed sample was used for the purpose of this study. Sample sizes ranged from 11,192-22,876 across K-grade 2.

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

Istation derived IRT-based reliability from Classical Test Theory standpoint to Item Response Theory. Test-retest reliability of ISIP was estimated as the Pearson correlation of student scaled scores for a set of students who took ISIP twice during the school year in 2018-19. The confidence interval (CI) for the test-retest reliability coefficient was obtained using the standard CI for a Pearson correlation (i.e., via the Fisher z-transformation).

*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.

Mathes, P., Torgeson, J., & Herron, J. (2022). Istation’s Indicators of Progress (ISIP)Reading: Technical Report. Dallas, TX: Istation.

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:

Manual cites other published reliability studies:

No

Provide citations for additional published studies.

Validity

Grade	Kindergarten	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Rating	d	d	d	d	d	d	d	d	d

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

The criterion measure in Kindergarten through grade 2 for both types of validity analyses (concurrent and predictive) is the NWEA MAP reading assessment. The measure is an appropriate criterion because it measures a construct hypothesized to be related to ISIP overall reading. NWEA MAP is external as MAP and ISIP are published by separate companies. Another criterion measure used was the Renaissance STAR assessment for Kindergarten through grade 2. Renaissance STAR is an appropriate measure because it provides a broad indicator of overall reading ability similar to ISIP overall reading scores. Both ISIP and STAR are independent of each other as each measure is published by different companies.

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

Concurrent and predictive analyses of NWEA MAP data were conducted on a sample of students across three states: CA, NM, and TX. The sample sizes ranged from 1,266-3,075 across grades. Renaissance STAR data was collected from one state in the South Atlantic census region in the US. Sample sizes ranged from 54-796 across grades.

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

Validity coefficients were calculated by computing Pearson product moment correlations between ISIP reading and the criterion measures. 95% confidence intervals were computed using the fisher z-transformation method. Predictive validity examines associations between BOY and EOY whereas concurrent validity considers associations between the measures at EOY.

*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.

Campbell, L. O., Sutter, C. C., Lambie, G. W., & Tinstman Jones, J. (2019). Measuring the predictability of Istation Indicators of Progress Early Reading (ISIP-ER) scores on Renaissance STAR Reading® scores. University of Central Florida. www.ucf.edu/mirc Sutter, C. C., Campbell, L. O., & Lambie, G. W. (2020). Predicting second-grade students’ yearly standardized reading achievement using a computer-adaptive assessment. Computers in the Schools, 37, 1 40-54.

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

The coefficients show strong support for predictive and concurrent validity evidence.

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:

Manual cites other published reliability studies:

Yes

Provide citations for additional published studies.

Campbell, L. O., Sutter, C. C, & Lambie, G. W. (2019). Predictability of Istation’s Indicators of Progress Scores on Students’ Virginia Standard of Learning Scores: Grades 3 through 8. Orlando, FL: University of Florida Morgridge International Reading Center. Available at www.istation.com/studies Campbell, L. O., Sutter, C. C., Lambie, G. W., & Tinstman Jones, J. (2019). Measuring the predictability of Istation Indicators of Progress Early Reading (ISIP-ER) scores on Renaissance STAR Reading® scores. University of Central Florida. www.ucf.edu/mirc Cook, M. A. & Ross, S. M. (2020a). SC-Ready Predictability Study. Baltimore, MD: Johns Hopkins University Center for Research and Reform in Education. Available at www.istation.com/studies Cook, M. A. & Ross, S. M. (2020c). NWEA MAP Predictability Study. Baltimore, MD: Johns Hopkins University Center for Research and Reform in Education. Available at www.istation.com/studies Cook, M. A. & Ross, S. M. (2020b). PARCC Predictability Study – 3rd grade. Baltimore, MD: Johns Hopkins University Center for Research and Reform in Education. Available at www.istation.com/studies Mathes, P., Torgesen, J. & Herron, J. (2016). Istation’s Indicators of Progress (ISIP) Early Reading Technical Report: Computer Adaptive Testing System for continuous Progress Monitoring of Reading Growth for Students Pre-K through Grade 3. Dallas, TX: Istation. Mathes, P. (2016). Istation’s Indicators of Progress (ISIP) Advanced Reading Technical Report: Computer Adaptive Testing System for Continuous Progress Monitoring of Reading Growth for Students Grade 4 through Grade 8. Dallas, TX: Istation. Patarapichayatham, C. (2019). Linking the Colorado Measures of Academic Success English Language Arts (CMAS ELA) Assessments to ISIP™ Reading Assessments Grades 3 through 5. Dallas, TX: Istation. Available at www.istation.com/studies. Patarapichayatham, C. (2018). Predictability Study of ISIP Reading and Virginia Standards of Learning (SOL) for English Reading: 3rd – 5th Grade Students. Dallas, TX: Istation. Available at www.istation.com/studies. Patarapichayatham, C. (2017). Predictability Study of ISIP Reading and Kansas Assessment Program: 3rd – 6th Grade Students. Dallas, TX: Istation. Available at www.istation.com/studies. Patarapichayatham, C. (2016). Predictability Study of ISIP Reading and Georgia Milestones Assessment System: 3rd – 6th Grade Students. Dallas, TX: Istation. Available at www.istation.com/studies. Patarapichayatham, C. (2014). Predictability Study of ISIP Reading and STAAR Reading: Prediction Bands. Dallas, TX: Istation. Available at www.istation.com/studies. Patarapichayatham, C., & Locke, V. N. (2020). Linking the ACT Aspire Assessments to ISIP Reading and Math. Dallas, TX: Istation. Available at www.istation.com/studies. Patarapichayatham, C. & Locke, V. N. (2020). Linking the Ohio AIR to ISIP. Dallas, TX: Istation. Available at www.istation.com/studies. Patarapichayatham, C. & Locke, V. N. (2020). Linking Study Between STAAR Reading and ISIP ER Assessment for Second and Third Grade Students. Dallas, TX: Istation. Available at www.istation.com/studies Patarapichayatham, C. & Wolf, R. (2022). Linking Study Between the California Smarter Balanced Assessment and ISIP Reading. Dallas, TX: Istation. Available at www.istation.com/studies. Sutter, C. C., Campbell, L. O., & Lambie, G. W. (2020). Predicting second-grade students’ yearly standardized reading achievement using a computer-adaptive assessment. Computers in the Schools, 37, 1 40-54. Wolf, B. (2020). Linking Istation ISIP Early Reading with the Idaho ISAT. Baltimore, MD: Johns Hopkins University Center for Research and Reform in Education. Available at http://jhir.library.jhu.edu/handle/1774.2/62380 Wolf, B. (2020). Linking 2nd Grade Istation ISIP Reading with 3rd Grade ISAT in English Language Arts. Baltimore, MD: Johns Hopkins University Center for Research and Reform in Education. Available at http://jhir.library.jhu.edu/handle/1774.2/63123

Bias Analysis

Grade	Kindergarten	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Grade 6	Grade 7	Grade 8
Rating	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:

Differential Item Functioning (DIF) analysis was conducted by grade level (K - 2) 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).