Istation’s Indicators of Progress (ISIP)
Early Reading

Summary

ISIP Early Reading (ISIP ER) is an engaging computer adaptive assessment of reading ability that automatically adjusts the difficulty of items delivered to limit the amount of frustration or boredom often associated with traditional assessments. ISIP ER includes comprehensive reporting for teachers and parents, as well as downloadable teacher-directed lesson and resources for differentiated instruction. ISIP ER is intended to be used with students in grades K-3, and can be administered simultaneously to an entire classroom in approximately 30 minutes.

Where to Obtain:
Istation
info@istation.com
8150 North Central Expressway, Suite 2000, Dallas, TX 75206
(866)883-READ
www.istation.com
Initial Cost:
$5.95 per student
Replacement Cost:
$5.95 per student per year
Included in Cost:
ISIP ER is priced at $5.95 per student per year. Training manuals/materials are included in the cost of the tool. In-person training conducted by a professional development specialist cost is $2800 per specialist per day.
ISIP ER assessment packages includes online assessment, data hosting, reporting, teacher resources, online training center, user and manuals.
Training Requirements:
1-4 hours of training
Qualified Administrators:
Paraprofessional at minimum
Access to Technical Support:
By email and phone (M-F 7am-6:30pm) CST)
Assessment Format:
  • Direct: Computerized
Scoring Time:
  • Scoring is automatic
Scores Generated:
  • Raw score
  • Percentile score
  • IRT-based score
  • Lexile score
  • Composite scores
  • Subscale/subtest scores
Administration Time:
  • 30 minutes per student/group
Scoring Method:
  • Automatically (computer-scored)
Technology Requirements:
  • Computer or tablet
  • Internet connection
Accommodations:
ISIP ER assessment packages includes online assessment, data hosting, reporting, teacher resources, online training center, user and manuals.

Descriptive Information

Please provide a description of your tool:
ISIP Early Reading (ISIP ER) is an engaging computer adaptive assessment of reading ability that automatically adjusts the difficulty of items delivered to limit the amount of frustration or boredom often associated with traditional assessments. ISIP ER includes comprehensive reporting for teachers and parents, as well as downloadable teacher-directed lesson and resources for differentiated instruction. ISIP ER is intended to be used with students in grades K-3, and can be administered simultaneously to an entire classroom in approximately 30 minutes.
The tool is intended for use with the following grade(s).
not selected Preschool / Pre - kindergarten
selected Kindergarten
selected First grade
selected Second grade
selected Third grade
not selected Fourth grade
not selected Fifth grade
not selected Sixth grade
not selected Seventh grade
not selected Eighth grade
not selected Ninth grade
not selected Tenth grade
not selected Eleventh grade
not selected Twelfth grade

The tool is intended for use with the following age(s).
not selected 0-4 years old
selected 5 years old
selected 6 years old
selected 7 years old
selected 8 years old
selected 9 years old
not selected 10 years old
not selected 11 years old
not selected 12 years old
not selected 13 years old
not selected 14 years old
not selected 15 years old
not selected 16 years old
not selected 17 years old
not selected 18 years old

The tool is intended for use with the following student populations.
not selected Students in general education
not selected Students with disabilities
not selected English language learners

ACADEMIC ONLY: What skills does the tool screen?

Reading
Phonological processing:
not selected RAN
not selected Memory
selected Awareness
selected Letter sound correspondence
selected Phonics
selected Structural analysis

Word ID
selected Accuracy
selected Speed

Nonword
selected Accuracy
selected Speed

Spelling
selected Accuracy
selected Speed

Passage
selected Accuracy
selected Speed

Reading comprehension:
selected Multiple choice questions
not selected Cloze
not selected Constructed Response
not selected Retell
selected Maze
not selected Sentence verification
not selected Other (please describe):


Listening comprehension:
selected Multiple choice questions
not selected Cloze
not selected Constructed Response
not selected Retell
not selected Maze
not selected Sentence verification
selected Vocabulary
not selected Expressive
not selected Receptive

Mathematics
Global Indicator of Math Competence
not selected Accuracy
not selected Speed
not selected Multiple Choice
not selected Constructed Response

Early Numeracy
not selected Accuracy
not selected Speed
not selected Multiple Choice
not selected Constructed Response

Mathematics Concepts
not selected Accuracy
not selected Speed
not selected Multiple Choice
not selected Constructed Response

Mathematics Computation
not selected Accuracy
not selected Speed
not selected Multiple Choice
not selected Constructed Response

Mathematic Application
not selected Accuracy
not selected Speed
not selected Multiple Choice
not selected Constructed Response

Fractions/Decimals
not selected Accuracy
not selected Speed
not selected Multiple Choice
not selected Constructed Response

Algebra
not selected Accuracy
not selected Speed
not selected Multiple Choice
not selected Constructed Response

Geometry
not selected Accuracy
not selected Speed
not selected Multiple Choice
not selected Constructed Response

not selected Other (please describe):

Please describe specific domain, skills or subtests:
BEHAVIOR ONLY: Which category of behaviors does your tool target?


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
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 ER is priced at $5.95 per student per year. Training manuals/materials are included in the cost of the tool. In-person training conducted by a professional development specialist cost is $2800 per specialist per day.
Provide information about special accommodations for students with disabilities.
ISIP ER assessment packages includes online assessment, data hosting, reporting, teacher resources, online training center, user and manuals.

Administration

BEHAVIOR ONLY: What type of administrator is your tool designed for?
not selected General education teacher
not selected Special education teacher
not selected Parent
not selected Child
not selected External observer
not selected Other
If other, please specify:

What is the administration setting?
not selected Direct observation
not selected Rating scale
not selected Checklist
not selected Performance measure
not selected Questionnaire
selected Direct: Computerized
not selected One-to-one
not selected 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)
selected Computer or tablet
selected Internet connection
not selected 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?
selected Individual
selected Small group   If small group, n=
selected Large group   If large group, n=
not selected Computer-administered
not selected Other
If other, please specify:

What is the administration time?
Time in minutes
30
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?
not selected No discontinue rules provided
not selected Basals
not selected Ceilings
selected 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
not selected 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:
By email and phone (M-F 7am-6:30pm) CST)

Scoring

How are scores calculated?
not selected Manually (by hand)
selected Automatically (computer-scored)
not selected 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?
not selected Age norms
selected Grade norms
not selected Classwide norms
not selected Schoolwide norms
not selected Stanines
not selected Normal curve equivalents

What types of performance level scores are available?
selected Raw score
not selected Standard score
selected Percentile score
not selected Grade equivalents
selected IRT-based score
not selected Age equivalents
not selected Stanines
not selected Normal curve equivalents
not selected Developmental benchmarks
not selected Developmental cut points
not selected Equated
not selected Probability
selected Lexile score
not selected Error analysis
selected Composite scores
selected Subscale/subtest scores
not selected Other
If other, please specify:

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. 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. The data used for the calibration was based on an ethically diverse regional sample, including urban and suburban students of varied ability and backgrounds. Annual reviews of item parameters, score scaling, and the setting of cut-points is practiced for ISIP.

Technical Standards

Classification Accuracy & Cross-Validation Summary

Grade Grade 3
Classification Accuracy Fall Partially convincing evidence
Classification Accuracy Winter Partially convincing evidence
Classification Accuracy Spring Partially convincing evidence
Legend
Full BubbleConvincing evidence
Half BubblePartially convincing evidence
Empty BubbleUnconvincing evidence
Null BubbleData unavailable
dDisaggregated data available

MAP Reading

Classification Accuracy

Select time of year
Describe the criterion (outcome) measure(s) including the degree to which it/they is/are independent from the screening measure.
MAP Reading is a computer adaptive assessment of reading ability. It is similar to ISIP ER in the fact that each student receives a set of items that is optimal for the student’s ability level. In this study, MAP is used as a separate criterion measure to provide further student performance information for the sample district. Analyzes were conducted to determine the classification accuracy of ISIP ER as compared to MAP Reading.
Do the classification accuracy analyses examine concurrent and/or predictive classification?

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 classification analyses conducted using MAP Reading Measures as criterion measure and ISIP Reading Measures. The data were collected in 2015-16 school year from one district in the State of Texas. Each student had both ISIP and MAP scores for each data point. The SPSS software was used to conduct the analyses. Both ISIP and MAP At-Risk cut-points were applied. The cut points were exact how both measures identify at-risk and/or intensive need students. To be more specific, the 20th Percentile Rank (Tier 3 cut-point: students perform seriously below grade level and in need of intensive intervention) ISIP cut-point and 33rd Percentile Rank (Tier 3 cut point) MAP cut point were used.
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.
Describe the criterion (outcome) measure(s) including the degree to which it/they is/are independent from the screening measure.
MAP Reading is a computer adaptive assessment of reading ability. It is similar to ISIP AR in the fact that each student receives a set of items that is optimal for the student’s ability level. In this study, MAP is used as a separate criterion measure to provide further student performance information for the sample district. Analyzes were conducted to determine the classification accuracy of ISIP AR as compared to MAP Reading.
Do the cross-validation analyses examine concurrent and/or predictive classification?

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 cross-validation analyses conducted using MAP Reading Measures as criterion measure and ISIP Reading Measures. The data were collected in 2014-15 school year from one district in the State of Texas. Each student had both ISIP and MAP scores for each data point. The SPSS software was used to conduct the analyses. Both ISIP and MAP At-Risk cut-points were applied. The cut points were exact how both measures identify at-risk and/or intensive need students. To be more specific, the 20th Percentile Rank (Tier 3 cut-point: students perform seriously below grade level and in need of intensive intervention) ISIP cut-point and 33rd Percentile Rank (Tier 3 cut point) MAP cut point were used.
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 Grade 3
Criterion measure MAP Reading
Cut Points - Percentile rank on criterion measure 33
Cut Points - Performance score on criterion measure
Cut Points - Corresponding performance score (numeric) on screener measure 20th percentile
Classification Data - True Positive (a) 310
Classification Data - False Positive (b) 745
Classification Data - False Negative (c) 16
Classification Data - True Negative (d) 1854
Area Under the Curve (AUC) 0.94
AUC Estimate’s 95% Confidence Interval: Lower Bound 0.93
AUC Estimate’s 95% Confidence Interval: Upper Bound 0.95
Statistics Grade 3
Base Rate 0.11
Overall Classification Rate 0.74
Sensitivity 0.95
Specificity 0.71
False Positive Rate 0.29
False Negative Rate 0.05
Positive Predictive Power 0.29
Negative Predictive Power 0.99
Sample Grade 3
Date
Sample Size 2925
Geographic Representation West South Central (TX)
Male 58.3 %
Female 50.3 %
Other  
Gender Unknown  
White, Non-Hispanic 1.6 %
Black, Non-Hispanic 1.7 %
Hispanic 84.8 %
Asian/Pacific Islander  
American Indian/Alaska Native 0.2 %
Other 20.2 %
Race / Ethnicity Unknown  
Low SES 90.9 %
IEP or diagnosed disability  
English Language Learner 31.8 %

Classification Accuracy - Winter

Evidence Grade 3
Criterion measure MAP Reading
Cut Points - Percentile rank on criterion measure 33
Cut Points - Performance score on criterion measure
Cut Points - Corresponding performance score (numeric) on screener measure 20th percentile
Classification Data - True Positive (a) 288
Classification Data - False Positive (b) 727
Classification Data - False Negative (c) 22
Classification Data - True Negative (d) 2049
Area Under the Curve (AUC) 0.93
AUC Estimate’s 95% Confidence Interval: Lower Bound 0.91
AUC Estimate’s 95% Confidence Interval: Upper Bound 0.94
Statistics Grade 3
Base Rate 0.10
Overall Classification Rate 0.76
Sensitivity 0.93
Specificity 0.74
False Positive Rate 0.26
False Negative Rate 0.07
Positive Predictive Power 0.28
Negative Predictive Power 0.99
Sample Grade 3
Date
Sample Size 3086
Geographic Representation 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 Grade 3
Criterion measure MAP Reading
Cut Points - Percentile rank on criterion measure 33
Cut Points - Performance score on criterion measure
Cut Points - Corresponding performance score (numeric) on screener measure 20th percentile
Classification Data - True Positive (a) 272
Classification Data - False Positive (b) 692
Classification Data - False Negative (c) 26
Classification Data - True Negative (d) 2191
Area Under the Curve (AUC) 0.92
AUC Estimate’s 95% Confidence Interval: Lower Bound 0.91
AUC Estimate’s 95% Confidence Interval: Upper Bound 0.94
Statistics Grade 3
Base Rate 0.09
Overall Classification Rate 0.77
Sensitivity 0.91
Specificity 0.76
False Positive Rate 0.24
False Negative Rate 0.09
Positive Predictive Power 0.28
Negative Predictive Power 0.99
Sample Grade 3
Date
Sample Size 3181
Geographic Representation 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 Grade 3
Criterion measure MAP Reading
Cut Points - Percentile rank on criterion measure 33
Cut Points - Performance score on criterion measure
Cut Points - Corresponding performance score (numeric) on screener measure 20th percentile
Classification Data - True Positive (a) 277
Classification Data - False Positive (b) 751
Classification Data - False Negative (c) 19
Classification Data - True Negative (d) 1759
Area Under the Curve (AUC) 0.92
AUC Estimate’s 95% Confidence Interval: Lower Bound 0.91
AUC Estimate’s 95% Confidence Interval: Upper Bound 0.94
Statistics Grade 3
Base Rate 0.11
Overall Classification Rate 0.73
Sensitivity 0.94
Specificity 0.70
False Positive Rate 0.30
False Negative Rate 0.06
Positive Predictive Power 0.27
Negative Predictive Power 0.99
Sample Grade 3
Date
Sample Size 2806
Geographic Representation 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 Grade 3
Criterion measure MAP Reading
Cut Points - Percentile rank on criterion measure 33
Cut Points - Performance score on criterion measure
Cut Points - Corresponding performance score (numeric) on screener measure 20th percentile
Classification Data - True Positive (a) 273
Classification Data - False Positive (b) 748
Classification Data - False Negative (c) 23
Classification Data - True Negative (d) 2006
Area Under the Curve (AUC) 0.92
AUC Estimate’s 95% Confidence Interval: Lower Bound 0.91
AUC Estimate’s 95% Confidence Interval: Upper Bound 0.94
Statistics Grade 3
Base Rate 0.10
Overall Classification Rate 0.75
Sensitivity 0.92
Specificity 0.73
False Positive Rate 0.27
False Negative Rate 0.08
Positive Predictive Power 0.27
Negative Predictive Power 0.99
Sample Grade 3
Date
Sample Size 3050
Geographic Representation 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 Grade 3
Criterion measure MAP Reading
Cut Points - Percentile rank on criterion measure 33
Cut Points - Performance score on criterion measure
Cut Points - Corresponding performance score (numeric) on screener measure 20th percentile
Classification Data - True Positive (a) 285
Classification Data - False Positive (b) 691
Classification Data - False Negative (c) 29
Classification Data - True Negative (d) 2167
Area Under the Curve (AUC) 0.93
AUC Estimate’s 95% Confidence Interval: Lower Bound 0.91
AUC Estimate’s 95% Confidence Interval: Upper Bound 0.94
Statistics Grade 3
Base Rate 0.10
Overall Classification Rate 0.77
Sensitivity 0.91
Specificity 0.76
False Positive Rate 0.24
False Negative Rate 0.09
Positive Predictive Power 0.29
Negative Predictive Power 0.99
Sample Grade 3
Date
Sample Size 3172
Geographic Representation 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 Grade 3
Rating Convincing evidence
Legend
Full BubbleConvincing evidence
Half BubblePartially convincing evidence
Empty BubbleUnconvincing evidence
Null BubbleData unavailable
dDisaggregated data available
*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 Early 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) and thus applied to ISIP Early Reading. 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.
*Describe the sample(s), including size and characteristics, for each reliability analysis conducted.
Sample derived from the total population of students using the ISIP assessment throughout the 2014-2015 school year. Large sample size ranges from 83,621 to 226,558 students across the United States.
*Describe the analysis procedures for each reported type of reliability.
Istation derived IRT-based reliability from Classical Test Theory standpoint to Item Response Theory.

*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. (2016). Istation’s Indicators of Progress (ISIP) Early Reading: Technical Report. Retrieved from https://www.istation.com/Content/downloads/studies/er_technical_report.pdf
Do you have reliability data that are disaggregated by gender, race/ethnicity, or other subgroups (e.g., English language learners, students with disabilities)?

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 3
Rating Partially convincing evidence
Legend
Full BubbleConvincing evidence
Half BubblePartially convincing evidence
Empty BubbleUnconvincing evidence
Null BubbleData unavailable
dDisaggregated data available
*Describe each criterion measure used and explain why each measure is appropriate, given the type and purpose of the tool.
Predictive Validities were conducted using the Texas Primary Reading Inventory (TPRI), the Iowa Test of Basic Skills (ITBS) and the State of Texas Assessments of Academic Readiness (STAAR) were used as criterion. TPRI is an assessment used in to measure early reading skills in primary grades. ITBS is a standardized measure used to assess students’ reading ability success at grade level. STARR is the testing program for students in Texas public schools. STAAR Reading is the assessment used to determine whether students are successful in meeting the reading standards of their current grade and able to make academic progress from year to year. ISIP ER was developed to measure the skills that are most predictive of students’ future reading success. Since TPRI, ITBS and STAAR Reading are measures of reading ability and often determine students’ grade level success, it is important to understand theEpredictive validity of ISIP eR; used as a screener, when compared to these assessments.
*Describe the sample(s), including size and characteristics, for each validity analysis conducted.
Sample is derived from urban school districts in the northeast area of the state of Texas. Sample size ranges from n=95 to 3,694.
*Describe the analysis procedures for each reported type of validity.
The predictive validity study was conducted to determine how well ISIP measures predicted students' performance on other reading tests. The data were collected from one district in the State of Texas in 2007-2008 & 2012-2013 school years. Each student had both ISIP reading ability scores and TPRI, ITBS and STAAR scores. SPSS software was used to conduct the analyses. Pearson Product-Moment correlation analysis, multiple linear regression, and multiple logistic regression were applied for each grade data by using SPSS software.

*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.
Mathes, P., Torgeson, J., & Herron, J. (2016). Istation’s Indicators of Progress (ISIP) Early Reading: Technical Report. Retrieved from https://www.istation.com/Content/downloads/studies/er_technical_report.pdf
Describe the degree to which the provided data support the validity of the tool.
The results of these studies suggest moderate to strong relationships between ISIP ER TPRI, ITBS and STAAR Reading. Our findings also add to the evidence that ISIP Reading measures are predictive of students’ reading success across grades. The ISIP tests can be used as a prediction of how a student will score on TPRI, ITBS and STAAR.
Do you have validity data that are disaggregated by gender, race/ethnicity, or other subgroups (e.g., English language learners, students with disabilities)?

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:
Provide citations for additional published studies.

Bias Analysis

Grade Grade 3
Rating 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 - 3) 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).

Data Collection Practices

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