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Predicting School Suspension Risk from Eighth Through Tenth Grade Using the Strengths and Difficulties Questionnaire

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Abstract

The current study examined (1) if the Strengths and Difficulties Questionnaire (SDQ) would yield alternative factor structures related to either symptoms or strengths with early adolescent students when an exploratory factor analysis (EFA) is used; (2) which scales best predicted suspensions of typically developing early adolescents; and (3) what cutoff scores were useful for identifying youth at risk for suspensions. The current study included 321 parent-student dyads, who were followed from the middle of eighth grade until the end of tenth grade. A symptoms-based EFA yielded three factors: Misbehavior, Isolation, and Agitation. A strength-based EFA yielded three factors, as well: Emotional, Social, and Moral competence. Logistic regression path analyses were used to predict risk of any suspension at the end of eighth, ninth, and tenth grades. The predictor variables were the original SDQ Conduct Problems and Hyperactivity scales in one model, the Misbehavior and Agitation scales in a second model, and the Emotional and Moral competence scales in the third model. Only the Misbehavior scale consistently predicted suspensions across each grade (b = .27, OR = 1.32, p < .001; b = .15, OR = 1.18, p = .029; b = .17, OR = 1.18, p = .029, respectively). For the Misbehavior scale, cutoff scores were established that reflected the 75th and 90th percentile; however, each cutoff demonstrated strengths and weaknesses for identifying at-risk students. The expectation of screening to identify youth at risk for suspensions, a complex school discipline decision, is discussed.

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References

  • Azzopardi, L. M., Camilleri, L., Sammut, F., & Cefai, C. (2016). Examining the model structure of the strengths and difficulties questionnaire (SDQ). Xjenza Online, 4, 100–108. https://doi.org/10.7423/XJENZA.2016.2.01.

    Article  Google Scholar 

  • Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588–606.

    Article  Google Scholar 

  • Berkeley, S., Bender, W., Peaster, L., & Saunders, L. (2009). Implementation of response to intervention: a snapshot of progress. Journal of Learning Disabilities, 42, 85–95. https://doi.org/10.1177/0022219408326214.

    Article  PubMed  Google Scholar 

  • Breunlin, D. C., Cimmarusti, R. A., Bryant-Edwards, T. L., & Hetherington, J. S. (2002). Conflict resolution training as an alternative to suspension for violent behavior. The Journal of Educational Research, 95, 349–357. https://doi.org/10.1080/00220670209596609.

    Article  Google Scholar 

  • Brooks, K., Schiraldi, V., & Ziedenberg, J. (2000). School house hype: two years later. Washington, DC: Justice Policy Institute/Children's Law Center [Online]. Available: http://www.cjcj.org/schoolhousehype/shh2.html.

  • Burke, R., Schuchmann, L. F., & Barnes, B. A. (2006). Common sense parenting trainer guide. Boys Town, NE: Boys Town Press.

    Google Scholar 

  • Catalano, R. F., Berglund, M. L., Ryan, J. A., Lonczak, H. S., & Hawkins, J. D. (2004). Positive youth development in the United States: research findings on evaluations of positive youth development programs. The Annals of the American Academy of Political and Social Science, 591, 98–124. https://doi.org/10.1177/0002716203260102.

    Article  Google Scholar 

  • Christle, C., Jolivette, K., & Nelson, C. (2007). School characteristics related to high school dropout rates. Remedial and Special Education, 28, 325–339. https://doi.org/10.1177/07419325070280060201.

    Article  Google Scholar 

  • Cohen, J., McCabe, L., Michelli, N. M., & Pickeral, T. (2009). School climate: research, policy, practice, and teacher education. Teachers College Record, 111, 180–213.

    Google Scholar 

  • Cook, C. R., Frye, M., Slemrod, T., Lyon, A. R., Renshaw, T. L., & Zhang, Y. (2015). An integrated approach to universal prevention: independent and combined effects of PBIS and SEL on youths’ mental health. School Psychology Quarterly, 30, 166–183. https://doi.org/10.1037/spq0000102.

    Article  PubMed  PubMed Central  Google Scholar 

  • Costello, A. B., & Osborne, J. W. (2005). Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, 10, 1–9.

    Google Scholar 

  • Crawford, L., & Ketterlin-Geller, L. R. (2008). Improving math programming for students at risk: introduction to the special topic issue. Remedial and Special Education, 29, 5–8. https://doi.org/10.1177/0741932507309685.

    Article  Google Scholar 

  • Curran, F. C. (2016). Estimating the effect of state zero tolerance laws on exclusionary discipline, racial discipline gaps, and student behavior. Educational Evaluation and Policy Analysis, 38, 647–668. https://doi.org/10.3102/0162373716652728.

    Article  Google Scholar 

  • Davis, J.A. (1971). Elementary survey analysis. Englewood Cliffs, NJ: Prentice-Hall.

  • De Voe, J. F., Peter, K., Kaufman, P., Miller, A., Noonan, M., Snyder, T. D., & Baum, K. (2004). Indicators of School Crime and Safety: 2004 (NCES 2005–002/NCJ 205290). U.S. Departments of Education and Justice. Washington, DC: U.S. Government Printing Office.

    Google Scholar 

  • Dickey, W. C., & Blumberg, S. J. (2004). Revisiting the factor structure of the strengths and difficulties questionnaire: United States, 2001. Journal of the American Academy of Child & Adolescent Psychiatry, 43, 1159–1167. https://doi.org/10.1097/01.chi.0000132808.36708.a9.

    Article  Google Scholar 

  • Dodge, K. A., Dishion, T. J., & Lansford, J. E. (2006). Deviant peer influences in intervention and public policy for youth. Social Policy Report. Volume 20, Number 1. Society for Research in Child Development.

  • Doyle, M. M., Murphy, J., & Shevlin, M. (2016). Competing factor models of child and adolescent psychopathology. Journal of Abnormal Child Psychology, 44, 1559–1571. https://doi.org/10.1007/s10802-016-0129-9.

    Article  PubMed  Google Scholar 

  • Feil, E. G., Small, J. W., Forness, S. R., Kaiser, A. P., Hancock, T. B., Serna, L. A., et al. (2005). Using different measures, informants, and clinical cut-off points to estimate prevalence of emotional or behavioral disorders in preschoolers: effects on age, gender, and ethnicity. Behavioral Disorders, 30, 375–391. https://doi.org/10.1177/019874290503000405.

    Article  Google Scholar 

  • Fabelo, T., Thompson, M., Plotkin, M., Carmichael, D., Marchbanks, M., and Booth, E. (2011). Breaking schools’ rules: a statewide study of how school discipline relates to students’ success and juvenile justice involvement. New York: Council of State Governments Justice Center. Retrieved from justicecenter.csg.org/resources/juveniles.

  • Fletcher, J., & Wolfe, B. (2009). Long-term consequences of childhood ADHD on criminal activities. The Journal of Mental Health Policy and Economics, 12, 119–138.

    PubMed  PubMed Central  Google Scholar 

  • Flouri, E., Midouhas, E., Joshi, H., & Tzavidis, N. (2015). Emotional and behavioural resilience to multiple risk exposure in early life: the role of parenting. European Child & Adolescent Psychiatry, 24, 745–755. https://doi.org/10.1007/s00787-014-0619-7.

    Article  Google Scholar 

  • Flynn, R. M., Lissy, R., Alicea, S., Tazartes, L., & McKay, M. M. (2016). Professional development for teachers plus coaching related to school-wide suspensions for a large urban school system. Children and Youth Services Review, 62, 29–39. https://doi.org/10.1016/j.childyouth.2016.01.015.

    Article  Google Scholar 

  • Forster, J. J., McDonald, J. W., & Smith, P. W. (1996). Monte Carlo exact conditional tests for log-linear and logistic models. Journal of the Royal Statistical Society. Series B (Methodological), 445–453.

  • Ganao, J. S. D., Silvestre, F. S., & Glenn, J. W. (2013). Assessing the differential impact of contextual factors on school suspension for Black and White students. The Journal of Negro Education, 82, 393–407. https://doi.org/10.7709/jnegroeducation.82.4.0393.

    Article  Google Scholar 

  • Gibson, P., Haight, W., & Kayama, M. (2016). Out-of-school suspensions of Black youths: culture, ability, disability, gender, and perspective. Social Work, 61, 235–243. https://doi.org/10.1093/sw/sww021.

    Article  PubMed  Google Scholar 

  • Gómez-Beneyto, M., Nolasco, A., Moncho, J., Pereyra-Zamora, P., Tamayo-Fonseca, N., Munarriz, M., Salazar, J., Tabarés-Seisdedos, R., & Girón, M. (2013). Psychometric behaviour of the strengths and difficulties questionnaire (SDQ) in the Spanish national health survey 2006. BMC Psychiatry, 13, 95. https://doi.org/10.1186/1471-244X-13-95.

    Article  PubMed  PubMed Central  Google Scholar 

  • Goodman, R. (1997). The Strengths and Difficulties Questionnaire: A research note. Journal of Child Psychology and Psychiatry, 38(5), 581–586.

    Article  Google Scholar 

  • Goodman, A., Lamping, D., & Ploubidis, G. (2010). When to use broader internalising and externalising subscales instead of the hypothesized five subscales on the Strengths and Difficulties Questionnaire (SDQ): data from British parents, teachers, and children. Journal of Abnormal Child Psychology, 38, 1179–1191. https://doi.org/10.1007/s10802-010-9434-x.

    Article  PubMed  Google Scholar 

  • Gross, T., Farmer, R., & Ochs, S. (2018). Evidence-based assessment: best practices, customary practices, and recommendations for field-based assessment. Contemporary School Psychology, Advanced On-line. https://doi.org/10.1007/s40688-018-0186-x.

  • Heilbrun, A., Cornell, D., & Lovegrove, P. (2015). Principal attitudes regarding zero tolerance and racial disparities in school suspensions. Psychology in the Schools, 52, 489–499. https://doi.org/10.1002/pits.21838.

    Article  Google Scholar 

  • Henson, R. K., & Roberts, J. K. (2006). Use of exploratory factor analysis in published research: common errors and some comment on improved practice. Educational and Psychological Measurement, 66, 393–416. https://doi.org/10.1177/0013164405282485.

    Article  Google Scholar 

  • Hooper, D., Coughlin, J., & Mullen, M. R. (2008). Structural equation modeling: Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6, 53–60.

    Google Scholar 

  • Hughes, C., & Dexter, D. (2015). Field studies of RTI programs, revised. RTI Action Network Web, National Center for Learning Disabilities, Retrieved January 18, 2017 from http://www.rtinetwork.org/learn/research.

  • Information for researchers and professionals about the Strengths & Difficulties Questionnaires. (n.d.). Retrieved from http://www.sdqinfo.com/.

  • Kamphaus, R., DiStefano, C., Dowdy, E., Eklund, K., & Dunn, A. (2010). Determining the presence of a problem: comparing two approaches for detecting youth behavioral risk. School Psychology Review, 39, 395–407.

    Google Scholar 

  • Kamphaus, R., & Reynolds, C. (2007). BASC-2 Behavioral and Emotional Screening System. Minneapolis, MN: Pearson.

    Google Scholar 

  • Kettler, R., Glover, T., Albers, C., & Feeney-Kettler, K. (2014). Universal screening in educational settings: evidence-based decision making for schools. Washington, D.C.: American Psychological Association.

    Book  Google Scholar 

  • Kilgus, S. P., Chafouleas, S. M., & Riley-Tillman, T. C. (2013). Development and initial validation of the Social and Academic Behavior Risk Screener for elementary grades. School Psychology Quarterly, 28, 210–226.

    Article  PubMed  Google Scholar 

  • Kilgus, S. P., Sims, W. A., von der Embse, N. P., & Riley-Tillman, T. C. (2015). Confirmation of models for interpretation and use of the Social and Academic Behavior Risk Screener (SABRS). School Psychology Quarterly, 30(3), 335–352. https://doi.org/10.1037/spq0000087.

    Article  PubMed  Google Scholar 

  • Kilgus, S. P., Eklund, K., Nathaniel, P., Taylor, C. N., & Sims, W. A. (2016). Psychometric defensibility of the Social, Academic, and Emotional Behavior Risk Screener (SAEBRS) Teacher Rating Scale and multiple gating procedure within elementary and middle school samples. Journal of School Psychology, 58, 21–39.

    Article  PubMed  Google Scholar 

  • Lane, K., Parks, R., Kalberg, J., & Carter, E. (2007). Systematic screening at the middle school level: score reliability and validity of the student risk screening scale. Journal of Emotional and Behavioral Disorders, 15, 209–222. https://doi.org/10.1177/10634266070150040301.

    Article  Google Scholar 

  • Liu, J. (2004). Childhood externalizing behavior: theory and implications. Journal of Child and Adolescent Psychiatric Nursing, 17, 93–103. https://doi.org/10.1111/j.1744-6171.2004.tb00003.x.

    Article  PubMed  PubMed Central  Google Scholar 

  • Losen, D. J., & Skiba, R. J. (2010). Suspended education: urban middle schools in crisis. Los Angeles, CA: The Civil Rights Project.

    Google Scholar 

  • Lynch, M., & Cicchetti, D. (2002). Links between community violence and the family system: evidence from children’s feelings of relatedness and perceptions of parent behavior. Family Process, 41, 519–532. https://doi.org/10.1111/j.1545-5300.2002.41314.x.

    Article  PubMed  Google Scholar 

  • Mason, W. A., Fleming, C. B., Gross, T. J., Thompson, R. W., Parra, G. R., Haggerty, K. P., & Snyder, J. J. (2016). Randomized trial of parent training to prevent adolescent problem behaviors during the high school transition. Journal of Family Psychology, 30(8), 944–954. https://doi.org/10.1037/fam0000204.supp.

    Article  PubMed  PubMed Central  Google Scholar 

  • Matjasko, J. L., Needham, B. L., Grunden, L. N., & Farb, A. F. (2010). Violent victimization and perpetration during adolescence: developmental stage dependent ecological models. Journal of Youth and Adolescence, 39, 1053–1066. https://doi.org/10.1007/s10964-010-9508-7.

    Article  PubMed  Google Scholar 

  • McHugh, M. L. (2013). The chi-square test of independence. Biochemia Medica, 23, 143–149. https://doi.org/10.11613/bm.2013.018.

    Article  PubMed  PubMed Central  Google Scholar 

  • Mendez, L. M. R., & Knoff, H. M. (2003). Who gets suspended from school and why: A demographic analysis of schools and disciplinary infractions in a large school district. Education and Treatment of Children, 26, 30–51.

    Google Scholar 

  • Neymotin, F. (2014). How parental involvement affects childhood behavioral outcomes. Journal of Family and Economic Issues, 35, 433–451. https://doi.org/10.1007/s10834-013-9383-y.

    Article  Google Scholar 

  • Niclasen, J., Skovgaard, A., Andersen, A., Sømhovd, M., & Obel, C. (2012). A confirmatory approach to examining the factor structure of the Strengths and Difficulties Questionnaire (SDQ): a large scale cohort study. Journal of Abnormal Child Psychology, 41, 355–365. https://doi.org/10.1007/s10802-012-9683-y.

    Article  Google Scholar 

  • Office for Civil Rights. (2016). 2013–2014 civil rights data collection: A first look. Key data highlights on equity and opportunity gaps in our nation’s public schools. Washington, DC: U.S. Department of Education. Retrieved from https://www2.ed.gov/about/offices/list/ocr/docs/2013-14-first-look.pdf.

  • Parker, C., Paget, A., Ford, T., & Gwernan-Jones, R. (2016). ‘.he was excluded for the kind of behaviour that we thought he needed support with…’ A qualitative analysis of the experiences and perspectives of parents whose children have been excluded from school. Emotional and Behavioural Difficulties, 21, 133–151. https://doi.org/10.1080/13632752.2015.1120070.

    Article  Google Scholar 

  • Petersen, I. T., Bates, J. E., Dodge, K. A., Lansford, J. E., & Pettit, G. S. (2015). Describing and predicting developmental profiles of externalizing problems from childhood to adulthood. Development and Psychopathology, 27, 791–818. https://doi.org/10.1017/S0954579414000789.

    Article  PubMed  Google Scholar 

  • Pratt, T. C., & Cullen, F. T. (2000). The empirical status of Gottfredson and Hirschi’s general theory of crime: a meta-analysis. Criminology, 38, 931–964. https://doi.org/10.1111/j.1745-9125.2000.tb00911.x.

    Article  Google Scholar 

  • Reef, J., Diamantopoulou, S., van Meurs, I., Verhulst, F., & van der Ende, J. (2009). Child to adult continuities of psychopathology: a 24-year follow-up. Acta Psychiatrica Scandinavica, 120, 230–238. https://doi.org/10.1111/j.1600-0447.2009.01422.x.

    Article  PubMed  Google Scholar 

  • Reinke, W. M., Thompson, A., Herman, K. C., Holmes, S., Owens, S., Cohen, D., & Copeland, C. (2017). The county schools mental health coalition: a model for community-level impact. School Mental Health, Advanced Online. doi: https://doi.org/10.1007/s12310-017-9227-2, 10, 173, 180.

  • Reynolds, C. R., & Kamphaus, R. W. (2004). BASC-2: Behavior assessment system for children, second edition manual. Circle Pines, MN: American Guidance Service.

    Google Scholar 

  • Richart, D., Brooks, K., & Soler, M. (2003). Unintended Consequences: The impact of "zero tolerance" and other exclusionary policies on Kentucky students. Washington, DC: Building Blocks for Youth.

    Google Scholar 

  • Roberts, B. W., Chernyshenko, O. S., Stark, S., & Goldberg, L. R. (2005). The structure of conscientiousness: an empirical investigation based on seven major personality questionnaires. Personnel Psychology, 58, 103–139. https://doi.org/10.1111/j.1744-6570.2005.00301.x.

    Article  Google Scholar 

  • Rumberger, R. W., & Losen, D. J. (2016). The high cost of harsh discipline and its disparate impact. Civil Rights Project-Proyecto Derechos Civiles.

  • Sasser, T. R., Kalvin, C. B., & Bierman, K. L. (2016). Developmental trajectories of clinically significant attentiondeficit/hyperactivity disorder (ADHD) symptoms from grade 3 through 12 in a high-risk sample: Predictors and outcomes. Journal of Abnormal Psychology, 125, 207–219.

    Article  PubMed  PubMed Central  Google Scholar 

  • Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7(2), 147–177. https://doi.org/10.1037/1082-989X.7.2.147.

    Article  Google Scholar 

  • Skiba, R. J., Peterson, R. L., & Williams, T. (1997). Office referrals and suspension: Disciplinary intervention in middle schools. Education and Treatment of Children, 20, 295–315.

    Google Scholar 

  • Skiba, R. J., Horner, R. H., Chung, C. G., Rausch, M. K., May, S. L., & Tobin, T. (2011). Race is not neutral: A national investigation of African American and Latino disproportionality in school discipline. School Psychology Review, 40, 85–107.

    Google Scholar 

  • Skiba, R., Chung, C., Trachok, M., Baker, T., Sheya, A., & Hughes, R. (2014). Parsing disciplinary disproportionality: contributions of infraction, student, and school characteristics to out-of-school suspension and expulsion. American Educational Research Journal, 51, 640–670. https://doi.org/10.3102/0002831214541670.

    Article  Google Scholar 

  • Suh, S., Suh, J., & Houston, I. (2007). Predictors of categorical at-risk high school dropouts. Journal of Counseling & Development, 85, 196–203. https://doi.org/10.1002/j.1556-6678.2007.tb00463.x.

    Article  Google Scholar 

  • Suh, S., & Suh, J. (2007). Risk factors and levels of risk for high school dropouts. Professional School Counseling, 10, 297–306. https://doi.org/10.5330/prsc.10.3.w26024vvw6541gv7.

    Article  Google Scholar 

  • Thapar, A., Harrington, R., & McGuffin, P. (2001). Examining the comorbidity of ADHD-related behaviours and conduct problems using a twin study design. The British Journal of Psychiatry, 179, 224–229. https://doi.org/10.1192/bjp.179.3.224.

    Article  PubMed  Google Scholar 

  • Thornberry, T. P., & Krohn, M. D. (2000). The self-report method for measuring delinquency and crime. Criminal Justice, 4, 33–83.

    Google Scholar 

  • Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–69. https://doi.org/10.1177/109442810031002.

    Article  Google Scholar 

  • Walker, H., Severson, H., Feil, E. (2014) Systematic Screening for Behavior Disorders (SSBD) technical manual: universal screening for preK-9, 2, Eugene, OR: Pacific Northwest Publishing.

  • Wang, Y., & Chen, H.-J. (2012). Use of percentiles and Z-scores in anthropometry. In V. R. Preedy (Ed.), Handbook of anthropometry: physical measures of human form in health and disease (pp. 29–48). New York: Springer.

    Chapter  Google Scholar 

  • Yong, A. G., & Pearce, S. (2013). A beginner’s guide to factor analysis: focusing on exploratory factor analysis. Tutorials in Quantitative Methods for Psychology, 9, 79–94. https://doi.org/10.20982/tqmp.09.2.p079.

    Article  Google Scholar 

  • Yuan, K. H., & Bentler, P. M. (2000). Three likelihood-based methods for mean and covariance structure analysis with non-normal missing data. Sociological Methodology, 30, 165–200. https://doi.org/10.1111/0081-1750.00078.

    Article  Google Scholar 

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The project described was supported by National Institute on Drug Abuse Grant 1R01DA025651 to Boys Town National Research Institute for Child and Family Studies. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies or the National Institutes of Health.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The authors adhered to the American Psychological Association (APA) ethical standards during the development of this manuscript. All procedures were approved by the human subjects review committees at the University, Community Organization, and the participating school district.

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Gross, T.J., Duncan, J., Kim, S.Y. et al. Predicting School Suspension Risk from Eighth Through Tenth Grade Using the Strengths and Difficulties Questionnaire. Contemp School Psychol 23, 270–289 (2019). https://doi.org/10.1007/s40688-018-00215-y

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