FAST

Social, Academic, & Emotional Behavior Risk Screener (SAEBRS)

Cost

Technology, Human Resources, and Accommodations for Special Needs

Service and Support

Purpose and Other Implementation Information

Usage and Reporting

Initial Cost:

$7.00 per student.

 

Replacement Cost:

$7.00 per student, per year. Subscription renewal fees subject to change annually.

 

Included in Cost:

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.

Technology Requirements:

  • Computer or tablet
  • Internet connection

 

Training Requirements:

  • Less than 1 hour of training

In addition to the online training modules embedded within the FAST application, FastBridge Learning offers onsite training options. One, two, and three day packages are available. Packages are determined by implementation size and which FAST assessments (e.g., reading, math, and/or behavior) a district intends to use: 1-day package: $3,000.00; 2-day package: $6,000.00; 3-day package: $9,000.00. Any onsite training purchase also includes a complimentary online Admin/Manager training session (2 hours) for users who will be designated as District Managers and/or School Managers in FAST. Additionally, FastBridge offers web-based consultation and training delivered by certified FAST trainers. The web-based consultation and training rate is $200.00/hour.

 

Qualified Administrators:

Must have been able to observe and interact with the student over the past month.

 

Accommodations:

As part of item development, all items were reviewed for bias and fairness.

Where to Obtain:

Website: www.fastbridge.org

Address: 520 Nicollet Mall, Suite #910, Minneapolis, MN 55402

Phone number: 612.254.2534

Email address: info@fastbridge.org

 

Access to Technical Support:

Users have access to professional development technicians as well as ongoing technical support.

The FAST™ SAEBRS is a brief and efficient tool to universally screen students individually, or by class, grade, or school for risk for social-emotional and behavioral problems. The FAST™ SAEBRS evaluates general student behavior, as well as behavior within the social, academic, and emotional domains. The FAST™ SAEBRS is considered a brief behavior rating scale, which is comprised of 19 items. To screen using the FAST™ SAEBRS, a teacher completes the scale once for each student in his/her classroom.

The FAST™ SAEBRS includes items from three domains. Each domain is defined as follows. Social Behavior (6 items) is defined as behaviors that promote (e.g., social skills) or limit (e.g., externalizing problems) one's ability to maintain age appropriate relationships with peers and adults. Academic Behavior (6 items) is defined as behaviors that promote (e.g., academic enablers) or limit (e.g., attentional problems) one's ability to be prepared for, participate in, and benefit from academic instruction. Finally, Emotional Behavior (7 items) is defined as actions that promote (e.g., social-emotional competencies) or limit (e.g., internalizing problems) one's ability to regulate internal states, adapt to change, and respond to stressful/challenging events.

Assessment Format:

  • Rating scale

 

Administration Time:

  • 1-2 minutes per student
  • 25-50 minutes per group

 

Scoring Time:

  • Scoring is automatic

 

Scoring Method:

  • Calculated automatically

 

Scores Generated:

  • Raw score
  • Percentile score
  • Composite scores
  • Subscale/subtest scores

 

Classification Accuracy

Grade: InformantK-5:
Teacher
Criterion 1 FallFull bubbled
Criterion 1 WinterFull bubble
Criterion 1 SpringFull bubble
Criterion 2 Falldash
Criterion 2 Winterdash
Criterion 2 Springdash

Primary Sample

 

Criterion 1: BASC-2 Behavioral and Emotional Screening System. Outcome corresponded to the overall Behavioral and Emotional Risk index, dichotomously scored as 0 = Normal or Elevated Risk and 1 = Extremely Elevated Risk. To note, those students falling within the “Elevated” risk range on the BESS were excluded from the present analyses.

Grade: K-5

Informant: Teacher

 

K-5, Teacher

Fall

K-5, Teacher

Winter

K-5, Teacher

Spring

Cut points

28

31

28

Base rate in the sample for children requiring intensive intervention

0.07

0.08

0.07

Base rate in the sample for children considered at-risk, including those with the most intensive needs

0.22

0.24

0.22

False Positive Rate

0.05

0.09

0.01

False Negative Rate

0.17

0.25

0.05

Sensitivity

0.83

0.72

0.95

Specificity

0.95

0.96

0.99

Positive Predictive Power

0.59

0.63

0.95

Negative Predictive Power

0.98

0.97

0.99

Overall Classification Rate

0.94

0.94

0.99

Area Under the Curve (AUC)

0.96

0.93

0.99

AUC 95% Confidence Interval Lower

0.94

0.87

0.99

AUC 95% Confidence Interval Upper

0.98

0.98

1.00

At 90% Sensitivity, specificity equals

0.90

0.85

0.99

At 80% Sensitivity, specificity equals

0.95

0.92

1.00

At 70% Sensitivity, specificity equals

0.97

0.97

1.00

 

 

Additional Classification Accuracy

The following are provided for context and did not factor into the Classification Accuracy ratings.

 

Disaggregated Data

Criterion 1: BASC-2 Behavioral and Emotional Screening System. Outcome corresponded to the overall Behavioral and Emotional Risk index, dichotomously scored as 0 = Normal or Elevated Risk and 1 = Extremely Elevated Risk. To note, those students falling within the “Elevated” risk range on the BESS were excluded from the present analyses.

Grade: K-5

Informant: Teacher

Time of Year: Fall

 

White/Caucasian Students

Minority Students (includes students identifying as Black/African American, Hispanic/Latino(a), Asian American, Native American/American Indian, and Multi-racial)

Cut points

28

28

Base rate in the sample for children requiring intensive intervention

0.06

0.08

Base rate in the sample for children considered at-risk, including those with the most intensive needs

0.21

0.22

False Positive Rate

0.04

0.07

False Negative Rate

0.28

0.09

Sensitivity

0.72

0.91

Specificity

0.96

0.93

Positive Predictive Power

0.59

0.58

Negative Predictive Power

0.98

0.99

Overall Classification Rate

0.95

0.93

Area Under the Curve (AUC)

0.96

0.97

AUC 95% Confidence Interval Lower

0.92

0.94

AUC 95% Confidence Interval Upper

0.99

0.99

At 90% Sensitivity, specificity equals

0.91

0.96

At 80% Sensitivity, specificity equals

0.94

0.97

At 70% Sensitivity, specificity equals

0.96

0.97

 

Cross-Validation Sample

Criterion 1: BASC-2 BESS

Grade: K-5

Informant: Teacher

Time of Year: Fall

 

Southeast Sample

Southwest Sample

Cut points

28

28

Base rate in the sample for children requiring intensive intervention

0.05

0.05

Base rate in the sample for children considered at-risk, including those with the most intensive needs

0.15

0.15

False Positive Rate

0.04

0.05

False Negative Rate

0.19

0.03

Sensitivity

0.81

0.97

Specificity

0.96

0.95

Positive Predictive Power

0.48

0.48

Negative Predictive Power

0.99

1.00

Overall Classification Rate

0.95

0.95

Area Under the Curve (AUC)

0.97

0.99

AUC 95% Confidence Interval Lower

0.95

0.98

AUC 95% Confidence Interval Upper

1.00

1.00

At 90% Sensitivity, specificity equals

0.94

0.96

At 80% Sensitivity, specificity equals

0.96

0.97

At 70% Sensitivity, specificity equals

0.99

0.99

 

Reliability

Grade: InformantK-5:
Teacher
RatingFull bubble
  1. Justification for each type of reliability reported, given the type and purpose of the tool:

 

Internal reliability: multiple statistics were used in evaluating FAST™ SAEBRS internal reliability. First, a series of omega coefficients were used as part of a model-based approach to the evaluation of FAST™ SAEBRS Total Behavior scale internal reliability. Second, alpha coefficients were used as a separate, non-model-based approach. Internal reliability is considered relevant given the presumption that all FAST™ SAEBRS items are related to the broader construct of general behavioral functioning. Consideration of a model-based coefficient like omega is considered particularly relevant given the presumption that the FAST™ SAEBRS is founded upon a bifactor model, wherein all items are related to both the general behavior factor and one of three narrow factors.

 

Test-retest reliability: a series of correlation analyses were used to evaluate the association between FAST™ SAEBRS data administered at two different time points within two weeks. Interest in test-retest reliability was founded in the assumption that the majority of students within a school should maintain their social-emotional and behavioral risk status across the school year (Dever, Dowdy, Raines, & Carnazzo, 2015). Accordingly, it was anticipated there should be some degree of consistency in scores. With that said, it was expected such consistency would be tempered by the inherent variability of behavior and the delivery of intervention and supports to a subsample of students in the school.

 

  1. Description of the sample(s), including size and characteristics, for each reliability analysis conducted:

 

Internal reliability was evaluated across two studies. First, omega coefficients were calculated as part of Kilgus, Bonifay, von der Embse, Allen, and Eklund (in press). The study was conducted in four urban elementary schools (K-5) located in the Midwestern United States. All general education teachers in each school chose to participate in this study. The teachers screened all students in their classroom, resulting in a sample of 68 teacher participants and 1,243 students. The sample was characterized by a diverse student population in regard to ethnicity, including sizeable subsamples of White (54.5%), Black (28.6%), Hispanic (5.3%), and Multiracial (8.4%) students. The free/reduced-price lunch rate across the four schools was equal to 65.1%. Second, alpha coefficients were evaluated as part of Kilgus, Eklund, von der Embse, Taylor, and Sims (2016). This study was conducted with 567 elementary students (52.9% female) and 34 classroom teachers. The sample was characterized by a diverse student population, including sizeable subsamples of White (50.1%), Black (34.4%), Hispanic/Latina(o) (11.3%), and multiracial (3.7%) students.

 

Test-retest reliability was evaluated with internal FastBridge Learning SAEBRS data. This sample included 53 students (42% female), all of whom were evaluated with the SAEBRS twice within 14 days. In terms of ethnicity, the sample consistent of 89% Black, 6% White, 2% Hispanic/Latina(o), and 4% multiracial students. SAEBRS scores at Time 1 spanned the range of expected performance, ranging from 13 to 57. This suggested that students from all performance levels (i.e., low, moderate, and high risk) were represented within this sample, despite its restricted size.

 

  1. Description of the analysis procedures for each reported type of reliability:

 

Internal reliability: Omega (ω) coefficients represent the proportion of variance to all factors common to an item set of interest. Hierarchical omega (ωH) coefficients represent the proportion of variance attributable to a particular factor after controlling for all other factors. These latter statistics are particularly informative when examining measures corresponding to bifactor structures (such as the FAST™ SAEBRS), as items are presumed to be multidimensional and driven by both general and specific factors. Beyond omega, coefficient alphas were also calculated in evaluating FAST internal reliability.

 

Test-retest reliability: Pearson product-moment correlation (r) coefficients were used to evaluate the association between FAST™ SAEBRS administrations.

 

  1. Reliability of performance level score (e.g., model-based, internal consistency, inter-rater reliability).

Type of Reliability

Informant

Age or Grade

n

Coefficient

Confidence Interval

Internal (omega)

Teacher

Elementary

1243

Omega = .98;

Hierarchical omega = .87

 

Internal (alpha)

Teacher

Elementary

567

Alpha = .93

 

Test-retest

Teacher

Elementary

53

r = 1.00

 

 

Disaggregated Reliability

The following disaggregated reliability data are provided for context and did not factor into the Reliability rating.

Type of Reliability

Subgroup

Age or Grade

n

Coefficient

Confidence Interval

None

 

 

 

 

 

 

Validity

Grade: InformantK-5:
Teacher
RatingFull bubble
  1. Description of each criterion measure used and explanation as to why each measure is appropriate, given the type and purpose of the tool:

 

SSIS: The Social Skills Improvement System (SSIS; Gresham & Elliott, 2008) is a comprehensive teacher rating scale (83 items), which is used to assess the broad areas of student Social Skills, Problem Behaviors, and Academic Competence. The FAST™ SAEBRS was compared to the SSIS as part of its initial validation study, during which only FAST™ SAEBRS only included the Social Behavior (6 items) and Academic Behavior (6 items) scales. The SSIS was considered a particularly important criterion given the scale’s assessment of social and academic functioning, as well as its pertinence to problem behaviors, which are also assessed within each FAST™ SAEBRS subscale.

 

BASC-2 BESS: The BASC-2 BESS was factor analytically derived from the broader BASC-2 measure, which is considered a goal standard of behavioral assessment. Research suggests that BASC-2 BESS scores are valid and diagnostically accurate indicators of the BASC-2, as well as other alternative measures. Like the FAST™ SAEBRS Total Behavior scale, the sole BASC-2 BESS scale score represents broad and general functioning within the social-emotional and behavioral domain.

 

SRSS: The Student Risk Screening Scale (SRSS; Drummond, 1994) is a brief, 7-item teacher rating scale, which research suggests is an indicator of student externalizing behavior. The SRSS is completed in a manner similar to the FAST™ SAEBRS, with a classroom teacher completing the scale once for each student in his or her classroom. The SRSS is considered relevant to the FAST™ SAEBRS given the latter scale’s incorporation of items either directly or indirectly related to externalizing behavior, particularly as it relates to social functioning.

 

SIBS: The Student Internalizing Behavior Screener (SIBS; Cook et al., 2011) is a brief, 7-item teacher rating scale used to assess student internalizing behavior. The SIBS is completed in a manner nearly identical to the SRSS. The SIBS is considered relevant to the FAST™ SAEBRS given the latter scale’s incorporation of items either directly or indirectly related to internalizing behavior, particularly as it relates to emotional functioning.

  

  1. Description of the sample(s), including size and characteristics, for each validity analysis conducted:

 

SSIS: The FAST™ SAEBRS was compared to the SSIS within Kilgus, Chafouleas, and Riley-Tillman (2013). This study was conducted across three public schools within a single school district in southeastern United States. During the 2010-2011 school year, 4% of the district’s students identified as English language learners, and 40% qualified for free or reduced lunch. Across the three schools, 56 K-5 teachers agreed to participate. Using a random number generator, researchers randomly selected five students for participation in each classroom, resulting in the identification of 276 student participants (four teachers requested to rate only four randomly selected students because of time constraints). In regards to ethnicity, 50.6% of students were White, 32.5% Black/African American, 10.3% Hispanic/Latino(a), 2.1% Asian, and 3.7% other.

 

BASC-2 BESS (Concurrent): The FAST™ SAEBRS was compared to the BASC-2 BESS within Kilgus, Eklund, von der Embse, Taylor, & Sims (2016). Participants included 34 elementary teachers and their 567 students (52.9% female). In regards to ethnicity, 50.1% of students were White, 34.4% Black/African American, 11.3% Hispanic/Latino(a), 0.5% Asian, and 3.7% multi-racial. Overall, 61.9% of students qualified for free/reduced-priced lunch.

 

BASC-2 BESS (Predictive): The SAEBRS was also compared to the BASC-2 BESS as part of predictive validity analyses, which have yet to be published. The sample consisted of 68 teacher participants and 1,243 students. The sample was characterized by a diverse student population in regard to ethnicity, including sizeable subsamples of White (54.5%), Black (28.6%), Hispanic (5.3%), and Multiracial (8.4%) students. The free/reduced-price lunch rate across the four schools was equal to 65.1%. SAEBRS data corresponded to that collected within the fall of the school year. BASC-2 BESS data were collected in the winter and spring (approximately 3-6 months later, respectively).

 

SRSS and SIBS: The FAST™ SAEBRS was compared to the SRSS and SIBS as part of Kilgus, Sims, von der Embse, and Taylor (2016). Participants included 17 teachers and 346 students. This sample comprised all the teachers and students from a single rural Midwestern elementary school enrolling third-, fourth-, and fifth-grade students (teacher and student participation rate = 100%). The student body in this school was characterized by a relatively even split in gender, homogeneous ethnicity profile (i.e., 95% White/Caucasian), a 20% free/reduced price lunch rate, and low English language learner enrollment. Students included in the analysis were both regular and special education students. The 17 teachers in the study were all Caucasian females. Years of teaching experience ranged from 1 to more than 25 years.

 

  1. Description of the analysis procedures for each reported type of validity:

 

Concurrent validity: The FAST™ SAEBRS was compared to each of the aforementioned criteria via Pearson product-moment correlation (r) coefficients.

 

  1. Validity for the performance level score (e.g., concurrent, predictive, 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 Validity

Informant

Age or Grade

Test or Criterion

n

Coefficient

Confidence Interval

Concurrent

Teacher

Elementary

SSIS-Social Skills

276

0.88

0.85, 0.90

Concurrent

Teacher

Elementary

SSIS-Problem Behaviors

276

-0.89

-0.91, -0.86

Concurrent

Teacher

Elementary

SSIS-Academic Competence

276

0.61

0.53, 0.68

Concurrent

Teacher

Elementary

BESS

567

-0.93

-0.94, -0.92

Concurrent

Teacher

Elementary

SRSS

346

-0.84

-0.87, -0.81

Concurrent

Teacher

Elementary

SIBS

346

-0.67

-0.73, -0.61

Predictive

Teacher

Elementary

BESS

1243

-0.76

-0.73, -0.78

Predictive

Teacher

Elementary

BESS

1243

-0.74

-0.71, -0.76

 

  1. Results for other forms of validity (e.g. factor analysis) not conducive to the table format:

Not provided

 

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

The presented data speak to the validity of nomological net upon which the FAST™ SAEBRS is founded. The FAST™ SAEBRS theoretical framework specifies that the measure should be capable of predicting a student’s broader social-emotional and behavioral functioning. It further specifies the measure should be capable of predicting a student’s behavior within the social, academic, and emotional domains. Evidence to date has supported this, as evidence by: (a) concurrent and predictive correlations with the BESS, an indicator of broad and general functioning, (b) correlations with the SSIS-Social Skills and SRSS, indicators of student social competence and externalizing behavior, respectively (both of which are theoretically captured through the FAST™ SAEBRS Social Behavior subscale), (c) correlations with the SSIS-Academic Competence scale, an indicator of student academic functioning (which is theoretically captured through the FAST™ SAEBRS Academic Behavior subscale), and (d) correlations with the SIBS, an indicator of student internalizing behavior (which is theoretically captured through the FAST™ SAEBRS Emotional Behavior subscale). When taken together, existing validity evidence supports all elements of the theoretical framework upon which the FAST™ SAEBRS is founded.

 

Disaggregated Validity

The following disaggregated validity data are provided for context and did not factor into the Validity rating.

Type of Validity

Subgroup

Age or Grade

Test or Criterion

n

Coefficient

Confidence Interval

None

 

 

 

 

 

 

 

Results for other forms of disaggregated validity (e.g. factor analysis) not conducive to the table format:

Not provided

 

Sample Representativeness

Grade: InformantK-5:
Teacher
RatingHalf-filled bubble

Primary Classification Accuracy Sample

 

Criterion 1: BASC-2 BESS

Representation

Local: one state, with the sample being urban in nature

Midwest: East North Central

Date

2015-2016 school year

Size

875 students

Male

43.8%

Female

40.6%

Unknown

15.7%

Free or reduced-price lunch

65.1%

White, Non-Hispanic

47.4%

Black, Non-Hispanic

23.1%

Hispanic

4.8%

American Indian/Alaska Native

0.6%

Asian/Pacific Islander

1.5%

Other

6.7% multi-racial

Unknown

15.8%

Disability classification

Unavailable

First language

Unavailable

Language proficiency status

Unavailable

 

Cross-Validation Sample

 

Criterion 1: BASC-2 BESS

 

Southeast Sample

Southwest Sample

Representation

Local: Includes one school district from one state, with the district being highly diverse and representative of its community

South: South Atlantic

Local: two school districts from one state, with both districts being highly diverse and representative of their community

South: West South Central

Date

Winter 2014

Fall 2014

Size

567

712

Male

47.1%

53.5%

Female

52.9%

45.5%

Unknown

Not provided

1.0%

Free or reduced-price lunch

61.9%

28.0%

White, Non-Hispanic

50.1%

52.1%

Black, Non-Hispanic

34.4%

2.8%

Hispanic

11.3%

26.7%

American Indian/Alaska Native

0.0%

4.6%

Asian/Pacific Islander

0.5%

2.7%

Other

3.7%

7.0%

Unknown

Not provided

4.1%

Disability classification

Not provided

Not provided

First language

Not provided

Not provided

Language proficiency status

Not provided

Not provided

 

Bias Analysis Conducted

Grade: InformantK-5:
Teacher
RatingYes
  1. Description of the method used to determine the presence or absence of bias:

Multi-group confirmatory factor analysis (MG-CFA) was used to examine measurement equivalence/invariance (Pendergast, von der Embse, Kilgus, & Eklund, 2017). Specifically, analyses considered the extent to which the FAST™ SAEBRS (inclusive of only Social Behavior and Academic Behavior items) was invariant across ethnic categories.

 

  1. Description of the subgroups for which bias analyses were conducted:

Participants from two racial groups (White n = 412, and Black n = 323) were included in analyses of SABRS ME/I across race. Participants from other racial groups were excluded because the sample sizes were too small (<100).

 

  1. Description of the results of the bias analyses conducted, including data and interpretative statements:

CFAs were conducted in Mplus 6.2 using WLSMV estimation (Beauducel & Herzberg, 2006). Overall model fit was evaluated based on the RMSEA and the CFI (Kline, 2010; Tanaka, 1993). Criteria for evaluating minimally acceptable model fit were established a priori: RMSEA values ≤ 0.08 and CFA values ≥ 0.90 (Browne & Cudeck, 1993; Hu & Bentler, 1995; Markland, 2007).

 

The analyses in this study focused on examining ME/I across race. The ME/I of the two-factor SABRS structure was assessed by applying increasingly restrictive equality constraints across groups to examine (a) configural invariance, (b) metric invariance, and (c) scalar/threshold invariance. Nested models (i.e., models with increasingly restrictive invariance tests) were compared using the change in SB χ2 (ΔSB χ2), change in (ΔCFI), and change in RMSEA (ΔRMSEA) values. Within the current study, each nested model was compared to its parent model, the latter of which possessed increasingly restrictive invariance specifications. As the models grew more restrictive, non-significant Δχ2 (p > 0.05), ΔCFI < 0.01 (Cheung & Rensvold, 2002), and ΔRMSEA < 0.015 indicated that the more restrictive model had a comparable fit to the data as less restrictive one (Byrne, 2011; Meade et al., 2008; Satorra & Bentler, 2001).

 

In the first step, configural invariance was established. Fit indices for the configural model fell within specified ranges (CFI = 0.993; RMSEA = 0.077). Next, a metric invariance model was tested wherein factor loadings were constrained to be equal across racial groups. The model had adequate fit based on the aforementioned fit criteria (CFI = 0.994; RMSEA = 0.069). The change-in-model fit indices suggested that the fit of the metric invariance model was not significantly worse, and, in fact, was slightly better relative to the configural invariance model (Δχ2 was non-significant, ΔCFI was < 0.01, and was ΔRMSEA < 0.015). Subsequently, a scalar/threshold invariance model was tested whereby factor loadings and thresholds were constrained to be equal across groups. The model had adequate fit (CFI = 0.994; RMSEA = 0.062), and the change-in-model fit indices indicated that the fit of the scalar/threshold model was not significantly different from that of the metric model (Δχ2 was non-significant, ΔCFI was < 0.01, ΔRMSEA was < 0.015). Therefore, scalar/threshold invariance was supported.

 

Administration Format

Grade: InformantK-5:
Teacher
Data
  • Individual
  • Administration & Scoring Time

    Grade: InformantK-5:
    Teacher
    Data
  • 1-2 minutes
  • Scoring Format

    Grade: InformantK-5:
    Teacher
    Data
  • Automatic
  • Types of Decision Rules

    Grade: InformantK-5:
    Teacher
    Data
  • None
  • Evidence Available for Multiple Decision Rules

    Grade: InformantK-5:
    Teacher
    Data
  • No
  • Usability Study Conducted

    Grade: InformantK-5:
    Teacher
    DataNo