DIBELS 6th Edition
Oral Reading Fluency
Summary
DIBELS Oral Reading Fluency is a 1-minute fluency measure that assesses both accuracy and fluency of reading connected text. Students read a set of three passages at the beginning, middle, and end of the school year, and it is their median score from these passages, at each assessment period, that is recorded and compared to the recommended cut points. Cut points for intensive intervention are addressed in this application. Benchmark cut points, as well as cut points for intensive intervention, are available at https://dibels.uoregon.edu/docs/marketplace/dibels/DIBELS-6Ed-Goals.pdf
- Where to Obtain:
- University of Oregon
- support@dibels.uoregon.edu
- 5292 University of Oregon, Eugene, OR 97403
- 1-888-497-4290
- https://dibels.uoregon.edu
- Initial Cost:
- Contact vendor for pricing details.
- Replacement Cost:
- Contact vendor for pricing details.
- Included in Cost:
- All materials required for administration are available for free download at https://dibels.uoregon.edu. 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. Included with the DDS service is optional tablet based administration through the HiFi Reading app available for free download at the Apple app store. Training is required for assessors and is available through online DDS training modules. The cost of the training ranges from $40 - $79 per person. Additional costs include the cost of printing and the cost of a computer (required) and tablets (optional).
- DIBELS 6th Edition approved accommodations for LNF are: • Enlarged student materials • Colored overlays, filters or lighting adjustments • Assistive technology (e.g., hearing aids, assistive listening devices) • Marker or ruler for tracking
- Training Requirements:
- 1-4 hrs of training
- 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:
-
- One-to-one
- Scoring Time:
-
- Scoring is automatic OR
- 1 minutes per student
- Scores Generated:
-
- Raw score
- Percentile score
- Developmental benchmarks
- Administration Time:
-
- 4 minutes per student
- Scoring Method:
-
- Manually (by hand)
- Automatically (computer-scored)
- Technology Requirements:
-
- Accommodations:
- DIBELS 6th Edition approved accommodations for LNF are: • Enlarged student materials • Colored overlays, filters or lighting adjustments • Assistive technology (e.g., hearing aids, assistive listening devices) • Marker or ruler for tracking
Descriptive Information
- Please provide a description of your tool:
- DIBELS Oral Reading Fluency is a 1-minute fluency measure that assesses both accuracy and fluency of reading connected text. Students read a set of three passages at the beginning, middle, and end of the school year, and it is their median score from these passages, at each assessment period, that is recorded and compared to the recommended cut points. Cut points for intensive intervention are addressed in this application. Benchmark cut points, as well as cut points for intensive intervention, are available at https://dibels.uoregon.edu/docs/marketplace/dibels/DIBELS-6Ed-Goals.pdf
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?
- Two benchmarks for first grade (middle and end of year) and three benchmarks for all other grades (beginning, middle, and end of year).
- If yes, for which months are benchmarks available?
- 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. If DIBELS ORF is administered outside of that one month time frame, it should not be entered as the benchmark score for the student.
- 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 hrs of training
- 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?
- No
- If No, please describe training costs:
- Online training for administration and scoring of DIBELS 6th edition is available at https://dibels.uoregon.edu/training/. The cost of the training ranges from $40 - $79 per person and includes all DIBELS 6th Edition subtests. Cost depends on whether a group discount is applied, and whether the trainee is a DIBELS Data System customer.
- 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.
- Grade-based, empirically determined cut points for risk and benchmark goals, based on ROC analyses predicting performance at the 20th and 40th percentile on the SAT-10 Total Reading.
- Can you provide evidence in support of multiple decision rules?
-
Yes
- 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.
- There is no cluster/composite score for ORF.
- 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.
- The DIBELS ORF is a standardized, individually administered test of accuracy and reading fluency with connected text for students in Grades 1 through 3. The measure consists of a standardized set of reading passages, all of which have been calibrated to an end-of-grade readability level (using Spache). Administration procedures are also standardized. The results of the measure can be used to identify children who may need additional instructional support, and to monitor their progress toward instructional goals. Student performance is measured by having students read a passage aloud for one minute. Words omitted, substituted, and hesitations of more than three seconds are scored as errors. Words self-corrected within three seconds are scored as accurate. The number of correct words per minute from the passages is the oral reading fluency rate. 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 |
Grade 1
|
Grade 2
|
Grade 3
|
|---|---|---|---|
| Classification Accuracy Fall |
|
|
|
| Classification Accuracy Winter |
|
|
|
| Classification Accuracy Spring |
|
|
|
Legend
Convincing evidencePartially convincing evidenceUnconvincing evidenceData unavailabledDisaggregated data available
Stanford Achievement Test: 10th Edition (SAT-10) Reading
Classification Accuracy
- Describe the criterion (outcome) measure(s) including the degree to which it/they is/are independent from the screening measure.
- In grades 1 and 2, we selected the Stanford Achievement Test—10th Edition (SAT10; Harcourt Assessment, 2004, 2007 Normative Update) Reading test as the outcome measure that would indicate "healthy" reading performance at the end of the school year. The SAT-10 is external to the DIBELS assessment system and is a nationally recognized test primarily developed to measure student achievement in reading in kindergarten through grade 12. The SAT-10 is a group-administered, norm-referenced test of overall reading proficiency. The measure is not timed, although guidelines with flexible time recommendations are given. Selected reading subtests from the SAT10 were combined to form a total reading composite, which served as the standard for healthy reading performance in the spring of each grade. In grades 3-5, SAT-10 subtests included word reading, reading vocabulary, and reading comprehension. In grade 6, subtests included reading vocabulary and reading comprehension. This battery takes about 110 minutes to complete. An alpha reliability coefficient for total SAT10 reading scores was .87. Validity coefficient with the Otis-Lennon School Ability Test ranged r = .61 – .75. The normative sample is representative of the U.S. student population. The 20th percentiles on both the SAT10 and the OAKS ELA were used as our cut point for intensive need.
- 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).
- We used a two-stage process for determining cut-points for intensive need. First, we plotted Receiver Operating Characteristic (ROC) curves at each time point and grade and the associated end-of-year criterion measure and determined the area under the curve (A). Prior to conducting our analyses, we decided to calculate cut points only for those measures and time points where the AUC met or exceeded .75. An AUC of less than .75 suggests that the measure may not represent accuracy beyond teacher judgment, and we believe that providing cut-points for measures with an AUC value less than .75 would imply greater confidence in the measures than is warranted. Second, we conducted a diagnostic analysis of each measure at each time point (i.e., season. For each analysis, we examined 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). Further, we emphasized sensitivity in our analyses because of its practical application in a prevention model in education. Specifically, we want to be confident that students receive the instructional support they require as early as possible. All cut-points were determined using an optimal decision threshold associated with sensitivity at or above .80. This criterion roughly corresponds to the statement that, we will miss an opportunity to provide additional support to only 20% of students who are likely to score below the 20th percentile on the SAT10.
- Were the children in the study/studies involved in an intervention in addition to typical classroom instruction between the screening measure and outcome assessment?
-
Yes
- If yes, please describe the intervention, what children received the intervention, and how they were chosen.
- All students were part of the Oregon Reading First study. Each participating school provided at least 90 minutes of daily, scientifically based reading instruction for all kindergarten through third-grade students with a minimum of 30 minutes of daily small-group, teacher-directed reading instruction.
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.
Oregon Assessment of Knowledge and Skills - English Language Arts (OAKS-ELA)
Classification Accuracy
- Describe the criterion (outcome) measure(s) including the degree to which it/they is/are independent from the screening measure.
- In grade 3, we used the Oregon Assessment of Knowledge and Skills – English Language Arts (OAKS-ELA; Oregon Department of Education [ODE], 2008) as a criterion measure, given that as Oregon state accountability assessment, it is external to the DIBELS 6th edition progress monitoring system. The OAKS ELA is an untimed, multiple-choice test. Reading passages representing literary, informative, and practical selections are included in the third-grade test. These passages represent selections that students might encounter in school settings and other daily reading activities. Seven individual subtests require students to: (a) understand word meanings in the context of a selection; (b) locate information in common resources; (c) answer literal, inferential, and evaluative comprehension questions; (d) recognize common literary forms, such as novels, short stories, poetry, and folk tales; and (e) analyze the use of literary elements and devices, such as plot, setting, personification, and metaphor. The ODE reports that OAKS ELA criterion validity was .75 with the California Achievement Tests and .78 with the Iowa Tests of Basic Skills (ODE, 2005). The four alternate forms used for the OAKS ELA demonstrated internal consistency reliability (Kuder–Richardson–20) of .95 (ODE, 2000). The 20th percentiles on both the SAT10 and the OAKS ELA were used as our cut point for intensive need.
- 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).
- We used a two-stage process for determining cut-points for intensive need. First, we plotted Receiver Operating Characteristic (ROC) curves at each time point and grade and the associated end-of-year criterion measure and determined the area under the curve (A). Prior to conducting our analyses, we decided to calculate cut points only for those measures and time points where the AUC met or exceeded .75. An AUC of less than .75 suggests that the measure may not represent accuracy beyond teacher judgment, and we believe that providing cut-points for measures with an AUC value less than .75 would imply greater confidence in the measures than is warranted. Second, we conducted a diagnostic analysis of each measure at each time point (i.e., season. For each analysis, we examined 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). Further, we emphasized sensitivity in our analyses because of its practical application in a prevention model in education. Specifically, we want to be confident that students receive the instructional support they require as early as possible. All cut-points were determined using an optimal decision threshold associated with sensitivity at or above .80.
- Were the children in the study/studies involved in an intervention in addition to typical classroom instruction between the screening measure and outcome assessment?
-
Yes
- If yes, please describe the intervention, what children received the intervention, and how they were chosen.
- All students were part of the Oregon Reading First study. Each participating school provided at least 90 minutes of daily, scientifically based reading instruction for all kindergarten through third-grade students with a minimum of 30 minutes of daily small-group, teacher-directed reading instruction.
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 | Grade 2 | Grade 3 |
|---|---|---|
| Criterion measure | Stanford Achievement Test: 10th Edition (SAT-10) Reading | Oregon Assessment of Knowledge and Skills - English Language Arts (OAKS-ELA) |
| 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 | 28 words correct | 57 words correct |
| 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) | 0.89 | 0.84 |
| AUC Estimate’s 95% Confidence Interval: Lower Bound | 0.88 | 0.83 |
| AUC Estimate’s 95% Confidence Interval: Upper Bound | 0.90 | 0.85 |
| Statistics | Grade 2 | Grade 3 |
|---|---|---|
| Base Rate | ||
| Overall Classification Rate | ||
| Sensitivity | ||
| Specificity | ||
| False Positive Rate | ||
| False Negative Rate | ||
| Positive Predictive Power | ||
| Negative Predictive Power |
| Sample | Grade 2 | Grade 3 |
|---|---|---|
| Date | 2003-06 | 2003-06 |
| Sample Size | ||
| Geographic Representation | Pacific (OR) | Pacific (OR) |
| 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 | Grade 1 | Grade 2 | Grade 3 |
|---|---|---|---|
| Criterion measure | Stanford Achievement Test: 10th Edition (SAT-10) Reading | Stanford Achievement Test: 10th Edition (SAT-10) Reading | Oregon Assessment of Knowledge and Skills - English Language Arts (OAKS-ELA) |
| Cut Points - Percentile rank on criterion measure | 20 | 20 | 20 |
| Cut Points - Performance score on criterion measure | |||
| Cut Points - Corresponding performance score (numeric) on screener measure | 13 words correct | 55 words correct | 76 words correct |
| 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) | 0.92 | 0.91 | 0.85 |
| AUC Estimate’s 95% Confidence Interval: Lower Bound | 0.91 | 0.90 | 0.84 |
| AUC Estimate’s 95% Confidence Interval: Upper Bound | 0.93 | 0.92 | 0.86 |
| Statistics | Grade 1 | Grade 2 | Grade 3 |
|---|---|---|---|
| Base Rate | |||
| Overall Classification Rate | |||
| Sensitivity | |||
| Specificity | |||
| False Positive Rate | |||
| False Negative Rate | |||
| Positive Predictive Power | |||
| Negative Predictive Power |
| Sample | Grade 1 | Grade 2 | Grade 3 |
|---|---|---|---|
| Date | 2003-06 | 2003-06 | 2003-06 |
| Sample Size | |||
| Geographic Representation | Pacific (OR) | Pacific (OR) | Pacific (OR) |
| Male | |||
| Female | |||
| Other | |||
| Gender Unknown | |||
| White, Non-Hispanic | |||
| Black, Non-Hispanic | |||
| Hispanic | |||
| Asian/Pacific Islander | |||
| American Indian/Alaska Native | |||
| Other | |||
| Race / Ethnicity Unknown | |||
| Low SES | |||
| IEP or diagnosed disability | |||
| English Language Learner |
Reliability
| Grade |
Grade 1
|
Grade 2
|
Grade 3
|
|---|---|---|---|
| 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.
- We evaluated alternate form reliability, test-retest reliability, and internal consistency to assess the reliability of DIBELS 6th edition ORF measure. Additionally, we examined delayed alternate form reliability as a supplementary reliability evidence by calculating correlations between two or more alternate form of the same test administered at different time points (e.g., different seasons). Alternate-form reliability: Alternate-form reliability indicates the extent to which test results generalize to different item samples. Students are tested with two different (i.e., alternate) but equivalent forms of the test within a relatively short interval of time, and scores from these two forms are correlated. 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. Test-retest reliability: Test-retest reliability is evaluated by administering the same test (i.e., set of items) to the same individuals twice within a short interval 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. Internal consistency: Internal consistency is the extent to which a group of items measure the same construct, as evidenced by how well they vary together, or intercorrelate. We included internal consistency to examine how reliable the items on a test are in terms of measuring the same construct of ORF.
- *Describe the sample(s), including size and characteristics, for each reliability analysis conducted.
- Study a test-retest reliability: Participants were students at 34 Oregon Reading First Schools across 16 school districts. The study included 17 schools in large urban areas, eight in midsize cities, and nine in rural areas. Subjects were four cohorts of students in Grades 1-3, with each cohort representing 2,400 students. 10% of the students received special education services and 32% were English language learners. Study b alternate form reliability: The analytic sample for the alternate form reliability were 182 first-, 217 second-, and 204 third-grade students from one school district in a northwest state. The first-grade sample included 44.7% female, 51.1% white, 32.8% Hispanic, 2.2% African-American, 22% American Indian/Alaska Native, 6.6% Asian, 0.7 Hawaiian/Pacific Islander, 5.4% two or more races, 9.8% of students receiving special education services, and 23.5% English language learners. The second-grade sample included 46% female, 54.8% white, 32.8% Hispanic, 2.4% African-American, 20.9% American Indian/Alaska Native, 6.1% Asian, 0.4 Hawaiian/Pacific Islander, 5.4% two or more races, 12.3% of students receiving special education services, and 20.7% English language learners. The third-grade sample included 44.6% female, 52.2% white, 31.8% Hispanic, 1.9% African-American, 20.2% American Indian/Alaska Native, 7.7% Asian, 0.7 Hawaiian/Pacific Islander, 4.9% two or more races, 9.5% of students receiving special education services, and 18.3% English language learners. Study d internal consistency: Participants were 666 second-grade students from a subset of nine of 51 schools taking part in the Oregon Reading First (RFO) program; these nine schools were located in six school districts throughout Oregon. For our analyses, we used student data from the academic years 2006-2007 and included any student with at least one passage score at the fall or spring benchmark assessments. Student characteristics are 10% received special education services, 21% percent were English learners, and 50% were female with gender missing for 3% of students. The racial/ethnic composition was 43% White, 30% Hispanic, 14% Black, and 10% other non-White ethnic groups with race/ethnicity missing for 4% of students.
- *Describe the analysis procedures for each reported type of reliability.
- Test-retest reliability: Students were re-administered the same test in the three weeks following the end-of-year benchmark assessment. Test-retest reliability was estimated as the correlation coefficient between the test and retest. Alternate form reliability: Students were administered three different forms for the middle of year ORF test. Alternate-form reliability of a single form was estimated by the correlation between the score recorded on the other forms. The median of correlation coefficients between three forms is reported. Delayed alternate form reliability was estimated by correlating ORF scores measured at different measurement points across year—beginning-, middle-, and end of year. The median of correlation coefficients between the three benchmark assessments is reported. Internal consistency: The reliability of the ORF fall to spring gain score, a confirmatory factor analysis (CFA) measurement model that included the three fall ORF passage scores to define latent fall ORF ability and the three spring ORF passages to define latent spring ORF ability to estimate the reliability was used. The covariance between the two fall median scores is an estimate of the fall true score variance, the covariance between the two spring median scores is an estimate of the spring true score variance and the four covariances between the two fall median scores and two spring median scores are each an estimate of the covariance between fall and spring true scores. The average of the four covariances as the best single estimate of the fall-spring true score covariance. The true gain score variance was computed as fall true score variance plus spring true score variance minus two times the covariance of fall and spring true scores. For the observed gain score variance, the observed fall and spring variances in the same formula in place of the true score variances were used. Reliability coefficient was computed as the ratio of the true gain score variance to the observed gain score variance.
*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:
- Type of Reliability Informant (Behavior Only) Grade n Coefficient 95% Confidence Interval*: Lower Bound 95% Confidence Interval*: Upper Bound Internal consistency (d) Second 666 .94-.97 .93-.95† .966-.974† Alternate form (b) Second 209 .92-.93 .90-.94† .91-.97† Alternate form (e) Second 134 .87-.96 .82-.91† .95-.95† *If model-based evidence is being submitted for reliability, note that providing Test Information Function (TIF) / Standard Error (SE) plots to judge the relative precision of the model-based estimate(s) is acceptable in place of providing confidence intervals. Such plots may be provided for aggregate and disaggregated data. a) Baker, S. K., Smolkowski, K., Katz, R., Fien, H., Seeley, J. R., Kame'Enui, E. J., & Beck, C. T. (2008). Reading fluency as a predictor of reading proficiency in low-performing, high-poverty schools. School Psychology Review, 37(1), 18-37. b) Center on Teaching and Learning (2017). Unpublished data [HSD Project]. Eugene, OR: University of Oregon. c) Roberts, G., Good, R., & Corcoran, S. (2005). Story retell: A fluency-based indicator of reading comprehension. School Psychology Quarterly, 20(3), 304-317. d) Cummings, K. D., Stoolmiller, M. L., Baker, S. K., Fien, H., & Kame’enui, E. J. (2015). Using school-level student achievement to engage in formative evaluation: comparative school-level rates of oral reading fluency growth conditioned by initial skill for second grade students. Reading and Writing, 28(1), 105-130. e) Francis, D. J., Santi, K. L., Barr, C., Fletcher, J. M., Varisco, A., & Foorman, B. R. (2008). Form effects on the estimation of students' oral reading fluency using DIBELS. Journal of School Psychology, 46(3), 315-342. † indicates the ranges of confidence interval depending on the sample size
- Manual cites other published reliability studies:
- Provide citations for additional published studies.
- Fien, H., Park, Y., Baker, S. K., Smith, J. L. M., Stoolmiller, M., & Kame’enui, E. J. (2010). An examination of the relation of Nonsense Word Fluency initial status and gains to reading outcomes for beginning readers. School Psychology Review, 39, 631-653. Good, R. H., & Jefferson, G. (1998). Contemporary perspectives on Curriculum-Based Measurement validity. In M. R. Shinn (Ed.), Advanced applications of Curriculum-Based Measurement (pp. 61-88). New York: Guilford. Roberts, G., Good, R., & Corcoran, S. (2005). Story retell: A fluency-based indicator of reading comprehension. School Psychology Quarterly, 20(3), 304-317. Stoolmiller, M., Biancarosa, G., & Fien, H. (2013). Measurement properties of DIBELS Oral Reading Fluency in grade 2: Implications for equating studies. Assessment for Effective Intervention,38(2), 76-90. Tindal, G., Marston, D., & Deno, S. L. (1983). The reliability of direct and repeated measurement (Research Rep. 109). Minneapolis, MN: University of Minnesota Institute for Research on Learning Disabilities
- 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 |
Grade 1
|
Grade 2
|
Grade 3
|
|---|---|---|---|
| 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.
- easyCBM Passage Reading Fluency (PRF): The easyCBM PRF has been developed for grades K-8 and assess grade level oral reading fluency. These assessments can be used as screening measures to establish benchmarks and for progress monitoring. The easyCBM PRF is individually administered. Participants are provided with a test form with approximately 250 words long, narrative fiction on a single piece of paper. Students read aloud the story for 1 minute, and word read incorrectly was counted as an error. Scores are the number of correct words per minute. The easyCBM PRF serves an appropriate criterion measure for validity analysis of DIBELS ORF measure because it measures theoretically related construct (i.e., oral reading fluency) and it has good technical adequacy. The median of alternate form reliability in grades 1 and 3 range, r = .94-.97 and construct validity in grades 1 through 3 range, CFI = .97-.99 and RMSEA = .02-.14. Stanford Achievement Test—10th Edition. The SAT-10 is a group-administered, norm-referenced test of overall reading proficiency (SAT10; Harcourt Assessment, 2004, 2007 Normative Update). The SAT-10 Reading subtests is administered at the end of year and assess the essential reading skills including phonemic awareness, decoding, phonics, vocabulary, and comprehension. The measure is not timed, although guidelines with flexible time recommendations are given. The SAT-10 was primarily developed to measure student reading achievement in kindergarten through grade 12. In grade 1, the SAT-10 reading test includes word study reading, word reading, sentence reading and reading comprehension. In grades 2-3, SAT-10 reading is comprised of word reading, reading vocabulary, and reading comprehension. The SAT-10 Reading test serves as an appropriate criterion measure for validity analysis of DIBELS ORF measure because it has been widely used across states as an established measure of reading. In particular, external to DIBELS progress monitoring system, the SAT-10 reading was normed on a nationally representative sample, which supports the generalizability of the scores. An alpha reliability coefficient for total SAT-10 reading scores was .87. Validity coefficient with the Otis-Lennon School Ability Test ranged r = .61-.75. The SAT-10 Reading test is also aligned with International Reading Association (IRA)/National Council of Teachers of English (NCTE) standards, state standards, and the National Assessment of Education Progress (NAEP). Note that because oral reading fluency is known to correlate successively less well with comprehension over the course of the elementary grades because of the increasing complexity and diversity of reading materials and expectations, ORF validity correlations with a measure like SAT-10 are expected to be somewhat weaker in upper elementary grades than in lower elementary grades. However, they are still expected to be strong relative to Cohen’s rule of thumb for interpreting correlations (i.e., over .50).
- *Describe the sample(s), including size and characteristics, for each validity analysis conducted.
- Grades 1 through 3 concurrent validity: The analytic sample was comprised of 197 first-, 259 second-, and 202 third-grade students from one school district in a northwest state. The first-grade sample included 44.7% female, 51.1% white, 32.8% Hispanic, 2.2% African-American, 22% American Indian/Alaska Native, 6.6% Asian, 0.7 Hawaiian/Pacific Islander, 5.4% two or more races, 9.8% of students receiving special education services, and 23.5% English language learners. The second-grade sample included 46% female, 54.8% white, 32.8% Hispanic, 2.4% African-American, 20.9% American Indian/Alaska Native, 6.1% Asian, 0.4 Hawaiian/Pacific Islander, 5.4% two or more races, 12.3% of students receiving special education services, and 20.7% English language learners. The third-grade sample included 44.6% female, 52.2% white, 31.8% Hispanic, 1.9% African-American, 20.2% American Indian/Alaska Native, 7.7% Asian, 0.7 Hawaiian/Pacific Islander, 4.9% two or more races, 9.5% of students receiving special education services, and 18.3% English language learners. Grade 1 through 2 predictive validity: The analytic sample included 4,973 first-grade and 4,826 second-grade students at 34 Oregon Reading First Schools across 16 school districts. The study included 17 schools in large urban areas, eight in midsize cities, and nine in rural areas. Approximately 10% of the students received special education services and 32% were English language learners. Grade 3 predictive validity: The analytic sample comprised of calibration sample (n = 16,539) and cross-validation sample (n = 16,908). Students in both samples enrolled in Florida Reading First Schools during 2004-2005 school year. Two samples on average included 48.7% female, 36% white, 36.2% African American, 22.4% Latino, 3.5% Multiracial, 1.5% Asian, and less than 1% as Native American, 75% of students eligible for free or reduced-price lunch, 17% of students receiving special education services, and 20% English language 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. Discriminant validity: Discriminant validity was evaluated by examining the strength of correlation between the screening measure and the criterion measures designed to measure theoretically different concepts.
*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:
- Type of Reliability Grade Criterion n Coefficient 95% Confidence Interval*: Lower Bound 95% Confidence Interval*: Upper Bound Predictive h First End of 1st Group Reading Assessment and Diagnostic Evaluation 1,027 – 1,224 Mdn: .63 .60-.67 .60-.66 Predictive l Third SAT-10 16,539 – 16,908 Mdn: .69 .68-.70† .68-.70† Predictive k Third FCAT-SSS 16,539 - 16908 Mdn: .68 .67-.69† .67-.69† *If model-based evidence is being submitted for validity, note that providing Test Information Function (TIF) / Standard Error (SE) plots to judge the relative precision of the model-based estimate(s) is acceptable in place of providing confidence intervals. Such plots may be provided for aggregate and disaggregated data. a Center on Teaching and Learning (2017). Unpublished data [HSD Project]. Eugene, OR: University of Oregon. b Burke, M. D., & Hagan-Burke, S. (2007). Concurrent criterion-related validity of early literacy indicators for middle of first grade. Assessment for effective Intervention, 32(2), 66-77. c Baker, S. K., Smolkowski, K., Katz, R., Fien, H., Seeley, J. R., Kame'Enui, E. J., & Beck, C. T. (2008). Reading fluency as a predictor of reading proficiency in low-performing, high-poverty schools. School Psychology Review, 37(1), 18-37. d Schilling, S. G., Carlisle, J. F., Scott, S. E., & Zeng, J. (2007). Are fluency measures accurate predictors of reading achievement?. The Elementary School Journal, 107(5), 429-448. e Harn, B. A., Stoolmiller, M., & Chard, D. J. (2008). Measuring the dimensions of alphabetic principle on the reading development of first graders: The role of automaticity and unitization. Journal of Learning Disabilities, 41(2), 143-157. f Roberts, G., Good, R., & Corcoran, S. (2005). Story retell: A fluency-based indicator of reading comprehension. School Psychology Quarterly, 20(3), 304-317. g Burke, M. D., & Hagan-Burke, S. (2007). Concurrent criterion-related validity of early literacy indicators for middle of first grade. Assessment for effective Intervention, 32(2), 66-77. h Riedel, B. W. (2007). The relation between DIBELS, reading comprehension, and vocabulary in urban first-grade students. Reading research quarterly, 42(4), 546-567. i Baker, S., & Smith, S. (2001). Linking school assessments to research-based practices in beginning reading: Improving programs and outcomes for students with and without disabilities. Teacher Education and Special Education, 24(4), 315-332. j Burke, M. D., Hagan-Burke, S., Kwok, O., & Parker, R. (2009). Predictive validity of early literacy indicators from the middle of kindergarten to second grade. The Journal of Special Education, 42(4), 209-226. k Buck, J., & Torgesen, J. (2003). The relationship between performance on a measure of oral reading fluency and performance on the Florida Comprehensive Assessment Test. Tallahassee, FL: Florida Center for Reading Research. l Roehrig, A. D., Petscher, Y., Nettles, S. M., Hudson, R. F., & Torgesen, J. K. (2008). Accuracy of the DIBELS oral reading fluency measure for predicting third grade reading comprehension outcomes. Journal of School Psychology, 46(3), 343-366. m Wilson, J. (2005). The relationship of dynamic indicators of basic early literacy skills (DIBELS) oral reading fluency to performance on Arizona instrument to measure standards (AIMS). Tempe, AZ: Tempe School District, No. 3. n Shaw, R., & Shaw, D. (2002). Technical Report DIBELS Oral Reading Fluency-Based Indicators of Third Grade Reading Skills for Colorado State Assessment Program (CSAP). Eugene, OR: University of Oregon. o Shapiro, E. S., Solari, E., & Petscher, Y. (2008). Use of a measure of reading comprehension to enhance prediction on the state high stakes assessment. Learning and individual differences, 18(3), 316-328. † indicates the ranges of confidence interval depending on the sample size.
- Manual cites other published reliability studies:
- No
- Provide citations for additional published studies.
- Barger, J. (2003). Comparing the DIBELS Oral Reading Fluency indicator and the North Carolina end of grade reading assessment (Technical Report). Asheville, NC: North Carolina Teacher Academy. Buck, J. & Torgeson, J. (2003). The relationship between performance on a measure of Oral Reading Fluency and performance on the Florida Comprehensive Assessment Test (Technical Report #1). Tallahassee, FL: Florida Center for Reading Research. Munger, K. A. & Blachman, B. A. (2013). Taking a "simple view" of the Dynamic Indicators of Basic Early Literacy Skills as a predictor of multiple measures of third-grade reading comprehension, Psychology in the Schools, 50(7), 722-737. Pressley, M., Hilden, K., & Shankland, R. (2005). An evaluation of end-grade-3 Dynamic Indicators of Basic Early Literacy Skills (DIBELS): Speed reading without comprehension, predicting little. East Lansing, MI: Michigan State University Vandermeer, C. D., Lentz, F. E., & Stollar, S. (2005). The relationship between oral reading fluency and Ohio proficiency testing in reading (Technical Report). Ohio: Southwest Ohio Special Education Regional Resource Center.
- Describe the degree to which the provided data support the validity of the tool.
- Barger, J. (2003). Comparing the DIBELS Oral Reading Fluency indicator and the North Carolina end of grade reading assessment (Technical Report). Asheville, NC: North Carolina Teacher Academy. Buck, J. & Torgeson, J. (2003). The relationship between performance on a measure of Oral Reading Fluency and performance on the Florida Comprehensive Assessment Test (Technical Report #1). Tallahassee, FL: Florida Center for Reading Research. Munger, K. A. & Blachman, B. A. (2013). Taking a "simple view" of the Dynamic Indicators of Basic Early Literacy Skills as a predictor of multiple measures of third-grade reading comprehension, Psychology in the Schools, 50(7), 722-737. Pressley, M., Hilden, K., & Shankland, R. (2005). An evaluation of end-grade-3 Dynamic Indicators of Basic Early Literacy Skills (DIBELS): Speed reading without comprehension, predicting little. East Lansing, MI: Michigan State University Vandermeer, C. D., Lentz, F. E., & Stollar, S. (2005). The relationship between oral reading fluency and Ohio proficiency testing in reading (Technical Report). Ohio: Southwest Ohio Special Education Regional Resource Center.
- 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 |
Grade 1
|
Grade 2
|
Grade 3
|
|---|---|---|---|
| Rating | No | 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.
