Star CBM
Letter Naming

Descriptive Information

Please provide a description of your tool:

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

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:

ACADEMIC ONLY: What are the discontinue rules?

No discontinue rules provided
Basals
Ceilings
Other

If other, please specify:

Are norms available?

Yes

Are benchmarks available?

Yes

If yes, how many benchmarks per year?

All seasons for Grade K

If yes, for which months are benchmarks available?

Fall, Winter, Spring

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

If yes, how many students can be rated concurrently?

Training & Scoring

Training

Is training for the administrator required?

Yes

Describe the time required for administrator training, if applicable:

Less than one hour training

Please describe the minimum qualifications an administrator must possess.

No minimum qualifications

Are training manuals and materials available?

Yes

Are training manuals/materials field-tested?

Yes

Are training manuals/materials included in cost of tools?

Yes

If No, please describe training costs:

Can users obtain ongoing professional and technical support?

Yes

If Yes, please describe how users can obtain support:

Renaissance Technical Support Staff

Scoring

How are scores calculated?

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

If other, please specify:

Do you provide basis for calculating performance level scores?

Yes

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

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

What types of performance level scores are available?

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

If other, please specify:

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.

Scores are shown as Correct Per Minute (CPM). Note that the score may be adjusted as a result of equating forms to ensure difficulty is consistent within grade.

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.

For Letter Naming assessments, students tell the teacher the names for lower- and uppercase letters. Letter sounds are a very early skill related to later reading achievement; they can be used to identify candidates for early intervention. Both criterion- and norm-referenced scores are automatically reported to inform instructional decisions. Reports and dashboards guide educators through screening and related decisions. Default risk categories, based on national norms, are provided, although they can be adjusted by local school leaders to best fit local populations. During test content development, every effort is made to avoid the use of stereotypes, potentially offensive language or characterizations, and descriptions of people or events that could be construed as being offensive, demeaning, patronizing, or otherwise insensitive. Field testing involved a diverse population. Additionally, bias analyses are performed for Gender, Ethnicity, Special Education Status, and English Language Learner Status.

Classification Accuracy & Cross-Validation Summary

Grade	Kindergarten
Classification Accuracy Fall
Classification Accuracy Winter
Classification Accuracy Spring

Legend

Convincing evidence

Partially convincing evidence

Unconvincing evidence

Data unavailable

^dDisaggregated data available

DIBELS Letter Naming Fluency

Classification Accuracy

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

In DIBELS Letter Name Fluency (LNF) task, a student is given a random list of upper- and lowercase letters and has 1 minute to identify the names of as many letters as possible. Schools often use these scores to determine which students are at risk for reading difficulties.

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).

Using the SAS program ROC analyses were conducted on the sample. Both Star CBM Letter Naming and DIBELS LNF was administered in the Winter of grade K to the same group of students. DIBELS was used to determine students “at-risk” and in “intensive need” of additional support. The 20th percentile on the LNF was used to determine the cut-points on the DIBELS. The results of the ROC analysis provided the Area Under the Curve (AUC) with 95% confidence intervals for determining the lower and upper bounds. In addition, the ROC analysis provides the Coordinates of the Curve provide Sensitivity and Specificity estimates. In addition, the coordinates are used to determine “risk” cut-points on LNF that are associated with different levels of sensitivity and specificity. The cut-points on the LNF were then used for further classification analysis in determining overall correct classification rates, false positives, false negative, Positive Predictive Power, and Negative Predictive Power.

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,

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

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 - Winter

Reliability

Grade	Kindergarten
Rating

Legend

Convincing evidence

Partially convincing evidence

Unconvincing evidence

Data unavailable

^dDisaggregated data available

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

The Star CBM Reading measures are analyzed within a classical test theory framework with scores reported in an equated correct per minute metric. CBMs are unique from other tests in that tests are typically stopped after a set amount of time (usually 1 minute of time), and most students are not expected to complete all of the items on a form within the time limit. As a result, reliability estimates that rely on total scores and that can be formulated within a classical test theory framework are appropriate for CBMs. Star CBM reports three different reliability coefficients, model based G-coefficients from generalizability theory analyses, alternate forms coefficients, and test-retest coefficients.

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

Each reliability coefficient is computed using a different sample of students that were drawn from a field test study that took place in the fall and winter of 2019-2020. Data were collected from schools in 26 different states. For the G-coefficients based on generalizability theory, the estimate was based on a total of sample of 3182 student test records where students were required to take two distinct CBM forms in the fall and two distinct CBM forms in the winter on the same day. For the alternate forms coefficients, the estimate was based on a total sample of 1591 matched student test records where students took two distinct CBM forms in the fall or two distinct CBM forms in the winter on the same day. For the test-retest coefficients, the estimate was based on a total sample of 1239 matched student test records students took a CBM form in the winter and took that same CBM form two to three weeks later.

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

G-coefficients from generalizability theory were estimated using a linear mixed effects model using the lme4 package in R. The mixed effect model estimated for each Star CBM Reading measure used equated correct per minute scores as the outcome and random effects for form, person, season, form order, the form by form order interaction, and the number of days since start of the field test. The variance component for persons was then divided by the variance component for persons plus the variance component of the residuals to estimate the G-coefficient from the generalizability theory analysis. The alternate forms coefficients were estimated in R by taking the Pearson product moment correlation between the equated correct per minute scores across the pairs of two alternate forms that were administered to students on the same day. The test-retest coefficients were estimated in R by taking the Pearson product moment correlation between the equated correct per minute scores between the pairs of identical tests forms that were administered to students two to three weeks apart.

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

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

Manual cites other published reliability studies:

No

Provide citations for additional published studies.

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

No

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

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
Rating

Legend

Convincing evidence

Partially convincing evidence

Unconvincing evidence

Data unavailable

^dDisaggregated data available

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

DIBELS Letter Naming Fluency (LNF) is a standardized, individually administered test that provides a measure of risk. Students are presented with a page of upper- and lower-case letters arranged in a random order and are asked to name as many letters as they can. LNF is based on research by Marston and Magnusson (1988). Students are told if they do not know a letter they will be told the letter. The student is allowed 1 minute to produce as many letter names as he/she can, and the score is the number of letters named correctly in 1 minute.

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

The concurrent sample consisted of 56 students enrolled in Kindergarten who took both Star CBM Letter Naming and DIBELS Letter Naming Fluency during the Winter. The predictive sample of 60 students enrolled in Kindergarten who took Star CBM Letter Naming in the Fall and DIBELS Letter Naming during the Winter.

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

For both the concurrent and predictive analyses, Pearson product-moment correlation coefficients were calculated along with a 95% confidence interval around the coefficient.

*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.

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

Manual cites other published reliability studies:

No

Provide citations for additional published studies.

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

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

No

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

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

Manual cites other published reliability studies:

No

Provide citations for additional published studies.

Bias Analysis

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:

Classification analyses were performed using a sample of Grade K data obtained from the spring of 2022 where students took the CBM Letter Sounds measure and Star Early Literacy. Receiver Operating Character (ROC) curve analyses were performed to evaluate the diagnostic classification accuracy of the Letter Sounds measure for identifying students as being at risk on Star Early Literacy. For Star Early Literacy, the cut point for defining at risk was set at the 20th percentile. The Area Under the ROC (AUC) was computed separately by subgroup with 95% confidence intervals estimated for each AUC estimate. The confidence intervals for the different subgroups were compared and evaluated to determine whether there was differential classification accuracy.

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

Separate analyses were performed for Gender, Ethnicity, Special Education Status, and English Language Learner Status.

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.

The 95% AUC confidence intervals for the subgroups compared overlapped and no statistically significant differences in classification accuracy were found for any of the subgroups compared.