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... Measure Performance of Returning Drop- outs in Trail Making Test - Form A / 74 Figure 5: Returning Drop- outs' Trail Making Test - Form A Error Rates / 75 ABSTRACT The increasing high school drop- out. .. day; 30% of Hispanic youths are drop- outs; 14% of African American youths are drop- outs; 8% of Caucasian youths are drop- outs; 41-46% of all prisoners are drop- outs; high school drop- outs make... baseline data that have assessed for attention or inattention before students drop- out Such data, of course, not exist Accordingly, "inattention" in drop- outs can only be operationally defined,
ASSESSMENT OF NEUROPSYCHOLOGICAL FUNCTION OF ATTENTION IN DROP-OUT STUDENTS RETURNING FOR DIPLOMA COMPLETION DISSERTATION SUBMITTED TO THE FACULTY OF THE ADLER SCHOOL OF PROFESSIONAL PSYCHOLOGY DISSERTATION CHAIR: JERRY WESTERMEYER, PH.D. BY DONG YOUNG HAN, M.A. IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PSYCHOLOGY CHICAGO, ILLINOIS JANUARY 21,2008 UMI Number: 3310330 INFORMATION TO USERS The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleed-through, substandard margins, and improper alignment can adversely affect reproduction. In the unlikely event that the author did not send a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. ® UMI UMI Microform 3310330 Copyright 2008 by ProQuest LLC. All rights reserved. This microform edition is protected against unauthorized copying under Title 17, United States Code. ProQuest LLC 789 E. Eisenhower Parkway PO Box 1346 Ann Arbor, Ml 48106-1346 Appendix K Acller School of Professional Psychology Dissertation Final Submission Form Name of Student YxM^l Titleof Dissertation O ^ ' ) L fatKKvw^ tA '^ ^ ^ i - H • • KWAA^'P^ycUloycM ^VA^C-^O^ r CA- This dissertation has been successfully defended and approved for final submission. Certified by: ( K , /pliaiE^ %^J^j^ ^ LP ^^ ^ £ j ^ Date ^ O 3 - 3 / - 0ff Date Member Copy of completed form to Registrar Original retained by student for inclusion with dissertation submitted to the Library ^ ii ACKNOWLEDGEMENTS Special acknowledgements are due to Drs. Jerry Westermeyer, Larry Maucieri, and Mark Stone. Thank you to all for your mentorship, guidance, and respective contributions. None of this, as well, could have been possible without the love and support of my family and friends. HI CURRICULUM VITA - Condensed DONG (DAN) YOUNG HAN, M.A. Phone:(312)201-3155 Email: dhan(5),adler.edu EDUCATION 08/08 08/08 03/04 05/00 Doctor of Psychology - Clinical Psychology Adler School of Professional Psychology (APA Accredited) Pre-doctoral Internship - Clinical Psychology Dreikurs Psychological Services Center (APA Accredited) Master of Arts - Counseling Psychology Adler School of Professional Psychology (APA Accredited) Bachelor of Science - Psychology major/Theology minor Loyola University Chicago PROFESSIONAL/CLINICAL EXPERIENCE 06/06-present 07/04-06/06 07/01-08/06 08/01-05/02 Psychometrician/Neuropsychometrist Rush University Medical Center Advanced Neuropsychology Extern (07/05-06/06); Clinical Neuropsychology Extern (07/04-06/05) The University of Chicago Medical Center Assistant Principal Truman Middle College Psychotherapy Extern Dreikurs Psychological Services Center GRANTSMANSHIP 07/00-06/07 07/00-06/07 07/00-06/07 07/00-06/07 07/06-06/07 11/04-10/05 Dropout Retrieval - Tuition • Total amount of allocated funds: $5,385,786.00 Truant's Alternative Optional Education Program • Total amount of allocated funds: $2,898,070.00 NCLB Chapter I • Total amount of allocated funds: $471,128.00 NCLB Title I • Total amount of allocated funds: $16,730.00 Re-Enrolled Student Project • Award amount: $80,000.00 Adult Education Innovative iv • Award amount: $18,000.00 PUBLISHED ARTICLES Hook, J. N., Han, D. Y., & Smith, C. A. (submitted). Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) and Depressive Complaints in Older Adults. Clinical Gerontologist. Han, D. Y. & Lee, E. J. (2007). Dementia in the Elderly: The Early Signs, and When and How to Seek Help. Yehyang, 30, 19. Han, D. Y. & O'Hale, H. T. (2004). Dropping Out of Traditional Schools: And the Psychosocial Stressors That Got Them There. Alternative Schools Network Bulletin, 2, 6-8. SELECTED PUBLISHED ABSTRACTS/PRESENTATIONS PUBLISHED ABSTRACTS & CONFERENCE PRESENTATIONS Pyykkonen, B. A., Smith, C. A., Han, D. Y., Bartt, R., Martin, E., & Stebbins, G. T. (2008). Depression Symptoms and Gray Matter Atrophy in Individuals with HIV Infection: Differential Patterns Associated with Unique Symptomatology. Poster session presented at the 36th Annual Meeting of the International Neuropsychological Society, Waikoloa, HI. Smith, C. A., Hook, J., Stebbins, G. T., Han, D., Martin, E., Bammer, R., & Moseley, M. (2008). Risky Decision Making and Whole-Brain RadiologicallyDefined Normal Appearing White Matter: A Diffusion Tensor Imaging Study. Poster session presented at the 36th Annual Meeting of the International Neuropsychological Society, Waikoloa, HI. Hook, J. N., Han, D. Y., & Smith, C. A. (2008). Effects of Depressive Symptoms on Older Adults' Performance on the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Submitted for presentation at the 6th Annual Conference of the American Academy of Clinical Neuropsychology, Boston, MA. Wise, J., Lacy, M., Oliveira, M., Han, D. Y. & Pyykkonen, B. A. (2006). Detecting Neuropsychological Malingering of Mild TBI: Effects of Internet Based Coaching and Mild TBI. Journal of International Neuropsychological Society, 12 (SI), 128. Pyykkonen, B. A., Han, D. Y., & Lacy, M. (2005). HVLT-R and BVMT-R: Correlation between Recognition Memory Scores. Journal of International Neuropsychological Society, 11 (SI), 133. Han, D. Y., Anderson, G. R., Yumoto, T., & Colon, M. (2004, May). Normal Executive Function in Dropout Students Returning for High School Completion: V Findings from the Brief Academic Neuropsychological Test. Poster session presented at the 16th Annual Convention of the Association for Psychological Science, Chicago, IL. Colon, M., Maloy, L., Anderson, G. R., Yumoto, T., & Han, D. Y. (2004, May). Examination ofLD/BD Students from a Neuropsychological Perspective. Poster session presented at the 16l Annual Convention of the Association for Psychological Science, Chicago, IL. Han, D. Y. (2001, September). Psychology of Contemporary Education. The 26th Annual National Middle College Conference, Chicago, IL. O'Hale, H. T. & Han, D. Y. (2001, September). Necessity of Social Counseling and Mentorship in Educating Inner-city Youths. The 26* Annual National Middle College Conference, Chicago, IL. INVITED SYMPOSIA/COLLOOUIA/MEDIA PRESENTATIONS Han, D. Y. (2006, September). Dementia with Lewy Bodies: Clinical Implications. Invited presentation for Clinical Neuropsychology Case Conference, Rush University Medical Center, Chicago, IL. Han, D. Y. (2006, April). The Impact of Neurological Disorders on the Life Tasks: An Adlerian Perspective. Invited presentation for The North American Society of Adlerian Psychology's Continuing Education Seminar hosted at the Symposium of The Individual Psychology Society, Chicago, IL. Han, D. Y. (2006, March). Neuropsychological Implications of Lewy Body Disease: A Case Study and Review of Clinical Diagnosis, Neuropathology, and Treatment Planning. Invited presentation for Clinical Neuropsychology Case Conference, The University of Chicago Medical Center, Chicago, IL. Han, D. Y. (2005, April). Case ofM: Neuropsychological Implications of Huntington's Disease. Invited presentation for Clinical Neuropsychology Case Conference, The University of Chicago Medical Center, Chicago, IL. Han, D. Y. (2003, May). Adolescent Counseling and Education: Considering Urban Subculture and Socioeconomic Status. Invited presentation for The North American Society of Adlerian Psychology's Continuing Education Seminar hosted at the Symposium of The Individual Psychology Society, Chicago, IL. Oellrich, M., O'Hale, H. T., & Han, D. Y. (2002, November). Alternative Education. Chicago, IL. WYCC-TV 20. Han, D. Y. (2002, August). Assessment and Counseling for Adolescents. Phoenix, AZ. "Possibilities Radio", KFNX AM 1100. vi TABLE OF CONTENTS Dissertation Final Submission Form / i Acknowledgements / ii Curriculum Vitae / iii Abstract / 2 Chapter I: Introduction / 4 1.1 General Statement / 5 1.2 Statement of the Problem / 6 1.3 Statement of Purpose / 8 1.4 Assumptions and Limitations / 9 Chapter II: Review of Literature / 12 2.1 The Drop-out Crisis / 1 3 2.2 Lack of National Data on Drop-outs / 1 5 2.3 The Neuropsychology of Attention / 18 2.4 Previous Data on Returning Students' Executive Functions / 21 2.5 Test Selection and Rationale / 27 Chapter III: Methodology / 29 3.1 Samples Employed / 30 3.2 Instruments Used / 30 3.3 Procedures Followed / 35 3.4 Data Analysis / 36 vii Chapter IV: Results / 39 4.1 Demographics and Descriptive Statistics / 40 4.2 Sample Comparisons / 46 Chapter V: Discussion / 50 5.1 Neuropsychological Implications of the Findings / 51 5.2 Psychosocial and Cultural Implications of the Findings / 53 5.3 Returning Population and Hope / 54 5.4 Political Implications of the Findings / 55 5.5 Suggested Future Research / 56 References / 58 Appendices / 62 Appendix A: Informed Consent Form / 62 Appendix B: Demographic Information / 63 List of Tables/64 Table 1: Sample Gender and Age / 64 Table 2: Sample Demographics / 65 Table 3: Reported Reasons for Withdrawal/Dropping Out / 66 Table 4: Elapsed Time between Dropping Out and Returning to School/67 Table 5: Reported Diagnosis Prior to Returning to School / 68 Table 6: Returning Students' Stroop Performances / 69 viii Table 7: /-test Results of Attention Performances between Groups / 70 ListofFigures/71 Figure 1: Executive Measure Performance of Returning Drop-outs in Stroop Word Test / 71 Figure 2: Executive Measure Performance of Returning Drop-outs in Trail Making Tests / 72 Figure 3: Executive Measure Performance of Returning Drop-outs in Stroop Word and Color Word Tests / 73 Figure 4: Executive Measure Performance of Returning Drop-outs in Trail Making Test - Form A / 74 Figure 5: Returning Drop-outs' Trail Making Test - Form A Error Rates / 75 2 ABSTRACT The increasing high school drop-out rate has been a silent epidemic for a number of years. Yet, there is a significant lack of data to help understand why. Some have suggested that possible pre-existing attentional deficits may be a significant factor in the etiology of the drop-out epidemic (Barriga, Doran, Newell, Morrison, Barbetti, & Robbins, 2002). However, there are limited data to support such a hypothesis. The present study examined the attentional performance of 70 students (43 males and 27 females; mean age =18) who dropped out of their traditional school settings, but later returned for diploma completion. Participants were administered eight neuropsychological measures of attention (Stroop Word, Color, & Color-Word; Trails Form A, B, & L; and the Digit Span and Digit Symbol-Coding subtests of the Wechsler Adult Intelligence Scale - III) to determine whether or not specific deficits were present. The performances on all attentional measures were average, suggesting intact attentional functioning in this population. The present findings were consistent with that of Han, Anderson, Yumoto, & Colon (2004), which revealed average executive functioning performances in a similar sample. Also similar to Han et al. (2004), the current participants' performances were somewhat better (faster) on the speeded task (Trails A), and somewhat worse (slower) on the more complex task (Trails B), relative to national norms. The data also illustrated that the most commonly identified reason for dropping out was due to poor grades (14%). This may be due 3 to various psychosocial stressors as opposed to attentional deficits, as the second most reported reason for dropping out was due to family problems (13%). Although Life-style/hanging out with the wrong crowd (10%) was similarly endorsed, reported issues of gang-related reasons (6%) and incarceration (3%) were comparatively lower. CHAPTER I INTRODUCTION 5 1.1 General Statement Increasing high school drop-out rate has been a silent epidemic for a number of years; in fact, a student drops out of school every nine seconds in America (Martin & Halperin, 2006). Students who withdraw from high school do so for a number of reasons: poor school performance, poor school attendance, gang related issues, disciplinary issues, family problems, pregnancy, etc., which frequently follow a long developmental history of multiple and variable conflicts (Jimerson, Egeland, Sroufe, & Carlson, 2000). Subsequently, many of these students are thought to have poor academic achievement and problems with attention, behavioral inhibition, etc. (Barriga, Doran, Newell, Morrison, Barbetti, & Robbins, 2002). However, at least for the drop-out population who elects to return for their diploma completion, executive functions (i.e., problem solving abilities, cognitive flexibility, inhibition, and set-shifting, etc.) appear to be relatively intact, which may be at least in part related to their corresponding level of motivation towards diploma completion. Accordingly, these data further suggest a more significant role of psychosocial stressors identified by Jimerson et al. (2000) in contrast to possible neuropsychological or neurobehavioral pre-existing deficits (Han, Anderson, Yumoto, & Colon, 2004). Subsequently, it can be further hypothesized that these students may not show substantial deficits in measures of attentional function, similar to the results of executive measures provided by Han et al. 6 (2004). By utilizing the neuropsychological measures of attention, the current study examined in this sample, the students' level of functioning in generalized cortical and reticular activating systems, i.e., attention. 1.2 Statement of the Problem Currently, more than one million students drop out of American high schools every year, while graduation rates are, at best, only up to 70 percent of all students nationally (Maxwell, 2007). Also, of the 30 percent of all students that fall by the waste side, more than a third of these high school drop-outs across the nation leave their academic settings before completing the ninth grade (Schemo, 2006). While the rate of high school students withdrawing from their traditional school settings has reached an epidemic proportion (Jones, 2005; Greater West Town Community Development Project, 2003) and the subsequently interrelated societal complications have been well documented (Ad Counsel, 2003), current review of the literature is significantly limited regarding the information on the attentional functioning of high school drop-outs, let alone returning students. Also, despite the data that many of these students may have poor academic achievement and problems with attention, behavioral inhibition, etc. (Barriga et al., 2002), limited studies are available to further validate this theory and to explore the "drop-out" issue. Additionally, the "problem with attention" as a variable in students identified by Barriga et al. (2002) is not operationally defined as an 7 inability in attentional functions. Accordingly, inattention in this context is defined more as a socio-behavioral trait rather than a neuropsychological deficit. Subsequently, the misuse of these data may misrepresent the idea that drop-out population may have pre-existing and underlying neuropsychological deficits in attention. Although this may be the case for some individuals, little is known regarding the ratio between the drop-outs' true neuropsychological deficits in attention and inattention as a socio-behavioral reaction to psychosocial stressors. Although heuristics may be inferred by exploring this population's predrop-out stage by assessing individual risks, such as behavioral features, truancy, psychosocial stressors, etc., current review of the literature is limited to the identification of the risks and the rate of withdrawals among this population (Jones, 2005; Metzer, 1997; Greater West Town Community Development Project, 2003; Ad Counsel, 2003). Accordingly, there is a realistic need for an establishment of a baseline, assessing the level of attentional functioning in this population. The subsequent research question is the following: "What is the level of generalized cortical and reticular activating system functioning in the high school drop-out population returning for diploma completion; and was there a correlative pre-existing neuropsychological deficit in their attentional functioning prior to dropping out?" 8 1.3 Statement of Purpose Again, very little is known regarding the ratio between the drop-outs' true neuropsychological deficits in attention and inattention as a socio-behavioral reaction to psychosocial stressors. This study attempted to determine whether possible deficits in measures of attentional function and related brain functioning are correlatively attributable as students' pre-existing conditions, to less than optimal school functioning, subsequent administrative withdrawal (dropping out), and the students opting to continue high school completion at an alternative setting. This study attempted to accomplish a baseline of attentional functioning for the aforementioned population as they are marginalized, and with limited representation and information. Accordingly, this study may play a contributory role in assessing appropriate methods for prevention and intervention. Additional implication includes the use of the analysis of attention among this population, as a variable that may be widely inferred as one of the contributing factors to a systemic problem, without the necessary corresponding data; examples include incorrect inferences (due to insufficient data) regarding students dropping out of high school, having possible ADHD, etc. It was hypothesized that the returning students may not show substantial deficits in measures of attentional function, similar to the results provided by Han et al. (2004). This study examined the generalized cortical and reticular activating system's neuropsychological test 9 functioning in a sample of North American high school drop-outs returning for diploma completion. 1,4 Assumptions and Limitations It was assumed that there will be no significant differences in the attentional functions between the retrieved drop-out students and the normative data. The eight research hypotheses were the following: HI: The performance of the drop-out group is in the average range when compared to the performance of the normative group on the Stroop Word test. H2: The performance of the drop-out group is in the average range when compared to the performance of the normative group on the Stroop Color test. H3: The performance of the drop-out group is in the average range when compared to the performance of the normative group on the Stroop Color-Word test. H4: The performance of the drop-out group is not significantly different from the performance of the normative group on the Trail Making Test - Form A. H5: The performance of the drop-out group is not significantly different from the performance of the normative group on the Trail Making Test - Form B. 10 H6: The performance of the drop-out group is not significantly different from the performance of the normative group on the Trail Making Test - Form L. H7: The performance of the drop-out group is not significantly different from the performance of the normative group on the Digit Span subtest of the Wechsler Adult Intelligence Scale - III. H8: The performance of the drop-out group is not significantly different from the performance of the normative group on the Digit Symbol subtest of the Wechsler Adult Intelligence Scale - III. As per the study done by Han et al. (2004), which demonstrated no significant differences in the executive functions between the retrieved drop-out students and the normative data, the current study was assumed to provide similar results; particularly since attention and corresponding processing speed are participating variables in the operational definition of executive functioning: cognitive flexibility, inhibition, set-shifting, self-monitoring and/or self-awareness. The sample used in the study consisted of 16 to 21-year-old students who dropped out of their traditional high school setting but have returned for diploma completion. The sample was limited to urban students who are current residents of the city of Chicago. The sample was also limited to exclude all students under the age of eighteen without the consent of their parent/legal guardian. The sample also excluded any former students who wished to participate but did not yet return 11 to a diploma completion program. The reported reasons and stressors contributing to their reasons for dropping out of high school vary significantly: poor school performance, poor school attendance, gang related issues, disciplinary issues, family problems, pregnancy, etc. However, it was the assumption of the study that attention, along with executive function as per Han et al. (2004), may not be a significant variable that may have contributed to the returning students' initial status as high school drop-outs. CHAPTER II REVIEW OF LITERATURE 13 2.1 The Drop-out Crisis According to Martin & Halperin (2006) of the American Youth Policy Forum, every nine seconds in America, a student becomes a dropout. As mentioned earlier, students who withdraw from high school do so for a number of reasons: poor school performance, poor school attendance, gang related issues, disciplinary issues, family problems, pregnancy, etc., which frequently follow a long developmental history of multiple and variable conflicts (Jimerson et al., 2000). Additionally, the complexity of this issue increases as the mentioned variables interrelate to facilitate additional stressors, such as poor attendance due to gang problems, leading to poor school performance and subsequent family problems. Psychosocial conflicts such as these invariably may contribute to individual socio-behavioral and rather logical responses such as inattention, distractibility, depression, agitation, state of hyper arousal, etc. Consequently, the current situation of high school student attrition rate in the United States, due to self-selected withdrawal process (dropping out), has reached an epidemic proportion in the recent past (Jones, 2005). Again, more than one million students drop out of American high schools every year, while graduation rates are, at best, only up to 70 percent nationally; graduation rate for black and Hispanic students, particularly males, are closer to only 50 percent (Maxwell, 2007). Also to stress again is the fact that many of these students do not even reach success in their first year of high school; more than a third of high 14 school drop-outs across the nation leave their academic settings before completing the ninth grade (Schemo, 2006). According to the local data presented by the Greater West Town Community Development Project (2003), Chicago Public High Schools graduated 60,814 students between 1999 and 2002. In that same academic period, 64,057 students have dropped out from Chicago Public High Schools; the number of students dropping out exceeded the number of students graduating by 3,243 between 1999 and 2002. In 2002, the number of high school students dropping out of Chicago Public Schools (17,404) reached the highest level ever recorded and it has been increasing. Within this cohort data set, 23% of all CPS freshmen have dropped out, and 74% of the drop-outs never completed their sophomore year. There are a number of societal complications that are interrelated with the current trend. A number of socio-economic, cultural, and developmental factors are affected by this trend. According to the research published by the national Ad counsel (2003), at least 1,300 students drop out of school every day; 30% of Hispanic youths are drop-outs; 14% of African American youths are drop-outs; 8% of Caucasian youths are drop-outs; 41-46% of all prisoners are drop-outs; high school drop-outs make 42% less money in the workplace than high school graduates; 50% of drop-outs are unemployed; drop-outs are three times as likely to face poverty and to receive public assistance than are high school graduates; 15 while 72% of students aged 10-13 say they would like to talk to their parents more about schoolwork. 2.2 Lack of National Data on Drop-outs Unfortunately, the current literature and available data are limited to the identification of the risks and the rate of withdrawals among this population, while there are significantly limited data regarding the information on the attentional functioning of high school drop-outs, let alone returning students. Also, publicly available data are often dated, making it difficult for independent researchers to infer any updated trends. This may be at least in part due to the fact that such population is difficult to track, especially when there is little to no published federal and state policies to track and intervene with students dropping out of secondary education. Even when the drop-outs return to complete their secondary education, there is little to no uniformed policy in tracking their progress and attrition nationally (Samuels, 2007). However, even with such limitations, available literatures tend to hastily speculate pre-existing deficits in drop-out population, without clarifying the definition of "pre-existing deficits." Drop-out students are simply thought to have poor academic achievement and global problems with attention, behavioral inhibition, executive functioning, among many other "deficits" (Barriga et al., 2002). However, the "problem with attention" as a variable in students identified by Barriga et al. (2002) cannot be operationally defined as an inability in 16 attentional functions, as no task-specific baseline assessment was done on this population. It would be impossible to infer such deficit in drop-outs; unless there are national baseline data that have assessed for attention or inattention before students drop-out. Such data, of course, do not exist. Accordingly, "inattention" in drop-outs can only be operationally defined, at best, as a possible sociobehavioral trait, and not as a neuropsychological deficit. Given this scenario, an operational definition of attentional deficit remains ambiguous especially when it pertains to drop-outs. However, the current literature, or lack there of, continues to default to an unclear definition of attentional deficit. This ambiguity serves to misrepresent the idea that drop-out population may have pre-existing and underlying neuropsychological deficits in attention. Although this may be the case for some individuals, little is known regarding the ratio between the drop-outs' true neuropsychological deficits in attention, and inattention as a rather contextually appropriate socio-behavioral reaction to psychosocial stressors. Again, given that there is no formal baseline assessment of attention in high school students (before dropping out); some heuristics may be inferred by exploring the students' individual socio-behavioral risks instead. These may include behavioral features, truancy, psychosocial stressors, etc. as opposed to risks attributed to pre-existing neuropsychological deficits (Jones, 2005; Metzer, 1997; Greater West Town Community Development Project, 2003; Ad Counsel, 17 2003). It is also important to note the shortcomings of the latter assumption as it conceptualizes such a significant socio-cultural phenomenon (i.e., dropping out) as a unitary variable (i.e., pre-existing "brain damage"). To simply brush off such a complex social phenomenon as possible brain damage, especially without supporting data, is far from being sufficient. The literature on neuropsychology already warns of the shortcomings associated with unitary conceptualization of "organicity" (or "brain damage") when discussing such a complex variable as attention (Lezak, Howieson, & Loring, 2004). This pattern of insufficient unitary conceptualization can be seen clinically, all around us, particularly regarding attention. As an example, the literature also suggests an over-diagnosis and overuse of medication for ADHD among schoolchildren (LeFever, Arcona, & Anonuccio, 2003). This is no surprise. Such data highlight the prevalence in the misuse of an ambiguous definition when discussing attention, and the shortcomings of an "easier" unitary conceptualization of attention and its disorders. Given the over-diagnostic trend of ADHD found by LeFever et al., it is important to note that there may be a trend to assume psychological disorders in students demonstrating difficulties, as opposed to exploring the etiology of their symptoms which may be socio-behavioral in nature, particularly those induced by exogenous stressors. The trend to lean on pre-existing pathology (with inattention being merely one of many) seems to be pessimistic in nature, and far from sufficient. Exploring the etiology of socio- 18 behavioral traits of attentional problems would prove to be a more pragmatic approach. 2.3 The Neuropsychology of Attention The neuropsychology of attention is a complex system to understand. Even with the brain's substantial capacity to process multiple amount of information simultaneously, it is still inherently limited in its process (Strauss, Sherman, & Spreen, 2006). The effectiveness of the human brain is demonstrated through its ability to select specific information for further processing (Banich, 2004). In essence, attention can be conceptualized as the gateway for information flow to the brain (Cohen, 1993). However, this "gateway" concept remains difficult to operationalize for the purpose of a study, as the concept of attention has been all over the place in psychology, especially depending on the context of the empirical question posed. Initially, the idea of attention as a measurable variable was often rejected, at least until the relatively recent emergence of the cognitive sciences. Brought with this emergence are the renewed interests in the concept of attention; ultimately further dividing into explorations of automatic processes taking part in attention and the conscious selection of sensory information (Kolb & Wishaw, 2003). However, in order to accurately differentiate contextually appropriate attentional problem from that of a clinical deficit, e.g., undiagnosed ADHD, one must have a working definition of attention that is not split by different methods 19 of conceptualization. The International Neuropsychological Society (INS) Dictionary of Neuropsychology defines attention as the "...processes that enable an individual to engage in certain cognitive operations while ignoring others. Thus, attention involves a selective awareness or responsiveness. Attention also refers to the ability to focus and maintain interest for given task or activity" (Loring, 1999). This operational definition of attention is notably differentiated from automatic processing, as this definition requires one's conscious effort to focus, and maintain interest for a specific task. For the purpose of this study, this clinical definition of conscious processing of attentiveness, specifically pertaining to the ability to focus, will be used throughout the study. As complex as it is, attention still does not have to be a grossly arbitrary construct. Whether or not attention is the consequence of neural signaling, it is still possible to identify the neural correlates of attention through functional imaging studies. Attention can be defined as the ability to focus, as a precondition for conscious behavior, which can also be mapped neurophysiologically. Specifically, given the INS dictionary definition of attention, the reticular activating system (RAS) can be conceptualized as the geographic definition of attention. The major portion of the reticular activating system can be found in the midbrain, or the mesencephalon, which is a small area just forward of the hindbrain (Lezak et al., 2004). 20 As for the functional conceptualization of attention, Knudsen (2007) defines the fundamental components of attention as the following general structural model. This includes four core processes with working memory at the center. First, working memory, or the immediate memory that "works", temporarily stores information for further analysis. Second, competitive selection process determines and selects prioritized information to gain access to working memory. Third, top-down sensitivity control process regulates signal intensity in information channels that compete for better access to working memory, influencing the selection of new information. This mediates voluntary control of attention in a recurrent loop. Fourth, Bottom-up saliency filters enhance the response to infrequent stimuli, and/or stimuli of instinctive or learned biological relevance. Accordingly, the neural correlate of attention, via the four core processes, is functionally apparent through enhanced firing, e.g., can be seen through imaging studies. These can also be behaviorally observed through subsequent eye movements; and, these can be mapped on the frontal eye fields (FEF) on the dorsolateral frontal cortex, which contain a retinocentric spacial map. A variety of these spacial maps are also stretched out to the parietal cortex. Particularly, the lateral intraparietal area (LIP) contains a saliency map and is interconnected both with the FEF and with sensory areas (as the parietal cortical region is primarily dominated by sensory circuitries). Also, subcortically, the superior colliculi (as 21 their primary function is related to vision) mediate certain automatic responses that influence attention, like orienting to a highly salient stimulus. All these neural correlates are of course, under the assumption of an intact RAS functioning. Again, given the importance of working memory highlighted by Knudsen (2007), the current study utilized an appropriate measure of working memory, often used in clinical settings along with other attention measures, i.e., WAIS-III Digit Span. 2.4 Previous Data on Returning Students' Executive Functions In the International Neuropsychological Society (INS) Dictionary of Neuropsychology, Loring (1999) elaborates and defines executive function as the following: Cognitive abilities necessary for complex goal-directed behavior and adaptation to a range of environmental changes and demands. Executive function includes the ability to plan and anticipate outcomes (cognitive flexibility) and to direct attentional resources to meet the demands of nonroutine events. Many conceptualizations of executive function also include self-monitoring and self-awareness since these are necessary for behavioral flexibility and "appropriateness." Because of individual variability and changing task demands required to demonstrate executive functions, they are often difficult to assess with standardized measures. Cerebral localization also remains elusive and controversial. Regions of 22 the prefrontal cortex may play a special role in recruiting other brain areas in a series of distributed networks that handle different components of executive functions, depending on the processing demands of the specific task (p. 64). Given this INS definition, attention may be conceptualized as one of the crucial components of executive function. Whether it is cognitive flexibility, problem-solving, self-monitoring and/or self-awareness, all variables appear to include involvement of attentive processing. Subsequently, one may even hypothesize that intact executive function may infer intact attentional function. Accordingly, data exploring the drop-out population's executive abilities have set the stage for the current attention study. According to data explored by Han et al. (2004), evaluation of the returning drop-outs' executive abilities found normal level of performances. After assessing the pattern of these students' executive functioning ability, at least for the drop-out population who elected to return for their diploma completion, normal attentional abilities in this sample have also been documented. The returning students' average performances on the executive function measures were suggestive of average attentional abilities, as these measures are also known to test for attentional properties, i.e., Trails A, Stroop Word and Color. Similar to the current attention study, Han et al. (2004) examined performance of 66 students who had withdrawn and returned for diploma 23 completion at an alternative high school program. Students were between 17 and 20 years of age, including 30 females and 36 males. Most self-reported reasons for dropping out of their previous school were due to poor attendance and poor grades. All but eleven students were returning after being out of high school for less than five months. The following tests were administered in this study: Trails A and B, Stroop (Word, Color, and Color-Word) and Cancellation of 4's. Data were analyzed in the following steps. First, comparisons were made to typically developing individuals and then to LD/BD students from data collected by Anderson and Yumoto (unpublished) using ANOVA/ANCOVA analyses for the three groups with age as the covariate. Then MANCOVA was used to identify any interaction effect(s). While the LD/BD population showed significantly lower performance from the national norms and the returning students, there were no significant differences between the returning students and normative samples on all but Stroop Word, which actually showed higher performance among returning students; F(l, 129)=17, p=.00004 (fig. 1). There was a significant interaction between the performances of returning students on the Trails A and Trails B. They seemed to show higher performance on the Trails A; Wilks lambda=.94821, F(2, 131)=3.5779, p=.03070 (fig. 2). There was also a significant interaction between the performances of returning students on the Stroop Word and Stroop 24 Color-Word. They seemed to show higher performance on the simpler tasks but not the complex tasks; Wilks lambda=83965, F(2,128)=12.222, p=. 00001 (fig. 3). Insert Figure 1 about here Insert Figure 2 about here Insert Figure 3 about here There were also trends suggesting higher speed of performance in the returning students (Trails A, Stroop Color, and Cancellation of 4's) while the error rates also seemed slightly higher (fig. 4 & 5). 25 Insert Figure 4 about here Insert Figure 5 about here Through this, Han et al. (2004) demonstrated that the students attending alternative high school completion programs appear to show no remarkable level of problems in executive function measures. As mentioned earlier, one could hypothesize that these students initially left school primarily due to their psychosocial stressors, i.e. gang related issues, disciplinary issues, family problems, pregnancy, etc. which may contribute to poor attendance and poor grades, rather than preexisting neuropsychological deficits of executive function and behavior. It can be further hypothesized that executive function deficits, if any, in the returning student population, may be compensated by their motivation to continue with their diploma completion. In terms of the interactions between Trails A/B and the Stroop Word/Color-Word, the data suggested higher performance on the simpler tasks but not the complex tasks. There were also findings suggesting slightly higher 26 error rates in the Trails A and B. These results may be demonstrating the influence of preexisting psychosocial stressors on their learning styles they have developed from previous school settings. All in all, the Han et al. (2004) data concluded that the performance of the students attending alternative high schools appeared to be no different from students in normative settings. Subsequently, this study made a significant political statement as well. Unfortunately, although federal acts such as No Child Left Behind address the necessity to increase national educational standards, there are little to no published guidelines for retrieving drop-out students at the federal level. Majority of the retrieval programs across the nation are currently funded at the state level, and are susceptible of being cut at any given fiscal year. Given the normal functioning in the executive domain of their neuropsychological profile, it can be argued that executive deficit may not have played a significant role in this population's decision to initially drop-out from school. Accordingly, it can further be argued that any fiscal threat to academic programs designed to retrieve this population would hinder reintegration of normal functioning students, who demonstrate the desire to return to mainstream education. This current study on this population's attentional function attempted to replicate the findings. 27 2.5 Test Selection and Rationale There is a host of measures of attention for the diagnostic purposes in clinical settings; some are evidenced to be more valid and reliable than others. The following measures were selected for the study: Stroop (Word, Color, & Color-Word), Trail Making Test (Forms: A, B, & L), and two subtests of the Wechsler Adult Intelligence Scale - III (WAIS-III), i.e., Digit Span and Digit Symbol. Many of these measurement tools are commonly used in clinical and neuropsychological assessments and have well-established validity and reliability. Additionally, the utility of the aforementioned measurement tools for the purpose of assessing attention has been standardized by national normative data (Heaton, Miller, Taylor, & Grant, 2004; Mitrushina, Boone, Razani, & D'Elia, 2005). Specifically, the wide utility of the Trail Making Tests in assessing orientation and attention has been well documented over time. Although most reports of reliability have been confounded with large variability, with reliability above .60 with some being in the .90s, Trail Making is widely utilized as one of the choice measures in the global assessment of brain functioning; specifically attentional functioning (Groth-Marnat, 2003; Lezak et al., 2004; Strauss et al., 2006). Additionally, the Digit Span and Digit Symbol - Coding subtests of the WAIS-III require the subjects to pay close attention to the tasks at hand, while assessing for attentional ability, processing speed, and working memory (GrothMarnat, 2003), making them appropriate for the battery used for this study. 28 The Stroop Test is a measure of cognitive control that assesses the ease with which an individual can sustain a goal while suppressing a habitual response in favor of a less familiar one (Strauss et al., 2006). Given its wide use, the Golden version of the Stroop test was utilized as one of the measures of attention in this study. However, it should be noted that the characteristics of the normative sample such as sample size, mean, and standard deviation are not clearly provided in the manual, making it difficult to undergo an independent samples Mest to evaluate corresponding hypotheses. Instead, the study used the manual's scoring formula to derive at the test performances of the drop-out group. CHAPTER III METHODOLOGY 30 3.1 Samples Employed This study examined the attentional performances of 70 students (43 males and 27 females) from an urban alternative high school in Chicago, Illinois, which is designed to retrieve drop-outs for diploma completion. All students were between 16 and 21 years of age, with the mean age of 18. Self-reported reasons for dropping out of their previous school were gathered, along with information regarding the length of time with drop-out status prior to the admission to the program (i.e., via self-report demographic questionnaire - appendix b). Sample selection was systematic in its approach as it was on a volunteer basis. All students were offered extra credit in their science class for participation (as approved by the school administration); their classes also provided additional options for extra credit assignments in order that no students were penalized for opting not to participate in the research. Exclusion criteria included any students who did not initially drop-out from a traditional high school, and any former students who have not returned for diploma completion. 3.2 Instruments Used The following tests were administered, scored, and analyzed to investigate the proposed hypotheses: Stroop (Word, Color, & Color-Word), Trail Making Test (Forms: A, B, & L), and two subtests of the Wechsler Adult Intelligence Scale - III (WAIS-III), i.e., Digit Span and Digit Symbol. All measures were administered individually, in a quiet setting outside of the classroom. The 31 completion time for the battery after filling out the demographic questionnaire {appendix b) was approximately 15-25 minutes. Stroop Test (Golden version - Forms: Word, Color, & Color-Word). The Stroop Test is a measure of selective attention and cognitive flexibility originally developed by Stroop (1935), and has been utilized through multiple versions over decades, e.g., Golden version, Victoria version, Comalli/Kaplan version, Dodrill version, Trenerry version, etc. One of the most widely used is the Golden version of the Stroop Color and Word Test (Golden, 1978; Golden & Freshwater, 2002), which boasts wide clinical and experimental applications. Given its wide use, the Golden version was utilized as one of the measures of attention in this study. The Golden version of the Stroop test reports reliabilities of .89 (Word), .84 (Color), and .73 (Color-Word; N = 450) for a group administered version, and reliabilities of .86, .82, and .73, respectively (N = 30), for the individual version (Golden, 1975). In addition, in assessing for validity, correlations among test trials tend to be moderate to high for the Golden version, while slightly higher than the competing Victoria version, suggesting that the Golden version is tapping similar abilities within the test. When compared to other measures of attention, the Golden version of the Stroop's interference score correlates moderately well with others such as the difference score between Trails A and B (.55) (Strauss et al., 2006). 32 The Golden version of the Stroop test consists of a Word page with 100 words identifying names of colors (i.e., red, green, blue), which are printed in black ink. The Color page also has 100 stimuli, but in the form of X's printed in either red, green, or blue ink. The Color-Word page has 100 words from the first page (red, green, blue) printed in colors from the second page, while the colors and the words do not match. The subjects were instructed to read the words as fast as possible in the Word page for 45 seconds, colors in the Color page for 45 seconds, and the color of the ink on the Col or-Word page for 45 seconds. These raw performances of how many correct answers they provided in 45 seconds per trial were translated into T-scores (M = 50, SD = 10) using formula and tables published in the manual. It should again be noted that the characteristics of the normative sample such as sample size, mean, and standard deviation are not clearly provided in the manual, making it difficult to undergo an independent samples /-test to evaluate corresponding hypotheses. Subsequently, the study used the manual's scoring formula to derive at the test performances of the drop-out group via T-scores (M = 50, SD = 10). T-score ranges between 43 and 56 were interpreted as average (unremarkable) performances (Lyman, 1971). Trail Making Test (Forms: A, B, & L). The Trail Making Test forms A and B were originally constructed in 1938 as "Partington's Pathways" or the "Divided Attention Test" as a part of the Army Individual Test battery, and were later 33 adopted by Reitan (1955) to be added into the Halstead Battery (Strauss et al., 2006). The form L of the Trail Making Test was developed by Stone and Stone in 2000, to add an additional dimension of difficulty in order to ascertain possible discrepancies between the subjects (Stone et al, 2003). The wide utility of the Trail Making Test (forms A & B) in assessing orientation and attention has been well documented over time. Although most reports of reliability have been confounded with large variability, with reliability above .60 with some being in the .90s, Trail Making is widely utilized as one of the choice measures in the global assessment of brain functioning; specifically attentional functioning (Groth-Marnat, 2003; Lezak et al., 2004; Strauss et al, 2006). As for the validity assessment, forms A and B correlate moderately well with each other (r = .31 - 6), suggesting that they measure similar yet somewhat different functions. When compared with other measures of attention, Trails B particularly showed high loadings on a visuomotor scanning factor, along with Digit Symbol Test, Letter Cancellation, and the Symbol-Digit Modality Test. As for the form L of the Trail Making Test, given its recent development of the test, further research on establishing the reliability and validity of the measure seemed to be necessary. The test requires the individual to connect, by making pencil lines, 25 encircled numbers randomly arranged on a page in proper order (form A) and 25 encircled numbers and letters in alternating order (form B) (Strauss et al., 2006). 34 The form L of the test requires the individual to connect, by making pencil lines, 26 encircled letters beginning with the letter A, then to Z, and then to B, then to Y, and so on, in an alternating sequence. The total time is then measured for comparison with normative dataset provided in the manual. Wechsler Adult Intelligence Scale - III (WAIS-III) Digit Span Subtest & Digit Symbol-Coding Subtest. The Wechsler Adult Intelligence Scale - III (WAIS-III) (The Psychological Corporation, 2002) is a revision of the WAIS-R (Wechsler, 1981), and is widely standardized as a choice instrument for assessing intellectual functioning. Among the construct definitions of intellectual functioning are working memory and processing speed. Working memory can be conceptualized as a person's information-processing capacity (The Psychological Corporation, 2002), which is an updated model of conceptualization of short-term memory. Working memory can be viewed as an active form of attentiveness, in which the process of attention is necessary in encoding and maintaining information in such an active state. Accordingly, the Digit Span subtest of the WAIS-III, which measures working memory by examining the individual's ability to repeat the number sequence in the same order as presented (and backwards), was included as a part of the study's battery. In addition, a measure of the speed of the information-processing was included in the battery, i.e., Digit Symbol- Coding subtest. 35 The reliability coefficient for the Digit Span subtest for ages 16 to 24 is .81. The reliability coefficient for the Digit Symbol-Coding subtest for ages 16 to 17 is .90, 18 to 19 is .91, and 20 to 24 is .90. Also, there is a substantial correlation (.80 and above) between the WAIS-III and the WAIS-R (The Psychological Corporation, 2002). There is also substantial correlation (.6-.92) between the WAIS-III and other measures of intelligence, including the Standard Progressive Matrices, Stanford-Binet-IV, the GAMA, the WASI, etc. (Strauss et al., 2006). See the WAIS-III/WMS-III Technical Manual (2002) for greater detail of test psychometric properties. Demographics Questionnaire. Individual demographic information was gathered from all participants. Demographic data included gender, date of birth, age, handedness, years of education, ethnicity, native language, reason for dropping out from previous high school, elapsed time between leaving school and applying to new program, and whether or not they have been diagnosed with Attention Deficit Hyperactivity Disorder (ADHD), Learning Disability (LD), Behavioral Disorder (BD), and/or Non-Verbal Learning Disorder (NVLD). These data were included to enhance the quality of the sample and to examine any notable findings. 3.3 Procedures Followed Upon receipt of consent, all participants were given the demographic questionnaire {appendix b) to fill out. Then, all tests were administered 36 individually by graduate level clinicians, in a quiet setting outside of the classroom. All students were offered extra credit in their science class for participation (as approved by the school administration); their classes were also provided additional options for extra credit assignments so that no students were penalized for opting not to participate in the research (as approved by the school administration). Upon completion of each battery, all tests were scored and analyzed. No feedback was given to the participants regarding the results. 3.4 Data Analysis The study incorporated descriptive statistics, independent samples Wests, and correlational analysis in its assessment of the eight proposed research hypotheses, which are the following: HI: The performance of the drop-out group is in the average range when compared to the performance of the normative group on the Stroop Word test. H2: The performance of the drop-out group is in the average range when compared to the performance of the normative group on the Stroop Color test. H3: The performance of the drop-out group is in the average range when compared to the performance of the normative group on the Stroop Color-Word test. 37 H4: The performance of the drop-out group is not significantly different from the performance of the normative group on the Trail Making Test - Form A. H5: The performance of the drop-out group is not significantly different from the performance of the normative group on the Trail Making Test - Form B. H6: The performance of the drop-out group is not significantly different from the performance of the normative group on the Trail Making Test - Form L. H7: The performance of the drop-out group is not significantly different from the performance of the normative group on the Digit Span subtest of the Wechsler Adult Intelligence Scale - III. H8: The performance of the drop-out group is not significantly different from the performance of the normative group on the Digit Symbol subtest of the Wechsler Adult Intelligence Scale - III. HI through H8 were first analyzed through descriptive statistics. Descriptive statistic of the mean and standard deviation of individual test performances for the group were established. Given the lack of clarity in the characteristics of the Stroop test normative data, HI through H3 were analyzed with descriptive statistics of the mean and standard deviation of Stroop test performances, then were determined whether these performances were considered 38 unremarkable. Comparisons were made with the manual's scoring formula, based on the normative dataset of the Stroop Color and Word Test manual - Golden version (Golden & Freshwater, 2002, p. 7). Then for H4 through H8, these test performances were analyzed with independent samples Mests, used to compare the drop-out sample to national normative mean data as possible. H4 through H6 were analyzed by comparing the drop-out group performances to those published as descriptive statistics of the total sample (i.e., Trails A, B, & L) in the manual by Stone et al. (2003). H7 and H8 were analyzed by comparing the drop-out group performances to those published by The Psychological Corporation in the WAIS-III/WMS-III Technical Manual (2002). CHAPTER IV RESULTS 40 4.1 Demographics and Descriptive Statistics The following data are based on the demographic questionnaires collected from the participants. There were a total of 70 participants, with 43 males (61.4%) and 27 females (38.6%). The sample's age ranges were between 16 and 21, with the mean age of 18 (table 1). Insert Table 1 about here A majority of the participants (92.9%) were right handed, while 5.7% were left handed and 1.4% was ambidextrous. 81.4% of the participants have completed 11 years of education and were in their 12th year, while 14.3% have completed 10 years of education and 4.3% have completed nine years of education total. Of the participants, 28.6% were Caucasians (non-Hispanic), 17.1% were African Americans, 44.3% were Hispanics, 4.3% were Asians, 1.4% was Pacific Islanders, and 4.3% reported "Other." Of the participants, 15.1% identified English as their native language, while 15.7% identified Spanish as their native language (table 2). 41 Insert Table 2 about here All participants were also asked to provide reason(s) for withdrawing their previous high school (table 3). They were also asked to identify how long of a time interval they have been a drop-out (table 4). Although "Other" category was identified the most (18.6%), with various reasons for withdrawal, the most commonly identified reason for dropping out was due to poor grades/not learning/not earning credits (14.3%). This may be due to various stressors; the second most reported reason for dropping out was due to family problems (12.9%). Life-style/hanging out with the wrong crowd (10%) came close to family problems, and problems relating to others, poor attendance, and disciplinary issues came close all three being 8.6% of the responses. Boredom was actually 7.1%> of the responses, while gang-related reasons (5.7%) and incarceration (2.9%) were also notably present. Pregnancy and financial problems only constituted 2.8% of the sample responses (1.4% each). Many of the students (47.1%) reported that they returned to school only after two months (or less) of dropping out, while up to 17.1% of the students have been dropped out for one year or more. Additionally, 90% of the participants reported that they have not been diagnosed with any psychological or learning disorder. However, up to 10%) of the participants reported that they have been diagnosed with Attention Deficit 42 Hyperactivity Disorder (2.9%), Learning Disorder (2.9%), Non-Verbal Learning Disorder (2.9%), or Behavior Disorder (1.3%) However, the sources of their reported diagnoses were not identified (table 5). Insert Table 3 about here Insert Table 4 about here Insert Table 5 about here The results of the descriptive statistical analysis rendered the following. As illustrated on table 6, Stroop Word, Color, and Color-Word mean (M) Tscores were 45.03, 45.2, and 49.99, with standard deviations (SD) of 7.47, 7.29, and 10.09 respectively. The group's Stroop Interference mean T-score was also 43 53.8, with SD of 8.9. Trail Making Tests, forms A and B mean T-scores were 45.27 and 51.20, with SD of 9.02 and 10.25 respectively, while mean performance of form L was in the 36* percentile (equal to T-score of 46.5), with SD of 26.90 (table 7). Digit Span and Symbol-Coding subtests of the WAIS-III were performed in the average range as well, with mean scaled scores of 9.8 and 9.79 (both equal to T-score of 49), SD's 2.84 and 2.53 respectively (table 7). For this study, T-score ranges between 43 and 56 were interpreted as average (unremarkable) performances (Lyman, 1971); all attention measure performances of the returning drop-outs were in the average (unremarkable) ranges, suggesting that their attentional measure performances were not significantly different when compared to the national normative data. Insert Table 6 about here The descriptive statistical analysis addressed the first three individual research hypotheses, which were the following: HI: The performance of the drop-out group is in the average range when compared to the performance of the normative group on the Stroop Word test. Given that the characteristics of the normative dataset such as sample size, mean, 44 and standard deviation are not clearly provided in the Golden version of the Stroop manual, the study used the manual's scoring formula to derive at the test performances of the drop-out group via T-scores (M = 50, SD = 10) (Golden & Freshwater, 2002, p. 7). T-score ranges between 43 and 56 were interpreted as average (unremarkable) performances (Lyman, 1971). Again, the Stroop Word test performance of the drop-out group (N = 70) was in the average range (T = 45, SD - 7.47), when compared to the national normative dataset based on the Golden version of the Stroop manual (Golden, 1978; Golden & Freshwater, 2002). This result was consistent with the research hypothesis; students with a history of dropping out of high school, who later returned for diploma completion, performed in the average range when compared to individuals recruited for normative data for the Stroop Word test. H2: The performance of the drop-out group is in the average range when compared to the performance of the normative group on the Stroop Color test. Given that the characteristics of the normative dataset such as sample size, mean, and standard deviation are not clearly provided in the Golden version of the Stroop manual, the study used the manual's scoring formula to derive at the test performances of the drop-out group via T-scores (M = 50, SD = 10) (Golden & Freshwater, 2002, p. 7). T-score ranges between 43 and 56 were interpreted as average (unremarkable) performances (Lyman, 1971). Again, the Stroop Color test performance of the drop-out group (N = 70) was in the average range (T = 45, 45 SD = 7.29), when compared to the national normative dataset based on the Golden version of the Stroop manual (Golden, 1978; Golden & Freshwater, 2002). This result was consistent with the research hypothesis; students with a history of dropping out of high school, who later returned for diploma completion, performed in the average range when compared to individuals recruited for normative data for the Stroop Color test. H3: The performance of the drop-out group is in the average range when compared to the performance of the normative group on the Stroop Color-Word test. Given that the characteristics of the normative dataset such as sample size, mean, and standard deviation are not clearly provided in the Golden version of the Stroop manual, the study used the manual's scoring formula to derive at the test performances of the drop-out group via T-scores (M = 50, SD = 10) (Golden & Freshwater, 2002, p. 7). T-score ranges between 43 and 56 were interpreted as average (unremarkable) performances (Lyman, 1971). Again, the Stroop ColorWord test performance of the drop-out group (N = 70) was in the average range (T = 50, SD = 10.09), when compared to the national normative dataset based on the Golden version of the Stroop manual (Golden, 1978; Golden & Freshwater, 2002). This result was consistent with the research hypothesis; students with a history of dropping out of high school, who later returned for diploma completion, performed in the average range when compared to individuals recruited for normative data for the Stroop Color-Word test. 46 Insert Table 7 about here 4.2 Sample Comparisons The analyses of the remaining five of eight individual research hypotheses were the following: H4: The performance of the drop-out group is not significantly different from the performance of the normative group on the Trail Making Test - Form A. An independent-samples /-test was conducted to evaluate the hypothesis that there is not a significant difference between the performances of the drop-out group and that of the normative group on the Trail Making Test - Form A. This test revealed no significant differences, t (341) = 1.89, p_ = .0595. These results were consistent with the research hypothesis; students with a history of dropping out of high school, who later returned for diploma completion, performed as well as individuals recruited for normative data for the Trail Making Test - Form A (table 7). However, it should be noted that the current result oft (341) = 1.89, p = .0595 does illustrate a trend in a form of a difference, as the returning students performed better on average (M=30.46, SD=8.28) on this speeded task of number 47 sequencing (compared to the normative sample's M=35.22, SD=20.62). Also to note is that this trend of higher performance on the simpler task is consistent with the executive function findings illustrated earlier by Han et al. (2004) (fig. 2). H5: The performance of the drop-out group is not significantly different from the performance of the normative group on the Trail Making Test - Form B. An independent-samples Mest was conducted to evaluate the hypothesis that there is not a significant difference between the performances of the drop-out group and that of the normative group on the Trail Making Test - Form B. This test revealed no significant differences, t (341) = 1.734, p = .0838. These results were consistent with the research hypothesis; students with a history of dropping out of high school, who later returned for diploma completion, performed as well as individuals recruited for normative data for the Trail Making Test - Form B (table 7). However, it should be noted that the current result oft (341) = 1.734, p_ = .0838 does illustrate a trend in a form of a difference, as the returning students performed worse on average (M=66.27, SD=23.36) on this more complex task of alternating sequences of numbers and letters (compared to the normative sample's M=58.11, SD=37.53). Also to note is that this trend of lower performance on more complex task is consistent with the executive function findings illustrated earlier by Han et al. (2004) (fig. 2). The result still remained similar when an additional independent-samples /-test was conducted only with the 63 students, excluding the seven students who 48 reported having a previously diagnosed clinical disorder, i.e., ADHD, LD, BD, NVLD, etc. (table 7). This adjusted result also revealed no significant difference, t (334) = 1.3076,p_ = .1919. Although the 63 students' performances were slightly better/faster without the seven clinical sample subjects, the trend remained the same, as the 63 returning students also performed worse on average (MN64.52, SD=21.14) on this more complex task of alternating sequences of numbers and letters (compared to the normative sample's M=58.11, SD=37.53). H6: The performance of the drop-out group is not significantly different from the performance of the normative group on the Trail Making Test - Form L. An independent-samples Mest was conducted to evaluate the hypothesis that there is not a significant difference between the performances of the drop-out group and that of the normative group on the Trail Making Test - Form L. This test revealed no significant differences, t (341) = 0.1129, p = .9102. These results were consistent with the research hypothesis; students with a history of dropping out of high school, who later returned for diploma completion, performed as well as individuals recruited for normative data for the Trail Making Test - Form L (table 7). H7: The performance of the drop-out group is not significantly different from the performance of the normative group on the Digit Span subtest of the Wechsler Adult Intelligence Scale - III An independent-samples t-test was conducted to evaluate the hypothesis that there is not a significant difference 49 between the performances of the drop-out group and that of the normative group on the Digit Span subtest of the Wechsler Adult Intelligence Scale - III. This test revealed no significant differences, t (168) = 0.98, p_ = .3332. These results were consistent with the research hypothesis; students with a history of dropping out of high school, who later returned for diploma completion, performed as well as individuals recruited for normative data for the Digit Span subtest of the Wechsler Adult Intelligence Scale - III (table 7). H8: The performance of the drop-out group is not significantly different from the performance of the normative group on the Digit Symbol subtest of the Wechsler Adult Intelligence Scale - III. An independent-samples West was conducted to evaluate the hypothesis that there is not a significant difference between the performances of the drop-out group and that of the normative group on the Digit Symbol subtest of the Wechsler Adult Intelligence Scale - III. This test revealed no significant differences, t (168) = 0.9341, p = .3516. These results were consistent with the research hypothesis; students with a history of dropping out of high school, who later returned for diploma completion, performed as well as individuals recruited for normative data for the Digit Symbol subtest of the Wechsler Adult Intelligence Scale - III (table 7). CHAPTER V DISCUSSION 51 5.1 Neuropsychological Implications of the Findings The proposed hypotheses of the study, HI through H8, illustrated that the performances of drop-outs that returned for diploma completion showed no significant level of problems in attentional function measures, when compared to the normative data. Specifically, students attending alternative high school completion programs showed no significant level of problems in attentional function measures. However, there were some interesting trends illustrated through the study sample's Trails A and B performances in H4 and H5. The returning students appeared to do somewhat better (faster) on average on the speeded task of number sequencing (Trails A), while performing somewhat worse (slower) on average on the more complex task of alternating sequences of numbers and letters (Trails B), compared to the national norm. Despite the fact that the returning students' Trails A and B performances remained statistically insignificant, these performance trends were consistent with the executive function findings illustrated earlier by Han et al. (2004). Seven (10%) of the participants reported that they have been diagnosed with Attention Deficit Hyperactivity Disorder (2.9%), Learning Disorder (2.9%), Non-Verbal Learning Disorder (2.9%), or Behavior Disorder (1.3%), although the sources of their reported diagnoses were not identified. These participants were not excluded from the study, yet they did not prove to be outliers as the mean 52 performances stayed within the average range, even with their alleged attentional and behavioral disorder diagnoses. In addition, when the worst of the group performances (Trails B: still in the average range) were reexamined after excluding the seven clinical sample subjects, the results remained the same, as there was still no significant difference, while the trend of the returning students' comparatively worse performance in the more complex task (Trails B) remained the same. As noted in the review of the literature, it would be irresponsible to attribute the drop-out epidemic to an undiagnosed neuropsychological condition, i.e., attentional deficit induced by ADHD, etc. However, given the over-diagnosed nature of the disorder and the difficulty of assessing students who drop out, it currently remains difficult to definitively infer the etiology of the drop-out epidemic. It would also be pre-mature to infer that drop-outs have not suffered from pre-existing attentional deficit from the current data, as it can still be argued that attentional function deficit, if any, in the returning student population, may be compensated by their motivation to continue with their diploma completion. Also, the current data are limited to the illustration of normal attentional functioning only among returning students, and not among individuals who stay dropped out. It still remains a mystery whether or not those who stay dropped out (and never 53 return) may actually have undiagnosed neuropsychological condition(s), i.e., attentional dysfunction. Either way, with a student dropping out of school every nine seconds in America, the current data illustrating intact attentional functioning among the retrieved/returning students seem optimistic: Again, this study's sample who returned for diploma completion, after initially dropping out of their traditional educational setting, did not show current attentional deficit when compared to the national normative data. 5.2 Psychosocial and Cultural Implications of the Findings One could hypothesize that these students initially left school primarily due to their psychosocial stressors, such as gang related issues, disciplinary issues, family problems, pregnancy, etc. which may contribute to inattentive behavior, poor attendance, and poor grades, rather than pre-existing neuropsychological deficits of attentional function. The demographics provided by the study's sample illustrated that the most commonly identified reason for dropping out was due to poor grades/not learning/not earning credits (14.3%). This may be due to various stressors; the second most reported reason for dropping out was due to family problems (12.9%). Although Life-style/hanging out with the wrong crowd (10%) came close to family problems, reported issues of gang-related reasons (5.7%) and incarceration (2.9%) were comparatively lower. This may be attributable to 54 more difficult access to return to school when a drop-out is distressed by gangrelated problems and/or incarceration. Pregnancy and financial problems only constituted 2.8% of the sample responses (1.4% each). Most of the students (47.1%) reported that they returned to school only after two months (or less) of dropping out, while up to 17.1% of the students have been dropped out for one year or more. It may be further hypothesized that the longer one stays out of school, the more difficult it becomes for one to return to school. Further research to assess for this hypothesis may be helpful. 5.3 Returning Population and Hope Given the mentioned strengths and motivation of the drop-outs returning for diploma, there is still hope. There are public and non-for-profit movements working together to provide a second chance for these returning students. In Chicago, Illinois, Youth Connection Charter Schools consortium is comprised of 23 campuses, serving students across the city, having conferred more than 4,000 high school diplomas to former drop-outs between 1997 and 2007. The Alternative Schools Network, also from Chicago, works closely with the Illinois Department of Children and Family Services to retrieve drop-outs in order that they are distributed throughout the city's charter school programs. In addition, the Washington-based American Youth Policy Forum recently examined school-based initiatives, youth employment programs, GED- 55 preparation efforts, and state and local policies that have had a positive effect on helping dropouts return to school in 12 states. Although the numbers behind the drop-out crisis are still alarming, the 12 states showed evidence that retrieval is possible when state and local policies come together to meet the need to address this public issue (Bushweller, 2006). 5.4 Political Implications of the Findings The review of the literature discussed the crisis of drop-out epidemic being exacerbated by lack of national data and funding. Although institutions like Youth Connection Charter Schools consortium, Alternative Schools Network, American Youth Policy Forum, etc. engage and maintain meaningful work to retrieve drop-outs and facilitate educational reform, lack of political interest in retrieving drop-outs make it difficult for corrective intervention. Especially given the current data showing that drop-outs who return for diploma completion are average and/or high functioning individuals, at least when measuring the attentional domain, it is crucial for sufficient funds to be aiding retrieval programs to maintain their operation. Without sufficient political interest and subsequently adequate funds to proliferate retrieval programs, countless individuals that are normally functioning may be losing out on their chance to be reintegrated into the educational mainstream. Further research and proposals for outreach seems essential. 56 5.5 Suggested Future Research As noted throughout the study, the lack of national data on the etiology of the current drop-out crisis is astounding. The present study revealed findings that are similar to that of Han et al. (2004), which revealed that executive deficit may not be a major contributing factor in the drop-out population, at least among those who return for diploma completion. Future replication of these findings, particularly addressing attention and executive functioning, may further strengthen to rule out the often blindly attributed notion of ADHD playing a major role in students dropping out. Further, future research and outreach for public awareness of the drop-out epidemic, subsequent policy, and political interest to foster appropriate intervention remain to be vital. Sample selection of those who remained dropped out; those who returned to school and those who never left, may also present interesting findings through the analyses of variance. In addition, given such a large variance of the Trail Making Tests in the compared sample provided by Stone et al. (2003), a more homogeneous sample, representative of the norm should benefit the study in the future. On a different note, to further scrutinize the current study, it appears necessary to reexamine the significance of the sample that was previously diagnosed with clinical syndrome(s). Although the seven participants did not turn out to be outliers for the present study, future studies should continue to identify 57 such samples within the subject pool and carefully examine their impact on the studies. Another area of further research may include performance comparisons between returning students who graduated from the retrieval program and those who failed to graduate. This may further scrutinize the impact of supposed attentional deficits among the drop-out population, as unaddressed deficit may continue to hinder success in academic settings. 58 REFERENCES Banich, M. T. (2004). Cognitive Neuroscience and Neuropsychology. Boston: Houghton Mifflin. Barriga, A. Q., Doran, J. W., Newell, S. B., Morrison, E. M., Barbetti, V., & Robbins, B. D. (2002). Relationships between Problems Behaviors and Academic Achievement in Adolescents: The Unique role of Attention Problems. Journal of Emotional and Behavioral Disorders, 10, 233-240. Bushweller, K. K. (2006). Dropout Recovery. Education Week, 25(26), 12. Chicago's Dropout Crisis: Hard facts about our high school's continuing problems. (2003). Retrieved April 10, 2006, from http ://www. gwtp. org/03 DropoutReport-FIN AL .pdf. Cohen, R. A. (1993). The Neuropsychology of Attention. New York: Plenum Press. Golden, C. J. (1978). Stroop Color and Word Test: A manual for clinical and experimental uses. 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Test Scores and What They Mean, Second Edition. Englewood Cliffs, N.J.: Prentice-Hall, Inc. Martin, N., & Halperin, S. (2006). Whatever It Takes: How Twelve Communities Are Reconnecting Out-of-School Youth. Washington, DC: American Youth Policy Forum. Maxwell, L. (2007). Conference Focuses on 'Silent Epidemic' of Dropouts. Education Week, 26(37), 5-15. Metzer, D. (1997). High School Dropouts Returning: A Study of the Education System's Efforts to Encourage their Return. Humanities & Social Sciences, 58, 2072. Mitrushina, M., Boone, K. B., Razani, J., & D'Elia, F. (2005). Handbook of Normative Data for Neuropsychological Assessment (2nd ed.). New York: Oxford University Press. 61 The Psychological Corporation (2002). WAIS-III/WMS-III: Updated Technical Manual. San Antonio: Author. Samuels, C. (2007). Lack of Research, Data Hurts Dropout Efforts, Experts Say. Education Week, 26(36), 8-8. Schemo, D. J. (2006, June 1). A Third of U.S. Dropouts Never Reach 10th Grade. The New York Times. Strauss, E., Sherman, M. S., & Spreen, O. (2006). A Compendium of Neuropsychological Tests: Administration, Norms, and Commentary (3rd ed.). New York: Oxford University Press. Stone, M., McLain, A., & Stone, K. M. (2003). Trails L: Manual for Administration, Scoring and Interpretation. Wechsler, D. (1981). Wechsler Adult Intelligence Scale-Revised. New York: The Psychological Corporation. 62 Appendix A Informed Consent Form I agree to give permission for myself (or for my dependent) to take part in the research project being conducted by D. Y. Han, M.A. I have read the letter describing the project and agree to testing for myself (or for my dependent) which will not exceed one hour. I also grant permission for the researcher to report other records related to school records addressing learning or school problems for research purposes; but no personal information will be made public. I understand that all information about me (or my dependent) will be kept strictly confidential. Subject's Rights / understand that: 1. This participation is voluntary and without force, and I am free to withdraw from this agreement at any time. 2. This research is for purposes of completing a requirement toward the researcher's attainment of his doctoral degree. 3. My participation involves participating in testing which may last up to 60 minutes. 4. I am free to ask questions of the researcher and receive explanations about the research study and my rights as a subject. 5. I will not be identified by name in this project. All information that might lead to my identity will be disguised. The researcher has explained the above rights to me as a subject and has informed me that this consent will remain in a confidential file. Subject's Name Printed: Signature: Date: (signature of the parent AND the subject if he/she is under 18 years of age) ID Number: (Assigned by the researcher) Researcher's Signature: 63 Appendix B DEMOGRAPHIC INFORMATION ID Number: Date: Gender: Male / Female Date of Birth: / / Age: Handedness: Right handed / Left handed / Ambidextrous Years of Education completed: (e.g., 9 = completed 9th grade, 12 = graduated from high school, etc.). Ethnic background: (please check one) Caucasian (Non-Hispanic) ; African-American Asian ; Pacific-Islander ; Native-American Native Language: English ; Hispanic ; Other ; / Other (Please describe) Reason for withdrawing from previous high school: (please check one) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. poor grades/not learning/not earning credits gang-related problems problems interacting with teachers or other students family problems money problems boredom life-style/hanging out with the wrong crowds pregnancy incarceration/jail/prison poor attendance ; if so, briefly describe what led to this. 11. discipline problems/expulsion 12. Other ; if so, briefly describe what led to this. ; please briefly describe. Elapsed time between leaving previous high school and applying to new program. (Please check one) 1. 2. 3. 4. 5. 2 months or less 3-5 months 6~8 months 9-11 months 1 year or more Check next to the item if a healthcare professional told you or your parent that you have: Attention Deficit Hyperactivity Disorder (ADHD) Learning Disability (LD) Behavioral Disorder (BD) Non-Verbal Learning Disorder (NVLD) 64 Table 1) Sample Gender and Age Gender Frequency Percent male 43 61.4 female 27 38.6 Age Frequency Percent 16 17 18 19 20 21 1 25 29 11 2 2 1.4 35.7 41.4 15.7 2.9 2.9 65 Table 2) Sample Demographics Handedness Frequency Percent right handed 65 92.9 left handed 4 ambidextrous 1 5.7 1.4 Education Frequency Percent 9 10 11 3 10 57 4.3 14.3 81.4 Ethnicity Frequency Percent Caucasian (non-Hispanic) 20 28.6 African American Hispanic Asian 12 31 3 17.1 44.3 4.3 Pacific Islander Other 1 3 1.4 4.3 Native Language Frequency Percent English Spanish Other 53 11 6 75.7 15.7 8.6 Table 3) Reported Reasons for Withdrawal/Dropping Out Reason for Withdrawal/Dropping out Frequency Percent poor grades/not learning/not earning credits gang-related reasons Problems relating to teachers or others family problems financial problems boredom 10 4 14.3 5.7 6 9 1 8.6 12.9 1.4 5 7.1 life-style/hanging out with the wrong crowd pregnancy incarceration/) ail/prison/arrested poor attendance discipline issues/expulsion Other 7 1 10.0 1.4 2 6 6 13 2.9 8.6 8.6 18.6 67 Table 4) Elapsed Time between Dropping Out and Returning to School Time between programs Frequency Percent 2 months or less 33 47.1 3-5 months 17 24.3 6-8 months 8 11.4 1 year or more 12 17.1 68 Table 5) Reported Diagnosis Prior to Returning to School Previous Diagnosis Frequency Percent ADHD 2 2~9 Learning Disorder 2 2.9 Behavior Disorder 1 1.3 Non-Verbal Learning Disorder None 2 2.9 63 90.0 Table 6) Returning Students' Stroop Performances Attention Measure N Mean Std. Deviation stroop word raw 70 98.2000 14.6234 stroop word T stroop color raw stroop color T stroop color word raw stroop color word T stroop interference raw stroop interference T 70 70 70 70 70 70 70 45.03 72.60 45.20 45.03 49.99 4.22 53.80 7.47 11.10 7.29 10.13 10.09 9.49 8.90 70 Table 7) t-test Results of Attention Performances between Groups Measures Sample N Mean trails a raw Returning drop-outs 70 30.46 8.28 National Norm* 273 35.22 20.62 Returning drop-outs 70 66.27 23.36 National Norm* 273 58.11 37.53 Returning drop-outs w/ no diagnosis 63 64.52 21.14 National Norm* 273 58.11 37.53 70 141.70 69.57 National Norm* 273 140.34 94.37 Returning drop-outs 70 9.80 2.84 trails b raw trails b raw trails 1 raw digit span ss digit symbol coding ss Returning drop-outs Std. Deviation National Norm** 100 10.2 2.5 Returning drop-outs 70 9.79 2.53 National Norm** 100 10.2 3 Source: * Adapted from Stone et al., 2003. **Adapted from The Psychological Corporation, 2002. t df Sig. (2tailed) 1.89 341 0.0595 1.734 341 0.0838 1.3076 334 0.1919 0.1129 341 0.9102 0.98 168 0.3332 0.9341 168 0.3516 71 Figure 1) Executive Measure Performance of Returning Drop-outs in Stroop Word Test Covariate means: Age: 17.35543 Needs; L S M e a n s C u r r e n t effect: F ( 1 , 1 2 9 ) = 1 7 . 9 9 7 , p = . 0 0 0 0 4 ( C o m p u t e d for c o v a r i a t e s at their m e a n s ) V e r t i c a l bars d e n o t e 0.95 c o n f i d e n c e intervals 114 112 110 108 106 104 102 100 98 96 94 92 90 N Needs Note. W=Stroop Word; N=norm group; D=drop-outs Source: Adapted from Han et al, 2004. 72 Figure 2) Executive Measure Performance of Returning Drop-outs in Trail Making Tests Covariate means: Age: 17.44198 Needs; LS Means Wilks lambda=.94821, F(2, 131)=3.5779, p=.03070 (Computed for covariates at their means) Vertical bars denote 0.95 confidence intervals Needs Note. TA=Trails A; TB=Trails B; N=norm group; D=drop-outs Source: Adapted from Han et al., 2004. 73 Figure 3) Executive Measure Performance of Returning Drop-outs in Stroop Word and Color Word Tests Covariate means: Age: 17.35543 Needs; l_S Means J/Vilks lambda=.83965, F(2, 128)=12.222, p=.00001 (Computed for covariates at their means) Vertical bars denote 0.95 confidence intervals Needs Note. W=Stroop Word; CW=Stroop Color Word; N=norm group; D=drop-outs Source: Adapted from Han et al, 2004. 74 Figure 4) Executive Measure Performance of Returning Drop-outs in Trail Making Test - Form A Covariate means: Age: 17.44198 Needs; LS Means Current effect: F(1, 132)=2.3927, p=.12430 (Computed for covariates at their means) Vertical bars denote 0.95 confidence intervals Needs Note. TA=Trails A; N=norm group; D=drop-outs Source: Adapted from Han et al., 2004. 75 Figure 5) Returning Drop-outs' Trail Making Test - Form A Error Rates Covariate means: Age: 17.7013 N e e d s ; LS M e a n s C u r r e n t effect: F ( 1 , 7 4 ) = 1 . 5 3 5 7 , p = 2 1 9 1 8 ( C o m p u t e d for c o v a r i a t e s at their m e a n s ) V e r t i c a l bars d e n o t e 0.95 c o n f i d e n c e intervals 0.5 0.4 0.3 0.2 0.1 HI 0.0 -0.1 -0.2 -0.3 -0.4 -0.5 N Needs Note. TAE=Trails A Error; N=norm group; D=drop-outs Source: Adapted from Han et al., 2004. [...]... following: "What is the level of generalized cortical and reticular activating system functioning in the high school drop- out population returning for diploma completion; and was there a correlative pre-existing neuropsychological deficit in their attentional functioning prior to dropping out? " 8 1.3 Statement of Purpose Again, very little is known regarding the ratio between the drop- outs' true neuropsychological. .. Ad counsel (2003), at least 1,300 students drop out of school every day; 30% of Hispanic youths are drop- outs; 14% of African American youths are drop- outs; 8% of Caucasian youths are drop- outs; 41-46% of all prisoners are drop- outs; high school drop- outs make 42% less money in the workplace than high school graduates; 50% of drop- outs are unemployed; drop- outs are three times as likely to face poverty... evaluation of the returning drop- outs' executive abilities found normal level of performances After assessing the pattern of these students' executive functioning ability, at least for the drop- out population who elected to return for their diploma completion, normal attentional abilities in this sample have also been documented The returning students' average performances on the executive function measures... drop- outs' true neuropsychological deficits in attention, and inattention as a rather contextually appropriate socio-behavioral reaction to psychosocial stressors Again, given that there is no formal baseline assessment of attention in high school students (before dropping out) ; some heuristics may be inferred by exploring the students' individual socio-behavioral risks instead These may include behavioral... in drop- outs; unless there are national baseline data that have assessed for attention or inattention before students drop- out Such data, of course, do not exist Accordingly, "inattention" in drop- outs can only be operationally defined, at best, as a possible sociobehavioral trait, and not as a neuropsychological deficit Given this scenario, an operational definition of attentional deficit remains ambiguous... deficits in attention and inattention as a socio-behavioral reaction to psychosocial stressors This study attempted to determine whether possible deficits in measures of attentional function and related brain functioning are correlatively attributable as students' pre-existing conditions, to less than optimal school functioning, subsequent administrative withdrawal (dropping out) , and the students opting... analysis of attention among this population, as a variable that may be widely inferred as one of the contributing factors to a systemic problem, without the necessary corresponding data; examples include incorrect inferences (due to insufficient data) regarding students dropping out of high school, having possible ADHD, etc It was hypothesized that the returning students may not show substantial deficits in. .. Making Test - Form B 10 H6: The performance of the drop- out group is not significantly different from the performance of the normative group on the Trail Making Test - Form L H7: The performance of the drop- out group is not significantly different from the performance of the normative group on the Digit Span subtest of the Wechsler Adult Intelligence Scale - III H8: The performance of the drop- out. .. stores information for further analysis Second, competitive selection process determines and selects prioritized information to gain access to working memory Third, top-down sensitivity control process regulates signal intensity in information channels that compete for better access to working memory, influencing the selection of new information This mediates voluntary control of attention in a recurrent... 60,814 students between 1999 and 2002 In that same academic period, 64,057 students have dropped out from Chicago Public High Schools; the number of students dropping out exceeded the number of students graduating by 3,243 between 1999 and 2002 In 2002, the number of high school students dropping out of Chicago Public Schools (17,404) reached the highest level ever recorded and it has been increasing