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Predictors of academic progress for youth with mood and trauma disorders in a day treatment facility

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Predictors of academic progress for youth with mood and trauma disorders in a day treatment facility
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Joel, Tiffany Helga Barbara ( author )
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The purpose of this study was to determine whether significant differences in academic growth exists for students with a Diagnostic and Statistical Manual, Fourth Edition (DSM-IV) diagnosis of Mood Disorder Not Otherwise Specified (MD-NOS) versus students diagnosed with Post Traumatic Stress Disorder (PTSD). A secondary purpose was to analyze whether demographic variables such as sex, minority status, or participation in an Extended School Year (ESY) program were significantly related to academic growth. Academic data for elementary-aged students with a diagnosis of MD-NOS or PTSD were obtained from a day treatment facility in a Western state and analyzed using one-way ANOVAs and factorial ANOVAs. The data reveal that significant difference in Word Recognition growth existed between groups, whereas students with PTSD made significantly more progress. In addition, a significant interaction between minority status and ESY participation was found on Math Computation growth. For students that did not attend ESY, White students made significantly more Math growth than students of color. Amongst students of color, those who did attend ESY made significantly more Math growth than those who did not attend. These findings can be used to help target interventions to increase the academic growth of students with Mood and Trauma disorders, as well as to inform future research.
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Thesis (D.Ed.)--University of Colorado Denver
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by Tiffany Helga Barbara Joel/

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Full Text
PREDICTORS OF ACADEMIC PROGRESS FOR YOUTH WITH MOOD AND
TRAUMA DISORDERS IN A DAY TREATMENT FACILITY
by
TIFFANY HELGA BARBARA JOEL B.A., Thompson Rivers University, 2012
A thesis submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment of the requirements for the degree of Doctor of Psychology School Psychology Program
2017


2017
TIFFANY HELGA BARBARA JOEL
ALL RIGHTS RESERVED


This thesis for the Doctor of Psychology degree by Tiffany Helga Barbara Joel has been approved for the School Psychology program by
Franci Crepeau-Hobson, Chair Bryn Harris, Advisor Colette Haunbaum
Date: May 13, 2017


Joel, Tiffany Helga Barbara (PsyD., School Psychology Program)
Predictors Of Academic Progress For Youth With Mood And Trauma Disorders In A Day Treatment Facility
Thesis directed by Assistant Professor Bryn Harris
ABSTRACT
The purpose of this study was to determine whether significant differences in academic growth exists for students with a Diagnostic and Statistical Manual, Fourth Edition (DSM-IV) diagnosis of Mood Disorder Not Otherwise Specified (MD-NOS) versus students diagnosed with Post Traumatic Stress Disorder (PTSD). A secondary purpose was to analyze whether demographic variables such as sex, minority status, or participation in an Extended School Year (ESY) program were significantly related to academic growth. Academic data for elementary-aged students with a diagnosis of MD-NOS or PTSD were obtained from a day treatment facility in a Western state and analyzed using one-way ANOVAs and factorial ANOVAs. The data reveal that significant difference in Word Recognition growth existed between groups, whereas students with PTSD made significantly more progress. In addition, a significant interaction between minority status and ESY participation was found on Math Computation growth. For students that did not attend ESY, White students made significantly more Math growth than students of color. Amongst students of color, those who did attend ESY made significantly more Math growth than those who did not attend. These findings can be used to help target interventions to increase the academic growth of students with Mood and Trauma disorders, as well as to inform future research.
The form and content of this abstract are approved. I recommend its publication.
Approved: Bryn Harris
IV


TABLE OF CONTENTS
CHAPTER
I. INTRODUCTION...........................................................1
II. LITERATURE REVIEW.....................................................3
III. METHOD...............................................................6
Participants...........................................................6
Measures...............................................................7
Analysis...............................................................8
IV. RESULTS.............................................................10
V. DISCUSSION...........................................................17
VI. LIMITATIONS & FUTURE RESEARCH........................................19
REFERENCES..................................................................21
v


INTRODUCTION
The educational experiences of students with mood and trauma related mental health disorders are often negatively impacted due to the manifestation of symptoms and behaviors related to their diagnosis (Lane, Wehby, Little & Cooley, 2005a; Nelson, Benner, Lane & Smith, 2004; Trout, Nordness, Pierce & Epstein, 2003). When the impact is severe, these students can qualify to receive Special Education (SPED) services under the disability category of Serious Emotional Disability (SED). Many of the students who are eligible for SPED services under the SED category also have a mental health diagnosis from the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV). These diagnoses include but are not limited to Post Traumatic Stress Disorder (PTSD), Major Depressive disorder, Opposition Defiant Disorder and Mood Disorder Not Otherwise Specified (MD-NOS). It should be noted that as this is a retrospective study, the data was recorded during a time period where the DSM-IV was in use, therefore all references to diagnoses will use DSM-IV terminology.
Students with SED commonly exhibit externalizing behaviors including verbal and physical aggression, which can be highly disruptive in a classroom environment. As a result, these students may require additional attention and support from the classroom teacher resulting in an extended portion of class time spent dealing with behaviors and reduced time spent receiving academic instruction (Lane et al., 2005a). This reduction in exposure to the curriculum and educational instruction can in turn contribute to skill deficits in foundational academic areas that impede academic progress over time (Kern & Sokol, 2009).
When the general education classroom is unable to appropriately support students with SED, they are often placed in specialized learning environments such as self-contained
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classrooms or schools, which incorporate therapeutic elements to assist in meeting their specific needs (Lane, Wehby, Little & Cooley, 2005b). Research has shown that many educators in these learning environments believe that student behavior must be managed before they can benefit from academics (Lane et al., 2005b). Therefore, in an effort to support students with SED, teachers may spend less time on academic instruction and more time trying to manage and remediate student behavior (Kern & Sokol, 2009). Even in educational settings that are structured to meet the needs of students with SED, they often still struggle academically and socially. This may be due to students emotional dysregulation and/or verbal and physical aggression, making it difficult for them to meet the demands of the learning environment and develop relationships with their peers and teachers.
The interaction of so many contributing factors is extremely complex, however the end result for these students is often large learning gaps that are difficult to remediate creating poorer educational outcomes (Nelson et al., 2004). It is not surprising then, that students with SED are also at a much greater risk for school dropout, incarceration, reduced employment options and dealing with further mental health problems as adults (Stromopolis etal., 2012; Wagner, 1996).
While the scholastic challenges and outcomes faced by children with SED has been well documented, research on how these students progress and where their strengths and weaknesses lie is much more scarce (Nelson et al., 2004). The knowledge gap becomes even greater when trying to identify what impact specific mental health diagnoses have on a students ability to learn. Yet in order to begin to improve the outcomes for these students, its imperative to have a greater understanding of the relationship between mental health disorders, skill acquisition and educational progress to accurately identify and create
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educational supports that will be most beneficial (Burke, Boon, Hatton, & Bowman-Perrott, 2015). This knowledge can also assist in developing the most appropriate and effective curricula and evidence-based interventions (EBIs) to enable these students to thrive in their learning environments.
The purpose of this study is to analyze the academic progress of students with either Mood Disorder Not Otherwise Specified or Post Traumatic Stress Disorder in the areas of math and reading to identify whether significant differences in academic progress exist between the two groups. The secondary focus of this study is to identify whether any differences in academic progress exist when comparing students based on demographics such as sex, minority status, and participation in an Extended School Year program.
LITERATURE REVIEW
Throughout the literature it has been well documented that students with SED struggle in classroom settings and commonly experience academic deficits in several areas (Lane, et al., 2005a; Nelson et al., 2004; Trout et al., 2003). For example, students may display a variety of behaviors that interfere with their learning including physical and verbal aggression (Esch et al., 2014). Their educational experience may also be impeded by a manifestation of behaviors related to their disability such as hyperactivity, difficulty attending to information or an inability to remain focused on specific tasks, all common symptoms related to SED (Nelson et al., 2004). Students with SED are also likely to experience poorer social relationships with peers and educators due to their externalizing behaviors and difficulty interpreting social situations (Lane et al., 2005a).
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All of these variables contribute to the fact that students with SED consistently experience academic deficits in several foundational areas such as mathematics and reading (Lane, Barton-Arwood, Nelson & Wehby, 2008). Previous research has found that students with SED score significantly lower than students without disabilities on academic achievement tests (Nelson et al., 2004; Wagner, 1995) and the disparity in academic functioning remains when comparing students with SED to students with other disabilities. Nelson et al. (2004) also reported that while the prevalence of academic deficits for children with SED is not known, previous research suggests that between 25-97% of students with SED are one or more years below grade level in one or more subject areas. Wagner (1995) also found that compared to students with any one of the other disability categories recognized by the Individuals with Disabilities Education Act (IDEA), those with SED had the lowest grade point averages (GPAs) and this occurred across all grade levels.
Previous studies have compared the academic progress of students with SED to students with a Specific Learning Disability (SLD) (Anderson, Kutash & Duchnowski, 2001). Findings consistently suggest that while students with SLD may initially have greater academic deficits than students with SED, those with SLD make steady and significant progress over time while the academic deficits of children with SED either remain stable or broaden in scope (Nelson et al., 2004). Anderson et al. (2001) compared the academic levels of Kindergarteners diagnosed with either SED or SLD and found that while those with SLD scored significantly lower in math and reading, by 5th grade the students with SLD had made significant and steady progress and outscored the students with SED in all areas. This tendency for the learning gaps of students with SED to widen in scope as they get older makes the need for early identification and intervention all the more critical.
4


While the educational careers of students with SED are often characterized by impeded academic progress and challenges in the classroom, prospects for students as they enter adulthood continue to be marked by difficulties. Studies have repeatedly shown that individuals with SED are less likely to graduate high school (Strompolis et al., 2012; Wagner, 1996; Trout et al., 2003), and those that do are less likely to attend college when compared to peers with different disabilities or none at all (Nelson et al., 2004). These students also face an elevated risk of incarceration, poverty, poorer employment prospects and an increased likelihood of dealing with additional mental health issues as adults (Trout et al., 2003; Wagner, 1995).
The fact that educational outcomes for students with SED are often poor and markedly more negative than peers with and without disabilities has been known for decades. However, few studies have analyzed which academic areas students with SED have the greatest strengths and deficits in and even fewer studies have looked at what type of interventions are most beneficial (Lane et al., 2005). While Anderson et al. (2001) found that students with SLD make consistent academic progress over time whereas students with SED do not, it is possible that this is due to the fact that EBIs are often developed specifically to address the needs of students with SLD. This brings into question whether the academic progress of students with SED would improve if they received academic interventions that were tailored to their specific academic and behavioral needs.
Another important point to consider is that students with an educational diagnosis of SED may have very different DSM-IV diagnoses (ie. PTSD, MD-NOS, Anxiety, Conduct Disorder, Schizophrenia etc.), yet very little research has been conducted that analyzes the educational experiences of students with a specific DSM-IV diagnosis. This study aims to
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further that conversation by analyzing how students with MD-NOS or PTSD progress academically in the areas of math and reading while subsequently comparing the average progress of each group to identify any potential similarities or differences.
METHOD
A data set was provided by a facility in a Western state that serves as a self-contained school for students with a variety of mental health disorders including MD-NOS and PTSD. The data set included demographic data (sex, minority status, DSM-IV diagnosis, and participation in an extended school year program) along with test scores denoting academic growth in the areas of reading and math. Due to the lack of availability of socioeconomic status for the children in this sample, this variable was not included in the analysis. Participants
Sample selection was done by first compiling a list of all records with a primary diagnosis of MD-NOS or PTSD with intake and discharge dates between January 2010 and August 2015. A five-year time period was used in order to maximize the number of qualifying participants. The facility had an organizational policy of administering the Wide Range Achievement Test 4 (WRAT-4) to students upon intake and subsequently every six months to assess academic progress, therefore records with a stay shorter than 180 days were removed from the sample. Client records that contained a change in primary DSM-IV diagnosis during enrolment were also removed in order to maintain consistency during data analysis.
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The final sample included 61 client records. Seventy-two percent (N=44) of the participants were White, 6.6% (N=4) were Hispanic, 6.6% (N=4) were Black, 6.6% (N=4) were Black/White, 4.9% (N=3) were Hispanic/White, and 3.3% (N=2) were Native American/White. Students with aDSM-IV diagnosis of Post Traumatic Stress Disorder (PTSD) made up 74% (N=45) of the sample while students with a diagnosis of Mood Disorder Not Otherwise Specified (MD-NOS) comprised 26% (N=16). Based on sex, the sample was 74% male (N=45) and 26% female (N=16) and 100% of participants were aged between 6 and 13 years old. The final data set was representative of the overall demographics of the student population. The data set are illustrated in Table 1.
Table 1. Participant Demographics
Variable Group Value N %
DSM-IV 1 PTSD 45 74%
Diagnosis 2 MD-NOS 16 26%
Sex 1 Female 16 26%
2 Male 45 74%
Minority 1 Yes 17 28%
Status 2 No 44 72%
ESY 1 Yes 18 30%
Participant 2 No 43 70%

Table 1.
Measures
Upon entry into the program, students academic skills were assessed using the Wide Range Achievement Test 4 (WRAT-4) (Wilkinson & Robertson, 2006). The WRAT-4 is a norm-referenced measure of basic academic skills in the areas of reading, spelling, and mathematical calculations. The WRAT-4 is designed for use with individuals aged 5 through 94 and consists of four subtests: Math Computation (MC), Sentence Comprehension (SC), Spelling (S), and Word Recognition (WR). The test kit includes two forms, blue and green,
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that can be used alternately with similar results (Dell & Dell, 2008). For the purpose of this study, only the Word Recognition and the Math Computation subtests were included in the analysis and were used as indicators of reading and math skills respectively.
The Math Computation subtest consists of two parts: oral math and math computation. This subtest is designed to measure an individuals ability to perform basic mathematics computations through counting, identifying numbers, solving simple oral problems and calculating written mathematics problems. The Word Reading subtest includes Letter Recognition (15 items) and Word Reading (55 words). This subtest is designed to measure letter and word recognition rather than speech or dictation (Wilkinson & Robertson, 2006, p. 2).
Analysis
For this study the predictor variables were DSM-IV diagnosis, sex, racial minority status and participation in an Extended School Year (ESY) program. These variables were selected because previous studies have shown each to have an impact on educational achievement. A report by Farbman & Kaplan (2005) found that students with extended school years outperformed students at schools with traditional calendars, while research conducted by Voyer & Voyer (2009) shows that female students routinely outperform male students. Finally, racial minority status was included due to the fact that students of color may experience lower academic achievement compared to White students due to the educational opportunity gap (Olszewski-Kubilius & Clarenbach, 2014). All predictor variables were coded to have a standard numerical representation in order to facilitate statistical analysis. The dependent criterion variables were Grade Equivalency (GE) growth scores on the Word Reading and Math Computation subtests of the WRAT-4. GE scores for
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both subtests ranged from below kindergarten level (12.9). GE scores that were already in numeric format were left unchanged for coding purposes. The below kindergarten level score (12.9 was converted to 13 in order to facilitate statistical analysis.
To examine the academic progress of participants based on their DSM-IV category, one-way ANOVAs were conducted for each WRAT-4 subtest. Lastly, differences in academic progress between the two groups were conducted using a combination of factorial ANOVAs and Simple Effects post-hoc tests for each WRAT-4 subtest.
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RESULTS
To answer the question of whether or not a significant difference in academic growth was present between students diagnosed with PTSD and students diagnosed with MD-NOS, a one-way ANOVA was conducted for each of the dependent variables. The dependent variables were Math Computation Growth difference and Word Recognition Growth difference. Both dependent variables were numeric scores representing the difference between expected academic growth in the subject area versus actual academic growth. The actual academic growth was obtained by calculating the amount of time the student attended the program in years (i.e. 0.5 years of attendance would equal 0.5 years of expected growth). Then the students final WRAT-4 scores for Math Computation and Word Recognition respectively were subtracted from their initial scores and the resulting number was used to represent their actual academic growth. The actual versus expected academic growth for students can be seen below in Figure 1 and Figure 2.
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AVERAGE ACTUAL ACADEMIC GROWTH VS. EXPECTED BY DEMOGRAPHICS
Minority
Expected Academic Growth
Actual Word Recognition Growth
Actual Math Computation Growth
Figure 1. Average Actual Academic Growth vs. Expected by Demographics
AVERAGE ACTUAL ACADEMIC GROWTH VS. EXPECTED
BY DIAGNOSIS
94%
Mood Disorder NOS
82%
PTSD
100%
90% Expected
80% Academic Growth
70%
60% Actual Word
50% Recognition
40% Growth
30% Actual Math
20% Computation
10% Growth
0%
Figure 2. Average Actual Academic Growth vs. Expected by Diagnosis
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The primary null hypothesis was that no significant difference in academic growth would exist between the students diagnosed with PTSD and the students diagnosed with MD-NOS. A secondary null hypothesis was that none of the demographic variables would have a significant impact on the academic growth of students. Assumptions for both the one-way ANOVA and the factorial ANOVA were tested and met.
For the primary null hypothesis, no main effect on DSM-IV diagnosis was found for Math Computation Growth. However, a main effect on DSM-IV diagnosis was found on Word Recognition growth, F(l,59) = 8.450, p = 0.005, eta= 0.125, with students diagnosed with MD-NOS making significantly less progress than students diagnosed with PTSD. ANOVA results can be seen in Table 2.
Table 2 TESTS OF BETWEEN-SUBJECTS EFFECTS
Dependent Variable: Word Recognition Growth Difference
Source Type III Sum of Squares df Mean Square F P
Corrected Model 2.414a 1 2.414 8.450 .005
Intercept 6.822 1 6.822 23.880 .000
DSM DIAG 2.414 1 2.414 8.450 .005
Error 16.855 59 .286
Total 23.803 61
Corrected Total 19.269 60
a. R Squared = Table 2 125 (Adjusted R Squared = .110)
To determine whether or not demographic variables were significantly associated with academic growth, a factorial ANOVA was conducted on each of the dependent variables using the following independent variables: sex, ethnic minority status, and participation in an extended school year program. To further analyze the significant interactions effects, Simple Effects post-hoc tests were also conducted.
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For the secondary null hypothesis, an interaction effect of ethnic minority status and participation in the ESY program was found on Word Recognition growth, F(l,57) = 8.110, p = 0.006, eta = 0.144 Factorial ANOVA results can be seen in Table 3.
Table 3 TESTS OF BETWEEN-SUBJECTS EFFECTS Dependent Variable: Word Recognition Growth Difference
Source Type III Sum of Squares df Mean Square F P
Corrected Model 2.770a 3 .923 3.190 .030
Intercept 2.300 1 2.300 7.945 .007
MINORITY STATUS .051 1 .051 .176 .676
ESY .286 1 .286 .988 .324
MINORITY STATUS 2.348 1 2.348 8.110 .006
* ESY
Error 16.500 57 .289
Total 23.803 61
Corrected Total 19.269 60
a. R Squared = .144 (Adjusted R Squared = .099)
Table 3
A Simple Effects post-hoc test was then conducted on Word Recognition growth. Significant differences were seen between White students and non-White students who did not attend an Extended School Year program, F(l,57) = 5.772,p = 0.020, with White students averaging greater academic progress. Differences can be seen in the Pairwise Comparisons table.
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Table 4 PAIRWISE COMPARISONS
Dependent Variable: Word Recognition Growth Difference
95% Confidence Interval for Difference b
ESY (I) NONWHITE STUDENTS (J) WHITE STUDENTS (I-J) Mean Difference Std. Error P Lower Bound Upper Bound
Yes 1 2 .578 .305 .063 -.033 1.188
2 1 -.578 .305 .063 -1.188 .033
No 1 2 -.429 .179 .020 -.787 -.071
2 1 .429 .179 .020 .071 .787

Based on estimated marginal means The mean difference is significant at the .050 level.
b Adjustment for multiple comparisons: Least Significant Difference (equivalent to no adjustment).
Table 4
A significant difference in Word Recognition progress was also found among students of color. At F(1,57) = 5.772, p = 0.031, those in the ESY program made significantly more progress than those who did not attend. Differences can be seen in the Pairwise Comparisons table below.
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PAIRWISE COMPARISONS
Table 5
Dependent Variable: Word Recognition Growth Difference__________________________________
95% Confidence Interval for Difference b
Non- White Students (I) ESY (J) ESY (I-J) Mean Difference Std. Error P Lower Bound Upper Bound
1 Yes No .679 .308 .031 .063 1.295
No Yes -.679 .308 .031 -1.295 -.063
2 Yes No -.328 .174 .065 -.676 .021
No Yes .328 .174 .065 -.021 .676
Based on estimated marginal means The mean difference is significant at the .050 level.
b Adjustment for multiple comparisons: Least Significant Difference (equivalent to no adjustments).
Table 5
For the secondary null hypothesis, an interaction effect of ethnic minority status and participation in an extended school year program was also found on the Math Computation Growth variable, F(1,57) = 5.891,p = 0.018, eta = 0.101. Factorial ANOVA results can be seen in Table 6.
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Table 6 TESTS OF BETWEEN-SUBJECTS EFFECTS
Dependent Variable: Math Computation Growth Difference
Source Type III Sum of Squares df Mean Square F P
Corrected Model 4.661a 3 1.554 2.132 .106
Intercept .450 1 .450 .618 .435
MINORITY STATUS 1.706 1 1.706 2.341 .132
ESY .431 1 .431 .592 .445
MINORITY STATUS ESY 4.292 1 4.292 5.891 .018
Error 41.530 57 .729
Total 48.889 61
Corrected Total 46.191 60
a. R Squared = .101 (Adjusted R Squared = Table 6 .054)
A Simple Effects post-hoc test was then conducted on Math Computation growth. Significant differences were found between White students and non-White students who attended an Extended School Year program, F(l,57) = 5.258, p = 0.26, with White students making greater progress on average. Differences can be seen in the Pairwise Comparisons table.
Table 7
PAIRWISE COMPARISONS
Dependent Variable: Math Computation Growth Difference
95% Confidence Interval for Difference b
ESY (I) NONWHITE (J) WHITE (I-J) Mean Difference Std. Error P Lower Bound Upper Bound
1 1 2 1.110 .484 .026 .141 2.079
2 1 -1.110 .484 .026 -2.079 -.141
2 1 2 -.252 .283 .379 -.819 .316
2 1 .252 .283 .379 -.316 .819
Based on estimated marginal means The mean difference is significant at the .050 level.
b Adjustment for multiple comparisons: Least Significant Difference (equivalent to no adjustments).
Table 7
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DISCUSSION
The purpose of this study was to identify whether any significant differences in academic progress existed for students based on their DSM-IV diagnosis, and secondly, to identify whether significant differences were present when controlling for demographic factors. Results of this study indicate that the average academic progress of students, regardless of diagnosis or demographics did not meet expectations in both Math Computation and Word Recognition. This is consistent with other findings indicating that students with SED have poor educational outcomes (Kern & Sokol, 2009).
On average, the students in this study did not make expected academic progress, however, based on demographic groupings, some performed better than others. Regardless of diagnosis, on average the students in this study did not make the expected academic progress in Word Recognition nor Math Computation. However, students diagnosed with MD-NOS and those diagnosed with PTSD did make similar progress overall in the area of Math Computation, although students with PTSD did make significantly better progress in Word Recognition than those with MD-NOS. In addition, significant differences in Math Computation was present amongst ESY participants with students of color making significantly more gains than White students. An interesting note about this result is that when looking at the average Math Computation progress of minority versus non-minority students as a whole, students of color made more progress on average than White students. This difference does not become significant until the interaction of attending ESY is considered. One interpretation of this result is that the Extended School Year program is more effective with students of color and another possibility is that students of color in ESY started out with stronger mathematics skills than White students.
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Analysis of student progress in the area of Word Recognition shows several significant findings when accounting for demographics. Among students who did not attend ESY, White students made better progress than students of color. Further, students of color who did not attend the ESY program were outperformed by other students of color who attended ESY in the area of Word Recognition. These results show that students of color who did not attend ESY made less progress than students of color who attended ESY and White students who did not attend. This could suggest that ESY is more impactful on students of color or it could suggest that White students entered the program with stronger reading skills. The lack of significant differences among White students regardless of their participation in ESY may suggest the extended school year program has less of an impact on these students.
However, it is difficult to make any assertions about these results due to the fact that socio-economic status data was unavailable for the participants. SES is often a determining factor for students medical insurance, which in turn greatly impacts whether students qualify for ESY, as it is insurance companies that often pay for the program. Therefore, students in ESY may have been eligible for the additional instruction due to financial factors rather than academic need. Without SES data it is also impossible to make a determination about the living conditions of the students in this study, yet children of color on average experience poverty at a greater rate than White students (Patten & Krogstad, 2014). Therefore, the racial minority students in this study had a higher probability rate of living below the poverty line. In which case it is possible that their lower economic status had a significant impact on their reading skills (Carroll, Maughan, Goodman & Meltzer, 2005), which would impact their Word Recognition progress.
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LIMITATIONS & FUTURE RESEARCH
This study had a number of limitations. The sample size of this retrospective study was modest with a total of 61 participants. During the analysis, some of the interactions between demographic variables were close to being significant, but their p values were just above alpha and thus were judged as inconclusive. Replication with a larger sample size might show additional significant interactions. This sample was also unevenly weighted, as there were more males than females, more White students than students of color and more students with a PTSD diagnosis versus MD-NOS. An additional limitation was that the sample was less racially diverse than the local school district population at the time and therefore was not a representative one. Additionally, SES data was not available for the students so we were unable to control for any impact this may have had on students academic progress. Thus, caution is in order about generalizing from this study.
This study did not differentiate groups of students based on their educator so the impact of different teaching styles was not taken into consideration. Behavioral management styles were also not analyzed, therefore student time spent out of class was not known and this may have had a significant impact on the amount of instruction students received.
This study did not include any data regarding the forms of individual therapy or medication students may have been receiving during the school day. This is an important factor to consider as therapeutic services and medical management may have had a significant impact on students mental health, which may have influenced their ability to perform academically.
Future research could include historical data on student academic progress, as there may be a significant relationship between performance in the general education classroom and performance in a day treatment environment. This study used an academic growth
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benchmark that assumed one calendar year would equal one year of academic growth, the same growth model that is used for students without a Serious Emotional Disability.
However, subsequent research could use a less aggressive growth model to compensate for the reduced instructions students receive as a result of behavior management and therapeutic interventions.
Inclusion of information regarding student medical insurance could also benefit future research by allowing analysis of whether having private insurance versus Medicaid impacts a students inclusion in ESY. Future studies could also implement the use of comparison groups to measure whether or not interventions that are tailored to students with behavioral disabilities have a significant impact on academic progress.
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Carroll, J. M., Maughan, B., Goodman, R. and Meltzer, H. (2005), Literacy difficulties and psychiatric disorders: evidence for comorbidity. Journal of Child Psychology and Psychiatry, 46: 524-532. doi:10.1111/j.l469-7610.2004.00366.x
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Kauffman, J. M., & Landrum, T. J. (2006). Children andyouth with emotional and behavioral disorders: a history of their education. Austin, Tex: Pro-Ed.
Kern, L., Hilt-Panahon, A. & Sokol, N. G. (2009). Further examining the triangle tip:
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PREDICTORS OF ACADEMIC PROGRESS FOR YOUTH WITH MOOD AND TRAUMA DISORDERS IN A DAY TREATMENT FACILITY by TIFFANY HELGA BARBARA JOEL B.A., Thompson Rivers University, 201 2 A thesis submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment of the requirements for the degree of Doctor of Psychology School Psychology Program 2017

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ii 2017 TIFFANY HELGA BARBARA JOEL ALL RIGHTS RESERVED

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iii This thesis for the Doctor of Psychology degree by Tiffany Helga Barbara Joel has been approved for the School Psychology program by Franci Crepeau Hobson, Chair Bryn Harris, Advisor Colette Haunbaum Date: May 13, 2017

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iv Joel, Tiffany Helga Barbara (PsyD., School Psychology Program) Predictors Of Ac ademic Progress For Youth With Mood And Trauma Disorders In A Day Treatment Facility Thesis directed by Assistant Professor Bryn Harris ABSTRACT The purpose of this study was to determine whether significant differences in academic growth exists for stude nts with a Diagnostic and Statistical Manual, Fourth Edition ( DSM IV ) diagnosis of Mood Disorder Not Otherwise Specified ( MD NOS) versus students diagnosed with Post Traumatic Stress Disorder (PTSD). A secondary purpose was to analyze whether demographic v ariables such as sex, minority status, or participation in an Extended School Year (ESY) program were significantly related to academic growth. Academic data for elementary aged students with a diagnosis of MD NOS or PTSD were obtained from a day treatment facility in a Western state and analyzed using one way ANOVA s and factorial ANOVA s The data reveal that significant difference in Word Recognition growth existed between groups whereas students with PTSD made significantly more progress In addition, a significant interaction between minority status and ESY participation was found on Math Computation growth. For students that did not attend ESY, White students made significantly more Math growth than students of color. Amongst students of color, those wh o did attend ESY made significantly more Math growth than those who did not attend. These findings can be used to help target interventions to increase the academic growth of students with Mood and Trauma disorders, as well as to inform future research. Th e form and content of this abstract are approved. I recommend its publication Approved: Bryn Harris

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1 INTRODUCTION The educational experiences of students with mood and trauma related menta l health disorders are often negatively impacted due to the manifestation of symptoms and behaviors related to their diagnosis ( Lane, Wehby, Little & Cooley, 2005a; Nelson, Benner, Lane & Smith, 2004; Trout, Nordness, Pierce & Epstein, 2003 ). When the impa ct is severe, these students can qualify to receive Special Education (SPED) services under the disability category of Serious Emotional Disability ( S ED). Many of the students who are eligible for SPED services under the SED category also have a mental he alth diagnosis from the Diagnostic and Statistical Manual of Mental Disorders, 4 th Edition ( DSM IV ) These diagnoses include but are not limited to Post Traumatic Stress Disorder (PTSD), Major Depressive disorder, Opposition Defiant Disorder and Mood Disor der Not Otherwise Specified ( MD NOS ). It should be noted that as this is a retrospective study, the data was recorded during a time period where the DSM IV was in use, therefore all references to diagnoses will use DSM IV terminology. Students with S ED com monly exhibit externali zing behavior s including verbal and physical aggression which can be highly disruptive in a classroom environment As a result these students may require additional attention and support from the classroom teacher resulting in an e xtended portion of class time spent dealing with behaviors and reduced time spent receiving academic instruction (Lane et al., 2005a) This reduction in exposure to the curriculum and educational instruction can in turn contribute to skill deficits in foun dational academic areas that impede academic progress over time ( Kern & Sokol, 2009 ) When the general education classroom is unable to appropriately support students with S ED, they are often placed in specialized learning environments such as self contai n ed

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2 classrooms or schools which i ncorporate therapeutic elements to assist in meeting their specific needs ( Lane, Wehby, Little & Cooley, 2005b ). Research has shown that many e ducators in these learning environments believe that student behavior must be m anaged before they can benefit from academics ( Lane et al., 2005b). Therefore, in an effort to support students with SED, teachers may spend less time on academic instruction and more time trying to manage and remediate student behavior ( Kern & Sokol, 2009 ) Even in educational settings that are structured to meet the needs of students with SED, they often still struggle academically and socially This may be due to students' emotional dysregulation and/or verbal and physical aggression, making it difficult for them to meet the demands of the learning environment and develop relationships with their peers and teachers T he interaction of so many contributing factors is extremely complex, however the end result for these students is often large learning gaps that are difficult to remediate creating poorer educational outcomes ( Nelson et al., 2004 ) It is not surprising then, that s tudents with S ED are also at a much greater risk for school dropout incarceration, reduced employment options and dealing with fu rther men tal health problems as adults ( Stromopolis et al., 2012; Wagner, 1996 ) While the scholastic challenges and outcomes faced by children with S ED has been well documented, research on how these students progress and where their strengths and weakne sses lie is much more scarce (Nelson et al., 2004). The knowledge gap b ecomes even greater when trying to identify what impact specific mental health diagnoses have on a student' s ability to learn. Yet in order to begin to improve the outcomes for these st udents, it's imperative to have a greater understanding of the relationship between mental health disorders, skill acquisition and educational progress to accurately identify and create

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3 educational supports that will be most beneficial ( Burke, Boon, Hatton & Bowman Perrott, 2015). This knowledge can also assist in developing the most appropriate and e ffective curricula and evidence based interventions (EBIs) to enable these students to thrive in their learning environments T he purpose of this study is t o analyze the academic progress of students with either Mood Disorder Not Otherwise Specified or Post Traumatic Stress D isorder in the areas of math and reading to identify whether significant differences in academic progress exist between the two groups The secondary focus of this study is to identify whether any differences in academic progress exist when comparing student's based on demographics such as sex, minority status, and participation in an Extended School Year program LITERATURE REVIEW Throug hout the literature it has been well documented that students with S ED struggle in classroom settings and commo nly experience academic deficits in several areas ( Lane, et al., 2005a; Nelson et al., 2004; Trout et al., 2003 ) For example, students may displ ay a variety of behaviors that interfere with their learning including physical and verbal aggression ( Esch et al., 2014). Their educational experience may also be impeded by a manifestation of behaviors related to their disability such as hyperactivity, d ifficulty attending to information or an inability to remain focused on specific tasks, a ll common symptom s related to S ED (Nelson et al., 2004). Students with S ED are also likely to experience poorer social relationships with peers and educators due to th eir externalizing behaviors and difficulty interpreting social situations (Lane et al., 2005a)

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4 All of these variables contribute to the fact that students with S ED consistently experience academic deficits in several foundational areas such as mathematic s and reading ( Lan e, Barton Arwood, Nelson & Wehby, 2008). P revious research has found that students with S ED score significantly lower than students without disabilities on academic achievement tests ( Nelson et al., 2004 ; Wagner, 1995 ) and the disparity i n academic functioning remains when comparing students with S ED to students with other disabilities. Nelson et al. (2004) also reported that while the prevalence of academic deficits for children with S ED is not known, previous research suggests that betw een 25 97% of students with S ED are one or more years below grade level in one or more subject areas Wagner (1995) also f ound that compared to students with any one of the othe r disability categories recognized by the Individuals with Disabilities Educati on Act ( IDEA ) those with S ED had the lowest grade point averages ( GPAs ) and this occurred across all grade levels Previous studies have compared the academic progress of students with S ED to students with a Specific Learning Disability (SLD) ( Anderson, Kutash & Duchnowski, 2001) Findings consistently suggest that while students with SLD may initially have greater academic deficits than students with S ED, those with SLD make steady and significant progress over time while the academic deficits of childr en with S ED either remain stable or broaden in scope ( Nelson et al., 2004 ) Anderson et al. (2001 ) compared the academic levels of Kindergarteners diagnosed with either S ED or SLD and found that while those with SLD score d significantly lower in math and r eading, by 5 t h grade the students with SLD had made significant and steady progress and outscored the students with S ED in all areas. This tendency for the learning gaps of students with S ED to widen in scope as they get older makes the need for early iden tification and intervention all the more critical

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5 While the educational careers of students with S ED are often characterized by impeded academic progress and challenges in the classroom prospects for students as they enter adulthood continue to be marke d by difficulties. Studies have repeatedly shown that individuals with S ED are less likely to graduate high school ( Strompolis et al., 2012; Wagner, 1996; Trout et al., 2003) and those that do are less likely to attend college when compared to peers with different disabilities or none at all (Nelson et al., 2004). These students also face an elevated risk of incarceration, poverty, poorer employment prospects and an increased likelihood of dealing wi th additional mental health issues as adults ( Trout et al ., 2003; Wagner, 1995 ) The fact that educational outcomes for students with S ED are often poor and markedly more negative than peers with and without disabilities has been known for decades. However, few studies have analyzed which academic areas stude nts with S ED have the greatest strengths and deficits in and even fewer studies have looked at what type of interventions are most beneficial (Lane et al., 2005 ) While Anderson et al. (2001) found that students with SLD make consistent academic progress o ver time whereas students with S ED do not, it is possible that this is due to the fact that EBIs are often developed specifically to address the needs of students with SLD. This brings into question whether the academic progress of students with S ED would improve if they received academic interventions that were tailored to their specific academic and behavioral needs Another important point to consider is that students with an educational diagnosis of S ED may have very different DSM IV diagnoses (ie. PTS D, MD NOS Anxiety Conduct Disorder, Schizophrenia etc.), yet very little research has been conducted that analyzes the educational experiences of students with a specific DSM IV diagnosi s. This study aims to

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6 further that conversation by analyzing how stu dents with MD NOS or PTSD progress academically in the areas of math and reading while subsequently comparing the average progress of each group to identify any potential similarities or differences. METHOD A data set was provided by a facility in a We stern state that serves as a self contained school for students with a variety of ment al health disorders including MD NOS and PTSD The data set included demographic data (sex, minority status, DSM IV diagnosis, and participation in an extended school yea r program) along with test scores denoting academic growth in the areas of reading and math Due to the lack of availability of socioeconomic status for the children in this sample, this variable was not included in the analysis. Participants Sample selec tion was done by first compiling a list of all records with a primary diagnosis of MD NOS or PTSD with intake and discharge date s between Jan uary 2010 and August 2015 A five year time period was used in order to maximize the number of qualifying participa nts. The facility had an organizational policy of administering the Wide Range Achievement Test 4 (WRAT 4) to students upon intake and subsequently every six months to assess academic progress therefore records with a stay shorter than 180 days were rem oved from the sample. Client records that contained a change in pr imary DSM IV d iagnosis during enrolment were also removed in order to maintain consistency during data analysis.

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7 The final samp le included 61 client records Seventy two percent (N= 44 ) of t he participants were White, 6.6 % (N= 4 ) were Hispanic 6.6 % (N=4 ) were Black, 6.6 % (N=4) were Black/White 4.9 % (N=3) were Hispanic/White, and 3 .3 % (N=2) were Native American/White St udents with a DSM IV diagnosis of Post Traumatic Stress Disorder (PTSD) m ade up 74% (N=4 5) of the sample while students with a diagnosis of Mood Disorder Not Otherwise Specified ( MD NOS ) comprised 26% (N=16). Based on sex, the sample was 7 4 % male (N= 4 5 ) and 2 6 % female (N=1 6 ) and 100% of participants were aged between 6 and 1 3 y ears old. The final data set was representative of the overall demographics of the student population. T he data set are illustrated in Table 1. Table 1. Participant Demographics Variable Group Value N % DSM IV Diagnosis 1 PTSD 4 5 74% 2 MD N OS 16 26% Sex 1 Female 16 26% 2 Male 45 74% Minority Status 1 2 Yes No 17 44 28% 72% ESY Participant 1 2 Yes No 18 43 30% 70% Table 1. Measures Upon entry into the program students' academic skills were assessed using the Wide Range Ac hievement Test 4 (WRAT 4) (Wilkinson & Robertson, 2006). The WRAT 4 is a norm referenced measure of basic academic skills in the areas of reading, spelling, and mathematical calculations. The WRAT 4 is designed for use with individuals aged 5 thr ough 94 and consists of four subtests: Math Computation (MC), Sentence Comprehension (SC), Spelling (S), and Word Recognition (WR). The test kit includes two forms, blue and green,

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8 that can be used alternately with similar results (Dell & Dell, 2008). For the purp ose of this study, only the Word Recognition and the Math Computation subtests were included in the analysis and were used as indicators of reading and math skills respectively. The Math Computation subtest consists of two parts: oral math and math comput ation. This subtest is designed to measure an individual's "ability to perform basic mathematics computations through counting, identifying numbers, solving simple oral problems and calculatin g written mathematics problems" The Wo rd Reading subtest includ es L e tter Recognition (15 items) and Word R eading (55 words). This subtest is designed to measure letter and word recognition r ather than speech or dictation (Wilkinson & Robertson, 2006, p. 2). Analysis For this study the predictor variables were DSM IV diagnosis sex, racial minority status and participation in an Extended School Year (ESY) program These variables were selected because previous studies have shown each to have an impact on educational achievement. A report by Farbman & Kaplan (2005) fou nd that students with extended school years outperformed students at sch ools with traditional calendars, while research conducted by Voyer & Voyer (2009) shows that female students routinely outperform male students. Finally, racial minority status was inc luded due to the fact that students of color may experience lower ac ademic achievement compared to W hite students due to the educational opportunity gap (Olszewski Kubilius & Clarenbach, 2014 ) All predictor variables were coded to have a standard numerical representation in order to facilitate statistical analysis. The dependent criterion variables were Grade E quivale ncy (GE) growth scores on the Word Reading and Math Computation subtests of the WRAT 4. GE score s for

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9 both subtest s ranged from below kindergarten level (12.9 ). GE scores that were already in numeric format were left unchanged for coding purposes T he below kindergarten level score (12.9 was converted to 13 in order to facilitate statistical analysis To examine the academic progress of participants based on the ir DSM IV category, one way AN OVAs were conducted fo r each WRAT 4 subtest. Lastly, differences in academic progres s between the two groups were conducted using a combi nation of factorial ANOVAs and Simple E ffects post hoc tests for each WRAT 4 subtest.

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10 RESULTS To answer the question of whether or not a significant difference in academic growth was present between s tudents diagnosed with PTSD and students diagnosed with MD NOS a one way ANOVA was conducted for each of the dependent variables. The dependent variable s were Math Computation Growth d ifference and Word Recognition Growth difference. Both dependent variab les were numeric scores representing the difference between expected academic growth in the subject area versus actual academic growth. The actual academic growth was obtained by calculating the amount of time the student attended the program in years ( i.e 0.5 years of attendance would equal 0.5 years of expected growth). Then the student's final WRAT 4 scores for Math Computation and Word Recognition respectively were subtracted from their initial scores and the resulting number was used to represent thei r actual academic growth. The actual versus expect ed academic growth for students can be seen below in Figure 1 and Figure 2.

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11 85% 85% 85% 85% 85% 85% 42% 63% 53% 60% 68% 54% 72% 61% 54% 68% 83% 57% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Minority NonMinority ESY Non-ESY Female Male AVERAGE ACTUAL ACADEMIC GROWTH VS. EXPECTED BY DEMOGRAPHICS Expected Academic Growth Actual Word Recognition Growth Actual Math Computation Growth Figure 1 Average Actual Academic Growth vs. Expected by Demographics 94% 82% 34% 66% 43% 72% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Mood Disorder NOS PTSD AVERAGE ACTUAL ACADEMIC GROWTH VS. EXPECTED BY DIAGNOSIS Expected Academic Growth Actual Word Recognition Growth Actual Math Computation Growth Figure 2 Average Actual Academic Growth vs. Expected by Diagnosis

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12 The primary null hypothesis was that no significant difference in academic growth would exist between the students diagnos ed with PTSD and the students diagnosed with MD NOS A secondary null hypothesis was that none of the demographic variables would have a significant impact on the academic growth of students. Assumptions for both the one way ANOVA and the factorial ANOVA w ere tested and met. For the primary null hypothesis, no main effect on DSM IV diagnosis was found for Math Computation Growth. However, a main effect on DSM IV diagnosis was found on Word Recognition growth, F(1,59) = 8.450, p = 0.005, eta = 0.125, with s tudents diagnosed with MD NOS making significantly less progress than students diagnosed with PTSD. ANOVA results can be seen in Table 2. Table 2 TESTS OF BETWEEN SUBJECTS EFFECTS Dependent Variable: Word Recognition Growth Diff erence Source Type III Sum of Squares df Mean Square F p Corrected Model 2.414 a 1 2.414 8.450 .005 Intercept 6.822 1 6.822 23.880 .000 DSM_DIAG 2.414 1 2.414 8.450 .005 Error 16.855 59 .286 Total 23.803 61 Corrected Total 19.269 60 a. R Squared = .125 (Adjusted R Squared = .110) Table 2 To determine whether or not demographic variables were significantly associated with academic growth, a factorial ANOVA was conducted on each of the dependent variables using the following indepe ndent variables: s ex, ethnic minority status and participation in an extended school year program To further analyze the significant interactions effects, Simple Effects post hoc tests were also conducted.

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13 For the secondary null hypothesis, an interacti on effect of ethnic minority status and participation in the ESY program was found on Word Recognition growth, F(1,57) = 8.110, p = 0.006, eta = 0.144 Factorial ANOVA results can be seen in Table 3. Table 3 TESTS OF BETWEEN SUBJECT S EFFECTS Dependent Variable: Word Recognition Growth Difference Source Type III Sum of Squares df Mean Square F p Corrected Model 2.770 a 3 .923 3.190 .030 Intercept 2.300 1 2.300 7.945 .007 MINORITY_STATUS .051 1 .051 .176 .676 ESY .286 1 .286 .9 88 .324 MINORITY_STATUS ESY 2.348 1 2.348 8.110 .006 Error 16.500 57 .289 Total 23.803 61 Corrected Total 19.269 60 a. R Squared = .144 (Adjusted R Squared = .099) Table 3 A Simple Effects post hoc test was then conducted on Word Recognit ion growth. Significant differences were seen between White students and non White students who did not attend an Extended School Year program F(1,57) = 5.772, p = 0. 0 20, with White students averaging great er academic progress Differences can be seen i n the Pairwise Comparisons table.

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14 A significant difference in Word Recognition progress was also fo und among students of color. At F(1,57) = 5.772, p = 0.031, those in the ESY program made significantly more progress than those who did not attend. Differences can be seen in the Pairwise Comparisons table below. Table 4 PAIRWISE COMPARISONS Dependent Variable: Word Recognition Growth Difference 95% Confidence Interval for Difference b ESY (I) NON WHITE STUDENTS (J) WHITE STUDENTS (I J) Mean Difference Std. Error p Lower Bound Upper Bound Yes 1 2 .578 .305 .063 .033 1.188 2 1 .578 .305 .063 1.188 .033 No 1 2 .429 .179 .020 .787 .071 2 1 .429 .179 .020 .071 .787 Based on estim ated marginal means The mean difference is significant at the .050 level. b Adjustment for multiple comparisons: Least Significant Difference (equivalent to no adjustment ). Table 4

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15 For the secondary null hypothesis, an interaction effect of ethnic minority status and participation in an extended school year program was also found on the Math Computation Growth variable, F(1,57) = 5.891, p = 0.018, eta = 0.101. Factorial ANOVA results can be seen in Table 6. Table 5 PAIRWISE COMPARISONS Dependent Variable: Word Recognition Growth Difference 95% Confidence Interval for Difference b Non White Students (I) ESY (J) ESY (I J) Mean Difference Std. Error p Lower Bound Upper Bound 1 Yes No .6 79 .308 .031 .063 1.295 No Yes .679 .308 .031 1.295 .063 2 Yes No .328 .174 .065 .676 .021 No Yes .328 .174 .065 .021 .676 Based on estimated marginal means The mean difference is significant at the .050 level. b Adjustmen t for multiple comparisons: Least Significant Difference (equivalent to no adjustments). Table 5

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16 Table 6 TESTS OF BETWEEN SUBJECTS EFFECTS Dependent Variable: Math Computation Growth Differenc e Source Type III Sum of Squares df Mean Square F p Corrected Model 4.661 a 3 1.554 2.132 .106 Intercept .450 1 .450 .618 .435 MINORITY_STATUS 1.706 1 1.706 2.341 .132 ESY .431 1 .431 .592 .445 MINORITY_STATUS ESY 4.292 1 4.292 5.891 .018 Error 41.530 57 .729 Total 48.889 61 Corrected Total 46.191 60 a. R Squared = .101 (Adjusted R Squared = .054) Table 6 A Simple Effects post hoc test was then conducted on Math Computation growth. Significant differences were found between White students and non White students who attended an Extended School Year program F(1,57) = 5.258, p = 0.26, with White students making greater progress on average Differences can be seen in the Pairwise Comparisons table. Table 7 PAIRWISE COMPARISONS Dependent Variable: Math Computation Growth Difference 95% Confidence Interval for Difference b ESY (I) NON WHITE (J) WHITE (I J) Mean Difference Std. Error p Lower Bound Upper Bound 1 1 2 1. 110 .484 .026 .141 2.079 2 1 1.110 .484 .026 2.079 .141 2 1 2 .252 .283 .379 .819 .316 2 1 .252 .283 .379 .316 .819 Based on estimated marginal means The mean difference is significant at the .050 level. b Adjustment for multiple comp arisons: Least Significant Difference (equivalent to no adjustments). Table 7

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17 DISCUSSION The purpose of this study was to identify whether any significant differences in academic progress existed for students based on their DSM IV diagnosis, and seco ndly, to identify whether significant differences were present when controlling for demographic factors. Results of this study indicate that t he average academic progress of students, regardless of diagnosis or demographics did not meet expectations in bo th Math Computation and Word Recognition This is consistent with other findings indicating that students with SED have poor educational outcomes ( Kern & Sokol, 2009 ) On average, the students in this study did not make expected academic progress, however, based on demographic groupings, some performed better than other s Regardless of diagnosis, on average the students in this study did not make the expected academic prog r ess in Word Recognition nor Math Comput at ion. However, s tudents diagnosed with MD NOS and those diagnosed with PTSD did make similar progress overall in the area of Math Computation although students with PTSD did make significantly better progress in Word Recognition than those with MD NOS In addition, significant differences in Math Co mputation was present amongst ESY participants with students of color making significantly more gains than White students. An interesting note about this result is that when looking at the average Math Computation progress of minority versus non minority s tudents as a whole, students of color made more progress on average than White students. This difference does not become significant until the interaction of attending ESY is c onsidered. One interpretation of this result is that the Extended School Year pr ogram is more effective with students of color and another possibility is that students of color in ESY started out with stronger mathematics skills than White students

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18 Anal ysis of student progress in the area of Word Recognition shows several significa nt findings when accounting for demographics. Among students who did not attend ESY, White students made better progress than students of color. Further, students of color who did not attend the ESY program were outperformed by other students of color who attended ESY in the area of Word Recognition These results show that students of color who did not attend ESY made less progress than students of color who attended ESY and White students who did not attend. This could suggest that ESY is more impactful o n students of color or it could suggest that White students entered the program with stronger reading skills. The lack of significant differences among White students regardless of their participation in ESY may suggest the extended school year program has less of an impact on these students. However, it is difficult to make any assertions about these results due to the fact that socio economic status data was unavailable for the participants. SES is often a determining factor for students' medical insuranc e, which in turn greatly impacts whether students qualify for ESY, as it is insurance companies that often pay for the program. Therefore, students in ESY may have been eligible for the additional instruction due to financial factors rather than academic n eed. Without SES data it is also impossible to make a determination about the living conditions of the students in this study, yet children of color on average experience poverty at a greater rate than White student s (Patten & Krogstad, 2014) Therefore, t he racial minority students in this study had a higher probability rate of living below the poverty line. In which case it is possible that their lower economic status had a significant impact on their reading skills (Carroll Maugha n, Goodman & Meltzer, 2 005 ) which would impact their Word Recognition progress.

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19 LIMITATIONS & FUTURE RESEARCH This study had a number of limitations. The sample size of this retrospective study was modest with a total of 61 participants During the analysis some of the int eractions between demographic variables were close to being significant but their p values were just above alpha and thus were judged as inconclusive. Replication with a larger sample size might show additional significant interactions This sample was al so unevenly weighted, as there were more males than females, more White students than students of color and more students with a PTSD diagnosis versus MD NOS. An additional limitation was that the sample was less racially diverse than the local school d ist rict population at the time and therefore was not a representative one. Additionally, SES data was not available for the students so we were unable to control for any impact this may have had on students' academic progress Thus, caution is in order about generalizing from this study. This study did not differentiate groups of students based on their educator so the impact of different teaching styles was not taken into consideration. Behavioral management styles were also not analyzed therefore student t ime spent out of class was not known and this may have had a significant impact on the amount of instruction students received. T his study did not include any data regarding the forms of individual therapy or medication students' may have been receiving d uring the school day This is an important factor to consider as therapeutic services and medical management may have had a significant impact on students' mental health which may have influence d their ability to perform academically Future research coul d include historical data on student academic progress, as there may be a significant relationship between performance in the general education classroom and performance in a day treatment environment This study used an academic growth

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20 benchmark that assu med one calendar year would equal one year of academic growth, the same growth model that is used for students without a Serious Emotional Disability. However, subsequent research could use a less aggressive growth model to compensate for the reduced instr uctions students' receive as a result of behavior management and therapeutic interventions. Inclusion of information regarding student medical insurance could also benefit future research by allowing analysis of whether having private insurance versus Medi caid impacts a student's inclusion in ESY. Future studies could also implement the use of comparison groups to measure whether or not interventions that are tailored to students with behavioral disabilities have a significant impact on academic progress.

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22 Lane, K. L., Barton Arwood, S. M., Nelson, J. R., & Wehby, J. (2008). Academic Performance of Students with Emotional and Behavioral Disorders Served in a Self Contained Setting. Journal of Behavioral Education, 17 (1), 43 62. Lane, K. L., & Menzies, H. M. (2010). Reading and Writing Interventions fo r Students With and at Risk for Emotional and Behavioral Disorders: An Introduction. Behavioral Disorders, 35 (2), 82 85. Lane, K. L., Wehby, J. H., Little, M. A., & Cooley, C. (2005a). Academic, Social, and Behavioral Profiles of Students With Emotional a nd Behavioral Disorders Educated in Self Contained Classrooms and Self Contained Schools: Part I Are They More Alike Than Different? Behavioral Disorders, 30 (4), 349 361. Lane, K. L., Wehby, J. H., Little, M. A., & Cooley, C. (2005b). Students Educated in Self Contained Classrooms and Self Contained Schools: Part II How Do They Progress Over Time? Behavioral Disorders, 30 (4), 363 374. McMackin, R. A., Tansi, R., & Hartwell, S. (2005). Proficiency in Basic Educational Skills as Related to Program Outcome a nd Escape Risk among Juvenile Offenders in Residential Treatment. Journal of Offender Rehabilitation, 42 (3), 57 74. Nelson, J. R., Benner, G. J., Lane, K., & Smith, B. W. (2004). Academic Achievement of K 12 Students with Emotional and Behavioral Disorder s. Exceptional Children, 71 (1), 59 73. Olszewski Kubilius, P., & Clarenbach, J. (2014). Closing the opportunity gap: Program factors contributing to academic success in culturally different youth. Gifted Child Today 37 (2), 103 110. Patten, E., & Krogstad, J. M. (2014, July 14). Black child poverty rate holds steady, even as other groups see declines | Pew Research Center. Retrieved from http://www.pewresearch.org/fact tank/2015/07/14/black chil d poverty rate holds steady even as other groups see declines/ Porche, M. V., Fortuna, L. R., Lin, J., & Alegria, M. (2011). Childhood Trauma and Psychiatric Disorders as Correlates of School Dropout in a National Sample of Young Adults. Child Development 82 (3), 982 998. Reid, R., Gonzalez, J. E., Nordness, P. D., Trout, A., & Epstein, M. H. (2004). A Meta Analysis of the Academic Status of Students with Emotional/Behavioral Disturbance. The Journal of Special Education, 38 (3), 130 143. Schelble, J. L ., Franks, B. A., & Miller, M. D (2010). Emotion Dysregulation and Academic Resilience in Maltreated Children. Child & Youth Care Forum 39 (4), 289 303.

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