Measurement invariance of the Student Risk Screening Scale across time and gender.

Fredrick, Stephanie S.; Drevon, Daniel D.; Jervinsky, M

doi:10.1037/spq0000278

Cited by 3 publications

(6 citation statements)

References 35 publications

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“…Third, we established invariance of internalizing and externalizing scores over time for students at the elementary, middle, and high school levels. Fredrick et al (2019) indicated a single externalizing factor on the SRSS was invariant for regional elementary student samples across time (fall, winter, spring). Extending their study, our analysis provides the first large-scale examination of measurement invariance for both externalizing and internalizing factors with scores appropriately nested within teachers for students at varying school levels including elementary, middle, and high school.…”

Section: Discussionmentioning

confidence: 99%

“…Building on previous SRSS and SRSS-IE inquiry (e.g., Fredrick et al, 2019), in this article we report on factor structure and score reliability of the internalizing and externalizing scales as well as measurement invariance across demographic subgroups and time in a large sample of K–12 U.S. students. Subsequent articles in this programmatic line of inquiry will follow with additional psychometric properties (e.g., predictive validity) informed by IES and NCII Technical Standards.…”

Section: Purposementioning

confidence: 94%

“…Our approach to missing data was similar to other analyses reported with this screener (e.g., Fredrick et al, 2019). For factor structure models for each time point, we included all students (a) with any SRSS-IE screening data at that time point and (b) a reported identification number for the screening teacher (necessary for calculating clustered effects).…”

Section: Missing Datamentioning

confidence: 99%

“…These studies consistently found SRSS scores met recommended reliability thresholds for screening practices with an internal consistency estimate of .83 across 17 studies and called for additional inquiry in high schools. Fredrick, Drevon, and Jervinsky (2019) tested measurement invariance of the SRSS using student samples ( N = 1,220) from three elementary schools in the U.S. Midwest. Results confirmed the factor structure of externalizing behaviors and supported measurement invariance across gender and time.…”

Section: Description and Psychometric Properties Of The Srss-iementioning

confidence: 99%

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Examination of the Factor Structure and Measurement Invariance of the SRSS-IE

Lane,

Oakes,

Buckman

et al. 2023

Remedial and Special Education

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Given the need for a swift, systematic way to identify students with internalizing and externalizing behavior patterns to connect students with appropriate supports, we present new findings of the Student Risk Screening Scale–Internalizing and Externalizing (SRSS-IE). In this article, we examined (a) factor structure of the SRSS-IE and (b) the extent to which measurement invariance holds across gender, special education status, race, and ethnicity, as well as time point (fall, winter, spring) within each school level (elementary, middle, high). The sample includes 124 schools from four U.S. geographic regions in their first year of implementing the SRSS-IE collected over a 10-year span. Using confirmatory factor analysis procedures accounting for the nesting of students within teachers’ classrooms, we confirmed a two-factor structure (internalizing and externalizing) and determined three items may be removed from the instrument while maintaining adequate model fit, pending replication with schools in later stages of screening implementation. All model comparisons between configural, metric, scalar, and strict models met invariance criteria within a time point. Longitudinal models also met these invariance criteria. We discuss educational implications, limitations, and directions for future research.

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Section: Discussionmentioning

confidence: 99%

Section: Purposementioning

confidence: 94%

Section: Missing Datamentioning

confidence: 99%

Section: Description and Psychometric Properties Of The Srss-iementioning

confidence: 99%

See 2 more Smart Citations

Examination of the Factor Structure and Measurement Invariance of the SRSS-IE

Lane,

Oakes,

Buckman

et al. 2023

Remedial and Special Education

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“…Our study highlights a number of challenges present in advancing the use of advanced measurement strategies in education and psychology research. First, large sample sizes are typically needed to conduct IRT, and significant computing power may be necessary for estimating multidimensional models with larger samples (Yao & Boughton, 2007, p. 90) Kamphaus, & Dever, 2015), the SAEBRS (von der Embse et al, 2016) and the SRSS (Fredrick, Drevon, & Jervinsky, 2019). Further, many schools have identified narrow-band mental health issues they wish to target with universal screening efforts, such as district-wide suicide risk screening (Aseltine & DeMartino, 2004;Hallfors et al, 2006), although this is not without controversy (Scherff, Eckert, & Miller, 2005;Fox, Eisenberg, McMorris, Pettingell, & Borowsky, 2013).…”

Section: Multidimensional Irt In Screening Researchmentioning

confidence: 99%

Refining school mental health screening using mixture modeling : evaluation for use in applied settings

Hodgson¹

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Universal socioemotional and behavior screening procedures continue to gain momentum and adoption in schools; however, a major research-to-practice gap remains in translating these data into meaningful interventions for identified students. Despite advances in methodology in school psychology research, parallel efforts have not been made to translate screening data into highly individualized, data-based interventions. The current study sought to apply person-centered analytic strategies to an existing youth mental health screening tool (EIS-Student; Reinke et al., 2020) to determine the extent to which empirically-derived latent subgroups could reveal clinically relevant conclusions in universal screening data. The sample was drawn from existing universal screening data from fall 2018 EIS-Student participants across nine high schools in a single Midwestern county (n = 5,860). Results revealed a stable 7-factor structure for the EIS-Student as well as a 5 latent profiles. Profiles corresponded with meaningfully different scores on subscales of mental health risk from the EIS-Student, and significant relationships were observed between profile membership and academic and behavioral outcomes. Characteristics of each profile are discussed for possible clinical conceptualization and applications within a socioemotional screening program. Implications for universal screening efforts are discussed, including the use of mixture modeling to identify subgroups of student need, the value of person-centered analyses in school intervention decision-making, and considerations for key stakeholders in applied contexts.

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