DIBELS 8th Edition
Letter Naming Fluency
Summary
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.
- Where to Obtain:
- University of Oregon, Center on Teaching and Learning
- support@dibels.uoregon.edu
- 5292 University of Oregon Eugene, OR 97403
- 1-888-497-4290
- https://dibels.uoregon.edu
- Initial Cost:
- Free
- Replacement Cost:
- Free
- Included in Cost:
- 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).
- 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.
- Training Requirements:
- 1-4 hours
- Qualified Administrators:
- Paraprofessional
- Access to Technical 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).
- Assessment Format:
-
- Direct observation
- Performance measure
- One-to-one
- Scoring Time:
-
- 1 minutes per student
- Scores Generated:
-
- Raw score
- Percentile score
- Developmental benchmarks
- Developmental cut points
- Administration Time:
-
- 2 minutes per student
- Scoring Method:
-
- Manually (by hand)
- Technology Requirements:
-
- Accommodations:
- 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.
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.
ACADEMIC ONLY: What skills does the tool screen?
- 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
Administration
- 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
- Do you provide basis for calculating performance level scores?
-
Yes
- 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.
- 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.
Technical Standards
Classification Accuracy & Cross-Validation Summary
| Grade |
Kindergarten
|
Grade 1
|
|---|---|---|
| Classification Accuracy Fall |
|
|
| Classification Accuracy Winter |
|
|
| Classification Accuracy Spring |
|
|
Legend
Convincing evidencePartially convincing evidenceUnconvincing evidenceData unavailabledDisaggregated data available
DIBELS Next Composite Score
Classification Accuracy
- 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.
- 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).
- 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,
- 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.
Iowa Assessment Total Reading Score
Classification Accuracy
- 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.
- 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).
- 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,
- 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 - 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 | ||
| Cut Points - Corresponding performance score (numeric) on screener measure | 15 | 41 |
| Classification Data - True Positive (a) | ||
| Classification Data - False Positive (b) | ||
| Classification Data - False Negative (c) | ||
| Classification Data - True Negative (d) | ||
| 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 | ||
| Overall Classification Rate | ||
| Sensitivity | ||
| Specificity | ||
| False Positive Rate | ||
| False Negative Rate | ||
| Positive Predictive Power | ||
| Negative Predictive Power |
| Sample | Kindergarten | Grade 1 |
|---|---|---|
| Date | Spring 2019 | Spring 2019 |
| Sample Size | ||
| 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 | ||
| 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 - 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 | ||
| Cut Points - Corresponding performance score (numeric) on screener measure | 30 | 53 |
| Classification Data - True Positive (a) | ||
| Classification Data - False Positive (b) | ||
| Classification Data - False Negative (c) | ||
| Classification Data - True Negative (d) | ||
| 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 | ||
| Overall Classification Rate | ||
| Sensitivity | ||
| Specificity | ||
| False Positive Rate | ||
| False Negative Rate | ||
| Positive Predictive Power | ||
| Negative Predictive Power |
| Sample | Kindergarten | Grade 1 |
|---|---|---|
| Date | Spring 2019 | Spring 2019 |
| Sample Size | ||
| 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 | ||
| 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 |
Kindergarten
|
Grade 1
|
|---|---|---|
| Rating |
|
|
Legend
Convincing evidencePartially convincing evidenceUnconvincing evidenceData unavailabledDisaggregated data available
- *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:
- 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)?
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 |
Kindergarten
|
Grade 1
|
|---|---|---|
| Rating |
|
|
Legend
Convincing evidencePartially convincing evidenceUnconvincing evidenceData unavailabledDisaggregated data available
- *Describe each criterion measure used and explain why each measure is appropriate, given the type and purpose of the tool.
- 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. 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 of Reading provided two criterion measures: Total Reading score and Word Analysis score. 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 Word Analysis measure focuses on decoding skills, while the Total Reading measure assesses reading achievement more broadly. 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 both kindergarten and first grade. It is a published, individually-administered assessment of phonological awareness and rapid naming ability. The Rapid Symbolic Naming Composite and Rapid Non-symbolic Naming Composites measure 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:
- 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. Concurrent and predictive validity coefficients for DIBELS Next LNF are moderately correlated and range from r = .75-.87 in kindergarten, and r = .72 in grade 1. Concurrent and predictive validity coefficients for DIBELS Next NWF are correlated well and range from r = .71-.72 in kindergarten, and r = .49 in grade 1. Concurrent and predictive validity coefficients for DIBELS Next ORF are moderately correlated and range from r = .61-.71 in grade 1. Predictive validity coefficients for Iowa Total Reading are moderately correlated, ranging from r = .42-.43 in kindergarten and r = .27-57 in grade 1. 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 are the moderate to strong correlations between CTOPP-2 rapid naming composite scores and LNF. As might be expected, relations are strongest with the symbolic naming composite, which incorporates a letter naming subtest (r = .31 for the beginning of kindergarten respectively and r = .59 for the beginning of first grade respectively). Relations are weaker for non-symbolic composite scores, but still moderate to strong in magnitude. These results suggest that LNF can operate as a reasonable screener for processing speed, particularly 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)?
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 |
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.
Data Collection Practices
Most tools and programs evaluated by the NCII are branded products which have been submitted by the companies, organizations, or individuals that disseminate these products. These entities supply the textual information shown above, but not the ratings accompanying the text. NCII administrators and members of our Technical Review Committees have reviewed the content on this page, but NCII cannot guarantee that this information is free from error or reflective of recent changes to the product. Tools and programs have the opportunity to be updated annually or upon request.
