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A Nationwide Epidemiologic Modeling Study of LD: Risk, Protection, and Unintended Impact

A Nationwide Epidemiologic Modeling Study of LD: Risk, Protection, and Unintended Impact
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   JOURNALOF LEARNING DISABILITIESVOLUME 39, NUMBER 3, MAY/JUNE 2006, PAGES 230–251 ANationwide EpidemiologicModeling Study of LD: Risk, Protection, and Unintended Impact Paul A. McDermott, Michelle M. Goldberg, Marley W. Watkins, Jeanne L. Stanley, and Joseph J. Glutting Abstract Through multiple logistic regression modeling, this article explores the relative importance of risk and protective factors associated withlearning disabilities (LD). Arepresentative national sample of 6- to 17-year-old students ( N  = 1,268) was drawn by random stratificationand classified by the presence versus absence of LD in reading, spelling, and mathematics according to ability–achievement discrepan-cies or low achievement levels. The dichotomous classifications were regressed on sets of explanatory variables indicating potential biological, social–environmental, and cognitive factors, problem behavior, and classroom learning behavior. Modeling revealed patternsof high risk for male students and students evincing verbal and nonverbal ability problems and processing speed problems. It was shownthat, absent controls for cognitive abilities (such as provided by the ability–achievement discrepancy definition), definitions keyed to lowachievement will substantially overidentify ethnic minority and disadvantaged students and will be confounded by significantly higherproportions of students who display oppositional and aggressive behavior problems. Alternatively, good learning behaviors uniformlyprovide substantial reduction in the risk for LD. A ccording to the U.S. Depart-ment of Education, NationalCenter for Learning Disabili-ties (NCLD), 2.8 million American stu-dents are currently receiving specialeducation services for learning disabil-ities (NCLD, 2002). This represents al-most 6% of all public school children.Not included in these numbers are thechildren in private schools, who may be receiving few, if any, learning sup-port services, and the children in bothpublic and private schools who haveserious learning difficulties but havenot been identified due to definitionalissuesLearning disabilities (LD) are con-sidered neurological deficits that inter-fere with a student’s ability to store,process, or produce information andthat create discontinuity between one’sability and performance leading to sig-nificant academic and social difficul-ties (Gettinger & Koscik, 2001; NCLD,2002). Although a student with LDmay have performance difficulties inone or more areas, such as reading,writing, spelling, arithmetic, listening,talking, and social perception, these in-dividuals generally have normal cog-nitive abilities (Culbertson & Edmonds,1996; NCLD, 2002). The impact of theLD on the student’s education anddaily life can range from mild to se-vere, with academic underachieve-ment or failure being the most com-mon outcome.Due to their continued academicproblems, children with LD often expe-rience social–emotional problems, suchas low self-esteem and difficulty withmaking and maintaining friendships(Gettinger & Koscik, 2001). As thesechildren move into adolescence, theymay exhibit characteristics such aslearned helplessness, decreased confi-dence in their ability to learn or suc-ceed, low motivation, attention prob-lems,and maladaptive behavior (Desh-ler, Ellis, & Lenz, 1996). As reported  by the NCLD (2002), 35% of childrenwith LD drop out of high school. Thisis twice the dropout rate of studentswithout LD. Of the students with LDwho do graduate, fewer than 2% at-tend a 4-year college, despite being of average or above-average intelligence(NCLD, 2002). The NCLD (2002) alsoreported that several studies haveshown that between 50% and 60% of adolescents in treatment for substanceabuse have LD.The formation of a knowledge baseregarding LD began in the early 1900s(Culbertson & Edmonds, 1996). Re-search since then has produced a com-plex array of terminology and con-ceptualizations of LD, all of whichhave led to the current issues of def-initions, subtypes, and research ap-proaches (Culbertson & Edmonds,1996). Although there seems to be a  VOLUME 39, NUMBER 3,MAY/JUNE2006 231fairly common set of terms used in thefield, there is great controversy overand variation in the issues of defini-tions and diagnostic criteria.These issues have been hotly de- bated in educational, behavioral, andmedical journals (Doris, 1993) duringthe past 20 to 25 years in particular. Anumber of arguments have been madethat intelligence tests have limited util-ity for the identification of childrenwith LD and that the traditional IQ–achievement discrepancy criterion forLD should be abandoned (Bryan, 1989;Dombrowski, Kamphaus, & Reynolds,2004; Fletcher et al., 1998; Siegel, 1989,1990, 1999; Sternberg & Grigorenko,2002). Siegel (1989) argued that tests of achievement might provide a clearerpicture of a child’s actual functioningthan IQ. Siegel (1989, 1999) also con-tended that individuals with LD oftenhave deficits in one or more of thecomponent skills that are measured by IQ tests and that their scores on thosetests are an underestimate of their abil-ity, thereby underidentifying childrenwith LD.Despite these arguments, a dis-crepancy between ability and achieve-ment has long been the major criterionfor diagnosing LD in the United States(Gettinger & Koscik, 2001; Gregg &Scott, 2000; Mercer, Jordan, Allsopp, &Mercer, 1996), and an intelligence testis one of the primary tools used toidentify LD (Siegel, 1999). In a surveyof the 51 departments of education rep-resenting the states and the District of Columbia, Mercer et al. (1996) foundthat 27% of the departments includedan ability–achievement discrepancycomponent in their definition of LDand 94% included the discrepancycomponent in their criteria for diag-nosing LD. Operationalization of thediscrepancy component varies some-what among the states, although re-gression analysis is the most commonprocedure for detecting such discrep-ancies (Mercer et al., 1996).The use of a regression approachis thought to be the most psychometri-cally defensible method for determin-ing an ability–achievement discrep-ancy (Culbertson, 1998; Heath & Kush,1991; Reynolds, 1984; Thorndike, 1963;Wilson & Cone, 1984). This methoduses a prediction equation based on thecorrelation between IQ and achieve-ment scores. The student’s IQ is usedto predict his or her expected achieve-ment test score, which in turn is com-pared to his or her actual achievementtest score. If there is a significant dif-ference between expected and actualachievement, the student is consideredto have a notable discrepancy.Previous research on the etiologyof LD has included many studies on ge-netic and neurodevelopmental factors,particularly in regard to reading dis-abilities. Anumber of anatomical cor-relates have been studied, includingcerebral lateralization abnormalities,cerebral asymmetry, minor cortical mal-formations, immune disturbances, andgenetics (Culbertson, 1998). The NCLD(2002) has reported that experts do notknow what exactly causes LD and thatfactors such as heredity, problems dur-ing pregnancy or childbirth, and in-cidents after birth (e.g., poisoning,head injury) may contribute. Gallicoand Lewis (1992) have also noted thatthe cause of LD remains unclear. How-ever, they reported that affected chil-dren do not seem to have an increasedincidence of birth trauma or remark-able environmental influences andtend to develop as rapidly as childrenwithout disabilities, except in the areaof language.Contemporary research includesstudies designed to explore the neuro-psychological processes that would ap-pear emblematic of LD and that arehoped to explain their unique mani-festations or srcins. Most often, thiswork takes the form of studies thatattempt to discover subtypes of neu-rocognitive, academic, and other mea-surable performance patterns that dis-tinguish LD (Kavale & Forness, 1987;Siegel, 2003). Each child’s pattern of functioning is represented by a profileof measured attributes. Similar profilesare grouped together through typalcluster or other structural analysis toform subtypes that are studied for theirrelative prevalence among childrenwith LD and are otherwise interpretedfor their inferential value in explain-ing mediating neuropsychological pro-cesses. Although very interesting re-search has been produced in this arena,the scientific advances are seriouslylimited by methodological impedi-ments. Such studies tend to be overlyreliant on relatively small samples of children who are classified a priori and by disparate criteria (Morris, 1988) ashaving LD, thereby failing to ensurethat the subtypes identified are trulyunique to LD populations and notcommonplace among non-LD popula-tions (e.g., Brewer, Moore, & Hiscock,1997; Davis, Parr, & Lan, 1997; Fletcher,Morris, & Lyon, 2003; Morris et al.,1998; Silver, Pennett, Black, Fair, & Ba-lise, 1999; Spreen & Haaf, 1986). Thesestudies also tend to construct chil-dren’s profiles based on attributes thathave no empirically established factor-ial validity or that are drawn from dif-ferent standardized test batteries. Inthe latter case, the clustering of profilesinto subtypes does not appear to ap-preciate the fact that performance ex-tracted through measures developedwith different normative samples atdifferent times will inevitably yieldvariation due to sampling error ratherthan true individual child variation.Such investigations alternatively re-quire large random samples of chil-dren drawn from the entire schoolpopulation, in which learning disabili-ties are uniformly identified post hoc,profile attributes are simultaneouslynormed and validated for factorial in-tegrity, and the resultant subtypes arefound emergent over multiple inde-pendent replication trials (see McDer-mott, 1998, on typal cluster replica-tion). Due caution is also warranted indrawing inferences about children’sinternal, mediating, neuropsycholog-ical processes as based on their psycho-metric and behavioral performances,rather than on direct neurological and biological evidence.According to Lyon (1996), limitedinformation exists on how race, ethnic-ity, and cultural factors may influence   JOURNALOF LEARNING DISABILITIES 232the development of LD. This lack of re-search may be due in part to the factthat all current state and federal defini-tions mandate that the deficits in LDcannot be attributed to cultural factors,including race and ethnicity (Lyon,1996). Culbertson (1998) suggested thatcultural and environmental factorshave important roles in learning acqui-sition. Children from impoverishedenvironments—both in terms of theamount of materials or toys availableto help learning and in terms of theavailability of educational or intellec-tual resources from caregivers—mayhave a distinct disadvantage (Culbert-son, 1998).Prevalence data are difficult todiscern for male and female students because of conflicting data dependingon whether the child is research identi-fied or school identified (Culbertson,1998). Among students actually iden-tified as having LD by schools, onlyabout 50% demonstrate a significantaptitude–achievement discrepancy (Ka-vale & Forness, 2000). It seems thatschools tend to identify more boysthan girls as having LD, due to boys’more disruptive and attention-getting behavior in the classroom. Girls tend tomanifest attention and learning prob-lems differently from boys, without asmuch acting-out behavior, and, there-fore, do not attract as much attentionfrom their teachers (Gallico & Lewis,1992). According to the NCLD (2002),equal numbers of girls and boys have been found to have reading disabili-ties, but boys are 3 times more likely to be evaluated and treated. Lyon(1996) reported that schools identify boys as having reading disabilitiesabout 4 times as often as girls, but that longitudinal and epidemiologicalstudies of clinical populations haveshown that approximately as manygirls as boys have reading disabilities.The current educational zeitgeistis clearly calling for empirical researchthat is multicultural, is multidimen-sional, and can serve as the foundationfor educational programming aimed atensuring that every child receives aquality education (Paige, 2002). To beinformative, such research must be rea-sonably generalizable across the nationand able to free the facts from the en-tanglement of controversies over defi-nition and disparities in the applica-tion of diagnostic criteria. Moreover,given the alarming prevalence and po-tentially detrimental outcomes of LD,new research must look for agents thatoperate to protect children from, orhelp to mitigate, LD, in addition to in-forming the relative precedence anddynamic pathways of risks that por-tend LD. Technically, this requires thatfuture research focus simultaneouslyon pertinent protective and risk factorsand that it not concentrate exclusivelyon the risk factors that are investigatedin the context of inconstant diagnosticcriteria.Whereas extant epidemiologic re-search has been quite informative, ithas been grounded primarily in stud-ies with children who were diagnoseda priori as having LD (i.e., clinical pop-ulations; see, e.g., Blair & Scott, 2002),notwithstanding the high likelihoodthat such populations will reflect allthe definitional inconsistencies andvariations in diagnostic practice thatplague the field (MacMillan & Siper-stein, 2001). Modern epidemiologicscience offers a viable alternative to in-vestigations hampered by such irregu-larities. Rather than drawing on extantpopulations that are loosely presumedto share a common and objectivelyidentified morbidity, epidemiologistsprefer to draw on large, randomlysampled populations ( community sam-ples) and thereafter apply uniform andscientifically reliable diagnostic criteriafor all individuals in the random sam-ple, thus distinguishing those individ-uals who in fact satisfy the criteriafrom those who do not. Once the diag-nostic distinctions are empirically de-fined, other common relevant factors(biological, environmental, behavioral,educational, etc.) are studied for all in-dividuals, with a special interest inthose factors that accurately differenti-ate those individuals diagnosed posi-tive versus negative for the conditionand a special focus on the relative riskor protection afforded by such factors.Multivariate statistical modeling isused to help disentangle the factorsthat portend disease versus health. Aprimary example of such epidemio-logic modeling is the work in the na-tional mental health arena carried outin the wake of the U.S. EpidemiologicCatchment Area Survey (J. W. Swan-son, Borum, Swartz, & Monohan, 1996).Here, given the widespread inconsis-tency in the application of criteria formental disorders, the federal govern-ment funded the identification of a na-tionwide, stratified random sample of persons who thereafter were examinedthrough rigorous structured interviewsto diagnose the presence or absence of mental illness and to identify the vari-ous life factors (other than the particu-lar diagnostic criteria) that constitutedrisks and protections. Similar large-population community studies haveassessed the longitudinal risk growthcurves that connect childhood expo-sure to birth anomalies, lead, and fam-ily stressors and subsequent cascadingschool failure (Tighe, McDermott, &Grim, 2001; Weiss & Fantuzzo, 2001).Within this epidemiologic frame,our research team designed a series of modeling studies to investigate theconnections between uniformly andempirically defined LD in a large andrepresentative community sample of American students. We took advan-tage of the overlapping cohorts of stu-dents obtained for the nationwidenorming of several standardized in-struments—one measuring academicachievement and cognitive ability, an-other a myriad of classroom behaviorproblems, and still another focusing onstudent learning strategies and reac-tion styles. Given the national sample,we defined LD de novo as either theexistence of a relatively rare discrep-ancy between expected and observedachievement (the ability–achievementdiscrepancy rule) or the presence of markedly low achievement (the lowachievement rule). For each definition,  VOLUME 39, NUMBER 3,MAY/JUNE2006 233and with respect to achievement inreading, spelling, and mathematics,multiple logistic models were con-structed to identify the specific leveland nature of risk for LD associatedwith five distinct classes of explana-tory variables. Whereas one might en-vision a nearly endless list of prospec-tive risks, we decided to concentrateon those variables that would be ex-pected to reasonably clarify the factsand that would comprise the types of information that researchers or practi-tioners could easily duplicate withoutthe necessity for less accessible or com-plete archival data.In our models, we applied a set of potentially biological markers. Theseincluded student age, sex, and ethnic-ity. Although one might argue that anyof these factors may well convey vari-ance that is more sociological than bio-logical, it is nonetheless clear that eachfactor carries variance linked directlyto conception or birth that cannot al-ternatively be caused by subsequentenvironmental factors. Another vari-able in this set was any major physicalimpairment that would not have pre-cluded a student’s participation in stan-dardized, individualized testing (e.g.,cardiac problems, speech impediments).Given the rich literature on therelationships between environmentalagents and school success (Weiss & Fan-tuzzo, 2001), we hypothesized that stu-dents attending large, urban schoolswould suffer some risk for learningdifficulties, but that those raised inhouseholds with progressively moreeducated parents would obtain anadvantage, especially over studentswhose parents had relatively little for-mal education. Parent education alsoserves as a viable proxy in Americanpopulation research for social and so-cioeconomic strata (Ceci, 1991). Be-cause of the new evidence for botheducational and behavioral problemsthat may associate with single-parenthouseholds (Tighe et al., 2001; Weiss &Fantuzzo, 2001), we decided to incor-porate those factors in our models aswell.Because cognitive ability is—atleast in the case of the ability–achieve-ment discrepancy definition of LD—apart of the identification mechanism,one might surmise that aspects of cog-nitive ability would not appear as in-dependent variables in a modeling in-quiry. However, its only direct role in definition is limited to estimatingachievement expectations, whereuponthe resultant dependent variable (un-derachievement) is actually formed insubsequent steps that incorporate dif-ferent achievement indices that, in ourmodeling routines, are further modi-fied through rubrics specifying whatconstitutes meaningful discrepancies.We believe—especially given the afore-mentioned controversies surroundingthe relevance of cognitive ability—thatno explanatory model would be com-plete were it to ignore the potential riskor protective effects attendant on gen-eral cognitive ability and performancesin major subdomains, such as verbal,nonverbal, and spatial abilities (seeMasten & Coatsworth, 1998, on theglobal protective aspects of children’scognitive ability). Also, researchersand practitioners who are interested inLD continue to contest the diagnosticor treatment relevance of peculiar orcharacteristic configurations of cogni-tive abilities presumably manifestedthrough substantial scatter of scoresamong the subtests that comprise abil-ity batteries or the appearance of pre-sumably rare and pathognomonicscore profiles for those same subtests.Indeed, ability subtest profile analysisis something of a mainstay in leadingtexts employed to prepare school psy-chologists (e.g., Kaufman, 1994), pro-moting subtest patterns that shouldcharacterize or raise the suspicion of LD. Another cognitive capacity, thespeed of information processing, has become a special focus of some re-searchers concerned about LD (Kail,2000, p. 52). Processing speed refers tothe ability to maintain a certain degreeof attention and concentration in rap-idly processing basic cognitive tasks(Sattler, 2000). Slower processing speedhas been linked with LD in general (H. L. Swanson, 1988; Weiler, Harris,Marcus, Bellinger, Kosslyn, & Walker,2000) and with reading disabilities inparticular (Aaron, Joshi, & Williams,1999).Pervasive behavior disorders inthe classroom constitute another classof explanatory factors that we haveventured to model. It is conventionalpractice that LD not be diagnosedwhen the learning problems are a con-sequence of primary emotional or so-cial maladjustment, but it is neverthe-less also true that many students withLD suffer dispositional and behavioralproblems that are either concomitantsor sequelae of frustrating experienceswith learning activities (Roeser, Eccles,& Stroebel, 1998). The sense of frustra-tion and other perceived pressure ap-pears to drive some students to with-draw effort or feign incompetence, orto become defiant or outright violent(see, e.g., Boekaerts, 1993; Brackney &Karabenick, 1995; Furlong & Morrison,1994). Thus, we have collected stan-dardized assessments of markedlyatypical and phenotypically distinct behavior patterns (attention-deficit/hyperactivity, aggressiveness, impul-siveness, oppositionality, diffidence,avoidance) over a 2-month period.Moreover, our assessments accommo-dated the more informed view of be-havior pathology as being not simplythe manifestation of certain intense re-actions in certain situations, but a moreconsistent pattern of similar manifesta-tions across multiple situations in theschool setting (Horn, Wagner, & Ia-longo, 1989). This perspective countersthe alternative prospect that problem behavior manifested only with certainpeople or in certain situations is farmore likely to be a reactive or randomoccurrence, and not indicative of anyreal pathology.As noted earlier, we endeavoredto explore the relative impact of con-ceivably viable protective factors. Theseincluded indicators of successivelyhigher parent education levels, dual-parent families, and cognitive ability.   JOURNALOF LEARNING DISABILITIES 234Higher cognitive ability alone has beendemonstrated to function as one of themost instrumental agents in protectingchildren from the vicissitudes of im-poverishment, maladjustment, and aca-demic failure (Mannuzza, Gittelman-Klein, Bessler, Malloy, & LaPadula,1993; Masten & Coatsworth, 1998; Weiss& Hechtman, 1993). Regrettably, afterdecades of research on aptitude–treatment interactions and learningpotential, most general cognitive abili-ties have been found to be relatively in-tractable to programmatic efforts thatwould improve them in malaffectedchildren (Brown & Campione, 1982;Ceci, 1990, 1991; Glutting & McDer-mott, 1990; Scarr, 1997; Snow, 1986;Spitz, 1986). It is this lack of successthat, in part, has led to the contempo-rary opinion that information drawnfrom cognitive ability measures is notvery useful for planning promisingeducational interventions (Gresham &Witt, 1997). In response, we haveturned to a final set of explanatory fac-tors that we believe to hold promise forinforming workable interventions. Wedrew on 20 years of empirical researchon students’ differential approaches tolearning (McDermott, 1999; McDer-mott, Mordell, & Stoltzfus, 2001; Stott,McDermott, Green, & Francis, 1988), or learning behaviors , that underpin thesuccessful mastery of academic tasks.These behaviors are assessed throughstandardized teacher observations overtime and encompass aspects of compe-tence motivation, task planning, per-sistence, responses to error and assis-tance, flexibility, and positive attitudestoward learning. These attributes, asmanifested in classroom behavior,have been deemed keystone elementsin successful school performance, andit has been found that many of themare responsive to teaching and educa-tional programming (Barnett, Bauer,Ehrhardt, Lentz, & Stollar, 1996). In-deed, the National Education GoalsPanel (1997) of the U.S. Department of Education has underscored the partic-ular significance of learning behaviors:First, they have embraced them as oneof the five essential components of chil-dren’s school readiness, making thema national strategic focus for early in-tervention with children at risk for pooracademic outcomes; second, howbeittheir apparent value as protectiveagents, they have been identified as the least understood and the least re-searched school readiness competen-cies (Kagan, Moore, & Bredekamp,1995). Method Participants The cross-sample ( N  = 1,268) was com-posed of the overlapping portions of the national standardization samplesfor the Differential Abilities Scales (DAS;Elliot, 1990),  Adjustment Scales for Chil-dren and Adolescents (ASCA; McDer-mott, Marston, & Stott, 1993), and Learning Behaviors Scale (LBS; McDer-mott, Green, Francis, & Stott, 1999).The cross-sample was designed to berepresentative of all noninstitutional-ized 6- through 17-year-old studentsattending school in the United Statesduring the 1990s. Participants were se-lected from 154 public school districtsand 47 private schools in 70 U.S. Cen-sus metropolitan statistical areas andassociated rural areas across the fourregions of the nation.The cross-sample conformed tothe parameters of the 1992 U.S. Census(U.S. Department of Commerce, 1992)with matrix blocking for sex, age, andgrade level (634 boys and 634 girls withapproximately balanced distributionsof students and sexes within 1-year ageand grade intervals). Stratified randomsampling was conducted by race, par-ents’ education level, community size,and geographic region. Sampling pre-cision included simultaneous within-cellmatching across all stratification vari-ables (e.g., correct proportions for race by parent education by region, etc.)and matching to marginal proportions.On the basis of census parame-ters, the cross-sample consisted of 68%European American students, 16% La-tino, 13% African American, and 3%other ethnic minorities. Also in accordwith census parameters, 74% of thesample resided in two-parent families,24% in mother-only families, and 2% infather-only families. Parent educationserved as the primary index of socialclass because of its strong ability to re-flect essential class differences, as dem-onstrated in other research on youthscholastic ability (Ceci, 1991) and be-havior (Farrington, 1986; Magnuson,Stattin, & Dunner, 1983). Parent edu-cation was defined as the averagenumber of years of formal schoolingcompleted between a student’s motherand father (or the total number of years completed by a single parent orguardian) and was categorized into a 3-point scale. As per the standard clas-sification system employed by the U.S.Census Bureau (U.S. Department of Commerce, 1992), 16% of sample stu-dents had parents who did not gradu-ate high school, 66% had parents whowere high school graduates, and 18%had parents who completed at least 4 years of college. Approximately 44%of students resided in major metropol-itan statistical areas, as characterized by total populations ≥ 1 million. Con-sistent with the design to draw a rep-resentative sample, the resulting meanscores for the DAS, ASCA, and LBS inthe cross-sample were within 1 stan-dard score point of the respective pop-ulation means.  Instrumentation Cognitive Ability. Various as-pects of cognitive ability were assessedwith the DAS (Elliot, 1990), an individ-ually administered, multidimensional battery for use with children ages 6 to17 years. The DAS is a hierarchicallystructured test in which scores on sixsubtests are combined to form mea-sures of three major cognitive subdo-mains: Verbal Reasoning, NonverbalReasoning, and Spatial Ability. Thesubdomains are combined to form theGeneral Conceptual Ability (GCA)score, which is a measure of generalintellectual functioning (i.e., Spear-man’s  g ). An additional three subtestsare usually administered as well, but
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