Sage Journals: Discover world-class research

Abstract

The Winston Essential Skills Survey (WESS) 3.0, the first social-emotional learning (SEL) assessment designed for students with learning disabilities (SwLD), has evidence for reliability and validity in a specific population of teachers. To build on this evidence, 180 educators responded to the WESS 3.0, along with new items and the Devereux Student Strengths Assessment (DESSA), to assess a sample of 397 SwLD. Minor modifications to the WESS 3.0 resulted in the WESS 4.0, which demonstrated internal consistency, an eight-factor structure, and convergent validity with the DESSA. The WESS 4.0 addresses a critical gap in SEL assessments for SwLD, providing researchers and educators with a valid and reliable method to assess SwLD's social-emotional skills. This survey may inform school interventions, policy decisions on SEL programming, and advance understanding of SEL in SwLD, potentially improving educational outcomes and well-being for this population.

Keywords

social-emotional learning learning disabilities assessment reliability validity

Introduction

Students with learning disabilities (SwLD), who constitute 32% of students receiving special education, face unique challenges that extend beyond academic difficulties (Grigorenko et al., 2020; National Center for Education Statistics, 2024). Studies have consistently shown that SwLD face a disproportionate burden of emotional and social challenges in educational settings (Grigorenko et al., 2020). Compared to their peers without disabilities, SwLD report higher levels of negative emotions, depression, physical complaints, anxiety, and stress while at school (Lawrence et al., 2019; Polak & Grossman, 2024; Polak & Grossman, 2024b). Furthermore, many SwLD struggle with peer acceptance and often exhibit difficulties in both intrapersonal and interpersonal domains, including challenges with regulating their emotions and forming social connections (Al-Yagon, 2014; Rivara & Menestrel, 2016; Rose et al., 2016). These findings underscore the critical need for targeted interventions and support systems to address the unique social-emotional needs of SwLD in educational environments.

Yet, how can researchers develop evidence-based interventions for SwLD when existing assessments of social-emotional needs were not designed with this population in mind? Few assessments included SwLD in their development process or measured the specific social-emotional skills known to be critical for their success (for a review, see DeBono et al., 2025). This means that most social-emotional learning (SEL) assessments may not be valid or reliable in the SwLD population and are also not measuring the SEL skills (e.g., self-advocacy) critical for their success. Consequently, education systems may not be able to establish effective support systems if SwLD’s social-emotional needs are not assessed accurately or reliably due to the absence of surveys tailored specifically to this group. Clearly, there is a need for a reliable and valid assessment explicitly designed for SwLD—one that captures the social-emotional skills essential for their success.

SwLD: The Role of Social Emotional Learning

SEL offers a promising approach to address SwLD’s unique challenges and support their holistic development (Weissberg et al., 2015). Defined as the lifelong process of learning to manage emotions and social interactions for success in school, work, relationships, and community, SEL encompasses understanding and regulating emotions, demonstrating empathy, setting and achieving goals, fostering healthy relationships, and making responsible decisions (Jones & Doolittle, 2017). Effective SEL necessitates a range of cognitive abilities, self-confidence, and social awareness; areas in which SwLD often require additional support (Milligan et al., 2016; Patterson & Thomas, 2014). This underscores the critical importance of accurately assessing SwLD’s social-emotional skills to provide targeted and appropriate interventions. A comprehensive evaluation of these skills enables educators to design SEL strategies addressing the unique needs of SwLD, which is likely to enhance their academic engagement, social relationships, and overall well-being (Cavioni et al., 2017).

Evidence suggests SEL has numerous benefits for all students, including improved mental health, better relational skills, enhanced self-esteem, and increased self-understanding (Darling-Hammond & Cook-Harvey, 2018). SEL can also enhance peer-to-peer relationships, decrease bullying, foster more trusting relationships between students and teachers, and improve the educational climate (Darling-Hammond & Cook-Harvey, 2018; Divecha & Brackett, 2020). For SwLD, SEL can be particularly beneficial as it emphasizes relationships and social interactions, helping students develop a sense of safety and belonging (Cavioni et al., 2017). With this feeling of safety and belonging, SEL can play a crucial role in empowering them to thrive academically, socially, and emotionally (Goldstein & Brooks, 2023).

Limitations in Current SEL Frameworks for SwLD

The spectrum of identified SEL skills is extensive and diverse, drawing from various yet interrelated theoretical frameworks (Jones & Doolittle, 2017). Although numerous SEL models exist, the most widely adopted SEL framework in educational settings is the Collaborative for Academic, Social, and Emotional Learning (CASEL) model (Frye et al., 2022). This framework delineates five interconnected competencies defined in Table 1: self-awareness, self-management, social awareness, relationship skills, and responsible decision-making.

Table 1.

The Overlap Between CASEL and WESS Frameworks: Abbreviated Definitions of SEL Competencies and Skills

CASEL SEL	CASEL definition	WESS SEL	WESS definition and sample item
CASEL SEL	The ability to…	WESS SEL	WESS definition and sample item
Relationship skills	Establish and maintain healthy and supportive relationships; to effectively navigate settings with diverse individuals and groups	Social skills	The student consistently both responds and adapts to diverse situations and understands how to communicate their needs and ideas to others without external structures of support
Relationship skills		Social skills	Item: The student is comfortable talking about their problems with at least one person
Responsible decision-making	Make caring and constructive choices about personal behavior and social interactions across diverse situations	Problem solving	The student is an adaptive, creative and effective problem-solver
Responsible decision-making		Problem solving	Item: The student is able to solve problems
Social awareness	Empathize and understand the perspectives of others, including those from diverse backgrounds/cultures/contexts	Social responsibility	The student is a consistently effective and helpful member of the community
Social awareness		Social responsibility	Item: The student is a helpful member of the school community
Self-awareness	Understands own emotions, thoughts, and values and how they influence behavior	Self-reflection	The student is reflective and self-aware
Self-awareness		Self-reflection	Item: The student knows what is difficult for them
Self-management	Manage one’s emotions, thoughts, and behaviors effectively in different situations and to achieve goals and aspirations	Management/organization	The student is able to prioritize, organize, and complete tasks effectively and efficiently
		Management/organization	Item: The student is organized
		Self-regulation	The student is able to remain goal-directed across time, even in the face of difficult situations, and emotions such as boredom/annoyance/frustration
		Self-regulation	Item: The student is usually able to concentrate fully when doing a task
Not addressed in CASEL model		Resilience	The student is able to recover from or adjust easily to misfortune or change by becoming strong and healthy in response to such experiences
Not addressed in CASEL model		Resilience	Item: The student is aware that effort leads to success
Not addressed in CASEL model		Self-advocacy	The student consistently represents themselves by appropriately asking for assistance inside and outside of the classroom
Not addressed in CASEL model		Self-advocacy	Item: The student knows how to get help when they need it

Note. Measuring individual SEL competencies through multiple subscales is standard practice, e.g., the DESSA evaluates CASEL’s Self-Management with Goal-Directed Behavior and Decision-Making subscales (LeBuffe et al., 2009). For a complete list of WESS items, please contact the corresponding author.

Despite the initial focus of many early social-emotional learning (SEL) improvement efforts on SwLD (Elias, 2004), few SEL frameworks address their social emotional needs. Although the CASEL model has demonstrated robustness across various grade levels and contexts (Ross & Tolan, 2018), it does not fully address the specific SEL competencies required for SwLD to succeed academically and socially. Because of SwLD’s unique challenges due to their disability, additional SEL skills and competencies are imperative for them to succeed (Cavioni et al., 2017). These challenges necessitate a more tailored approach to SEL addressing their specific needs.

Recent research indicates an alternative SEL model may better accommodate the unique SEL needs of SwLD, especially for SEL skills known to predict student success (DeBono et al., 2025). Developers of the Winston Essential Skills Survey (WESS) proposed a SEL model that overlaps with the CASEL model, while also addressing two critical SEL skills known to predict success for SwLD, particularly for educational and employment outcomes: resilience and self-advocacy. These skills are consistently associated with improved academic performance, persistence in education, and successful transitions to employment for SwLD (Daly-Cano et al., 2015; Lee et al., 2015; McNaught & Pope, 2022; Piers & Duquette, 2016; White et al., 2014).

Resilience and self-advocacy are critical factors in the educational persistence of SwLD. Research indicates that SwLD with higher resilience levels are more likely to overcome academic challenges and maintain their educational trajectories (Lee et al., 2015). Similarly, targeted self-advocacy training enhances skills necessary for success in higher education, empowering students to effectively communicate their needs and access appropriate accommodations (White et al., 2014).

A qualitative study by Piers and Duquette (2016) revealed that SwLD attribute their academic success to factors related to resilience and self-advocacy, including developing robust self-advocacy skills, setting ambitious yet attainable goals, maintaining perseverance, and proactively utilizing available resources. These findings underscore the importance of incorporating both resilience and self-advocacy into comprehensive SEL models to enable accurate assessment and targeted interventions for SwLD.

The impact of having strong resilience and self-advocacy skills extends beyond immediate academic achievement. Studies show that resilience and self-advocacy contribute to positive long-term developmental outcomes and may serve as protective factors against the potential negative effects of learning disabilities on academic progress, employment, and psychological well-being (Daly-Cano et al., 2015; Lee et al., 2015; McNaught & Pope, 2022; Piers & Duquette, 2016; White et al., 2014). Therefore, an SEL tool that assesses these skills may be a critical component for teachers’ toolboxes for promoting success in SwLD.

Current SEL Assessments

Because SEL assessments are based largely on frameworks that do not address the social-emotional needs of SwLD, a significant gap exists in reliable and valid SEL assessments for this population. A comprehensive review of 27 SEL assessments listed in the CASEL assessment guide revealed a concerning lack of inclusivity and specificity for SwLD (DeBono et al., 2025). This review highlighted that only a small number of the assessments included students with disabilities in their development or validation processes, which would include SwLD but also students with other disabilities, e.g., visual impairments. More critically, none of the 27 assessments had been specifically validated for use with SwLD. This oversight is particularly problematic given the unique social-emotional challenges commonly faced by SwLD, including: difficulties with peer relationships, decreased self-esteem, and impaired emotional regulation (Kopelman-Rubin et al., 2020; Musetti et al., 2019).

The lack of reliable and valid SEL assessments for SwLD has several significant implications. Existing assessments may not accurately capture the social-emotional competencies of SwLD, potentially leading to misinterpretation of their strengths and needs. This inaccuracy can result in ineffective or inappropriate interventions (Fuchs et al., 2004). Furthermore, the absence of validated assessments may result in these students being overlooked in school-wide SEL initiatives or receiving inadequate support, raising equity concerns. From a research perspective, the lack of validated measures hinders efforts to understand and improve SEL outcomes for SwLD, limiting the advancement of knowledge and evidence-based practices in special education. Without accurate SEL data on SwLD, policymakers are unable to make informed decisions about how to best allocate resources in special education. Collectively, these implications underscore the urgent need for developing and validating SEL assessments that are developed for and tested on SwLD, to ensure accuracy in assessing their needs.

Development of the Winston Essential Skills Survey (WESS)

Winston Preparatory School (WPS), a specialized, nonprofit, independent school with multiple campuses nationwide for SwLD, collaborated with the National School Climate Center to develop the WESS. Teachers, school leaders, and researchers developed the initial version of the WESS. The WESS expands upon the CASEL model by explicitly addressing those skills not clearly addressed in the CASEL framework (i.e., resilience and self-advocacy). In total, the WESS assesses eight SEL skills: resilience, self-advocacy, self-reflection, self-regulation, social skills, social responsibility, problem solving and management/organization (see Table 1 for definitions and sample items).

As an SEL assessment, the WESS provides an evaluation of students’ social-emotional skills from an educator's perspective. Assessing both intrapersonal (e.g., self-reflection) and interpersonal (e.g., social skills), initial evaluations of the WESS demonstrated promising validity and reliability, resulting in a 41-item survey (version 2.0; Thapa et al., 2014). The survey was further refined into a 26-item measure (DeBono et al., 2025) that had compelling evidence for its validity (e.g., construct and convergent validity) and reliability (e.g., internal consistency and test-retest reliability). However, this 26-item version of the WESS (version 3.0) was developed exclusively with WPS faculty, who evaluated their students with the WESS annually. To further demonstrate the WESS’s validity and reliability, the WESS 3.0 requires testing with educators outside of WPS. The objective of the following study was to further refine the WESS 3.0 into the WESS 4.0 using a sample of educational professionals outside of WPS. This study will determine if the WESS continues to demonstrate its eight factor structure and is reliable and valid in a general population of educators. This includes assessing the WESS 3.0’s internal consistency and its construct and convergent validity.

The convergent validity of the newly developed WESS 4.0 was assessed by examining the correlations between its subscales and those of another established SEL survey. Based on previous research, we anticipated that correlations between related subscales would fall within the moderate-to-strong range, with Pearson correlations ranging between .40 and .70, which is consistent with published evidence for convergent validity in SEL measurement (Anthony et al., 2022; Anvari et al., 2024). Correlations in this range are generally considered indicative of satisfactory convergent validity for measures assessing similar constructs (Anvari et al., 2024).

Method

Participants

Participants (N = 180, M_age = 44.69, additional demographics in Table 2) were recruited via listservs, conferences, and word of mouth, resulting in a sample of educators nationwide and internationally (e.g., Canada), with most participants residing in Texas. These educators mostly included teachers, but also included the following personnel: school psychologists, counselors, social workers, teacher’s assistants, speech language pathologists, and school nurses (for details, see Table 2). A majority of the educators in the sample worked in a public school.

Table 2.

Sample Demographics

Demographic	Categories	Percent
Gender	Female	88.3%
	Male	10.6%
	Non-binary	1.1%
Grade	K-5	44.8%
	6-8	25.4%
	9-12	29.0%
	Skipped question	.8%
Race/ethnicity	American Indian/Alaskan native	3.2%
	Asian	2.1%
	Black/African-American	2.1%
	Native Hawaiian/other Pacific Islander	2.1%
	White	80.9%
	Other	3.2%
	Prefer not to say/skipped question	6.4%
Hispanic	Yes	18%
	No	77.7%
	Prefer not to say	4.3%
Role	Teacher	70%
	School psychologist/counselor/social worker	10.6%
	Teacher’s assistant	4.4%
	Speech language pathologist	3.9%
	School nurse	1.1%
	Skipped question	13.3%
School type	Public	77.2%
	Private school (for general population)	3.9%
	Private school (for learning disabilities)	13.3%
	Skipped question	2.2%

Note. Sample represented 38 states, the District of Columbia, and 3 countries.

This sample included educators across 38 states, the District of Columbia, and three countries. Participants evaluated up to three SwLD students (Mode_grade = 8; Mode_LD = Dyslexia - 30%), resulting in a total of 397 students evaluated. In addition to their specific learning disabilities, 51.1% of these students had Attention-Deficit/Hyperactivity Disorder.

Measures

WESS 4.0

The original WESS 3.0 (DeBono et al., 2025) was a 26-item, educator-response survey. To make the WESS 3.0 gender neutral, all references to gender in each item were replaced with they/them language. Expecting the WESS 3.0 might require additional items for this general sample, that included educators beyond teachers, 1-2 additional test items were added for each subscale to create the WESS 4.0 (e.g., “The student uses their skills to successfully navigate social situations.” to measure Social Skills). Consistent with the development process for the original WESS items, the new items were created based on the SEL definitions outlined in Table 1. Item development was a collaborative effort involving experts in learning disabilities, educational researchers, and school administrators to ensure both content validity and practical relevance. Several of these new items were pre-tested with Winston teachers during earlier iterations of the WESS, providing preliminary evidence of their clarity and appropriateness for the target population. Ratings for all items were on a 1 (strongly disagree) to 5 (strongly agree) scale. The WESS 3.0 has evidence for its validity (convergent, discriminant, divergent, and construct validity) and reliability (internal consistency, test-retest reliability, interrater reliability).

Devereux Student Strengths Assessment (DESSA)

The DESSA is a 72-item survey to further determine the convergent validity of the WESS 4.0. The DESSA measures eight core social-emotional competencies: self-awareness, social awareness, self-management, relationship skills, goal-directed behavior, personal responsibility, decision making, and optimistic thinking. All items were rated on a 1 (never) to 5 (very frequently) scale. The DESSA has strong evidence for reliability and validity (LeBuffe et al., 2009, 2018; Naglieri et al., 2011), and the subscales were all internally consistent in this study, αs > .81 (see Table 3).

Table 3.

McDonald’s Omegas, Cronbach’s alphas, Descriptive Statistics, and Correlations Between the WESS 4.0 (1-8) and DESSA (9-16) Subscales

Subscales		1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16
	ω	.74	.74	.79	.83	.77	N/A	.82	.84	.91	.87	.88	.88	.92	.93	.85	.90
	α	.74	.73	.78	.82	.77	.76	.83	.84	.89	.87	.88	.88	.91	.93	.82	.90
	M	3.24	3.15	3.30	3.50	2.56	2.92	2.96	2.77	30.11	21.03	28.28	27.57	24.74	31.51	20.30	33.89
	SD	.90	.94	.96	.91	.95	1.07	.87	.63	7.18	5.56	7.01	6.71	6.30	8.29	5.49	7.90
1. Resilience		1.00
2. Social skills		.51	1.00
3. Self-advocacy		.59	.46	1.00
4. Social responsibility		.54	.61	.54	1.00
5. Self-regulation		.59	.48	.45	.51	1.00
6. Self-reflection		.61	.62	.52	.66	.71	1.00
7. Problem solving		.65	.59	.58	.59	.63	.72	1.00
8. Management/organization		.50	.38	.47	.41	.69	.56	.55	1.00
9. Personal responsibility		.57	.56	.52	.66	.62	.67	.64	.63	1.00
10. Optimistic thinking		.55	.52	.57	.63	.44	.54	.55	.41	.71	1.00
11. Goal directed behavior		.52	.47	.49	.57	.50	.53	.53	.49	.76	.76	1.00
12. Social awareness		.52	.63	.51	.75	.46	.63	.62	.43	.79	.74	.62	1.00
13. Decision making		.55	.59	.56	.71	.55	.72	.66	.52	.85	.71	.71	.83	1.00
14. Relationship skills		.48	.55	.51	.76	.39	.55	.55	.34	.69	.75	.64	.81	.80	1.00
15. Self-awareness		.52	.53	.64	.58	.47	.57	.59	.44	.72	.77	.66	.68	.80	.76	1.00
16. Self-management		.50	.55	.47	.63	.59	.61	.61	.56	.82	.62	.62	.77	.82	.72	.67	1.00

Note. The WESS subscores are calculated by taking the average of the scores. The DESSA subscores are calculated by summing up the scores. McDonald’s omega cannot be computed for the Self-Reflection subscale because it has less than 3 items (indicated with N/A). The Self-Reflection subscale’s Spearman-Brown coefficient was .76. All correlations were significant at the p < .001 level. Moderate correlations range from .40-.59 and strong correlations from .60-.70 (Evans, 1996).

Procedure

After providing informed consent and before completing the SEL assessments, participants received the following instructions:

As you are considering your level of agreement to each statement please keep in mind a same-aged, mainstream peer as a comparison point - focus on your thoughts and feelings based on your own personal experiences. There are no right or wrong answers - we just want to know how you feel.

Participants were provided an opportunity to rate three students. After completing assessments for the desired number of students, participants were prompted to provide their email address in order to receive a gift card. Subsequently, participants were thanked for their time and participation.

Study and Data Analysis Procedures

Prior to data collection, Institutional Review Board (IRB) approval was provided by Sterling IRB. Participants completed the study in Qualtrics which included a consent form, a demographic questionnaire about themselves (e.g., age) and about each student they evaluated (e.g., grade, diagnoses), and evaluations of up to three SwLD with the WESS 4.0 and the DESSA. They received a $15 electronic gift card for each student they evaluated.

For the data analysis, subscale scores for the WESS and DESSA were calculated according to each survey’s instructions. To determine if the WESS 4.0 maintains its eight-factor structure, demonstrating its structural validity, a Confirmatory Factor Analysis (CFA) was performed using Analysis of Moment Structures (AMOS 28). Internal consistency was determined by calculating each subscale’s Cronbach’s alpha in the Statistical Package for the Social Sciences (SPSS) 29.0. Minor modifications to the WESS 3.0 were required to improve internal consistency and to improve the model fit in this sample, resulting in the final version of the WESS 4.0. Correlations between the subscales in the WESS 4.0 and the DESSA were calculated to establish the WESS 4.0’s convergent validity.

A post-hoc multivariate analysis of variance (MANOVA) was conducted to examine whether student grade level, used as a proxy for age (given that teachers may not reliably report exact ages), influenced WESS scores. For this analysis, students were categorized into three groups: elementary (K–5), middle school (6–8), and high school (9–12), with grade level serving as the single predictor and the WESS subscale scores as the outcome variables. This approach allowed us to assess whether younger students were rated lower than older students on SEL skills, with particular attention to higher-order competencies such as self-reflection and self-regulation—skills that are known to develop with age and can be challenging even for adults (Gage & Thomas, 2019).

Results

The CFA of the original WESS 3.0 provided evidence supporting the retention of its eight-factor structure (see Figure 1 and Table 4). Correlations between the latent variables ranged from moderate to strong (Table 5) and several goodness-of-fit indices met or exceeded generally accepted criteria, indicating an adequate model fit. The Root Mean Square Error of Approximation (RMSEA) and Standardized Root Mean Squared Residual (SRMR) both met established thresholds, suggesting a good absolute fit of the model to the data. Notably, the RMSEA, in addition to being a measure of absolute fit, also favors more parsimonious models (Hooper et al., 2008), further supporting the efficiency of the eight-factor structure. The Parsimony Normed Fixed Index (PNFI) also met acceptable criteria, indicating that the model achieved a satisfactory balance between fit and complexity (Mulaik et al., 1989). Two additional indices nearly met criteria: the Comparative Fit Index (CFI) and Incremental Fit Index (IFI). These findings combined indicated that the hypothesized eight factor structure fits the data adequately, but some adjustments may be needed to meet most criteria.

Figure 1.

Confirmatory Factor Analysis: WESS 3.0 - 26-Item Model. Note: All Items Were Entered as Manifest Variables With Error Terms Associated With Each of Them; all Subscales (Factors) Were Entered as Latent Variables With Error Terms Associated With Each of Them (Indicated to the Top Right of Each Latent Variable). All Subscales Were Allowed to Correlate With One Another. Covariances Between Subscales Ranged From .11 (Between Social Skills and Self-Regulation) to .49 (Between Self-Regulation and Management and Organization) and all Were Significant, ps < .001. Numbers to the Upper Right of the Eight Latent SEL Variables and to the Right of the Error Terms (e) Indicate Their Variances

Table 4.

Goodness-of-Fit Indices for the WESS 3.0 and 4.0 in a General Population of Educators

Criteria for goodness-of-fit	WESS 3.0	WESS 4.0	WESS 4.0	WESS 4.0	WESS 4.0
Criteria for goodness-of-fit	26 items	27 items	27 items, Model A	27 items, Model B	27 items, Model C
NFI >.90	.83	.87^a	.86	.85	.83
CFI >.90	.88	.91 ^a	.90	.89	.88
RMSEA <.08	.07	.07	.07	.07	.08
SRMR <.08	.06	.05 ^a	.05 ^a	.06	.06
RFI close to 1	.80	.84 ^a	.84 ^a	.83 ^a	.81 ^a
IFI >.90	.88	.91 ^a	.90^a	.89^a	.88
PNFI >.50	.69	.73 ^a	.74^a	.75 ^a	.73 ^a
GFI >.95	.86	.87^a	.86	.85	.82
AGFI >.90	.81	.83^a	.82^a	.81	.78
χ2/df ≤ 2	2.84	2.70^a	2.83^a	3.00	3.29

Note. Model A collapsed the self-regulation and self-reflection latent factors. Model B collapsed the self-regulation and management/organization latent factors. Model C additionally collapsed management/organization with the self-regulation and self-reflection factors. Bolded goodness-of-fit measurements meet criteria (Hu & Bentler, 1999).

^aIndicate goodness-of-fit measurements that were improved over the 3.0 version.

Table 5.

Correlations Between Latent Factors for the WESS 3.0 from the Confirmatory Factor Analysis

Subscales	1	2	3	4	5	6	7	8
1. Resilience	1.00
2. Social skills	.80	1.00
3. Self-advocacy	.87	.69	1.00
4. Social responsibility	.62	.83	.65	1.00
5. Self-regulation	.55	.46	.41	.40	1.00
6. Self-reflection	.80	.72	.72	.51	.40	1.00
7. Problem solving	.82	.76	.69	.69	.51	.72	1.00
8. Management/organization	.70	.61	.59	.59	.85	.55	.65	1.00

Note. All correlations are significant at the p < .05 level.

The analysis of the WESS 3.0 subscales demonstrated room for improvement (Table 4). Several WESS 3.0 subscales did not reach the criteria of Cronbach’s alpha >.70, indicating questionable internal consistency (Mallery & George, 2003) in the following subscales: resilience, self-advocacy, social skills, and self-reflection. Improving the internal consistency of these subscales is an appropriate next step, as it will reduce the amount of error in the survey and increase the power of the CFA (Costa & Sarmento, 2019; Shultz et al., 2020). To address the shortcomings in internal consistency, several minor adjustments to these subscales were required: the Resilience subscale was strengthened by item replacement, Social Skills and Self-Advocacy subscales were augmented with new items, and Self-Reflection was optimized by removing two items (e.g., The student is aware of their academic goals) and adding a new one (The student accepts responsibility for their actions). These modifications resulted in all subscales achieving Cronbach's alpha coefficients above .70. These new subscales were entered into the CFA and demonstrated that the model fit improved substantially (Table 4 and Figure 2). This finalized 27-item version of the WESS, version 4.0, met the same goodness-of-fit criteria as the 3.0 version, but also met the following criteria: CFI, RFI, IFI, and PNFI.

Figure 2.

Confirmatory Factor Analysis: WESS 4.0 - 27-Item Model. Note: All Items Were Entered as Manifest Variables With Error Terms Associated With Each of Them; all Subscales (Factors) Were Entered as Latent Variables With Error Terms Associated With Each of Them (Indicated to the Top Right of Each Latent Variable). All Subscales Were Allowed to Correlate With One Another. Covariances Between Subscales Ranged From .49 (Between Social Skills and Management/Organization) to .94 (Between Self-Regulation and Self-Reflection) and all Were Significant, ps < .001. Numbers to the Upper Right of the Eight Latent SEL Variables and to the Right of the Error Terms (e) Indicate Their Variances

A review of the correlations among latent factors in the CFA suggested that some degree of factor collapsing might be warranted (see Table 6). Notably, self-regulation and self-reflection exhibited a particularly high correlation, prompting us to test a model in which these two factors were combined into a single construct. However, this modification resulted in a decline in model fit indices (see Table 4), indicating that the distinctiveness of these factors contributes meaningfully to the overall model structure. We further explored a model in which management/organization was collapsed with self-regulation and self-reflection, but this approach led to an even greater reduction in model fit. Specifically, fit indices such as the NFI, CFI, IFI, GFI, AGFI, and χ² all decreased relative to the original 8-factor model, and both the CFI and IFI fell below commonly accepted thresholds for adequate fit (Hu & Bentler, 1999). These results collectively indicate that the original 8-factor structure remained the best-fitting model.

Table 6.

Correlations Between Latent Factors for the WESS 4.0 from the Confirmatory Factor Analysis

Subscales	1	2	3	4	5	6	7	8
1. Resilience	1.00
2. Social skills	.69	1.00
3. Self-advocacy	.77	.59	1.00
4. Social responsibility	.67	.75	.62	1.00
5. Self-regulation	.80	.65	.59	.69	1.00
6. Self-reflection	.82	.82	.66	.83	.94	1.00
7. Problem solving	.83	.74	.70	.69	.80	.89	1.00
8. Management/organization	.70	.49	.60	.53	.90	.74	.65	1.00

Note. All correlations are significant at the p < .05 level.

Additionally, a review of the CFA solutions for both the WESS 3.0 and 4.0 revealed no evidence of Heywood cases; that is, there were no standardized factor loadings exceeding 1.0 and no negative error variances. Thus, both models demonstrated proper solutions.

Convergent Validity

The revised WESS 4.0 exhibited positive and significant correlations with the DESSA subscales, with many showing moderate to strong associations (Table 3). The size of these correlations consistently fit within the .40 to .70 range that was expected between these surveys (Anthony et al., 2022). Notably, the WESS Social Responsibility subscale demonstrated particularly robust correlations (rs > .75) with DESSA's Social Awareness and Relationship Skills subscales, underscoring the instrument's convergent validity. The smallest correlation was between the WESS 4.0’s management/organization and DESSA’s relationship skills subscales, which indicates that they may be both measuring SEL — given the partial overlap in scores — but they are likely measuring different constructs.

Student Grade Level and SEL Scores

To examine the impact of student grade level on SEL skills, we conducted a MANOVA with grade level as the predictor (elementary: K–5; middle school: 6–8; high school: 9–12) and WESS 4.0 subscale scores as outcome variables (see Table 7). The analysis indicated that, overall, grade level did not significantly affect most WESS scores. However, as anticipated based on developmental theory, significant differences emerged for the two SEL domains most closely tied to developmental progression: self-regulation and self-reflection (Lyons & Zelazo, 2011).

Table 7.

Multivariate ANOVA: Grade as a Predictor of SEL Scores From the WESS

WESS subscales	F	p	Partial η²
Resilience	.44	.643	.002
Social skills	.01	.990	.000
Self-advocacy	.52	.593	.003
Social responsibility	.36	.700	.002
Self-regulation	3.52	.031	.018
Self-reflection	3.90	.021	.020
Problem solving	1.40	.248	.007
Management/organization	2.35	.097	.012

Specifically, SwLD in elementary grades scored significantly lower on self-reflection (M = 3.06, SD = 0.87) and self-regulation (M = 2.28, SD = 1.12) compared to their middle school (Self-Reflection: M = 3.21, SD = 0.86; Self-Regulation: M = 2.57, SD = 1.14) and high school peers (Self-Reflection: M = 3.33, SD = 0.79; Self-Regulation: M = 2.59, SD = 1.16). These findings are consistent with the literature indicating that self-regulation and self-reflection are developmental skills that typically improve with age and experience (Lyons & Zelazo, 2011).

Notably, while statistically significant, the observed differences were small in magnitude (partial η² = .02 for both self-regulation and self-reflection). This suggests that although age and grade level contribute to the development of these SEL skills, other factors may also play a substantial role.

Discussion

The WESS 3.0 demonstrated acceptable fit and efficiency with an eight-factor structure (Hooper et al., 2008) in a general population of educators. However, the WESS 4.0, with minor refinements to the 3.0 version, improved the WESS’s psychometric properties, including internal consistency and convergent validity, supporting its use as a reliable and valid tool for assessing SEL in SwLD. The WESS 4.0 offers valuable information for educators to design programming addressing SwLD's strengths and weaknesses, and for researchers to assess the effectiveness of special education, particularly SEL, interventions.

The increasing recognition of SEL as a crucial component for SwLD necessitates targeted tools and interventions (Elias, 2004). Although broad SEL models exist, they often lack focus on resilience and self-advocacy, skills vital for SwLD (Schoon, 2021). The WESS 4.0 directly addresses this need, offering a reliable and valid SEL measure specifically designed and validated for this population, contributing to the call for more assessments providing greater equity for diverse learners (Jagers et al., 2019).

Addressing a Critical Gap

The WESS 4.0 fills a critical gap by providing an SEL assessment specifically designed and validated for SwLD, whereas most existing measures developed primarily with general student populations. Prior SEL assessments often excluded or statistically controlled for SwLD (DeBono et al., 2025), yet the WESS 4.0 reflects the unique strengths and challenges of this group (Fuchs et al., 2004). Its focus on resilience and self-advocacy—skills essential for SwLD success in secondary and postsecondary settings (Daly-Cano et al., 2015; Peddigrew et al., 2022)—sets it apart from established measures. For example, the Resiliency Scales for Children and Adolescents (Prince-Embury, 2008) assesses resilience through a self-report measure and executive functioning assessments such as the Behavior Rating Inventory of Executive Function (BRIEF; Gioia et al., 2000) target narrower cognitive and behavioral regulation domains like planning and organizing through teacher and parent ratings, which may be useful in targeted interventions. Several of the executive functioning skills assessed in the BRIEF (e.g., planning and organization) are captured in the WESS 4.0’s Management/Organization subscale, which incorporates them within a broader SEL framework tailored to SwLD. By integrating these domains into a cohesive SEL framework, the WESS 4.0 complements existing tools while advancing more equitable, population-specific assessment practices (Aronson & Laughter, 2016; Durlak et al., 2022; Li et al., 2024; Montenegro & Jankowski, 2017). Moreover, this integrated approach may help identify specific SEL skills that require further assessment and targeted intervention to effectively support SwLD’s needs.

Limitations and Future Research Directions

The WESS 4.0 development followed rigorous scale development procedures (Boateng et al., 2018; Hinkin, 1995). While these steps were carefully undertaken in the present investigation, further validation by independent researchers is encouraged to fully establish its reliability and validity (DeVellis & Thorpe, 2022).

Several substantial correlations emerged among factors in the CFA of the 8-factor model, with the most notable being those between self-reflection, management/organization, and self-regulation. The high correlation between self-reflection and self-regulation is theoretically understandable, as effective self-regulation inherently involves some degree of self-reflection (Lyons & Zelazo, 2011). However, attempts to collapse these factors into a single construct resulted in poorer model fit, suggesting that, despite their conceptual overlap, these constructs are empirically distinguishable within the WESS framework.

This finding aligns with recommendations in the literature (Geiser et al., 2015), which caution against collapsing factors that are grounded in established theoretical models. CFA is designed to test a priori frameworks (Kline, 2023; Liu et al., 2020), and collapsing factors post-hoc can distort the intended factor structure, complicating the interpretation of relationships between observed variables and latent constructs. Such an approach risks diminishing the depth and accuracy of the assessment, as important nuances between constructs may be lost (Kyriazos & Poga, 2023). Moreover, empirical evidence indicates that collapsing factors can introduce bias into parameter estimates, potentially leading to over or underestimation of factor loadings and misrepresentation of the true relationships in the data (Geiser et al., 2015).

Based on these considerations, it was determined that maintaining the original factor structure in the WESS 4.0 was most appropriate. Nonetheless, future research should continue to explore whether further refinement can enhance the distinctiveness of these factors or, alternatively, whether there are empirical or theoretical justifications for combining them in specific contexts.

This study, in line with previous research (DeBono et al., 2025) demonstrating convergent validity for the WESS 3.0 with the Mindsets, Skills, and Habits Survey (Transforming Education, 2016), shows evidence for the WESS 4.0 having convergent validity due to its correlations with the DESSA. Combined, these findings provide multiple sources of evidence supporting the convergent validity of the WESS. However, further evidence is warranted. In particular, the DESSA subscales in our sample showed high intercorrelations—exceeding .80 among Social Awareness, Decision Making, Relationship Skills, and Self-Awareness (see Table 3)—suggesting these subscales may not be well differentiated and could be assessing the same or overlapping constructs. This observation highlights the importance of comprehensive construct validation, which requires the evaluation of both convergent and discriminant validity (Cronbach & Meehl, 1955). Future studies should consider using SEL assessments with well-differentiated factors to further evaluate the convergent and discriminant validity of the WESS 4.0.

Additionally, the relationship between the evaluator and the student may influence the accuracy of WESS ratings. Variables such as the duration of the evaluator’s experience with the student and the frequency of their interactions could affect the reliability of the assessment. Future research should examine whether a minimum threshold of familiarity or number of interactions is necessary to ensure accurate evaluation of students’ SEL skills.

Our findings also suggest that grade level and developmental stage play important roles in SEL skill acquisition. As expected, younger students, particularly those with SwLD, were rated lower in self-regulation and self-reflection—skills that typically develop with age and experience (Butler & Schnellert, 2015; Lyons & Zelazo, 2011). Although these effects were statistically significant, they were small in magnitude. This small effect is likely attributable to the instructions, which asked participants to evaluate their students in comparison to a “same-aged, mainstream peer.” Nonetheless, these results underscore the need to consider age and developmental factors when developing and interpreting SEL assessments.

The design of a brief SEL survey for teachers assessing SwLD offers several practical advantages. Shorter measures reduce rater fatigue, improve data quality, and minimize disruptions to instructional time (Lane et al., 2014; Raver et al., 2011). Briefer assessments are also more feasible for teachers who must assess large numbers of students within limited time frames (McKown et al., 2009). However, the emphasis on brevity resulted in a two-item subscale for Self-Reflection in the WESS 4.0. Although the overall model fit was excellent and the Self-Reflection subscale was internally consistent with strong factor loadings (.78 and .79; see Figure 2), two-item subscales can pose challenges for stability and model specification (Knekta et al., 2019). To mitigate these risks, we employed a large sample, ensured careful model specification, and confirmed high reliability for the subscale (Knekta et al., 2019). Nevertheless, ongoing research should continue to monitor the stability of this subscale and consider adding items as needed to maintain both internal consistency and model fit.

To contribute greater equity in SEL assessments, a Spanish translation of the WESS is underway (Raisch et al., 2024). This addresses the demand for accessible assessments for non-English speakers. As the second most spoken native language globally (Instituto Cervantes, 2023), a Spanish version of the WESS will enable comprehensive assessment of SEL skills among SwLD in Spanish-speaking communities, potentially leading to more targeted interventions. This aligns with developing more inclusive SEL frameworks. Future research should explore SwLD’s unique SEL needs and trajectories worldwide, informing comprehensive and inclusive educational practices.

Implications for Practice and Policy

The WESS 4.0 may provide an important contribution to schools with Multi-Tiered Systems of Support (MTSS) by providing crucial data on SwLD's social and emotional needs (Arden, 2021). Incorporating the WESS 4.0 into assessment protocols allows schools to accurately identify and respond to the unique SEL challenges faced by SwLD. This facilitates targeted interventions, enabling educators to design individualized strategies based on specific SEL strengths and weaknesses (Cook et al., 2015). Regular WESS 4.0 administration would allow for tracking SEL development, enabling timely adjustments to support strategies (Fuchs & Fuchs, 2006), informing resource allocation (Eagle et al., 2015), and guiding professional development to enhance educators’ capacity to support SwLD's social-emotional needs (Desimone & Garet, 2015), ultimately improving outcomes for SwLD.

Accurate SEL assessment may demonstrate SEL's benefits, encouraging policymakers to integrate SEL into education standards and curricula. This could prompt a revision of assessment systems, inspiring more holistic methods incorporating social-emotional skills alongside academic achievements. These findings could catalyze cross-sector collaboration, fostering policies that promote partnerships between education, mental health, and social services sectors to provide comprehensive support for SwLD’s social and emotional needs and addressing unintended consequences of existing education policies. As SEL gains recognition globally, the WESS 4.0 and its translations could inform education policies internationally, potentially leading to collaborations and knowledge sharing worldwide. Utilizing the WESS in special education may shape education policies to improve student outcomes and well-being for SwLD.

Footnotes

ORCID iD

Amber DeBono

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: support has been received from the Learning Disabilities Foundation of America however, the opinions expressed are those of the authors and do not necessarily reflect the views of the Foundation.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data supporting this study’s findings are available from the corresponding author, Amber DeBono, upon reasonable request.*

IRB Statement

The research reported in this manuscript was approved by an Institutional Review Board (IRB), Sterling IRB.

References

Al-Yagon

(2014). Child–mother and child–father attachment security: Links to internalizing adjustment among children with learning disabilities. Child Psychiatry and Human Development, 45(1), 119–131. https://doi.org/10.1007/s10578-013-0383-9

Anthony

C. J.

Elliott

S. N.

Yost

Lei

P. W.

DiPerna

J. C.

Cefai

Camilleri

Bartolo

A. P.

Grazzani

Ornaghi

Cavioni

Conte

Vorkapic

T. S.

Poulou

Martinsone

Simões

Colomeischi

A. A.

(2022). Multi-informant validity evidence for the SSIS SEL brief scales across six European countries. Frontiers in Psychology, 13, Article 928189. https://doi.org/10.3389/fpsyg.2022.928189

Anvari

Billinger

Analytis

P. P.

Franco

V. R.

Marchiori

(2024). Testing the convergent validity, domain generality, and temporal stability of selected measures of people’s tendency to explore. Nature Communications, 15(1), 7721. https://doi.org/10.1038/s41467-024-51685-z

Arden

(2021). Multi-tiered system of supports (formerly RTI). American Institutes for Research. https://www.air.org/our-work/education/multi-tiered-system-supports-formerly-rti

Aronson

Laughter

(2016). The theory and practice of culturally relevant education: A synthesis of research across content areas. Review of Educational Research, 86(1), 163–206. https://doi.org/10.3102/0034654315582066

Boateng

G. O.

Neilands

T. B.

Frongillo

E. A.

Melgar-Quiñonez

H. R.

Young

S. L.

(2018). Best practices for developing and validating scales for health, social, and behavioral research: A primer. Frontiers in Public Health, 6(149), 1–18. https://doi.org/10.3389/fpubh.2018.00149

Butler

D. L.

Schnellert

(2015). Success for students with learning disabilities: What does self-regulated learning have to do with it? In Cleary

(Ed.), Self-regulated learning interventions with at-risk populations: Enhancing adaptability, performance, and well-being (pp. 89–111). American Psychological Association Press. https://doi.org/10.1037/14641-005

Cavioni

Grazzani

Ornaghi

(2017). Social and emotional learning for children with learning disability: Implications for inclusion. International Journal of Emotional Education, 9(2), 100–109. https://hdl.handle.net/10281/17728

Cook

C. R.

Frye

Slemrod

Lyon

A. R.

Renshaw

T. L.

Zhang

(2015). An integrated approach to universal prevention: Independent and combined effects of PBIS and SEL on youths’ mental health. School Psychology Quarterly: The Official Journal of the Division of School Psychology, American Psychological Association, 30(2), 166–183. https://doi.org/10.1037/spq0000102

10.

Costa

Sarmento

R. P.

(2019). Confirmatory factor analysis-a case study. https://arxiv.org/pdf/1905.05598

11.

Cronbach

L. J.

Meehl

P. E.

(1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281–302. https://doi.org/10.1037/h0040957

12.

Daly-Cano

Vaccaro

Newman

(2015). College student narratives about learning and using self-advocacy skills. Journal of Postsecondary Education and Disability, 28(2), 213–227.

13.

Darling-Hammond

Cook-Harvey

C. M.

(2018). Educating the whole child: Improving school climate to support student success. Learning Policy Institute.

14.

DeBono

A. E.

Ewoldt

K. B.

Heimbauer

Mendelsohn

E. A.

Cohen

(2025). A social and emotional learning survey for students with learning disabilities: Reliability and validity analyses. Learning Disability Quarterly, 47(3), 159–172. https://doi.org/10.1177/07319487241301400

15.

Desimone

L. M.

Garet

M. S.

(2015). Best practices in teacher's professional development in the United States. Psychology, Society, & Education, 7(3), 252–263. https://doi.org/10.25115/psye.v7i3.515

16.

DeVellis

R. F.

Thorpe

C. T.

(2022). Scale development: Theory and applications. Sage Publications.

17.

Divecha

Brackett

(2020). Rethinking school-based bullying prevention through the lens of social and emotional learning: A bioecological perspective. International Journal of Bullying Prevention, 2(2), 93–113. https://doi.org/10.1007/s42380-019-00019-5

18.

Durlak

J. A.

Mahoney

J. L.

Boyle

A. E.

(2022). What we know, and what we need to find out about universal, school-based social and emotional learning programs for children and adolescents: A review of meta-analyses and directions for future research. Psychological Bulletin, 148(11–12), 765–782. https://doi.org/10.1037/bul0000383

19.

Eagle

J. W.

Dowd-Eagle

S. E.

Snyder

Holtzman

E. G.

(2015). Implementing a multi-tiered system of support (MTSS): Collaboration between school psychologists and administrators to promote systems-level change. Journal of Educational and Psychological Consultation, 25(2–3), 160–177. https://doi.org/10.1080/10474412.2014.929960

20.

Elias

M. J.

(2004). The connection between social-emotional learning and learning disabilities: Implications for intervention. Learning Disability Quarterly, 27(1), 53–63. https://doi.org/10.2307/1593632

21.

Evans

J. D.

(1996). Straightforward statistics for the behavioral sciences. Thomson Brooks/Cole Publishing Co.

22.

Frye

K. E.

Boss

D. L.

Anthony

C. J.

Xing

(2022). Content analysis of the CASEL framework using K–12 state SEL standards. School Psychology Review, 53(3), 208–222. https://doi.org/10.1080/2372966X.2022.2030193

23.

Fuchs

L. S.

(2006). Introduction to response to intervention: What, why, and how valid is it? Reading Research Quarterly, 41(1), 93–99. https://doi.org/10.1598/rrq.41.1.4

24.

Fuchs

L. S.

Compton

D. L.

(2004). Identifying reading disabilities by responsiveness-to-instruction: Specifying measures and criteria. Learning Disability Quarterly, 27(4), 216–227. https://doi.org/10.2307/1593674

25.

Gage

Thomas

(2019). Elementary teachers' perceptions of social and emotional learning and its effects on school climate, student behavior, and academic achievement. Alabama Journal of Educational Leadership, 6, 41–51. https://files.eric.ed.gov/fulltext/EJ1327616.pdf

26.

Geiser

Bishop

Lockhart

(2015). Collapsing factors in multitrait-multimethod models: Examining consequences of a mismatch between measurement design and model. Frontiers in Psychology, 6, 946. https://doi.org/10.3389/fpsyg.2015.00946

27.

Gioia

G. A.

Isquith

P. K.

Guy

S. C.

Kenworthy

(2000). Behavior rating inventory of executive function. Child Neuropsychology, 6(3), 235–238. https://doi.org/10.1076/chin.6.3.235.3152

28.

Goldstein

Brooks

R. B.

(2023). Handbook of resilience in children. Springer Nature. https://doi.org/10.1007/978-3-031-14728-9

29.

Grigorenko

E. L.

Compton

D. L.

Fuchs

L. S.

Wagner

R. K.

Willcutt

E. G.

Fletcher

J. M.

(2020). Understanding, educating, and supporting children with specific learning disabilities: 50 years of science and practice. The American Psychologist, 75(1), 37–51. https://doi.org/10.1037/amp0000452

30.

Hinkin

T. R.

(1995). A review of scale development practices in the study of organizations. Journal of Management, 21(5), 967–988. https://doi.org/10.1177/014920639502100509

31.

Hooper

Coughlan

Mullen

(2008). Structural equation modelling: Guidelines for determining model fit. Electronic Journal of Business Methods, 6(1), 53–60.

32.

L. T.

Bentler

P. M.

(1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

33.

Instituto Cervantes . (2023). El español en el mundo 2023: Anuario del instituto cervantes. https://cvc.cervantes.es/lengua/anuario/anuario_23/el_espanol_en_el_mundo_anuario_intituto_cervantes_2023.pdf

34.

Jagers

R. J.

Rivas-Drake

Williams

(2019). Transformative social and emotional learning (SEL): Toward SEL in service of educational equity and excellence. Educational Psychologist, 54(3), 162–184. https://doi.org/10.1080/00461520.2019.1623032

35.

Jones

S. M.

Doolittle

E. J.

(2017). Social and emotional learning: Introducing the issue. The Future of Children, 27(1), 3–11. https://doi.org/10.1353/foc.2017.0000

36.

Kline

R. B.

(2023). Principles and practice of structural equation modeling (5th edition). Guilford Publications.

37.

Knekta

Runyon

Eddy

(2019). One size doesn’t fit all: Using factor analysis to gather validity evidence when using surveys in your research. CBE Life Sciences Education, 18(1), 1–17. https://doi.org/10.1187/cbe.18-04-0064

38.

Kopelman-Rubin

Siegel

Weiss

Kats-Gold

(2020). The relationship between emotion regulation, school belonging, and psychosocial difficulties among adolescents with specific learning disorder. Children & Schools, 42(4), 216–224. https://doi.org/10.1093/cs/cdaa022

39.

Kyriazos

Poga

(2023). Dealing with multicollinearity in factor analysis: The problem, detections, and solutions. Open Journal of Statistics, 13(3), 404–424. https://doi.org/10.4236/ojs.2023.133020

40.

Lane

K. L.

Oakes

W. P.

Menzies

H. M.

(2014). Comprehensive, integrated, three-tiered models of prevention: Why does my school—and district—need an integrated approach to meet students’ academic, behavioral, and social needs? Preventing School Failure: Alternative Education for Children and Youth, 58(3), 121–128. https://doi.org/10.1080/1045988X.2014.893977

41.

Lawrence

B. C.

Harrison

G. L.

Milford

T. M.

(2019). Effects of positive parenting on mental health in adolescents with learning disabilities. Learning Disabilities: A Contemporary Journal, 17(2), 223–244. https://files.eric.ed.gov/fulltext/EJ1234951.pdf

42.

LeBuffe

P. A.

Shapiro

V. B.

Naglieri

J. A.

(2009). The devereux student strengths assessment (DESSA): Assessment, technical manual, and user's guide. Apperson, Inc.

43.

LeBuffe

P. A.

Shapiro

V. B.

Robitaille

J. L.

(2018). The devereux student strengths assessment (DESSA) comprehensive system: Screening, assessing, planning, and monitoring. Journal of Applied Developmental Psychology, 55, 62–70. https://doi.org/10.1016/j.appdev.2017.05.002

44.

Lee

I. H.

Rojewski

J. W.

Gregg

Jeong

S. O.

(2015). Postsecondary education persistence of adolescents with specific learning disabilities or emotional/behavioral, disorders. The Journal of Special Education, 49(2), 77–88. https://doi.org/10.1177/0022466914524826

45.

Y. F.

Zhang

Dulas

H. M.

Whirley

M. L.

(2024). Academic learning experiences and challenges of students with disabilities in higher education. Journal of Postsecondary Student Success, 3(4), 79–102. https://doi.org/10.33009/fsop_jpss134617

46.

Liu

Luo

Wang

(2020). Convergence analysis of single latent factor-dependent, nonnegative, and multiplicative update-based nonnegative latent factor models. IEEE Transactions on Neural Networks and Learning Systems, 32(4), 1737–1749. https://doi.org/10.1109/TNNLS.2020.2990990

47.

Lyons

K. E.

Zelazo

P. D.

(2011). Monitoring, metacognition, and executive function: Elucidating the role of self-reflection in the development of self-regulation. Advances in Child Development and Behavior, 40, 379–412. https://doi.org/10.1016/B978-0-12-386491-8.00010-4

48.

Mallery

George

(2003). SPSS for windows step by step: A simple guide and reference. 11.0 update, (4th ed.). Allyn & Bacon.

49.

McKown

Gumbiner

L. M.

Russo

N. M.

Lipton

(2009). Social-emotional learning skill, self-regulation, and social competence in typically developing and clinic-referred children. Journal of Clinical Child and Adolescent Psychology, 38(6), 858–871. https://doi.org/10.1080/15374410903258934

50.

McNaught

Pope

E. M.

(2022). Youth leaders with learning disabilities and their decision to enroll in higher education. Career Development and Transition for Exceptional Individuals, 45(4), 187–199. https://doi.org/10.1177/21651434221076063

51.

Milligan

Phillips

Morgan

A. S.

(2016). Tailoring social competence interventions for children with learning disabilities. Journal of Child and Family Studies, 25(3), 856–869. https://doi.org/10.1007/s10826-015-0278-4

52.

Montenegro

Jankowski

N. A.

(2017). Equity and assessment: Moving towards culturally responsive assessment. National Institute for Learning Outcomes Assessment. Occasional Paper #29. https://files.eric.ed.gov/fulltext/ED574461.pdf

53.

Mulaik

S. A.

James

L. R.

Van Alstine

Bennett

Lind

Stilwell

C. D.

(1989). Evaluation of goodness-of-fit indices for structural equation models. Psychological Bulletin, 105(3), 430–445. https://doi.org/10.1037/0033-2909.105.3.430

54.

Musetti

Eboli

Cavallini

Corsano

(2019). Social relationships, self-esteem, and loneliness in adolescents with learning disabilities. Clinical Neuropsychiatry, 16(4), 165–172. https://pmc.ncbi.nlm.nih.gov/articles/PMC8650192/

55.

Naglieri

J. A.

LeBuffe

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

56.

National Center for Education Statistics . (2024). Students with disabilities. Condition of education. U.S. Department of Education, Institute of Education Sciences. https://nces.ed.gov/programs/coe/indicator/cgg

57.

Patterson

Thomas

(2014). Life skills group: Increasing foundation knowledge and motivation in offenders with a learning disability. Journal of Intellectual Disabilities and Offending Behaviour, 5(1), 4–13. https://doi.org/10.1108/JIDOB-08-2013-0016

58.

Peddigrew

McNamara

Andrews

N. C.

Sendzik-Sinclair

(2022). Transitioning to postsecondary education with a learning disability: Identifying similarities and gaps between first-hand experiences and university transition programs. International Journal for Research in Learning Disabilities, 5(2), 55–69.

59.

Piers

Duquette

C. A.

(2016). Facilitating academic and mental health resilience in students with a learning disability. Exceptionality Education International, 26(2), 21–41. https://doi.org/10.5206/eei.v26i2.7739

60.

Polak

Grossman

E. S.

(2024). Exploring psychological distress among individuals with specific learning disabilities: The impact of age, gender, and disability type. Disabilities, 4(4), 1044–1064. https://doi.org/10.3390/disabilities4040065

61.

Polak

Grossman

E. S.

(2024b). The inherent shield: Learning-based stress symptoms among people diagnosed with specific learning disability and its relationship with dispositional mindfulness. Societies, 14(11), 236. https://doi.org/10.3390/soc14110236

62.

Prince-Embury

(2008). The resiliency scales for children and adolescents, psychological symptoms, and clinical status in adolescents. Canadian Journal of School Psychology, 23(1), 41–56. https://doi.org/10.1177/0829573508316592

63.

Raisch

Bailey

Jones

S. M.

(2024). SEL insights: Applying behavioral insights to social and emotional learning programs in global settings. Social and Emotional Learning: Research, Practice, and Policy, 4, 1–10. https://doi.org/10.1016/j.sel.2024.100056

64.

Raver

C. C.

Jones

S. M.

Li‐Grining

Zhai

Bub

Pressler

(2011). CSRP’s impact on low‐income preschoolers’ preacademic skills: Self‐regulation as a mediating mechanism. Child Development, 82(1), 362–378. https://doi.org/10.1111/j.1467-8624.2010.01561.x

65.

Rivara

Menestrel

S. L.

(2016). The scope of the problem. In Rivera

Menestrel

S. L.

(Eds.), Preventing bullying through science, policy, and practice (pp. 31–68). National Academies Press (US). https://doi.org/10.17226/23482

66.

Rose

C. A.

Simpson

C. G.

Preast

J. L.

(2016). Exploring psychosocial predictors of bullying involvement for students with disabilities. Remedial and Special Education, 37(5), 308–317. https://doi.org/10.1177/0741932516629219

67.

Ross

K. M.

Tolan

(2018). Social and emotional learning in adolescence: Testing the CASEL model in a normative sample. The Journal of Early Adolescence, 38(8), 1170–1199. https://doi.org/10.1177/0272431617725198

68.

Schoon

(2021). Towards an integrative taxonomy of social-emotional competences. Frontiers in Psychology, 12, 1–9. https://doi.org/10.3389/fpsyg.2021.515313

69.

Shultz

K. S.

Whitney

Zickar

M. J.

(2020). Measurement theory in action: Case studies and exercises. Routledge. https://doi.org/10.4324/9781003127536

70.

Thapa

Ice

Ramdass

J. V.

Cohen

(2014). Report on construct validity and internal consistency for the qualities of a sustainable and independent learner scale. National School Climate Center.

71.

Transforming Education . (2016). Measuring MESH: Student and teacher surveys curated for the CORE districts. https://files.eric.ed.gov/fulltext/ED605395.pdf

72.

Weissberg

R. P.

Durlak

J. A.

Domitrovich

C. E.

Gullotta

T. P.

(2015). Social and emotional learning: Past, present, and future. In Durlak

J. A.

Domitrovich

C. E.

Weissberg

R. P.

Gullotta

T. P.

(Eds.), Handbook of social and emotional learning: Research and practice (pp. 3–19). The Guilford Press. https://journals.ub.uni-koeln.de/index.php/IJRLD/article/view/1892/1929

73.

White

G. W.

Summers

J. A.

Zhang

Renault

(2014). Evaluating the effects of a self-advocacy training program for undergraduates with disabilities. Journal of Postsecondary Education and Disability, 27(3), 229–244. https://files.eric.ed.gov/fulltext/EJ1048818.pdf

The Winston Essential Skills Survey: A Comprehensive Social-Emotional Assessment for Students With Learning Disabilities

Abstract

Keywords

Introduction

SwLD: The Role of Social Emotional Learning

Limitations in Current SEL Frameworks for SwLD

Current SEL Assessments

Development of the Winston Essential Skills Survey (WESS)

Method

Participants

Measures

WESS 4.0

Devereux Student Strengths Assessment (DESSA)

Procedure

Study and Data Analysis Procedures

Results

Convergent Validity

Student Grade Level and SEL Scores

Discussion

Addressing a Critical Gap

Limitations and Future Research Directions

Implications for Practice and Policy

Footnotes

ORCID iD

Funding

Declaration of Conflicting Interests

Data Availability Statement

IRB Statement

References