Istation’s Indicators of Progress (ISIP)
Early Reading

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).

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

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?

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/group

Additional scoring time:

Time in minutes

per (student/group/other unit)

ACADEMIC ONLY: What are the discontinue rules?

No discontinue rules provided
Basals
Ceilings
Other

If other, please specify:

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:

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:

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.

Classification Accuracy & Cross-Validation Summary

Grade	Grade 3
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.

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?

Concurrent
Predictive

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

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

The 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.

Fall

Winter

Spring

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?

Concurrent
Predictive

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

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

The 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

*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

*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).