MANEJO DE RESIDUOS NO PELIGROSOS

Multiple regression models examined the effects of the inventions on self-report measures of academic behavior, self-efficacy, and life satisfaction, affective engagement, behavioral engagement perceived competence after controlling for pre-test academic behavior and any predictor variable that was significantly related to the dependent variable of interest (i.e., socio-economic status, age, ethnicity, and honors class status). The effect sizes for the self-report academic behavior and psychosocial measures were small and non-significant.

CHAPTER 4

CONCLUSION

This study was designed to examine the separate and joint effects of the SBM and MI interventions, using data from 195 middle school students randomly assigned to one of four conditions: SBM only, MI only, SBM plus MI, and a waitlist control group. This study builds on two previously developed interventions based on the SBM procedures of McQuillin et al. (2012) and the MI intervention based on the procedures of the Strait et al. (2012). Two preliminary studies of these interventions provide support for the efficacy of MI and SBM as interventions to improve math performance in middle school students. The current study found a significant effect for math in the MI+SBM group and found significant effects for MI+SBM, SBM, and MI in Year One of this study for math. However, given the results seem to be unstable from year to year it is uncertain if there is any benefits of combining MI with SBM. Taken together, these findings along with the Strait et. al. and McQullin et. al. studies provide preliminary support for SBM and MI interventions with middle school students in an effort to improve math grades, however further investigation is needed for the MI+SBM intervention.

This study attempts to build on previous studies examining two novel MI and SBM interventions. It aims to replicates results of two previous. Unfortunately, dissemination of interventions often proceeds developing a strong evidence-based. According to guidelines of program dissemination, an intervention is not ready for dissemination until it meets requirements for both efficacy and effectiveness. Replication is required for both and is one of the most

important steps in establishing an empirically supported treatment (Flay et. al., 2005; Valentine et al., 2011). For math grades, during Year One of this study, similar significant effects where found

as McQuillin and Strait, however comparisons across studies remain difficult due to poor measurement of fidelity to procedures. Additionally, this study builds on prior research by comparing three competing interventions, which allows for stronger inferences about respective effectiveness as opposed to only comparing interventions to a no treatment control.

The current study has several major methodological limitations that threaten internal validity and generalization to other settings. First, as was the case with the original Strait et al. (2012) and McQuillin et al. (2012) studies, academic grades as well as several constructs of theoretical interests were measured, but only a handful of these variables were found to be

significant. While effects for math were found to be significant in the Strait and McQuillin studies and the first year of this study, this was the only effect found to be significant fairly consistently. It is unclear why these interventions appear to be affecting math but not other academic areas. Owing to limited experimental control and poor measurement of fidelity it is unclear whether the intervention itself differentially effects math grades or variation with the interventions themselves explains this result. A major assumption of experimental techniques in social science is that procedures will be delivered systematically and identically. Even with randomized studies, when interventions are not followed exactly, so called “broken randomized experiments,” there is little ability for inference (West, 2010, p 19). This study uses an intent to treat design whereas each student was assigned to a group and then analyzed the same regards if they received less than full intervention. This design is a more conservative test of the interventions’ treatment effects, however specific information considering dose-response is lost.

Secondly, middle school students completed self-reports of academic behavior. Several of these measures used in this study demonstrated poor psychometric properties in terms of internal consistency reliability, test retest reliability, and ecological validity (Pearson Correlations between academic grades and measures used in this study). The internal consistency reliability of these measures ranged from α = 0. 48 to 0.87, indicating that some of these scales were not

measuring the same construct of interest. Future studies should employ measures that

psychometrically sound with an emphasis on choosing constructs that aid in the investigation of potential mechanisms of action and are aimed at developing stronger theories of how these interventions may produce change. Additionally, self-reports of behavior are problematic because participants may know what is being measured. Meaning some of the items in this study have high content validity (i.e., how important is it for you to make good grades) resulting in an increased chance of biases responding. Furthermore, in the case of children and adolescents’ behavior, self-reports are best used in the context of other information, such as parent ratings, teacher ratings, and objectively observed behavior. Teacher ratings and classroom observations would substantially improve this line of research. Having teachers who are blind to the

intervention complete ratings on participation and homework completion would be a major methodological step forward. Some of these data may be readily accessible because most teachers give students grades for participation and homework completion, which are ecologically valid measures of positive academic behavior. However, the precision and validity of teacher ratings should be substantiated by direct observation. Future research studies have the opportunity to increase confidence in previous findings by adding multiple measures of multiple constructs of interest. Additionally, measures with both well-established nomological networks (i.e., construct validity evidence) and ecological valid measures that correspond with measures that are germane in real world applications (e.g., parent report, teacher report, standardized testing data, and objective fidelity measurement) would serve to substantiate current self-report data from students (Cronbach & Meehl, 1955; Schmuckle, 2001).

A final consideration was that this program of research has, so far, relied on psychology graduate students (and highly trained recent graduates), as opposed to school personnel, to provide MI. The supply of university students to provide MI is limited, thus threatening the reach of school-based MI. To address this issue, future studies should address the acceptability and

feasibility of recruiting other providers to implement MI. This may include school personnel (including teachers, school administrators, school counselors, school mental health staff), or paraprofessional volunteers to provide MI. Future studies should also address the practical issues (i.e., acceptability, feasibility, and sustainability) of combining MI with SBM. This may include having SBM and MI provided by the same person, or having a coordinator work with separate mentors and MI providers to coordinate their efforts.

R

American Psychiatric Association: Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision. Washington, DC, American Psychiatric Association, 2000.

American Psychological Association Task Force on Evidence-Based Practice for Children and Adolescents. (2008). Disseminating evidence-based practice for children and

adolescents: A systems approach to enhancing care.Washington, DC: American Psychological Association.

Aos, S., Cook, T., Elliott, D., Gottfredson, D., Hawkins, D., Lipsey, M., Tolan, P. (2011). Commentary on Valentine, Jeffrey, et al. Replication in Prevention Science. The

Advisory Board of Bluepreings for Violence Prevention. Prevetion Science, vol 12, 121- 122.

Baekeland, F., & Lundwall, L. (1975). Dropping out of treatment: A critical review. Psychological Bulletin, 82, 738–783.

Chitiyo, M., May, M., Chitiyo, G. (2012). An Assessment of the Evidence-Base for School-Wide Positive Behavior Support. Education and Treatment of Children, Vol. 35, No.1.

Cronbach, L. J. & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52, 281-302.

Barrett, S., Bradshaw, C., & Lewis-Palmer, T. (2008). Maryland state-wide PBIS initiative.

Journal of Positive Behavior Interventions, 10, 1005-114.

Blonigen, B., Harbaugh, W., Singell, L., Horner, R.H., Irvin, L., & Smolkowski, K. 2008). Application of economic analysis to school-wide positive behavior support programs.

Journal of Positive Behavior Interventions

Bradshaw, C., Reinke, W., Brown, L., Bevans, K., & Leaf, P. (2008). Implementation of school- wide positive behavioral interventions and supports (PBIS) in elementary schools: Observations from a randomized trial. Education and Treatment of Children, 31, 1-26. Bradshaw, C. P., Mitchell, M. M., & Leaf, P. J. (2010). Examining the effects of schoolwide

randomized controlled effectiveness trial in elementary schools. Journal Of Positive Behavior Interventions, 12(3), 133-148.

Campbell, D. (1986). Advances in Quasi -Experimental Design and Analysis.

New Directions for Program Evaluation, no.31. San Francisco:Jossey-Bass.

Child Trends (2010). Individualized Education Plans. Retrieved from

www.childtrendsdatabank.org/?q=node/95 Last update: August, 2010


Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155-159.

Cornelius, J. R., Douaihy, A., Bukstein, O. G., Daley, D. C., Wood, S. D., Kelly, T. M., & Salloum, I. M. (2011). Evaluation of cognitive behavioral therapy/motivational enhancement therapy (CBT/MET) in a treatment trial of comorbid MDD/AUD adolescents. Addictive Behaviors, 36(8), 843-848.

Cortina, J. M. (1993). What is coefficient alpha? An examination of theory and applications. Journal Of Applied Psychology, 78(1), 98-104.

Dunlap, K., Goodman, S., McEvoy, C., & Paris, F. (2010). School-wide positive behavioral interventions and supports: Implementation guide. Lansing, MI: Michigan Department of Education.

Flay, B., Biglan, A., Boruch, R., Castro, F., Gottfredson, D., Kellam, S., Moscicki, E., Schinke, S., Valentine, J., & Ji, P. (2005) Standards of evidence: Criteria for efficacy, Effectiveness, and Dissemnation. Prevention Science, vol (10).

Frey, A., Cloud, R., Lee, J., Small, J., Seeley, J., Feil, E., Walker, H., Golly, A. (2011). The Promise of Motivational Interviewing in School Mental Health. School Mental Health. vol (3).

Green, J., McLaughlin, K. A., Alegría, M., Costello, E., Gruber, M. J., Hoagwood, K., & ... Kessler, R. C. (2013). School mental health resources and adolescent mental health service use. Journal Of The American Academy Of Child & Adolescent Psychiatry, 52(5), 501-510.

Greenspoon, P. J., & Saklofske, D. H. (1998). Confirmatory factor analysis of the multidimensional Students' Life Satisfaction Scale. Personality And Individual Differences, 25(5), 965-971.

Grossman, J., Chan, C., Schwartz, S., Rhodes, J. (2012). The Test of Time in School-Based Mentoring: The Role of Relationship Duration and Re-Matching on Academic Outcomes. American Journal of Community Psychology Vol (49) 43–54

Herrera, C., Grossman, J., Kauh, T. J., & McMaken, J. (2011). Mentoring in schools: An impact study of Big Brothers Big Sisters school‐based mentoring. Child Development, 82(1), 346-361.

Huebner. E. S. (1991). Initial development of the student|s life satisfaction scale. School Psychology International, 120-139.

Horner, R., Sugai, G., Smolkowski, K., Todd, A., Nakasato, J., & Esperanza, J., (2009). A Randomized Control Trial of School-wide Positive Behavior Support in Elementary Schools. Journal of Positive Behavior Interventions.

Kazdin, A. E. (2011). Evidence-based treatment research: Advances, limitations, and next steps. American Psychologist, 66(8), 685-698.

Karcher, M. J. (2003). The Hemingway: Measure of adolescent connectedness:

Validation studies. ERIC no. ED477969;ERIC/ CASS no. CG032433. Retrieved June 15th, 2009 from http://www.adoelscentconnectedness.com

Kertes, A., Westra, H. A., Angus, L., & Marcus, M. (2011). The impact of motivational

interviewing on client experiences of cognitive behavioral therapy for generalized anxiety disorder. Cognitive And Behavioral Practice, 18(1), 55-69.

Kloos, B., Hill, J.L., Thomas, E., & Wandersman, A. (2012). Community Psychology: Linking individuals and communities, 3rd Edition. Menlo Park, CA: Cengage Publishing. Leary-Tevyaw, T., Monti, P. (2004). Motivational enhancement and other brief interventions for

adolescent substance abuse: foundations, applications and evaluation. Addiction 99. 63- 75.

McFall, R. M. (1991). Manifesto for Science of Clinical Psychology. The Clinical Psychologist,

44, 75-88

Mercer, S.W., Watt, G., Maxwell, M., & Heaney, D. (2004), The consultation and

relational empathy (CARE) measure: preliminary validity and reliability of an empathy- based consultation process measure. Family Practice, 21, 699-705.

Merikangas, K., He, J., Burstein, M., Swanson, S., Avenevoli, S., Cui, L., Benjet, C., Georgiades, K., Swendsen, J. (2010). Lifetime Prevalence of Mental Disorders in U.S. Adolescents: Results from the National Comorbidity Survey Replication– Adolescent Supplement (NCS-A). Journal of the American Academy of Child & Adolescent Psychiatry. Vol 49 (10).

Miller, L. M., Southam-Gerow, M. A., & Allin, R. r. (2008). Who stays in treatment? Child and family predictors of youth client retention in a public mental health agency. Child & Youth Care Forum, 37(4), 153-170.

Miller, W., Rollnick, S. (2012). Motivational Interviewing: Helping People Change (3rded.) New York: The Guilford Press.

Mueller, M., Phelps, E., Bowers, E., Agans, J., Urban, J., Lerner, R. (2011).Youth development program participation and intentional self-regulation skills: Contextual and individual

bases of pathways to positive youth development. Journal of Adolescence. Vol. 34. 1115–1125.

McQuillin, S., Smith, B., Strait, G. (2011). Randomized Evaluation of a Single Semester Transitional Mentoring Program for First Year Middle School Students: A Cautionary Result For Brief, School-based Mentoring Programs. Journal of Community Psychology Vol. 39, No. 7, 844–859.

Naar-King, S., Suarez, M. (2011). Motivational Interviewing with Adolescents and Young Adults. New York: The Guilford Press.

National Registry of Evidence-Based Programs and Practices. Retrieved on 9/5/2012 http://www.nrepp.samhsa.gov/

Pajares, F. & Miller, D.M. (1995). Mathematics self-efficacy and mathematics

performances: the need for specificity of assessment. Journal of Counseling Psychology. 42(2), 190-198.

Peugh, J. L. (2010). A practical guide to multilevel modeling. Journal Of School Psychology, 48(1), 85-112.

Podell, J. L., Mychailyszyn, M., Edmunds, J., Puleo, C. M., & Kendall, P. C. (2010). The Coping Cat Program for anxious youth: The FEAR plan comes to life. Cognitive And Behavioral Practice, 17(2), 132-141.

Prinz, R., Sanders, M. (2007). Adopting a population-level approach to parenting and family support interventions. Clinical Psychology Review, 27, 739–749

Randolph, K., Johnson, J. (2008). School-based Mentoring Programs: A Review of the Research. Children and Schools Vol (30) Number 3.

Rotheram-Borus, M., Swendeman, D., & Chorpita, B. F. (2012). Disruptive innovations for designing and diffusing evidence-based interventions. American Psychologist, 67(6), 463-476.

Schmuckler, M. A. (2001). What is ecological validity? A dimensional analysis. Infancy, 2(4), 419-436.

Smith, B., Molina, B., Massetti, G., Waschbush, D., Pelham, W. (2007). School-Wide Interventions-The Foundation of a Public Health Approach to School-Based Mental Health. Advances in School-Based Mental Health Interventions: Best Practices and Program Models. Volume 2. Civic Research Institute

Strait, G., Smith, B., McQuillin, S., Terry, J., Swan, S., Malone, P. (2012). A Randomized Trial of Motivational Interviewing to Improve Middle School Students’ Academic

Sugai, G., & Horner, R. (2010). Schoolwide positive behavior supports: Establishing a continuum of evidence-based practices. Journal Of Evidence-Based Practices For Schools, 11(1), 62- 83.

Terry, J., Strait, G., Smith, B., McQuillin, S. (In press). Replication of Motivational Interviewing to Improve Middle School Students’ Academic Performance. Journal of Community Psychology. Excepted & Awaiting Publication.

Urbaniak, G. C., & Plous, S. (2011). Research Randomizer (Version 3.0) [Computer software]. Retrieved on April 22, 2011, from http://www.randomizer.org/

U.S Census School Enrollment Data. http://www.census.gov/hhes/school/ retrieved August 24th 2012.

U.S. Department of Education: http://idea.ed.gov retrieve August 24th 2012.

U.S. Department of Education, Office of Special Education and Rehabilitative Services, Office of Special Education Programs, 28th Annual Report to Congress on the Implementation of the Individuals with Disabilities Education Act, 2006, vol. 1, Washington, D.C., 2009.

U.S. Public Health Service, Report of the Surgeon General’s Conference on Children’s

Mental Health: A National Action Agenda. Washington, DC: Department of

Health and Human Services, 2000.

Valentine, J., Biglan, A., Boruch, R., Gonzalez, F., Collins, L., Flay, B., Kellam, S., Moscicki, E., Schike, S. (2011). Replication in Prevention Science. Prevention Science 12, 103-117. Walker, H. M., & Shinn, M. R. (2002). Structuring School-Based Interventions to Achieve

Integrated Primary, Secondary, and Tertiary Prevention Goals for Safe and Effective Schools. In M. R. Shinn, H. M. Walker, G. Stoner (Eds.) , Interventions for academic and behavior problems II: Preventive and remedial approaches (pp. 1-25). Washington, DC US: National Association of School Psychologist.

Waxman, R. P., Weist, M. D., & Benson, D. M. (1999). Toward collaboration in the growing education-mental health interface. Clinical Psychology Review, 19(2), 239-253.

West, S. G., & Thoemmes, F. (2010). Campbell’s and Rubin’s perspectives on causal inference. Psychological Methods, 15(1), 18-37.

Weist, M. D., Goldstein, J., Evans, S. W., Lever, N. A., Axelrod, J., Schreters, R., & Pruitt, D. (2003). Funding a full continuum of mental health promotion and intervention programs in the school. Journal Of Adolescent Health, 32(Suppl6), 70-78.

Weist, M. D. (2003). Challenges and opportunities in moving toward a public health approach in school mental health. Journal Of School Psychology, 41(1), 77-82.

Wilkinson, L. (1999). Statistical methods in psychology journals: Guidelines and explanations. American Psychologist, 54(8), 594-604.