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TRANSCRIPT
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THE RELATIONSHIP BETWEEN EARLY CHILDHOOD SPEECHLANGUAGE IMPAIRMENT
AND YOUNG ADULTHOOD ANTISOCIAL BEHAVIOUR
David Aaron Rebuck, Hon. B.Sc.
A thesis submitted in conformity with the requirernents for the degree of Master of Science, Graduate Department of Community Health,
Faculty of Medicine, University of Toronto
O Copyright by David Aaron Rebuck, 1998
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THE RELATIONSHIP BETWEEN EARLY CHlLDHOOD SPEECH/LANGUAGE IMPAIRMENT AND YOUNG ADULTHOOD
ANTISOCIAL BEHAVIOUR
David Aaron Rebuck, Hon. %.Sc.
A thesis submitted in conformity with the requirements for the degree of Master of Science, Graduate Departrnent of Cornrnunity Health, Faculty of Medicine,
University of Toronto.
ABSTRACT
This investigation used data from the Ottawa Language Study to examine the
relationship between early chiIdhood speechnanguage impairments and young adulthood
antisocial behaviour. At age 5, 284 chiidren were categonzed as either speech-only
impaired, language irnpaired or controls. While accounting for a large variety of age 5
covariates, their age 19 antisocial behaviour was assessed. Children who were
Ianguage impaired had the worst outcome. These children were at significant risk for a
diagnosis of antisocial personality disorder (odds ratio=3.66, 95%CL: 1.49-9.01) and were
also considered by parents and/or teachers to be significantly more antisocial than those
without a history of such im pairrnents. However, when antisocial behaviour was
measured in terms of criminal activity or when subjects rated their own antisocial
behaviour, no significant differences were found arnong the age 5 speechtlanguage
groups. Despite this, these findings are the first to report the long-term, young adulthood
psychiatrie consequences of early childhood language impairments.
I am especially grateful to Dr. Joseph H. Beitchrnan for the use of the Ottawa
Language Study (OLS) data on which this study is based as well as for his guidance and
supervision of this thesis. I am also indebted to my thesis cornmittee members: Prof.
Susan J. Bondy, for her patience, assistance and support and Prof. Michael Escobar for
his advice and explanations on statistical matters. I also acknowledge my indebtedness
to Beth Wilson, whose knowledge of the OLS data was invaluable to me throughout the
development and completion of this work. Finally, I acknowledge the constructive
feedback provided by rny thesis examiners: Prof. Mary Chipman, Prof. Bruce Ferguson
and Prof. Rhonda Love.
TABLE OF CONTENTS
-. ABSTRACT ............ ...,. ............................................................................................. t I
ACKNOWLEDGMENTS.. ......................................................................................... iii ... LIST OF TABLES.. ................................................................................................... vil I
UST OF FIGURES .................................................................................................... x
LIST OF APPENDICIES.. ................................................................................... .... xi
1. Introduction ......................................................................................................... 1
1.1 Objective ............. ... .......................................................................... 1
1.2 Study Rationale and Strengths ....................................................... 1
2. Background ........................................................................................................ 2
2.1 Speech and Language Irnpairments .............................................. 3
............................................................................ 2.1.1 Definitions ..3
................................................. ...................... 2.1 -2 Prievalence ,. 4
2.1 .3 Problems in Defining Speechllanguage
Impairments ... .................................................................... 6
2.2 Longitudinal Outcome of Speechllanguage Impairnent ......... 7
2.2.1 Behavioual Findings .......................................................... 7
2.3 Relevance ............................................................................................. 1 O
2.4 Evidence that Suggests an Association Between
SpeechRanguage Impairment and Antisocial Behaviour.. 1 1
2.4.1 Speechllanguage Impairment as an
......................................................... Antecedent Variable 1 1
2.4.2 Speechllanguage Impairment, ADHD and Antisocial
Behaviour ............................................................................ 1 3
2.5 From Early Childhood Impairment to Young Adulthood Outcome:
...................................................... Possible Mechanisms 14
2.6 The Present Study .............................................................................. 1 5
2.6.1 Data Source and Conceptual Framework ..................... 15
2.6.2 Antisocial Behaviour: Conceptualizations ..................... 18
2.6.3 Risk Factors to be Controlled ........................................ 19
2.6.4 Hypothesis ............................................................................ 28
3 . Methods ............................................................................................................... 29 3.1 The Ottawa Language Study ......................... .. ............................... 29
3.2 lndependent Variable of Primary Interest: Speechllanguage
.............................................................................................. Status 2 9
3.3 Covariates ............................................................................................ 30
............................................................................ 3.3.1 Intelligence 30
3.3.2 Socioeconomic Status (SES) .......................................... 31
3.3.3 Childhood Adversity ........................................................... 31
3.3.4 Inattention, Hyperactivity and Conduct Problems ....... 33
3.3.5 Speechllang uage Treatrnent ......................................... - 3 3
......................................................................... 3.4 Dependent Variables 33
3.4.1 Antisocial Behaviour as a Diagnosis .............................. 33
3.4.2 Antisocial Behaviour as a Legal Entity ........................... 34
3.4.3 Antisocial Behaviour as a Dimension ............................. 34
.. .................................................................... 3.5 Statistical Analysis ... 37
3.5.1 Data Exploration ............................................................... 37
......................................................... 3.5.2 Logistic Regression 38
.......................................... 3.5.3 Linear Regression .............. ... -40
3.6 Missing Data ........................................................................................ 40
...................................................... 3.6.1 Independent Variables 4 0
................................ ............ . 3.6.2 Dependent Variables ... ....... 4 0
.......... 3.7 Descriptive Statistics ; .......................................................... 41
............................................................................ 3.8 Power Calculations 41
......................................... 3.9 Tests of Reproducibility of the Results 42
3.9.1 Independent Variables ....................................................... 42
.......................................................... 3.9.2 Dependent Variables 44
..................... 3.9.3 Reproducibility of the Results: Surnmary -44
4 . Results 1: Prirnary Analyses ...................... .. ................................................ 46 . .
4.1 Summary Stahstics ................ .. .................................................... 46
4.2 Conceptualization 1: Diagnoses of ASPD ....................................... 48
4.2.1 Unadjusted Resuits ............................................................. 48
4.2.2 Adjusted Results ............................................................... 49
4.2.3 Summary of Unadjusted and Adjusted Results for the
Analysîs of ASPD ................................................... 52
4.3 Conceptualization II: Criminal Activity ............................................ 52
4.3.1 Unadjusted Results ............................................................. 52
.............................................................. 4.3.2 Adjusted Results 53
4.3.3 Sumrnary of Unadjusted and Adjusted Results for the
Analysis of Criminal Activity ........................................... 56
4.4 Conceptualization III: Antisocial Behaviour as a Dimension .... 57
............................................... 4.4.1 Non-su bject Measures 57
4.4.2 Subject Measures ............................................................ 6 1
4.5 Summary of Unadjusted and Adjusted Results ........................ 64
5 . Results II: Supplementary Analyses ............................................................ 67
5.1 The Composite Nature of the Dimensional Measure of
Antisocial Behaviour ..................................................................... 67
5.2 The Composite Nature of the Non-subject Measure of
Antisocial Behaviour .......... .... ................................................. 69
5.3 Group Differences in Subject-perceived Social Suppo R ......... 71
5.4 ASPD and Subject Measures of Antisocial Behaviour .............. 71
.......................................................................................................... . 6 Discussion 74
6.1 Ovenriew of Findings ....................................................................... 74
6.2 Poorer Outcome Among those Language versus Speech-only
Impaired ........................................................................................... 76
6.3 Evidence of Antisocial Personality Disorder versus Criminal
Behaviour ............... ,., .................................................................... -77
6.4 Subject versus Non-subject Measures ..................................... 79
6.5 Confounding ......................................................................................... 82
.................... 6.5.1 The Covariates as Confounding Variables 83
......................................... 6.5.2 Ancillary Findings ............ .... 84
6.6 Implications .......................................................................................... 86
6.6.1 Language-based Problems and Vulnerability for
.............. Antisocial Behaviour .............................. ...... 86
6.6.2 Implications for Preventi on ............................................. 88
6.7 Study Limitations ................................................................................ 89
......................................................................... 6.7.1 Missing Data 89
6.7.2 lntegrated Analysis of the Data for Both Males and
............-...........*.........*.. ............... ..................... Fernales .,, ... 90
................... 6.7.3 Merging of Parent and Teacher Measures 90
6.8 Recommendations .....................................~....................................... 90
6.9 Summary .............................................................................................. 91
.................................,,.............. .............................. REFERENCES , -92
APPENDIX A Missing Data . Tables A1 and A2 ............................................ 103
APPENDW B Comparisons of subjects with and without missing time-3
antisocial outcome data . Tabfes B I OB5 .................................. 106
APPENDIX C Multicollinearity and Diagnostics . Tables Cl-C6 .................. 112
vii
UST OF TABLES
Table 3.1 :
Table 3.2:
Table 3.3:
Table 4.1 :
Table 4.2:
Table 4.3:
Table 4.4:
Table 4.5:
Table 4.6:
Table 4.7:
Table 4.8:
Table 4.9:
Table 4.1 0:
Table 4.1 1 :
Correlations (and their p-values) of the Aggression and Delinquency Subscale T scores and raw scores for the CBCL, TRF and YSR instruments, respectively ........... ..,, ..........~........................................ 36
Correlations (and their p-values) of the parent-rated (from the CBCL) and teacher-rated (frorn the TRF) rnean antisocial measures for the T scores and raw scores,respectively ....................................... 37
Approximate decile groups for the Conners variables of Hyperactivity, Conduct and Inattention, the range of values within each group and
............................................................................................. their size 43
Descriptive statistics (and standard deviations) for the speech1 language groups on time-1 sample size, each of the time-1 covariates, and time-3 speechnanguage treatment variables. ........... 47
Unadjusted proportions with diagnoses of ASPD for each speechllanguage group .....................~............................................... 48
Odds ratios (Y) and their 95% CLs for the diagnosis of an ASPD for the speech-only impaired and language irnpaired groups unadjusted for any covariates .............~............................................. 48
Results from the univanate and ANCOVA analyses for each covariate and for each alternately defined covariate for the diagnosis of an ASPD.. ...................................................................... 50
Relevant odds ratios and their 95% CLs for the variables in ............................................................................................... Model 1 51
Cornparison of the unadjusted and the adjusted speechllanguage odds ratio estimates and their 95%CLs for the analysis of ASPD ..... 5 2
Unadjusted proportions of subjects who reported some fonn of criminal behaviour for each speechllanguage group ......................... 53
Odds ratios (Y) and their 95% CLs for the occurrence of criminal behaviour for the speech-only irnpaired and language irnpaired groups unadjusted for any covariates ............................................ 53
Results from the univariate and ANCOVA analyses for each covariate for the occurrence of criminal behaviour ........................... 54
Odds ratios and their 95% CLs for the variables in Model2a ............ 55
Relevant odds ratios and their 95% CLs for the variables in .................................................. ..................................... Model 2b .. 55
S..
Vlll
Table 4.12: Cornparison of the unadjusted and the adjusted (Model Sa) speechtlanguage odds ratio estimates and their 95%CLs for the
* .
analysis of criminal activity .....................,.....+~~.................. 5 ï
Table 4.13: Mean non-subject measured antisocial T ratings for each speechllanguage impaired group, unadjusted for any covariates ..... 57
Table 4.14: Results from the univariate and ANCOVA analyses for each covariate for the non-subject measures of antisocial behaviour ...... 58
Table 4.15: Adjusted means for each speech/language group from Mode1 3a .... 59
Table 4.16: Adjusted means for each speechftanguage group frorn Mode! 3b .... 60
Table 4.17: Cornparison of the unadjusted and the adjusted (Model 3a) speechtlanguage mean antisocial behaviour estimates for the analysis of non-subject measures ..................................................... 61
Table 4.18: Mean subject measured antisocial T ratings for each speech/ .................. language impaired group, unadjusted for any covanates 61
Table 4.19: Results from the univariate and ANCOVA analyses for each covariate for the subject measures of antisocial behaviour .............. 62
Table 4.20: Adjusted means for each speechtianguage group from Model 4...... 63
Table 4.21: Cornparison of the unadjusted and the adjusted (Model 3a) speech/ language mean antisocial behaviour estirnates for the analysis of subject measures ............................................................ 64
Table 4.22 Sumrnary table of the results unadjusted for any of the covanates for the dichotomous outcomes (diagnoses of ASPD and occurrence of criminal behaviour) and dimensional outcomes (non-subject and subject measures), respectively.. ...................................................... 65
Table 4.23: Summary table of the results adjusted for the covariates for the dichotomous outcornes (diagnoses of ASPD and occurrence of criminal behaviour) and dimensional outcomes (non-subject and subject measures), respectively ....................................................... 66
Table 5.1: SpeechAanguage group mean T scores and raw scores for the subject-measured Delinquency and Aggression Subscales ............ .68
Table 5.2: Speech/language group mean T scores and raw scores for the ...... non-subject measured Delinquency and Aggression Su bscales 68
Table 5.3: Subject-measured mean antisocial behaviour T scores for those with and without a diagnosis of Antisocial Personality Disorder (ASPD) ............................................................................................... 71
Table 5.4: Subject measures of antisocial behaviour for those with and without an ASPD and the associated Pearson correlation coefficients for each speechnanguage group ................................... 72
LIST OF FIGURES
Figure 2.1 : Follow-up and attrition in the Ottawa Language Study ...................... 17
Figure 5.1 : Adjusted subject and non-subject measures of antisocial be haviour by time-1 speechflanguage grou p .................. ..... .... -70
UST OF APPENDICES
. ................................................. APPENDIX A: Missing Data Tables A I and A2 103
APPENDK B: Cornparisons of subjects with and without missing time-3 antisocial outcome data . Tables BI-85 ................... ,.. ............. 106
APPENDiX C: Multicollinearity and Diagnostics . Tables Ci .C6 ..... ..... ...................... 112
1. Introduction
1.1 Objective
This thesis made use of data from the Ottawa Language Study (OLS), a
cornmunity-based, three-wave panel investigation. Children were recruited into the study
ai age five and were categorized as either speech-only impaired, language impaired or
as controls. The present investigation adds to the literature by exploring the relationship
between early childhood speechltanguage impairnent and subsequent antisocial
behaviour in young adulthood. Specifically, it assesses whether speechllanguage
impairrnents, identifieci in children 5 years of age. are associated with antisocial
behaviour in young adults 19 years of age.
1.2 Study Rationale and Strengths
The rationale and strengths of this study are many and each justified its
implernentation. First, the study is unique as it is the first to address the long-term
psychiatric outcome of early chiidhood speechnanguage impairment. Second, it
addresses this outcome during a critical period of Iife, young adulthood, during which
success and failure have long-term consequences. Third, multiple measures of the
antisocial outcorne are used, thus allowing for the cornparison of different data sources
and different charactenzations of antisocial behaviour. Fourth, a large variety of possible
confounding variables are controlled, thus strengthening the conclusions drawn from the
results. Fifth, inferences frorn this study are strengthened by the nature of the data on
which it is based. For example, the data represents a community (rather than chic)
sample of children, thereby improving its extemal validity. As well, the OLS followed both
well defined speechllanguage impaired and control subjects over a period of 14 years - one of the longest follow-up periods in the speechnanguage literature.
2. Background Language is a means of representing and cornmunicating information. In young
children, the development of language and thought are believed to develop in a parallel
and reciprocal fashion with each contributing to the growth and evolution of the other
(Paul & Jackson, 1993). One of the hallmarks of psychopathology, however, is irrational
thought (Crittenden, 1996) and that children with impaired speech or language would be
at riçk for psychopathology seems likely. Thus, the effects of speech and language
impairment could be particularly handicapping in two ways: first, by interfering with
normal cognitive development and promoting the onset of irrational thought and
psychopathology, and second, by isolating the child, restricting social, educational and
occupational opportunities which may also increase the risk of subsequent
psychopathology.
Indeed, the CO-existence of speech and language impairment and psychiatric
disorders has k e n well docurnented and empirical research continues to show that this
association occurs ai a rate greater than chance (Beitchman et al., 1994). This has been
observed in a number of cross-sectional and retrospective studies that find large
proportions of speechllanguage irnpaired children with psychiatric disorder and
conversely, in studies that find large proportions of psychiatrically disturbed children with
speechllanguage disorders (Howlin 8 Rutter, 1987). Moreover, prospective longitudinal
studies that have followed their subjects through to middle or late childhood have shown
that speechllanguage impairment puts children at greater risk for subsequently
developing psychiatric disorders of al1 kinds (Baker & Cantwell, 1987; Cohen, Devine,
Horodezky, Lipsett, & Issacson, 1993).
Unfortunately, there have been no studies of speechllanguage impairment that
have explored the long-term psychiatrie outcome of these subjects in young adulthood.
This period in life is a pivotal transition point in the passage from adolescence to
adulthood during which decisions have Iifelong consequences. This study addresses
this conspicuous lack of information on the long-term psychiatric outcome of
speechtlanguage irnpaired children. Specifically, it explores the relationship between
early childhood speechllanguage impairment and young adulthood antisocial behaviour.
2.1 Speech and Language Irnpainnents
2.1.1 Definitions
Perhaps the rnost general definition of speech and language irnpairment was
given by Pinker (1995). He describes them as "any speech or language syndrome in
which the person fails to develop properly the requisite skills and the blarne cannot be
pinned on hearing deficits, low intelligence, social problems or difficulty controlling the
speech muscles" (p. 481). This definition is useful in that it highlights the nature of these
impainnents as k i n g specific to the domain of speech or language rather than being the
resuIt of some other physical, neurological, psychological or intelledual deficit.
The Diagnostic and Statistical Manual (DSM-IIIR, Arnerican Psychiatric Association,
1987) also recognizes speech and language disorders and provides sirnilar definitions. It
describes these disorders as usually first evident in infancy, childhood or adolescence
and therefore refers to thern as "developrnental disorders" characterized by "the
inadequate development of specific language or speech skills and that are not due to
demonstrable physical or neurological disorders, a pervasive developmental disorder
(such as Autism), mental retardation or deficient educationai opportunities" (p. 3940).
The essential features of a child with only an articulation disorder is a constant
failure to produce the correct speech sounds at the developrnentally appropriate age. It
is manifested by frequent misarticulations, substitutions or omissions. The rnost
frequently rnisarticulated sounds are those acquired late in the deveioprnental sequence:
r, sh, th, f, z, 1 and ch. This is in contrast to the linguistic features in children with
language delay which include, among others, sirnplified and Iimited grammatical
structures, unusual word orders and difficulty in the acquisition of new words (DSM-IIIR,
American Psychiatric Association, 1987).
Both of the above definitions raise important issues. First, there is an important
distinction between disorders in the ability to express language and disorders in the
ability to produce speech. The proficiency and clarity of speech is only one aspect of
language expression. As well, the [iterature has shown differences in the amount and
severity of social, emotional, academic and behavioural problems associated with varying
degrees of expressive language ability (Beitchman, Nair, Clegg, Ferguson, & Patel,
1986b). These differences have been obsewed between both normal children and
children with speech-only problems and children with speech-only problems and children
with language-only problems. For exarnple, children with unclear speech articulation are
known to be more Iikely to have slow language development and behaviour problems
than children with clear speech irrespective of IQ (Morely, 1965; Silva, Justin, McGee, &
Williams, 1984) but there is also evidence that a "pure articulation disorder" is less likely
3
than a "pure language disorder" to be associated with psychiatn'c and psychosocial
problems (Cantwell & Baker, 1980; Baker & Cantwell, 1982a).
Second, these definitions raise an issue conceming the relationship between
language ability and intelligence. Thought (or cognition) can be regarded as the mind's
ability to integrate and manipulate information, (Crittenden, 1996) while intelligence, a
closely related concept, can be regarded as a rneasure of this ability (Paul & Jackson,
1993). Language and thought are closely intertwined in development, so too are
language and intelligence. Indeed, the items in tests of language and intelligence are
often the same and, while some language tests appear to assess language, they also
require the child's sustained attention and involve a certain amount of reasoning (Silva,
1987). Likewise, many IQ test items involve language comprehension and expression.
For example, the Stanford-Binet Intelligence Scale and Reynell Developrnental Language
Scale correlate highly (0.6-0.7) and both show similar correlations with both WSC-IQ
(Wechsler, 1974) and reading tests given several years later (Silva, Bradshaw, &
Spears, 1978). For these reasons, intelligence may easily confound language ability and
must therefore be controlled in speech and language disorder studies. However, much
of the literature has failed to incorporate 10 when defining speech and ianguage impaired
groups thus Iimiting the intemal validtty of their findings.
Third, as language includes both the ability to express and to comprehend
information, the DSM-IIIR krrther divides specific language disorders into developmental
expressive and receptive subcategories. Although this distinction seerns intuitive, much
of the early Iiterature does not report data for each type of disorder, but rather refers
only to a "language impaired" group perhaps consisting of children with one or the other
or both impairrnents grouped together (Silva, 1987). Although many recent studies
continue to group subjects in this way, a number of studies that have used language
disorder subgroups have found differences between them (e-g. Silva, Williams, & McGee,
1987; Beitchman, Hood, Rochon, & Peterson, 1989a; Beitchman, Kruidenier, Clegg, Hood,
& Comadini, 1989b).
2.1.2 Prevalence
Many of the early epidemiological studies of speech and language disorders, as
reviewed by Mac Keith and Rutter (1972), were handicapped by the lack of standardized
tests of speech/language development. Instead, they generally used criterion-referenced
approaches and assessed aspects such as "intelligibility" of expressive language. They
also tended to be based on expressive language only, as language comprehension is
more dificult to assess (Silva, 1987). In recent years, however, standardized tests have
becorne available (eg. Test of Adolescent and Adult Language, Harnmill, Brown, Larsen,
& Wiederhold, 1994). Furthemore, atmost al1 studies of disordered language focus on
preschool children (0-5 years of age) because it is during this period that language
develops dramatically. Both clinic- and cornmunity-based studies comprise the literature
on prevalence. Since clinic-based studies are lirnited in their extemal validity, only
community-based epidemiological surveys will be reviewed.
Studies conducted over the last three decades have observed prevalence rates
of speecManguage disorders ranging from approximately 3%-25%. The most notable
studies include the Waltham Forest Study (Stevenson & Richman, 1976), the Dunedin
Study (Silva, McGee, & Williams, 1983), the Newcastle study (Fundudis, Kolvin, &
Garside, 1979) and the Ottawa Language Study (OLS) (Beitchman, Nair, Clegg, & Patel,
1986a). The Waltham Forest Study reported a prevalence rate of expressive language
delay of 3.1 O h . The authors used standardized tests which assessed a 1 -in-4 sample of
3 year old children in an outer London (UK) borough, the criterion for delayldeviance
being that the child fell at least 6 months below the chronologid age nom. IQ, however,
was not controlled in this study.
The Newcastle study reported a 4% prevalence rate of "speech retardation"
which was based on the sample of al1 children born in Newcastle Upon-Tyne, UK.
However, this figure is lirnited by its initial definition of speech retardation: "the failure to
use three or more words stning together to make some sort of sense by the age of 36
months." This definition is insensitive to the extent that children with only the more
severe forms of delay would have been identified. ChiIdren with mild and moderate
articulation problems, as well as those with disorders primarily of syntax, would have
been missed.
The Dunedin study measured different subtypes of language disorder. The
investigators found a prevalence rate of 3% in delayed verbal comprehension, a 2.5%
rate in delayed verbal expression, a 3% rate for those delayed in both and an overall
prevalence rate of 8.4%. These figures were based on al1 infants born at one maternity
hospital, however, and the survey iesults may not have been representative of the
population as there were large social class and family status differences between the
children in the sample and those not included.
Beitchman et al. (1986a) in the OLS found a speech-only impairment prevalence
rate of 6.4%, a language-only impairmentprevalence rate of 8.04%. a speech and
language impairment rate of 4.56% and an overall prevalence rate of 19.0%. These
figures were based on a 1-in-3 sample of al1 5 year old English-speaking children from
the Ottawa-Carlton region. The investigators used a 2-stage series of standardized tests
where cases had to have failed (greater than or equal to 1 standard deviation below the
mean on) any 1 of 4 tests, or failed (greater than or equal to 2 standard deviations below
the mean on) any subtest of the Test of Language Development (Newcomer & Harnmill,
1 977).
Another investigation, the lowa Epidemiologic Study of Specific Language
Impairment (SLI) (Tomblin, Buckwater, Zhang, O'Brian, & Smith, in press; Tomblin, 1996)
represents a well-controlled, population sampled prevalence study of SLI. Among the
rnonolingual English-speaking kindergarten population, 7,218 children (age 5) were
sampled via a stratified cluster sampling of children living in lowa or western Illinois.
Among those sampled, 216 children (3%) were diagnosed as SLI. This diagnosis was
based on 1) a language assessrnent battery of standardized tests; 2) performance-IQ
estimates; 3) a pure tone hearing test, and 4) parental reports of developmental, sensory
and neuromotor problems. A diagnosis of SLI consisted of an oral language impairment in
association with normal performance-IQ (>87), normal hearing and no parental report of
mental retardation, autism, visual impairment, cerebral palsy or severe head injury.
A final study worthy of mention is one performed by Tuomi and lvanoff (1977).
These investigators found a prevalence of speech articulation problems to be 24.5% and
of language problems to be 6.7% among almost 900 kindergarten and grade 1 children in
public schools near London, Ontario. These figures were based on a 2-stage procedure,
the second of which made use of a standardized test. However, the initial screening
stage was quite subjective (college students judged a child's response to the question
"What do you do at home?" as well as the descriptions of three pictures). Because the
critical screening stage was not standardized, it is difficult to be confident of these
results.
2.1 3 Problems in Defining SpeechILanguage lmpairments
The range of the observed prevalence rates of speech and language disorders is
quite large (3%-25%). It is at least partially due to the fact that there is Iittle or no
agreement as to how exactly to define and operationalize these irnpairments. The criteria
provided by the DSM-IIIR are suffkiently vague that they can only be used as a guide for
investigatorç who choose to define the disorders on their own. For exarnple, the DSM's
diagnostic criteria for Developrnental Receptive Language Disorder States that a child
"should score substantially lower in a standardized test of language reception than in a
standardized test of intellectual ability" (p. 48). The DSM does not, however, state what
is considered "substantially fower" nor does it state which standardized tests are
acceptable.
Because of these reasons, investigators have been left with much fiexibility in
how they regard speech and language irnpaiments. This is refiected in the literature by
the many different types of language tests in use, the varying methodology in which
these tests are utilized, and the variation in the constructs that are actually measured.
For example, some investigations lump together children with receptive disorders and
children with expressive disorders into a single group. The authors of other studies have
constructed their own nosologies by using terms like "speech retardation" and
"articulation deviance". Although intuition guides us in their interpretation, they are an
example and representation of the difficulties in the speech/language Iiterature. As a
result, the responsibility of defining and operationatizing impairrnents is left up to the
investigator.
Stevenson and Richman (1976) suggested that "a decision about what
constitutes abnormality would be strengthened if prognostic validation data were
available" (p. 431). Since then, a number of longitudinal studies of speech and language
disorder have been perforrned and have highlighted the longer-terni course and
significance of these disorders.
2.2 Longitudinal Oütcome of SpeechlLanguage lmpainnent
The longitudinal outcomes of speech/language impaired children that have been
studied generally fall into three domains: linguistic outcorne, which may include changes
in speechllanguage deficits; acadernic ouicornes, which may include changes in school
performance and reading ability; and psychological outcomes, which may include
changes in the rates of emotional, behavioural or psychiatric problems. Because this
investigation is concerned pnmarily with psychiatnc outcorne, a summary of literature
only on the emotional, behavioural or psychiatric outcomes will be provided.
2.2.1 Behavioural Findings
The most common late childhood behavioural outcorne among speechllanguage
impaired children are increased rates of extemalizing behaviours of inattention and
hyperactivity. However, internalizing behaviour such as anxiety and socially withdrawn
behaviours have also been reported. Ammg clinic-based samples, Baker and Cantwell
(1 987) reported on a foliow-up study of children first seen at a speech and language
clinic. The age range of the children was 2-1 5.9 years (with a mean age of 5.i years).
AI1 children were initially diagnosed as having a speech-only impairment, a language-only
7
impairment or a combined impairment. There were no controls. The overall prevalence of
psychiatric disorders increased frorn 44% initially to 60% at follow-up five years later - a
significant change. The major diagnoses showing changes were attention-deficit
disorder (ADD) which more than doubled in prevalence (from 16% to 37%) and anxiety
disorder which alrnost doubled in prevalence (from 8% to 14%). The third rnost prevalent
diagnosis was oppositional/conduct disorder.
The above study, however, had a number of weaknesses. For example, in
addition to the absence of controls, only 300 of the 600 subjects initially assessed were
followed-up. Furthemore, the initial age range of the subjects was large and the authors
assumed that the effects of speechnanguage impainnent on outcome were uniform over
this range which may not necessarily be true.
Another clinic-based study (Benasich, Curtiss, & Talial, 1993), which did use
control groups repcirted similar findings. This study did not use psychiatric diagnoses as
the outcornes measures, but instead used parent-assessed behavioural rneasures. The
investigators followed a group of language impaired and control children from age 4 to
age 8. At age 8, the language irnpaired group had a significantly higher total behaviour
problem score as well as a significantly higher nurnber of parent-endorsed behavioural
problem items than the control group. With respect to particular types of behavioural
problerns, both language impaired boys and girls scored significantly higher levels of
hyperactivity than the controls. However, language impaired girls also scored
significantly higher levels of social withdrawal symptoms than controls. These findings
are similar to those of Baker and Cantwell (1987) which show increased rates of both
intemalizing and externalizing behavioural symptoms among language impaired children
compared to control children followed over time.
A third clinic-based study (Cohen at al., 1993) investigsted the occurrence of
unsuspected language impairments in a sarnple of 4-12 year old psychiatnc outpatients.
The study also examined a group of psychiatric outpatients who did have a previously
identified language impairment. The authors showed that of 288 children referred solely
for psychiatric disorders, 99 (34.4%) had more subtle language irnpairments than did the
other children with previously recognized impaiments. This study used parent- and
teacher-assessed scores of behaviour problems. As in the other two clinic-based
studies reviewed above, children in both groups in this case had symptoms associated
with attention-deficit and hyperactivity disorder (ADHD). As well, children with
unsuspeded language impaiments had the rnost senous externalizing behaviour
problems while children with previously identified language impaiments had more
internalizing problems. Again, because these studies were based on clinic samples, their
generalizability is limited.
Among the comrnunity-based investigations, the same four major studies that
assessed the prevalence of speechtlanguage impairments have also followed these
children over tirne to assess their behavioural outcomes. They were the Dunedin study
(Silva, Williams, & McGee, 1987) which followed children from age 3 to age Il, the
Waltham Forest study (Stevenson, Richman, & Graham, 1985) which followed children
from age 3 to age 8, the Newcastle study (Fundudis et al., 1979) which also followed
children from age 3 to age 8 and the Ottawa Language Study (OLS) (Beitchman et al.,
1993) which followed children from age 5 to age 12.5. The Dunedin study showed that
children with delayed verbal comprehension and general Ianguage delay at three years
of age were most at risk for later behaviour problems. The type of behaviour problern,
however, was not reported. The Newcastle study found the children with language
delays to be more Iikely to show rnarked introversion and withdrawal than the control
group at follow-up five years later. The Waltham Forest study, however, reported that
early problems with language structure were specifically associated with later problems
in neurotic deviance, rather than with antisocial deviance, even after controlling for initiai
behaviour problems.
Rutter and Mawhood (1991) reviewed the literature on the Iink between language
problems in early childhood and later psychopathology. They concluded that: "As judged
by al1 the available data, the main increase in psychopathology seems to be in the domain
of anxiety, social relationships and attention-deficit problems rather than in conduct
disturbance or antisocial behaviour" (p. 121). Beitchman et al. (1 993) in the OLS,
however, did not show such specificity of early speech/language impairments on later
internalizing problerns. Although they did corroborate others' finding that report high rates
of inattention and hyperactivity among impaired children as well as intemalizing symptorns
such as anxiety, they also reported conduct disorders. Furthemore, overalt measures of
externalizing symptoms were significant among the impaired groups.
Unfortunately, almost al1 longitudinal investigations of speech~language impaired
children that have studied behavioural outcomes have only followed their subjects to late
childhood or adolescence and it is not known to what extent early speechllanguage
impairment is a precursor of young adulthood behavioural deviance.
2.3 Relevance
Speechnanguage impaired children have been observed to shaw increased rates
of intemalizing and extemalking behaviours. It may be argued ?an an epidemiotogical
and clinical point of view, however, that the extemalizing problerns represent the more
significant outcome as they are more stable than intemalizing behaviours and cany (except in the instances of severe inhibition or depression) a worse prognosis as well as
resistance to most forms of intervention (Robins, 1979).
Antisocial activity represents one type of extemalizing behaviour that is found to
develop arnong speech and ;anguage impaired children and although the studies
reviewed above have only equivocally pointed towards this outcome, this fact should not
deter its exploration among thzs2 children at a much later point in their lives - young
adulthood. There are four main reasons for this. First, the young adulthood period in
one's Iife is an important developmental stage. Theorists have emphasized it as a pivotal
transition point from adolescence to adulthood. Young men and women gain significant
freedoms and are forced to make important decisions that will have long-term
consequences. A successful transition secures an adult role, identity and opportunities
for social advancement, while failure rnay tead into pathways that have negative social
and health consequences (Beitchrnan, 1995; Rutter & Giller, 1983).
Second, young adulthood represents the ages during which antisocial behaviour
reaches a peak (Robins & Reiger, 1991) and antisocial behaviour is predorninantly a male
phenornenon (Henggeler, 1989; Robins, 1986). The recent Ontario Health Survey-Mental
Health Supplement (mord et al., 1996) and other epidemiological psychiatrie studies
such as the Epiderniologic Catchment Area Study (Robins & Reiger, 1991), the National
Comorbidity Study (Kessler et al., 1994) and the Edmonton Psychiatrie Epidemiology Study
(Biand, Newman, & Om, 1988; Bland, Om, & Newman, 1988) have al1 reported the
highest rates of antisocial behaviour in the 18-24 year-old age group as well as
significantly greater prevalence rates for males than for fernales. As there have been no
controlled prospective studies which have followed speech and language impaired
children to adulthood, a relationship between earfy childhood speechnanguage impairment
and the peak in prevalence in antisocial behaviour during the young adulthood years
seems worthy of investigation. F~irthemare, the fact that these high prevalence rates
represent prirnarily male antisocial behaviour and that males are at greater nsk for speech
and language impairment (Silva, 1987) substantiates the need to investigate the long-term
outcorne of these domains.
Third, antisocial behaviour is a critical social concem demanding attention. At a time
when antisocial behaviour in young people seerns to be increasingly significant, it is of
great importance that interventions be designed to modify such behaviours at the earliest
possible age (Maughan & McCarthy, 1997). In the case of children with speechnanguage
impairment, should these impairment prove to be an underiying cause of antisocial
behaviour in adulthood, interventions through speech or language therapy in childhood
may prove to be a more desired treatment strategy rather than attempting to intervene
many years later in young adulthood when the rnany other long-term effects of
speechAanguage impaiment have already taken their toll on the individual.
Finally, there are two main areas of research that have alluded to this association.
The first is the literature that suggests a possible antecedent role of speechllanguage
impaiment in the relationship between academic underachievernent and extemalizing
behaviour; and the second is the literature that suggests a mediating role of hyperactive
and inattentive symptoms for the indirect effect of speechnanguage impairment on young
adulthood antisocial behaviour.
2.4 Evidence that Suggests an Association Between SpeechlLanguage
Impairnient and Antisocial Behaviour
2.4.1 Speechllanguage Impairment as an Antecedent Variable
Much interest in the role of speechllanguage impairment and its relationship to
acting-out behaviour cornes from the large number of studies which have explored the
association between extemalizing behaviour problerns and academic underachievement.
A variety of causal models have been tested: some studies support the model that
unde rachievement leads to extemalizing behaviour, others support the opposite model
that extemalizing behaviour leads to academic underachievernent, while others support a
bi-directional or reciprocal mode1 (Lynam, Moffit, & Stouthamer-Loeber, 1993). In his
review of the literature, however, Hinshaw's (1 992) overarching conclusion was that
research of unidirectional rnodels to explain the association between academic
underachievement and externalizing behaviour were iriconclusive and as a whole,
research in the field pointed toward the action of an antecedent variable.
This model, that an antecedent or third variable explains the association requires
that the antecedent variable precedes the association of interest and that the effect of
the antecedent variable should not be explainable by the other background variables.
Early childhood speecManguage problerns are an excellent candidate as an underlying
variable as they correlate with acadernic and extemalizing behaviour problems and
because they predate the emergence of the association. However, because such a test
of this model requires a prospective longitudinal evaluation that includes sensitive
rneasures of al1 the hypothesized variables at multiple time points, only two such studies
have foflowed children beyond the elementary school years.
In the first study (Olweus, 1983). the author made an explicit attempt to establish
the credibility of unidirectional, reciprocal and antecedent variable models. The study
used peer-rated rneasures of aggression and averaged school grades at both the sixth
and ninth grades in a sarnple of Swedish boys. Using structura1 equation rnodeling, when
background variables of social class, parents' ages, divorce, birth out of wedlock, and
birth order were controlled for, the unidirectior;al and reciprocal rnodels were not
supported, leading to the conclusion that antecedent variables accounted for the
association between poor grades and aggressive status.
In the second study, Schonfeld et al. (1988) used path analyses and provided
some evidence for a link between cognitive deficits in childhood and conduct disorder 10
years later. More s?ecifically, the investigators found an indirect path from age 7 Verbal
IQ and severïty ratings of conduct disorder at age 17 - rnediated by adolescent IQ
scores. This finding held up even when: i) early aggressive behaviour and ii)
background variables of environmental disadvantage, neurological soft signs and
parental psychopathology were controlled. Although methodological problems cloud the
viability of their findings (the sarne psychologists measured both initial behaviour ratings
and the initial cognitive ability and were, therefore, not blinded), this study is interesting
because it: i) performed measlirements of each dornain at multiple time points, ii)
assessed relevant antecedent variables, and most importantly, iii) found a Iink between
childhood Verbal IQ - a variable highly correlated with speechllanguage ability - and
antisocial behaviour in late adolescence.
As mentioned above, a sizable literature has emerged on the relationship between
early language problems and the development of both behavioural difficulties and
academic deficits. In the case of academic achievement, there is a consensus in the
literature of studies of speechnanguage impairment showing higher rates of academic
underachievement than controls (Schacter, 1996). Underachievement has k e n
measured in many different ways including reading ability, high school completion,
grades, parent and teacher reports on studying and hornework habits, standardized
tests, the presence of learning disabilities as well as placement in special education
classes. Though al1 the studies described in Schacter's recent review (1 996) obsewed
an increase in rate among speechnanguage impaired children, methodoiogical problems
rnay lirnit their extemal validity. For example, most did not control for potentially
confounding variables such as social class or intelligence. Nevertheless, in those
studies that did statisticafly control for the effect of these variables, two of them
continued ta observe an independent effed of speechnanguage status on achievement
(Beitchman et al., 1993; Menyuk et al., 1991) while one did not (Ararn, Ekelman, & Nation,
1 984).
The close relationship between speechnanguage impairment and academic
underachievement sheds new light on previous findings between academic achievement
and extemalizing behaviour. Thus outcornes once thought to be related to academic
underachievement may now be seen within a new, although indirect, causal context of
speech/language ability.
2.4.2 Speechllanguage Impairment, ADHD and Antisocial Behaviour
Upon examining externalizing behaviours, mild-to-moderate language problems
seem to be associated with several types of difficulties and, as rnentioned above,
attention and hyperactivity problems are at the top of the list (Hinshaw, 1992).
Specifically, a developmental trend in the nature of the behavioural difficulties among
underachieving children has been noted. In the middle to late childhood yean academic
deficits seem most correlated with inattention and hyperactivity while in adolescence,
academic problems become most strongly correlated with problems of conduct and
antisocial behaviour (Maughan, Pickles, Hagell, Rutter, & Yulc, 1996).
Finally, inattention and hyperactivity are well established risk factors for conduct
disorder (Taylor, 1994) and a number of studies have found increased rates of antisocial
disorders in adulthood arnong individuals with ADHD in childhood (Gittelman, ~annuzza,
Shenker, & Bonagura, 1985; Greenfield, Hechtman, & Weiss, 1988; Weiss & Hechtrnan,
1986). For example, Loney, Kramer, & Milich (1981) found that young hyperactive adults
when compared to their nonhyperactive brothers were found to have a greater incidence
of antisocial personality disorder (45% vs. 18%) and more frequent episodes of
incarcerations (41 % vs. 5%). A controlled prospective follow-up study (Hechtman &
Weiss, 1986) examined 64 patients with a rnean age of 25 from a group of 104 who had
been assessed as hyperactive 15 years earlier. The patients in the study had more court
referrals than controls and 23% also met DSM-III criteria for antisocial personality disorder
while only 2.3% of controls did. The two fields of research, that is, the one which
studies the relationship between inattentionlhyperactivity and subsequent antisocial
behaviour and the second which studies academic underachievernent and its
subsequent association with externalizing behaviour rnay overlap considerably and have
speechnanguage impairment in cornmon as a latent variable.
2.5 From Early Childhood Impairment to Young Adulthood Outcorne:
Possible Mechanisms
Over time, the proportion of speechAanguage irnpaired individuals with a
psychiatric problem increases (Baker & Cantwell, 1987). White the persistence of the
speecManguage impairment may decrease over time, the clinic-based and community-
based studies reviewed above suggest that sorne longer-terni vulnerability remains that
increases the chances of developing a psychiatric problem even after the impairment
diminishes. Cuvent evidence supports this contention only up until late childhood or early
adolescence. This longer-term vulnerability rnay exercise its effects by irnpeding the
ability to communicate. That is, children who have trouble communicating rnay be at risk
for considerable frustration and a poor self-esteem. This may lead to dysfunctional
parent-child relationships andlor child-peer relationships both of which could precipitate
or maintain externalizing behaviour problerns such as antisocial behaviour (Howlin &
Rutter, 1987). Becaase of the importance of communication in a social world, it seems
likely that children with speechnanguage impairments would be at risk for behavioural
problems.
lt is also possible that early speech/language impairment does not play a causal
role in the emergence of later antisocial behaviour. The association between these two
variables may be due to an antecedent variable that leads to both. Beitchman (1985)
suggested that neurodeveloprnental immaturity could be such a variable and that
speechllanguage impairment may in fact be a marker for it. However, even if
neurodevelopmental immatunty could account for both speechAanguage impaiments and
later antisocial behaviour, the above rnentioned difkulties in communication would, most
likely, also play a role in the developrnent of behaviour problerns. Thus, the action of a
neurodevelopmental immaturity and diffÏculties in communicating would most likely not be
mutually exclusive in causing later antisocial problems.
Another possible mechanism alluded to above involves the relationship between
speechnanguage impairment and subsequent symptoms of inattention and hyperactivity.
Symptoms of inattention and hyperactivity are a known risk factor for subsequent
conduct disorder (Taylor, 1994). Furthemore, the rnost comrnon behavioural outcome in
late childhood among speechnanguage impaired children is attention-deficit and
hyperactivity disorder (ADHD) (Hinshaw, 1992). It is possible that this outcome rnay act
as a subsequent exposure van'able for later antisocial behaviour.
Finally, it is possible that variables related to speechnanguage impairment may
fully or partially account for its association with later antisocial behaviour. That is,
antisocial behaviour arnong speecNlanguage impaired children may be a function of
covariates such as a low intelligence, childhood adversity or other factors that are both
associated with speechllanguage impairment and risk factors for antisocial behaviour.
The rote of these variables in explai~ing antisocial outcome is very likely because not al1
speechnançvage impaired children rnay become antisocial and also because many non-
speechAangliage impaired children do becorne antisocial. If this is the case, when these
covariates are accounted for statistically, the association between speechilanguage
impairment and later antisocial behaviour would be attenuated.
2.6 The Present Study
2.6.1 Data Source and Conceptual Framework
This study makes use of the most recent wave of data from the Ottawa
Language Study (OLS) (Dr. J.H. Beitchman, Principle Investigator), a comrnunity-based
three-wave panel study. The OLS was initiated in 1982 (time-1) when subjects were 5
years of age, followed-up approximately 7 years later in 1989 (tirne-2) when the subjects
were 12.5 years of age and most recently followed-up for a third time in 1996 (tirne-3)
when the subjects were 19 years of age. Predictor variables have been measured at
both tirne-1 and tirne-2 in a number of dornains including a) speech and language
competence; b) intellectual functioning competence; c) academic achievement ; d)
psychiatric disorders; e) psychological distress and impairment; e) psychological
adjustment; and f) data pertaining to family and parental mental health. Subject treatment
history was also recorded.
ln the present study, only time-1 measures are utilized to assess their association
with antisocial outcornes. The choice of variables that are controlled in this investigation
was driven by the Iiterature. Once specific risk factors for antisocial behaviour are
identified and entered into a predictive model, it is important to determine whether
speechnanguage impairment expIains an additional significant amount of variance in the
occurrence of antisocial behaviour. If it does, then the knowledge of speechllanguage
impairment identified at as early an age as 5 years provides investigators in the field (and
subsequently parents of such children and their physicians) knowledge that the
impairment puts the child at significant risk for later antisocial behaviour. If it does not,
15
then the knowledge of such irnpairments would not be considered to provide any
information on the risk of subsequent antisocial outcorne and that the variables controlled
may explain rnost of the antisocial outcorne variance.
Figure 2.1, below, illustrates the follow-up and attrition of subjects in the OLS.
Although no tirne-2 data is used, it is represented in the figure to provide a complete
depiction of the study. The figure aiso shows the number of subjects that were
categorized as speechllanguage impaired and as controls at each wave of the study.
Finally, the figure displays that 90.1 % of the subjects at time-1 provided at least some
data at time-3, while 85.2% of the subjects at üme-1 provided complete data at time-3.
Differences between subjects with and without missing data are described in section 3.6
and in appendicies A and B.
2.6.2 Antisocial Behaviour: Conceptualizations
In the field of psychopathology, behavioural deviance can be characterized in
several ways. For example, to convey a dimension of antisocial behaviour, investigators
can sum or aveage data gathered from rating scales or behaviour observations, yielding
a quantitative score. Through the application of cutoff scores or multivariate clustering
strategies, the same instruments rnay yield a category - that is, a subgroup of children
with common characteristics #en investigators use clinical judgments, inclusionary
markers, and exclusionary criteria, they can obtain a diagnosis. Finally, alternative
definitions, such as the legal entity of delinquency with respect to antisocial activities c m
also be employed. This study intends to measure antisocial behaviour in each of these
three ways: First, as a diagnosis; second, as a legat entity; and third, as a dimension-
Generafly, antisocial behaviour involves the violation of the nghts of others, 3 lack
of conformity to social noms and is usually present in variety of settings including farnily,
work and interpersonal contexts. Antisocial individuals tend to be aggressive and
impulsive and some are thought to lack normal capacities for love, guilt and cooperation
with authority figures. Many, but not alII antisocial individuals come into contact with the
law and the notion of equating antisocial behaviour with cn'minality has been discussed
(Robins & Reiger, 1991). For exarnple, the psychiatric nosology has been criticized as an
attempt to "medicalize" bad behaviour and that diagnoses of Antisocial Personality
Disorder (ASPD) express the alienation of the disadvantaged and are representative of a
"sick" society rather than of a sick patient (Robins & Reiger, 1991). Robins (1 979),
however, argued four main reasons as to why antisocial behaviour can be considered a
psychiatric illness.
First, its syrnptoms are highly intercorrelated, making it a coherent syndrome and
not just an assemblage of various types of deviance. Second, it has been shown to
have a heritable cornponent (Rutter, 1 996). Third, it occurs in and is recognized by every
society, no matter what its econornic system and throughout history, showing that it is
not purely an indication of a modem "sick" society. Although its prevalence varies with
time and place, the same can be said with almost every psychiatric and nonpsychiatric
disorder. Finally, if the criticism were warranted that the diagnosis of ASPD was simply
a medicalization of criminality, then one would expect that most cnminals would be
diagnosed with ASPD and that most indivi&als with ASPD would also be criminals.
Robins and Reiger (1991) argued that this is not the case. In their large investigation of
psychiatric disorders, The Epidemiologic Catchment Area Study (Robins 8 Regier, 1991),
these authors reported that less than half (47%) of those with ASPD had a significant
record of arrest (which they defined as more than one arrest other than for a rnoving
violation or any felony conviction).
One could argue that the criteria used by Robins and Regier was strict and that a
much a larger proportion of those diagnosed with ASPD may have had a significant
history of arrest that would have still distinguished thern from the non-ASPD sample.
Thus, the overlap between these h o domains depends rnuch on the way in which
"cn'rninality" is defined. To avoid this problem, this study addresses its research objective
by measuring antisocial behaviour as a diagnosis, as a legal entity and as a dimension,
each separately. Perfomiing separate analyses for each measure of antisocial
behaviour improves the extemal validity of the study by allowing for differences in not
only the sources of the antisocial behaviour data, but also in the ways in which antisocial
behaviour can be conceptualized.
2.6.3 Risk Factors to be Controlled
Intelligence
Low intelligence is a factor that has been shown to be both associated with early
childhood speechllanguage impairment and a risk factor for subsequent antisocial
behaviour. In order for intelligence to be considered a confounding variable by the
classical definition (see Last, 4995) it must satisfy three criteria: i) it must be a variable
that can cause or prevent the outcorne of interest; ii) it rnust be associated with the factor
under investigation; and iii) it must not be an intemediate variable. Evidence thus far
suggests that intelligence, as measured by IQ, satisfies the first two criteria. There is
however, no information pertaining to the third cnteria. The reasons for this are related to
the early developmental association between thought and language which are
considered to develop in a parallel and reciprocal fashion. Because thought and
language develop so closely to each other and at such a young age, it is very difficult to
parse out their effect on each other (Silva, 1987). There is, however, research showing
a clear association between speech/language impairment and later IQ. The four main
longitudinal studies of speechllanguage impairment have al1 shown later significant
deficits in intelligence which were not initially present (Beitchman et al., 1994; Fundudis et
al., 1979; Silva, McGee, & Williams, 1 983; Richman, Stevenson, & Graham, 1982).
An association between low IQ and delinquency has also long been noted.
Furthemore, this association is stronger for verbal tests than for performance or full
scale tests (Lynam et al, 1993). There were three general ways in which IQ was
thought to be related to antisocial behaviour: a delinquent lifestyle was thought to lead to
a low IQ; a low IQ was thought to lead to delinquency; or a third (or antecedent) variable
waç thought to explain the association between delinquency and low IQ, in which case
the relationship between delinquency and low IQ was considered to be only spun'ous.
Two "antecedent" hypotheses were put forward. The first was the differential
detection hypothesis (Moffitt & Silva, 1988; Murchison, 1926; Stark, 1975; Sutherland,
i 931) which suggested that the population of delinquents and nondelinquents were not
distinguished by their IQ1s, but rather, it was only the lower IQ delinquents who were
detected by the police and were thus more likely to be represented in research samples
(Feldrnan, 1977; Hirschi and Hindelberg, 1977). Moffitt and Silva (1 988) tested this
hypothesis and found no significant difference in IQ between a group of self-reported
delinquents and a group that had been detected by the police. The second "antecedent"
hypothesis was that some third variable lead to both IQ and delinqtiency. The most often
tested candidates were race and social class (Lynam et al,, 1993). However, many
studies found the low IQ-delinquency relation to be maintained even when race or social
class were controlled (Hirschi, 1969; Reiss & Rhodes, 1961 ; Short & Strodtbeck, 1965;
Toby & Toby, 1961 ; West & Famngton, 1973). Lynam et al. (1 993) also controlled for
both at the same time and reported that the relationship still held.
The hypothesis that delinquency leads to low IQ has also been tested. A number
of authors have suggested that a delinquent lifestyle results in lower intellectual
functioning (Hare, 1984; Shanok & Lewis, .198l). One expianation describes that
delinquents score more poorly on IQ tests because they are not interested in doing well
(Tamopo!, 1970). Thus, the lower IQ scores do not reflect impaired cognitive functioning,
but rather that they are personally opposed to test taking and do not value it. However,
Lynam et al. (1993) found that IQ differences between delinquents and nondelinquents
r2mained significant even after controlling for the boys' observed level of effort while
taking the 1Q test. Furthemore, prospective studies of juvenile delinquents indicate that
lower IQ scores are present well before the initiation of delinquent activities (Denno,
1990; Moffitt, Gabrieli, Mednick, & Schulsinger, 1981 ; West & Farrington, 1973).
The rernaining hypothesis that low IQ leads to delinquency has received the most
support. The specific explanations generally faIl into one of hrvo categories: the direct-
effect formulation and the indirect-effect formulation (Moffitt, 1990). In the case of the
direct-effect formulation, many interpret the 1Q as a broad index of neuropsychological
health and deficits in neuropsychologicai abilities, referred to "executive functions,"
interfere with a person's ability to monitor or control his or her own behaviour. In
addition, the most popular tests of executive functions al1 share significant proportions of
variance with tQ scores and to this extent IQ should have direct effects on delinquency.
For example, some low IQ children are more impulsive, less attentive and less adept at
seIf-control, thus setting the stage for delinquency and antisocial behaviour. Using path
analyses, Lynam et al. (i993), however, found that onty 17-25% of the effect of low IQ
on delinquency operated indirectly through rneasures of impulsivity while most of the
effect acted frorn low IQ directly to delinquency.
Altematively, many authors have suggested that low IQ leads to delinquency
indirectly through school failure (Hirschi, 1969; Hirschi & Hindelang, 1977). Children who
experience failure in school will be more Iikely to become delinquents than those who do
not. In this model, IQ acts as an index of ability to succeed in school. Again, Lynam et ai.
(1 993) tested this hypothesis and did not find a simple answer. They found that this
hypothesis proved correct only for A f r h n Amencan subjects but not for Caucasians. In either case, there is considerable evidence in the literature for the risk factor status of IQ
on subsequent antisocial behaviour thaï warrants its control when detennining the risk
factor status of early chiidhood speechtlanguage impairment on young adulthood
antisocial behaviour. As such, IQ was controlled in this investigation.
Socioeconomic Status
Many studies report an association between low socioeconomic status (SES) and
poor Ianguage ability. In the Dunedin study the average language score of the lowest
SES group was about a year below that of the children in the highest SES group (Silva,
McGee, Thomas & Williams. 1982b). Similar SES gradients relating to language ability
have also been found by others (Lawton, 1968). For example, Wooster (1 970) found
social class differences in the ability to produce and understand language. As well,
Beitchman et al. (1 994) found similar SES differences between a speech/language
irnpaired group and a control group and Fundudis et al. (1 979) also found an association
between low SES and language delay.
In an attempt to determine whether social class variables accounted for the
relationship between speechllanguage impairment and psychiatrie problems, Beitchman,
Peterson and Clegg (1988) measured family income, occupation, parental marital status,
educational attainment, family size and birth order. Although occupation, marital status
and matemal education distinguished psychiatrically disturbed children from normal
children, none of the variables studied distinguished these children within the
speechllanguage impaired group. The authors concluded that these variables did not
appear to mediate the relationship in It should be noted that this study was
based on cross-sectional data and its findings do not preclude the possibility that social
class variables rnight act as confounding variables in explaining the relationship between
speecManguage impairment and later antisocial behaviour. As such, socioeconomic
status was controlied in this investigation.
Childhood Adversity
The relationship between speechnanguage ability and low socioeconornic status
as been discussed above. What rernains unclear is why this association exists. SES or
poverty are useful measures, but Bloom (1974) pointed out that they are too general and
too static to serve as the environmental measure for such a specific human
characteristic as language ability. Bloom argued that it is what parents and others in the
environment do in their interaction with the child that is iikely to influence the development
of a specific characteristic rather than the more static or status attributes o f the parent.
Maughan and McCarthy (1 997) argue that the effects of childhood poverty and broader
social variables like SES on various constructs Iike cognition and language abilrty seem
largely mediated through their impact on various aspects of family fundioning (Sampson
& Laub, 1993).
Research on early and middle childhood environmental corre:ates of disruptive
behaviour problems have been identified with impressive consistency. A meta-analysis
(Loeber & Stouthamer-Loeber, 1986) identified four main paradigms of childhood
adversity: i) a neglect paradigrn, refiecting poor parent-child relationships and a lack of
appropriate parental supervision; ii) a conflict paradigm, reflecting hostile, erratic or
threatening discipline and rejecting relationships; iii) a deviant parent paradigm, reflecting
deviant parent behaviours and attitudes, including parent criminality; and iv) a disruption
paradigm, including marital discord between parents and family breakdown. Findings
from two long-term British studies illustrate these patterns and the risk factor status of
childhood adversity on antisocial behaviour. The Cambridge Study of Delinquent
Development (Farrington and Hawkins, 1991) reported that the nsk of offending by age
21 doubled for males with a convicted parent and that early onset of delinquency had a
strong association with poor parental childrearing and harsh or erratic discipline. The
Newcastle Tho~tsand Family Study (Kolvin, Miller, & Fleeting, 1988) reported that the risk
of offending by age 33 was tripled for those whose parents' marnages were unstable as
well as for those males who experienced poor early mothering or physical care. Fer
fernale offenders, very high rates of childhood adversity were reported. Overall for
males, multiple early stressors were associated with the highest rates of n'sk.
Because childhood adversity seems to have a crucial effect on increasing the nsk
of later antisocial behaviour there is reason to control for this construct when detemining
the relationship between eariy childhood speechnanguage impairment and subsequent
young adulthood antisocial behaviour.
Esrly Antisocial Behaviour - Among the most frequently cited predictors of antisocial behaviour is early
aggressive behaviour (Huesman, Eron, Lefkowitz, & Walder, 1984; McCord, 1983; Rutter
& Giller, 1983). For example, Pakiz, Reinherz and Giaconia (1997) found that early
aggression was strongly associated with delinquent behaviour ai age 15. As well,
Farrington (1 991) showed that early aggression and violent behaviour showed
significant continuity and predicted chronic offending in young adulthood in a sample of
males. In Maughan and McCarthy1s (1997) review of the Merature, these authors
observed that severe antisocial disorders in adulthood are almost invanably preceded by
overt antisocial behaviour in childhood and aggressive and disruptive behaviours show
high levels of persistence across development.
As to why antisocial behaviours persist into adult life, previous research
suggested that the continuity of antisocial behaviour reflected the direct unfolding of the
pathogenic process over time where the course of chronic delinquency follows a
succession of anticipated paths (Patterson, DeBaryshe, & Ramsey, 1989). including a
progression from Iess serious to more serious acts (Loeber, 1991). Other evidence,
however, (Quinton, Pickles, & Maughan, 1993) suggests a somewhat different model.
Here, persistence of antisocial behaviour appears to depend at least in part on an
individual's continued exposure to other later risks and eariy behaviour probfems to be of
significance in increasing exposure to those risks. For example, eariy disruptive
behaviour is known to be associated with a range of "real world" social handicaps such
as acadernic underachievernent, unempIoyment, early family formation, deviance and a
lack of support in partners and relationship breakdown (Maughan 8 McCarthy, 1997).
These consequences may be particulariy important at key transition points in
development, such as puberty or adolescence, increasing the chance that individuals will
be exposed to later risks for antisocial behaviour (Scarr, 1992). By the same token,
reduction or absence of such later risks can allow for tuming points out of dysfunctional
trajectories (Pickles & Rutter, 1991).
With respect to the association between eariy childhood speechAanguage
impairment and early antisocial, oppositional or conduct problems, studies which have
followed speechdanguage impaired children to late childhood do not report that these
dornains are related with a high degree of specificity. Nevertheless, conduct problems
are present at rates greater than controls. For example, Beitchman, Wilson, Brownlie,
Walters and Lancee (1996) reported significant differences between low overall and
high overall language functioning groups in self-reported conduct problems.
Nevertheless, enough of an association exists between early speechflanguage
impairment and concurrent or later antisocial problems to warrant the control of this
constnict. Moreover, early childhood conduct problems are one of the strongest
predictors of adult antisocial behaviour (Pakiz , Reinherr, & Gianconia, 1997). The
purpose of this study is to determine whether knowledge of speechllanguage impairment
at as early as 5 years of age provides us with additional information on risk beyond that
provided by the other known risk factors for young adulthood antisocial behaviour. If not,
and al1 eise being equal, then parents and dinicians rnay have to wait until later factors,
such as antisocial behaviour in rniddle or late childhood, emerge before solid predictions
about risk for future antisocial behaviour can be made.
Inattention and Hyperactivity
Mild-to-moderate language problems are associated with severa! types of
behavioural difficulties and symptoms of inattention and hyperactivity are at or near the
top of the list. For example, Beitchman, Hood and Inglis (1990) reported that the rates for
both boys and girls of DSM-III Axis-l diagnosis of Attention Deficit Disorder (ADD) were
not only significantly greater for the speech/language groups than for the control groups,
but were also the most cornmon DSM-Ill diagnosis made. Sirnilariy, in a ciinic-based study,
Baker and Cantwell (1 987) reported that of the 50% of the 628 children in their sarnple
who were assigned a DSM-III Axis-l diagnosis, the most cornmon diagnosis was Attention
Deficit Disorder with Hyperactivity (ADHD). There was, however, no controt group in the
study. Nevertheless, other more methodologically sound cornrnunrty-based studies have
also shown this. For example, Beitchman et al. (1989a) used cluster analysis to classify
speechllanguage impairment in a sampie of 347 five year old children using scores on a
variety of speech and language tests. Four linguistic profiles were identified: high
overall, low overall, poor auditory cornprehension and poor articulation. The authors
reported that increased risk of behaviour disturbance was related to the child's linguistic
profile. Specifically, the rate of behavioural disturbance was greatest when the
language involved both comprehension and articulation problems. When they did, the
major outcome was hyperactivity or ADHD.
In addition, speech/language impaired children are ai increased risk for later
inattention and hyperactivity. For exarnple, Baker and Cantwell (1 987), although still not
using a control group, showed that those who were initially free of any psychiatrie
disorders but who were language disordered were at an increased flsk for subsequently
acquiring ADD. Beitchman et al. (1996) also followed up their sample of children from
age 5 to age 12-5 and found that even after controlling for initial behavioural status, that
the early childhood language profile was still associated with an increase in teacher-
rated hyperactivity. Thus, there appears to be an association between speechAanguage
impairment and later emergence of inattention and hyperactivity symptorns.
The risk factor status of these symptoms, however, is less clear but the following
will show that these symptoms still warrant control. Weiss and Hechtrnan (1 986) studied
the adolescent and adult antisocial outcome of hyperactive children. They also reported
their own long-tem prospective follow-up data results of hyperactive children and
concluded that the concern that hyperactive children grow up to become antisocial is
exaggerated and that, in fact, few hyperactive children grow up to become serious adult
offenders. They elaborated, however, by stating that although hyperactive adults are not
significantly different from controls on measures of antisocial behaviour, one can still see
trends of greater antisocial involvernent in individuak with a history of hyperactivrty. On
the other hand, two studies (Gittefman et al., 1985; Greenfield, Hechtrnan, & Weiss, 1988)
published at approxirnately the same time reported that among those diagnosed as ADHD
in childhood, the greatest risk factor for the development of antisocial behaviour in
adulthood is the maintenance of ADHD symptoms. Gittelman et al. (1 985) conducted a 15
year follow up of subjects, with a final age-range of 22-29 years, who were either
diagnosed as hyperactive during childhood or were controls. They identified two
subgroups of the hyperactive population: those with moderate or severe continuing
symptoms and those with none or only rnild continuing symptoms. The former group was
characterized by significant emotional difficulties, alcohol use and antisocial behaviour,
while the latter group did not have dificulties in psychosocial functioning and were, in
addition, similar to the control group in many respects. Because of the evidence
supporting the risk factor status of symptoms of inattention and hyperactivity on later
antisocial behaviour, as well as the association between these symptoms and chitdhood
speechllanguage impairment, they were controlled in the analysis.
Gender
A relationship exists between speechflanguage problems and gender. For
example, gender differences in language development and developmental language delay
favoring girls have been found in a number studies. The Dunedin studies (Silva, 1980;
Silva, McGee, 8 Williams, 1982a, 1983) found that girls began to ta!k, on average, a rnonth
earlier than the boys and they gained significantly higher scores on al1 language tests.
AIso, about twice as rnany boys as girls are usually found to have a language delay
(Morley, 1965; Stevenson & Richman, 1976; Fundudis et al., 1979; Silva, 1980). Although
most studies find more speechAanguage problems among boys, this is not always
reported. Beitchman et al. (1 986a), for example, found that the rates of speech-only and
language-only impaiment were slightly greater for girls than for boys, while the rate of
combined speechflanguage impairment was more than double for giris than for boys. It
should also be noted that the instruments used to assess these rates were gender-
referenced and gender-specific (Beitchman et al., 1986a).
As rnentioned earlier, there is also an association between gender and
delinquency. Male predominance in antisocial behaviour has been well documented
across studies (Henggeler, 1989; Robins, 1986). Male rates of antisocial behaviour
personality disorder greatly exceed female rates for every age and ethnic group (Robins
& Reiger, 1991). In addition, research suggests that antisocial behaviour develops within
different pathways for each gender. For example, in their review, Offord and Bennett
(1 994) note that the predictive power of earlier conduct problems on adult antisocial
behaviour is stronger in men than in women and that earlier factors rnay explain different
amounts of later antisocial variance. As well, early conduct problems predict different
types of later disorders for each gender. For example, in females, conduct problems
predict later intemalizing disorderç more strongly than they do antisocial behaviours,
while the opposite is true for males (Robins, 1986). Finally, the specific consequences of
antisocial behaviour have been reported as different for each gender. For example,
Robins and Reiger (1 991) showed that while the overall symptom patterns for antisocial
males and females were similar, men exceeded women in rates of traffic and nontraffic
arrests (as they do in the population at large), while wornen exceeded men in reporting
marital difficulties.
Almost al1 research on antisocial behaviour is based on male-only samples and
there is a paucity of data on antisocial outcorne in girls. This is noteworthy as the
authors of the Ottawa Language Study found that ratings by both a psychiatrist and the
child's mother indicated that girls with speechllanguage impaiment were at greater risk
than boys for a psychiatric disorder (Beitchman, Hood, & Inglis, l99O). The reasons for
this, however, are not known and had not been previously reported in the Iiterature. The
authors suggested that the gender with the lower prevalence rate, when affected, tend
to be more severely affected and since speechllanguage disorders are generally less
frequent in females they may be more severe and consequently contribute to a greater
vulnerability to other problems as well. Nevertheless, as the present study intends to
draw on the data from the OLS, it has the opportunity to contribute knowledge to this
issue. In summîry, because of gender differences in antisocial behaviour and
speecManguage ability, in addition to the difference in pathways leading to antisocial
behaviour, gender was controlled in assessing the relationship between eariy childhood
speecManguage impairment and young adulthood antisocial behaviour.
SpeechLang uage Treatment
The occurrence of treatment for speechllanguage irnpairments also needs to be
controlled. It is associated with the exposure variable of interest and may be related to
antisocial outcorne. At tirne-3, parents reported that 33%, 62% and 12% of the language
impaired, speech-only impaired and control subjects, respectively received sorne form of
speech andior ianguage therapy some tirne between ages 5 and 19. Thus, subjects who
had some forrn of impairment at time-1 were more Iikely to have received some fom of
treatment before time-3 than the subjects who were normal at tirne-1. There is no data,
however, to detemine whether speechAanguage therapy is effective in preventing
youog adulthood antisocial behaviour. if speechnanguage irnpainnent does lead to an
increase in risk for such behaviour, then conceptually, therapy for such irnpairments
might prove helpful in that it would be expected to increase the child's ability to relate to
others without resorting to aggressive or delinquent behaviours.
Evidence as to the effectiveness of such therapy is not positive. Beitchman et al.
(1 994) failed to demonstrate an effect of speechllanguage treatment. However, because
that investigation was not intended as an evaluation study (for example, the subjects
were not randomly assigned to receive treatrnent) no reliable conclusions can be drawn.
As well, despite the meta-analysis by Nye, Foster and Seaman (1987), tfiere is Iittte
documented evidence of the effectiveness of speechllanguage therapy. Beitchman et al.
(1994) pointed out that the above meta-analysis based its conclusions on studies with
serious methodologicstl problems such as the lack of control groups and random
assignments as well as non-blind ratings, al1 of which cast doubt on the conclusions
drawn by Nye, Foster and Seaman ('l987).
Despite the lack of data supporting the effectiveness of speechllanguage
therapy, as well as data supporting its relationship to later antisocial behaviour, the
occurrence of therapy is still highly associated with the present study's exposure group.
Therefore, it still proves worthwhiie to account for this variable when determining the
relationship between early childhood speechnanguage impairment and young adulthood
antisocial behaviour.
2.6.4 Hypothesis
The most common behavioural outcome in late childhood among speecManguage
impaired children are symptoms of inattention and hyperactivity (Hinshaw, 1992). These
symptorns are also a known risk factor for subsequent conduct and antisocial problerns
(Taylor, 1994). It seems Iikely, therefore, that individuals with earIy childhood
speecManguage irnpairrnents rnay be at risk for antisocial problems in young adulthood.
The truth of this hypothesis, however, rnay depend on the type of antisocial outcorne
measure used. For example, it rnay most Iikely be true for diagnoses of ASPD which
depend on the occurrence of conduct problems before age 15 and which are also
present in speechllanguage impaired children in late childhood. it rnay not be true, however, for measures of crirninal behaviour. For example, even if there is a strong
association between speechnanguage impairment and syrnptoms of inattention and
hyperactivity, it is still unclear as to whether such children grow up to becorne serious
adult offenders (Weiss & Hechtrnan, 1986). The dimensional measures of antisocial
behaviour are useful in their ability to report on the more subtle tendencies associated
with antisocial behaviour that rnay not be of a severity such that these individuals would
exhibit crÏminaI activity. Furtherrnore, these subtle differences rnay prove to be
statistically significant and clinically relevant.
3. Methods
3.1 The Ottawa Language Study
The present study comprises a secondary analysis that makes use of data from
the Ottawa Language Study (OLS). The present author did not take part in the collection
of the OLS data at any of the tirne points, but did contnbute in constnicting a nurnber of
the variables presently used. Specifically, independent variable data was obtained from
the first wave of the study (time-1 , 1982) when the subjects were 5 years of age and
dependent variable data was obtained from the most recent, third wave of the study
(time-3, 1996) when the subjects were 18 years of age. A description of the relevant
data acquired frarn the OLS and how it was utilized in the present study now follows.
3.2 lndependent Variable of Prirnary Interest: SpeechlLanguage Status
One of the purposes of the OLS during tirne-l was to determine the prevalence of
speech and language disorders in kindergarten children (5 years of age) attending
English-tanguage schools in the Ottawa-Carlton region. A one-in-three sample of al1 5
year old children (for whom English was their mother tongue) was administered the first
stage of a three-stage screening procedure, each stage consisting of standardized test
batteries. This original study reported that the Ottawa-Carlton population was
represented well by the studyls sample (Beitchrnan et al., 1986b).
Ali standardized tests were completed within one academic year and were
administered to the students on an individual basis. Stage I consisted of a 30 minute
speech and language screening. All children scoring below the Stage 1 cutoffs, as welt
as a small randorn sample of those scoring above the cutoffs (the purpose of which was
to estimate the rate of false-negatives) were asked to participate in a more intensive
testing session at Stage II. Those children falling below the Stage Il cutoff points were
identified as either speech-only impaired, languageanly impaired or cornbined speech
and language impaired (for a more detailed description of the screening procedure and
the test batteries used, see Beitchman et al., 1986b).
These speech andfor fanguage irnpaired children and a control sarnple (matched
forage and sex, but scoring above the Stage II cutofis) comprised the current
investigation's study sample. This information was also used to comtruct the
independent variable of pnrnary interest in this investigation: time-1 speechflanguage
status. Because, however, there are few, if any, notable differences in psychiatnc and
developmental disorders that distinguish children with language-only impaiments and
children with speech and language impaiments (e-g. see Baker & Cantwell, 1987a; and
Cantwell & Baker, 1987a), these two groups were cornbined into a single "Ianguage
impaired" group. This was also performed because the speech and language impaired
group had the largest proportion of missing values compared to any of the others (see
sections 3.6.2). Merging these two groups helped reduced possible bias. Thus, the
independent variable of primary interest was composed of three categories: a control
group, a speech-only impaired group and a language impaired group.
At tirne-1, the speecManguage impaired and control subjects were also asked to
participate in Stage II1 testing which was designed to ascertain a variety of background,
developrnental and farnily information (as previously described). The relevant information
from this final stage of testing waç the source of the following covariate data.
3.3 Covariates
As described in chapter 2, the çelection of additional risk factors for young
adulthood antisocial behaviour that needed to be controlled was guided by the literature.
These Rsk factors incfuded: intelligence, socioeconomic status (SES), childhood
adversity, being reared in a single-parent family, eariy conduct problerns, inattention,
hyperactivity, gender and speech/language therapy. Except for the latterrnost covariate,
each one of the risk factors was assessed as part of the Stage III procedures dunng
tirne-?. The occurrence of speechllanguage therapy was assessed at time-2 and tirne-3.
A description of the instruments used and the operationalization of each covanate is
provided below.
3.3.1 Intelligence
At time-1, the Wechsler Primary Preschool Scale of Intelligence (WPPSI) was used
to assess IQ (Wechsler, 1987). Silverstein (1 985) reports very good psychometric
properties for this instrument. The WPPSI provides three scores: a full-scale IQ, a verbal
IQ and a performance IQ.
The risk factor status of low intelligence on subsequent antisocial behaviour has
been discussed. Although this relationship has been shown to be stronger for measures
of verbal IQ than for either performance or full-sale 1Q (Prentice & Kelly, 1963; West &
Famngton, 1973), the use of verbal IQ as a covariate poses a problern. Because of the
strong association between language and thought, measures of verbal intelligence and
measures of language ability are very likely to be collinear, such that controlling for verbal
30
IQ would simultaneously control for speechnanguage ability. A similar problem would
exist if full-sale IQ, which represents a measure of both verbal and nonverbal
intelligence, was used. For Our purposes, it is preferable to have a rneasure of
intelligence that is most independent of language ability. Using performance IQ achieves
this and allows for a more complete separation of language abilities and intelligence.
Therefore, measures of performance IQ were used as a covariate when assessing the
risk factor status of eariy childhood speechnanguage impairment on later antisocial
behaviour.
3.3.2 Socioeconornic Status (SES)
Part of the time-l Stage Ill testing involved a parent questionnaire which assessed
total family income. Respondents were asked to report in what category out of a
possible 13 their income fell. The lowest category was less than $5,000 peryear, the
largest was greater than $60,000 per year and each intermediate category encompassed
a $5,000 per year range. Although SES was coded as a 14-level categorical variable (1 3
income categories and 1 missing observation category), it was treated as an ordinal
variable in the analyses.
3.3.3 Childhood Adversity
Research on eariy childhood environmental correlates of disniptive behaviour has
been discussed and the concept of childhood adversity described. Although no specific
measures of this construct were provided at time-1 , a similar construct, parent adversity,
was assessed. This variable was originatly intended to provide a composite measure of
parental mental health that included information from mothers and fathers. However,
dunng the collection of time-l data, almost al1 respondents (97%) were only the children's
mothers. As a result, this variable was re-termed "rnother's adversity".
The mother's adversity variable is a composite score based on two main
subfactors: 1) parent psychopathology, as represented by scores on the Center for
EpidemioIogic Studies-Depression Scale (CES-D) (Radloff, 1977) and scores fom the
Global Symptorn Index Subscale of the Behavioural Symptom lnventory (BSI) (Derogatis,
1975); and 2) scores on marital adjustment and discord from the Marital Adjustment Scale
(MAS) (Locke 8 Wallace, 1959). The CES-D is a self-administered scale which assesses
depression in adults. lts psychometric properties are good and it has been shown to
have high correlations with other self-report measures of depression and with clinical
ratings of depression (Radloff, 1977); The BSI is self-report questionnaire that measures
general psychopathology. Derogatis (1 983) reported that this instrument had good
psychometric properties; and the MAS is a widely used self-report of marital satisfaction
and it too has been shown to have good psychometric properties (Cohen, 1988).
For two-parent families, each score (CES-D, BSI and MAS) was given a weight of
0.333 to create the composite. For single-parent families only the CES-D and the BSI
were available and each was given a weight of 0.5 for the composite. The analysis in
the curent study did not distinguish between mother and father respondents as there
were relatively few of the latter (only 3%). Thus, mother's adversity is used as a proxy
for childhood adversity. This was deemed appropriate for three reasons: First, rnother's
adversity was the closest approximation to childhood adversity available for use in this
study. Second, mother's adversity was previously shown to be related to the children's
psychiatrie outcome at time-2 (Beitchman et al., 1993); and third, it was assumed that
because the mother traditionally plays a significant role in child rearing, especially during
the preschool years, the adversity experienced by the mother would correlate well with
the adversity experienced by the child if such a variable were available. The limitations,
however, of using the mother's adversity construct should be noted. Such a variable
might not accurately represent the adversity experienced by the child if the parents are
separated or divorced and the child spent a rnajority of the time with the parent not
interviewed in the OLS. As weli, in farnilies in which there are a large nurnber of children,
the older ones might be delegated a large amount of responsibility that rnay increase their
adversity relative to their mother as well as to their younger siblings. The assumption that
the adversity experienced by the mother of the subject during the subject's childhood is
related to the subsequent adult functioning of the subject was tested in the course of the
analysis.
It was not assumed, however, that mothers (or children) from single-parent
homes would experience the same amounts of adversity as those from two-parent
homes. In fact, 30% of mother respondents indicated that they were single parents. (An
anonyrnous 4 mother respondents reported that they had partners but were single
parents. For these data, the MAS was not used in the calculation of mother's adversity
and were thus treated as single-parent families). To account for this effect associated
with adversity and which may be related to later antisocial outcorne, a dichotornous
variable indicating whether a child was reared in a single- or two-parent home was
considered in the analyses. Conceptually, child rearing in single-parent homes are
associated with the neglect and disruption paradigrns descnbed by Loeber & Stouthamer-
Loeber (1 986). For example, Henggeler (1 989) identified a modest association between
broken homes and antisocial acts. This categorical variable was operationalized,
however, with three levels: a two-parent family category, a single-parent family category
and a missing data category. The lattermost group had only 6 observations (see Missing
Data, below).
3.3.4 Inattention, Hyperactivity and Conduct Problems
Tirne-1 measures of inattention, hyperactivity and conduct problems were
assessed using the respective subscale percent scores from the Revised Conners
Teacher Rating Scale (CTRS) (Goyette, Conners, & Ulrich, 1978). This instrument
includes items that are rated by the child's teacher as not at al1 present, just a little
present, pretty much present or very much present (scored as 0-3, respective1 y). The
above subscales are ernpirically derived factors. This scale has good psychometnc
properties and has also repeatedly been shown to be sensitive to treatment effects in
hyperactive children (Miller, Koplewicz, & Klein, 1997). A potential limitation in the use of
this instrument in the operationalkation of these risk factors lies in the fact that it reIies on
a single teacher source for the information. Previous research shows that the stability of
early behaviour problerns is particulariy high for preschoolers who exhibit problems both
at home and in preschool settings. ldeally, had parent-rated data been available, it would
have been used in conjunction with the teacher-rated data. Previous work, however,
has also shown that teacher-rated information is more sensitive in assessing the
significance and impact that children's disruptive behaviour will have on Iater functioning
(Miller et al., 1997; Egeland, Kalkoske, Gottesman, & Erikson, IWO).
3.3.5 SpeechILanguage Treatment
Data from the parent and subject time-3 interviews was used to assess the
occurrence of speechllanguage therapy during the period between times-1 and 3.
Specifically, the respondents were asked whether the subject had ever received
treatment for a speech andlor language impairment. Both parent- and subject-assessed
data were used in the analyses and both were treated as dichotomous variables.
3.4 Dependent Variables
3.4.1 Antisocial Behaviour as a Diagnosis
The time-3 measure of antisocial behaviour conceptualized as a diagnosis utilized
the Composite lntemational Diagnostic Interview (CIDI) (World Health Organization, 1990).
The CIDI is a comprehensive interview scheduie designed for the assessment of major
diagnostic categories according to the criteria in the lntemational Classification of
Diseases (ICD-1 O)-Diagnostic Criten'a for Research (World Health Organization, 1991 a, b)
33
and the DSM-IIIR (Amerkm Psychiatnc Association, 1987). It is a highly stnictured
instrument intended for use by trained lay interviewers and in epidemiological studies of
mental disorders in general populations. CID1 questions are fully spelled-out and positive
answers are further explored with a strictly specified probing system. A symptom
assessed by the CID1 is considered clinically significant if it led to professional
consultation, treatment with medication or was believed by the respondent to have
interfered significantly with Iife activities. This latter criterion is assessed with the Global
Assessrnent of Functioning (GAF) s a l e which is listed in the DSM-IIIR as an Axis V
diagnostic criterion test (American Psychiatric Association, 4 987).
The GAF is also an interviewer-rated scale and provides a rating from 1 to 90 of
an individual's overall psychological, social and occupational functioning. In the present
study, a GAF score of 69 or less was required (in addition to endorsement of the
relevant itemslbehaviourç on the CIDI) for a subject to be "diagnosedn with an antisocial
personality disorder (ASPD). Ali "layn interviewers were research assistants working
with the OLS and took part in a 2day intensive training course for the administration of
these instruments. The training was sirnilar to that employed by the investigators of the
Ontario Health Survey-Mental Health Supplement (mord et al., 1996). Finaily, the CIDI
has been shown to have good psychometric properties (Kessler et al., 1994) and the
GAF has also been shown to be adequate (Hall, 1995; Piersma & Boes, 1997).
3.4.2 Antisocial Behaviour as a Legal Entity
Antisocial behaviour was also assessed with respect to the law. Specifically, an
atternpt was made to detennine whether or not antisocial behaviour elicited by the
subjects was of a nature (or perhaps, severity) such that they would have had
increased contact with the police andior courts. To address this problem, data from one
of the time-3 subject interviews on contact with the law was used. From this
questionnaire, a simple dichotomous variable called criminal behaviour was created.
Criminal behaviour was considered present if a subject reported at least one of any of
the following: an arrest, a court trial for having been accused of something, a conviction,
tirne on probation, time in a juvenile detention center or time in a police station or jail.
3.4.3 Antisocial Behaviour as a Dimension Measured Continuously
Antisocial behaviour conceptualized as a dimension was assessed using three
different instruments, each of which relied on a different informant: parents (mother or
prirnary parent if mother is unavailable), teachers (subject-identified teacher likely to
know thern best) and the subjects themselves. The corresponding instruments used
were the Achenbach Child Behaviour Checklist (CBCL) (Achenbach, 1991 a), Teacher
Report Fom (TRF) (Achenbach, 1991b) and Youth Self Report (YSR) (Achenbach,
1991 c). Each test is a standardized and reliable checklist that has been shown to
differentiate between children referred for psychiatrie treatment and normal children. As
well, the psychometric properties for each are reported to be good (Achenbach, 1991d).
Each checklist contains a large nurnber of problem items which the respondents
rate as not true, somewhat or sometimes true or very true or oiten true (scored 0-2,
respectively). The problems items are used in the construction of a variety of syndrome-
specific scales. ln this study, the syndrome-specific scores that were used to measure
antisocial behaviour are the Aggressive and Delinquent Behaviour Scales. Both of these
scales are produced by al1 three instruments. In other words, the constructs of
aggression and delinquency are each as assessed by different informants from their
respective contexts or environments. For each instrument, there are two types of
syndrome-specific scales: those that rely only on the problem items common to alt three
instruments (the cross-informant syndrome scales) and those that rely on the common
problems items plus the items specific to a particular instrument (instrument-specit7c
syndrome scales). Only the latter scales were used in this study.
The reason for this is that there are behaviours for which some types of
informants can report on while other types of informants cannot. For example, teachers
may be better assessors of how a subject interacts with other subjects in a controlled
environment (such as a classroom) than a parent. For this reason, the TRF contains the
problern items such as "disnipts classn or "disturbs othersn while the CBCL does not.
One reason for using multiple sources is that different types of informants are likely to
observe different samples of subject behaviour. Using the instrument-specific syndrome
scales rather than the cross-informant syndrome scales allows for this study to capture
better the different ways in which antisocial behaviour is manifested by the subjects
instead of how different informers report on the same subject behaviour.
For each syndrome-specific scale, a raw score is produced which is converted
to a norrnalized T score. In this study, each instrument's Aggressive Behaviour T score
and Delinquent Behaviour T score was combined to form a mean "antisocialn T score
which was used in the study's analyses. This was done for four main reasons: First,
although problem items in difFerent syndrome scales have subtle conceptual differences
that warranted the grouping of them separately in the original Achenbach scales, ail of
the items in both scales can still be considered antisocial in nature. Second, considering
al1 problems items together as a single score yields a measure of antisocial behaviour
which is most similar to the notion of antisocial behaviour as conceptualized in the
diagnosis of an ASPD. Third, combining these two T scores is reasonable in that it yields
a useful and easily interpretable summary score that does not involve a loss of
information. Finally, the T scores and raw scores for each of the syndrome-specific
scales correlated highly (see Table 3.1, below).
ssion and Delinquency Subscale strurnents, respectively.
Table 3.1 : Correlations (and their p-values) of the Aggr T scores and raw scores for the CBCL, TRF and YSR
Finally, because of a large proportion of rnissing values arnong the teacher-rated
(36%) and parent-rated antisocial behaviour T scores (23%), these two measures were
merged into a single variable conceptualized as "non-subject rneasured" antisocial
behaviour (see below for a more complete discussion of missing values and how they
were dealt with). This merging invoived using the mean of both rneasures if both data
were present and using either datum if the other was rnissing. Although cornbining
parent and teacher data resulted in a substantial loss of information by losing the ability to
distinguish behnreen parent and teacher scores (for example, they only share
approximately 20% of their variance), it was the only way to salvage and make good use
of the data available. As well, useful distinctions were still made between subject-
measured and non-subject rneasured antisocial behaviour. Furthemore, combining these
two rneasures reduced the proportion of values missing to 16.5% frorn the higher values
of 23% and 36%. Table 3.2 shows how well parent- and teacher-rated antisocial
behaviour rneasures correlated for T scores and raw scores, respectively.
YSR
Aggression &
Delinquency 0.45408
(p=0.0001)
TRF Aggression
& Delinquency 0.621 76
(p=0.000 1) T score
CBCL Aggression
& Delinquency
0,73004 (p=0.0001)
?ir p-values) of the parent-rated (from the CBCL) and iean antisocial measures for the T scores and raw scores,
Table 3.2: Correlations (and th teacher-rated (from the TRF) i respectively.
To bnefiy summarize:
T score
raw score
antisocial behaviour as a dimension was operationalized by using scores from
parents, teachers and su bjects
due to missing values, parent and teacher measures were merged into a single score
referred to as "non-subject measures" of antisocial behaviour
a separate analysis was performed for subject and non-subject measures of
antisocial behaviour
within each analysis, the actual score used was the mean of the Aggressive
Behaviour and Delinquent Behaviour T scores from the respective instruments'
Parent- & Teacher. Measured Antisocial
Behaviour 0.43776
(p=O.O001)
0.43307 (p=0.0001)
3.5 Statistical Analysis
3.5.1 Data Exploration
The analyses used to explore the data have been partially described. In order to
make decisions about how the data would be treated a variety of simple analyses were
performed. For categoncal variables, frequency calculations were perfomed and
missing value categories were created. For continuous variables, distributions were
analyzed and issues conceming whether assumptions underlying the various analyses
would be violated were addressed. Decisions about the ways in which variables were
treâted are discussed in more detait in sections 3.3. 3.6 and 3.9.
' ln the case of non-subject measured antisocial behaviour with both parent and teacher data available, the T score used was a mean of 4 T scores: parent- rated and teacher-rated aggressive behaviour T scores and parent-rated and teacher-rated delinquent behaviour T scores.
3.5.2 Logistic Regression
Young adulthood antisocial behaviour was first conceptualized as an illness and
was operationalized as a diagnosis of antisocial personaIity disorder being either present
or absent at time-3. This tirne-3 outcome was therefore, dichotornous and the logistic
model was used to obtain odds ratio estimates (ps) for a time-1 speech-only impairment
and a tirne-1 language impainnent (each of which was defined relative to the control
group). The same set of analyses of the model building procedure was used for the
second conceptualization, that of antisocial behaviour as a legal entity. As discussed
above, the outcome variable far this conceptualization was termed cnminal behaviour and
was also operationalized as either present or absent at tirne-3 and was, therefore,
dichotomous, Thus, the logistic model was used a second time to obtain a second set of
the above mentioned odds ratio estirnates.
Model Building
Because this investigation studied the relationship between an independent
variable of primary interest and an outcome for which a number of potential confounders
required control, a combination of Analysis of Covariance (ANCOVA) and model building
techniques were employed. For the logistic analyses, this was accomplished via the
following steps:
Step 1 : ANCOVA
A set of ANCOVA procedures were performed involving the independent variable
of interest and each covariate to assess whether the latter confounded the relationship
between speechAanguage impairment and risk of an antisocial outcome. This involved
two substeps: First, an assessment of an interaction between speech~language status
and the covariate was perforrned (it should be noted, however, that there were no a
prion hypotheses of any interactions); and second, if no interaction existed, an
assessment of the confounding status of the covanate was performed. A covariate
was defined as a confounder if the adjusted ps differed by greater than 10% from the
unadjusted (i.e. cade) iys. This definition of confounding has been shown to perform
acceptab!~ and the 10% difference between adjusted and unadjusted p s to be an
acceptable cut-point (Maldonado & Greenland, 7 993).
Step 2: Variable Selection
At least one of the two following criteria were required for a covariate to be
considered for inclusion in the model building process: 1) the adjusted and unadjusted Ws
differed by 10% or more; and/or 2) the covariate's Wald x2 statistic was significant at the
0.1 O level in the wrresponding ANCOVA performed in Step 1.
Step 3: Mode1 Building
A backward-deletion model building strategy similar to that suggested by Hosmer
and Lemeshow (1 989) and Rothrnan and Greenland (1 998) was employed. The
advantage of a backward-deletion approach (as opposed to a fonnrard-addition
approach) is the fact that it takes into consideration the possibility that a collection of
variables, each of which may be weakly associated with the outcome in a multivariate
model, can strongly effect outcome when taken together (Hosmer & Lemeshow, 1989).
A fonnrard-addition process does not account for this because it considers the impact of
the addition of only one variable in the model at a time.
The backwarddeletion process first involves the fitting of the largest multivariate
model containing the independent variable of primary interest (time-l speechnanguage
status) and the covariates identified in step 2. The corresponding p s of
speechllanguage status are determined and the probability of each covariate's Wald X2
statistic is also noted. The covariate with the least statistically significant value is deleted
from the model and a new model is fit. The p s for speechlianguage status are then
recalculated and campared to the p s from the largest multivariate rnodel. If at least one
pair of @s differ by 10% or more, the deleted covariate is retained, the model is
considered final and the speechAanguage @s are reported. If the iys do not differ by
10% or more, the deleted variable is eliminated from consideration and the process is
repeated using the new, reduced model.
Step 4: Iterations
This process of deleting covariates, refitting reduced models and cornparing the
new p s to iys from the fullest multivariate rnodel is repeated up until at least one pair of
corresponding Ys differ by 10%. This is the final model.
3.5.3 Linear Regression
Essentially the same ANCOVA-rnodel building steps that were used for the logistic
regression analyses were also used for the linear regression analyses; the major
difference being that now, the mean antisocial T scores for the speech-only impaired
group, the language irnpaired group and the control group were the estirnates of interest
rather than odds ratios. Regression diagnostics were also performed to assess the
validity of the assumptions involved in linear regression analyses. As well, an analysis
of multicollinearity arnong the independent variables in the final Iinear and logistic rnodels
was assessed with painvise correlations and multiple R ~ S . The results from these
analyses appear in Appendix C.
3.6 Missing Data
3.6.1 Independent Variables
OnIy four of the nine independent variables had missing data and arnong each
one, a relatively small proportion was rnissing: performance IQ (1.8%), rnother's adversity
(7.7%), single-parent family (2.1 %) and SES (8.5%). For each of these, missing values
were substituted with the variable's respective mean. For the covariates of rnother's
adversity and SES, those with rnissing data at time-1 were most Iikely to be those most
impaired. Because those most irnpaired tend to have the poorest tevels for the
covanates, such missing covanate data might bias the results toward rejecting the nul1
hypothesis. See Appendicies A and B for a more complete discussion of missing data.
3.6.2 Dependent Variables
Missing data among dependent variables was more senous: parent-rated
antisocial behaviour, 22.5%; teacher-rated antisocial behaviour, 36.3%; (as mentioned
a bove), subject-rated antisocial behaviour, 1 0.6%; diagnosis of ASPD, 14. A %; and
criminal behaviour, 1 1 -6%. Observations with rnissing dependent variables data were
not included in any of the main analyses. However, cornparisons of subjects with
missing data to those without missing data for each dependent variable were made on all
the variables in the study. This was intended to expose any patterns in missing data that
rnay be of use in when interpreting results. As well, cornparisons of the proportions of
subjects with missing dependent variable data among each tirne-? speechllanguage
status group were also made. The results for these analyses appear in Appendix A. Al1
of this information was used to assess the reliability of the results.
3.7 Descriptive Staüstics
A set of descriptive statistics was calculated for each time-1 speechnanguage
group on each independent variable. Because this study was concemed oniy with
correlates that were also risk factors for antisocial behaviour, these data are only a
partial representation of the time-1 study sample characteristics.
3.8 Power Calculations
Before the start of time-3 data collection, power calculations were perfonned
based on a participation rate estirnated to be equivalent to the tirne-2 participation rate.
Specifically, 210 subjects were expected to take part and sample sizes of 225, 200 and
175, respectiveiy, were used to estimate the srnallest standardized effect size (Beta)
detectable. These calculations were also carried out with respect to: number of
independent variables in final regression models, overall explained variance ( R ~ ) and
tolerance levels among the independent variables (Greenland, 1985). All calculations
were performed setting ~ ~ ~ û . 0 5 and fk0.20 (power=0.80). Calculations were performed
for models containing 5 and 7 independent variables, respectively. As well. time-3 R~
values considered were 0.50, 0.40 and 0.25 respectively. Tolerance levels of 0.60 and
0.80 were also considered.
For a 5-independent variable regression model, the Beta ranged from the smallest
value of 0.1640 (for an n=225, an ~'=0.40 and tolerance of 0.80) to the largest value of
0.2410 (for an n-175, an ~~=0.25 and a tolerance of 0.60). For a 7-independent variable
regression rnodel, the Beta ranged from the smallest value ~f 0.1 504 (for an n=225, an
~*=0.50 and tolerance of 0.80) to the largest value of 0.2168 (for an n=175 an ~~=0.40
and a tolerance of 0.60). These analyses deemed that even with a smaller than
anticipated sarnple size of 175, the detectable effect sizes (the targest of which was
0.241 0) remained, from a dinical point of view, adequately small.
A preliminary check of the data for the curent study showed that the model with
the smallest number of observations exceeds the value of 225 used in the above
calculations. It was not assumed, however, that this study would find as large
proportions of explained variance, or as high levels of tolerance among the independent
variables which may exist in particular final models. Because of this, the above
mentioned a priori power calculations can only be used as a guide in the attempt to
explain any non-significant results that rnay be found.
3.9 Tests of Reproducibility of the Results
The variables in this study can be operationalized in rnany ways. To detemine
whether variations in covariate definition affects the results, three variable modifications
were considered: first, the use of the Conners measures of inattention, hyperactivity and
conduct as ordinal vs. continuous; second, the use of subject- vs. parent-assessed data
on whether the subject had ever received speechiianguage therapy; and third, the use of
raw scores vs. T scores in the definitions of the dependent variables used in the linear
regression analyses.
3.9.1 Independent Variables
Conners Measures of Inattention, Hyperactivity and Conduct
These percentage scores were found to be positively skewed. That these
covariates need to be normally distributed is not an assumption of Iinear or logistic
regression. There was concem, however, that the few observations at the far right of
each distribution were influential values. An attempt to solve this problem while retaining
these observations was made by forming groups of deciles, nurnbering each group and
then treating these nurnbers as continuous in the ANCOVA and model building processes
(Hosmer & Lemeshow, 1989). Although the new variables are actually transformed
continuous linear terms, for simplicity of argument, these new variables are referred to
as "ordinaln- A caveat should be noted: Because of the preponderance of certain values
(e-g. 39% of the subjects had a value of 0.00 for the Conduct variable), in none of the
Conners variables did the decile groups have an equivalent 10% of the total number of
subjects. This also caused the Conduct and Inattention variables not to have 10 decile
groups, but instead to have only 7 and 6 approximate "decilen groups, respectively. The
percentage scores at which the approximate decile groups were defined for each new
ordinal Conners variable appear below in Table 3.3.
Subject-assessed Versus Parent-assessed SpeechILanguage Treatment Information
As described in section 3.3.5, both the subjects and their parents were
interviewed conceming whether the subject had ever received any speecManguage
therapy. To account for any possible bias in either of these rneasures, both were
assessed in the analyses.
3.9.2 Dependent Variables
Dimensional Measures of Antisocial Behaviour
There are two reasons to consider the Achenbach raw scores versus the
normalized T scores. First, Achenbach (1 991 6) suggested that for purposes of
statistical analysis, it is preferable to use the raw scores from the syndrome-specific
scales rather than the T scores in the order to capitalize on the full range of variation in
the raw data. This is because the transformation of raw scores into T scores often
involves truncation where the assignment of a single T score to multiple raw scores may
eliminate variations that are important to detect. Second, the subject- and non-subject-
measured mean antisocial T scores described above were found to be very positively
skewed. Although norrnality of the dependent variable is not an assumption in Iinear
regression, it was found that corresponding distributions of the raw scores were not as
severely skewed.
A disadvantage of using raw scores over T scores is the resulting difficulty in
their interpretation. The main function of the T scores is to facilitate cornparisons of the
degree of deviance indicated by a child's standing on different scales and different
instruments. However, because this study does not intend to make such comparisons (it
only intends to make comparisons among different subjects using the same scale and
then comparing the resulting pattern of results to those frorn the use of a different
instrument), it was still of use to compare results from analyses using raw scores and
analyses using T scores.
3.9.3 Reproducibility of the Results: Summary
This study also determines whether different operationalizations of particular
covariates changes the conclusions drawn from the results. As such, for the analysis
of each antisocial outcome measure (diagnoses of ASPD, criminal behaviour, non-subject
dimensional measures and subject dimensional measures), there were four difierent sets
of covariates that were considered when detemining the relationship between early
childhood speechnanguage impairment and young adulthood antisocial behaviouc
Set 1:
Set 2:
Set 3:
Set 4:
Conners variables, as measured continuously, subject-assessed
speechllanguage treatrnent variable and the rernaining covariates (Le. P-IQ,
mother's adversity, SES and gender).
Conners variables (continuous), parent-assessed speechllanguage
treatment variable and the remaining covariates.
Conners variables, as measured ordinally, subject-assessed speechllanguage
treatment variable and the remaining covariates.
Conners variables (ordinal), parent-assessed speechflanguage treatment variable
and the remaining covariates.
For the dimensional measures of antisocial behaviour, there were actually 8 different
sets of models; four rnodels using the above sets of covariates while using the
Achenbach T scores as the dependent variable and another four models using the same
four sets of covariates but using the Achenbach raw scores as the dependent variable.
However, because the raw scores do not allow for easy interpretation and
generalization, only the results from the analyses using the T scores are reported. Thus,
for the analyses of non-subject and subject measures of antisocial behaviour, sets 1
through 4 refer to the modets that used the Achenbach T scores as the dependent
variable.
4. Results I: Primary Analyses
Table 4.1 provides descriptive statistics for each time-1 speechllanguage group.
The pattern of results is such that the ianguage impaired children were generally worse
off than the speech-only impaired and control children. For example, ianguage impaired
children had significantly lower (pc0.05) performance-la (1 00.58) than both the speech-
only impaired group (1 13.72) and controls (1 17.96). They also had significantly higher
(pc0.05) rates of inattention (25.43%), hyperactivity (32.72%) and conduct problerns
(1 7-20 %) than speech-only impaired children (1 6-31 %, 24.75% and 1 1.57%,
respedively) and controls (1 4.06%, 18.25% and 10.31 %, respectively). Furthermore, a
larger proportion (23.23%) of language impaired children had been raised in single-parent
families than speech-only impaired children (10.26%) or controls (12.86%), however, the
differences in these proportions were only nearly significant (p=0.056). As well, a
significantly lower level of family income was reported for the language impaired group
(mean income level 6.02) than for the speech-only irnpaired (7.92) and control groups
(7.45), corresponding to less than $1 0,000 per year.
Tirne-1 Sarnple Size (n)
Performance IQ
Mother's Adversity
Inattention
Hyperactivity
Conduct
E S
Single-Parent Familyo
Gendet
Treatment (subject)O
Treatment (parent)d
Table 4.1 : Descriptive statistics (and standard deviations) for the speechllanguage groups on time-1 sample sbe, each of the time-l covariates, and time-3 speechllanguage treatment variables.
F(2,281)=63,38, p=0.0001; C S , S>L, C>L, al1 p<O.05
F(2,281)=2,16, p=0.1171
F(2,28l)=lO.2OI p=0.0001; L A , L>C, bolh p<0.05
F(2,281)=13,26, p=0.0001; L>S, LIC, both pc0.05
F(2,281)=4.45, p=0.0125; L>C, pc0.05
F(2,281)=8.68, p=0.0002; C>L, S>L, both p<0.05
~2(2df)=5.78, p=0,056
;~2(2df)=2,16, p=0.339
22(241)=40.487, p=0,001
~2(2df)=47.416, p=0.001
Tirne-1 sample size, covariates and tirne-3
treatment data
Note: aproportion from single-parent families. b proportion male. Csubject-reported proportion having received any speech or language therapy. d parent-reported proportion having received any speech or language therapy. "for post hoc tests: C=control group, S=speech-only impaired group and L=language impaired group.
Control Speech-only Language lmpaired Group ~lfferences' Group lmpaired Group Group
A sirnilar, but less dramatic, pattern of results was observed for the speech-only
impaired group. For example, they had significantly lower performance-IQs than controls,
and had higher, although not significantly higher, rates of inattention, hyperactivity and
conduct problems.
4.2 Conceptualization 1: Diagnoses of ASPD
4.2.1 Unadjusted Results
Table 4.2 shows the unadjusted time-3 ASPD results. The language impaired
group had a significantly higher proportion with diagnoses than the controi group
(19.48% vs. 7.75%, z=2.61, pc0.05), but not significantly higher than the speech-only
impaired group (13.16%). f i e proportions of speech-only impaired children and controls
with diagnoses were also not significantly difFerent from each other- Table 4.2 also
shows the unadjusted ~2 comparing the proportions with a diagnosis of ASPD arnong the
speechllanguage groups.
Table 4.2: Unadjusted proportions with diagnoses of ASPD for each speechllanguage
I~iagnosis of ASPD 1 1 O 7.75 1 5 13.161 1 5 19.481 p=0.045 1
Control
n 70
The corresponding unadjusted +s and their 95% confidence Iimits (CLs) for the speech-
only irnpaired and language impaired groups appear in Table 4.3, below. Table 4.3 also
shows the likelihood ratio ~2 value for the crude mode1 which contains only the tirne-1
+ \
Speech-only
n %
speechllanguage variable.
Table 4.3: Odds ratios ( p) and their 95% CLs for the diagnosis of an ASPD for the
Language
n Yt
Group Differences
~ 2 ( 2 d f ) = 6 . 1 8 2 ,
speech-only impaired and language irnpaired groups unadiusted for any covariates.
95%CL(Y)
(0 .576-5 .641 ) ( 1 -222-6.783)
' LRTa: ~2(2df)=6.027, p-0.0491
Speech-only Lang uage
Note: aLRT=likelihood ratio test.
tjis defined with respect to the control group.
Pb 1.803 2.879
4.2.2 Adjusted Results
Thirteen separate univariate analyses were perfomed (for nine covan'ates, with
the Conners variables of Inattention, Hyperactivity and Conduct having two definitions
each, continuous and ordinai, and with the speecManguage treatment variables having
two definitions - subject- and parent-assessed). An equal number of ANCOVAs were
also perfomed. The results from each of these analyses appear in Table 4.4, below.
Table 4.4: Results from the univariate and ANCOVA analyses for each covariate and for
Performance lQ
Mother's Adversity
Inattention (continuous).
Hyperactivity (continuous)
Conduct (continuous)
SES
Single-Parent
Gender*
rreatment (subject).
covariate for tC. Covariate
Jnivariate Analysis
! diagnosis of an ASPD. ANCOVA
Covariate 1 1 Speechnanguage Status
Language 0.0323 3.002 (1 .097-8.212)
Language 0.021 O 2.763 (1 -1 66-6.548) 0.01 O8 0.2501 Speech-only 0.3330 1.759 (0.561 -5.597)
Language 0.0336 2.589' (1 -097-6.226) 0.00741 0.3927 Speech-only 0.3603 1 -710 (0.542-5.344)
Language 0.031 8 2.630 (1 -088-6.357) -0.00526 0.6453 Speech-only 0.3053 1-81 7 (0.580-5.689)
Language 0.01 41 2.948 (1 -244-6.985)
Language 0.01 99 2.809 (1 -1 78-6.702)
0.3073 0.5704 Speech-only 0.2957 1,840 (0.587-5.768)
Language 0.01 98 2.786 (1.177-6.595) 1 -8857 0.0029* Speech-only 0.3995 1 -646 (0.51 7-5.243)
Language 0.0073 3 . 3 5 ~ ~ (1 -385-8.1 25)
Language 0.01 12 3.089 (1 -293-7.381 ) L
Alternate Definitions for the Conners and SpeechILanciuaqe Treatment Variables - - Inattention (ordinal). ) 0.21 94 0.0594 1 0.1 637 0.1799 Speech-only 0.3563 1,714 (0.546-5.390)
I I Language 0.041 2 2.505' (1 -037-6.047) dyperactivity (ordinal)' 0.1 454 0.0453 0.1 1 15 0.1 417 Speech-only 0.4905 1 -633 (0.51 6-5.1 70)
I I Language 0.8973 2.453' (1 -01 6-5.925)
Sonduct (ordinal) 1 0.0718 0.4290 1 0.0493 0.5970 Speech-only 0.3144 1.797 (0.574-5.625)
1 1 Language 0.01 88 2.809 (1 -1 86-6.650) rreatment (parent), 1 0.5596 0.2173 1 0.1 163 0.8174 Speech-only 0.1 541 2.636' (0.695-9.995)
I I Languaqe 0.01 39 3.647* (1 -300-1 0.230)
Note: a f i =parameter estimate. bp=pro ba bility. " p=adjusted odds ratio estimate. d95%C~( @)=confidence limits of tjk '=adjusteci and unadjusted odds ratios differ by 10% or more. '=parameter estimate is significant at the p=O.l O level in its ANCOVA. *=covariate entered into the model building process (Le. was significant at the p=0.10
level in its ANCOVA (*) andfor changed 4 by 10% or more (')).
As described in section 3.5.1, only those covariates which were either significant
at the p=0.10 level in their ANCOVA andior changed at least one of the speechllanguage
odds ratio estimates by 10% or more were considered to be entered into a model building
process. The satisfaction of this criterion for the particular covariates for each covariate
50
set (described in section 3.9.3) was assessed. Of the four covariate sets, set 1 and 3
yielded identical models and sets 2 and 4 yielded identical models. Thus, the four
covan'ate sets yielded only two distinct final models. These were:
From covariate sets 1 and 3:
logit[P(Dx ASPD=present)]=a+Pr(Speech-only)+B2(Language)+~~(Gender)+~,(Treatment-S)
From covariate sets 2 and 4:
logit[P(Dx ASPD=present)]=a+Bl(Speech-only)+~2(Language)+P3(Gender)+P,ITreatment-P)
Because each of these two final models are almost the sarne and because each
produced speech/language odds ratios that were virtually identical (differed only at the
third decimal place), only the final rnodel from covariate sets 1 and 3 will be reported.
This final model is henceforth referred to as 'Mode1 1". This rnodel and its liklihood ratio X2
value appear below. The associated odds ratios for each of the variables in Model 1
appear in Table 4.5.
Model 1 : Liklihood Ratio Test: k(3df)=l9.?'84. p=0.0006 (n=241). logit[P(Dx ASPD=present)]=a+~l(Speech-only)+~~(Language)+~3(Gender)+p~(Treatment-S)
Table 4.5 shows that a time-1 language impairment was associated with a
significant odds ratio of 3.662 for a tirne-3 diagnosis of an ASPD, while a time-1 speech-
only impairment was associated with a non-significant odds ratio of 1.973. Only Gender
and Subject-assessed speechllanguage treatment were controlled as none of the eleven
other covariates proved to be confounding variables and were, therefore, not included in
the above final model. It is also worth noting that the male gender had a highly significant
Table 4.5: Relevant odds ratios and their 95% CLs for the variables in Model 1. 95%CL(I9)
(0.575-6.767) (1 -488-9.01 2) (1 -907-1 2.31 0) (0.247- 1 -784)
Speech-onfya Languagea Gender"
Treatment (S)= Note: aodds ratio defined with respect to the control group. bodds ratio for males defined wRh respect to females. 'odds ratio for having received treatment (subject-assessed) defined with respect to not
having received treatment.
Y 1.973 3.662 6.636 0.664
odds ratio of 6.591 defined with respect to fernales, but that the treatment odds ratio of
0.664, which identifies a protective effect. was not signifiwnt at the p=0.05 level. As
well, that covariate sets 1 through 4 could al1 be surnrnarized by a single rnodel (Mode1 1)
shows that varying the operationalizations of the Conners and speechnanguage
treatment covariates did not affect the conclusions drawn from these results.
4.2.3 Summary of Unadjusted and Adjusted Results for the Analysis of ASPD
When companng the adjusted to the unadjusted speechAanguage odds ratios. it
can be seen that only their magnitude increased white their significance at the p=0.05
level did not. After accounting for the variables of gender and speechnanguage
treatment, the adjusted odds ratios can be considered as more precise estimates of the
risk for a diagnosis of ASPD. A comparison of the adjusted and unadjusted odds ratios
for the speechflanguage groups appears below in Table 4.6.
Table 4.6: Cornparison of the unadjusted and the adjusted speechllanguage odds ratio - - estirnates and their 95%CLs for the analysis of ASPD.
-
Note: "LRT=likelihood ratio test. bodds ratio defined with respect to the control group.
Unadjusted Resuits
speech-mly Language
4.3 Conceptualization II: Criminal Activity
4.3.1 Unadjusted Results
When antisocial outcome was operationalized in ternis of illegal activity, both the
language impaired (28.92%) and speech-only impaired (27.03%) groups had significantly
higher (z=2.37 and ~2.29, respectively, ~ ~ 0 . 0 5 for both) proportions of subjects who
reported some criminal behaviour than the controls did (15.27%) (see Table 4.7). The
speech-only impaired and language impaired groups did not differ from each other. Table
4.7 also shows the likelihood ratio 5 value for the crude model which contains only the
time-1 speechAanguage variable.
LM: ~2(2df)=6.027, ~=0.0491
Adjusted Results I
LRT~: x2(4df)=1 9.784, 0=0-0006 qb
1.803 2.879
95%CL(*) yb (0 -576-5 -641 ) (1 -222-6.783)
9S%CL(@)
(0.575-6.767) (1 -488-9.01 2)
Speech-only Language
1 -973 3.662
Table 4.7: Unadjusted proportions of subjects who reported sorne form of criminal
- - -- - - - - -
Note: 'LRT=likelihood ratio test.
behaviour for each speechnanguage group.
The corresponding unadjusted ys and their 95% CLs for the speech-only impaired and
language impaired group appear, in Table 4.8, below:
Table 4.8: Odds ratios ( q ) and their 95% CLs for the occurrence of criminal behaviour for the speech-only impaired and ianguage irnpaired groups unadjusted for any
Criminal Behaviour
covariates -
Control
n YO 2 0 15.27
Speech-only
n %
10 27.03
Note: ' 4 s defined with respect to the control group.
Language
n YO 24 28.92
Speech-only Language
4.3.2 Adjusted Results
A similar set of univariate and ANCOVA analyses were perfomed with criminal
behaviour as the outcome variable. The results from these analyses are summarized in
Table 4.9, below:
Group Differences
~2(2df )=6 .386 ,
p=0.041
>
2,056 2.258
(0.863-4.896) (1 -1 53-4.422)
Table 4.9: Resutts from the univariate and ANCOVA analyses for each covariate for the occurrence of cnminal bt
I Performance lQ
Mother's Adversity
Inattention (continuous)*
Hyperactivity (continuous)
Conduct (continuous)
SES*
Single-Parent.
llnattention (ordinal)
Hyperactivity (ordinal)
Conduct (ordinal)
Treatment (parent)
~aviour. Covanate
Jnivan'ate Analysis
I
Definitions for the
Covariate 1 1 SpeechKanguage Status
Language 0.0475 2.247 (1 -009-5.003)
0.01 23 0.3937 Speech-only 0.1 018 2.066 (0.866-4.927)
Language 0.0221 2.201 (1 -1 20-4.325)
0.01 57 0-0380* Speech-only 0.1 250 1.982 (0.827-4-749)
Language 0.0660 1,918' (0.958-3.840) 0.01 83 0.0080* Speech-only 0.1 950 1.797' (0.741 -4.361 )
Language 0.1089 1.7~7' (0.880-3.587)
0.01 41 0.0743* Speech-only 0.1 163 2.01 3 (0.841 -4.81 9)
Language 0-0360 2.077 (1 -049-4.1 12)
-0.1 360 0.01 Speech-0nly 0.0661 2.294' (0.946-5.561)
Language 0.0529 1 -972' (0.992-3.920)
1.3979 0-0005* Speech-only 0.0756 2.250 (0.920-5-506)
Language 0.031 6 2.1 50 (1 -070-4.323)
0.8877 0-01 51 * Speech-only 0.1 546 1.892 (0.786-4.555)
Language 0.01 24 2.429 (1 -207-4.742)
-0.4587 0.2427 Speech-oniy 0.0448 2-637' (1.023-6.800)
Language 0.01 60 2.350 (1.1 72-4.71 0)
Conners and SpeechILanguage Treatrnent Variables
Note: ' =parameter estimate. 'p=probability. ' p=adjusted odds ratio estimate. d95% CL( @)=confidence limits of p.
0.2372 O.O1 51 * Speech-only 0.1 472 1.914 (0.796-4.606)
Language 0.0893 1 -83zt (0.91 1-3.683)
0.1 788 0-0030* Speech-only 0.21 29 1.760' (0.723-4.286)
Language 0.1 282 1.724~ (0.855-3.478)
0.1 753 0.01 53* Speech-only 0.1 1 10 2.044 (0.849-4.924)
Language 0.0398 2.048 (1 -034-4.057)
-0.5401 0.21 46 Speech-only 0.061 3 2.729' (0.954-7.808)
Language 0.0050 3.082' (1 -405-6-761 )
'=adjusted and unadjusted odds ratios differ by 10% or more. '=parameter estimate is significant at the p=O. 10 level in its ANCOVA. *=covanate entered into the model building process (Le. was significant at the p=0.10
level in its ANCOVA (') andlor changed g by 10% or more (3).
For the analysis of criminal behaviour, covariate sets 1 and 2 yielded the same
final model, This mode1 is henceforth referred to as "Mode1 2an. This model and its
likiihood ratio x2 value appear below. The associated odds ratios for each of the
variables in Model2a appear in Table 4.1 0.
Model 2a: LikIihood Ratio Test: x2(3df)=1 3.396, p=0.0039 (n=251). logit[P(Crim.Beh.=present)]=a+~~(Speech-only)+Pz(language)+$~(Hyperactivity) 1
Table 4.1 0: Odds ratios and their 95% CLs for the variables in Model Sa. I w I 95C/oCL(tp)
Note: aodds ratio defined with respect to the control group. bodds ratio defined by a 10% increase in Hyperactivity.
Similady, covanate sets 3 and 4 yielded the sarne final model. This model is henceforth
referred to as "Model 2bn. This model and its liklihood ratio X2 value appear below. The
associated odds ratios for each of the variables in Model2b appear in Table 4.1 1.
Model2b: Liklihood Ratio Test: X2(3df)=26.842, p=0.0001 (n=251).
1 logit[P(Crim.Beh~=present)]=a+~~(Speech-only)+~~(Language)+ 1
Table 4.1 1 : Relevant odds ratios and their 95% CLs 1 ? 1 9 5 % ~ ~ ( y )
Speech-onlya Languagea
Hyperactivity- ORD SES"
GendeP Note:
or the variables in Model2b.
aodds ratio defined with respect to the control group. bodds ratio defined by a decile increase in Hyperactivity. 'odds ratio defined by a $5,000 increase in total yearfy family income. dodds ratio for males defined with respect to females.
Although Model2b contained more variables than Model 2a, both yielded sirnilar odds
ratios for each of the speechllanguage groups. For Model 2a, the odds ratios for a
speech-only impairment and a language impairment (1.797 and 1.777, respectively) were
both non-significant at the p=0.05 level (see Table 4.10). For Model2b, the
corresponding odds ratios were also non-significant (1 -887 and 1.660, respectively) (see
Table 4.1 A ) . The four covariate sets were surnman'zed by only two separate models (2a
and 2b) and these two models produced very similar odds ratios for the risk of criminal
behaviour for each speechllanguage group. This suggests that differences in the
operationalizations of the Conners and speechnanguage treatment covariates do not alter
greatly the odds ratios of prirnary interest-
Both Models 2a and 2b controlled for Hyperactivity (defined continuously in the
former model and ordinally in the latter model). Each definition of Hyperactivity was
associated with a significant odds ratio of 1 .O18 and 1.141, respectively. Model2b,
however, controlled for the additional variables of SES and Gender. SES had a
significant odds ratio of 0.857 indicating a protective effect against the occurrence of
criminal activity as socioeconomic status increased (as measured by total family incorne).
As well, Gender had a significant odds ratio of 2.498 indicating a higher risk for males
than for females. As covariates are deleted from the final model during the model building
process there is a Ioss in the precision of the estimates. It should also be pointed out,
therefore, that even though few covan'ates rernained in the final Models (2a and 2b), the
resulting loss in precision did not prevent a significant odds ratio for either
speechllanguage group from being observed. Even in the fullest muftivariate case for
each Model (2a and 2b) the speechllanguage odds ratios were still not significant at the
p=0.05 level.
4.3.3 Summary of Unadjusted and Adjusted Results for the Analysis of
Criminal Activity
It can be seen that the effect of adjustment on the speechllanguage odds ratios
was to decrease their magnitude. Furthemore, afier adjustment, the odds ratio for the
language impaired group was no longer significant at the p=0.05 level. This was tme
when referring to either Model 2a or 2b. In the latter, when the additional variables of
SES and gender were controlled, this odds ratio decreased even further. A comparison
of the adjusted and unadjusted odds ratios for the speechnanguage groups appears
below in Table 4.12.
4.4 Conceptualization III: Antisocial Behaviour as a Dimension
Table 4.12: Cornpanson of the unadjusted and the adjusted (Model 2a) speecManguage odds ratio estimates and their 95%CLs for the analysis of criminal activity.
4.4.1 Non-subject Measures
Unadjusted Results
Unaditrsted Results
Table 4.1 3 shows the unadjusted parent andfoi teacher measured ratings for
each speechllanguage group. Differences were reported with the language impaired
children (rnean antisocial T score=56.89) scoring significantl y higher than both the
speech-only impaired children (54.49) and the controls (53.48). These latter two groups
were not significantly different from each other. Table 4.1 3 also shows the overall F-
statistic for the crude mode1 which contains only the tirne-l speechllanguage variable.
Note: "LRT=likelihood ratio test. odds ratio defined with respect to the control group.
-
Adjusted Results (Mode1 2a)
Table 4.1 3: Mean non-subject measured antisocial T ratings for each speechnanguage
95% CL(@)
(0.863-4.896) (1 -1 53 -4 .422 ) -
B
LW: ~2(2df)=6.027, p=0.0491
C
Speech-only Language
95% CL(@)
(0.741-4.361) (0.880-3.587)
LR?: x2(4df)=1 9.784, p=0.0006
Speech-only Language
Note: afor post hoc tests: C=control group, S=speech-only impaired group and L=language impaired group.
@b
2.056 2.258
qb 1.797 1.777
impaired group, unadjusted for any covariates.
Adjusted Results
A set of Iinear univanate and ANCOVA analyses were performed with parent
Control Speech-only
Languaqe
andlor teacher measures of subject antisocial behaviour as the outcome. These results
are summarized in Table 4.14, below:
- X
53.478 54.486 56.892
F(2,234)=7.98, p=0.0004
vs C, p=0.3605 L vs S, p=0.0432 L vs C, p=0.0001
1 able 4.14: Results Rom the univariate and ANCOVA analyses for each covariate and for each altemately definec
Performance IQ
Mothefs Adversity
Inattention (continuous)*
Hyperactivity (continuous)
Conduct (continuous)
SES*
Single-Parent*
Sender
Treatment (subject)
Covanate
Univanate Analysis Covariate 1 1 Speechnanguage Status
" S ] b P
b P 1 R 1 d ~ - ~ differencs
covariate for the non-subject measures of antisocial behaviour. ANCOVA
- m - F
- - -0.0215 0.4952 Controls 53.602
Language 0.0023 56,668
0.051 4 0.1 598 Controls 53.517
Language 0.0002 56.797 0.0388 0-05 17* Controls 53.628
Speech-only 0.4267 54.500 S vs L, p=0.07d
Language 0.0007 56.628
0.0682 0-0002* Controls 53.833
Speech-only 0.6769 54.285 S vs L, p=0.07(
Language 0.0033 56.381
0.0713 0-001 6* Controls 53.639
Speech-only 0.4461 54.464 S vs L, p=0.06!
Language 0.0005 56.625
-0.2073 0 -0955~ ControIs 53.541
Speech-only
Language
0.7523 0,5120 Controls
Speech-only
Language
0.9683 0.2247 Controls
Speech-onty
Language
0.2334 0.8083 Controls
Speech-only
Lanauaae 0.0001 57.03
nattention (ordinal)
4yperactivity (ordinal)
Zonduct (ordinal) *
rreatment (parent)
Alternate - - 05862 0.01 62* Controls 53.689
J - - Definitions for the Conners and S~eech/Lanauaae Treatment Variables
Speech-only 0.4853 54.453 S vs L, p=O.O77
Language 0.001 2 56.546
0.4939 0-0004* Controls 53.81 4
Speech-only 0.6422 54.317 S vs L, p=0.074
Language 0.0030 56.399
0.671 1 0 . 0 ~ 0 2 ~ Controls 53.656
Speech-only 0.4740 54.531 S vs L, p=0.080
Language 0.0006 56.565
0.21 05 0.8305 Controls 53.22
Speech-only 0.3525 54.38 S vs L, p=0.068
Language 0.0003 56.65
From Table 4-14, above: d Note: "B =parameter estirnate; bp=probability; X=adjusted mean estimate; S-L differences=p-values
cornparing the rneans of the speech-only group and the language group; '=adjusted and unadjusted means differ by 10% or more; *=parameter estimate is significant at the p=û.10 level in its ANCOVA; .=covariate
entered into the model building process (Le. was significant at the p=0.10 level in its ANCOVA (*) and/or t changed by 10% or more ( )).
For the analysis of non-subject measures of antisocial behaviour, covariate sets
1 and 2 yielded the same final model. This model is henceforth referred to as 'Model 3a".
This mode1 and the overall F-test for the mean parent andor teacher rneasured antisocial
ratings appear below. The associated means for each speechiianguage group for Model
3a appear in Table 4.15.
Model 3a: Overall F-test: F(4,231)=8.36, p=0.0001 ( ~ 2 3 7 ) . ASBNon-suwea = a+~l(Speech-oniy)+~zfLanguage)+~~(Hyperactivi~)+~4(Single-Parent) 1
"for post hoc tests: C=control group, S=speech-only impaired group and L=language
Table 4.1 5: Adjusted means for each speechllanguage group from Model3a.
impaired group.
Similarly, covariate sets 3 and 4 yielded the same final model. This model is henceforth
referred to as "Model 3b". This model and the overall F-test for the mean non-subject
measured antisocial ratings appear below. The associated means for each
speechflanguage group for Model3b appear in Table 4.1 6.
aPost hoc Tests 7
S vs C, p=0.6544 L vs S, p=O.llO3 L vs Cl p=0.0071
Control Speech-only bnguage Note:
T
56.895 57.372 59.200
Models 3a and 3b yielded very similar results. For Model 3a, the mean antisocial T score
for the language impaired group (X=59.200) was significantly higher (p=0.0071) than the
control group ( X=56.895), but not significantly higher (p=0.1103) than the speech-only
impaired group (F=57.372) while the control and speech-only irnpaired groups were not
significantly different from each other (p=0.6544) (see Table 4.1 5). The pattern of
results from Model 3b (Table 4.16) was essentially the sarne as both Models controlled
for similar confounding variables. Thus, differences in the operationalizations of the
Conners and speechllanguage treatment covariates did not appear to alter greatly the
mean parent andior teacher measures of subject antisocial behaviour.
Finally, the exact same patîern of results was found when the raw scores
instead of the T scores were used as the dependent variable. This was true for each of
the above analyses (Models 3a and 3b). Because the raw scores do not allow for easy
interpretation and generalization, the raw score models and means are not reported.
Table 4.16: Adjusted means for each speech/lan$uage group from Model3b.
Comparison of the Unadjusted and Adjusted Results for the Non-subject Measures
After the results were adjusted for the potential confounders, the pattern of
significance among the speechJlanguage groups did not change. The magnitude of the
means, however, increased by approxirnately 3 T points for each group. Thus,
increasing the precision of the estimated means by accounting for both hyperactivity and
being reared in a single-parent family adjusted the level of antisocial behaviour across al1
of the speech/language groups. A cornparison of the adjusted and unadjusted estirnated
rneans for the speechAanguage and control groups appears below in Table 4.1 7.
Control Speech-only Language Nofe: afor post hoc tests: C=control group, S=speech-only impaired group and L=language
impaired group.
X 1 'Pest hoc Tests
56.916 57.460 59.267
S vs C, p=0.6106 L vs S, p=O.ll65 L vs C, p=0.0064
4.4.2 Subject Measures
Table 4.1 7: Cornparison of the unadjusted and the adjusted (Model3a) speechlianguage mean antisocial behaviour estimates for the analysis of non-subject rneasures.
Unadjusted Results
Table 4.1 8 shows that when subjects rated their own antisocial behaviour, there
Unadjusted Results
were no differences in the unadjusted mean antisocial T ratings among the tirne-1
Note: aOverall F-test, bSpeech/language group means. 'for post hoc tests: C=control group. S=speech-only impaired group and L=language
irnpaired group.
a~(2.234)=7.92. p=0.0004
Controls Speech-only
Languaqe
Adjusted Results (Model 3a)
speechflanguage groups. Table 4.1 8 also shows the overall F-statistic for the crude
'~(5.231)=8.36, p=0.0001
Controls Speech-only
Language
model which contains only the time-1 speechnanguage variable.
XI
53.478 54.486 56.892
Table 4.1 8: Mean subject rneasured antisocial T ratings for each speechnanguage
Post hoc Tests
S vs C, p=0.3605 L vs S, p=0.0432 L vs C. p=0.0001
x
56.895 57.372 59.200
Post hoc Tests
" S v s C , p=0.6544 L vs S, p=O.llO3 LvsC,p=0.0071
Note: afor post hoc tests: C=control group, S=speechsnly impaired group and L=language
impaired group.
- - impaired group, unadjusted for any covariates.
Adjusted Results
Control Speech-only
A similar set of linear univariate and ANCOVA analyses were perforrned with
subject-measured antisocial behaviour as the outcome variable. The results frorn these
Lanquaqe 55.177 L vs C, p=0.4330 L
z 55.791 5 5.1 84
analyses are surnrnarized in Table 4.1 9, below.
F(2,251)=0.38, p=0.6836 vs C, p=0.5544
L vs S, p=0.9946
Table 4.19: Results from the univariate and ANCOVA analyses for each covariate and for 3ach altemately defin€
3erformance IQ
ulother's Adversity*
nattention (continuous)
fyperactivity (continuous)
2onduct (continuous)
SES*
single-Parent*
Sender
'reatment (subject)
covariate for tt Covariate
Jnivariate Analysis
! subject measures of antisocial behaviour. ANCOVA
Covariate 1 1 SpeecNtanguage Status
Controls
Speech-only
Language
Controls
Speech-only
Language
Controls
Speech-only
Language
Controls
Speech-only
Language
Controls
Speech-only
Language
Controls
Speech-only
Language
Controls
Speech-onIy
Language
Controls
Speech-only
Language
Controls
Speech-only
Language
Alternate Definitions for the Conners and Speech/Language Treatment Variables - nattention (ordinal)
iyperactivity (ordinal).
:onduct (ordinal)
'reatment (parent)
- -
0.3094 0.1 656 Controls 55.90
Speech-only 0,4827 55.1 8 S vs 1. p=0-861
Language 0.2639 55.00
0.1 982 0.1 263 Controls ~ 4 . 9 8 ~
Speech-only 0-4376 55.13 S vs L, p=0.89;
Language 0.2448 ~ 5 . 9 3 ~
0.1 143 0.4949 Controls - 55.82
Speech-only 0.5491 55.20 S vs L, p=0.95[
Language 0.3865 55.13
-0.4688 0.6228 Controls 55.73
Speech-only 0.9655 55.69 S vs L, p=0.37(
Language 0.2486 54.70
From Table 4.19, above: Note: a =parameter estimate: bp,probability; X =adjusted mean estimate; d ~ - ~ differences=p-values comparing the means of the speech-only group and the language group; '=adjusted and unadjusted means
differ by 10% or more; *=parameter estimate is significant at the p=0.10 levei in its ANCOVA; *=covariate
entered into the modei building process (i.e. was significant at the p=0.10 level in its ANCOVA (*) andlor t
changed by 10% or more ( )).
Each one of the four covaBate sets described in section 3.9.3 yielded the same final
model. This final mode1 is henceforth referred to as "Model4". The modei, and the overall
F-test for the subject measured antisocial scores appears below, The mean subject
measured antisocial scores for each speecMlanguage group appear in Table 4.20.
Model4: Overa Il F-test: F(4,249)=1.48, p=0.2079 (n=254). ASBçubjea = a+~1(Speech-only)+~2(Language)+Pî(Single-Parent)
group from Model4.
Control
Note: "for post hoc tests: C=conirol group, S=speech-only impairec! group and L=language
impaired grou p.
The results from the subject-measured analyses did not yield significant differences at
the p=0.05 level. The language impaired group ( %=56.62) was not significantly different
from either the speech-only impaired group ( X =S6.83, p=0.8518) or from the control
group (X=57.36, p=0.3438). The speech-only impaired and control groups were also not
significantly different (p=0.5998). Once again, that al1 four covariate sets yielded the
same final model suggests that variations in the operationalization of the Conners and
speechllanguage treatment covariates does not alter the conclusions drawn from these
results.
Finally, the exact same pattern of results was found when the raw scores
instead of the T scores were used as the dependent variable. Because the raw scores
do not allow for easy interpretation and generalization, the raw score models and means
are not reported.
Comparïson of the Unadjusted and Adjusted Results for the Subject Measures
In this analysis, there were no significant differences found among the
speechllanguage groups before or after adjustment. After adjusting for the variable of
single-parent rearing, the increase in the precision of estimated means was exhibited by
an increase in their magnitude. This increase of approximately 2 T points, however, is
small.
4.5 Summary of Unadjusted and Adjusted Results The first part of this section summarizes the results when they were not adjusted
Table 4.21 : CompaRson of the unadjusted and the adjusted (Model3a) speechllanguage mean antisocial behaviour estimates for the analysis of subject measures.
by any of the covariates used in this investigation. Among the dichotomous measures of
antisocial behaviour, a language impairment at time-1 was associated with a significant
risk for a diagnosis of an ASPD ( @=2.879, 95%CL: 1 -222-6-783) as well as for the
Unadjusted Results
occurrence of cnminal behaviour at time-3 ( tj7 =2.258, 95%CL: 1.1 53-4.422). A speech-
Note: "Overall F-test. bSpeecManguage group means.
Adjusted ResuIts
only impairment at time-?, however, was not associated with a significant risk for either
of these outcornes at time-3. Among the dimensional measures of antisocial behaviour,
non-subject measures of antisocial behaviour rated those subjects who had been
language impaired at time-1 as significantly more antisocial ( Z=56.9) than either those
who had been speech-only impaired ( F=54.5) or controls ( Z=53.5) at time-1 (p=0.0001
and p=0.04, respectively). Subject measures of antisocial behaviour, however, did not
yield any differences at time-3 among the speechAanguage groups. The unadjusted
results are summarized in Table 4.22, below.
Post hoc Tests
S vs C, p=0.5544 L vs S, p=0.9946 L v s C,p=0.4330
aF(z,251)=o-38, p=0.6836
Controls Speech-only
Language
'F(4,249)=1.48, p=0.2079
Controls Speech-only
- Language
X
5 S . 7 9 1 55.184 55.177
K
57.36 56.83 56.62
Post hoc Tests
S vs C, p=0.5998 L vs S, p=0.8518 L vs C, p=0.3438
Table 4.22: Summary table of the results unadjusted for any of the covariates for the dichotomous outcomes (diagnoses of ASPD and occurrence of criminal behaviour) and dimensional outcomes (non-subject and subject measures), respectively.
Dichotomous Outcomes 1 Diagnoses of ASPD I Criminal Behaviour I
I Dimensional Outcomes I
Speech-only
Language
1 Non-Subject Measures 1 Subject Measures 1
"w 1 -803
2.879
--
Note: aodds ratios defined with respect to the control group. b95% confidence limits for the #S.
I m
'unadjusted mean antisocial behaviour score. d for post hoc tests: C=control group, S=speech-only impaired group and L=language
impaired group.
~S%CL(W>
(0.576-5.641 )
(1.222-6.783)
The adjusted results differ somewhat from the unadjusted results. In the latter,
Controls
S~eech-on l~
Language
when analyzing cnminal behaviour, the language impaired group had a significant
associated odds ratio of 2.258 (95%CL:I -1 534.422). However, when the variable of
Speech-only
Language
cx 53,478
5 4.48 6
56.892
hyperactivity was controlled (as in Model 2a), the corresponding odds ratio of 1.777 was
no longer significant (95%CL:0.880-3.587). A similar change between adjusted and
unadjusted results was observed when the variables of hyperactivtiy, SES and gender
F(2,251)=0.38, p=0.6836
dS vs C, p=0.5544
L vs SI p=0.9946
L vs CI p=0.4330
F(2,234)=6.82, p=0.0004
dS vs CI p=0.3605
L vs SI p=0.0432
L vs CI p=0.0001
were controlled, as in Model2b. The associated odds ratio for criminal behaviour for the
"@ 2 - 0 5 6
2.258
speech-only impaired group, however, was non-significant before and after adjusting for
9 5 8 ~ ~ ( ~ )
(O .863-4.896)
( 1 - 1 53-4.422)
C~ntrols
S ~ e e c h - o n l ~
Language
covariates. In addition to the pattern of significance changing, it should also be noted that
"X
55.791
5 5.1 8 4
55.177
when this relationship was controlled, the magnitude of the odds ratios decreased from
2.056 and 2.258 to 1.797 and 1.777, for the speech-only and language impaired groups,
respectively.
For the analysis of ASPD, the pattern of results for the associated odds ratios for
the speech-only impaired and language impaired groups did not change. Both before and
after adjustment, the odds ratio for the speech-onty impaired group was non-significant.
As well, the odds ratio for the language impaired group was significant before adjustment
(2.879, 95%CL:1.222-6.783) and after adjustment (3.662, 95%CL: 1 -448-9.01 2). Although
the pattern of significance did not change, the magnitude of the odds ratios did change,
increasing from 1.803 and 2.879 to 1.973 and 3.662. for the speech-only impaired and
language impaired groups, respedively.
For the dimensional measures of antisocial behaviour, the pattern of results did
not change greatly. Both before and after the results were controlled, parent andlor
teacher measures of the antisocial behaviour of the subjects, rated those who had been
language impaired the most antisocial. Before adjustment. this group was considered
significantly more antisocial than both the control (p=0.0001) and speech-only impaired
(p=0.0432) groups (see Table 4.8). However, after controlling for the covariates. the
antisocial behaviour of the language impaired group ( F=59.2) and speech-only impaired
group (%=57.4) were not rated as significantly different from each other (p=0.1103).
Among the non-subjed measures of antisocial behaviour. the pattern of results did not
change. Both before and after control, subjects did not rate themselves any more or less
antisocial based on their time-1 speechnanguage status. These adjusted results are
summarked in Table 4.23.
Table 4.23: Sumrnary table of the results adjusted for the covariates for the dichotomous outcornes (diagnoses of ASPD and occurrence of criminal behaviour) and dimensional
Diagnoses of ASPD (Mode1 1) 1 Criminal Behaviour (Mode1 2a) 1
I Dimensional Outcornes I I Non-Subject Measures (Model 3a) I Subject Measures (Model 4)
Speech-only
Language
Controls s
56.895 S vs C, p=0.6544 Contmls 57.36 d S vs C, p=0.5998
Speech-only
Language
L vs C, p=0.0071 Language 56.62 L vs C, p=0.3438
Note: Only the results from Models 1, 2a, 3a and 4 are repoited in this table.
a @
1 -973
3.662
'odds ratios defined with respect to the control group. -
b95% confidence lirnits for the Ys. 'adjusteci mean antisocial behaviour score.
for post hoc tests: C=control group, S=speech-only impaired group and L=language impaired group.
b 95% CL($)
(0.575-6.767)
(1.488-9.01 2)
a$
1 -797
1.777
95% CL(@)
(0.741 -4.361 ) (0.880-3.587)
5. Results II: Supplementary Analyses The dependent variables of antisocial behaviour were operationalized in two main
ways, as a dichotornous outcome and as a dimensional outcorne. For the former type,
significant differences were found arnong the speechnanguage groups when antisocial
behaviour was defined as a diagnosis of antisocial personality disorder (ASPD),
however, non-significant differences were found when the dependent variable was
defined as the presence or absence of criminal behaviour. For the latter type, significant
differences were found when antisocial behaviour was measured by parents and/or
teachers, however, non-significant differences were found when subjects measured
their own antisocial behaviour. The purpose of the present section was to explore the
apparent inconsistency between subject and non-subject measured results. The
discrepancy between the ASPD and criminal behaviour results was not explored in this
study (see Discussion). Although differences between subject and non-subject ratings
are not uncornmon in the psychiatrie and psychological literature, four main
supplernentary analyses were performed in an effort to more fully understand the
differences observed,
5.1 The Composite Nature of the Dimensionai Measure of Antisocial
Behaviour
As mentioned in section 3.4.2, the dimensional measure of antisocial behaviour is
the mean of two separate subscale scores; the Delinquency subscale and the
Aggression subscale. Table 2.1 showed, however, that the correlation between these
scores was the lowest (r=0.45, p=0.0001) for the subject-measured data. It was
hypothesized that one of the subscaIes, rnight in fact, be more sensitive in the ability to
detect differences among the speech/language groups. Furthemore, that these two
subscale scores were not highly correlated, using the mean of the subscales might
inadvertently be obscunng the ability of the composite variabte to detect any differences.
To test this hypothesis, a separate analysis was performed for each subscale. If this
hypothesis was correct, the two analyses would yield different results, one of which
would resemble the non-subject results. A paraflel analysis was also performed using
the non-subject measures. Finally, these analyses were also performed with the
corresponding raw scores. A surnmary of the findings from the analyses of the subject-
measures appear in Table 5.1 and the summary of the findings from the non-subject
measures appear in Table 5.2 (the corresponding final models are not shown).
Table 5.1 : SpeechJlanguage group mean T scores and raw scores for the subject- rneasured Delinquency and Aggression Subscales.
1 Delinquency Aggression F(4,249)=2.04, p=0.0891 F(4.249)=0.65, p=0.6286
1
F Post hoc Tests % Post hoc Tests Control 59.206 S vs C, p=0.9501 55.51 9 S vs C, pd.3619
T scores Speech-only 59.1 25 L vs S, pa.6858 54.530 L vs S. pd.8920 Language 58.558 L vs C. p=0.5169 54.687 L vs C. pd.3166
r Post hoc Tests x Post hoc Tests Control 4.964 S vs C, pd.8046 9.408 S vs C. p=û.6021
Raw scores Speech-only 5.093 L vs S. p=0.5594 9.560 L vs S. ~ 4 . 8 7 8 1 Language 4.766 L vs C, pa.6212 t 0.037 L vs C, pa.3704
Note: 'for post hoc tests: C=control group. S=speechsnly irnpaired group and L=language
impaired group.
Table 5.2: Speechllanguage group rnean T scores and raw scores for the non-subject measured Delinquency and Aggression Subscales.
Control
I -
Control Raw scores Speech-on1 y
1 Language
Note: 'for post hoc tests: C=control group, S=speech-only impaired group and L=language
impaired group.
The Table 5.1 results show that when the Aggression subscale was used, no
Delinquency
F(4.232)=5.84, p=0.0002
significant differences were found among the speechllanguage groups (for either the T
x 58.1 78 59.260 61.606
Aggression
F(4,232)=4.23, p=0.0025
scores or the raw scores) when subject-measured scores were analyzed. However,
Post hoc Tests S vs C. pd.3895 L vs S. p=0.0847 L vs C, p=0.0005
X 54.773 55.843 57.781
x 3.538 4.067 4.969
near significant differences were found when the Delinquency subscale was used
F(4,232)=6.02, p=0.0001
Post hoc Tests S vs C, pd.3349 L vs S. p=0.1061 L vs C. p=0.0006
(overali F-tests: p=0.0891 and p=0.0990, for the T scores and raw scores, respectively).
F(4.232)=4.03, p=0.0035
Post hoc Tests S vs C, pd.3293 L vs S, p=0.1240 L vs C, p=0.0008
Table 5.2 shows that a sirnilar pattern was found when the non-subject measured
scores were analyzed: both scales found significant differences among the
- X
5.676 6.276 8.577
speechllanguage groups, but the ~elinquency scale yielded far more significant results
Post hoc Tests S vs C, p=0.581 O L vs S, p=0.0508 L vs C. p4.0007
(p=0.0002 and p=0.0001 for the T and raw scores, respectiveiy) than did the Aggression
subscale (p=0.0025 and p=0.0035 for the T raw scores, respectively).
The above hypothesis appears to be only partly correct: the separate analyses
did produce different results, however, the pattern of differences among the
speechnanguage scores was not significant at the pc0.05 level and the pattern of
differences was dissimilar tu that of the non-subject results. For example, the language
impaired group had the lowest scores (58.558 and 54.687 for the Delinquency and
Aggression subscales, respectively) for the subject-measured data, while the same
group had the highest scores (61.606 and 57.781 for the Delinquency and Aggression
subscales, respectively) for the non-subject measured data. Because the standard
errors of the means for the subject-measured data are approximately the same as for the
equivalent means from the non-subject scores, it would be unreasonable to assume that
with an increase in power, the pattern of speechllanguage scores would change. The
above hypothesis, therefore, does not appear to explain the discrepancy between the
subject and non-subject measured scores.
5.2 The Composite Nature of the Non-subject Measure of Antisocial
Behaviour
As mentioned in section 3.4.2, due to the large proportion of missing values
among the teacher ratings, the teacher and parent ratings were combined to form a "non-
subject" measure of antisocial behaviour. The possibility that the pattern of means for the
speechllanguage groups as measured by the parent or teacher ratings was similar to the
pattern from the subject ratings and that the composite rating was obscuring this was
also considered. It was hypothesized that if the pattern of differences among the
speechllanguage groups for parent and teacher measures were grossly dissirnilar
where either was similar to the subject-measured pattern, then this would detract from
the validity of the non-subject measures and allow for re-consideration of the differences
between the subject and non-subject results. The results form this analysis are
presented in Figure 5.1, below:
Controls Speech- Language only
Time-1 Speech/Language Status Group
+ Subject-measured data -i- Non-subject measured data -+- Parent-measured data --
* Teacher-measured data
:igure 5.1 : Adjusted subject and non-subject measures of antisocial behaviour by time-1
This figure shows the adjusted mean antisocial T score for the subject and non-
subject rneasures, as welt as the individual parent and teacher measures, for each
speechllanguage group. Three points about this figure and the data it illustrates can be
made: First, by cornpanng the non-subject and parent measures, it can be seen that the
non-subject means closely resemble the parent-measured means. This is due to the fact
that a majority of the non-subject measures were composed of parent-rated data as a
much greater proportion of teacher-rated data was missing (36% vs 23% for teachers
and parents, respectively). Second, parent- and teacher-measured data are similar with
respect to the pattern of differences among the speechllanguage groups in that both
perceived the language impaired group to be very different from the speech-only irnpaired
and control groups and the speech-only irnpaired group not to be very different frorn the
control group. This corresponds to the correlation of parent- and teacher-measured data
that appeared in Table 3.2 ( ~0 .43 , p=0.0001). Third, that when al1 four rneasures of
antisocial behaviour are plotted together the pattern of the subject-rneasured means are
clearly different from the patterns observed for the other three measures. This analysis,
therefore, lends credence to the non-subject measures of antisocial behaviour, but does
not help explain the discrepancy between the subject and non-subject measured results.
5.3 Group Differences in Subject-perceived Social Support
In an atternpt to understand why the subject and non-subject ratings differed rnost
for the language impaired group, differences among the speechAanguage groups in the
ways in which the subjects believed they were perceived by others was assessed.
To rneasure this, the Social Support Appraisal (SSA) scale2 was used. No significant
difierences were found, however, on any of the individual ratings or summary scores
(al1 p>0.17). In a second atternpt to address this issue, the sarne set of analyses were
perfomed only on those subjects who had been diagnosed with an ASPD. Similarly, no
significant differences (or near significant differences) among the speechllanguage
groups were found (al1 p>0.11). Because this set of analyses only yielded negative
findings, they do not irnprove Our understanding of the differences between the subject
and non-su bject results.
5.4 ASPD and Subject Measures of Antisocial Behaviour
Using a diagnosis of an ASPD as a gold-standard, subjects with and without a
diagnosis were compared as to how they rated their own antisocial behaviour. The
same analysis was also performed for non-subject measures. The results for this
analyses are presented in Table 5.3, below:
Table 5.3: Subiect-measured mean antisocial behaviour T scores for those with and without a diagnosis of Antisocial Personality Disorder (ASPD).
Subject Measures 1 Non-subject Measures
ASPDAbsent 154.79 1 p=O.OOOl 1 1 p=O-0001
ASPD Present 6 1 -35
This table shows that subjects with an ASPD do indeed perceive themsetves as more
antisocial than those without an ASPD (p=0.0001). The same was true with non-subject
'The SSA is a 23 item instrument that taps the extent to which the individual believes that he or she is loved by, esteemed by, and involved with family. friends and others. The SSA has been reported to have very good psychometric properties (Vaux, 1 986).
measures, that is, subjects with an ASPD are rated independently by parents andlor
teachers as more antisocial than those without an ASPD (p=0.0001). ln this respect,
there appears to be agreement between diagnoses of ASPD and self-rated scores of
antisocial behaviour. It was hypothesized that having a speechnanguage impairment
might be associated with a poorer tendency for self-rated scores to agree with the gold-
standard diagnosis of an ASPD. If this were true, then rneasures o f agreement would be
srnallest for the language impaired group than for the speech-only irnpaired or control
groups. To detennine this, Pearson correlation coefficients were calculated for each
speecManguage group to measure the agreement between ASPD and subject-
rneasures. Table 5.4 shows these correlations as well as the subject rneasured scores
for those with and without an ASPD for each speechflanguage group.
Table 5.4: Subject measures of antisocial behaviour for those with and without an ASPD
1 Pearson Correlation 1 0.3536. p =0.0001 1 0.6845. p=O.OOO1 1 0.3454. p=0.0021 1
and the associated Pearson correlation coefficients for each speech/language group.
These data show that the self-reports frorn the language impaired subjects tend to agree
the least (r=0.345) with the gold-standard of a diagnosis of an ASPD, however, for
reasons unknown, the control group also had a very low level of agreement r=0.354).
The correlation coefficients for both the control and language impaired groups were not
significantly different from each other, but each was significantly different ( ~ ~ 0 . 0 5 ) from
the correlation coefficient for the speech-only impaired group. To formalize the
relationship between tevel of agreement and speechAanguage group, the interaction
between subject rneasures of antisocial behaviour and speechAanguage group was
tested in the following model:
ASPD Absent
lagit [P(Dx ASPD=present))=a+fl~(subject measures)+&(speechilanguage group)+b(Gender)+ &(subject masures X speedinanguage group)
The likelihood ratio test for the interaction t e n approached significance k2(2df)=5.1 194,
p=O.O773).
A similar model was used to test for the interaction between non-subject rneasures of
antisocial behaviour and speechnanguage group:
Control
55.30 1 p=O.OOOI S~eech-onlv
53.73 1 p=0.0001 Lanuuaqe
54.39 1 p = 0 . 0 0 2 1
logit [P(Dx ASPD=present)~+~~(non-subjed measures)+&(speeManguage group)+&(Gender)+
&(non-subjed measures X speecManguage group)
The liklihood ratio test for the interaction term in this model was non-significant
(X2(2d9=1 -4929, p=O.4?4O).
Thus, these results show that when subjects rate their own antisocial behaviour, the
degree to which the subject measures distinguish those with and without an ASPD may
depend on the speechAanguage group king measured- But, when non-subjects rate the
antisocial behaviour, the degree to which non-subject measures agree with a diagnosis
of an ASPD does not depend on the speechllanguage group being rneasured. Although
the former interaction only approached significance as well as the fact that the control
group appeared to have a low level of agreement, these findings appear to partially
confirm the above hypothesis and warrant the further study of subject measure
reliability.
6. Discussion
6.1 Ovewiew of Findings
The purpose of this study was to determine whether speechllanguage
impairments identified in children as young as five years of age were associated with
antisocial behaviour in young adulthood. Antisocial behaviour was measured among
three groups of 19 year olds which had been fonned by the age 5 speechjlanguage
status of their members. These three groups that were studied comprised a language
impaired group (some of the subjects of which may have had an additional speech
impairment), a speech-only irnpaired group and a control group. In summary, the main
findings of this study were:
Early childhood speechllanguage impairments were associated with subsequent
antisocial behaviour. For the outcome of antisocial personality disorder (ASPD), the
risk was approximately triple for individuals with a history of language impairment
than for those who were controls.
A Ianguage impairment at 5 years of age was associated with an increased risk (as
compared to controls) for being diagnosed with an ASPD at age 19, while a speech-
only impairment at 5 years of age was not.
Neither a language impairment nor a speech-only impairment at 5 years of age was
associated with an increased risk for exhibiting criminal behaviour by age 19 when
adjusted for the covariates.
When parents andlor teachers rated the antisocial behaviours of the subjects at age
19, those who had been language impaired at age 5 were considered more antisocial
than those who had been controls, but not more antisocial than those who had been
speech-only irnpaired. Those who had been speech-only impaired were not
considered different from those who had been controls.
Although the language impaired group was rated by parents and/or teachers as more
antisocial than controls, whether the level of antisocial behaviour of the language
impaired group is of dinical significance is unclear.
The differences in age 19 antisocial behaviour among the age 5 speechllanguage
groups still held after controlling for the confounders of speechllanguage treatment,
hyperactivity, SES, gender and single-parent reanng.
When the subjects measured dirnensionally their own antisocial behaviour at age 19,
no differences were found arnong the groups as defined by age 5 speechllanguage
status.
This study appears to be the first to have evaluated in a prospective and
longitudinal design a comrnunity sample of speechnanguage impaired and control children
over 14 years, from kindergarten to young adulthood, in assessing the long-terrn
psychiatnc implications of eariy childhood speechnanguage impairments. As it is known
that speechnanguage impairments are associated with concurrent and subsequent
symptorns of inattention and hyperactivity, this study explored the hypothesis that eariy
childhood speechlianguage impairments would be associated with young adulthood
antisocial behaviour. The hypothesis proved to be partly true: depending on how
antisocial behaviour is rneasured in young adulthood early childhood speechJlanguage
impairments did predict subsequent antisocial behaviour in the sample of individuals
studied. When it was operationalized dichotomously in ternis of diagnoses of ASPD or
dimensionally in ternis of parents and/or teacher reports, antisocial behaviour was found
to be associated with speecManguage impairments. When operationalized
dichotomously in terms of criminal activity or when operationalized dimensionally in ternis
of subject self-reports, when adjusted for covariates, no association was found. This
study reports important long-term prospective findings that have substantial implications
for speech/language impaired children and the parents and health care providers who
care for thern. The main features of this study's findings that warrant discussion are
sumrnarized as follows:
Language impaiments were found to be associated with antisocial behaviour while
speech-only irnpairments were not. This is consistent with the Iiterature that reports
poorer outcome arnong language impaired children versus speech-only impaired
children.
Early childhood speechllanguage impairments have antisocial behavioural
consequences, but they were not associated with increased rates of criminal activity
after controlling for the covariates.
When the subjects reported their own antisocial behaviour, their age 5
speechJlanguage status was unrelated to their scores. However, when parents
andfor teachers measured the subject's antisocial behaviour, the scores were related
to the subjects' age 5 speechJlanguage status.
The inff uence of several covariates was controlled. Only five of them, however,
proved to be confounding variables: hyperactivity, single-parent rearing, SES, gender
and having received speechJlanguage treatment. As well, variations in the definitions
of some covariates did not affect the results.
6.2 Poorer outcome among those language versus speech-only impaired
Children who had been Ianguage impaired had consistently worse outcomes than
those who had been speech-only irnpaired when differences in the long-tem outcornes
of the age 5 speechflanguage impaired groups (ASPD and non-subject rated analyses)
were shown. Although no other studies have examined specifically the long-term
psychiatnc outcornes among speechnanguage irnpaired children in the young adulthood
years, this pattern of outcome is consistent with studies that have followed such children
over shorter periods of time. Cantwell& Baker (1980) and Baker & CantwelI (1982a)
followed children aged 2-15.9 years (with a mean age of 5.1 years) for 5 years and
reported that children with "pure language" disorders were more likely than those with
'pure articulation" disorders to have later psychiatric and psychosocial problems. The
Newcastle study (Fundudis et al., 1979) which followed children from age 3 to age 8
reported that children with language delays were more likely than children with "speech
retardationn to show marked introversion and withdrawal. The present study extends
such behavioural findings into the young adulthood years by showing that children with a
history of language impairments continue to be more affected than those with a history of
speech-only impairments.
This pattern of outcome is also consistent with studies that have followed
speech/language irnpaired children into the young adulthood years but which did not
assess behavioural or psychiatric outcomes (Felsenfeld, Broen & McGue, 1992.1 994;
Hall & Tomblin, 1978; King, Jones & Lasky, 1982; Lewis & f reebairn, 1992; Tomblin,
Freese & Records, 1992). Johnson recently reported a 14-year follow-up study on the
speech/language stability of the same OLS sarnple studied in the present investigation
(Johnson, 1998, submitted for publication). This author reports three related long-terni
findings. First, those with initia1 language irnpairments fared worse than those with initial
speech-only impaiments, showing lower rates of recovery. Second, at age 19, those
who had been speech-only impaired at âge 5 were comparable to controls on most
measures of language, cognition and academic performance, while those who had
language impairment performed significantly worse than those who had been speech-
only impaired or controls. Third, considerable long-terni stability of individual differences
in language ability were also apparent and adult levels of language skills were well
predicted by eariy language measures. The present investigation is significant because it
adds antisocial behaviour to the list of long-terni potential problems among those initially
language, but not speech, impaired.
The issue as to why those with language impairment fare worse than those with
speech-only impaiments is likely related to the degree to which early childhood language,
76
as opposed to speech, impaiments represent a cognitive and developmental obstacle to
the child. A chiid with only a speech articulation problem faiis to make the correct
articulation of speech sounds at the developmentally appropriate age and may also have
a problem with phonology and discriminating certain sounds, Speech articulation,
however, is only one aspect of language ability. Problerns with articulation are mild
compared to and are in sharp contrast with the linguistic features of language
impaiments which may be of an expressive or receptive nature. Children with such
language impairments tend to exhibit unusual word orden, vocabulary errors or difficulty
understanding spatial ternis or complex "if-thenn statements (APA, 1987). The
development of these fundamental language skills, especially dun'ng earfy childhood, are
closely related to the development of cognition and the maturation of the brain (Crittenden,
1996). Since the c l a m of speech articulation is only one aspect of language ability, it
seems less likely that speech-only impaiments rather than language impaiments would
have such long-term effects.
The findings from studies of hearing impaired children supports this idea. Many
such children struggle with speech impairments secondary to sensorineural deafness
(Howlin & Rutter, l987), but nevertheless develop fundamental language skills by
acquiring a non-spoken mode of communication such as Amencan Sign Language (ASL).
Hearing impaired children who are immersed in ASL-based education earîy in childhood
acquire greater overall language skills and report fewer psychosocial problems later in
life (Paul & Jackson, 1993). Crittenden (1 996) argues that the "pragmaticsn or practical
and functional aspects of language are crucial in the developing structure and function of
the minds of young children. Because of the importance of effective communication in a
social worid, language impaiments seern more detrimental than speech-only impaiments
in impeding the practical aspects of communication and socialization.
The notion that language but not speech-only irnpairments lead specifically to
problems of antisocial behaviour remains unclear and requires the investigation of other
long-tem psychiatric outcomes. lt does seem unlikely, given that some of the major
psychiatric outcomes of speechnanguage impaired individuals in late childhood include
problems in the domains of anxiety and social relationships (Rutter & Mawhood, 1991).
That these problems continue or perhaps lead to other non-antisocial problems in the
young adulthood years remains to be shown.
6.3 Evidence of antisocial personality disorder versus criminal behaviour
Adult antisocial behaviour is defined and studied several ways. Criminologists
tend to focus on persistent offending as they study crirninal careers. Clinical behavioural
scientists tend to focus on antisocial personality disorder, the officia1 diagnostic category
of the DSM-III-R (APA, 1987). Personality psychologists rnay focus on the psychopath,
an antisocial designation that depends as much on personality style as it does on illegal
behaviour (Moffitt & Lynarn, 1994). Although the three designations have partially
overlapping critena sets, they are conceptually distinct (Robins 8 Reiger, 1991). This
study addressed antisocial behaviour using the first two definitions in addition to a
dimensional conceptualization. That each of the definitions used is conceptually distinct
seems to have been supported by this study's findings. Perhaps the antisocial behaviour
exhibited by the language impaired group is of neither the severity nor the nature such
that these individuals would have increased rates of contact with the police or courts.
Perhaps this present study failed to measure ctiminal activity in a way that was
sufficiently sensitive. This is possible as many contacts with the law may have
occurred, but remained unofficia1 (police may have not pressed charges but rather given
a waming). The interview which collected this data did not assess such information.
Whether the language impaired subjects in this present study will go on to develop
more severe antisocial behaviour is uncertain. However, it suggests that the antisocial
behaviour exhibited by the language irnpaired subjects is of a different nature from the
antisocial behaviour observed in persistent cnminals. The finding that antisocial
behaviour in adults is almost invariably associated with a history of such behaviour in
childhood usually describes a subset of individuals whose antisocial behaviour has
remained severe and stable throughout their Iives (Moffitt & Lynarn, 1994). The language
irnpaired subjects in the present study do not match that description with respect to the
severity or the development of their antisocial behaviour. For example, when parents
andlor teachers rated the antisocial behaviour of the subjects, the highest mean score,
given to the language impaired group, was only 59.2. This mean T score is not very large
and only corresponds to approximately the 84th percentile in the population. As well, this
score does not meet the Achenbach designated clinical rating of 70. Furthermore, 1
(0.7%), O (0%) and 5 (4.9%) subjects scored above the clinical cutoff in the control,
speech-only impaired and language impaired groups, respectively. These findings seem
to suggest that the majority of children with language impairments do have a statistically
significant, although clinically rnild level of antisocial behaviour, but that a small subgroup
(5%) have clinically significant antisocial behaviour.
Although this study atternpted to assess criminal activity as one measure of
antisocial behaviour, it does not assume that crirninal behaviour represents the rnost
severe form of antisocial behaviour. The inclusion of criminal activity as an outcome was
perfomed in an effort to identify the possible ways in which antisocial behaviour might
express itself in individuals with a history of speechllanguage impairment. This study
also does not assume that the pathways leading to crirninal activity might necessarily
involve a history of speechnanguage impairment. It is possible, however, that
speechllanguage impairments might predispose individuals to other risk factors that are
more closely related to subsequent increased contacts with the law.
Although this study reports odds ratios for the outcomes of ASPD and criminal
behaviour, they rnay be thought of as relative risks as the study was perfomed
prospectively and because these outcomes can be considered "raren (7.75% and
15.27% arnong the controls for ASP D and criminal behaviour, respectively) (Rothman €4
Greenland, 1998). Although the odds ratio for the language irnpaired group was 3.66 for
ASPD, odds ratios are known to slightly overestimate the relative risk. The unadjusted
risks were 7.75% and 19.48% for the control and language irnpaired groups,
respectively. This corresponds to an unadjusted relative risk of 2.51 while the
unadjusted odds ratio was 2.88. Thus, the adjusted odds ratio of 3.66 is a slight
overestimate of the adjusted relative risk. Therefore, it can be assumed that true relative
rÎsk is approximately three. Equivalently, those with a history of language impaiments
were approximately 3 tirnes more Iikely than controls to be diagnosed with an ASPD.
6.4 Subject versus non-subject measures
That the present study found confiicting results between subject and non-subject
(parent andlor teacher) measures of antisocial behaviour was not unexpected. Lack of
agreement among multiple sources is a cornmon finding in the psychiatrie and
psychological literature (Achenbach, McConaughy & Howell, i987). The purpose of
using subject, in addition to parent and teacher, measures of antisocial behaviour was to
detemine whether earfy childhood speechllanguage impairments affected the subjects'
perception and reports of their own behaviour. At 19 years of age, the subjects did not
report differences in their levels of antisocial behaviour based on their age 5
speechIlanguage impairment status. However, non-subject measures did report
differences, scoring those children who had been language impaired as significantly
more antisocial than controls. Subjects who had been speechlonly impaired at tirne-1,
were not considered to be any more antisocial than controls. These parent andlor
teacher findings are similar ta those for the analysis of ASPD. That is, those with a
history of speech-only impairment were not considered to be at greater risk than controls
for antisocial behaviour, while those with a history of language impaiments were at
greater risk than controls.
79
A number of analyses were performed in an effort to understand why such
differences in the results would have occurred between the two main rater types. It
was first hypothesized that subjects really did report differences in antisocial behaviour
based on their tirne-1 speechiianguage status, but that this was being obscured by the
composite nature of the dependent variable. Subsequent analyses were performed on
the individual delinquency and aggression subscales; significant differences were still
not found. In a second attempt, it was hypothesized that the merging of parent and
teacher measures obscured the fact that one of the rater cornponents had actually rated
the subjects in the sarne way the subjects rated themselves. This was also proven false
as separate parent and teacher analyses showed that they both perceived language
impaired subjects as being significantly more antisocial than controls and speech-only
impaired subjects as not being different from controls. A third hypothesis, that possible
differences existed among the speech/language groups in how they believed their
families, friends and others perceived them also did not prove to be revealing. All three
speechnanguage groups reported similar scores on al1 such rneasures.
A fourth attempt was made by considering the reliability of the subject measures.
It was hypothesized that the subjects were not perceiving andlor reporting their true
levels of antisocial behaviour. This was assessed by first determining whether or not
those subjects who had been diagnosed with an ASPD considered themselves any more
antisocial than those who had not been diagnosed. In this respect, a diagnosis of ASPD
was thought of as a "gold-standardn. This analysis was useful. Those who had been
diagnosed did see themselves as significantly more antisocial than those who had not. A
parallel analysis with non-subject measures also provided the same result. Thus, it
appeared as though both subject and non-subjects ratings could be used to distinguish
between those who were truly antisocial and those who were not. It was then
considered that this rnay not be the case across each of the time-1 speechllanguage
groups. That is, for any one of the groups, subject rneasures rnay be less reliable than
the others in identifying those who were tmly antisocial. This appeared to be sornewhat
true as an analysis of the interaction between speechllanguage status and subject-
measured scores was performed: Speechllanguage status did alter the way subjects
rated their own behaviour, with those who had been language impaired and diagnosed
with ASPD giving themselves the lowest antisocial ratings. A parallel analysis using the
non-subject measures did not yield an equivalent interaction between speechAanguage
status and non-subject measured scores. This suggests that self-reports of antisocial
behaviour may be under-rated from subjects with a history of language impairment and
may, therefore, not be as accurate as the reports from non-language impaired subjects.
It should also be noted that another possible explanation is that the parents
andfor the teachers of the language impaired subjects rnay have been influenced by the
knowledge of the subject's impairment. For example, although the parents were blinded
to the tirne-î speechnanguage status of their children, the disabilities of those who were
most impaired likely Ied to the disclosure of the irnpaitment to the parents. The handicaps
of some children rnay have been such that the parents sought out assessrnent and
treatment for the child. Either way, the parent's knowledge of such impairments rnay
have biased the antisocial ratings of their children, incorredly leading them to believe that
because their child was language impaired, he or she must, therefore, be somewhat
antisocial. It is unclear, however, whether this bias is specific to the domain of antisocial
behaviour or whether it includes other behaviours, as well.
As well, the non-language impaired subjects rnay be more able to hide or disguise
their antisociaI behaviour from their parents andlor teachers while those language
impaired subjects rnay be iess able. The ability to hide such behaviours rnay be indexed
by their intelligence. The summary statistics would support such a hypothesis as the
controls had the highest levels of performance IQ while those language impaired had the
lowest. If such an explanation were correct, the parent andlor teacher ratings of
language impaired subjects would, in fact, be the most accurate rather than the least.
The finding, however, that language impaired subjects diagnosed with an ASPD assign
themselves the lowest antisocial ratings compared to non-language impaired subjects
with an ASPD does not make this hypothesis seem likely.
It is possible that the differences between the subject and non-subject rneasures
are at least partly due to the differences in the type of behaviours that are actually
scored arnong the subject, parent and teacher instruments (YSR, CBCL and TRF,
respectively). For example, the TRF assesses behaviours that are not assessed by the
YSR and would, therefore, not be endorsed by the subjects. These behaviour include
among others: disturbs classrnates, talks out of turn, is explosive and disrupts class.
Unfortunately, inspection of the individual behaviour items that were endorsed by each
rater was not possible, but might prove illustrative in future studies to reveal specifically
which behaviaurs are most readily endorsed by each rater type as well as for each
speechnanguage group.
What seems likely in explaining the differences between subject and non-subjed
findings is situational specificity (Pervin 1985). The situations, environments or contexts
in which parents and/or teachers observe the subjects rnay have allowed for
differences in antisocial tendencies among the speecWlanguage groups to be
rnanifested. While subject-measures, on the other hand, are more akin to the subjects'
perception of themseives across situations which rnay, in fact, not differ arnong
speechAanguage groups.
6.5 Confounding
M i l e examining the relationship between early childhood speechhanguage
impairment and young adulthood antisocial behaviour, this study also assessed the
influence of nine covariates. The covariates were: intelligence, mother's adversity,
hyperactivity, inattention, conduct problems, single-parent rearing, SES, gender and
having received speechnanguage treatment. All of these covariates were either known
or suspected risk factors for antisocial behaviour. Few of thern, however, proved to
confound the relationship under study. The use of the multiple regression and model
building techniques used in this study needs to be descnbed and emphasized to ensure
the correct interpretation of the various final rnodels as well as the estimates of antisocial
behaviour that they provide.
Wth an independent variable of pnmary interest (in this study, speechnanguage
status), multiple regression techniques allow the investigator to estimate odds ratios (in
the case of logistic regression) or rneans (in the case of linear regression) that take into
account the other independent variables which appear in the regression equation. These
other independent variables rnay be risk factors andfor confounding variables. Although
any number of independent variables rnay appear in a regression equation, the larger the
number, the larger the standard errors of the parameters (and therefore, the odds ratio or
rnean estimates) becorne. This inevitably increases the chance of cornmitting a type II
erro r.
Because the purpose of this study was to investigate the association between
early childhood speechllanguage impairment and young adulthood antisocial behaviour,
this necessarily involved the estimation of the "best" rneasures of this association. The
"bestn measures are those estimates that balance precision with stability. Covariates
which proved to be risk factors, but did not prove to be confounding variables were not
included in the various final models as they had no effect on the odds ratio or mean
estimates of antisocial behaviour. If they had been included in the final models, the
estimates of antisocial behaviour would have been less stable and the chance of not
finding an effect of speechllanguage impairment when one really existed (Le. type II
error) would have increased substantially. Whether or not a particuiar covariate appears
in a final model is not an indication as to whether it has k e n controlled. Al1 the
82
covariates in this study have been controlled. Only covariates that proved to confound
the speechhanguage-antisocial behaviour relationship appear in the various final models.
6.5.1 The Covariates as Confounding Variables
Every covariate in this study, except for intelligence and speechllanguage
treatrnent (see section 6.5.2 Ancillary Findings, below) proved to be a rïsk factor in at
least one of the four major analyses of antisocial behaviour. Only the covariates of
speechnanguage treatment, hyperactivity, SES, gender and single-parent rearing proved
to be confounding variables. That these covariates proved to be confounders is
important in that this reiterates the necessity for their control that similar studies may have
failed to perforrn. Despite the fact that these variables were controlled, significant
differences were found among the speechllanguage groups on later antisocial behaviour
thereby lending strength to this study's findings. For the control of hyperactivity this is
especially relevant. MoffÏtt's (1 993) hypothesis of life-course persistent antisocial
behaviour, suggests that syrnptoms of inattention and hyperactivity in childhood are
continuous with antisocial behaviour problems in adulthood. This study suggests that
eariy childhood language impairrnents, independently of hyperactivity problems, lead to
the subsequent emergence of antisocial behaviour many years later.
Among the dichotomous analyses of antisocial behaviour, gender also proved to
be a confounding variable as well as a significant predictor of antisocial behaviour. This
is consistent with the body of literature that shows males being at a much higher n'sk for
antisocial behaviour than are fernales. In the dimensional analyses of antisocial
behaviour, gender was neither a confounding variable nor a significant predictor of
antisocial behaviour. This was surprising, especially for the parent andfor teacher
ratings. Most antisocial behaviour in general occurs among males. Thus, it was
unexpected that gender was not used by these raters to distinguish levels of antisocial
behaviour. While gender was an important variable in the dichotomous analyses but not
in the dimensional analyses, being reared in a single-parent family was important in the
dimensional analyses but not the dichotornous analyses. This may be related to the
observation that single parents may have been biased in their perception of their child's
antisocial behaviour. This hypothesis seems to be supported by the results, as single-
parent rearing was a significant predictor of antisocial behaviour when parents andlor
teachers rated the antisocial behaviour of the subjects, but not when subjects rated their
own antisocial behaviour.
Variations in the precise methods in which some of the covariates were defined
did not affect the results. The Conners variables of Inattention, Hyperactivity and
Conduct were defined as continuous and ordinal and the variables used to represent
speechllanguage treatment were both parent- and subject-assessed. Although different
covanate definitions caused sorne of the analyses to yietd more than one final model,
these different final rnodels did not yield large differences in the odds ratios (in the
analysis of criminal behaviour) or means (in the analysis of non-subject measures). That
differences in the operationalization of these covariates did not affect the results
appears to add weight to this study's conclusions.
One caveat should be noted. The coverage of the risk factors and possible
confounders in this study is tilted towards the psychopathological approach. This should
be desired as this study was conceived for a psychiatric/epidemiologic audience and
intended to make use of clinical psychiatric constructs in present use. It should be noted,
however, that a criminologist's approach might not weigh as heavily the variables on
which this study has placed rnuch emphasis. For exarnple, Andrews and Bonta (1 994)
argue that, for the forensic psychologist, the clinical constructs of low self-esteern and
hyperactivity play a very minor rote in explaining risk for criminal conduct within the
general paradigm of personality and social psychology. Nevertheless, the criminologist's
approach is not as different from the present psychopathological approach as it rnay
seern. For example, the other major risk factors Andrews and Bonta (1 994) report on
were constructs that were captured in this study. These include among others: history
of antisocial behaviour evident from a young age, familylneighborhood adversity as well
low levels of verbal intelligence. That such constructs which have relevance across
disciplines which study antisocial behaviour and which were accounted for in the
present study strengthens the external validity of this study's findings.
6.5.2 Ancillary Findings
Although not part of this study's objective, a number of additional findings related
to the covariates and relevant to the speechllanguage and antisocial literature. have been
described below.
Performance lQ
In section 2.6.3, it was noted that a common neuropsychological correlate of
delinquency is a deficit in IQ and that this elationship is stronger for verbal tests of 1Q
than for performance or full-scale measures (Lynam et al., 1993). In this study,
performance rather than verbal IQ was controlled. The justification for this approach
was that verbal IQ would be highly collinear with the independent variable of primary
interest: time-? speechllanguage status. In none of this study's analyses did
84
performance IQ prove to be either a confounding variable or a significant predictor of
antisocial behaviour. This finding is important for two reasons. First, it contrasts with
much research that shows that low IQ, as a broad index of neuropsychological
functioning, predicts delinquency. Second, if the speechllanguage status variable is twly
collinear with verbal IQ, it suggests that when verbai and non-verbal components are
separated, the non-verbal component fails to predict antisocial behaviour.
The mechanism by which low IQ leads to delinquency is still unclear. Some
investigators have suggested that the effect of Iow IQ on antisocial behaviour is mediated
indirectly by school failure (Hirschi, 1969; Hirschi & Hindelang, 1977). That is, children
who experience failure in school will be more likely to becorne delinquents than those
who do not. Thus, in this model, IQ acts as an index of ability to succeed acadernically. It
is possible that performance IQ does in fact lead to antisocial behaviour, but that the
subjects in this study have not experienced such failure. Hence, these subjects would
have avoided later exhibition of antisocial behaviour as a result of low performance IQ.
History of speechnanguage treatment
A second, ancillary finding worthy of mention was the overall lack of an effect of
the speechllanguage treatment variable on the study results. This observation should be
interpreted with caution. The inclusion of this variable was perforrned merely as a "poor
man's tooln to control for any possible effect on the speechllanguage-antisocial behaviour
relationship that speechnanguage treatment may have had. This variable changed the
odds ratios for the risk of an antisocial outcome only in the analyses of ASPD for both the
speech-only impaired and language impaired groups, respectively. The variable did not
change the significance of the odds ratios, but rather, increased their magnitude slightly.
It is possible that this was the result of the fact that those who rernained irnpaired tended
to be those who were most Iikely to receive treatment (Beitchrnan et al., 1994) and these
subjects are usually the most impaired. Although this implies that speechllanguage
treatment had the effect of attenuating the relationship between speechAanguage
impairment and antisocial behaviour, it must be remembered that such an interpretation
should not be made. The effect of such treatment on speechllanguage ability cannot be
tested as the subjects were not randomly assigned to treatrnent groups. Furthemiore, as
mentioned in section 2.6.3, the effectiveness of such treatment for speechnanguage
impainent remains to be established (Beitchman et al, 1994).
6.6 Implications
What are the potential implications to be derived from this study? First, the study
results imply that language-based problerns predispose individuals for the development of
antisocial behaviour. By drawing this implication, this study would appear to support a
large body of research which proposes that neuropsychological deficits underlie this
vulnerability. Second, because conduct and antisocial problerns do not respond well to
treatrnent, this study irnplicates the importance of prevention and therapy for those
children most at risk. Each of these implications will be discussed in turn followed by an
assessment of the study's limitations and recommendations for future research.
6.6.1 Language-based Problems and Vulnerability for Antisocial Behaviour
As mentioned in section 2.5, there are many ways in which language-based
impairments might lead to psychiatric disorders in generaI and antisocial behaviour in particular. That this study found such a long-term association between antisocial
behaviour in young adulthood and language impaiments identified in children as young as
5 years of age suggests something very fundamental about language abilities and their
relationship to aggression and delinquency. Given the role early childhood language
abilities have on the development of thought, it is not suprising that they would be
associated with psychiatric disorders. This study, however, is not the first to suggest
the importance of the relationship between language and antisocial behaviour and
supports the studies which link neuropsychological deficits with externalizing problems.
Neuropsychological theories may be useful in explaining the association behveen
speechnanguage irnpairments and subsequent antisocial behaviour. The belief that
neuropsychological factors are among the causes of antisocial behaviour is long
established. Benjamin Rush (1 81 2, cited in Elliott, 1 W8), for example, referred to the
"total perversion of the moral facultiesn in people who displayed "innate pretematural
moral depravityn. He also proposed that "there is an original defective organization in
those parts of the body which are occupied by the moral faculties of the mindn. Indeed,
almost 150 years later, Luria (1961) outlined a comprehensive theory of the importance of
normal language for the acquisition of morals and for the self-control of behaviour (see
also Wilson 8 Henstein, 1985; Eysenck, 1977; and Savitsky & Czyzewski, 1978).
The most commonly found neuropsychological deficits associated with antisocial
behaviour involve language-based verbal skills and self-control or "executive functionsn
(Moff~tt & Lynam, 1994). Because children with speechnanguage impairments often
show or acquire symptoms of inattention and hyperactivity and such symptoms are often
associated with neuropsychological deficits, it is ternpting to suggest an association
86
between speechnanguage impainents and neuropsychological impaiments.
Such an argument for the relationship between eariy childhood speechilanguage
impairments and young adulthood antisocial behaviour should must be made cautiously as
these associations are only partially adequate in explaining this study's results. Fint,
studies involving the relationship behnreen neuropsychological deficits and antisocial
behaviour usually involve individuals with a history of severe and stable antisocial
behaviour throughout their lives. The subjects in this study may or rnay not ffi that
description as the evidence of their antisocial behaviour is neither particularly severe nor
stable throughout their lives. For example, although many of the subjects who had been
Ianguage irnpaired were diagnosed with an ASPD at age 19, there was Iittle other
corroborating evidence from the other antisocial behaviour measures.
Second, these theories tend to provide only proximal explanations for syrnptoms
of antisocial behaviour. It has been said that children with verbal deficits will rely more
on physical modes of selfexpression: resorting to hitting rather than discussion, when
disagreements anse. These proximal descriptions do not explain why the subjects in the
OLS appear to have developed antisocial behaviour over time; they apply as well to a 19-
year old as they do to a 5-year old. To aid our understanding of how and why such
behaviour is acquired, such explanations must take a developmental perspective. If eariy
childhood language impaiments. such as those in this study, are markers for ianguage-
based verbal and executive function deficits, then it is possible that they wreak their
effects early in the life course. A more realistic description of how they would effect
later emergence of antisocial behaviour would involve their interaction with subsequent
exposures,
For example, Moffit and Lynam (1994) illustrate how the effects of early childhood
neuropsychological deficits might be amplified over time as children interact with their
environments, to culminate later in antisocial behaviour. They describe a preschooler
who may have a neuropsychological vulnerability related to language and therefore
resists his mother's efforts to read to hirn. This causes the child to have delays in school
readiness. Poor academic performance, therefore results, especially arnong schools in
low SES areas that do not have the resources for his special needs. Repeated years of
school failure may lead to peer rejection andfor exposure to remedial classes containing
other students who have behavioural problems and learning disabilîties. Familiarity with
delinquent behaviours inevitably follows as the child attempts to win peer acceptance. A
small neuropsychological deficit, therefore, might be multiplied insidiously over time
culminating in an antisocial personality style. This explanation embodies the notion that the
long-terni ernergence of antisocial behaviour should by studied by inctuding the influence
of many contributing factors each of which continue to put the child at risk.
Loeber and Stouthamer-Loeber (1 998) asserted that explanations of antisocial
behaviour that ernerge in adolescence are unlikely to be found only among the preschool
causes. These authon suggest that explanatory models have to deal with later emerging
risk factors that, together with earlier causes in a cumulative fashion, can help to explain
the later onset of aggression in adolescence. A good example of such a developmental
approach is Loeber and colleagues' (Loeber et al., 1993; Loeber, Keenan, & Zhang,
1997) triple-pathway model that integrates bath predelinquent behaviour nsk factors and
subsequent delinquent acts. The model is airned at descnbing which youth are at highest
nsk of becoming chronic offenders. The first pathway concems overt antisocial
behaviour which starts with minor aggression (e.g. bultying, annoying others), followed
by physical fighting which is followed by serious violence (e.g. rape, assault, strong-
aming). The second pathway concems covert antisocial behaviour which first consists
of minor covert behaviours (e.g. shoplifting, frequent lying), followed by property damage
(e-g. fire setting, vandalism) which is followed by moderate to serious forrns of
delinquency (e-g. fraud, burglary, serious theft). The third pathway concems individuals
whose antisocial behaviour is of a nature such that they exhibit conflict with authority.
This pathway consists of those who first exhibit stubbom, defiant or oppositional
behaviour, followed by more serious acts of disobedience which is followed by acts of
authority avoidance (e.g. truancy, running away, staying out late at night).
It would appear as though the antisocial behaviour exhibited by those with a
history of language impainent is more of a covert or authority defiant nature rather than
of a covert nature as it is unclear whether they are involved in violence and physical
aggression. They were not at significantly increased risk for exhibiting crÎminal
behaviour, but were ai risk for diagnoses of ASPD. These diagnoses incorporates many
behaviours which may not be violent or criminal in their manifestation, but are still
antisocial and were captured by the diagnosis. Within the context of Loeber et a l 3 triple-
pathway model, more detailed analyses of the exact nature of these subjects' antisocial
behaviour might prove useful in elucidating the mechanisms by which this behaviour
developed over tirne.
6.6.2 Implications for Prevention
The ability to make long-terni behavioural predictions based on early childhood
attributes bestows a powerful diagnostic and preventative capacity on the child's health
care provider. This study shows that young children with language impairments deserve
special attention as they are at significant risk for antisocial behaviour during the young
adulthood years. There is IittIe evidence for the effectiveness of interventions for
conduct and antisocial problems in adolescence and especially in young adulthood
(ûfford & Bennett, 1994). A compelling argument in favour of an increased emphasis on
primary prevention thus evolves. It seems logical that children with language impairrnents
who are at risk for antisocial behaviour should undergo speechnanguage therapy. An
important caveat for the role of prevention relates to the underlying causes of the
speechnanguage impairments themselves. For example, Beitchrnan (1 985) has
suggested that the association between speech/language impairrnents and antisocial
behaviour may be due to an antecedent variable that leads to both. He suggested that a
gene or group of genes might influence or cantrol neurodevelopmental maturation and
that speechnanguage irnpairments rnight be one of the more obvious manifestations of
delayed neurodeveiopment. Psychiatnc problems also can then be anticipated. If the
language impairment observed in this study were the result of such neurodevelopmental
delays, then prevention of antisocial behaviour in the form of speechllanguage therapy
might not prove useful.
Another form in which prevention might take place is in the assessrnent of
individual differences and protective factors. The outcome among language impaired
children is not uniformly negative and many do not develop antisocial behaviour problems.
Wth respect to psychiatnc disorders in general, Rae-Grant, Thomas, Offord and Boyle
(1989) reported that the developrnent of non-school skills (e.g. sports and music)
reduces the risk of emotional disorder arnong children and adolescents. Whether these
variables woufd serve as protective factors in young adults who had been identified as
speechllanguage impaired in childhood is unknown. The search for other "non-clinicaln
modes of prevention andfor protective factors might prove to be useful in the prevention
of antisocial behaviour.
6.7 Study Limitations
6.7.1 Missing Data
One significant limitation to the implications to be drawn from this study is the
absence of time-3 antisocial outcome data. This was especially true among teacher
(36.3% missing) and parent (22.5% missing) assessed antisocial data The analysis of
subjects with and without each measure of outcome data showed that those without
data at tirne3 tended to be worse off at time-1 showing, among others, lower levels of
IQ, a greater proportion of diagnoses of ASPD and occurrences of criminal behaviour, a
greater likelihood of coming from a single-parent family and having a lower level of family
incorne. Most of the missing information at time-3 occurred for the subjects who had
been language impaired at tirne-1. Missing data such as these would tend to bias the
results towards the nul1 hypothesis (Rothman & Greenland, 1998). Furthemore, that
such large proportions of data were missing for the parent and teacher dimensional
scores limits the generalizability of those findings. Undoubtedly, the reported results
would have been strengthened had there been a more complete set of data.
6.7.2 lntegrated Analysis of the Data for Both Males and Females
Compared to other longitudinal studies of speecManguage impainnent, the OLS
had a relatively small sample size. The largest regression model included 254 subjects
while the Dunedin Study involved almost 1,000 subjects. Because of sample size
limitations, separate analyses could not be performed for males and females. This was
unfortunate as research has suggested that the pathways to and adult consequences of
antisocial behaviour may be different for males and females (ûfford & Bennett, 1994).
lntegrated analysis of both genders may have resulted in the loss of useful information.
6.7.3 Merging of Parent and Teacher Measures
A potential weakness of the study is that parent and teacher data had to be
combined into a single scale because of a large amount of missing teacher data.
Although useful information was still acquired by comparing subject and non-subject
measures, this may have also resulted in the loss of useful information pertaining to the
specificity of the behaviour exhibited by the subjects in different contexts.
6.8 Recommendations
Recommendations can be made for future research projects. The relationship
between neuropsychological deficits such as those involving language-based mediation
and executive functions (MoffÏtt & Lynam, 1994) and specific language irnpainnents
should prove illustrative. More information concerning the effectiveness of
speechnanguage treatment and its effect on the later emergence of antisocial problems
might yield important preventative information. A replication of this study on each gender
individually would shed further Iight on the strength of the relationship between
speechnanguage irnpairments in eariy childhood and antisocial behaviour in young
adulthood.
90
6.9 Summary
The purpose of this study was to detemine whether eariy childhood
speecManguage impaiments identified in children as young as 5 years of age were
associated with antisocial behaviour in young adulthood. This was confirmed for the
situation in which antisocial behaviour was measured in tems of a diagnosis of antisocial
personality disorder (ASPD) and when non-subject raters (parents andlor teachers)
measured dimensionally the level of antisocial behaviour in the subjects. When antisocial
behaviour was measured in terms of contact with the law and when subjects measured
dirnensionally their own antisocial behaviour the association was not observed. When
differences among the groups were found, those subjects who had been language
irnpaired at age 5 were rated more antisocial than those who had been controls, while
those who had been speech-only impaired were not different than controls. With
respect to the analyses which did not yield differences arnong the speechllanguage
groups at age 79, the measures of criminal behaviour may not have been sufFiciently
sensitive to detect the true rates of contact with the law that the subjects may have had.
Subsequent research needs to study further the issue as to whether the antisocial
behaviour exhibited by individuals with a history of language impaiments is of the nature
or severity such that they would have increased rates of criminal behaviour.
Furthermore, supplernentary analyses suggested that the self-reports from individuals
with a history of language impairnent may be less accurate than those from individuals
without such a history of impairment. This was suggested as a possible explanation as
to why differences among subject measures of antisocial behaviour were not found.
Subsequent research might usefully address the neuropsychological features of those
with speechAanguage impairments and how such features relate to the developmental
pathways these individuals travel as they mature.
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APPENDlX A Missing Data.
Tables A l and A2.
Missing Observations
As was mentioned in sedion 3.6.2, most of the missing dependent variable data
occurred among the subjects who were categorized as having a cornbined speech and
language impairment. This can be seen in Table A l , below, which shows the proportion
of missing dependent variable data for each speechllanguage group and for each
dependent variable used.
Table A l : Proportion of missing values for each speecManguage group on each
When the combined speech and language impaired group was merged with the
language-only impaired group, the severity of the missing data was slightly diminished,
although there were still significant differences in the proportions of missing values
among the newly-defined speechllanguage groups. This can be seen in Table 3.29,
below.
Outcome Variable
DX ASPD
Criminal Behaviour
Non-subject measures
Subject rneasures
Table A 2 Proportion of missing values for the newly speechllanguage groups on each dependent variable.
I Outcorne Variable Controls I Group Differences I
Controls
9.1 5
7-75
10.25
5.63
Subjects with and without missing dependent variable data were aiso compared on al1
the other variables in the study. The results of these analyses appear in Tables B I to B5.
As a surnrnary description for al1 of these tables, the rnost conspicuous observation is
that subjects with missing dependent variable data tend to be worse off than those
without rnissing data. For example, Table B I shows that subjects with missing parent-
measured antisocial behaviour data have significantly higher (p=0.045) teacher rneasured
Speech-onIy
2.56
5.13
7.69
2.55
C Dx ASPD
Criminal 8ehaviour
Non-subject measures
Subject measures
Language-only
19.35
14.52
29.03
16.1 3
x2(3df)=1 7.727, p=0.001
x2(3df)=9.773 p=0.008
x2(3df)=1 5.946, p=0.001
x2(3df)=1 6.81 8, p=0.001
9.1 5
7.75
10.25
5.63
Speech&Language
34.1 5
26.83
26.83
26.83
Group Diff erences
x2(3df)=22.1 89, p=0.001
z2(3df)=1 3.41 7 p=0.004
x2(3df)=1 6.032. p=O.OOl
x2(3df)=1 9.809. p=0.001
2.56
5.1 3
7.69
2.56
25.24
19.42
28.1 6
20.39
antisocial behaviour ratings. significantly lower (p=0.0036) performance IQ scores,
significantly higher rates of hyperactivity and conduct problems (p=0.0095 and p=0.0406,
respectively), have a significantly larger proportion of subject eliciting criminal behaviour
(p=0.047), have a significantly larger @=O-001) proportion of subjects who corne from single-parent families and have a significantly lower family income (p=0.009). A similar
pattern is repeated for each of the dependent variables used in this study.
Cornpansons of subjects with and without missing tirne-3 antisocial outcome data. Tables B I -B5.
Variable Teacher-measured dimensional ASB
Subject-measured dimensional ASB
Performance IQ
Childhood Adversity
Inattention
Hy peractivi ty
Early conduct problems
Table B 1: Cornparisons of subjects with and without rnissing data on parent-rated antisocial behaviour for dl variables. Parent-rated data is rnissing for 64 subjects (22.5%).
I I 7
I
-
- - - "
- I
- I
-
1
Single parent status a
n= Mean/Proportion Test p-value present=I 64 54.1 3 t( 179df)=-2.0285 0.045
missing4 1 31.15%
present=207 P U P 9 ~ 2 ( I2df)=26.538 0.009
missing=53 p u p s 2 & 5
presen t=220 65.00% x2(ldf)=.135 0.7 13 missing=64 62.50%
b % missing in combined speech & language impaired group, language-only impaired
group, speech-only im aired group and controls, respectively. '9=4045K$, 2=5- I OK$, 5=28-2X$. d% male.
Table B2: Cornparison of subjects with and without missing data on teacher-rated antisocial behaviour for al1 variables. Teacher-rated data is missing for 103 subjects
le of interest.
Variable 'arent-measured Iimensional ASB
iubject-measured
limensional ASB
'erformance 1Q
:hildhood Adversity
nattention
i yperactivity
M y conduct problems
)X of ASPD'
lriminal I3ehavioura
peecManguage rtaturb
Iingie parent status "
b% missing in combined speech & language impaired group, language-only impaired
n= MeanIProportion Test p-value present=164 54.17 t(74.5df)=- 1.1589 0.25û2 missing=56 55.58
present=l80 55.2 t(252df)=-1.3387 0.1819
missing=74 56.23
present= 1 79 11251 t(277df)=2.2 186 0.0273
missing= 100 1085
present=l7 1 50.56 t(260df)=0.064 1 0.9489
missing=9 1 50.47
present=18 1 1?.1 t(282df)=- 1.5402 O. 1246
rnissing= 1 03 20.95
present=l8 1 23.17 t(282df)=- 1.2039 0.2296 missing=103 26.53
present=18 1 12.32 t(182.8df+0.7638 0.446
niissing==l03 14.14
present=I 76 9.66% x2( 1 df) =4.069 0.044
missing=68 19.12%
present=l77 18.08% ~ 2 ( ldf)=4.195 O.# 1 rnissing=74 29.73%
pesent=' 81 41.46%. 45.16% ~2(3df)=5.727 0.126 missing=l03 23.08%, 34.5 1 %
present=i 79 1329% x2( ldf')=5.625 0.0 18
speech-only im aired group and controls, respectively. '9=40-4K$, 2=5- 10K$, 8=3!-40~$. d 5% male.
ncomer
;enderd
missing=99 23.238
present= 170 P U P 9 ~ 2 ( 12df)=28.13 1 0.005 rnissing=90 groups 2 & 8
present= 1 8 1 6 1.88% ~ 2 ( 1 df)=I .425 0.233 missing= IO3 68.93%
Variable 'arent-measured limensional ASB
éacher-measured limensional ASB
'erformance 1Q
Ihildhood Adversity
nattention
Iyperactivity
larly conduct probIerns
b peecManguage status
ingle parent status"
n= MeanIProportion Test p-value present=2 1 8 54.57 t(217df)=10.1331 0.000 1
missing=2 50
present=180 54.4 I t( 179df)=.3358 0.7374
missing= 1 52-5
present=250 11 1.97 t(277df)=3.06 14 0.0024 missing=29 1 03 34
present=239 50.42 t(260df)=--50 14 0.6 165
rnissing=Z 51.6
present=254 17.63 t(282df)=-2.10 15 0.0365
missing=30 25.83
present=254 23 -43 t(282df)=-2.0827 0.0332 missing=30 32.48
presen t=254 12-33 t(33.3dî+1.4320 0.1615 missing-30 18-46
present=244 12.30% x2=da n/a
missin@ nia present=249 2 1.69% x2( 1 df)=.553 0.457
missing=2 0.00%
present=254 26.83% 16.13% ~2(3df)= 19.809 0.00 1
missing=30 2.56%. 5.63%
present=25 1 13.55% ~ 2 ( ldf)=13.289 0.00 1
rnissing=27 40.74%
present=238 groups 7.8 & 9 ~ 2 ( 1 2df)=36.263 0.0 1
rnissing~22 P U P 2 present=254 63.39% ~2(ldf)=1.159 0.282
rnissing=30 73.33%
b% missing in combined speech & languitge impaired group, language-only impaired group, speech-oniy impaired group and controls, respectiveIy.
'7~30-35K$, 8=35-40K$, 9~40-45K$. d % male.
Table B4: Compansons of subjects with and without missing data on diagnoses of ASPD for al1 variables.
8
Continuous Variables
L
Categorical VariabIes
peecManguage s t n d
ingle parent status"
ncomer
1
iubject-rneasured ) present=244 55.6 t(252df)= 1.3662 0.1731
Diagnoses of ASPD data is missing for 40 subjects (14.1 %).
F C m
d - C
d - F
L
- 1
- k
- E
C
- S
- s -
Variable n= Mean/Proportion Test p-val u c
rnissing=ll 9.09%
present=254 34.15%. 19.35% ~2(3df)=22.189 0.00 1
missing=30 2.56R.9.158
present=24 1 13.288 ~ 2 ( 1 d e 1 1.295 0.00 1
rnissing=37 35.149
present=228 F O U P 9 x2( 1 2df)=27.122 0.007
l - l "'33 with outcome of interest-
b 55 missing in combined speech & language impaired group, language-only impaired
group, speech-only impaired group and controis, respectively. '940-45K$, S=5- 1 0K$. d% male.
'arent-measured
limerisional ASB
reacher-rneasured
Iirnensional ASB
present=212 54.37 t(2 18df)=,4464 0.6557
missing=8 565
present=176 54.34 t(179df)=-3757 03824
missi ng=5 56.6
limensional ASB
'erformance IQ
:hildhood Adversity
nattention
missing= 1 0 53.15
present=242 1 12.26 t(47.9df)=3.563 0.0005
missing=37 10332
present-229 50.48 t(260df)=.2065 0.8366 missing=33 50.89
present=24 1736 t(282df)=-2.3395 0.02 missing== 25.42
brly conduct problems present=2# 12.09 t(282df)=-2.0264 0.0437 missing4.4 18.4
Table B5: Cornparisons of subjects with and without missing data on criminal behaviour ES. Criminal behaviour data is missing for 33 sibjects (1 1.6%).
Variable IF MeadProportion Test p-value
interest.
'arerit-measured
Iimensionai ASB
reacher-measured
Limensional ASB
iubject-rneasured
lirnensiond AS B
'erformance 1Q
Zhildhood Adversity
nattention
i yperactivi ty
3rly conduct problems
l x of ASPD a
:peecManguage statusb
ingle parent status"
ncomee
ienderd .
b 5% missing in combined speech & language irnpaired group, language-only impaired
group, speech-only im aired group and controls. respectively. '8=35-40K%, 9=45-50K$, 2=!- 1 OK$. d% male.
present=2 16 54.62 t(218df)=1.3788 0.1694
rnissing--4 50
presenr= 177 54.4 t( 179df)--.O778 0.938 1
missing4 54.63
present=249 55.54 t(252df)=.7307 0.4656 missing=5 53.7
present=247 11 1.85 t(277df)=2.5 1 57 0.01 24
rnissing=32 1 05 .O3
present=236 50.6 1 t(260df)=.3796 0.7045 rnissing=26 49.77
present=23 1 17-87 t(282df)z- 1.4252 0.1552 missing=33 23 -23
present=25 1 23.7 t(282df)z- 1.422 1 0.156 1
missing=33 29.65
present=25 1 12.63 t(282df)=-0.9039 0.3668 missing=3 1 15.69
present=240 1 2.08% ~ 2 ( 1 df)=0.609 0.43 5
missin@ 25.W'
26.83%. 14.52% x2(3df)= 13.4 17 0.004
missing=30 5.13%,7*75%
present=248 13.31% ~ 2 ( 1 df)= 14.056 0.00 1
missing=30 40.00%
presen t=234 groups 8 & 9 ~ 2 ( 12df)=25.638 0.012
missing=26 group 2
present=25 1 63.759 ~ 2 ( 1 df)=.35 1 0.502
missing=33 69.70%
APPENDIX C Multicollinearity and Diagnostics.
Tables Cl-C6.
Multicollinearity and Diagnostics
The purpose of this section is to address two important statisticâl issues present
in this investigation: multicollinearity and linear regreçsion diagnostics.
Because this study involved the construction of various models that contained a
number of independent variables, the degree of rnulticotlinearity among these variables
needed to be addressed. Multicollinearity in regression analysis exists when the
independent variables are highly correlated with one another, or more fomally, when one
independent variable is regressed on another and the R' approaches 1 .O (Berry &
Feldman, 1985; Lewis-Beck, 1990). There is always sorne degree of multicollinearity, but
high multicollinearity creates a problem with estimation. In general, as the degree of
correlation among the independent variables increases, the standard errors of the siope
coefficients increase thus decreasing the chance of rejecting the nuIl hypothesis that the
dope coefficients are zero. A number of indicators may be used to detect high
rnulticollinearity. Observed pairwise correlations between independent variables larger
than 0.8 rnay be one sign, but multicollinearity rnay still be a problern when bivariate
correlations are lower or not a problem when they are higher (Bondy, 1995). A
somewhat better test is to examine the joint influence of the independent variables on
each other by examining the multiple R~ values obtained. However, there is no cntical
value of R' which signifies the presence of absence of multicollinearity.
Tables Cl through C5 show the pairwise correlations and multiple R~ for Models
14, respectively. Generally, these results did not identify any multicollinearity of
concem, however, some important points will be made. For example, the highest
pairwise correlation found was -0.3405 (in Table Cl) which represented the association
between having received sorne form of treatment and having been categorized as either
Speech-only impaired or Language impaired at time-7. Although this pairwise correlation
is quite high it should be noted that it does not alter the conclusions that would have been
made from the model that contained this variable. This is explained by two related facts.
First, the standard errors of the speech-only and language impaired groups before and
after the treatment variable was included differed very little. Specifically, the standard
error of speech-only variable increased by only 3.7% and the language irnpaired group
increased by only 1.8%; and second, the ,significance of the odds ratios associated with
each of these speechtlanguage variables did not change when the treatment variable
was added. (It should be noted that the sarne was tme with the model (not shown) that
included the parent-reported treatrnent variable).
Another large paiwise correlation coefficient was 0.2781 (in Table C2) which
represented the association between Hyperactivity (continuous measure) and
membership in the Language impaired group and appeared in Model2a. This high level of
multicollinearity would also not have altered the conclusions drawn from that model. The
standard errors of the Speech-only and Language impaired variables increased by only
2.1 % and 1.6%. respectively. It should also be noted that the largest multiple R* found
was 0.1779 which existed for the Hyperactivity (ordinal) variable when it was regressed
onto speechllanguage status, Gender and SES. Thus, while some multicollinearity was
found, it would not be considered so high as to preclude the inclusion of the independent
variables in their respective models in which the multicollinearity occurred.
Linear regression diagnostics were also performed to assess the assumptions
involved in the linear regression analyses. Four types of graphs were plotted for each
multiple linear regression rnodel (Models 3a & b and 4): i) a plot of the distribution of the
studentized residuals; ii) a plot of the studentized residuals versus each of the
independent variables that were included in the respective final modeis; iii) a plot of the
studentized residuals versus the predicted antisocial behaviour T score values; and iv) a
plot the extemally studentized residuals versus the predicted antisocial behaviour T
score values. The purpose of the latter most graph type was to highlight outlying values
by determining how they affected the estimates of the regression parameters. Ideally,
the studentized and externally studentized plots (types iii and iv) should be sirnilar.
For each model. the distribution plots of the residuals were roughly normal. As
well, the plots of the studentized residuals versus each independent variables and the
plots of the studentized residuals versus the predicted values were very scattered, each
with no apparent pattern. Finally, the plots of the externally studentized residuals versus
the predicted values were almost identical to the plots of the studentized residuals
versus the predicted values indicating that in none of the Models were there any outlying
values the largely effected the respective parameter estimates.
In summary, based on the results from al1 of the above mentioned plots, none of
the assurnptions involved in multiple linear regression appeared to be violated.
TableCl:Pairwise--andmultipk~%f~~Modd 1. -
1 -cInebtiots I I Gender 1 (~ribject) ( M- R~
Gender ID I I ml31 Treatment (Subject) 0-0296ûQ~4I~64ûû) 1 If] [ 0.1607
ControI -0.27 16 (p=O.ûûO 1 ) Speec WLanguage Speech-onty 0.006 1 (pû.9 1 87) 11) OB86 1
Status Lariguage 0.2787 (p-d.0001)
TabieC3: PaiFwiçe amdations and mulople R ~ S for Model2.b.
I
Gender ID 1
SES 0-1 805 (p=û-0023) i
t Statu5 . . I R2 Hyperactmty ID 1 0.1338 Single-Parent 0251 2(p=û~ûûûl) 11) 0.0695
Contrd -0.27 1 6 (p=û.Oûûl) -0.08ô7 (pû- 1 446) S~eech/Language ' Speechnfy 0.006Tcp.O.9187) -0.067 7 (p0.3052) 1.0 0.0887
S t a h b L a m * 0-2781 (p=û.ûûû1) 0-1 340 (p=O.0240) }
I 1 P a i i C~r~etatiors I
Status [ , 0-1 243
t ?Co 1 I t O-? t O7
m 1 1 I I
p e m z N i (wt%~tl) 1 0 . 2 5 7 3 ( ~ . ~ l ) ' 4-1 597 1 1 .O I 1 0,1779
Gender SES i
Speech/ bwuage
Status
I HY- - - speaW (ordinal) ~anguage
~1452CpS).U.C2t4î) : bf 1837 (m0463)
C m d 1 -CLûû74(@.9ols)
, Mdt.i~AeR~
412719{p--Op--00001) : iaO?3C)(W-827 3)
1
, 113 , only
Language -02360 (p=û.ûûûl)
l
0-1 1 42 . 0-0827(@-1646)
-0,051 55 @=O3867) 02734(p=0-0001) , rn
Table C5: Pairwise correlations and multiple R ~ S for Model3b. 1
1 1 Hyperactivity
Hyperactivity
- Pairwise Correlations
Single-Parent 1 SpeechlLanguage Multiple (ordinal)
1 .O I
- -
Status 1 FP
0.2279 (p=O.OOOl) -0.271 9 (p=O.O.OOOl)
0.01 30 (pd.8273) 0.2734 (p=O.OOOl)
Single-Parent
Table C6: Pairwise correlations and multiple R2s for Model4. 1 Pairwîse Conelations
SpeechlLanguage Status
1 ( Single-Parent 1 Speech/Language
Control Speech-only
Language
Multipfe Fe
0.01 84 Sinqle-Parent 1 .O
-0.0868 (p=0.1446) -0.061 1 (p=0.3052) 0.1 340 (p=0.0240)
SpeechILanguage Status
1 .O Control
Speech-only Language