DESSA
DESSA-mini

Descriptive Information

Please provide a description of your tool:

The DESSA System is a comprehensive suite of standardized, norm-referenced screening and assessment tools designed to measure the social and emotional competence of students in grades K–12. Using a strengths-based approach, the system includes measures completed by educators, parents, and students to support universal screening, detailed assessment, progress monitoring, and outcome evaluation within multi-tiered systems of support (MTSS). The DESSA-mini is a brief and efficient teacher-completed behavior rating scale used for universal screening of students’ overall social-emotional competence. It consists of four parallel 8-item forms that may be used interchangeably and repeatedly throughout the school year for screening, progress monitoring, and evaluation of student outcomes. Each DESSA-mini form includes a representative sample of items drawn from the full DESSA assessment, a 40-item measure of overall social and emotional competence and six competency domains: Optimistic Thinking, Self-Awareness, Self-Management, Social Awareness, Relationship Skills, and Responsible Decision Making. In practice, the DESSA-mini is used to identify students who may benefit from additional social and emotional instruction or support, while the full DESSA serves as a more comprehensive follow-up assessment to identify specific student strengths and needs and to inform differentiated instruction for individual students or groups.

The tool is intended for use with the following grade(s).

Preschool / Pre - kindergarten
Kindergarten
First grade
Second grade
Third grade
Fourth grade
Fifth grade
Sixth grade
Seventh grade
Eighth grade
Ninth grade
Tenth grade
Eleventh grade
Twelfth grade

The tool is intended for use with the following age(s).

0-4 years old
5 years old
6 years old
7 years old
8 years old
9 years old
10 years old
11 years old
12 years old
13 years old
14 years old
15 years old
16 years old
17 years old
18 years old

The tool is intended for use with the following student populations.

Students in general education
Students with disabilities
English language learners

ACADEMIC ONLY: What skills does the tool screen?

Reading

Phonological processing:

RAN
Memory
Awareness
Letter sound correspondence
Phonics
Structural analysis

Word ID

Accuracy
Speed

Nonword

Accuracy
Speed

Spelling

Accuracy
Speed

Passage

Accuracy
Speed

Reading comprehension:

Multiple choice questions
Cloze
Constructed Response
Retell
Maze
Sentence verification
Other (please describe):

Listening comprehension:

Multiple choice questions
Cloze
Constructed Response
Retell
Maze
Sentence verification
Vocabulary
Expressive
Receptive

Mathematics

Global Indicator of Math Competence

Accuracy
Speed
Multiple Choice
Constructed Response

Early Numeracy

Accuracy
Speed
Multiple Choice
Constructed Response

Mathematics Concepts

Accuracy
Speed
Multiple Choice
Constructed Response

Mathematics Computation

Accuracy
Speed
Multiple Choice
Constructed Response

Mathematic Application

Accuracy
Speed
Multiple Choice
Constructed Response

Fractions/Decimals

Accuracy
Speed
Multiple Choice
Constructed Response

Algebra

Accuracy
Speed
Multiple Choice
Constructed Response

Geometry

Accuracy
Speed
Multiple Choice
Constructed Response

Other (please describe):

Please describe specific domain, skills or subtests:

BEHAVIOR ONLY: Which category of behaviors does your tool target?

Internalizing
Externalizing
Internalizing and Externalizing

BEHAVIOR ONLY: Please identify which broad domain(s)/construct(s) are measured by your tool and define each sub-domain or sub-construct.

Broad domain: Overall social and emotional competence. Sub-domains: 1. Optimistic Thinking: The belief and demonstration of confidence, hopefulness, and positive thinking regarding oneself, others, and one’s life situations in the past, present, and future. 2. Self-Awareness: The ability to understand emotions, thoughts, and values and how they influence one’s behavior; recognize strengths and limitations; and develop healthy identities and a sense of purpose. 3. Self-Management: The ability to manage emotions and behaviors across different situations and environments and to demonstrate agency as one works to set and achieve personal and collective goals. 4. Social Awareness: The understanding of social norms for behavior; the ability to empathize with, respect, and take the perspectives of others; and the feeling of connection and belonging with family, peers, schools, and community groups. 5. Relationship Skills: The abilities to establish and maintain healthy and positive relationships, including effective communication, collaborative problem solving, negotiating conflict, and demonstrating helpful and supportive behaviors. 6. Responsible Decision Making: The ability to make careful, reliable, and constructive choices about personal and social behavior that are appropriate across diverse situations; to consider the personal, social, and collective impact of one’s actions; and to demonstrate curiosity and an open-mindedness to learning.

Acquisition and Cost Information

Where to obtain:

Email Address

800-323-9540

Address

One Pierce Place, Suite 900W, Itasca, IL, 60143

Phone Number

1-800-323-9540

Website

https://riversideinsights.com/

Initial cost for implementing program:

Cost

Unit of cost

Student license

Replacement cost per unit for subsequent use:

Cost

Unit of cost

Student license

Duration of license

Year

Additional cost information:

Describe basic pricing plan and structure of the tool. Provide information on what is included in the published tool, as well as what is not included but required for implementation.

The DESSA System is offered through an annual subscription model based on student license counts, with separate plans available for grades K–5 and grades 6–8/9–12. The subscription includes access to the DESSA screeners and assessments, DESSA strategies and interventions, data-driven recommendations, and DESSA System implementation training resources. The published tool and associated digital platform include the materials and resources necessary for implementation; no additional required components are needed beyond access to the online DESSA System. Optional implementation supports and services are also available, including virtual or on-site professional learning, single sign-on (SSO), and student information system (SIS) rostering integrations. Districts and schools may contact Riverside Insights for detailed pricing information, licensing options, implementation supports, and technical integration services.

Provide information about special accommodations for students with disabilities.

The DESSA tools can be used with students with disabilities. We recommend teacher ratings be completed in the same manner as ratings for all students, although some adaptations may be needed for interpretation and use of results. More specific guidance is provided for users in the technical manuals and DESSA implementation guides.

Administration

BEHAVIOR ONLY: What type of administrator is your tool designed for?

General education teacher
Special education teacher
Parent
Child
External observer
Other

If other, please specify:

What is the administration setting?

Direct observation
Rating scale
Checklist
Performance measure
Questionnaire
Direct: Computerized
One-to-one
Other

If other, please specify:

Does the tool require technology?

Yes

If yes, what technology is required to implement your tool? (Select all that apply)

Computer or tablet
Internet connection
Other technology (please specify)

If your program requires additional technology not listed above, please describe the required technology and the extent to which it is combined with teacher small-group instruction/intervention:

What is the administration context?

Individual
Small group   If small group, n=
Large group   If large group, n=
Computer-administered
Other

If other, please specify:

What is the administration time?

Time in minutes

1

per (student/group/other unit)

student

Additional scoring time:

Time in minutes

per (student/group/other unit)

ACADEMIC ONLY: What are the discontinue rules?

No discontinue rules provided
Basals
Ceilings
Other

If other, please specify:

Are norms available?

Yes

Are benchmarks available?

No

If yes, how many benchmarks per year?

If yes, for which months are benchmarks available?

BEHAVIOR ONLY: Can students be rated concurrently by one administrator?

Yes

If yes, how many students can be rated concurrently?

Typical use case is that a teacher rates all students in their class with the DESSA-mini (1 minute per student).

Training & Scoring

Training

Is training for the administrator required?

Yes

Describe the time required for administrator training, if applicable:

Less than 1 hour of training.

Please describe the minimum qualifications an administrator must possess.

The opportunity to observe the student for at least four weeks before completing the rating.

No minimum qualifications

Are training manuals and materials available?

Yes

Are training manuals/materials field-tested?

Yes

Are training manuals/materials included in cost of tools?

Yes

If No, please describe training costs:

Can users obtain ongoing professional and technical support?

Yes

If Yes, please describe how users can obtain support:

Users have access to self-paced courses, an on-demand video library, office hours, and support portal resources to accommodate diverse learning preferences. Dedicated teams are available to provide implementation success guidance, technical support, and data integrations assistance. Additional customized in-person and virtual trainings are available for an additional cost.

Scoring

How are scores calculated?

Manually (by hand)
Automatically (computer-scored)
Other

If other, please specify:

Do you provide basis for calculating performance level scores?

Yes

What is the basis for calculating performance level and percentile scores?

Age norms
Grade norms
Classwide norms
Schoolwide norms
Stanines
Normal curve equivalents

What types of performance level scores are available?

Raw score
Standard score
Percentile score
Grade equivalents
IRT-based score
Age equivalents
Stanines
Normal curve equivalents
Developmental benchmarks
Developmental cut points
Equated
Probability
Lexile score
Error analysis
Composite scores
Subscale/subtest scores
Other

If other, please specify:

Does your tool include decision rules?

Yes

If yes, please describe.

Scores are categorized into three descriptive ranges based on the students’ T-score: strength range (T-scores above 60), typical range (T-scores between 41-59), and need for instruction range (T-scores of 40 and below).

Can you provide evidence in support of multiple decision rules?

Yes

If yes, please describe.

The developers followed a process that examined both the sensitivity and specificity of different cut scores for the strength and need for instruction ranges while also taking into consideration ease of use and practicality for users. The outcome of this examination yielded the cut scores of one standard deviation above the mean for the strength range (T-score = 60) and one standard deviation below the mean for the need for instruction range (T-score of 40). The DESSA tools have a mean of 50 and standard deviation of 10. These cut score rules were both easy for users to understand and were very close to the optimal cut off points observed during the empirical examination. For more information, please see the DESSA-mini technical manual.

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.

The DESSA measures are automatically scored within the online DESSA System upon submission of the rating form. For the DESSA-mini, each of the four parallel forms consists of 8 items rated on a 5-point frequency scale: Never (0), Rarely (1), Sometimes (2), Often (3), and Almost Always (4). Raw scores are calculated by summing item responses across all 8 items on the form, resulting in a single composite score referred to as the Social-Emotional Total (SET) score. Raw scores are converted to norm-referenced T-scores and percentile ranks using national norms specific to each DESSA-mini form. T-scores are classified into descriptive ranges indicating Strength (T-score ≥ 60), Typical (T-score = 41–59), or Need for Instruction (T-score ≤ 40). Items are organized into six subscales representing key social-emotional competency domains: Self-Awareness, Self-Management, Social Awareness, Relationship Skills, Responsible Decision Making, and Optimistic Thinking. Raw scores for each subscale are calculated by summing item responses within the respective domain. Subscale raw scores are then converted to norm-referenced T-scores and percentile ranks using national norms. In addition to subscale scores, the DESSA provides an overall Social-Emotional Composite (SEC) score. The SEC is derived from the sum of the six subscale T-scores, allowing equal weighting across all competency domains, and is converted to a norm-referenced T-score and percentile rank. For each subscale and the SEC, T-scores are classified into descriptive ranges indicating Strength (T-score ≥ 60), Typical (T-score = 41–59), or Need for Instruction (T-score ≤ 40).

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 DESSA measures were developed using a rigorous, research-based process to support universal screening and assessment of students’ social and emotional competence within multi-tiered systems of support (MTSS). Development procedures included expert review of item content, construct alignment, developmental appropriateness, and potential sources of bias. The measures were designed using a strengths-based framework aligned with widely recognized social and emotional competencies. The DESSA measures has been standardized and validated using large, diverse samples of students representative of the U.S. school-age population across demographic characteristics, including race/ethnicity, gender, geographic region, and socioeconomic status. Over time, the DESSA measures have been implemented and studied across a wide range of educational settings and student populations, including students receiving special education services and students from culturally and linguistically diverse backgrounds, providing ongoing evidence to support appropriate interpretation and use across groups. Additional details regarding the development process, standardization samples, reliability and validity evidence, and considerations for diverse populations are provided in the DESSA manuals and related peer-reviewed publications.

Usability

Has a usability study been conducted on your tool (i.e., a study that examines the extent to which the tool is convenient and practicable for use?)

No

Has a social validity study been conducted on your tool (i.e., a study that examines the significance of goals, appropriateness of procedures (e.g., ethics, cost, practicality), and the importance of treatment effects)?

No

Classification Accuracy & Cross-Validation Summary

Age / Grade Informant	Grades 6-8 Teacher
Classification Accuracy Fall
Classification Accuracy Winter
Classification Accuracy Spring

In-School Disciplinary Incidents

Classification Accuracy

Select time of year

Fall

Winter

Spring

Describe the criterion (outcome) measure(s) including the degree to which it/they is/are independent from the screening measure.

In-school disciplinary incidents, also referred to here as in-school suspension (ISS), were defined as removal from the student’s regular classroom setting for two or more class periods, indicating that the student’s behavior was sufficiently serious or persistent to warrant a formal disciplinary response. Students with 1 or more ISS incidents were coded as '1' and students with no ISS were '0'. ISS was selected as an important outcome measure because it is assigned for significant behavior infractions and results in the student missing instruction. ISS was selected as the external criterion because it represents a meaningful and consequential indicator of behavioral risk. Although students remain physically in school during ISS, they are removed from their regular instructional environment and therefore lose access to typical classroom instruction and learning opportunities (Cholewa et al., 2018). Evidence from large-scale, longitudinal research supports the relevance of ISS as a meaningful risk indicator. Using a nationally representative longitudinal sample of public high school students, Cholewa et al. found that students who received ISS had significantly lower 11th-grade and cumulative GPAs, even after accounting for prior achievement and other student and school characteristics. Students who received ISS were also substantially more likely to drop out of high school than students who did not receive ISS. Findings from New York City schools further support the importance of ISS as a consequential disciplinary outcome (LiCalsi et al., 2021). The NYCDOE study treated ISS as a distinct form of exclusionary discipline and examined variation in ISS length, including 1-day, 2- to 3-day, and 4- to 5-day ISS assignments. Among middle school students, even moving from a 1-day ISS to a 2- or 3-day ISS was associated with poorer attendance in each of the following four years. More broadly, the study found that greater severity of exclusionary discipline was associated with more days absent, more days suspended, lower likelihood of earning ELA and math credits, and lower likelihood of graduation. Taken together, these findings support ISS as an appropriate criterion for evaluating DESSA-Mini classification accuracy. ISS reflects behavior that has progressed beyond routine classroom management to a level requiring formal removal from instruction, and it is associated with subsequent educational risk. At the same time, ISS is generally less severe than out-of-school suspension, making it a relatively more proximal indicator of escalating behavioral difficulty. Further, in this study sample, 15% of students had an ISS incident during the course of the school year. This rate approximates the MTSS framework's estimated 15-20% of the student population that is expected to demonstrate a moderate (Tier 2) to high (Tier 3) level of risk. Thus, the ability of the DESSA-Mini to identify students who are more likely to experience ISS provides meaningful evidence that the screener is detecting students at elevated behavioral risk before more severe exclusionary outcomes occur.

Do the classification accuracy analyses examine concurrent and/or predictive classification?

Concurrent
Predictive

Describe when screening and criterion measures were administered and provide a justification for why the method(s) you chose (concurrent and/or predictive) is/are appropriate for your tool.

The screening measure was administered in the Fall and ISS was measured at the end of the school year (includes ISS across the school year). A predictive validity design was selected because the value of the DESSA Mini as a screener lies in identifying student risk early enough to act on it. If the DESSA Mini administered early in the school year can predict later in-school suspension, schools can intervene before that risk materializes, potentially reducing missed instructional time associated with suspension.

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).

Classification analyses for the ISS outcome were conducted using Receiver Operating Characteristic (ROC) analyses. Specifically, the analyses examined the degree to which SEC scores classified students into “at-risk” versus “not at-risk” groups based on school discipline outcomes. Sample: The sample came from one large, suburban school district in a mid-Atlantic state. Students in each grade level were as follows: Grade 6: 2,109; Grade 7: 2,163; Grade 8: 2,192. For the ISS analyses, students were classified into two groups: 1. Students identified as “at-risk” for ISS involvement (i.e., students with one or more in-school suspensions), and 2. Students identified as “not at-risk” (i.e., students with no ISS events). The classification analyses did not compare low-, moderate-, and high-risk groups simultaneously; rather, a binary classification approach was used in which students meeting the behavioral risk criterion were contrasted with students not meeting the criterion. Because lower SEC T-scores reflect greater social-emotional and behavioral concern, the analyses accounted for the inverse relationship between SEC scores and behavioral outcomes. Lower SEC scores therefore corresponded to increased likelihood of ISS incidents. Cut-points were determined using Youden’s Index (Sensitivity + Specificity – 1), which identifies the score that maximizes the balance between sensitivity and specificity. This approach is commonly used in screening and classification research because it identifies the threshold that most efficiently differentiates students at elevated risk from those not demonstrating risk. SEC T-scores demonstrated good discrimination for identifying students with ISS outcomes.

Were the children in the study/studies involved in an intervention in addition to typical classroom instruction between the screening measure and outcome assessment?

No

If yes, please describe the intervention, what children received the intervention, and how they were chosen.

Cross-Validation

Has a cross-validation study been conducted?

Yes

If yes,

Select time of year.

Fall

Winter

Spring

Describe the criterion (outcome) measure(s) including the degree to which it/they is/are independent from the screening measure.

In-school disciplinary incidents, also referred to here as in-school suspension (ISS), were defined as removal from the student’s regular classroom setting for two or more class periods, indicating that the student’s behavior was sufficiently serious or persistent to warrant a formal disciplinary response. Students with 1 or more ISS incidents were coded as '1' and students with no ISS were '0'. ISS was selected as an important outcome measure because it is assigned for significant behavior infractions and results in the student missing instruction. ISS was selected as the external criterion because it represents a meaningful and consequential indicator of behavioral risk. Although students remain physically in school during ISS, they are removed from their regular instructional environment and therefore lose access to typical classroom instruction and learning opportunities (Cholewa et al., 2018).

Do the cross-validation analyses examine concurrent and/or predictive classification?

Concurrent
Predictive

Describe when screening and criterion measures were administered and provide a justification for why the method(s) you chose (concurrent and/or predictive) is/are appropriate for your tool.

The screening measure was administered in the Fall and ISS was measured at the end of the school year (includes ISS across the school year). A predictive validity design was selected because the value of the DESSA Mini as a screener lies in identifying student risk early enough to act on it. If the DESSA Mini administered early in the school year can predict later in-school suspension, schools can intervene before that risk materializes, potentially reducing missed instructional time associated with suspension.

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).

Classification analyses for the ISS outcome were conducted using Receiver Operating Characteristic (ROC) analyses. Specifically, the analyses examined the degree to which SEC scores classified students into “at-risk” versus “not at-risk” groups based on school discipline outcomes. Sample: The cross-validation sample came from one large, suburban school district in a mid-Atlantic state. Students in each grade level were as follows: Grade 6: 2,085; Grade 7: 2,101; Grade 8: 2,219. For the ISS analyses, students were classified into two groups: 1. Students identified as “at-risk” for ISS involvement (i.e., students with one or more in-school suspensions), and 2. Students identified as “not at-risk” (i.e., students with no ISS events). The classification analyses did not compare low-, moderate-, and high-risk groups simultaneously; rather, a binary classification approach was used in which students meeting the behavioral risk criterion were contrasted with students not meeting the criterion. Because lower SEC T-scores reflect greater social-emotional and behavioral concern, the analyses accounted for the inverse relationship between SEC scores and behavioral outcomes. Lower SEC scores therefore corresponded to increased likelihood of ISS incidents. Cut-points were determined using Youden’s Index (Sensitivity + Specificity – 1), which identifies the score that maximizes the balance between sensitivity and specificity. This approach is commonly used in screening and classification research because it identifies the threshold that most efficiently differentiates students at elevated risk from those not demonstrating risk. SEC T-scores demonstrated good discrimination for identifying students with ISS outcomes.

Were the children in the study/studies involved in an intervention in addition to typical classroom instruction between the screening measure and outcome assessment?

No

If yes, please describe the intervention, what children received the intervention, and how they were chosen.

---

Classification Accuracy - Fall

Evidence	Grades 6-8
Criterion measure	In-School Disciplinary Incidents
Cut Points - Percentile rank on criterion measure
Cut Points - Performance score on criterion measure	1 or more
Cut Points - Corresponding performance score (numeric) on screener measure	49.5
Classification Data - True Positive (a)	602
Classification Data - False Positive (b)	1611
Classification Data - False Negative (c)	344
Classification Data - True Negative (d)	3907
Area Under the Curve (AUC)	0.72

Statistics	Grades 6-8
Base Rate	0.15
Overall Classification Rate	0.70
Sensitivity	0.64
Specificity	0.71
False Positive Rate	0.29
False Negative Rate	0.36
Positive Predictive Power	0.27
Negative Predictive Power	0.92

Sample	Grades 6-8
Date
Sample Size	6464
Geographic Representation	South Atlantic (VA)
Male	51.8%
Female	48.2%
Other
Gender Unknown
White, Non-Hispanic	42.2%
Black, Non-Hispanic	26.2%
Hispanic	22.2%
Asian/Pacific Islander
American Indian/Alaska Native	0.2%
Other	5.4%
Race / Ethnicity Unknown
Low SES
IEP or diagnosed disability	12.5%
English Language Learner	9.0%

Cross-Validation - Fall

Evidence	Grades 6-8
Criterion measure	In-School Disciplinary Incidents
Cut Points - Percentile rank on criterion measure
Cut Points - Performance score on criterion measure	1 or more
Cut Points - Corresponding performance score (numeric) on screener measure	52.5
Classification Data - True Positive (a)	708
Classification Data - False Positive (b)	2004
Classification Data - False Negative (c)	281
Classification Data - True Negative (d)	3412
Area Under the Curve (AUC)	0.73

Statistics	Grades 6-8
Base Rate	0.15
Overall Classification Rate	0.64
Sensitivity	0.72
Specificity	0.63
False Positive Rate	0.37
False Negative Rate	0.28
Positive Predictive Power	0.26
Negative Predictive Power	0.92

Sample	Grades 6-8
Date
Sample Size	6405
Geographic Representation	South Atlantic (VA)
Male	51.6%
Female	48.4%
Other
Gender Unknown
White, Non-Hispanic	41.7%
Black, Non-Hispanic	26.3%
Hispanic	22.7%
Asian/Pacific Islander
American Indian/Alaska Native
Other	5.4%
Race / Ethnicity Unknown
Low SES
IEP or diagnosed disability	11.8%
English Language Learner	8.5%

Reliability

Age / Grade Informant	Grades 6-8 Teacher
Rating

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

Inter-rater reliability: Per NCII guidance, acceptable forms of inter-rater reliability include ICCs, d-study results, multilevel models of ratings within judges and students, generalizability theory, or invariance testing in structural equation modeling. Because each student in this dataset was rated by only one teacher we used a multilevel (mixed-effects) modeling approach.

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

Inter-rater reliability: A linear mixed-effects model was fit with DESSA-Mini total score (sum of the 8 item ratings) as the outcome. Student grade level, gender, race/ethnicity, special education status, and English Language Learner (ELL) status were included as fixed effects, and rating educator was included as a random intercept. The intraclass correlation coefficient (ICC) was calculated as the proportion of total variance (educator variance plus residual variance) attributable to the educator random effect, after adjusting for the fixed-effect covariates. This approach quantifies systematic rater-level variance in ratings, consistent with a generalizability-theory framing of inter-rater consistency.

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

Inter-rater reliability: The analysis included 23,929 DESSA-Mini ratings (Forms 1-4 combined) covering 12,869 unique students in Grades 6-8, rated by 744 unique educators. Each educator rated a median of 31 students (M = 32.2, range 1-132). Student demographics included the following. Gender: Male 51.7%, Female 48.3%. Grade: 6th Grade 32.6%, 7th Grade 33.1%, 8th Grade 34.3%. Race/Ethnicity: White 42.0%, Black/African American 26.3%; Hispanic/Latinx 22.4%, Other 5.4%, Asian 3.5%, American Indian/Alaskan Native 0.2%, Native Hawaiian/Pacific Islander 0.2%. Special Populations: Gifted/Talented 17.6%, Special Education Services 12.2%, English Language Learner (ELL) 8.8%, Section 504 Plan 5.9%.

*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:

Inter-rater reliability: The estimated variance components were: educator (rater) variance = 8.03 and residual variance = 92.46, yielding an intraclass correlation coefficient of ICC = .080. That is, after accounting for measured student characteristics, approximately 8% of the remaining variance in DESSA-Mini total scores was attributable to systematic differences between educators, with the remaining 92% reflecting student-to-student variability. This low ICC indicates minimal systematic rater-level bias or drift in scoring: ratings are driven predominantly by real differences among students rather than by which educator completed the rating.

Manual cites other published reliability studies:

Yes

Provide citations for additional published studies.

Naglieri, J.A., LeBuffe, P.A., & Shapiro, V.B. (2011). Universal screening for social emotional competencies: A study of the reliability and validity of the DESSA-mini. Psychology in the Schools, 48(7): 660-671. https://doi.org/10.1002/pits.20586 Shapiro, V.B., Kim, B.K.E., Robitaille, J.L., & LeBuffe, P.A. (2017). Protective factor screening for prevention practice: Sensitivity and specificity of the DESSA-Mini. School Psychology Quarterly, 32(4), 449-464. http://dx.doi.org/10.1037/spq0000181

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:

Provide citations for additional published studies.

Validity

Age / Grade Informant	Grades 6-8 Teacher
Rating

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

Criterion Measure 1: DESSA (Full Assessment). The DESSA is a standardized, norm-referenced, teacher-completed behavior rating scale that provides a comprehensive assessment of students' social-emotional competence. The 40-item DESSA yields an overall Social-Emotional Composite (SEC) score, along with scores across six social-emotional competency domains. The DESSA was selected as a criterion measure because the DESSA-Mini is intended to serve as a brief universal screener of the same underlying construct measured more comprehensively by the full DESSA. Examining the relationship between the DESSA-Mini Social-Emotional Total (SET) score and the DESSA SEC score therefore directly evaluates whether the brief screener produces results consistent with the more comprehensive assessment. Because the DESSA is an internal criterion measure that shares item content with the DESSA-Mini, additional analyses were conducted to address potential inflation of validity estimates due to this overlap. Specifically, the relationship between the DESSA-Mini and DESSA was evaluated after correcting for overlapping items, and validity evidence also included an examination of the consistency with which the DESSA-Mini and DESSA identified individual students as demonstrating a need for social-emotional instruction. Together, these analyses provide evidence of the DESSA-Mini's validity for its intended screening purpose while addressing the limitations associated with an internal criterion measure. Criterion Measure 2: Office Discipline Referrals (ODRs) Resulting in Classroom Removal. Office discipline referrals (ODRs) resulting in removal from the classroom were used as an external criterion measure of students' behavioral functioning in school. Disciplinary records were provided by a large, diverse school district in the Mid-Atlantic United States. A qualifying ODR was identified using the district's disciplinary disposition code indicating that a student was removed from the classroom due to negative behavior. The criterion was coded dichotomously: students with no qualifying ODRs were coded 0, and students with one or more qualifying ODRs were coded 1. DESSA-Mini ratings were completed in September, near the beginning of the 2023-2024 school year, and ODR records covered the full school year. ODRs were selected as a second criterion because they provide a relevant, external indicator of observable behavioral difficulty that interferes with successful classroom functioning. Examining whether higher DESSA-Mini SET T-scores predict a lower likelihood of disciplinary removal therefore provides validity evidence relevant to the DESSA-Mini's intended use as a behavioral screener.

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

Sample 1: DESSA-Mini and DESSA. This analysis used a sample of 450 teacher ratings of students in Grades 6-8 who were rated using both the DESSA-Mini and the DESSA in one sitting. Grades were evenly represented, with 150 students at each of the three grade levels. Approximately 52% of students were male; 58% were White, 30% Black/African American, 19% Hispanic/Latinx, 4% American Indian/Alaskan Native, 2% Arab, Middle Eastern, or North African, and 3% Two or More Races; and 42% were eligible for free or reduced-price lunch. Students attended schools in all four geographic regions of the U.S. (Northeast, Midwest, South, and West). T-scores in the analysis sample ranged from 28 to 71, spanning the Need for Instruction, Typical, and Strength ranges and demonstrating representation across the full continuum of social-emotional competence. Sample 2: DESSA-Mini and ODRs. This analysis included 7,415 middle school students attending 14 schools in a large, diverse school district in the Mid-Atlantic United States during the 2023-2024 school year. Students ranged from 10 to 15 years old at the time of the DESSA-Mini rating (M ≈ 12.2 years). Approximately 52% of students were male and 48% were female. The sample was 42.0% White, 26.5% Black/African American, 22.2% Hispanic/Latinx, 3.4% Asian, 5.5% another race/ethnicity, and 0.2% American Indian/Alaskan Native; race/ethnicity was unavailable for 0.2% of students. Approximately 12.6% received special education services, 6.1% had a Section 504 plan, 8.5% were identified as English learners, and 15.7% were identified as gifted or talented. All students had an available DESSA-Mini SEC T-score and ODR outcome. SET T-scores ranged from 28 to 72 and represented the full continuum of social-emotional competence: 604 students (8.1%) were classified in the Need for Instruction range, 4,300 (58.0%) in the Typical range, and 2,511 (33.9%) in the Strength range. Of the 7,415 students, 540 (7.3%) received at least one ODR resulting in removal from the classroom, while 6,875 (92.7%) received no qualifying ODR. Observed ODR rates were 22.2% for students in the Need for Instruction range, 7.9% for students in the Typical range, and 2.7% for students in the Strength range.

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

Analysis 1: DESSA-Mini and DESSA. To address potential inflation of correlations due to overlapping items, correlation coefficients were calculated between each DESSA-Mini form's Social-Emotional Total (SET) T-score and DESSA total raw scores, calculated after excluding the items contained on the respective DESSA-Mini form. Classification agreement was also examined by calculating the percentage of students consistently identified by the DESSA-Mini and full DESSA as either in Need for Instruction (T-score ≤ 40) or not in Need for Instruction (T-score > 40). Percent agreement was based on the number of cases where the DESSA-Mini and DESSA Social-Emotional Composite yielded the same conclusion (need or no need for instruction) out of the total number of cases. The median value was calculated across DESSA-Mini forms. Analysis 2: DESSA-Mini and ODRs. Binary logistic regression was used because the criterion variable indicated whether each student received no qualifying office discipline referrals (0) or one or more qualifying referrals (1). The continuous DESSA-Mini SET T-score was examined as the primary predictor. An initial unadjusted model estimated the change in the odds of receiving an ODR associated with each one-point increase in SET T-score. A secondary categorical model examined differences among the Need for Instruction, Typical, and Strength ranges, with the Typical range as the reference category. Adjusted logistic regression models then examined whether SET T-score and SEC classification predicted ODR status after accounting for gender, age at rating, race/ethnicity, special education services, Section 504 status, English learner status, and gifted/talented status. These covariates were included because student characteristics and service status may be associated with differences in disciplinary referrals. Because the American Indian/Alaskan Native group included only 12 students and no ODR events, it was combined with the Other and unreported race/ethnicity categories for modeling. Baseline models containing the covariates were compared with models that added SET T-score or SET classification to evaluate whether DESSA-Mini results contributed information beyond the included student characteristics; both additions significantly improved model fit. Odds ratios, Wald 95% confidence intervals, and significance tests were calculated for the continuous and categorical models, both before and after controlling for student characteristics.

*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:

Results 2: DESSA-Mini and ODRs. After controlling for gender, age, race/ethnicity, special education services, Section 504 status, English learner status, and gifted/talented status, higher DESSA-Mini SEC T-scores were significantly associated with a lower likelihood of receiving at least one qualifying ODR. Each one-point increase in SEC T-score was associated with approximately 5.7% lower odds of receiving an ODR (adjusted OR = 0.943, Wald 95% CI [0.935, 0.952], p < .001). SEC classification also significantly predicted ODR status after controlling for the same student characteristics. Compared with students in the Typical range, students in the Need for Instruction range had 2.68 times the odds of receiving at least one ODR (adjusted OR = 2.68, Wald 95% CI [2.13, 3.38], p < .001). Students in the Strength range had approximately 59.7% lower odds of receiving an ODR than students in the Typical range (adjusted OR = 0.40, Wald 95% CI [0.31, 0.53], p < .001).

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.

Study 1: DESSA-Mini and DESSA. This evidence provides strong support for the use of the DESSA-Mini as a universal screener of students' social-emotional competence. DESSA-Mini SET scores were strongly correlated with scores from the full DESSA even after correcting for overlapping items, indicating that the brief screener measures the same underlying construct as the more comprehensive assessment. The DESSA-Mini also demonstrated a high level of agreement with the full DESSA in identifying students as either in Need for Instruction or not in Need for Instruction. Together, these findings support the validity of the DESSA-Mini for its intended purpose of identifying students who may benefit from additional social-emotional instruction or support. Study 2: DESSA-Mini and ODRs. This evidence provides strong support for the use of the DESSA-Mini as a behavioral screener for middle school students. Higher SET T-scores were associated with a significantly lower likelihood of receiving an office discipline referral resulting in removal from the classroom, even after controlling for relevant student characteristics. Students in the Need for Instruction range had substantially greater odds of receiving an ODR than students in the Typical range, while students in the Strength range had substantially lower odds—demonstrating a clear and practically meaningful pattern across levels of social-emotional competence and classroom behavior. Together, these findings support the DESSA-Mini's intended use in identifying students who may benefit from additional social-emotional instruction or support.

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:

Provide citations for additional published studies.

Bias Analysis

Age / Grade Informant	Grades 6-8 Teacher
Rating	Provided

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.

Yes

If yes,

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

Item-level bias was evaluated using the ordinal logistic regression method for detecting differential item functioning (DIF), an approach appropriate for polytomous (multi-category) items such as those on the DESSA-Mini. For each item, a rest score (the sum of the remaining seven items) was used to match students on overall social-emotional competence level, so that group comparisons reflect differences in item response conditional on ability rather than true group differences in the underlying construct. Three nested cumulative logit (proportional odds) models were fit for each item: (1) rest score only; (2) rest score plus group membership, testing for uniform DIF (the item is systematically harder or easier for one group regardless of ability level); and (3) rest score, group membership, and their interaction, testing for non-uniform DIF (the item behaves differently across ability levels for a given group). Nested models were compared using likelihood ratio chi-square tests. Because statistical significance is heavily influenced by sample size and can flag trivial differences as significant in large samples, effect size was used as the primary indicator of practically meaningful bias. Effect size was calculated as the change in pseudo-R² between the baseline (rest score only) and full (rest score, group, and interaction) models. Effect sizes were classified using the Zumbo and Thomas (1997) thresholds: R² difference less than .035 was considered negligible, .035 to .070 was considered moderate, and greater than .070 was considered large. Only moderate or large effect sizes were considered evidence of practically meaningful item bias.

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

DIF analyses were conducted separately for DESSA-Mini Form 1 (N = 13,151) and Form 2 (N = 10,712), since the two forms contain different item content, across the following four grouping variables: Race/Ethnicity: White and Black/African American, Hispanic/Latinx, Asian, Other*; Gender: Female and Male; Special Education Services: Not receiving services and Receiving services; English Language Learner (ELL) Status: Not identified as ELL and Identified as ELL. *American Indian/Alaskan Native (n = 37) and Native Hawaiian/Pacific Islander (n = 55) were pooled into the “Other” category across both forms due to insufficient sample size to support stable, independent estimates for these groups. In total, 64 item-level tests were conducted (8 items × 2 forms × 4 grouping variables).

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.

Across all 64 item-level tests, every item showed a negligible effect size (R² difference < .035) for both uniform and non-uniform DIF. The largest effect size observed across the entire analysis was R² difference = .0028 (Form 2, Q6, special education status) — more than twelve times smaller than the .035 threshold for even a “moderate” effect. No item on either form reached the moderate or large effect size threshold for any of the four grouping variables examined. A number of uniform DIF likelihood ratio tests were statistically significant (p < .05), particularly for race/ethnicity (15 of 16 form-by-item tests) and gender (12 of 16). This pattern is expected given the large sample sizes (N > 10,000 per form) and does not indicate meaningful bias; even very small group differences reach statistical significance at this sample size. Non-uniform DIF was significant less often (1 to 8 of 16 tests per variable) and, as with uniform DIF, corresponded to negligible effect sizes throughout. Table: Summary of DIF Findings by Grouping Variable Grouping Variable Items Tested (both forms) Items with Significant Uniform DIF (p < .05) Items with Significant Non-uniform DIF (p < .05) Maximum R² Difference Effect Size Race/Ethnicity 16 15 3 .0020 Negligible Gender 16 12 8 .0020 Negligible Special Education Services 16 10 5 .0028 Negligible ELL Status 16 9 1 .0021 Negligible Interpretation: No items on either DESSA-Mini form showed practically meaningful differential item functioning by race/ethnicity, gender, special education status, or ELL status. Although several items showed statistically significant uniform or non-uniform DIF, all associated effect sizes fell in the negligible range, indicating that observed differences were not large enough to represent meaningful item bias. Because no item met the threshold for moderate or large DIF, no magnitude-of-effect follow-up (e.g., item removal or revision, differential scoring) is warranted. These findings support the conclusion that DESSA-Mini items function equivalently across the subgroups examined.