FAST earlyReading
Letter Names
Summary
FAST™ earlyReading Letter Names is used to monitor student progress in early reading in the early primary grades and assesses the student’s ability and automaticity to name upper- and lower-case letters in isolation. The examiner and student each have the same page of letters available and that page is organized systematically as described later in this protocol. As the student names the letters aloud from a paper copy, the examiner marks errors on his/her paper or electronic copy. The resulting score is the number of letters named correctly in one minute. All 26 letters in the English alphabet were used. Each letter was used once in upper-case and once in lower-case. Every form includes each letter once in upper-case and once in lower-case before repeating. Each form is organized so that every row alternates with all upper-case or lower-case letters. For example, the first row is all lower-case and the second row all upper-case, and so on. Within the first 26 letters, each letter of the English alphabet is represented either in upper-case or lower-case. The second set of 26 letters contains the opposite upper or lower-case letter. Upper-case and lower-case letters were categorized as “dissimilar” or “same/moderate similarity.” The first two lower-case letters were randomly chosen from the “same/moderate similarity” category. The third letter was randomly chosen from the “dissimilar” category. Each set of three letters thereafter contained one randomly chosen “dissimilar” letter and two “same/moderate similarity” letters. The order for each set of three was randomly chosen after the first set. After the first appearance of each letter (upper and lower-case), letters were randomly chosen, regardless of similarity. There are a total of 10 rows and 10 columns. The first 6 rows can be completed for an inventory of all upper- and lower-case letter names. The last 4 rows are randomly ordered and are included to account for variation in student letter name reading.
- Where to Obtain:
- FastBridge Learning, LLC
- sales@fastbridge.org
- 150 South Fifth Street, Suite 600, Minneapolis, MN 55402
- 612-254-2534
- www.fastbridge.org
- Initial Cost:
- $7.50 per student
- Replacement Cost:
- $7.50 per student per year
- Included in Cost:
- FAST™ assessments are accessed through an annual subscription offered by FastBridge Learning, priced on a “per student assessed” model. The subscription rate for school year 2019–20 is $7.50 per student. There are no additional fixed costs. FAST subscriptions are all inclusive providing access to: all FAST reading and math assessments for universal screening, progress monitoring and diagnostic purposes including Computer Adaptive Testing and Curriculum-Based Measurement; Behavior and Developmental Milestones assessment tools; the FAST data management and reporting system; embedded online system training for staff; and basic implementation and user support. Several online training options in each assessment and report are available with the FAST subscription. In addition, schools can purchase additional training, or attend regional learning sessions, should they so choose.” A 2-day, onsite training, at the rate of $7000, provides in-depth training in screening and progress monitoring in reading and math. 2-day regional learning sessions are offered at a rate of $425/person.
- The FAST™ application is a fully cloud-based system, and therefore computer and Internet access are required for full use of the application. Teachers will require less than one hour of training on the administration of the assessment. A paraprofessional can administer the assessment as a Group Proctor in the FAST application. The application allows for the following accommodations to support accessibility for culturally and linguistically diverse populations: o Enlarged and printed paper materials are available upon request. o Extended time in untimed portions of earlyReading. o Extra breaks as needed. o Preferential seating and use of quiet space. o Proxy responses.
- Training Requirements:
- Less than one hour of training.
- Qualified Administrators:
- Access to Technical Support:
- Users have access to ongoing technical support.
- Assessment Format:
-
- Individual
- Other: The stimulus material is presented in paper form and responses are recorded by the administrator on a digital record form in the FAST system.
- Scoring Time:
-
- Scoring is automatic OR
- 0 minutes per student
- Scores Generated:
-
- Raw score
- Percentile score
- Developmental benchmarks
- Other : Items correct per minute
- Administration Time:
-
- 2 minutes per student
- Scoring Method:
-
- Automatically (computer-scored)
- Technology Requirements:
-
- Computer or tablet
- Internet connection
Tool Information
Descriptive Information
- Please provide a description of your tool:
- FAST™ earlyReading Letter Names is used to monitor student progress in early reading in the early primary grades and assesses the student’s ability and automaticity to name upper- and lower-case letters in isolation. The examiner and student each have the same page of letters available and that page is organized systematically as described later in this protocol. As the student names the letters aloud from a paper copy, the examiner marks errors on his/her paper or electronic copy. The resulting score is the number of letters named correctly in one minute. All 26 letters in the English alphabet were used. Each letter was used once in upper-case and once in lower-case. Every form includes each letter once in upper-case and once in lower-case before repeating. Each form is organized so that every row alternates with all upper-case or lower-case letters. For example, the first row is all lower-case and the second row all upper-case, and so on. Within the first 26 letters, each letter of the English alphabet is represented either in upper-case or lower-case. The second set of 26 letters contains the opposite upper or lower-case letter. Upper-case and lower-case letters were categorized as “dissimilar” or “same/moderate similarity.” The first two lower-case letters were randomly chosen from the “same/moderate similarity” category. The third letter was randomly chosen from the “dissimilar” category. Each set of three letters thereafter contained one randomly chosen “dissimilar” letter and two “same/moderate similarity” letters. The order for each set of three was randomly chosen after the first set. After the first appearance of each letter (upper and lower-case), letters were randomly chosen, regardless of similarity. There are a total of 10 rows and 10 columns. The first 6 rows can be completed for an inventory of all upper- and lower-case letter names. The last 4 rows are randomly ordered and are included to account for variation in student letter name reading.
- Is your tool designed to measure progress towards an end-of-year goal (e.g., oral reading fluency) or progress towards a short-term skill (e.g., letter naming fluency)?
-
ACADEMIC ONLY: What dimensions does the tool assess?
- BEHAVIOR ONLY: Please identify which broad domain(s)/construct(s) are measured by your tool and define each sub-domain or sub-construct.
- BEHAVIOR ONLY: Which category of behaviors does your tool target?
Acquisition and Cost Information
Administration
Training & Scoring
Training
- Is training for the administrator required?
- Yes
- Describe the time required for administrator training, if applicable:
- Less than one hour of training.
- Please describe the minimum qualifications an administrator must possess.
-
No minimum qualifications
- Are training manuals and materials available?
- Yes
- Are training manuals/materials field-tested?
- No
- Are training manuals/materials included in cost of tools?
- Yes
- If No, please describe training costs:
- Can users obtain ongoing professional and technical support?
- Yes
- If Yes, please describe how users can obtain support:
- Users have access to ongoing technical support.
Scoring
- 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.
- Two raw scores are calculated: 1) items correct per minute; and 2) accuracy – items correct divided by number of items taken. There are 100 items on each form.
- Do you provide basis for calculating slope (e.g., amount of improvement per unit in time)?
- Yes
- ACADEMIC ONLY: Do you provide benchmarks for the slopes?
- Yes
- ACADEMIC ONLY: Do you provide percentile ranks for the slopes?
- Yes
- Describe the tool’s approach to progress monitoring, behavior samples, test format, and/or scoring practices, including steps taken to ensure that it is appropriate for use with culturally and linguistically diverse populations and students with disabilities.
- Users define a progress monitoring schedule that includes frequency of administration, an end goal date, and target score. For each data point, a single form is administered. Accuracy and rate scores are computed and plotted across time. Scores and trends are compared to a performance target to determine student response to instruction and intervention.
Rates of Improvement and End of Year Benchmarks
- Is minimum acceptable growth (slope of improvement or average weekly increase in score by grade level) specified in your manual or published materials?
- Yes
- If yes, specify the growth standards:
- National growth norms, representing average weekly rates of improvement (ROI) from fall to spring , and derived from the full population of students are used as the basis for acceptable growth. Using the median ROI growth rate for each measure and grade, the FAST system computes growth rate benchmarks based on 80% , 100%, 120% and 150% of the median. The 120% rate, called "Ambitious" in the system serves as the FAST recommended growth target. This target was chosen because research show that Tier 2 and Tier 3 students receiving appropriate, research-based intensive intervention should expect to growth about 20% faster than the general population.
- Are benchmarks for minimum acceptable end-of-year performance specified in your manual or published materials?
- Yes
- If yes, specify the end-of-year performance standards:
- Minimum acceptable end-of-year performance are based on a national percentile threshold. For all grades and assessments the spring 40th national percentile is used.
If norm-referenced, describe the normative profile.
Local representation (please describe, including number of states)
We have national demographically matched norms based on data obtained in 20 states.
- Date
- 2017-2018
- Size
- 91639
Gender (Percent)
- Male
- 51
- Female
- 49
- Unknown
SES indicators (Percent)
- Eligible for free or reduced-price lunch
- 48
- Other SES Indicators
Race/Ethnicity (Percent)
- White, Non-Hispanic
- 46
- Black, Non-Hispanic
- Hispanic
- American Indian/Alaska Native
- Asian/Pacific Islander
- Other
- Unknown
- Disability classification (Please describe)
- Not reported
- First language (Please describe)
- Not reported
- Language proficiency status (Please describe)
- Not reported
Performance Level
Reliability
| Grade |
Kindergarten
|
|---|---|
| Rating |
|
Legend
Convincing evidence
Partially convincing evidence
Unconvincing evidence
Data unavailabledDisaggregated data available
- *Offer a justification for each type of reliability reported, given the type and purpose of the tool.
- Inter-rater reliability is an appropriate measure of form reliability because it demonstrates the stability of scores across administrators. Because earlyReading Letter Names uses fixed forms for progress monitoring, it is important to demonstrate that alternate forms provide consistent measurement of students' basic math computation skills. Alternate form reliability was calculated to demonstrate consistency between progress monitoring forms. This reliability coefficient was calculated from actual progress monitoring data using scores from forms administered no more than two weeks apart.
- *Describe the sample(s), including size and characteristics, for each reliability analysis conducted.
- For alternate form reliability, the data file consisted of all scores collected during the 2018-2019 school year. The sample demographics represent at least 15 states in each grade, all race/ethnicity categories, urban, suburban, and rural school districts, and the full range of socio-economic status. See results below for specific sample sizes by tool, grade, and analysis. Data for the inter-rater reliability analysis came from administrations done in 2014 by experienced graduate students who attended a 2-hour training collected the data. After the first testing session. Subtests were administered to students in nine elementary schools within three school districts in a metropolitan area in the Midwest. Students were administered five randomly selected progress monitoring forms. District A was about 56% White, 14% Black, 10% Hispanic, and 19% Asian/Pacific Islander. About 45% of students were eligible to receive free/reduced lunch and 13% were eligible for special education services. District B was about 93% White, 4% Black, 3% Hispanic, and 4% Asian/Pacific Islander. About 17% of students were eligible to receive free/reduced lunch and 10% were eligible for special education services. District C was about 80% White, 7% Black, 5% Hispanic, and 11% Asian/Pacific Islander. About 45% of students were eligible to receive free/reduced lunch and 10% were eligible for special education services.
- *Describe the analysis procedures for each reported type of reliability.
- For alternate form reliability the reliability coefficient was calculated from actual progress monitoring data using scores from forms administered no more than two weeks apart. Inter-rater reliability is a measure of the extent to which student scores are consistent across different examiners or scorers. Estimates of inter-rater reliability are based on two independent scorers, and the coefficients represent the level of agreement between examiners or raters.
*In the table(s) below, report the results of the reliability analyses described above (e.g., model-based evidence, internal consistency or inter-rater reliability coefficients). Include detail about the type of reliability data, statistic generated, and sample size and demographic information.
| Type of | Subscale | Subgroup | Informant | Age / Grade | Test or Criterion | n (sample/ examinees) |
n (raters) |
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 | Subscale | Subgroup | Informant | Age / Grade | Test or Criterion | n (sample/ examinees) |
n (raters) |
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
|
|---|---|
| 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.
- FAST earlyReading Decodable Words is an appropriate criterion measure for earlyReading Letter Names. The criterion tool is part of the FAST progress monitoring system; but, measures a different skill from earlyReading Letter Names. The measures have no overlapping items. Additionally, the only exposure to Decodable Words for the students who were progress monitored on Letter Names was the end of year, spring screening. earlyReading Letter Names assesses student letter recognition, which is a prerequisite skill for the development of word reading and decoding skills. Reading skills exist on a continuum from pre-reading skills and phonemic awareness to comprehending long texts. Letter Names measures the pre-reading skill of letter recognition and it is reasonable to compare it to the nearly adjacent, but distinct, skill of whole word decoding as measured by Decodable Words.
- *Describe the sample(s), including size and characteristics, for each validity analysis conducted.
- The data file consisted of all scores collected during the 2018-2019 school year. The sample demographics represent at least 15 states in each grade, all race/ethnicity categories, urban, suburban, and rural school districts, and the full range of socio-economic status. See results below for specific sample sizes by tool, grade, and analysis.
- *Describe the analysis procedures for each reported type of validity.
- Concurrent validity was calculated by correlating student scores during the same season administration of the earlyReading Letter Names tool and the earlyReading Decodable Words criterion measure. Predictive validity was calculated by correlating student scores adjacent season administrations (for example, Fall to Winter) of the earlyReading Letter Names tool and the earlyReading Decodable Words criterion measure.
*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 | Subscale | Subgroup | Informant | Age / Grade | Test or Criterion | n (sample/ examinees) |
n (raters) |
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.
- earlyReading Letter Names is designed to measure letter recognition skills that are critical pre-reading skills, and are sensitive to interventions delivered through MTSS. As such demonstrating the relationship between scores on earlyReading Letter Names and a related but distinct task reflects this purpose. Concurrent and predictive validity are both reasonable metrics for evaluating the validity of a progress monitoring tool. The results provide strong evidence of the value and validity of using earlyReading Letter Names to predict later pre-reading and reading skills.
- 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 | Subscale | Subgroup | Informant | Age / Grade | Test or Criterion | n (sample/ examinees) |
n (raters) |
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
|
|---|---|
| Rating | 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.
Growth Standards
Sensitivity: Reliability of Slope
| Grade | Kindergarten |
|---|---|
| Rating |
|
Legend
Convincing evidencePartially convincing evidenceUnconvincing evidenceData unavailabledDisaggregated data available
- Describe the sample, including size and characteristics. Please provide documentation showing that the sample was composed of students in need of intensive intervention. A sample of students with intensive needs should satisfy one of the following criteria: (1) all students scored below the 30th percentile on a local or national norm, or the sample mean on a local or national test fell below the 25th percentile; (2) students had an IEP with goals consistent with the construct measured by the tool; or (3) students were non-responsive to Tier 2 instruction. Evidence based on an unknown sample, or a sample that does not meet these specifications, may not be considered.
- The data file consisted of all scores collected during the 2018-2019 school year in which the students were below the 30th national percentile or lower. The sample demographics represent at least 15 states in each grade, all race/ethnicity categories, urban, suburban, and rural school districts, and the full range of socio-economic status. See results below for specific sample sizes by tool, grade, and analysis.
- Describe the frequency of measurement (for each student in the sample, report how often data were collected and over what span of time).
- Data for this analysis was comprised of students for whom there was data for at least 10 data points collected over at least 20 weeks.
- Describe the analysis procedures.
- The split-half method, in which the slope from all odd administrations was correlated with the slope from all even administrations was used to compute the reliability of slope. The sample was limited to at-risk students with a national percentile below the 30th percentile, and those who had at least 10 observations over a 20 week span.
In the table below, report reliability of the slope (e.g., ratio of true slope variance to total slope variance) by grade level (if relevant).
| Type of | Subscale | Subgroup | Informant | Age / Grade | Test or Criterion | n (sample/ examinees) |
n (raters) |
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 of the slope data that is disaggregated by subgroups (e.g., race/ethnicity, gender, socioeconomic status, students with disabilities, English language learners)?
- No
If yes, fill in data for each subgroup with disaggregated reliability of the slope data.
| Type of | Subscale | Subgroup | Informant | Age / Grade | Test or Criterion | n (sample/ examinees) |
n (raters) |
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.
Sensitivity: Validity of Slope
| Grade | Kindergarten |
|---|---|
| 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.
-
- Describe the sample(s), including size and characteristics. Please provide documentation showing that the sample was composed of students in need of intensive intervention. A sample of students with intensive needs should satisfy one of the following criteria: (1) all students scored below the 30th percentile on a local or national norm, or the sample mean on a local or national test fell below the 25th percentile; (2) students had an IEP with goals consistent with the construct measured by the tool; or (3) students were non-responsive to Tier 2 instruction. Evidence based on an unknown sample, or a sample that does not meet these specifications, may not be considered.
- Describe the frequency of measurement (for each student in the sample, report how often data were collected and over what span of time).
- Describe the analysis procedures for each reported type of validity.
In the table below, report predictive validity of the slope (correlation between the slope and achievement outcome) by grade level (if relevant).
NOTE: The TRC suggests controlling for initial level when the correlation for slope without such control is not adequate.
| Type of | Subscale | Subgroup | Informant | Age / Grade | Test or Criterion | n (sample/ examinees) |
n (raters) |
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 validity studies:
- No
- Provide citations for additional published studies.
- Describe the degree to which the provided data support the validity of the tool.
- Do you have validity of the slope data that is disaggregated by subgroups (e.g., race/ethnicity, gender, socioeconomic status, students with disabilities, English language learners)?
- No
If yes, fill in data for each subgroup with disaggregated validity of the slope data.
| Type of | Subscale | Subgroup | Informant | Age / Grade | Test or Criterion | n (sample/ examinees) |
n (raters) |
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 validity studies:
- No
- Provide citations for additional published studies.
Alternate Forms
| Grade | Kindergarten |
|---|---|
| Rating |
|
Legend
Convincing evidencePartially convincing evidenceUnconvincing evidenceData unavailabledDisaggregated data available
- Describe the sample for these analyses, including size and characteristics:
- Forms were developed carefully to achieve content balance. Thus, each form was derived from a common blueprint. To demonstrate empirically the equivalence of forms, we used progress monitoring data. After selecting students who scored below the 30th percentile and had at least 10 observations over 20 weeks, we calculated a mean score for each form adjusted for typical growth. The form means were subtracted for the grand mean for all forms within the grade of interest. That difference was converted to standard deviation units. Comparability of the entire set of 20 forms is also summarized using analysis of variance where Form is treated as a fixed factor. The results indicate that Form accounts for a very small amount of the total score variance (between 0% and 2%). This is a very small percent and will have a trivial effect on the growth slope over the 20 or so administrations that are common for progress monitoring. The data file consisted of all scores collected during the 2018-2019 school year in which the students were below the 30th national percentile or lower. The sample demographics represent at least 15 states in each grade, all race/ethnicity categories, urban, suburban, and rural school districts, and the full range of socio-economic status. See results below for specific sample sizes by tool, grade, and analysis.
- What is the number of alternate forms of equal and controlled difficulty?
- 20
- If IRT based, provide evidence of item or ability invariance
- If computer administered, how many items are in the item bank for each grade level?
- N/A
- If your tool is computer administered, please note how the test forms are derived instead of providing alternate forms:
- N/A
Decision Rules: Setting & Revising Goals
| Grade | Kindergarten |
|---|---|
| Rating |
|
Legend
Convincing evidencePartially convincing evidenceUnconvincing evidenceData unavailabledDisaggregated data available
- In your manual or published materials, do you specify validated decision rules for how to set and revise goals?
- No
- If yes, specify the decision rules:
-
What is the evidentiary basis for these decision rules?
NOTE: The TRC expects evidence for this standard to include an empirical study that compares a treatment group to a control and evaluates whether student outcomes increase when decision rules are in place.
Decision Rules: Changing Instruction
| Grade | Kindergarten |
|---|---|
| Rating |
|
Legend
Convincing evidencePartially convincing evidenceUnconvincing evidenceData unavailabledDisaggregated data available
- In your manual or published materials, do you specify validated decision rules for when changes to instruction need to be made?
- No
- If yes, specify the decision rules:
-
What is the evidentiary basis for these decision rules?
NOTE: The TRC expects evidence for this standard to include an empirical study that compares a treatment group to a control and evaluates whether student outcomes increase when decision rules are in place.
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.
