11
journal of Computer Assisted Learning (1987) 3. 2- 12 How do children do mathematics with LOGO? R. NOS, University of London Institute of Education, London Abstract This paper considers some results of a study designed to investigate the kinds of mathematical activity undertaken by children (aged between 8 and 11) as they learned to program in LOGO. A model of learning modes is proposed, which attempts to describe the ways in which children used and acquired under- standing of the programming/mathematical concepts involved. The remainder of the paper is concerned with discussing the validity and limitations of the model, and its implications for further research and curriculum development. Keywords: LOGO; Mathematics; Primary school; Programming; Learning Process. The case for student programming has been examined from two distinct but related points of view. The first has been concerned with investigating the effectiveness of a programming approach to the learning of mathematical content. Such studies have employed a variety of programming languages (mainly BASIC and LOGO), and have explored the effectiveness of programming on a wide range of mathematical concepts (see e.g., Johnson et al., 1966; Hatfield & Kieren, 1972; Milner, 1973; Howe et a]., 1982). Viewed from this perspective, the objective of student programming is to provide the learner with a tool with which to model mathematical content. The second strand of research has been concerned with the relationship between programming and problem-solving. Once again, such studies have been based both on BASIC (Foster, 1972; Andersen, 1977), and on LOGO (Statz, 1973; Weyer & Cannara. 1975; Papert et al., 1979). The results suggest that, apart from a wide disparity in what is meant by ‘problem- solving’, there is a range of specific heuristics which can be learned through programming. Such studies, and particularly those based on LOGO, have tended to concentrate on the process of programming itself, and have considered heuristic concepts independently of specific (and explicitly taught) mathematical content. The recent advent of LOGO on machines that are &widely available for Accepted: 6 March 1986. Correspondence: Richard Noss, Department of Mathematics. Statistics and Computing, University of London Institute of Education, 20 Bedford Way, London WClH OAL. 9

Do social information-processing models explain aggressive behaviour by children with mild intellectual disabilities in residential care?

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Page 1: Do social information-processing models explain aggressive behaviour by children with mild intellectual disabilities in residential care?

Journal of Intellectual Disability Research doi: 10.1111/j.1365-2788.2005.00773.x

pp

©

Blackwell Publishing Ltd

801

Blackwell Science, LtdOxford, UKJIRJournal of Intellectual Disability Research

-

Blackwell Publishing Ltd,

11801812

Original Article

SIP models to explain aggression in MID childrenM. van Nieuwenhuijzen

et al.

Correspondence: M. van Nieuwenhuijzen, Department of Developmental Psychology, Utrecht University, P.O. Box

,

TC Utrecht, The Netherlands (e-mail: [email protected]).

Do social information-processing models explain aggressive behaviour by children with mild intellectual disabilities in residential care?

M. van Nieuwenhuijzen,

1

B. O. de Castro,

1

I. van der Valk,

2

L. Wijnroks,

3

A. Vermeer

3

& W. Matthys

3,4

1

Department of Developmental Psychology, Utrecht University, The Netherlands

2

Department of Child and Youth Studies, Utrecht University, The Netherlands

3

Department of Special Education, Utrecht University, The Netherlands

4

Department of Child and Adolescent Psychiatry, and Rudolf Magnus Institute of Neuroscience, University Medical Center, Utrecht, The Netherlands

Abstract

Background

This study aimed to examine whether the social information-processing model (SIP model) applies to aggressive behaviour by children with mild intellectual disabilities (MID). The response-decision element of SIP was expected to be unnecessary to explain aggressive behaviour in these children, and SIP was expected to mediate the relation between social schemata and aggressive behaviour.

Method

SIP and aggressive behaviour of

- to

-year-old children with MID in residential care were assessed. The fit of various SIP models was tested with structural equation modelling.

Results

The response-decision process was found not to be necessary to explain aggressive behaviour. Social schemata were indirectly related to aggressive behaviour with aggressive response generation as mediating variable.

Conclusions

Implications for SIP theory and inter-vention are discussed.

Keywords

externalizing behaviour, intellectual disabilities, social cognition, social information processing

Introduction

Recent studies have shown children with mild intel-lectual disabilities (MID), especially those receiving residential care, to have high rates of aggressive behaviour problems (Einfeld & Tonge

; Linna

et al.

; Cormack

et al.

; Dekker

et al.

). Researchers (Leffert & Siperstein

; Van Nieu-wenhuijzen

et al.

) have tried to explain these aggressive behaviour problems in terms of the social information-processing model (SIP model) put forth by Dodge (

) and Crick & Dodge (

). Within the SIP model, it is assumed that social behaviour is preceded by the mental steps of encoding, interpre-tation, goal clarification, response generation, and response decision – with the latter including response evaluation, judgements of self-efficacy, and response selection. That is, individuals encode and interpret the information in a given social situation and then set goals, search for possible responses, evaluate their response options, and select a response for enactment upon the basis of the information that they have processed. It is proposed that individual differences in the different steps of social information processing lead to different behavioural responses. There is con-siderable evidence supporting the hypothesis that SIP is related to aggressive behaviour in children without MID (Crick & Dodge

; Lochman & Wells

; Orobio de Castro

et al.

; Matthys & Lochman

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M. van Nieuwenhuijzen

et al. •

SIP models to explain aggression in MID children802

©

Blackwell Publishing Ltd,

Journal of Intellectual Disability Research

,

). The steps in the SIP model are assumed to occur in a strict order, where each step predicts the next step. According to Crick & Dodge (

), this proposed structural relation among the SIP steps should be examined, and ‘the hypothesized temporal nature of the social information processes’ (p.

) should be taken into consideration. In addition, the steps in the processing of social information are pro-posed to be continuously influenced by a database, containing social schemata that are an accumulation of information from various social experiences over time.

The aim of the present study was to address two issues. The first aim of the study was to test whether the structure proposed by the SIP model applies to aggressive behaviour in children with MID in resi-dential care. The second aim of the study was to investigate how the above-mentioned database including social schemata is related to the SIP steps and aggressive behaviour in children with MID in residential care. With regard to the first question, research has indicated that different SIP steps are related to aggressive behaviour in children with MID (Healy & Masterpasqua

; Leffert & Siper-stein

; Van Nieuwenhuijzen

et al

.

; Van Nieuwenhuijzen

et al.

), but how the steps in the model interrelate to explain individual aggres-sive behaviour has yet to be revealed. Only a few researchers have attempted to study the hypothe-sized structure of SIP in children without MID. The use of structural equation modelling has been recommended (Crick & Dodge

) and research making use of this technique has shown the SIP steps of interpretation (i.e. hostile intent attribu-tion), aggressive response generation, evaluation of aggressive responses, and goal orientation to explain aggressive behaviour in children without MID (Zelli

et al.

; Dodge

et al.

). In addition, all of the steps within the SIP model have been found to be strongly interrelated (Zelli

et al.

; Dodge

et al.

; Orobio de Castro

et al.

). However, the structure of the relations between the different SIP steps has not been examined to date. In other words, only whether variable x, variable y and vari-able z separately predict aggressive behaviour has been considered. Not whether variable x predicts variable y, variable y predicts variable z, and vari-able z subsequently predicts aggressive behaviour (Crick & Dodge

).

The above-mentioned studies of SIP structure have involved only children without MID, which means that the results cannot be generalized to children with MID as yet. Thus, it is unclear whether the aggressive behaviour of children with MID can be explained by the same SIP patterns as have been proposed by the SIP model (Dodge

; Crick & Dodge

). Pos-sibly, children with MID skip certain steps due to their limited cognitive abilities. These children may, for example, experience problems with the evaluation of response alternatives due to difficulties with means-end thinking or the cognitive demands of comparing multiple response alternatives (Bebko & Luhaorg

). Previous studies of children with MID with or without externalizing behaviour problems have revealed major group differences in aggressive response generation but not in the response-decision process (i.e. response evaluation, judgements of self-efficacy and selection of aggressive responses) (Van Nieuwenhuijzen

et al.

; Van Nieuwenhuijzen

et al.

). Moreover, only aggres-sive response generation and not aggressive response selection have been found to be related to aggressive behaviour in staged standardized real-life conflict sit-uations with peers and teacher reports of classroom behaviour (Van Nieuwenhuijzen

et al.

). We therefore hypothesized that the response-decision process would not contribute to the explanation of aggressive behaviour in children with MID. If so, aggressive behaviour in children with MID can be explained by fewer SIP steps than have been pro-posed for children without MID (Dodge

; Crick & Dodge

) and have been confirmed by Zelli

et al.

(

) and Dodge

et al.

(

).In keeping with Dodge

et al.

(

) and Zelli

et al.

(

), encoding, hostile interpretation, aggressive response generation, evaluation of aggressive responses, self-efficacy of aggressive responses, and aggressive response selection were examined. Next, the structural relations between the different SIP variables were tested. Thus, we investigated whether encoding predicts interpretation, interpretation pre-dicts response generation, response generation pre-dicts response-decision processes, and response decision predicts actual behaviour. To measure SIP steps, we used hypothetical vignettes, developed for the Dutch context (Matthys

et al.

) and validated in a sample of children with MID (Van Nieuwen-huijzen

et al.

).

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,

The second issue addressed in this study was the relation between the database, SIP and aggressive behaviour. In both the SIP models proposed by Crick & Dodge (

) and Huesmann (

), children’s social knowledge plays a central role in their behav-iour. In children without MID, Huesmann has stud-ied schemata underlying normative beliefs with regard to aggression (i.e. cognitions regarding the appropriateness of particular aggressive behaviours) and found clear associations between normative beliefs and aggressive behaviour (Guerra

et al.

; Huesmann & Guerra

). In addition, Burks

et al.

(

) have shown that the tendency to think of others in aggressive terms is related to aggressive behaviour on the part of young children. Direct rela-tions between aggressive behavioural schemata and aggressive behaviour have thus been found in chil-dren without MID.

Examination of the relations between underlying behavioural schemata and SIP using structural equa-tion modelling, in contrast, has revealed only indirect effects of such schemata on aggressive behaviour. That is, underlying behavioural schemata were directly related to hostile intent attributions (Zelli

et al.

; Dodge

et al.

), spontaneous genera-tion of aggressive responses (Burks

et al.

; Zelli

et al.

), response evaluation, and instrumental goals (Dodge

et al.

), but only indirectly pre-dicted aggressive behaviour. Perhaps schemata may

appear

to directly affect aggressive behaviour when studied in isolation, but only indirectly affect such behaviour once SIP variables are taken into account. That is, the relation between underlying schemata and actual behaviour appears to be mediated by SIP.

It should be noted that the above results are all based on research with samples without MID and cannot simply be generalized to children with MID. It is nevertheless possible that the same – or at least similar – relations hold for both children without MID and children with MID. We therefore hypothe-sized that the relation between schemata concerning aggression and actual aggressive behaviour is medi-ated by hostile interpretations, aggressive response generation, positive evaluations of aggressive responses, positive judgements of self-efficacy with regard to aggressive responses, and aggressive response selection among children with MID.

In sum, the questions to be answered by this study were: (

) whether aggressive behaviour by children

with MID can be explained by a SIP model not including the response-decision process; and () whether knowledge structures (schemata) affect hos-tile interpretation, spontaneous aggressive response generation, evaluation of aggressive responses, self-efficacy, and aggressive response selection and thereby indirectly affect aggressive behaviour. The hypotheses were: () the response-decision process does not contribute to the explanation of aggressive behaviour in children with MID; and () the relation between schemata concerning aggression and actual aggressive behaviour is mediated by hostile interpre-tations and aggressive response generation among children with MID.

Method

Participants

A total of - to -year-old children with MID participated in this study. According to the most recent definition of intellectual disability provided by the American Association on Mental Retardation (AAMR), children with intellectual disabilities are characterized by ‘significant limitations both in intel-lectual functioning and in adaptive behaviour as expressed in conceptual, social and practical adaptive skills’ (Luckasson et al. ; p. ).

In the Netherlands, children are referred to resi-dential treatment institutes if they have limitations in intellectual functioning and if the limitations in their adaptive behaviour problems significantly impair social functioning and prohibits participation at home and in regular education, according to parents, teachers and diagnosticians (NVGz ).

Therefore, in this study, children were recruited from nine residential treatment institutes for children with MID, evenly spread geographically in the Netherlands. Children in the age of – in the nine institutions were selected, because children had to be old enough to understand the questions asked, and the only validated measurement was designed for this age range.

A summary of descriptive statistics for the sample is presented in Table . The mean age of the children was . years (SD = .), their IQ ranged from to (M = , SD = .), and % of the children were boys. Mean scores and standard deviations are provided for the SIP variables, social schemata, and

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CBCL and TRF scores. The Child Behaviour Check-list (CBCL) (Achenbach a; for the Dutch ver-sion see Verhulst et al. ) and Teacher Report Form (TRF) (Achenbach b; for the Dutch ver-sion see Verhulst et al. ) aggressive, externalising and total problem mean scores for the entire group of participants fell within the clinical range (see Measures).

Measures

Aggressive behaviour

For each child, the care worker and teacher com-pleted the Dutch version of the CBCL (Achenbach a; for the Dutch version see Verhulst et al. ) or the TRF (Achenbach b; for the Dutch version see Verhulst et al. ), respectively. The Dutch ver-sions of the CBCL and TRF have been shown to have good reliability and validity for both the general population (Verhulst et al. ) and for children with MID (Dekker et al. ). Using the Dutch

norms, T-scores were obtained for the syndrome scale Aggressive behaviour.

Intelligence

An estimate of intelligence was obtained using Raven’s Standard Progressive Matrices (SPM; Raven et al. ), which consists of incomplete arrays of abstract geometrical figures and requires respondents to identify the relations between the elements in an array in order to select the correct figure to complete the array. The internal consistency of the test has been found to be around ., and the test–retest reliability of the test has been found to vary from . for short term to . for long term. Moderate to high correlations between the SPM and other non-verbal tests of intelligence have been reported (Raven et al. ). Using percentile scores, the Raven scores were transformed into IQ scores.

Social information processing

The Social Problem Solving Test (SPT, Sociale Probleem oplossings Test) (Cuperus ; Matthys et al. ) was designed to measure social information processing. The original SPT consists of video vignettes with a structured interview. For the present study, the original version of the SPT was revised for use with children with MID (SPT-MID; Van Nieuwenhuijzen et al. ). Because a pilot study had shown the vignettes to demand too much from children with MID with regard to their attention and concentration, the number of video vignettes was reduced to five and the questions were simplified. The remaining vignettes include both peer-entry and provocation situations, which have shown to be part of one and the same factor ‘Being disadvantaged’ (Matthys et al. ). With the assistance of child actors, the social problem of being placed at a disad-vantage is displayed in each vignette. Each video-taped vignette consisted of two parts. First, the social problem was presented (e.g. the protagonist trying to build a Lego plane but not succeeding followed by another boy offering to help but breaking the plane). Thereafter, the protagonist enacted three solutions to the problem: a pro-social/assertive response, an anti-social/aggressive response and a passive/submissive response.

Prior to the presentation of each video vignette, participants were asked to identify with the protago-

Table 1 Means and standard deviations for TRF, CBCL, SIP vari-ables and NOBAGS

M SD

TRF T-scoresTotal problems 67.47 10.83Externalizing problems 65.32 11.48Aggressive behaviour 66.23 10.26

CBCL T-scoresTotal problems 71.58 8.71Externalizing problems 70.50 10.24Aggressive behaviour 71.07 11.31

SIP variablesNegative cues 1.05 0.40Hostile interpretation 2.68 1.01Aggressive response generation 1.16 1.15Evaluation 1.33 1.38Self-efficacy 1.49 1.42Aggressive response selection 0.57 1.00

NOBAGSTotal scale 36.22 8.41Verbal aggression 13.79 3.64Physical aggression 8.64 3.09General questions 13.95 4.07

CBCL, Child Behaviour Checklist; NOBAGS, Normative Beliefs About Aggression Scale; SIP, Social Information-Processing; TRF, Teacher Report Form.

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nist. Although the girls in this study had to identify with opposite sex protagonists, as the protagonist in the vignettes were boys, this did not affect the results. Following the viewing of a vignette but prior to the presentation of the three different solutions, a num-ber of questions were posed. The interviewers noted participants’ responses. The first question assessed the encoding of cues: ‘What happened in this frag-ment?’ The mention of verbal, situational, emotional or negative cues was coded. Phrases literally repeated from the segment were coded as verbal cues. Verbal descriptions of what happened in the vignette, such as ‘they are playing with Lego’, were coded as situa-tional cues. Remarks regarding the feelings of one of the children in the vignette (e.g. ‘he looks a bit sad’ or ‘he gets angry and starts shouting’) were coded as the encoding of emotional cues. When cues were interpreted negatively (e.g. ‘he said it in an unfriendly way’), they were coded as negative cues. The average number of cues encoded and the average number of verbal, situational, emotional and negative cues encoded were calculated by summing the relevant number of cues across the five vignettes and dividing by .

The second question assessed children’s interpreta-tion of the information presented: ‘[event] happened, why did this happen?’ The participant was asked to select one of three answers: () benign intent (‘it wasn’t his fault, he tried to help me’); () unkind (but not hostile) intent (‘he is clumsy’); or () hostile intent (‘he did it on purpose’). A total hostile intent attribution score was obtained by counting the num-ber of vignettes for which an unkind or hostile answer was provided, with a minimum of (for none of the vignettes) and a maximum of (for all of the vignettes).

The next question assessed response generation: ‘What would you do?’ After provision of an initial response, participants were asked to think of other ways of responding to the situation. The total number of responses generated was then coded along with the quality of each response: pro-social/assertive, antiso-cial/aggressive or passive/submissive. Spontaneous response scores were obtained by counting the num-ber of times each kind of response was provided as the initial response, with a minimum of (never) and a maximum of (always). A percentage score was then calculated for each response category by summing the number of responses representing a

particular category across the five vignettes and divid-ing this number by the total number of responses. A total response generation score was also obtained by summing the number of generated responses across the five vignettes and dividing this number by . A response variability score (i.e. indicator of a child’s response repertoire) was obtained by counting the number of qualitatively different responses across the five vignettes and dividing by .

Next, the different enacted solutions to the problem were viewed. Each solution was followed by a number of questions to assess response evaluation and self-efficacy. The first question concerned evalu-ation on the basis of moral values: ‘Was this a good way for the child to respond?’ to be answered ‘yes’ or ‘no’. The second question addressed participants’ confidence in enacting the response (self-efficacy): ‘Would you be able to behave in the same way?’ For each solution, a total evaluation score and a total self-efficacy score were calculated by summing the num-ber of positive answers across the five vignettes, with a minimum of (never positive) and a maximum of (always positive). After presentation of the three alternative solutions and assessment of the partici-pant’s reactions, the three videotaped solutions were again presented to assess response selection. Partici-pants were asked: ‘Which of the three responses would you choose?’ The total number of assertive and aggressive solutions chosen was summed over the five vignettes.

To assess inter-rater reliability for coding of parti-cipants’ answers to the open questions, both a grad-uate student and the first author scored these answers for randomly chosen vignettes. The Kappa values were found to be . for encoding and . for response generation. The answers to the response generation question correlated moderately with actual behaviour in real-life problem situations (r = .) and highly with behaviour in class as reported by the teacher (r = .) (Van Nieuwen-huijzen et al. ).

Schemata

To assess children’s schemata regarding aggression, a translation of the Normative Beliefs About Aggres-sion Scale (NOBAGS; Huesmann & Guerra ) was used. The NOBAGS is a -item self-report measure of children’s beliefs about the appropriate-

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ness of physical and verbal aggression. The retaliatory beliefs subscale consists of items regarding the appropriateness of aggressive retaliatory responses. The general beliefs subscale consists of eight ques-tions regarding more general beliefs about aggressive behaviour. Participants were asked to respond along a -point scale ranging from (‘I think it is not good to hit/call names’) to (‘I think it is very good to hit/call names’).

A total NOBAGS score is calculated by adding all of the response scores, with a minimum of reflec-ting a negative attitude towards aggression and a maximum of reflecting a positive attitude towards aggression. Research by Huesmann & Guerra () has shown the total scale to have good reliability (α = .) and found significant correlations between scores on the NOBAGS and aggressive behaviour. For the present sample, Cronbach’s α was found to be . for the total scale and to range from . to . for the subscales.

Procedure

After parental permission was obtained, participants were individually seen for min in a quiet room, usually located at participants’ schools. In cases of limited school space, participants were tested at the residential setting. Participants first completed the intelligence tests and then the SPT-MID. All parti-cipants’ responses were audio-recorded and also recorded on paper. The teacher who knew a partici-

pant best – typically the mentor or main teacher – was asked to complete the TRF. One of the care workers – in most cases the personal tutor – com-pleted the CBCL.

Data analyses

Structural equation modelling based on maximum likelihood estimation (AMOS, Arbuckle & Wothke ) was used to test the two hypotheses under study. The analyses were motivated by the assump-tion that bivariate relations exist between the vari-ables under study. As described above, the SIP model consists of the variables negative cues, hostile inter-pretation, aggressive response generation, and the decision process represented by positive evaluation of aggressive response, self-efficacy related to aggressive response, and aggressive response selection. This assumption was tested and found to produce signifi-cant zero-order correlations (see Results, Table ). The covariance matrix for all the variables was used as the input for the linear structural relations models.

Hypotheses concerning the structure of the SIP model were tested by examining which of two models provided a better fit for the data: a model in which aggressive behaviour is explained by a SIP structure including the response-decision process (Model , Fig. ) vs. a model with the response-decision process omitted from the SIP structure (Model , Fig. ). Based on the first hypothesis, we expected Model to fit significantly better than Model . Given that the

Table 2 Bivariate correlations between aggressive behaviour, SIP and schemata

1. 2. 3. 4. 5. 6. 7. 8. 9.

Aggressive behaviour1. Care worker –2. Teacher 0.44*** –

SIP3. Encoding 0.11 0.10 –4. Hostile interpretation 0.03 0.06 0.20* –5. Aggressive response generation 0.24** 0.23* 0.16 0.14 –6. Evaluation 0.02 0.10 −0.11 0.06 0.26** –7. Self-efficacy 0.12 0.11 −0.03 0.08 0.35*** 0.71*** –8. Aggressive response selection 0.06 −0.02 −0.07 −0.02 0.24** 0.70*** 0.52*** –9. Schemata 0.01 −0.04 −0.07 −0.12 0.26** 0.30*** 0.22* 0.36*** –

*P < ., **P < ., ***P < ..SIP, social information-processing.

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two models are nested, they could be directly com-pared to each other with a chi-square difference test (Bollen ).

The second hypothesis stated that participants’ schemata would be significantly related to hostile interpretations and, aggressive response generation, and thereby indirectly affect aggressive behaviour. This hypothesis was tested by including the schemata measure in the above models.

Results

Bivariate relations

In Table , the bivariate correlations between the SIP, aggressive behaviour and schemata scores for the entire group are presented. As can be seen, each SIP variable is related to the next variable in the model, with the exception of hostile interpretation and

aggressive response generation. The children’s sche-mata are related to aggressive response generation and the response-decision process but not to aggres-sive behaviour. While these correlations provide ten-tative support for the hypotheses put forth here, bivariate correlations could not be used to examine all of the relevant interrelations, and structural equa-tion modelling was therefore undertaken to shed greater light on the interrelations of the constructs.

Structural equation models

Social information-processing structure

As can be seen from Table , the overall fit of Model was found to be acceptable, as indicated by χ2 (, n = ) = ., P = ., goodness-of-fit indices [comparative fit index (CFI) = .; normed fit index (NFI) = .], and the root mean square error of approximation (RMSEA = .). Inspection of the parameter estimates showed all the variables to be significantly related, with the exception of one. No significant relation was found between aggressive response selection and aggressive behaviour (β = ., ns) (see Table ). Aggressive response selection did not relate to aggressive behaviour in children with MID.

As can be seen from Table , Model fitted the data too, as indicated by χ2 (, n = ) = .,

Figure 1 Model : SIP model with inclusion of response-decisionprocess and schemata. Note: *P < ., **P < ., ***P < ..SIP, Social Information-Processing.

Negative cues

Aggressive responsegeneration

Positive evaluationaggressive response

Self-efficacyaggressive response

Selectionaggressive response

Aggressivebehavior

Hostileinterpretation

Schemata

0.19*

0.19*

0.46***

0.02

0.26***

0.03

0.71***

0.17*

0.01

0.25**

0.28***

–0.11

Figure 2 Model : SIP model without response-decision process.Note: *P < ., ***P < .. SIP, Social Information-Processing.

Negative cues

Aggressive responsegeneration

Aggressivebehavior

Hostileinterpretation

Schemata

0.19*

0.17*

0.28***

–0.070.34***

–0.11

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P = ., goodness-of-fit indices (CFI = ; NFI = .), and the root mean square error of approximation (RMSEA = .). Inspection of the parameter estimates revealed significant relations between negative cues and hostile interpretation (β = ., P = .), hostile interpretation and aggressive response generation (β = ., P = .),

and aggressive response generation and aggressive behaviour (β = ., P < .).

To examine which model fitted the data best, we compared the chi-square values for the two models and tested the significance of change. Model was found to fit the data significantly better than Model (∆χ2 = ., ∆df = , P < .). These findings support the first hypothesis under study. Although the response-decision variables are strongly interre-lated, aggressive response generation directly affects externalizing behaviour. The response-decision vari-ables (i.e. evaluation, self-efficacy and selection) were not needed to explain aggressive behaviour in chil-dren with MID.

Schemata

To examine whether hostile interpretation, aggressive response generation, evaluation of aggressive responses, self-efficacy and aggressive response selec-tion mediated the relation between schemata and aggressive behaviour, the parameter estimates for Models and were inspected. In both models, schemata affected aggressive response generation (β = ., P < .), evaluation of aggressive responses (β = ., P = .), and aggressive response selection (β = ., P < .), but not aggressive behaviour (β = . in Model /β = −. in Model ). As expected, schemata affected SIP (i.e. aggressive response generation and evaluation) and indirectly affected aggressive behaviour via aggressive response generation (see Fig. ). These results are partly in line with the second hypothesis.

Discussion

The structure of the social information processing or SIP model (Dodge ; Crick & Dodge ) was tested for children with MID. Given the limited intel-lectual capacities of children with MID, it was sug-gested that the response-decision process of the SIP model may not be particularly relevant to this popu-lation of children. It was therefore examined whether all the SIP variables were necessary to explain the aggressive behaviour of children with MID. In addi-tion, the effects of schemata on SIP and aggressive behaviour were examined.

It was first hypothesized that the response-decision process would not be necessary to explain the aggres-

Table 3 Summary of goodness-of-fit indices for the two models ofsocial information processing

χχχχ2 df P CFI NFI RMSEA

Model 1 72.85 23 0.00 0.97 0.96 0.13Model 2 4.56 8 0.80 1 0.997 0.00

CFI, Comparative Fit Index; NFI, Normed Fit Index; RMSEA, Root Mean Square Error of Approximation.

Table 4 Standardized maximum likelihood estimates and fit coeffi-cients for models†

Model 1 Model 2

Negative cues to hostileinterpretation

0.19* 0.19*

Hostile interpretation to aggressiveresponse generation

0.17* 0.17*

Aggressive response generation toevaluation

0.19*

Positive evaluation of aggression toself-efficacy

0.71***

Self-efficacy to aggressive responseselection

0.46***

Aggressive response selection toaggression

0.03ns

Schemata to hostile interpretation −0.11ns −0.11ns

Schemata to aggressive responsegeneration

0.28*** 0.28***

Schemata to evaluation of aggressiveresponses

0.25**

Schemata to self-efficacy 0.01ns

Schemata to aggressive responseselection

0.26***

Schemata to aggression −0.02ns −0.07ns

Aggressive response generation toaggression

0.34***

*P < ., **P < ., ***P < .. ns, non-significant.†Model : model with response-decision process; Model : model with no response-decision process.

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sive behaviour of children with MID, which is in contrast to the assumptions underlying the SIP model (Dodge ; Crick & Dodge ). This was indeed found to hold for the children studied here. A model without response-decision process was found to fit the data significantly better than a model including the response-decision process. Aggressive response generation was found to directly affect aggressive behaviour in the children with MID studied here, and the response-decision process (i.e. evaluation, self-efficacy and selection of aggressive responses) was therefore not necessary to explain their aggressive behaviour.

An alternative explanation for the findings con-cerning response decision is a difference in the methods used to assess the response-decision process and aggressive response generation. The response selection variables were assessed with three behav-ioural solutions presented after the video vignettes while aggressive response generation was measured in terms of the child’s own spontaneous behaviour. It may be that these indicators of response evaluation based on video scenes do not relate as directly to the behaviour of the children with MID as the response generation assessment did. One methodological alternative may be to ask children to evaluate and select the responses they generated themselves. It should be noted, however, that Matthys et al. () found clear differences in the relevance of the response-decision process and the relations between response-decision and behaviour for a sample of par-ticipants without MID using the same instrument we did, which implies that the assessment of response decision may be valid.

Our hypothesis that children’s schemata regarding aggression would affect SIP and thereby aggressive behaviour was partially supported by the present results. We expected schemata regarding aggression to be directly linked to hostile interpretation, aggres-sive response generation, positive evaluation of aggressive responses, self-efficacy with regard to aggressive responses, and aggressive response selec-tion. Findings revealed effects of schemata on aggres-sive response generation and aggressive response decision, which is in keeping with the results of other studies (Burks et al. ; Zelli et al. ), and stresses the importance of studying social schemata in relation to SIP and aggression (Arsenio & Leme-rise ). In contrast to Zelli et al. () and

Dodge et al. (), however, no relation was found between schemata and hostile interpretation. This may be explained by the content of the schemata measure used in our study. The schemata assessed in our study concern normative beliefs about aggression and may thus relate more to the generation of responses than to the interpretation of other people’s behaviour. It is therefore possible that other schemata relate more specifically to the interpretation of other people’s behaviour than the presently assessed schemata.

In conclusion, aggressive behaviour in children with MID is related to encoding, hostile interpreta-tion and aggressive response generation as specified by the SIP model, and indirectly affected by social schemata. However, for these children the response-decision process seems not necessary in explaining aggressive behaviour.

While the model without response decision appears to fit the data from the present sample quite well, this does not mean that the model cannot be improved. It is likely that factors other than only the ones considered here play a role in SIP by children with MID. Given the large differences between MID children in their cognitive capacities, it may very well be that the exact processes involved in SIP differ between children with different cognitive strengths and weaknesses. Also, we have examined primarily cognitive processes and not emotional processes, which may nevertheless play an important role and therefore merit further consideration (Lemerise & Arsenio ). In boys referred for aggressive beha-viour problems, for example, the experimental induc-tion of a negative mood has been found to increase the attribution of hostile intent (Orobio de Castro et al. ).

We suggest that ego control, or impulsivity, may influence the course of SIP. Ego control and the regulation of attention and emotions have proven critical for understanding social behavior (Block & Block ; Lemerise & Arsenio ; Orobio de Castro et al. ). It has been shown that low levels of ego control (i.e. ego under control) are related to adjustment problems, especially externalizing beha-vior problems (Robins et al. ; Huey & Weisz ; Asendorpf & van Aken ). To process social information in the manner prescribed by the SIP model may require considerable ego control, as var-ious response options must be clearly controlled dur-

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ing the response-decision process. If such control is not exerted, the entire process of evaluation and explicit response selection may simply not occur with impulsive behavior and perhaps a higher incidence of aggressive behavior as a result. Thus, the steps of the response-decision process may simply not occur in individuals with limited ego control.

Low levels of ego control may be related to the omission of the response-decision process. Children who show under control of their emotions and beha-vior behave directly according to what comes in mind. They do know that there are more adequate forms of responding, if they take more time to think about these responses (Huesmann ; Anderson & Bushman ; Orobio de Castro et al. ), but their preemptive processing style may prevent them from generating and selecting these responses spon-taneously. Research is needed to investigate the role of ego control in relation with SIP and aggressive behaviour in children with MID.

What are the implications of the present findings for intervention? First, we found children with MID to select competent over aggressive responses when asked to do so in a SIP task, but found no relation between this skill and actual behaviour in the struc-tural model. Given that the response-decision pro-cess does not appear to be particularly relevant for the explanation of aggressive behaviour in children with MID, one can question the utility of training response-decision skills with this population. In cog-nitive behavioural intervention programmes for both children with and without MID (e.g. Greenberg et al. ; Lochman & Wells ), all SIP steps are explicitly trained. One may wonder whether the part of these programmes concerning response decision is useful for children with MID. Research indicated that training in SIP improves social-problem solving skills (O’Reilly et al. a,b) and decreases aggres-sive behaviour in people with MID (Taylor et al. ). It is, however, unclear whether all SIP steps contribute equally to the positive effect of the training.

Note, however, that the present results are based on structural relations so they cannot demonstrate causality. Only with experimental studies can we hope to definitely answer the question of whether the training of response decision is effective or not. Ide-ally, a randomized trial should be conducted with experimental manipulation of response-decision

training as the independent variable. Our finding that the response-decision process plays little or no role in the aggressive behaviour of children with MID begs the question in which children the response-decision process is related to aggressive behaviour at all. Perhaps children are less rational than the SIP model presumes? Empirical study of this issue with general population samples is clearly needed.

Acknowledgements

This study was supported by Hondsberg La Salle, OCB, Stichting St Anna/Gastenhof and Stichting De Bruggen, the Netherlands. The authors would like to thank the children, their teachers and the staff of the following institutes for their participation: De Bruggen, De Eik, Gastenhof, Groot Emaus, Honds-berg La Salle, Meilust, Orthopedagogisch Centrum Brabant (OCB), OPL and Van Arkel. The authors also gratefully acknowledge the contributions of their graduate students to data collection.

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Accepted 7 June