DIBELS 8th Edition
Letter Naming Fluency

Descriptive Information

Please provide a description of your tool:

Letter Naming Fluency (LNF) is a standardized, individually administered test that provides a measure of risk for reading achievement. LNF is based on research by Marston and Magnusson (1988) and is administered to students in the fall of kindergarten through the spring of first grade. In LNF, students are presented with a page of 100 upper- and lowercase letters stratified by letter frequency and asked to name as many letters as they can in 1 minute. If a student does not know a letter name, the examiner provides the letter name and marks the letter name incorrect.

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

support@dibels.uoregon.edu

Address

5292 University of Oregon Eugene, OR 97403

Phone Number

1-888-497-4290

Website

https://dibels.uoregon.edu

Initial cost for implementing program:

Cost

$0.00

Unit of cost

Replacement cost per unit for subsequent use:

Cost

$0.00

Unit of cost

Duration of license

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.

All materials required for administration are available for free download at https://dibels.uoregon.edu. Printed materials are also available at https://dibels.uoregon.edu/market for a cost of $53 to $91 for a classroom set of benchmark screening materials. The DIBELS Data System (DDS) is not required, but is available for online data entry, management and reporting for a cost of $1.00 per student per year. A multi-year discount is currently available. The DDS is free-of-charge to schools in Oregon. For the most current pricing information see: https://dibels.uoregon.edu/help/pricing. Additional costs are associated with printing, and computer and internet access if also using the DIBELS Data System. Starting in the 2019-20 school year, tablet-based administration will be available from Amplify (https://www.amplify.com).

Provide information about special accommodations for students with disabilities.

DIBELS 8th Edition approved assessment accommodations involve minor changes to assessment procedures that are unlikely to change the meaning of the results and have been approved either by DIBELS developers or assessment professionals. They should be used only when: • An accurate score is unlikely to be obtained without the accommodation; and/or • Specified in a student’s 504 plan or Individualized Education Plan (IEP). The accommodations approved for DIBELS 8th Edition are: quiet setting for testing; breaks in between measures; assistive technology (e.g., hearing aids, assistive listening devices, glasses); enlarged student materials; colored overlays, filters, or lighting adjustments; and marker or ruler for tracking.

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?

No

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:

Administering the measure does not require technology, but if users choose to use the DIBELS Data System for management and reporting of data, an internet connected computer is required. Additionally, if schools choose to administer the DIBELS 8th Edition measures using a tablet, they should contact Amplify for technology requirements.

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

2

per (student/group/other unit)

student

Additional scoring time:

Time in minutes

1

per (student/group/other unit)

student

ACADEMIC ONLY: What are the discontinue rules?

No discontinue rules provided
Basals
Ceilings
Other

If other, please specify:

No correct letter sounds in the first row.

Are norms available?

Yes

Are benchmarks available?

Yes

If yes, how many benchmarks per year?

3

If yes, for which months are benchmarks available?

Benchmarks are available for the beginning, middle and end of the school year. Beginning months are typically September, October and November; middle months are December, January, and February; and end months are typically March, April, May and June. Regardless of when the benchmark occurs, we recommend that all students are tested within a one-month window.

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

Please describe the minimum qualifications an administrator must possess.

Paraprofessional

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:

Information about online training is available through the DIBELS Data System (https://dibels.uoregon.edu/training). Online training is free-of-charge for ‘early adopters’ (i.e., schools or districts that sign up for the next school year by a specified date in spring.) For people not associated with the ‘early adopter’ program the charge is $40 to $79 per person, depending on the number of people purchasing the training, and whether an individual is associated with a DDS account.

Can users obtain ongoing professional and technical support?

Yes

If Yes, please describe how users can obtain support:

Technical support is available from the DIBELS Data System at the University of Oregon, https://dibels.uoregon.edu (phone: 1-888-497-4290, email: support@dibels.uoregon.edu, hours of operation: 6:00am to 5:30pm Pacific Time, Monday through Friday).

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?

Yes

If yes, please describe.

DIBELS 8th Edition LNF provides two cut points to help educators determine where to allocate resources and how much intervention students may need. One cut point indicates that students are likely at risk for future difficulty in learning to read. The other is a benchmark cut point that indicates if students are likely to be on track in learning to read. Students between the two cut points are considered to be somewhere between “at-risk” and “on track”.

Can you provide evidence in support of multiple decision rules?

Yes

If yes, please describe.

This application addresses the “at-risk” cut point. Information about benchmark cut points is available on the DIBELS Data System website https://dibels.uoregon.edu.

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.

Raw scores: The scorer marks and sums the incorrect items and subtracts that from the total number of items attempted.

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.

DIBELS 8th Edition LNF is a standardized, individually administered measure of a student’s accuracy and fluency with naming a series of upper- and lower-case letters of the alphabet. The measure is designed as an indicator of later reading risk for students in kindergarten and first grade. The measure is timed for one-minute. Letters omitted, substituted, and hesitations of more than three seconds are scored as errors. Letters self-corrected within three seconds are scored as accurate. The number of correct letters named in 1-minute is the letter naming fluency rate. In DIBELS 8th Edition, LNF accounts for how frequently letters appear in both upper- and lower-case forms. To better control differences in difficulty between forms, consistent rules are used in both kindergarten and first grade regarding when less frequent letters can appear on the forms. Each form in both grades begins with a sampling of the 20 most frequently seen letters (Jones & Mewhort, 2004), thereby preventing students from getting frustrated by forms that begin with rarer letters, such as X or q. The kindergarten version of LNF also only assesses the 40 most commonly seen upper- and lower-case letters, while the first-grade version assesses 49 upper and lower case letters. LNF excludes three letters on all forms: upper- and lower-case W and lower-case L. Although these are obviously important letters for students to know, they introduce real problems in a fluency assessment. W is the only letter with a multi-syllabic name: three syllables to be exact. As a result, any time W appears, it takes three times as long to name as other letters, which negatively affects a student’s LNF score. The lower-case L (l) was eliminated because it is easily confused with both the upper-case I and the number 1. Not only does this visual similarity pose problems for students, but it has also historically created scoring problems for the adult administering the assessment. By avoiding these letters, each included item (or letter) is equally challenging, other than in terms of its frequency in printed language. There are specific scoring rules regarding articulation and dialect to mitigate linguistic bias. Students are not penalized for differences in speech production that are the result of dialect, first-language, or articulation. There are specific scoring rules regarding articulation and dialect to mitigate linguistic bias. Students are not penalized for differences in speech production that are the result of dialect, first-language, or articulation.

Classification Accuracy & Cross-Validation Summary

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

DIBELS Next Composite Score

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.

In kindergarten, the criterion measure was the DIBELS Next Composite score administered in the spring. The DIBELS Next Composite score in the spring of kindergarten combines scores on Letter Naming Fluency, Phoneme Segmentation Fluency, and Nonsense Word Fluency Correct Letter Sounds. Although it assesses similar constructs, DIBELS Next was developed separately from DIBELS 8th Edition using different development specifications and is not part of the same measurement system.

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.

Screening measures were administered in the fall, winter, and spring of the 2018-19 school year. The DIBELS Next Composite was administered in the spring of 2019. All else being equal, concurrent administrations are preferable because they reduce the likelihood of inflated false positives due to intervention delivery on the part of schools. Thus, all spring benchmarks predicted end of year performance on the concurrent spring 2019 administration. Fall and winter benchmarks predicted end of year performance on the spring 2019 DIBELS Next Composite administration.

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).

DIBELS 8th Edition cut scores were established by using the composite score at each time point to predict end of year performance on a criterion measure of reading achievement. We used a two-stage process for determining cut-points for the DIBELS 8th Edition Composite score. First, we plotted a Receiver Operating Characteristic (ROC) curve for the selected end-of-year criterion measure at each time point and grade and determined the area under the curve (A). Second, we conducted a diagnostic analysis of each measure at each time point (i.e., season). For each analysis, we focused on two statistics: sensitivity and specificity. We chose to focus on sensitivity and specificity (rather than PPV and NPV) because they remain stable indicators regardless of the prevalence of reading difficulties in the population (Pepe, 2003). We attempted to balance sensitivity and specificity in our analyses because of their complimentary roles in a prevention model in education. Specifically, we want to be confident that as many students as possible receive the level of instructional support they require as early as possible, without overburdening teachers by asking them to deliver intervention to students who do not need additional instruction. Thus, wherever possible, the recommended cut points for DIBELS 8thedition were determined using an optimal decision threshold that maximized sensitivity among scores with a specificity at or above .80. That is, at each time point, we selected the score with the highest sensitivity among scores with a specificity at or above .80, unless the maximum sensitivity value exceeded .90, in which case the cut point selected was the score that minimized the difference between sensitivity and specificity among scores with specificity at or above .80. For measures and periods with no cut scores that met the minimum threshold for specificity, the cut point represents the score that best balances the goals of providing additional instruction where needed while keeping demands on teachers reasonable.

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.

---

Iowa Assessment Total Reading Score

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.

In grade 1, the criterion measure was the Iowa Assessment Total Reading Score, administered in spring. The Iowa Assessment is a published, group-administered, multiple-choice, norm-referenced measure of reading achievement. It is completely independent of DIBELS 8th Edition measures.

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.

Screening measures were administered in the fall, winter, and spring of the 2018-19 school year. The Iowa Assessment was administered in the spring of 2019. All else being equal, concurrent administrations are preferable because they reduce the likelihood of inflated false positives due to intervention delivery on the part of schools. Thus, all spring benchmarks predicted end of year performance on the concurrent spring 2019 administration. Fall and winter benchmarks predicted end of year performance on the spring 2019 Iowa administration because no concurrent administration was available.

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).

DIBELS 8th Edition cut scores were established by using the composite score at each time point to predict end of year performance on a criterion measure of reading achievement. We used a two-stage process for determining cut-points for the DIBELS 8th Edition LNF. First, we plotted a Receiver Operating Characteristic (ROC) curve for the selected end-of-year criterion measure at each time point and grade and determined the area under the curve (A). Second, we conducted a diagnostic analysis of each measure at each time point (i.e., season). For each analysis, we focused on two statistics: sensitivity and specificity. We chose to focus on sensitivity and specificity (rather than PPV and NPV) because they remain stable indicators regardless of the prevalence of reading difficulties in the population (Pepe, 2003). We attempted to balance sensitivity and specificity in our analyses because of their complimentary roles in a prevention model in education. Specifically, we want to be confident that as many students as possible receive the level of instructional support they require as early as possible, without overburdening teachers by asking them to deliver intervention to students who do not need additional instruction. Thus, wherever possible, the recommended cut points for DIBELS 8th edition were determined using an optimal decision threshold that maximized sensitivity among scores with a specificity at or above .80. That is, at each time point, we selected the score with the highest sensitivity among scores with a specificity at or above .80, unless the maximum sensitivity value exceeded .90, in which case the cut point selected was the score that minimized the difference between sensitivity and specificity among scores with specificity at or above .80. For measures and periods with no cut scores that met the minimum threshold for specificity, the cut point represents the score that best balances the goals of providing additional instruction where needed while keeping demands on teachers reasonable.

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
Criterion measure	DIBELS Next Composite Score	Iowa Assessment Total Reading Score
Cut Points - Percentile rank on criterion measure	20	20
Cut Points - Performance score on criterion measure	108	140
Cut Points - Corresponding performance score (numeric) on screener measure	15	31
Classification Data - True Positive (a)	37	14
Classification Data - False Positive (b)	38	17
Classification Data - False Negative (c)	14	14
Classification Data - True Negative (d)	217	78
Area Under the Curve (AUC)	0.85	0.71
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.79	0.61
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.91	0.82

Statistics	Kindergarten	Grade 1
Base Rate	0.17	0.23
Overall Classification Rate	0.83	0.75
Sensitivity	0.73	0.50
Specificity	0.85	0.82
False Positive Rate	0.15	0.18
False Negative Rate	0.27	0.50
Positive Predictive Power	0.49	0.45
Negative Predictive Power	0.94	0.85

Sample	Kindergarten	Grade 1
Date	Fall 2018 screening; Spring 2019 criterion	Fall 2018 screening; Spring 2019 criterion
Sample Size	306	123
Geographic Representation	East North Central (OH) Middle Atlantic (PA) West North Central (MO) West South Central (AR, TX)	East North Central (OH) Mountain (AZ) Pacific (OR, WA) South Atlantic (GA) West North Central (MO)
Male	52.6%	32.5%
Female	47.1%	32.5%
Other
Gender Unknown	0.3%	35.0%
White, Non-Hispanic	44.8%	24.4%
Black, Non-Hispanic	1.3%	30.1%
Hispanic	53.3%	2.4%
Asian/Pacific Islander		0.8%
American Indian/Alaska Native		4.9%
Other	0.7%	2.4%
Race / Ethnicity Unknown		35.0%
Low SES
IEP or diagnosed disability
English Language Learner

Classification Accuracy - Winter

Evidence	Kindergarten	Grade 1
Criterion measure	DIBELS Next Composite Score	Iowa Assessment Total Reading Score
Cut Points - Percentile rank on criterion measure	20	20
Cut Points - Performance score on criterion measure	108	140
Cut Points - Corresponding performance score (numeric) on screener measure	30	50
Classification Data - True Positive (a)	37	22
Classification Data - False Positive (b)	52	27
Classification Data - False Negative (c)	15	13
Classification Data - True Negative (d)	210	75
Area Under the Curve (AUC)	0.81	0.73
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.74	0.63
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.89	0.82

Statistics	Kindergarten	Grade 1
Base Rate	0.17	0.26
Overall Classification Rate	0.79	0.71
Sensitivity	0.71	0.63
Specificity	0.80	0.74
False Positive Rate	0.20	0.26
False Negative Rate	0.29	0.37
Positive Predictive Power	0.42	0.45
Negative Predictive Power	0.93	0.85

Sample	Kindergarten	Grade 1
Date	Winter 2018/2019 screening; Spring 2019 criterion	Winter 2018/2019 screening; Spring 2019 criterion
Sample Size	314	137
Geographic Representation	East North Central (OH) Middle Atlantic (PA) West North Central (MO) West South Central (AR, TX)	East North Central (OH) Mountain (AZ) Pacific (OR, WA) South Atlantic (GA) West North Central (MO)
Male	52.9%	35.8%
Female	47.1%	33.6%
Other
Gender Unknown		30.7%
White, Non-Hispanic	44.6%	22.6%
Black, Non-Hispanic	1.3%	37.2%
Hispanic	53.5%	2.2%
Asian/Pacific Islander		0.7%
American Indian/Alaska Native		4.4%
Other	0.6%	2.2%
Race / Ethnicity Unknown		30.7%
Low SES
IEP or diagnosed disability
English Language Learner

Classification Accuracy - Spring

Evidence	Kindergarten	Grade 1
Criterion measure	DIBELS Next Composite Score	Iowa Assessment Total Reading Score
Cut Points - Percentile rank on criterion measure	20	20
Cut Points - Performance score on criterion measure	108	140
Cut Points - Corresponding performance score (numeric) on screener measure	34	52
Classification Data - True Positive (a)	43	17
Classification Data - False Positive (b)	49	23
Classification Data - False Negative (c)	12	15
Classification Data - True Negative (d)	217	79
Area Under the Curve (AUC)	0.87	0.73
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.81	0.64
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.93	0.83

Statistics	Kindergarten	Grade 1
Base Rate	0.17	0.24
Overall Classification Rate	0.81	0.72
Sensitivity	0.78	0.53
Specificity	0.82	0.77
False Positive Rate	0.18	0.23
False Negative Rate	0.22	0.47
Positive Predictive Power	0.47	0.43
Negative Predictive Power	0.95	0.84

Sample	Kindergarten	Grade 1
Date	Spring 2019 screening; Spring 2019 criterion	Spring 2019 screening; Spring 2019 criterion
Sample Size	321	134
Geographic Representation	East North Central (OH) Middle Atlantic (PA) West North Central (MO) West South Central (AR, TX)	East North Central (OH) Mountain (AZ) Pacific (OR, WA) South Atlantic (GA) West North Central (MO)
Male	52.6%	35.1%
Female	47.4%	31.3%
Other
Gender Unknown		33.6%
White, Non-Hispanic	44.9%	24.6%
Black, Non-Hispanic	1.2%	31.3%
Hispanic	53.3%	3.0%
Asian/Pacific Islander		0.7%
American Indian/Alaska Native		4.5%
Other	0.6%	2.2%
Race / Ethnicity Unknown		33.6%
Low SES
IEP or diagnosed disability
English Language Learner

Reliability

Grade	Kindergarten	Grade 1
Rating

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

To assess the reliability of DIBELS 8th Edition, we evaluated multiple forms of reliability, including test-retest reliability, concurrent alternate form reliability, and delayed alternate form reliability. We include delayed alternate form reliability as a supplementary source of reliability evidence by reporting correlations between two or more alternate form of the same test administered at different time points (e.g., different seasons). Test-retest reliability: Test-retest reliability was evaluated by administering the same test (i.e., set of items) to the same individuals two times and correlating scores from the two test administrations. We included test-retest reliability in cases where the only source of alternate form reliability was delayed alternate form. In those instances, test-retest reliability provides some measure of reliability without the confound of the (expected) student growth between administrations. Alternate-form reliability: Alternate-form reliability indicates the extent to which test results generalize to different item samples. To assess alternate-form reliability, students were administered multiple forms of each subtest, and scores from these two forms were correlated. Concurrent alternate-form reliability of a single (i.e., benchmark) form was estimated by the correlation between the score on that form and the score on an alternate (i.e., progress monitoring) form. Delayed alternate form reliability was estimated by correlating scores measured at different benchmark administrations across year—beginning-, middle-, and end of year. The use of alternate form reliability is justified because it uses different but equivalent forms, thereby preventing practice effects inherent in test-retest reliability where the same form is administered twice. In addition, it is important to establish that different forms are equivalent given the use of different forms for progress-monitoring across the year.

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

Sample 1, 2017-18 DIBELS 8th Edition was administered to 4,453 students in grades K – 8 in twenty-nine schools. Participating students came from throughout the country: all four census regions were represented. 48.1% of the participating students were female, 50.9% were male. 18.1% of students were Hispanic. The sample included 0.6% Asian students, 14.3% Black/African American students, 0.4% Native Hawaiian or other Pacific Islander, 3.9% American Indian or Alaskan Native, 64.3% white, 3.2% two or more races, and 13.3% unknown or not reported. 6.3% of students were English Learners, and 13.9% were eligible for Special Education services. 57.4% of the students were eligible for the free or reduced lunch program. Sample 2, 2018-19 Twenty-one schools administered DIBELS 8th Edition to 5,259 students in grades K - 8. The schools were located in the Pacific, East North Central, West North Central, Mountain, and South Atlantic census divisions. Schools represent towns, large cities, suburbs and rural areas. The sample of students was 50.6% male and 48.9% female; 1.5% American Indian or Alaskan Native; 2.5% Asian, 17.2% Black, 20.9% Hispanic, 4.1% two or more races, 0.4% Native Hawaiian/Pacific Islander, and 53.0% White. 13.9% of students had disabilities, 59.6% were eligible for free or reduced lunch, and 7.3% were English learners.

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

Test-retest reliability: Students were re-administered the same version of test (i.e., same item pool) at multiple benchmark assessments. Test-retest reliability was estimated as the correlation coefficient between the test and retest. Alternate form reliability: Students were administered multiple forms of each subtest, and scores from these two forms were correlated. Concurrent alternate-form reliability of a single (i.e., benchmark) form was estimated by the correlation between the score on that form and the score on an alternate (i.e., progress monitoring) form. Delayed alternate form reliability was estimated by correlating scores measured at different benchmark administrations across year—beginning-, middle-, and end of year.

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

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

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

Manual cites other published reliability studies:

No

Provide citations for additional published studies.

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

No

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
Rating

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

Two criterion measures were drawn from DIBELS Next: Letter Naming Fluency (LNF) and the Composite score. The DIBELS Next Composite combines scores for DIBELS Next subtests administered in a given benchmark period. For example, in the spring of kindergarten, DIBELS Next Composite scores are calculated using scores from Letter Naming Fluency, Phoneme Segmentation Fluency, and Nonsense Word Fluency Correct Letter Sounds. DIBELS Next assesses similar constructs, was developed separately from DIBELS 8th Edition using different development specifications, and is not part of the same measurement system. In first grade, the criterion measure was the Iowa Assessment, administered in spring. The Iowa Assessment is a published, group-administered, multiple-choice, norm-referenced measure of reading achievement. It is completely independent of DIBELS 8th Edition measures. The Iowa Assessment is a commonly accepted measure of reading achievement. It is a published, group-administered, multiple-choice, norm-referenced test of reading. The Total Reading measure assesses broad reading achievement. Iowa assessments are completely independent of DIBELS 8th Edition measures. The Comprehensive Test of Phonological Processing – 2nd Edition was used as an additional criterion measure in first grade. It is a published, individually-administered assessment of phonological awareness and rapid naming ability. The Rapid Symbolic Naming Composite measures an individual’s efficiency in retrieving and processing phonological information from long-term memory. This criterion measure was selected to validate LNF’s use for identifying rapid naming deficits.

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

Sample 1, 2017-18 DIBELS 8th Edition was administered to 4,453 students in grades K – 8 in twenty-nine schools. Participating students came from throughout the country: all four census regions were represented. 48.1% of the participating students were female, 50.9% were male. 18.1% of students were Hispanic. The sample included 0.6% Asian students, 14.3% Black/African American students, 0.4% Native Hawaiian or other Pacific Islander, 3.9% American Indian or Alaskan Native, 64.3% white, 3.2% two or more races, and 13.3% unknown or not reported. 6.3% of students were English Learners, and 13.9% were eligible for Special Education services. 57.4% of the students were eligible for the free or reduced lunch program. Sample 2, 2018-19 Twenty-one schools administered DIBELS 8th Edition to 5,259 students in grades K - 8. The schools were located in the Pacific, East North Central, West North Central, Mountain, and South Atlantic census divisions. Schools represent towns, large cities, suburbs and rural areas. The sample of students was 50.6% male and 48.9% female; 1.5% American Indian or Alaskan Native; 2.5% Asian, 17.2% Black, 20.9% Hispanic, 4.1% two or more races, 0.4% Native Hawaiian/Pacific Islander, and 53.0% White. 13.9% of students had disabilities, 59.6% were eligible for free or reduced lunch, and 7.3% were English learners. Sample 3, 2018-19 Six public schools in five school districts administered DIBELS 8th Edition to 1,275 students in grades K - 3. The schools were located in Arkansas, Missouri, Ohio, Pennsylvania and Texas. The sample of students was 52.0% male and 47.3% female; 0.1% American Indian or Alaskan Native; 0.5% Black, 25.1% Hispanic, 1.2% two or more races, and 72.2% White. 7.2% of students were eligible for Special Education, 49.3% were eligible for free or reduced lunch, and 1.1% were English learners.

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

Concurrent validity: Concurrent validity was evaluated by examining the strength of correlation between the screening measure and the criterion measures administered at approximately the same time of the year. Predictive validity: Predictive validity was evaluated by examining the strength of correlation between the screening measure and the student future performance on the criterion measures.

*In the table below, report the results of the validity analyses described above (e.g., concurrent or predictive validity, evidence based on response processes, evidence based on internal structure, evidence based on relations to other variables, and/or evidence based on consequences of testing), and the criterion measures.

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

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

Manual cites other published reliability studies:

No

Provide citations for additional published studies.

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

Overall, the validity of LNF for DIBELS 8th Edition is well supported by a range of concurrent and predictive validity correlations across multiple criterion measures. For example, DIBELS 8th Edition LNF scores in kindergarten and first grade are moderately to relatively strongly correlated with a range of DIBELS Next (e.g., LNF, NWF, ORF) and Iowa Total Reading measures. Lower correlations are indicative of greater lengths of time between administrations (and thus, more opportunity for student growth) and/or weaker alignment between constructs being measured. Of particular interest is the strong correlation between CTOPP-2 Rapid Symbolic Naming Composite scores and LNF. These results suggest that LNF can operate as a reasonable screener for rapid naming in grade 1.

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

No

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:

No

Provide citations for additional published studies.

Bias Analysis

Grade	Kindergarten	Grade 1
Rating	No	No

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.

No

If yes,

a. Describe the method used to determine the presence or absence of bias:

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

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.