DESSA
DESSA Student Self-Report

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 Student Self-Report (DESSA-SSR) is designed for students in grades 6–12 and provides a self-reported measure of social and emotional competence. The DESSA-SSR yields an overall competence score as well as scores across the same six competency domains measured by the DESSA. It may be used for universal screening, in-depth assessment, progress monitoring, and evaluation of intervention outcomes within MTSS frameworks.

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. For secondary grade levels, the subscription also includes access to the DESSA Student Portal, which supports administration of the DESSA Student Self-Report (DESSA-SSR). 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. Support may be provided for students requiring additional help completing the DESSA Student Self-Report, including reading the items or enabling text to speech functionality. 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

5

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?

For the student self-report, all students in a class can complete their ratings at the same time, it typically takes 5-7 minutes.

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.

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 full DESSA assessments, raters complete the items using a 5-point frequency scale: Never (0), Rarely (1), Sometimes (2), Often (3), and Almost Always (4). The numbers of items vary by form; the DESSA Student-Self-Report form is 50 items. 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 Child	Grades 9-12 Child
Classification Accuracy Fall
Classification Accuracy Winter
Classification Accuracy Spring

Emotional Behavioral Concern Scale (Externalizing)

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.

The criterion measure used for the externalizing analyses was the Emotional and Behavioral Concerns (EBC) Externalizing scale developed by Elliott and colleagues. The EBC Externalizing scale is designed to identify students demonstrating negative emotions and behaviors mostly directed toward others, including indicators such as verbal or psychological aggression, threatening and bullying others, poor control of temper, arguing with others, and actively excluding others from activities. Students are classified into concern categories (e.g., Typical, Possible Concern, and Concern) based on established interpretive guidelines and normative thresholds. The EBC Externalizing scale functions as an outcome criterion that is distinct from the screening measure (the SSR SEC T-score). Although both instruments assess aspects of students' social-emotional functioning, the SSR was designed as a universal social-emotional screening measure assessing broad social-emotional competencies, whereas the EBC Externalizing scale specifically measures elevated emotional and behavioral concern indicators associated with externalizing risk. Thus, the criterion measure represents a related but more clinically oriented construct emphasizing behavioral risk rather than social-emotional competence.

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 DESSA and the EBC-E were administered at the same time in the Fall. Social-emotional and behavioral status can change over time due to intervention, maturation, or shifting circumstances. Administering the DESSA and the Emotional/Behavioral Concern scale concurrently controls for this variability, allowing for a clean evaluation of the DESSA's accuracy in identifying students who are also showing elevated risk on an independent, established indicator at that same point in time. This directly supports the DESSA's intended use as a universal screener of current, rather than future, risk status.

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 accuracy for the Externalizing Concern outcome was evaluated using Receiver Operating Characteristic (ROC) analysis, which examined how well SEC scores classified students as "at-risk" versus "not at-risk" based on their EBC Externalizing Scale classification. The middle school sample came from twelve unique districts across the U.S., with the following grade-level counts: 6th Grade: 468; 7th Grade: 480; 8th Grade: 520. The high school sample came from three unique districts across the U.S., with the following grade-level counts: 9th Grade: 146; 10th Grade: 79; 11th Grade: 82; 12th Grade: 87. Students were classified into two groups for this analysis: those identified as at-risk for externalizing concerns (an EBC Externalizing classification of "Possible Concern" or "Concern," coded as 1) and those identified as not at-risk (a classification of "No Concern," coded as 0). Rather than comparing low-, moderate-, and high-risk groups simultaneously, a binary classification approach was used, contrasting students who met the behavioral risk criterion against those who did not. The SSIS-SEL Emotional Behavior Concerns–Externalizing (EBC-E) scale was selected as the external criterion for this analysis. The EBC scales were specifically developed for screening and classifying students into three criterion-referenced levels — No Concern, Possible Concern, and Concern — with the goal of identifying students who may be experiencing behavioral concerns rather than establishing a diagnosis. Possible Concern and Concern were combined into a single at-risk group because this grouping is consistent with the intended interpretation, as outlined in Chapters 6 and 7 of the SSIS-SEL technical manual: the Possible Concern category identifies students whose observed scores may, once measurement error is taken into account, actually reflect functioning at the Concern level, and for whom continued monitoring or follow-up is recommended, while students in the Concern range warrant prompt follow-up and possible comprehensive assessment. Combining these two levels therefore provides an appropriately sensitive criterion for evaluating a universal screener intended to identify students at risk for poor emotional and behavioral outcomes, rather than limiting the criterion only to students already showing the most severe level of concern. Because lower SEC T-scores reflect greater social-emotional and behavioral concern, the analyses accounted for this inverse relationship between SEC scores and EBC-Externalizing outcomes — lower SEC scores corresponded to a greater likelihood of externalizing concerns. Cut-points were determined using Youden's Index (Sensitivity + Specificity – 1), which identifies the threshold that maximizes the balance between sensitivity and specificity and is commonly used in screening research to find the score that most efficiently differentiates at-risk students from those not at risk. Using this approach, SEC T-scores demonstrated good discrimination for identifying students with externalizing concerns.

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

No

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

Cross-Validation

Has a cross-validation study been conducted?

No

If yes,

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.

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.

Describe how the cross-validation analyses were performed and cut-points determined. Describe how the cut points align with students at-risk. Please indicate which groups were contrasted in your analyses (e.g., low risk students versus high risk students, low risk students versus moderate risk students).

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

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

---

Classification Accuracy - Fall

Evidence	Grades 6-8	Grades 9-12
Criterion measure	Emotional Behavioral Concern Scale (Externalizing)	Emotional Behavioral Concern Scale (Externalizing)
Cut Points - Percentile rank on criterion measure
Cut Points - Performance score on criterion measure	≥5	≥5
Cut Points - Corresponding performance score (numeric) on screener measure	48	45
Classification Data - True Positive (a)	292	71
Classification Data - False Positive (b)	371	70
Classification Data - False Negative (c)	125	35
Classification Data - True Negative (d)	680	218
Area Under the Curve (AUC)	0.72	0.74

Statistics	Grades 6-8	Grades 9-12
Base Rate	0.28	0.27
Overall Classification Rate	0.66	0.73
Sensitivity	0.70	0.67
Specificity	0.65	0.76
False Positive Rate	0.35	0.24
False Negative Rate	0.30	0.33
Positive Predictive Power	0.44	0.50
Negative Predictive Power	0.84	0.86

Sample	Grades 6-8	Grades 9-12
Date	Fall 2025	Fall 2025
Sample Size	1468	394
Geographic Representation	East North Central (IL, MI, OH) Middle Atlantic (NY) Mountain (MT) New England (VT) Pacific (WA) West South Central (TX)	East North Central (IL) Middle Atlantic (NY) West South Central (TX)
Male	50.2%	49.5%
Female	49.8%	50.0%
Other
Gender Unknown		0.5%
White, Non-Hispanic	52.3%	52.3%
Black, Non-Hispanic	7.2%	3.0%
Hispanic	11.6%	34.0%
Asian/Pacific Islander
American Indian/Alaska Native	0.4%
Other	0.8%
Race / Ethnicity Unknown	19.0%	10.2%
Low SES
IEP or diagnosed disability	5.0%	3.0%
English Language Learner	6.4%	1.0%

Reliability

Age / Grade Informant	Grades 6-8 Child	Grades 9-12 Child
Rating

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

Internal reliability: The DESSA Student Self-Report (DESSA-SSR) includes 50 items aligned to six social-emotional competency domains: Self-Awareness, Self-Management, Social Awareness, Relationship Skills, Responsible Decision Making, and Optimistic Thinking. Therefore, it is important to establish the internal consistency of the items within each scale and the overall Social-Emotional Composite (SEC). Internal reliability evaluates the degree to which items within a scale are correlated and can be considered to measure the same underlying construct. Establishing strong internal reliability provides evidence that the scale and composite scores represent coherent and reliable indicators of students’ self-reported social and emotional competence. Test-retest reliability: The DESSA Student Self-Report (DESSA-SSR) is designed to support universal screening and repeated administration for progress monitoring across the school year. Therefore, it is important to establish the stability of students’ self-reported scores across repeated administrations over short intervals when meaningful changes in social-emotional competence are not expected to occur. Test–retest reliability evaluates the consistency of scores obtained for the same student across two administrations and provides evidence that observed score differences are not primarily attributable to measurement error or the passage of time. Establishing strong test–retest reliability increases confidence that meaningful changes in scores across administrations reflect actual changes in students’ self-reported social-emotional competence rather than instability in the measure itself.

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

Internal reliability: The middle school (Grades 6-8) analysis was conducted using the DESSA-SSR standardization sample which included a total of 1,350 students in grades 6–8 across the four regions (Northeast, Midwest, South, West) of the United States. The sample closely approximated the 6–8 population of the United States with respect to age, gender, geographic region of residence, race, ethnicity, socioeconomic status, and special education status, based on U.S. Census data. The sample included a representative and diverse student population, with 51% male, 63% White, 29% Hispanic/Latinx, 16% Black or African American, 13% Two or More Races, 5% Asian, 3% American Indian/Alaskan Native, 0.5% Native Hawaiian/Pacific Islander, 53% eligible to receive free or reduced-price meals, and 15% receiving special education services. Students in the standardization sample had scores that covered the full T-score range of 28 to 72, with scores spanning the Need for Instruction, Typical, and Strength ranges, indicating representation of students across the full continuum of social-emotional competence performance levels. Full details of the sample are available in the DESSA-SSR technical manual. The high school sample included a total of 50,109 students in grades 9–12 across the four regions (Northeast, Midwest, South, West) of the United States. The sample closely approximated the high school student population of the United States with respect to age, gender, geographic region of residence, race, ethnicity, and socioeconomic status based on U.S. Census data. The sample included a representative and diverse student population, with 49% male, 46% White, 27% Hispanic/Latinx, 17% Black or African American, 5% Asian, 2% Two or More Races, 1% American Indian/Alaskan Native, 0.3% Native Hawaiian/Pacific Islander, 3% Other Race, and 57% eligible to receive free or reduced-price meals. Students in the standardization sample had scores that covered the full T-score range of 28 to 72, spanning the Need for Instruction, Typical, and Strength ranges, indicating representation of students across the full continuum of social-emotional competence performance levels. Test-retest reliability: This analysis was conducted using a sample of ratings completed by 121 students in grades 6–8 who rated themselves on two separate occasions. Approximately 50% of students were female, 42% of students were White, 31% Black/African American, 12% Hispanic/Latinx, 6% Two or More Races, 2.5% Asian, and 1% American Indian/Alaskan Native, and 36% eligible for free or reduced-price lunch. Students attended five schools in the Northeast, South, and West regions of the United States. Students in the analysis sample had scores that covered the full T-score range of 28 to 72, with scores spanning the Need for Instruction, Typical, and Strength ranges, indicating representation of students across the full continuum of social-emotional competence performance levels. The high school sample included ratings completed by 85 students in grades 9–12 who rated themselves on two separate occasions. Approximately 49% of students were male, 62% of students were Black/African American, 24% White, 13% Two or More Races, 2% American Indian/Alaskan Native, with 25% of students indicating Hispanic/Latinx ethnicity, and 82% eligible for free or reduced-price lunch. Students attended schools in all four geographic regions of the United States (Northeast, Midwest, South, and West). Students in the analysis sample had scores that covered the full T-score range of 28 to 72, with scores spanning the Need for Instruction, Typical, and Strength ranges, indicating representation of students across the full continuum of social-emotional competence performance levels.

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

Internal reliability: Cronbach’s alpha coefficients were calculated for each of the six DESSA-SSR competency scales and the overall Social-Emotional Composite (SEC) score using data from the standardization sample. The SEC reliability coefficient was calculated using the formula provided by Nunnally and Berstein (1994) for the reliability of a linear composite. This process evaluates the degree to which items within each scale consistently measure the same underlying construct. High School Internal reliability: Because the high school DESSA SSR used a matrix sampling design in which students responded to different subsets of items, internal reliability was evaluated using an Item Response Theory (IRT)-based approach rather than Cronbach’s alpha. Item and test information functions were used to calculate conditional standard errors of measurement (CSEM) across the latent trait continuum, and marginal reliability coefficients were calculated for each of the six competency scales and the overall Social-Emotional Composite (SEC). This process evaluates the overall precision and consistency of scores while accounting for differences in the items administered to individual students. Test-retest reliability: Correlation coefficients were calculated between DESSA-SSR scores obtained from the same students across two administrations separated by a 4- to 9-day interval, and pretest and posttest mean scores, standard deviations, and mean T-score differences were compared. This process evaluates the stability and consistency of students’ self-reported scores across repeated administrations over time.

*In the table(s) below, report the results of the reliability analyses described above (e.g., internal consistency or inter-rater reliability coefficients).

Type of	Subgroup	Informant	Age / Grade	Test or Criterion	n	Median Coefficient	95% Confidence Interval Lower Bound	95% Confidence Interval Upper Bound

Results from other forms of reliability analysis not compatible with above table format:

Manual cites other published reliability studies:

Yes

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	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 Child	Grades 9-12 Child
Rating

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

Middle School Concurrent Validity: The SSIS SEL Brief Scales (Elliott et al., 2020) is a strength-based behavior rating scale designed to assess the social and emotional learning competencies of children and adolescents. The student self-report form includes 20 self-statements assessing SEL skills and provides an overall SEL Composite score as well as scores across the five CASEL competency domains. The SSIS SEL Brief Scales was selected as an appropriate criterion measure because it is a psychometrically sound self-report assessment that measures social-emotional competencies conceptually aligned with those assessed by the DESSA Student Self-Report (DESSA-SSR). In addition, both measures use a strengths-based framework and are intended to support screening and assessment of students’ social-emotional competencies in school settings. High School Concurrent Validity: The Social Emotional Assets and Resilience Scales (SEARS, Merrell, 2011) is a strength-based behavior rating scale that assesses positive social and emotional attributes of children and adolescents. The Adolescent Long Form includes 35 self-statements and provides a total score as well as scores across four social-emotional skill domains (Self-Regulation, Social Competence, Empathy, and Responsibility). The SEARS was selected as an appropriate criterion measure because it is a psychometrically sound self-report assessment that measures social-emotional competencies conceptually aligned with those assessed by the high school DESSA SSR. In addition, both measures use a strengths-based framework and are intended to support screening and assessment of students’ social-emotional competencies in school settings. Internal Structure: Confirmatory Factor Analysis. To better explore the validity of the DESSA SSR’s scale structure through factor analysis, confirmatory factor analysis was completed, examining whether the item set reflects the six theorized social-emotional competency domains: Self-Awareness, Self-Management, Social Awareness, Relationship Skills, Responsible Decision Making, and Optimistic Thinking.

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

Middle School Concurrent Validity: This analysis was conducted using a sample of ratings completed by 154 students in grades 6–8 who completed the DESSA SSR and the SSIS SEL Brief Scales in one sitting. Approximately 52% of students were female, 38% of students were White, 31% Black/African American, 10% Hispanic/Latinx, 6% Two or More Races, 2% Asian, and 1% American Indian/Alaskan Native, and 35% eligible for free or reduced-price lunch. Students attended schools in the Northeast, South, and West regions of the United States. Students in the analysis sample had scores that covered the full T-score range of 28 to 72, with scores spanning the Need for Instruction, Typical, and Strength ranges, indicating representation of students across the full continuum of social-emotional competence performance levels. High School Concurrent Validity: This analysis was conducted using a sample of ratings completed by 127 students in grades 9–12 who completed the DESSA SSR and the SEARS in one sitting. Approximately 48% of students were female, 60% of students were Black/African American, 25% White,13% Two or More Races, 1% American Indian/Alaskan Native, 20% of students indicated Hispanic/Latinx ethnicity, and 79% eligible for free or reduced-price lunch. Students attended schools in all four geographic regions (Northeast, Midwest, South, and West). Students in the analysis sample had scores that covered the full T-score range of 28 to 72, with scores spanning the Need for Instruction, Typical, and Strength ranges, indicating representation of students across the full continuum of social-emotional competence performance levels. Middle school confirmatory factor analysis: Analysis was completed among the standardization sample for the Middle School, excluding cases missing one or more item response(s) (N = 1,350). High school confirmatory factor analysis: For high school, the analysis included N = 61,538 complete student self-report ratings on all 50 items (0-4 response scale), with no missing item-level data.

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

Concurrent Validity: Correlation coefficients were calculated between DESSA SSR scores and scores from the SSIS SEL Brief Scales. Correlations were examined between overall composite scores on the two instruments as well as theoretically aligned subscales across the two measures. Internal Structure: Confirmatory Factor Analysis. Middle school: We fit a six-factor model in which each item was assigned to one factor in alignment with its earlier assignment to one of the six DESSA-MSE SSR scales (Self-Management, Relationship Skills, etc.). Chapter 2 of the technical manual provides a discussion of assignment of items to the six scales. Confirmatory factor analysis was completed in R using the lavaan package (Rosseel, 2012). Weighted Least Square Mean and Variance Adjusted Estimators (WLSMV) were used, given the ordinal nature of the data (Li, 2016). For the purposes of comparison, two alternative models were explored, representing other popular conceptualizations of social and emotional competencies: 1. A three-factor model that assigned items to three factors: Intra-Personal (comprised of the DESSA-MSE SSR scales of Self-Awareness, Optimistic Thinking, and Self-Management; Inter-Personal (comprised of the DESSA-MSE SSR scales of Social Awareness and Relationship Skills); and Decision Making (comprised of the DESSA SSR scale Responsible Decision Making). 2. A one-factor model that assigned all items to a single factor. High school: Two nested measurement models were evaluated. Model 1 was a correlated six-factor CFA, with each item loading only on its theorized subscale and all six factors allowed to freely intercorrelate. Model 2 was a bifactor model, in which every item loaded on both a single general social-emotional competence factor and its theorized specific subscale factor, with all factors specified as orthogonal. Per the analysis plan, the bifactor model was estimated because the six-factor model's CFI did not exceed the conventional .90 threshold. All analyses used maximum likelihood estimation.

*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:

Internal Structure: Confirmatory Factor Analysis: Middle school: The six-scale solution exhibited a good model fit as described by Hu and Bentler (1999), indicated by a Tucker-Lewis Index (TLI) value of 0.990 and a Root Mean Square Error of six (RMSEA) value of 0.032. This evidence suggests that the six-factor DESSA-MSE SSR model fits the standardization data well. Fit indices for the six-scale model and the two additional models are presented in Table 3.6 of the technical manual. Each model tested exhibits a high TLI value (ranging from 0.985 for the one-scale model to 0.990 for the six-scale model) and a low RMSEA value (ranging from 0.032 for the six-scale model to 0.039 for the one-scale model), indicating a good fit to the data. The model fit indices suggest that all tested models fit the data well. To evaluate the fit of the proposed DESSA-MSE SSR model relative to the alternative models, the proposed DESSA-MSE SSR model was compared to the three-scale model and one-scale model, pairwise, via a series of scaled chi-square difference tests. The results of the pairwise comparisons are included in Table 3.7 of the technical manual. These results indicate that the proposed DESSA-MSE SSR six-scale model fits the data significantly better than the tested three-scale model and the one-scale model. Marginal improvements in TLI and RMSEA values suggest that the model that assigns DESSA-MSE SSR items to scales as described in Chapter of the technical manual 2 fits the data as well as, if not slightly better than, the alternatives tested. High school: Model Fit results were as follows. For the Six-Factor (correlated) model, χ² (df) = 228,832.21 (1160); CFI = .830; TLI = .820; RMSEA = .057; SRMR = .051. For the bifactor model, χ² (df) = 195,716.93 (1125); CFI = .855; TLI = .842; RMSEA = .053; SRMR = .045. The bifactor model showed improved fit over the correlated six-factor model on every index. RMSEA and SRMR reached the conventional “good fit” range for both models, but CFI and TLI remained below .90 for both models, indicating that neither model achieves fully acceptable fit by incremental fit criteria, even though absolute fit indices (RMSEA, SRMR) are favorable. This pattern — acceptable RMSEA/SRMR alongside subpar CFI/TLI — is a recognized feature of CFA models with large item sets (50 items here), where CFI/TLI are known to be more sensitive to the low average inter-item correlation inherent to broad, multi-domain instruments. General Factor Strength (Bifactor Model): The Explained Common Variance (ECV) for the general factor was .81, indicating that approximately 81% of the variance items share with one another is accounted for by a single general social-emotional competence factor, with the six specific subscale factors accounting for the remaining common variance. Subscale-specific ECVs ranged from .77 (Optimistic Thinking) to .87 (Responsible Decision Making), indicating the six subscales retain relatively little unique variance once the general factor is accounted for. Explained common variance (ECV) by subscale: Overall (general factor): .81; Responsible Decision Making: .87; Self-Management: .85; Self-Awareness: .81; Social Awareness: .79; Relationship Skills: .78; Optimistic Thinking: .77.

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.

The validity evidence provided demonstrates that the DESSA measures are meaningfully related to well-established, psychometrically sound assessments designed to measure similar social-emotional and behavioral constructs. Moderate to strong correlations across composite and domain-level scores support the intended interpretation and use of the DESSA measures as indicators of students’ social-emotional competence. Collectively, the findings provide evidence that the DESSA measures validly assess the constructs they were designed to measure.

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 Child	Grades 9-12 Child
Rating	Provided	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:

Middle school: Bias analyses were conducted using two complementary psychometric approaches: Differential Item Functioning (DIF) analyses and measurement invariance testing. DIF analyses were conducted using the Standardized P-DIF method to evaluate whether individual items functioned differently across gender, race/ethnicity, and special education groups after controlling for overall ability levels. Measurement invariance was evaluated using multigroup confirmatory factor analysis (MGCFA), which examined whether the underlying factor structure and measurement properties of the scales operated equivalently across demographic groups. Together, these approaches provided evidence regarding both item-level fairness and scale-level equivalence across groups. Overall, results demonstrated largely negligible to slight DIF effects and supported measurement invariance across most demographic comparisons. High school: To minimize potential bias, DIF was evaluated for the DESSA norming items using the Standardized P-Difference (STD P-DIF) approach. STD P-DIF is a statistical index used to detect whether different groups of examinees respond differently to specific test items, after controlling for differences in overall ability. In this context, P refers to the probability of success on an item. The term standardized implies that the analysis adjusts for group differences in overall ability levels. The STD P-DIF statistic is defined as: STD P-DIF = (∑_mN_fm P_fm)/(∑_mN_fm )-(∑_mN_fm P_rm)/(∑_mN_fm ), where P_fm=R_fm/N_(fm ) and P_rm=R_rm/N_(rm )are the proportions of examinees who answer correctly over the total number of examinees in the focal and reference groups at score level m (Dorans & Holland, 1993). The resulting STD P-DIF value ranges from –1 to +1, with values near zero indicating minimal group differences. All DESSA items were evaluated for DIF across the Spanish and English versions using the STD P-DIF approach, with the Spanish version designated as the focal group. The overall raw score served as the matching criterion to control for differences in overall ability. The analytic sample included 65,321 examinees in the English version and 646 in the Spanish version. Visual inspection indicated that the raw score distribution for the Spanish version was broadly similar to that of the English version, suggesting that distributional differences were unlikely to meaningfully affect the results. Analyses were conducted in R using the difR package (Magis et al., 2010).

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

Middle school: Bias analyses were conducted across several demographic subgroups to evaluate fairness and equivalence of the measures. Specifically, analyses examined potential bias across gender, race/ethnicity, and special education status groups. For gender analyses, male and female students were compared. For race/ethnicity analyses, White students served as the reference group and were compared separately with Black/African American students and Hispanic students. For special education analyses, students receiving special education services were compared with students in general education settings. These subgroup comparisons were evaluated using both Differential Item Functioning (DIF) analyses and measurement invariance testing. High school: All DESSA items were evaluated for DIF across the Spanish and English versions using the STD P-DIF approach, with the Spanish version designated as the focal group. The final analytic sample consisted of 65,967 examinees, including 65,321 in the English version and 646 in the Spanish version.

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.

Middle school: DIF: Overall, the results of the DIF analyses were largely favorable, with the vast majority of items demonstrating negligible to slight-to-moderate DIF. Measurement invariance: For the gender group, the configural model showed adequate fit for both male (N = 695) and female (N = 655) groups (CFI = 0.935, TLI = 0.932, RMSEA = 0.05, SRMR = 0.05). Fit was maintained across the threshold (CFA = 0.936, RMSEA = 0.05), metric (CFI = 0.939, RMSEA = 0.05) and scalar (CFI = 0.934, RMSEA = 0.05) models, with all change statistics within acceptable thresholds (ΔCFI < .01, ΔTLI < .01, ΔRMSEA < .015, ΔSRMR < .01), supporting full scalar invariance across gender. For racial group comparisons, the configural model showed adequate fit across Non-White (N = 760) and White (N = 588) examinees (CFI = 0.928, TLI = 0.924, RMSEA = 0.05, SRMR = 0.05). Fit was maintained through the threshold, metric and scalar models (scalar: CFI = 0.932, RMSEA = 0.05), with all change statistics within acceptable ranges (ΔCFI < .01, ΔTLI < .01, ΔRMSEA < .015, ΔSRMR < .01), supporting full scalar invariance across racial groups. For comparisons between students receiving special education services (N = 172) and those not receiving services (N = 1,004), the configural, threshold, metric, and scalar models did not converge. Therefore, measurement invariance across special education status could not be evaluated for the DESSA MS SSR. High school: No items were identified as exhibiting Category C DIF; rather, all items were classified as showing either negligible DIF or slight to moderate DIF effects (see Table 4). To further examine the potential influence of the substantial sample size imbalance, we randomly selected a subsample of 646 examinees from the English group and repeated the DIF analysis. The results were consistent with the primary analysis, again indicating that no items exhibited Category C DIF. Detailed methods and item-level results are available from the Center upon request.