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Evidence-Based Assessment: Best Practices, Customary Practices, and Recommendations for Field-Based Assessment

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Abstract

The purpose of the current review is to examine three frequently employed types of assessment: (a) standardized tests, (b) screening, and (c) behavioral assessment. The aims are to advocate for best practices with evidence-based assessments (EBAs) and provide guidance to implement EBAs within applied settings. Information regarding the current best practices, customary field-based practices, and recommendations for improved practices are provided for each assessment type. Further, a framework is provided for using standardized tests, screening, and behavioral assessment within best practices to determine student intervention needs and potential for disability.

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Notes

  1. Test-retest reliability criteria vary depending upon the purpose of the testing with scores used for diagnostic or classification purposes requiring higher criteria (e.g., above .85) than those used in progress monitoring (Beidas et al. 2015).

References

  • Achenbach, T. M. (2005). Advancing assessment of children and adolescents: commentary on evidence-based assessment of child and adolescent disorders. Journal of Clinical Child and Adolescent Psychology, 34(3), 541–547. https://doi.org/10.1207/s15374424jccp3403_9.

    Article  PubMed  Google Scholar 

  • Aitken, M., Martinussen, R., & Tannock, R. (2017). Incremental validity of teacher and parent symptom and impairment ratings when screening for mental health difficulties. Journal of Abnormal Child Psychology, 45(4), 827–837.

    Article  PubMed  Google Scholar 

  • Alter, P. J., Conroy, M. A., Mancil, G. R., & Haydon, T. (2008). A comparison of functional behavior assessment methodologies with young children: descriptive methods and functional analysis. Journal of Behavioral Education, 17, 200–219. https://doi.org/10.1007/s10864-008-9064-3.

    Article  Google Scholar 

  • American Association on Intellectual and Developmental Disabilities. (2010). Intellectual disability: definition, classification, and systems of supports (11th ed.). Washington, DC: Author.

    Google Scholar 

  • American Educational Research Association, American Psychological Association, National Council on Measurement in Education, Joint Committee on Standards for Educational & Psychological Testing (US). (2014). Standards for educational and psychological testing. Washington, DC: American Educational Research Association.

    Google Scholar 

  • American Psychiatric Association. (2013). Diagnostic and Statistical Manual of Mental Disorders (5th ed.). Washington, DC: American Psychiatric Publishing.

    Book  Google Scholar 

  • Andersen, C. M., Rodriguez, B. J., & Campbell, A. (2015). Functional behavior assessment in schools: current status and future directions. Journal of Behavioral Education, 24, 338–371. https://doi.org/10.1007/s10864-015-9226-z.

  • Armbruster, B. B., Lehr, F., Osborn, J., & Adler, C. R. (2009). Put reading first: the research building blocks of reading instruction: kindergarten through grade 3 (3rd ed.). Washington, D.C.: National Institute for Literacy.

    Google Scholar 

  • Arndorfer, R. E., Miltenberger, R. G., Woster, S. H., Rortvedt, A. K., & Gaffaney, T. (1994). Home-based descriptive and experimental analysis of problem behaviors in children. Topics in Early Childhood Special Education, 14, 64–87.

    Article  Google Scholar 

  • Baker, D. L., Biancarosa, G., Park, B. J., Bousselot, T., Smith, J. L., Baker, S. K., ... & Tindal, G. (2015). Validity of CBM measures of oral reading fluency and reading comprehension on high-stakes reading assessments in grades 7 and 8. Reading and Writing, 28(1), 57–104.

  • Ball, C. R., & O’Connor, E. (2016). Predictive utility and classification accuracy of oral reading fluency and the measures of academic progress for the Wisconsin knowledge and concepts exam. Assessment for Effective Intervention, 41(4), 195–208.

    Article  Google Scholar 

  • Bardon, J. L. (1979). How best to establish the identity of professional school psychology. School Psychology Digest, 8, 162–167.

    Google Scholar 

  • Bardon, J. L. (1994). Will the real school psychologist please stand up: is the past a prologue for the future of school psychology? The identity of school psychology revisited. School Psychology Review, 23, 584–588.

    Google Scholar 

  • Beavers, G. A., Iwata, B. A., & Lerman, D. C. (2013). Thirty years of research on the functional analysis of problem behavior. Journal of Applied Behavior Analysis, 46, 1–21. https://doi.org/10.1002/jaba.30.

  • Beidas, R. S., Stewart, R. E., Walsh, L., Lucas, S., Downey, M. M., Jackson, K., ... Mandell, D.S. (2015). Free, brief, and validated: standardized instruments for low-resource mental health settings. Cognitive Behavioral Practice, 22, 5–19. doi:https://doi.org/10.1016/j.cbpra.2014.02.002.

  • Bergeron, R., & Floyd, R. G. (2013). Individual part score profiles of children with intellectual disability: a descriptive analysis across three intelligence tests. School Psychology Review, 42, 22–38.

  • Bergeron, R., Floyd, R. G., & Shands, E. L. (2008). State eligibility guidelines for mental retardation: an update and consideration of part scores and unreliability of IQs. Education and Training in Developmental Disabilities, 41, 123–131.

    Google Scholar 

  • Berninger, V. W., & Fayol, M. (2008). Why spelling is important and how to teach if effectively. In D. G. Jamieson & J. Balayeva (Eds.), Encyclopedia of language and literacy development. London: Canadian Language and Literacy Research Network.

    Google Scholar 

  • Bickman, L. (2008). A measurement feedback system (MFS) is necessary to improve mental health outcomes. Journal of the American Academy of Child and Adolescent Psychiatry, 47(10), 1114–1119. https://doi.org/10.1097/CHI.0b013e3181825af8.

    Article  PubMed  Google Scholar 

  • Bieber, G., Hulac, D. M., & Schweinle, W. (2015). An independent evaluation of the technical features of the Basic Reading Inventory. Journal of Psychoeducational Assessment, 33(3), 199–209.

    Article  Google Scholar 

  • Bijou, S. W., Peterson, R. F., & Ault, M. H. (1968). A method to integrate descriptive and experimental field studies at the level of data and empirical concepts. Journal of Applied Behavior Analysis, 1, 175–191.

    Article  PubMed  PubMed Central  Google Scholar 

  • Blood, E., & Neel, R. S. (2007). From FBA to implementation: a look at what is actually being delivered. Education and Treatment of Children, 30, 67–80.

  • Bloom, S. E., Iwata, B. A., Fritz, J. N., Roscoe, E. M., & Carreau, A. B. (2011). Classroom application of a trialbased functional analysis. Journal of Applied Behavior Analysis, 44, 19–31. https://doi.org/10.1901/jaba.2011.44-19.

  • Bloom, S. E., Lambert, J. M., Dayton, E., & Samaha, A. L. (2013). Teacher-conducted trial-based functional analyses as the basis for intervention. Journal of Applied Behavior Analysis, 46, 208–218. https://doi.org/10.1002/jaba.21.

    Article  PubMed  Google Scholar 

  • Bracken, B. A. (2000). Maximizing construct relevant assessment: the optimal preschool testing situation. In B. A. Bracken (Ed.), The psychoeducational assessment of preschool children (pp. 34–44). Boston, MA: Allyn and Bacon.

  • Borckardt, J. J., Nash, M. R., Murphy, M. D., Moore, M., Shaw, D., & O’Neil, P. (2008). Clinical practice as natural laboratory for psychotherapy research: a guide to case-based time-series analysis. American Psychologist, 63, 77–95. https://doi.org/10.1037/0003-066X.63.2.77.

  • Boyajian, A. E., DuPaul, G. J., Handler, M. W., Eckert, T. L., & McGoey, K. E. (2001). The use of classroom-based brief functional analyses with preschoolers at-risk for attention deficit hyperactivity disorder. School Psychology Review, 30, 279–293.

    Google Scholar 

  • Braden, J. P., & Shaw, S. R. (2009). Intervention validity of cognitive assessment: knowns, unknowables, and unknowns. Assessment for Effective Intervention, 34, 106–115.

    Article  Google Scholar 

  • Bradshaw, C. P., Buckley, J. A., & Ialongo, N. S. (2008). School-based service utilization among urban children with early onset educational and mental health problems: The squeaky wheel phenomenon. School Psychology Quarterly, 23(2), 169–186.

    Article  Google Scholar 

  • Broussard, C., & Northup, J. (1997). The use of functional analysis to develop peer interventions for disruptive classroom behavior. School Psychology Quarterly, 12, 65–76.

    Article  Google Scholar 

  • Bruhn, A. L., Woods-Groves, S., & Huddle, S. (2014). A preliminary investigation of emotional and behavioral screening practices in K–12 schools. Education and Treatment of Children, 37(4), 611–634. https://doi.org/10.1353/etc.2014.0039.

    Article  Google Scholar 

  • Burke, M. D., Davis, J. L., Lee, Y. H., Hagan-Burke, S., Kwok, O. M., & Sugai, G. (2012). Universal screening for behavioral risk in elementary schools using SWPBS expectations. Journal of Emotional and Behavioral Disorders, 20(1), 38–54.

    Article  Google Scholar 

  • Burns, M. K., Haegele, K., & Petersen-Brown, S. (2014a). Screening for early reading skills: using data to guide resources and instruction. In R. J. Kettler, T. A. Glover, C. A. Albers, & K. A. Feeney-Kettler (Eds.), Universal screening in educational settings: evidence-based decision making for schools (pp. 171–197). Washington, DC: American Psychological Association.

    Chapter  Google Scholar 

  • Burns, M. K., Karich, A. C., Maki, K. E., Anderson, A., Pulles, S. M., Ittner, A., McComas, J. J., & Helman, L. (2014b). Identifying classwide problems in reading with screening data. Journal of Evidence-Based Practices for Schools, 14(2), 186–204.

    Google Scholar 

  • Burns, M. K., Pulles, S. M., Maki, K. E., Kanive, R., Hodgson, J., Helman, L. A., ... & Preast, J. L. (2015). Accuracy of student performance while reading leveled books rated at their instructional level by a reading inventory. Journal of School Psychology, 53(6), 437–445.

  • Canivez, G. L. (2013). Psychometric versus actuarial interpretation of intelligence and related aptitude batteries. In D. H. Saklofske, C. R. Reynolds, & V. L. Schwean (Eds.), The Oxford handbook of child psychological assessments (pp. 84–112). New York: Oxford University Press.

    Google Scholar 

  • Castro-Villarreal, F., Villarreal, V., & Sullivan, J. R. (2016). Special education policy and response to intervention: identifying promises and pitfalls to advance social justice for diverse students. Contemporary School Psychology, 20(1), 10–20.

    Article  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.

    Article  Google Scholar 

  • Chafouleas, S. M., Riley-Tillman, T. C., & Christ, T. J. (2009). Direct Behavior Rating (DBR): an emerging method for assessing social behavior within a tiered intervention system. Assessment for Effective Intervention, 34(4), 195–200. https://doi.org/10.1177/1534508409340391.

    Article  Google Scholar 

  • Christ, T. J., & Nelson, P. M. (2014). Developing and evaluating screening systems: practical and psycho-metric considerations. In R. J. Kettler, T. A. Glover, C. A. Albers, & K. A. Feeney-Kettler (Eds.), Universal screening in educational settings: evidence-based decision making for schools (pp. 79–110). Washington, DC: American Psychological Association.

    Chapter  Google Scholar 

  • Christ, T. J., Scullin, S., Tolbize, A., & Jiban, C. L. (2008). Implications of recent research: curriculum-based measurement of math computation. Assessment for Effective Intervention, 33, 198–205.

    Article  Google Scholar 

  • Cipani, E. (2017). Functional behavioral assessment, diagnosis, and treatment (3rd ed.). New York: Springer Publishing.

  • Clarke, B., Gersten, R., Dimino, J., & Rolfhus, E. (2011). Assessing student proficiency in early number sense (ASPENS) [measurement instrument]. Longmont: Cambium Learning Sopris.

    Google Scholar 

  • Clarke, B., & Shinn, M. R. (2004). A preliminary investigation into the identification and development of early mathematics curriculum-based measurement. School Psychology Review, 33(2), 234–248.

    Google Scholar 

  • Codding, R. S., Hilt-Panahon, A., Panahon, C. J., & Benson, J. L. (2009). Addressing mathematics computation problems: a review of simple and moderate intensity interventions. Education and Treatment of Children, 32(2), 279–312.

    Article  Google Scholar 

  • Cohen-Schotanus, J., & van der Vleuten, C. P. (2010). A standard setting method with the best performing students as point of reference: practical and affordable. Medical Teacher, 32(2), 154–160. https://doi.org/10.3109/01421590903196979.

    Article  PubMed  Google Scholar 

  • Compton, D. L., Fuchs, D., Fuchs, L. S., & Bryant, J. D. (2006). Selecting at-risk readers in first grade for early intervention: a two-year longitudinal study of decision rules and procedures. Journal of Educational Psychology, 98(2), 394–409.

    Article  Google Scholar 

  • Connors, E. H., Arora, P., Curtis, L., & Stephan, S. H. (2015). Evidence-based assessment in school mental health. Cognitive and Behavioral Practice, 22(1), 60–73.

    Article  Google Scholar 

  • Conroy, M. A., Fox, J. J., Bucklin, A., & Good, W. (1996). An analysis of the reliability and stability of the Motivation Assessment Scale in assessing the challenging behaviors of persons with developmental disabilities. Education and Training in Mental Retardation and Developmental Disabilities, 31, 243–250.

    Google Scholar 

  • Cook, C. R., Volpe, R. J., & Livanis, A. (2010). Constructing a roadmap for future universal screening research beyond academics. Assessment for Effective Intervention, 35(4), 197–205. https://doi.org/10.1177/1534508410379842.

    Article  Google Scholar 

  • Cooper, J. O., Heron, T. E., & Heward, W. L. (2007). Applied behavior analysis (2nd ed.). Upper Saddle River: Pearson.

  • Couvillon, M. A., Bullock, L. M., & Gable, R. A. (2009). Tracking behavior assessment methodology and support strategies: a national survey of how schools utilize functional behavioral assessments and behavior intervention plans. Emotional and Behavioural Difficulties, 14, 215–228.

  • 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(1), 5–8. https://doi.org/10.1177/0741932507309685.

    Article  Google Scholar 

  • Daly, E. J., Witt, J. C., Martens, B. K., & Dool, E. J. (1997). A model for conducting a functional analysis of academic performance problems. School Psychology Review, 26, 554–574.

  • Deary, I. J., Strand, S., Smith, P., & Fernandes, C. (2007). Intelligence and educational achievement. Intelligence, 35, 12–21. https://doi.org/10.1016/j.intell.2006.02.001.

    Article  Google Scholar 

  • Demaray, M. K., & Jenkins, L. N. (2011). Relations among academic enablers and academic achievement in children with and without high levels of parent-rated symptoms of inattention, impulsivity, and hyperactivity. Psychology in the Schools, 48(6), 573–586.

    Article  Google Scholar 

  • Deno, S. L., Marston, D., & Mirkin, P. (1982). Valid measurement procedures for continuous evaluation of written expression. Exceptional Children Special Education and Pediatrics: A New Relationship, 48, 368–371.

    Google Scholar 

  • Deno, S. L., Reschly, A. L., Lembke, E. S., Magnusson, D., Callender, S. A., Windram, H., & Stachel, N. (2009). Developing a school-wide progress-monitoring system. Psychology in the Schools, 46, 44–55. https://doi.org/10.1002/pits.20353.

    Article  Google Scholar 

  • DiPerna, J. C. (2006). Academic enablers and student achievement: implications for assessment and intervention services in the schools. Psychology in the Schools, 43(1), 7–17.

    Article  Google Scholar 

  • Dombrowski, S. C., McGill, R. J., & Canivez, G. L. (2017). Hierarchical explanatory factor analyses of the woodcock-Johnson IV full test battery: implications for CHC application in school psychology. School Psychology Quarterly. https://doi.org/10.1037/SPQ0000221.

  • Dowdy, E., Furlong, M., Raines, T. C., Bovery, B., Kauffman, B., Kamphaus, R. W., … & Murdock, J. (2015). Enhancing school-based mental health services with a preventive and promotive approach to universal screening for complete mental health. Journal of Educational and Psychological Consultation, 25(2–3), 178–197. doi:https://doi.org/10.1080/10474412.2014.929951.

  • Drummond, T. (1994). The student risk screening scale (SRSS). Grants Pass: Josephine County Mental Health Program.

  • Duhon, G. J., Noell, G. H., Witt, J. C., Freeland, J. T., Dufrene, B. A., & Gilbertson, D. N. (2004). Identifying academic skill and performance deficits: the experimental analysis of brief assessments of academic skills. School Psychology Review, 33, 429–443.

    Google Scholar 

  • Dvorsky, M. R., Girio-Herrera, E., & Owens, J. S. (2014). School-based screening for mental health in early childhood. In M.D. Weist, N.A. Lever, C.P. Bradshaw, & J.S. Owens (Eds) Handbook of school mental health:  Research, training, practice, and policy (pp. 297-310). New York, NY: Springer US.

  • Eklund, K., & Dowdy, E. (2014). Screening for behavioral and emotional risk versus traditional school identification methods. School Mental Health, 6(1), 40–49.

    Article  Google Scholar 

  • Eisenberg, N., Cumberland, A., Spinrad, T. L., Fabes, R. A., Shepard, S. A., Reiser, M., … & Guthrie, I. K. (2001). The relations of regulation and emotionality to children’s externalizing and internalizing problem behavior. Child Development, 72(4), 1112–1134.

  • Eisenberg, N., Valiente, C., Spinrad, T. L., Cumberland, A., Liew, J., Reiser, M., ... & Losoya, S. H. (2009). Longitudinal relations of children’s effortful control, impulsivity, and negative emotionality to their externalizing, internalizing, and co-occurring behavior problems. Developmental Psychology, 45(4), 988–1008.

  • Epstein, M. H. (2004). Behavioral and emotional rating scale – Second edition. Austin: Pro-Ed.

  • Espin, C., De La Paz, S., Scierka, B. J., & Roelofs, L. (2005). The relationship between curriculum-based measures in written expression and quality and completeness of expository writing for middle school students. The Journal of Special Education, 38, 208–217.

    Article  Google Scholar 

  • Espin, C., Wallace, T., Campbell, H., Lembke, E. S., Long, J. D., & Ticha, R. (2008). Curriculum-based measurement in writing: predicting the success of high-school students on state standards tests. Exceptional Children, 74, 174–193.

    Google Scholar 

  • Fagan, T. K. (2014). Trends in the history of school psychology in the United States. In P. L. Harrison & A. Thomas (Eds.), Best practices in school psychology VI (pp. 383–400). Bethesda: National Association of School Psychologists.

    Google Scholar 

  • Farmer, R. L., & Floyd, R. G. (2016). An evidence-driven, solution-focused approach to functional behavior assessment report writing. Psychology in the Schools, 53(10), 1018–1031.

    Article  Google Scholar 

  • Farmer, R. L. & Floyd, R. G. (2018). Use of intelligence tests in the identification of children and adolescents with intellectual disability. In D. P. Flanagan & E. M. McDonough (Eds.), Contemporary Intellectual Assessment: Theories, Tests, and Issues (4th ed.). New York: Guilford Press.

  • Flanagan, D. P., & Alfonso, V. C. (2017). Essentials of WISC-V assessment. New York: Wiley.

  • Flanagan, D. P., Alfonso, V. C., & Ortiz, S. O. (2012). In D. P. Flanagan & P. L. Harrison (Eds.), Contemporary Intellectual Assessment (3rd ed., pp. 459–483). New York: Guilford Press.

    Google Scholar 

  • Fletcher, J. M., Francis, D. J., Morris, R. D., & Lyon, G. R. (2005). Evidence-based assessment of learning disabilities in children and adolescents. Journal of Clinical Child and Adolescent Psychology, 34, 506–522. https://doi.org/10.1207/s15374424jccp3403_7.

    Article  PubMed  Google Scholar 

  • Floyd, R. G., Phaneauf, R. L., & Wilczynski, S. M. (2005). Measurement properties of indirect assessment methods for functional behavior assessment: a review of research. School Psychology Review, 34, 58–73.

    Google Scholar 

  • Foegen, A., Jiban, C., & Deno, S. (2007). Progress monitoring measures in mathematics: a review of the literature. The Journal of Special Education, 41(2), 121–139.

    Article  Google Scholar 

  • Forness, S. R., Freeman, S. F., Paparella, T., Kauffman, J. M., & Walker, H. M. (2012). Special education implications of point and cumulative prevalence for children with emotional or behavioral disorders. Journal of Emotional and Behavioral Disorders, 20(1), 4–18.

    Article  Google Scholar 

  • Freberg, M. E., Vandiver, B. J., Watkins, M. W., & Canivez, G. L. (2008). Significant factor score variability and the validity of the WISC-III full scale IQ in predicting later academic achievement. Applied Neuropsychology, 15, 131–139. https://doi.org/10.1080/09084280802084010.

  • Fuchs, D., & Fuchs, L. S. (2005). Responsiveness-to-intervention: a blueprint for practitioners, policymakers, and parents. Teaching Exceptional Children, 38(1), 57–61. https://doi.org/10.1177/004005990503800112.

    Article  Google Scholar 

  • Fuchs, L. S., & Fuchs, D. (2007). A model for implementing responsiveness to intervention. Teaching Exceptional Children, 39(5), 14–20.

    Article  Google Scholar 

  • Fuchs, L. S., & Vaughn, S. (2012). Responsiveness-to-intervention: a decade later. Journal of Learning Disabilities, 45(3), 195–203. https://doi.org/10.1177/0022219412442150.

    Article  PubMed  PubMed Central  Google Scholar 

  • Gansle, K. A., Noell, G. H., VanDerHeyden, A. M., Naquin, G. M., & Slider, N. J. (2002). Moving beyond total words written: the reliability, criterion validity, and time cost of alternative measures for curriculum-based measurement in writing. School Psychology Review, 31, 477–497.

    Google Scholar 

  • Gansle, K. A., Noell, G. H., VanDerHeyden, A. M., Slider, N. J., Hoffpauir, L. D., & Whitmarsh, E. L. (2004). An examination of the criterion validity and sensitivity to brief intervention of alternate curriculum-based measures of writing skill. Psychology in the Schools, 41, 291–300.

    Article  Google Scholar 

  • Geary, D. C. (2004). Mathematics and learning disabilities. Journal of Learning Disabilities, 37(1), 4–15.

    Article  PubMed  Google Scholar 

  • Gersten, R., Clarke, B., Jordan, N. C., Newman-Gonchar, R., Haymond, K., & Wilkins, C. (2012). Universal screening in mathematics for the primary grades: beginnings of a research base. Exceptional Children, 78(4), 423–445.

    Google Scholar 

  • Good, R. H., & Kaminski, R. A. (Eds.). (2002). Dynamic indicators of basic early literacy skills (6th ed.). Eugene: Institute for the Development of Educational Achievement.

    Google Scholar 

  • Graham, S., Harris, K. R., & Fink, B. (2000). Is handwriting casually related to learning to write? Treatment of handwriting problems in beginning writers. Journal of Educational Psychology, 92, 620–633.

    Article  Google Scholar 

  • Graham, S. (2013). It all starts here: fixing our national writing crises from the foundation. Columbus: Saperstein Associates Retrieved from https://www.zaner-bloser.com/products/pdfs/C3316_It_All_Starts_Here.pdf.

    Google Scholar 

  • Granpeesheh, D., Dixon, D. R., Tarbox, J., Kaplan, A. M., & Wilke, A. E. (2009). The effects of age and treatment intensity on behavioral intervention outcomes for children with autism spectrum disorders. Research in Autism Spectrum Disorders, 3(4), 1014–1022.

    Article  Google Scholar 

  • Gresham, F. (2015). Evidence-based social skills interventions for students at risk for EBD. Remedial and Special Education, 36, 100–104.

  • Gresham, F. M. (2004). Current status and future directions of school-based behavioral interventions. School Psychology Review, 33(3), 326–343.

    Google Scholar 

  • Gresham, F. M. (2005). Response to intervention: an alternative means of identifying students as emotionally disturbed. Education and Treatment of Children, 28, 328–344.

  • Gresham, F. M. (2007). Response to intervention and emotional and behavioral disorders: best practices in assessment for intervention. Assessment for Effective Intervention, 32(4), 214–222. https://doi.org/10.1177/15345084070320040301.

    Article  Google Scholar 

  • Gresham, F., & Elliott, S. N. (2008). Social Skills Improvement System (SSIS) Rating Scales. Bloomington: Pearson Assessments.

    Google Scholar 

  • Gresham, F. M., & Elliott, S. N. (2014). Social skills assessment and training in emotional and behavioral disorders. In H. M. Walker & F. M. Gresham (Eds.), Handbook of evidence-based practices for emotional and behavioral disorders: applications in schools (pp. 152–172). New York: The Guilford Press.

    Google Scholar 

  • Gresham, F. M., Watson, T. S., & Skinner, C. H. (2001). Functional behavioral assessment: principles, procedures, and future direction. School Psychology Review, 30(2), 156–172.

    Google Scholar 

  • Gross, T. J., Hurley, K. D., Lambert, M. C., Epstein, M. H., & Stevens, A. L. (2015). Psychometric evaluation of the Symptoms and Functioning Severity Scale (SFSS) short forms with out-of-home care youth. Child & Youth Care Forum, 44, 239–249.

    Article  Google Scholar 

  • Hambleton, R. K., & Pitoniak, M. (2006). Setting performance standards. In R. L. Brennan (Ed.), Educational measurement (4th ed., pp. 433–470). Westport: Praeger.

    Google Scholar 

  • Hanley, G. P. (2012). Functional assessment of problem behavior: dispelling myths, overcoming implementation obstacles, and developing new lore. Behavior Analysis in Practice, 5, 54–72. https://doi.org/10.1007/BF03391818.

  • Hanley, G. P., Jin, C. S., Vanselow, N. R., & Hanratty, L. A. (2014). Producing meaningful improvements in problem behavior of children with autism via synthesized analyses and treatments. Journal of Applied Behavior Analysis, 47, 16–36. https://doi.org/10.1002/jaba.106.

  • Hasbrouck, J., & Tindal, G. A. (2006). Oral reading fluency norms: a valuable assessment tool for reading teachers. The Reading Teacher, 59(7), 636–644.

    Article  Google Scholar 

  • Harn, B. A., Stoolmiller, M., & Chard, D. J. (2008). Measuring the dimensions of alphabetic principle on the reading development of first graders: the role of automaticity and unitization. Journal of Learning Disabilities, 41(2), 143–157.

    Article  Google Scholar 

  • Herschell, A. D., Greco, L. A., Filcheck, H. A., & McNeil, C. B. (2002). Who is testing whom? Ten suggestions for managing the disruptive behavior of young children during testing. Interventions in School and Clinic, 37, 140–148. https://doi.org/10.1177/105345120203700302.

  • Hershenberg, R., Drabick, D. A., & Vivian, D. (2012). An opportunity to bridge the gap between clinical research and clinical practice: implications for clinical training. Psychotherapy, 49(2), 123–134. https://doi.org/10.1037/a0027648.

    Article  PubMed  Google Scholar 

  • Hintze, J. M., Ryan, A. L., & Stoner, G. (2003). Concurrent validity and diagnostic accuracy of the dynamic indicators of basic early literacy skills and the comprehensive test of phonological processing. School Psychology Review, 32(4), 541–556.

    Google Scholar 

  • Hirsch, S. E., Bruhn, A. L., Lloyd, J. W., & Katsiyannis, A. (2017). FBAs and BIPs: avoiding and addressing four common challenges related to fidelity. Teaching Exceptional Children, 49(6), 369–379.

    Article  Google Scholar 

  • Humphrey, N., & Wigelsworth, M. (2016). Making the case for universal school-based mental health screening. Emotional and Behavioural Difficulties, 21(1), 22–42. https://doi.org/10.1080/13632752.2015.1120051.

    Article  Google Scholar 

  • Hunsley, J. (2007). Addressing key challenges in evidence-based practice in psychology. Professional Psychology: Research and Practice, 38(2), 113–121. https://doi.org/10.1037/0735-7028.38.2.113.

    Article  Google Scholar 

  • Hunsley, J. (2015). Translating evidence-based assessment principles and components into clinical practice settings. Cognitive and Behavioral Practice, 22(1), 101–109.

    Article  Google Scholar 

  • Hunsley, J., & Mash, E. J. (2005). Introduction to the special section on developing guidelines for the evidence-based assessment (EBA) of adult disorders. Psychological Assessment, 17, 251–255.

    Article  PubMed  Google Scholar 

  • Hunsley, J., & Mash, E. J. (2007). Evidence-based assessment. Annual Review of Clinical Psychology, 3, 29–51. https://doi.org/10.1146/annurev.clinpsy.3.022806.091419.

    Article  PubMed  Google Scholar 

  • Individuals with Disability Education Act Amendments of 1997 [IDEA]. (1997). Retrieved from https://www.congress.gov/105/plaws/publ17/PLAW-105publ17.pdf.

  • Individuals with Disabilities Education Act of 2004, 20 U.S.C. § 1400 et seq. (2004).

  • Ingram, K., Lewis-Palmer, T., & Sugai, G. (2005). Function-based intervention planning: comparing the effectiveness of FBA function-based and non-function-based intervention plans. Journal of Positive Behavior Interventions, 7(4), 224–236.

    Article  Google Scholar 

  • Iwata, B. A., & Dozier, C. L. (2008). Clinical applications of functional analysis methodology. Behavior Analysis in Practice, 1, 3–9. https://doi.org/10.1007/BF03391714.

    Article  PubMed  PubMed Central  Google Scholar 

  • January, S.-A. A., Ardoin, S. P., Christ, T. J., Eckert, T. L., & White, M. J. (2016). Evaluating the interpretations and use of curriculum-based measurement in reading and word lists for universal screening in first and second grade. School Psychology Review, 45, 310–326.

    Article  Google Scholar 

  • Jenkins, L. N., & Demaray, M. K. (2015). An investigation of relations among academic enablers and reading outcomes. Psychology in the Schools, 52(4), 379–389.

    Article  Google Scholar 

  • Jenkins, J. R., Hudson, R. F., & Johnson, E. S. (2007). Screening for at-risk readers in a response to intervention framework. School Psychology Review, 36(4), 582–600.

    Google Scholar 

  • Jensen-Doss, A., & Hawley, K. M. (2010). Understanding barriers to evidence-based assessment: clinician attitudes toward standardized assessment tools. Journal of Clinical Child & Adolescent Psychology, 39(6), 885–896.

    Article  Google Scholar 

  • Jensen-Doss, A., & Hawley, K. M. (2011). Understanding clinicians’ diagnostic practices: attitudes toward the utility of diagnosis and standardized diagnostic tools. Administration and Policy in Mental Health and Mental Health Services Research, 38(6), 476–485.

    Article  PubMed  Google Scholar 

  • Jewell, J., & Malecki, C. K. (2005). The utility of CBM written language indices: an investigation of production-dependent, production- independent, and accurate-production scores. School Psychology Review, 34(1), 27–44.

    Google Scholar 

  • Kamei-Hannan, C., Holbrook, M. C., & Ricci, L. A. (2012). Applying a response-to-intervention model to literacy instruction for students who are blind or have low vision. Journal of Visual Impairment & Blindness, 106(2), 69–80.

    Article  Google Scholar 

  • Kaufman, A. S., & Kaufman, N. L. (2004). Kaufman assessment battery for children, Second Edition. Circle Pines: American Guidance Service.

  • Kaufman, A. S., Raiford, S. E., & Coalson, D. L. (2015). Intelligence testing with the WISC-V. Hoboken: John Wiley & Sons.

    Google Scholar 

  • Kettler, R. J., Glover, T. A., Albers, C. A., & Feeney-Kettler, K. A. (2014). An Introduction to Universal Screening in Educational Settings. In R.J. Kettler, T.A. Glover, C.A. Albers, & K.A. Feeney-Kettler (Eds.) Universal screening in educational settings: evidence-based decision making for schools (pp. 3-16). Washington, D.C.: American Psychological Association.

  • 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(3), 210–226.

    Article  PubMed  Google Scholar 

  • Kilgus, S. P., & Eklund, K. R. (2016). Consideration of base rates within universal screening for behavioral and emotional risk: a novel procedural framework. School Psychology Forum, 10(1), 120–130. https://doi.org/10.1037/pas0000355.

    Article  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. https://doi.org/10.1016/j.jsp.2016.07.001.

    Article  PubMed  Google Scholar 

  • Kilgus, S. P., Methe, S. A., Maggin, D. M., & Tomasula, J. L. (2014). Curriculum-based measurement of oral reading (R-CBM): a diagnostic test accuracy meta-analysis of evidence supporting use in universal screening. Journal of School Psychology, 52(4), 377–405.

    Article  PubMed  Google Scholar 

  • Koegel, L., Matos-Freden, R., Lang, R., & Koegel, R. (2012). Interventions for children with autism spectrum disorders in inclusive school settings. Cognitive and Behavioral Practice, 19(3), 401–412.

    Article  Google Scholar 

  • Kranzler, J. H., Benson, N., & Floyd, R. G. (2015). Using estimated factor scores from a bifactor analysis to examine the unique effects of the latent variables measured by the WAIS-IV on academic achievement. Psychological Assessment, 27, 1402–1416. https://doi.org/10.1037/pas0000119.

  • Kranzler, J. H., Benson, N., & Floyd, R. G. (2016a). Intellectual assessment of children and youth in the United States of America: past, present, and future. International Journal of School & Educational Psychology. https://doi.org/10.1080/21683603.2016.1166759.

  • Kranzler, J. H., & Floyd, R. G. (2013). Assessing intelligence in children and adolescents: A practical guide. New York: Guilford Press.

  • Lambert, J. M., Lopano, S. E., Noel, C. R., & Ritchie, M. N. (2017). Teacher-conducted, latency-based functional analysis as basis for individualized levels systems in a classroom setting. Behavior Analysis in Practice, 10, 422–426. https://doi.org/10.1007/s40617-017-0200-1.

    Article  PubMed  PubMed Central  Google Scholar 

  • Lane, K. L., Oakes, W. P., Carter, E. W., Lambert, W. E., & Jenkins, A. B. (2013). Initial evidence for the reliability and validity of the Student Risk Screening Scale for Internalizing and Externalizing Behaviors at the middle school level. Assessment for Effective Intervention, 39(1), 24–38.

    Article  Google Scholar 

  • Lang, R., O’Reilly, M., Healy, O., Rispoli, M., Lydon, H., Streusand, W., et al. (2012). Sensory integration therapy for autism spectrum disorders: a systematic review. Research in Autism Spectrum Disorders, 6(3), 1004–1018.

    Article  Google Scholar 

  • LeBuffe, P. A., Shapiro, V. B., & Robitaille, J. L. (2017). The Devereux Student Strengths Assessment (DESSA) comprehensive system: screening, assessing, planning, and monitoring. Journal of Applied Developmental Psychology. Advance online publication. doi:https://doi.org/10.1016/j.appdev.2017.05.002.

  • Lembke, E., Deno, S. L., & Hall, K. (2003). Identifying an indicator of growth in early writing proficiency for elementary school students. Assessment for Effective Intervention, 28, 23–35.

    Article  Google Scholar 

  • Lembke, E., & Foegen, A. (2009). Identifying early numeracy indicators for kindergarten and first-grade students. Learning Disabilities Research & Practice, 24(1), 12–20.

    Article  Google Scholar 

  • Lee, Y. S., & Lembke, E. (2016). Developing and evaluating a kindergarten to third grade CBM mathematics assessment. ZDM, 48(7), 1019–1030.

    Article  Google Scholar 

  • Lerman, D. C., & Iwata, B. A. (1993). Descriptive and experimental analyses of variables maintaining self-injurious behavior. Journal of Applied Behavior Analysis, 26, 293–319.

    Article  PubMed  PubMed Central  Google Scholar 

  • Lilienfeld, S. O., Ammirati, R., & David, M. (2012). Distinguishing science from pseudoscience in school psychology: science and scientific thinking as safeguards against human error. Journal of School Psychology, 50(1), 7–36. https://doi.org/10.1016/j.jsp.2011.09.006.

    Article  PubMed  Google Scholar 

  • Luckasson, R., & Schalock, R. L. (2015). Standards to guide the use of clinical judgment in the field of intellectual disability. Intellectual and Developmental Disabilities, 53, 240–251. https://doi.org/10.1352/1934-9556-53.3.240.

    Article  PubMed  Google Scholar 

  • Mace, F. C., & Lalli, J. S. (1991). Linking descriptive and experimental analyses in the treatment of bizarre speech. Journal of Applied Behavior Analysis, 24, 553–562.

    Article  PubMed  PubMed Central  Google Scholar 

  • Maki, K. E., Floyd, R. G., & Roberson, T. (2015). State learning disability eligibility criteria: a comprehensive review. School Psychology Quarterly, 30, 457–469.

    Article  PubMed  Google Scholar 

  • Malecki, C. K., & Jewell, J. (2003). Developmental, gender, and practical considerations in scoring curriculum based measurement writing probes. Psychology in the Schools, 40, 379–390. https://doi.org/10.1002/pits.10096.

  • Marsh, J. K., De Los Reyes, A., & Lilenfeld, S. O. (2017). Leveraging the multiple lenses of psychological science to inform clinical decision-making. [Special section]. Clinical Psychological Science, 5(2), doi: https://doi.org/10.1177/2167702617736853.

  • Martens, B. K., & Gertz, L. E. (2009). Brief experimental analysis: a decision tool for bridging the gap between research and practice. Journal of Behavioral Education, 18, 92–99. https://doi.org/10.1007/s10864-009-9079-4.

    Article  Google Scholar 

  • Mash, E. J., & Hunsley, J. (2005). Evidence-based assessment of child and adolescent disorders: issues and challenges. Journal of Clinical Child and Adolescent Psychology, 34(3), 362–379. https://doi.org/10.1207/s15374424jccp3403_1.

    Article  PubMed  Google Scholar 

  • Mason, J. M. (1984). Early reading from a developmental perspective. In P. D. Pearson, R. Barr, M. L. Kamil, & P. Mosenthal (Eds.), Handbook of reading research (pp. 505–543). New York: Longman.

    Google Scholar 

  • Mather, N., & Wendling, B. J. (2014). Examiners manual. Woodcock-Johnson IV Tests of Cognitive Abilities. Rolling Meadows: Riverside.

    Google Scholar 

  • Mathews, D., & Clark, J. (2003). Successful students’ conceptions of mean, standard deviation, and the central limit theorem. Unpublished paper. Retrieved February 20, 2018. Retrieved from: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.636.8870&rep=rep1&type=pdf.

  • Mazur, J. E. (2013). Learning and behavior (7th ed.). New York: Routledge.

    Google Scholar 

  • McClarty, K. L., Way, W. D., Porter, A. C., Beimers, J. N., & Miles, J. A. (2013). Evidence-based standard setting: establishing a validity framework for cut scores. Educational Researcher, 42(2), 78–88. https://doi.org/10.3102/0013189x12470855.

    Article  Google Scholar 

  • McGill, R. J. (2016). Invalidating the full scale IQ score in the presence of significant factor score variability: clinical acumen or clinical illusion? Archives of Assessment Psychology, 6, 49–79.

  • McMaster, K. L., Du, X., & Petursdottir, A. (2009). Technical features of curriculum-based measures for beginning writers. Journal of Learning Disabilities, 42, 41–60.

    Article  PubMed  Google Scholar 

  • McMaster, K. L., Parker, D., & Jung, P. G. (2012). Using curriculum-based measurement for beginning writers within a response to intervention framework. Reading Psychology, 33(1–2), 190–216. https://doi.org/10.1080/02702711.2012.631867.

    Article  Google Scholar 

  • McMaster, K. L., Ritchey, K. D., & Lembke, E. (2011). Curriculum-based measurement for beginning writers: recent developments and future directions. In T. E. Scruggs & M. A. Mastropieri (Eds.), Assessment and intervention: advances in learning and behavioral disabilities (Vol. 24, pp. 111–148). Bingley: Emerald Group Publishing Limited.

    Chapter  Google Scholar 

  • McNicholas, P., Floyd, R. G., Woods, I. L., Singh, L. J., Manguno, M., & Maki, K. (2017). State special education criteria for identifying intellectual disability: a review following revised diagnostic criteria and Rosa’s law. School Psychology Quarterly. Advance online. doi: http://dx.doi.org/10.1037/spq0000208 

  • Mellard, D. F., McKnight, M., & Woods, K. (2009). Response to intervention screening and progress-monitoring practices in 41 local schools. Learning Disabilities Research & Practice, 24, 186–195. https://doi.org/10.1037/spq0000208.

    Article  Google Scholar 

  • Merrell, K. W. (2010). Better methods, better solutions: developments in school-based behavioral assessment. School Psychology Review, 39(3), 422–426.

    Google Scholar 

  • Meyer, K. A. (1999). Functional analysis and treatment of problem behavior exhibited by elementary school children. Journal of Applied Behavior Analysis, 32, 229–232.

    Article  PubMed  PubMed Central  Google Scholar 

  • Miura Wayman, M., Wallace, T., Wiley, H. I., Tichá, R., & Espin, C. A. (2007). Literature synthesis on curriculum-based measurement in reading. The Journal of Special Education, 41(2), 85–120.

    Article  Google Scholar 

  • Moore, S. A., Widales-Benitez, O., Carnazzo, K. W., Kim, E. K., Moffa, K., & Dowdy, E. (2015). Conducting universal complete mental health screening via student self-report. Contemporary School Psychology, 19(4), 253–267. https://doi.org/10.1007/s40688-015-0062-x.

    Article  Google Scholar 

  • Mueller, M. M., Nkosi, A., & Hine, J. F. (2013). Functional analysis in public schools: a summary of 90 functional analyses. Journal of Behavior Analysis, 44, 807–818. https://doi.org/10.1901/jaba.2011.44-807.

  • National Mathematics Advisory Panel. (2008). Foundations for success: the final report of the national mathematics advisory panel. Washington, DC: U.S. Department of Education.

    Google Scholar 

  • National Research Council. (2002). Mental retardation: Determining eligibility for social security benefits. Washington, DC: National Academy Press.

  • Nichols, P., Twing, J., Mueller, C. D., & O’Malley, K. (2010). Standard-setting methods as measurement processes. Educational Measurement: Issues and Practice, 29(1), 14–24. https://doi.org/10.1111/j.1745-3992.2009.00166.x.

    Article  Google Scholar 

  • Nickerson, R. S. (2000). Null hypothesis significance testing: a review of an old and continuing controversy. Psychological Methods, 5, 241–301.

    Article  PubMed  Google Scholar 

  • Northup, J., Wacker, D., Sasso, G., Steege, M., Cigrand, K., Cook, J., & DeRaad, A. (1991). A brief functional analysis of aggressive and alternative behavior in an outclinic setting. Journal of Applied Behavior Analysis, 24, 509–522. https://doi.org/10.1901/jaba.1991.25-509.

  • Oliver, A. C., Pratt, L. A., & Normand, M. P. (2015). A survey of functional behavior assessment methods used by behavior analysts in practice. Journal of Applied Behavior Analysis, 48, 817–829. https://doi.org/10.1002/jaba.256.

  • O’Neill, R. E., Albin, R. W., Storey, K., Horner, R. H., & Sprague, J. R. (2015). Functional assessment and program development for problem behavior: A practical handbook (3rd ed.). Stamford: Wadsworth Publishing.

  • Ortiz, S. O. (2011). Separating cultural and linguistic differences (CLD) from specific learning disability (SLD) in the evaluation of diverse students: difference or disorder? In D. P. Flanagan & V. C. Alfonso (Eds.), Essentials of specific learning disability identification (pp. 299–325). Hoboken: Wiley.

    Google Scholar 

  • Patterson, S. Y., Smith, V., & Jelen, M. (2010). Behavioural intervention practices for stereotypic and repetitive behaviour in individuals with autism spectrum disorder: a systematic review. Developmental Medicine & Child Neurology, 52(4), 318–327.

    Article  Google Scholar 

  • Pennefather, J. T., & Smolkowski, K. (2015). Validation of the Elementary Social Behavior Assessment: a measure of student prosocial school behaviors. Assessment for Effective Intervention, 40(3), 143–154. https://doi.org/10.1177/1534508414557562.

    Article  Google Scholar 

  • Plavnick, J. B., & Ferreri, S. J. (2011). Establishing verbal repertoires in children with autism using function-based video modeling. Journal of Applied Behavior Analysis, 44(4), 747–766.

    Article  PubMed  PubMed Central  Google Scholar 

  • Prewett, S., Mellard, D. F., Deshler, D. D., Allen, J., Alexander, R., & Stern, A. (2012). Response to intervention in middle schools: practices and outcomes. Learning Disabilities Research & Practice, 27(3), 136–147. https://doi.org/10.1111/j.1540-5826.2012.00359.x.

    Article  Google Scholar 

  • Price, K. W., Meisinger, E. B., Louwerse, M. M., & D’Mello, S. (2016). The contributions of oral and silent reading fluency to reading comprehension. Reading Psychology, 37(2), 167–201.

    Article  Google Scholar 

  • Raines, T. C., Dever, B. V., Kamphaus, R. W., & Roach, A. T. (2012). Universal screening for behavioral and emotional risk: a promising method for reducing disproportionate placement in special education. The Journal of Negro Education, 81(3), 283–296. https://doi.org/10.7709/jnegroeducation.81.3.0283.

    Article  Google Scholar 

  • Reynolds, C. R., & Livingston, R. B. (2014). A psychometric primer for school psychologists. In P. L. Harrison & A. Thomas (Eds.), Best practices in school psychology (6th ed., pp. 281–300). Bethesda: National Association of School Psychologists.

    Google Scholar 

  • Rhodes, R. L., Ochoa, S. H., & Ortiz, S. O. (2005). Assessing culturally and linguistically diverse students: A practical guide. New York: Guilford Press.

  • Rohde, T. E., & Thompson, L. A. (2007). Predicting academic achievement with cognitive ability. Intelligence, 35, 83–92. https://doi.org/10.1016/j.intell.2006.05.004.

    Article  Google Scholar 

  • Rutherford-Becker, K. J., & Vanderwood, M. L. (2009). Evaluation of the relationship between literacy and mathematics skills as assessed by curriculum-based measures. The California School Psychologist, 14, 23–34.

    Article  Google Scholar 

  • Sattler, J. M. (2008). Assessment of children: Cognitive foundations (5th ed.). San Diego: Author.

  • Schill, M. T., Kratochwill, T. R., & Gardner, W. I. (1996). An assessment protocol for selective mutism: analogue assessment using parents as facilitators. Journal of School Psychology, 34, 1–21.

    Article  Google Scholar 

  • Schneider, W. J., & Roman, Z. (2017). Fine-tuning cross-battery assessment procedures: after follow-up testing, use all valid scores, cohesive or not. Journal of Psychoeducational Assessment, 36, 34–54. https://doi.org/10.1177/0734282917722861.

  • Shapiro, E. S. (2004). Academic skills problems: Direct assessment and intervention (3rd ed.). New York: Guilford Publications.

  • Shapiro, E. S. (2011). Academic skills problems: direct assessment and intervention (4th ed.). New York: The Guilford Press.

    Google Scholar 

  • Shapiro, E. S., & Heick, P. F. (2004). School psychologist assessment practices in the evaluation of students referred for social/behavioral/emotional problems. Psychology in the Schools, 41(5), 551–561.

    Article  Google Scholar 

  • Schmidt, J. D., Shanholtzer, A., Mezhoudi, N., Scherbak, B., & Kahng, S. (2014). The utility of brief experimental analysis for problem behavior maintained by escape from demands. Education and Treatment of Children, 37, 229–248. https://doi.org/10.1353/etc.2014.0012.

    Article  Google Scholar 

  • Shumate, E. D., & Wills, H. P. (2010). Classroom-based functional analysis and intervention for disruptive and off-task behavior. Education and Treatment of Children, 33, 23–48. https://doi.org/10.1353/etc.0.0088.

    Article  Google Scholar 

  • Sigafoos, J., Kerr, M., Roberts, D., & Couzens, D. (1993). Reliability of structured interviews for the assessment of challenging behaviour. Behaviour Change, 10, 47–50.

    Article  Google Scholar 

  • Snow, C. E., Burns, M. S., & Griffin, P. (1998). Preventing reading difficulties in young children. Washington, DC: National Research Council.

  • Sotelo-Dynega, M., & Dixon, S. G. (2014). Cognitive assessment practices: a survey of school psychologists. Psychology in the Schools, 51(10), 1031–1045.

    Google Scholar 

  • Sotelo-Dynega, M., Flanagan, D. P., & Alfonso, V. C. (2011). Overview of specific learning disabilities. In D. P. Flanagan & V. C. Alfonso (Eds.), Essentials of specific learning disability identification (pp. 1–20). Hoboken: John Wiley & Sons.

    Google Scholar 

  • Spector, J. E. (2005). How reliable are informal reading inventories? Psychology in the Schools, 42(6), 593–603.

    Article  Google Scholar 

  • St. Peter, C. C., Vollmer, T. R., Bourret, J. C., Borrero, C. S. W., Sloman, K. N., & Rapp, J. T. (2005). On the role of attention in naturally occurring matching relations. Journal of Applied Behavior Anlaysis, 38, 429–443.

    Article  Google Scholar 

  • Stage, S. A., Sheppard, J., Davidson, M. M., & Browning, M. M. (2001). Prediction of first-graders’ growth in oral reading fluency using kindergarten letter fluency. Journal of School Psychology, 39(3), 225–237.

    Article  Google Scholar 

  • Steege, M. W., & Watson, T. S. (2009). Conducting school-based functional behavioral assessments: a practitioner’s guide (2nd ed.). New York: Guilford Press.

    Google Scholar 

  • Steege, M. W., & Scheib, M. A. (2014). Best practices in conducting functional behavioral assessments. In P. L. Harrison & A. Thomas (Eds.), Best practices in school psychology: data-based and collaborative decision making. National Association of School Psychologists: Bethesda.

    Google Scholar 

  • Suhr, J. A. (2015). Psychological assessment: a problem-solving approach. New York: Guilford Press.

    Google Scholar 

  • Suhr, J. A. (2015). Psychological assessment:A problem-solving approach. Baltimore, MD: Guilford Press.

  • Swets, J. A., Dawes, R. M., & Monahan, J. (2000). Psychological science can improve diagnostic decisions. Psychological Science in the Public Interest, 1(1), 1–26. https://doi.org/10.1111/1529-1006.001.

    Article  PubMed  Google Scholar 

  • Therrien, W. J. (2004). Fluency and comprehension gains as a result of repeated reading: a meta-analysis. Remedial and Special Education, 35, 252–261. https://doi.org/10.1177/07419325040250040801.

  • Thompson, R. H., & Iwata, B. A. (2007). A comparison of outcomes from descriptive and functional analyses of problem behavior. Journal of Applied Behavior Analysis, 40, 333–338. https://doi.org/10.1901/jaba.2007.56-06.

  • Tindal, G. (2013). Curriculum-based measurement: a brief history of nearly everything from the 1970s to the present. ISRN Education, 2013, 1–29.

    Article  Google Scholar 

  • Tindal, G., & Parker, R. (1991). Identifying measures for evaluating written expression. Learning Disabilities Research & Practice, 6, 211–218.

    Google Scholar 

  • Torrance, M., & Galbraith, D. (2006). The processing demands of writing. In C. A. MacArthur, S. Graham, & J. Fitzgerald (Eds.), Handbook of writing research (pp. 67–80). New York: Guilford Press.

    Google Scholar 

  • Unruh, S., & McKellar, N. A. (2017). Assessment and intervention for English language learners: translating research into practice. Cham, Switzerland: Springer International Publishing.

    Book  Google Scholar 

  • U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance, What Works Clearinghouse. (2016). Children identified with or at risk for an emotional disturbance topic area intervention report: Functional behavioral assessment-based interventions. Retrieved from https://ies.ed.gov/ncee/wwc/Docs/InterventionReports/wwc_fba_011017.pdf.

  • Valencia, R. R., & Lopez, R. (1992). Assessment of racial and ethnic minority students: Problems and prospects. In M. Zeidner & R. Most (Eds.), Psychological testing: An inside view (pp. 399–439). Palo Alto: Consulting Psychologists Press.

  • VanDerHeyden, A. M. (2013). Universal screening may not be for everyone: using a threshold model as a smarter way to determine risk. School Psychology Review, 42(4), 402–414.

    Google Scholar 

  • Vaughn, M. G., Salas-Wright, C. P., DeLisi, M., & Maynard, B. R. (2014). Violence and externalizing behavior among youth in the United States: is there a severe 5%? Youth Violence and Juvenile Justice, 12(1), 3–21.

    Article  Google Scholar 

  • Veenendaal, N. J., Groen, M. A., & Verhoeven, L. (2015). What oral text reading fluency can reveal about reading comprehension. Journal of Research in Reading, 38(3), 213–225.

    Article  Google Scholar 

  • Wacker, D., Berg, W., Harding, J., & Cooper-Brown, L. (2004). Use of brief experimental analysis in outpatient clinic and home settings. Journal of Behavioral Education, 13, 213–226. https://doi.org/10.1023/B:JOBE.0000044732.42711.f5.

    Article  Google Scholar 

  • Walcott, C. M., Charvat, J., McNamara, K. M., & Hyson, D. M. (2016). School psychology at a glance: 2015 member survey results. Special session presented at the Annual Convention of the National Association of School Psychologists, New Orleans, LA.

  • Walker, H. M. (2015). Perspectives on seminal achievements and challenges in the field of emotional and behavioral disorders. Remedial and Special Education, 36(1), 39–44. https://doi.org/10.1177/0741932514554106.

    Article  Google Scholar 

  • Walker, H. M., Severson, H., Stiller, B., Williams, G., Haring, N., Shinn, M., & Todis, B. (1988). Systematic screening of pupils in the elementary age range at risk for behavior disorders: development and trial testing of a multiple gating model. Remedial and Special Education, 9(3), 8–20.

    Article  Google Scholar 

  • Walker, H. M., Small, J. W., Severson, H. H., Seeley, J. R., & Feil, E. G. (2014). Multiple-gating approaches in universal screening within school and community settings. In R. J. Kettler, T. A. Glover, C. A. Albers, & K. A. Feeney-Kettler (Eds.), School psychology book series. Universal screening in educational settings: evidence-based decision making for schools (pp. 47–75). Washington, DC: American Psychological Association.

    Google Scholar 

  • 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 

  • Watkins, M. W. (2000). Cognitive profile analysis: a shared professional myth. School Psychology Quarterly, 15, 465–479. https://doi.org/10.1037/H0088802.

  • Watkins, M. W., Crosby, E. G., & Pearson, J. L. (2001). Role of the school psychologist: perceptions of school staff. School Psychology International, 22, 64–73.

    Article  Google Scholar 

  • Watson, T. S., & Sterling, H. E. (1998). Brief functional analysis of a vocal tic. Journal of Applied Behavior Analysis, 31, 471–474.

    Article  PubMed  PubMed Central  Google Scholar 

  • Wechsler, D. (2014). Administration and scoring manual. Wechsler Intelligence Scale for Children—Fifth Edition. Bloomington: PsychCorp.

    Google Scholar 

  • White, P. A. (2000). Causal judgment from contingency information: relation between subjective reports and individual tendencies in judgment. Memory & Cognition, 28, 415–526. https://doi.org/10.3758/BF03198557.

  • Wilder, D. A., Chen, L., Atwell, J., Pritchard, J., & Weinstein, P. (2006). Brief functional analysis and treatment of tantrums associated with transitions in preschool children. Journal of Applied Behavior Analysis, 39, 103–107. https://doi.org/10.1901/jaba/2006.66-04.

    Article  PubMed  PubMed Central  Google Scholar 

  • Wilder, D. A., Harris, C., Reagan, R., & Rasey, A. (2007). Functional analysis and treatment of noncompliance by preschool children. Journal of Applied Behavior Analysis, 40, 173–177. https://doi.org/10.1901/jaba.2007.44-06.

    Article  PubMed  PubMed Central  Google Scholar 

  • Youngstrom, E. A. (2013). Future directions in psychological assessment: combining evidence-based medicine innovations with psychology’s historical strengths to enhance utility. Journal of Clinical Child & Adolescent Psychology, 42(1), 139–159. https://doi.org/10.1080/15374416.2012.736358.

    Article  Google Scholar 

  • Youngstrom, E., Loeber, R., & Stouthamer-Loeber, M. (2000). Patterns and correlates of agreement between parent, teacher, and male adolescent ratings of externalizing and internalizing problems. Journal of Consulting and Clinical Psychology, 68(6), 1038–1050.

    Article  PubMed  Google Scholar 

  • Youngstrom, E. A., & Van Meter, A. (2016). Empirically supported assessment of children and adolescents. Clinical Psychology: Science and Practice, 23(4), 327–347.

    Google Scholar 

  • Zimmerman, B. J., & Dibenedetto, M. K. (2008). Mastery learning and assessment: implications for students and teachers in an era of high-stakes testing. Psychology in the Schools, 45(3), 206–216.

    Article  Google Scholar 

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Authors and Affiliations

  1. Western Kentucky University, 3012 Gary A. Ransdell Hall, 1906 College Heights Blvd., #21030, Bowling Green, KY, 42101-1030, USA

    Thomas J. Gross, Ryan L. Farmer & Sarah E. Ochs

  2. Department of Psychology, Western Kentucky University, 3045 Gary A. Ransdell Hall, 1906 College Heights Blvd., #21030, Bowling Green, KY, 42101-1030, USA

    Thomas J. Gross

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  1. Thomas J. Gross
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  2. Ryan L. Farmer
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Correspondence to Thomas J. Gross.

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Gross, T.J., Farmer, R.L. & Ochs, S.E. Evidence-Based Assessment: Best Practices, Customary Practices, and Recommendations for Field-Based Assessment. Contemp School Psychol 23, 304–326 (2019). https://doi.org/10.1007/s40688-018-0186-x

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