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An Ecobehavioral Assessment of a Special Education Classroom
Applied Printed
Research in Menfol Relordadon. in the USA. All rights reserved.
Vol. 5. pp. 395-408.
1984
0270.30!92/84
Copyright
TRAINING
AND
0
1984 Pcrgamon
$3.00+ .Oil Press Ltd.
DEMONSTRATION
An Ecobehavioral Assessment of a Special Education Classroom Stuart University
Vyse
of Rhode
/s/and
James A. Mulick Development
Child
Center,
Rhode
Hospital
Island
and
Brown
University
Program
in Medicine
and Brooks Dr.
/oseph
Exeter,
The presenr a correlarional behavioral
study
combined
analysis assessmen
in order insrtutnenr
a muliiple
M. Thayer H. Ladd Center Rhode
/s/and
caregory
lo [es1 rhe value and derermine
observarional
of such whether
data
a combination rhis package
sysrem
with
as an ecowould reveal
Requests for reprints may be addressed to: Stuart Vyse, Dept. of Psychology, University of Rhode Island, Kingston, RI 02881. This paper was presented in a somewhat different form at the 16th Annual Gatlinburg Conference on Research in Mental Retardation and Developmental Disabilities, March 16, 1983, Gatlinburg, Tennessee. James A. Mulick is now at Department of Pediatrics, Ohio State Univ., 700 Children’s Drive, Columbus, OH 43205. This research was supported in part by Maternal and Child Health Grant No. 01-H-000-109-09-0 to the Child Development Center and Title I Project No. 1430-519-901-7-07-72 to the Dr. Joseph H. Ladd Center. The authors are indebted to George Gunther, Lynda Kahn, and B. .I. Whaley for assistance in obtaining parental consent and institutional approval for this research, and to Siegfried M. Pueschel of the Child Development Center of Rhode Island Hospital for his support of this project. 395
396
S. Vyse, J. A. Mulick,
and B. M. Thayer
relationships both between and within the behavioral repetoires of several children. Four severely and profoundly mentally retarded children between the ages of 9 and 12 years were observed in their self-contained special education classroom, and the reacher collected the data over a 20-day period using an interval recording system. Bivariate correlational analysis of the 38 observed categories with the school-day as the unit of analysis revealed significant relationships both between and within children. Two children showed a pattern of relationships in which social interaction in either child was associated with maladaptive behavior in the other, and another child’s destructive and self-injurious behaviors were positively associated with his noncompliance. Patterns of related child behaviors suggested ways in which they may have been affected by the behavior of adults in the classroom. The value of such an instrument for ecological assessment is discussed.
A few early behavior analysis studies (Risley, 1968; Sajwaj, Twardosz, & Burke, 1972) demonstrated that interventions often have important “side effects” on non-target behaviors. This research, a concern for the adequate planning of interventions, and a desire to determine the etiologies of behavior problems have led some behavior analysts to advocate an “ecological psychology” or an “ecobehavioral” approach (Schroeder, Mulick, & Rojahn, 1980; Wahler & Fox, 1981; Willems, 1974, 1977) which is characterized by a conceptual and methodological expansion designed to more thoroughly assess the subject’s interaction with the environment. One of the important ingredients in this ecological approach is the observation of multiple response categories for both target and non-target behaviors and, in some cases, the recording of important setting events (Willems, 1977). A number of multicategory data systems have been developed (Maerov, Brummett, Patterson, & Reid, 1978; Wahler, House, & Stambaugh, 1976); however, while these coding systems provide for the collection of much valuable data, the appropriate method of analysis of this information is not always clear. In the field of mental retardation, a number of studies have employed multiple category data systems for recording several behaviors of a single subject or a group of subjects, but the methods of data analysis have varied widely. Lichstein and Wahler (1976) observed 16 categories of behavior in a single autistic child as well as six categories related to important setting characteristics. Observations were made in a structured school environment, an unstructured school environment, and at home. A cluster analysis revealed that the subject exhibited different response patterns in each of the three settings. This result suggests that patterns of responding within an individual change as a result of changes in the environmental conditions. In another study, Schroeder, Rojahn, and Mulick (1978) employed a data collection system consisting of 13 behavioral categories and six social stimulus codes to assessthe effect of various dosages of psychotropic medications on the behavior of a 16-year-old mentally retarded boy. In this case, ecobehavioral data were reported as multiple histograms indicating the percent occurrence of each category under each of the drug conditions. The results indicated that
Ecobehavioral Assessment
397
the drugs used had a general suppressive effect which was not correlated with the effects of behavioral procedures on the target response. In a similar study (Rojahn, Schroeder, & Mulick, 1980), seven behavioral categories and three social stimulus categories were scored in an analysis of the effects of two types of self-protective devices (SPDs) on three profoundly mentally retarded adults. In this case, the use of SPDs was associated with reduction of the target behaviors of pica and rectal digging; however, they also produced a reduction of such behaviors as work and appropriate play and an increase in stereotypy. As in the earlier study (Schroeder et al., 1978), ecobehavioral data were presented in several histograms indicating the percent occurrence of each category under each SPD condition. Finally, Schroeder, Kanoy, Mulick, Rojahn, Thios, Stephens, and Hawk (1982) used a 21 category data collection system in a day care unit for selfinjurious clients to assess the effects of a number of environmental conditions, such as high vs. low staff ratio, time of day, and the presence or absence of toys. In this case, data analysis involved a nonparametric partial correlation technique available as the MATPAR procedure in the Statistical Analysis System (SAS) (Johnson & Quade, 1980; Quade, 1974) which provided for an assessment of the effect of a single environmental variable upon each behavioral category. While a number of significant effects were found, the only environmental condition which produced a significant reduction in selfinjurious behavior (SIB) was the presence or absence of toys. In short, applied research in ecobehavioral analysis suggests that data collection systems employing multiple categories of behavior and setting characteristics show tremendous promise for the assessment of the multiple effects of behavioral interventions and the impact of broad environmental variables on adaptive and maladaptive behaviors. However, if ecobehavioral analysis is to have practical value in applied settings, several obstacles must be overcome. First, each of the studies above employed at least one non-participant observer, a resource which is not often available to schools and institutions. In order to be of wider value, ecological data systems must be adapted for use by teachers, teacher aides, school psychologists, and other support staff. Second, sophisticated statistical procedures such as cluster analysis and MATPAR are too unwieldly for applied use. The techniques for analysis of ecological data must be simple enough to be employed by clinical professionals, yet powerful enough to give a useful picture of classroom and residential environments. The goal of the present study was, therefore, to evaluate a behavioral assessment package consisting of a multicategory data system adapted for use by direct care staff and a relatively simple correlational analysis procedure. A second important goal was directed at a conceptual question within ecological psychology. The studies cited above used ecological coding systems either summed across several subjects or reported for a single subject under different treatment or environmental conditions. While these approaches represent major methodological advances, they do not address an important
398
S. Vyse. J. A. Mulick,
and B. M. Thayer
aspect of the conceptual expansion urged by Willems (1974) and Wahler and Fox (1981). When individuals share a common environment, such as a special education classroom, it follows that they will be confronted with many common stimuli. The manner in which they respond to these stimuli may be very different, but some behavioral covariation is to be expected. In addition, given the likely assumption that the other people in an environment represent especially salient stimuli, the behavior of one student may set the occasion for behavior in another. This view of the interplay of individual subjects within a common environment, if proven valid, would represent a fuller acknowledgement of the multiple determination of behavior implicit in the ecological approach. As a result, the question posed by this study can be summarized as follows: If this kind of behavioral covariation is present in classrooms for the severely and profoundly mentally retarded, is the present package sensitive enough to detect it? METHOD Subjects
The subjects were four members of a self-contained special education classroom within a state-operated residential facility for the mentally retarded. The class roster included a fifth client who was excluded from the study due to her frequent absences. The names used in this report are not the children’s actual names. Roy was a 12-year-old profoundly mentally retarded boy (Vineland Social Age Equivalent = 1.9 years) who had been institutionalized for five years. He exhibited a high frequency of self-injurious behaviors which typically involved rapidly hitting or slapping his face and other areas of his head. This behavior appeared to be equally probable in any setting and was frequently accompanied by tantrum-like outbursts which included screaming and out-of-seat behavior. Tim was a lo-year-old profoundly mentally retarded boy (Vineland Social Age Equivalent = 1.9 years) who had been institutionalized for six years. He exhibited a number of self-injurious behaviors including biting his fingers, hands, and wrists and pinching himself in the stomach, back, and face. He spent much of his time engaged in various forms of self-restraint which he accomplished by twisting his arms through and under his shirt or sweater or by wrapping a towel around his neck. Ed was a lo-year-old severely mentally retarded boy (Vineland Social Age Equivalent = 2.3 years) who had been institutionalized for 11 months. He ex-
Ecobehavioral Assessment
399
hibited infrequent tantrum-like outbursts which included screaming, stamping his feet, and pulling his hair. Occasionally he would aggressively grab staff members or other children. In a nonacademic situation, Ed spent the majority of his time playing with toys or engaged in stereotyped tapping of objects, flapping of his hands, and rocking. Sue was a 9-year-old severely mentally retarded girl (Vineland Social Age Equivalent = 2.3) who had been institutionalized for six months. She exhibited a variety of disruptive behaviors including wandering around the classroom, throwing objects, spitting, and aggressively grabbing other students and staff. Setting All observations were conducted entirely within the classroom in the morning between the hours of 9:30 a.m. and 12:00 p.m. and in the afternoon between 1:30 p.m. and 3:00 p.m. The room was approximately 9.0 m x 9.0 m and was divided roughly in half by partitions which allowed one side of the classroom to be designated as a play area and the other to be arranged with tables and chairs as the primary teaching area. Adjacent to the teaching area and behind another partition were a toilet and sink. Throughout the day the children were involved in a number of activities in all areas of the room. Often while some children were involved in group or individual instruction, others were engaged in free play or toileting. The teacher and the classroom aide spent most of their time in the teaching area of the classroom, from which vantage point students in both the teaching and play areas could be easily supervised; however, the arrangement of the room made it necessary for one of the adults to withdraw from the teaching area in order to monitor children who were in the toilet area. Roy’s disruptive SIB and stereotypy made it necessary for the classroom aide to spend the majority of her time in one-to-one instruction or supervision, while the teacher circulated among the other students providing instruction and reinforcing appropriate behavior. Observational
Procedures and Data Analysis
All data were collected by the teacher during the course of normal classroom operations. An adapted interval recording system was employed in which once every 10 min the teacher would observe and score the behavior of each child in turn. Ten-second observe and record intervals were employed in combination with a partial interval time sampling system in which all behaviors observed for any part of the 10 set period were scored. The 10 min periods between observations were timed on a kitchen timer, and 10 set observe and record intervals were timed on the classroom wall clock. Thus, each child was scored once in a fixed sequence at the end of each 10 min interval.
400
S. Vyse, J. A. Mulick.
and B. M. Thayer
A 10 category coding system was employed with each child, and for Tim and Sue an additional personal behavioral category was recorded. The coding system, which was a modified form of the instrument designed by Wahler, House, and Stambaugh (1976), is presented in Table 1. The frequency of each category of response was tallied for each child daily, and the results were divided by the total number of intervals scored and multiplied by 100 to produce the percent of occurrences per day. Since children were present for varying periods of the day, an additional variable (PR) was added to the analysis which reflected the percent of the total daily intervals during which each child was present. Finally, the percent occurrence of all 46 possible variables was submitted to analysis by bivariate correlation (Sal1 & Delong, 1982) to produce a 46 x 46 matrix of all possible subject/category correlations. Reliability
In order to assessthe reliability of teacher observations, a second observer scored a number of intervals on 11 of the 20 days of the study. On these days both the teacher and the observer scored the same children simultaneously using the same 10 set observe and 10 set record procedure. Percent agreement was calculated by the occurrence, nonoccurrence, and total agreement methods using the formula agreements + (agreements + disagreements) x 100.
Codes
and Definitions
TABLE 1. of Observational
Categories
Code
Definition
AGA AGC D ST
Aggression towards adults. Aggression towards children. Destructive behavior, including banging toys and tasks or tearing paper. Stereotypy: rocking, waving arms, flicking objects, staring at objects, making noises, etc. Self-injurious behavior, such as hitting self and biting self. Personal category (self restraint for Tim and spitting for Sue). Appropriate academic work. Appropriate social interaction, including hugs and physical affection. Time off: play time or time during which no structured activity was scheduled. Toileting. Noncompliance. This category could only be scored if academic work or toileting were the scheduled activities. Presence code.
SIB PC WP SI TO SL NC PR
Ecobehavioral Assessment
401
RESULTS
Table 2 shows the mean number and range of intervals scored per day for each child, as well as the number of days each child was present during the 20 days of the study. The number of intervals scored per day for an individual child varied as a function of the special services or other activities he or she was involved in outside of the classroom. Reliability was assessed for at least 18% of the total intervals for each child, and inter-observer agreement calculated across all behaviors was above 80% for all four children under the occurrence method and above 90% for the nonoccurrence and total agreement methods. An examination of individual behavioral categories for each child reveals the variability of percent of occurrence as well as inter-observer agreement across children and categories (see Table 3). Two low frequency categories (AGC and SI) were not scored by either observer during reliability sessions, and two others (AGA and D) were scored so infrequently that relatively few absolute disagreements produced lower occurrence agreement; however the reliability data calculated by the total agreement and nonoccurrence methods, in combination with the data in Table 2, suggests that inter-observer agreement is within acceptable limits for multiple category data systems. Substantial amounts of stereotypy were observed in all four children, and self-injury was observed in everyone but Sue, with Roy exhibiting SIB in 17% of all intervals. Only Ed and Sue exhibited social interaction (SI) and aggressive behaviors (AGA and AGC); however, for both children SI was observed in less than one percent of all intervals. Correlational
Analysis
Data collection yielded a total of 34 observed behavioral categories across all four children, which, with the four presence codes, produced a bivariate correlation matrix that was 38 x 38 variables. An initial concern in the analysis of a matrix of this size is the potential of such a large number of correlation coefficients (703) producing many significant correlations purely by chance. In order to assess this possibility, the number of observed correlations at various significance levels was compared to the number expected by chance alone. Table 4 shows that at the p < .05 level the number of observed correlations is only slightly higher than that expected by chance alone; however, as the p values decrease the observed correlations occur at greater relative frequencies until at thep < .OOl level 7.11 times as many correlations are observed than would be expected by chance. Based on the results summarized in Table 4, a critical value of )r 1 > .60 was chosen for acceptance of a correlation coefficient as clinically valid. This
S. Vyse, J. A. Mulick,
402
and B. M. Thayer
TABLE 2. Mean number and range of intervals scored per child per day, child attendance and percent of intervals during which interobserver agreement was assessed. Reliability data across all behaviors also is shown for each child. Data
Reliability
Collection
Child
Mean Intervals Per Day
Range
ROY Tim Ed Sue
12.1 13.1 13.7 13.0
7-17 8-17 9-20 7-17
Attendance (in days)
Percent of Overall Intervals Agreement
17 20 20 19
27 18 18 24
Data
Occurrence Agreement
95.0 93.1 95.1 98.2
Nonoccurrence Agreement
85.7 81.0 81.6 87.1
92.8 90.2 94.8 97.6
value was chosen because it was equivalent to ap value of < .Ol for TSbased on 17 days of data and p< .005 for 20 days of data. Correlation coefficients with values greater than .60 for each child are shown in Figure 1. Each child’s results are presented as a bar graph indicating the percentage of correlations between that child’s behaviors and behaviors of each of the other children. The results reveal some interesting differences in patterns of relationships for the four children. Ed and Sue showed correlations with three children, while Roy and Tim showed correlations with only two children. Differences in the patterns of correlated behaviors are further shown by
Reliability data for individual for each child.
categories Reliability
Category AGA AGC D ST SIB Pea WP Sl TO SL NC PR
Data
Overall Agreement
Occurrence Agreement
99. I 100.0 97.4 84.0 96.2 97.1 96.2 100.0 96.2 100.0 94.3 -
SO.0
*
33.3 13.9 78.3 93.5 90.0 * 90.0 100.0 88.4 -
aPC (Personal Category) was only *No occurrences of these categories
TABLE 3. and the mean percentages
Nonoccurrence Agreement 99.1 100.0 97.4 70.8 95.6 95.1 94.2 100.0 94.2 100.0 89.8 -
of occurrence
of each category
Mean
Percentage
ROY
Tim
Ed
Sue
0.0 0.0 0.0 69.1 17.5 21.5 0.0 54.3 12.6 18.4 69.4
0.0 0.0 1.0 42.4 4.6 83.6 15.4 0.0 65.2 II.3 9.6 88.4
0.4 0.7 0.4 75.2 3.1 4.4 0.8 61.0 14.6 21.4 92.1
4.5 I.2 1.9 29.8 0.0 4.2 27.8 0.4 48.9 8.3 12.1 83.3
employed for Tim and Sue. were scored by either observer
during
of Occurrence
reliability
sessions.
Ecobehavioral Assessment
Mean
number
TABLE 4. and observed correlations of observed to expected
of expected The ratio
at various significance is also shown.
Significance
Expected
aBased on a total
levels.
level
.05
.Ol
,005
.OOl
70.30 90 1.28
14.06 40 2.84
7.03 22 3.14
1.41 10 7.11
Source Expecteda Observed Observed:
403
of 38 observed
categories.
the striped bars, which indicate the percentage of each child’s “selfcorrelations.” “Other-correlations” are represented by the solid bars. Both Roy and Sue showed all of their correlations as relationships between their own behavior and that of another child and exhibited no correlations between different behaviors in their own repetoires. Ed showed 20% self- and 80% othercorrelations. In contrast to the other children, Tim’s correlations were evenly divided between self and other. Table 5 shows the specific correlations observed for each child (I rl > .60) and the observed r values. Note that the negative or positive r value is important to an understanding of the behavioral relationships. For example, the negative correlation between ST-R and SI-S indicates that on days when Roy showed less stereotypy, Sue had more social interaction, and vice versa. Conversely, the positive correlation between SIB-T and NC-T indicates that on days when Tim had more self-injurious behavior he also had more noncompliance. DISCUSSION
While the present study was exploratory in nature, it establishes that significant behavioral covariation within and between children existed in this classroom and that the data system was usable in the context of a functioning special education classroom. In addition, the types of relationships which have been revealed could be of great value in the functional analysis of behavior problems, as well as, in suggesting the possible effects of various interventions. However, the relationships between behaviors represented in Table 5 are correlational and not causal; and their interpretation must be approached with care. For instance, a positive relationship between a behavior of Roy’s and another behavior of Sue’s could have three possible explanations: 1) some aspect of Roy’s behavior may influence that of Sue’s; 2) some aspect of Sue’s behavior may influence that of Roy’s; or 3) some third stimulus (such as
404
S. Vyse, J. A. Mulick, and B. M. Thayer
ROY
TIM 60 40 20
i
0
‘RTES
I
1 RTES
SUE
ED 60
SELF IXSJ
OTHER -
FIGURE 1. Four histograms showing the percentages of each child’s total correlations which were shared with each of the other children in the study. The letters below each bar correspond to the initial of the child with whom correlations were shared. Striped bars represent self-correlations, and solid bars represent other-correlations. For all children, only correlations with an absolute value of .60 or greater are presented.
teacher behavior or another environmental condition) may influence the relative frequency of both behaviors. All of these explanations are possible with each of the correlations in the present study, making it important to use this method of analysis as a technique for selecting hypotheses regarding functional relationships which can then be tested through direct manipulation of environmental variables. It should also be mentioned that the criterion for accepting an r value has been based on the ratio of the number of observed correlations at a particular
- .646 - .647 ,605 ,612 - ,656 ,649
r D-T/NC-T D-T/SI-S SIB-T/NC-T SIB-T/ST-S WP-T/TO-T WP-T/PC-S
correlated behaviors
Tim
correlated
TABLE
,632 ,687’ .644 .682* - ,764’ ,615
r
behaviors
D-E/ST-R D-E/SL-R D-E/SI-S SI-E/NC-S TO-E/NC-E
Ed
observed
correlated behaviors
and
5.
- .646 ,605 1.000* ,624 - .736*
r
R values.*
AGC-S/NC-R ST-S/SIB-T PC-S/WI’-T SI-S/ST-R SI-S/SL-R SI-S/D-T SI-S/D-E NC-S/SLR NC-S/SI-E
correlated behaviors
Sue
,649 ,682 ,615 .647* .612 ,687’ 1.000* ,656 ,624
r
Nore: The letter following each behavioral category code is the initial of the child who showed that behavior, e.g. ST-R refers to stereotypy for Roy. aeach correlation coefficient involving the categories for two different children is listed for both children. *p< ,001
ST-R/D-E ST-R/SI-S SL-R/D-E SL-R/SI-S SL-R/NC-S NC-R/AGC-S
correlated behaviors
ROY
Individual
406
S. Vyse, J. A. Mulick, and B. M. Thayer
p value to the number expected by chance alone. A more conservative method
would be to divide thep level by the number of variables (38), which for these data would mean accepting only rs with p< .OOl (indicated by asterisks in Table 5). However the purpose of this technique is not to establish the statistical significance of any individual correlation coefficient, but rather to aid the planning of behavioral interventions. Therefore setting a criterion value based on the data in Table 2 is appropriate. Nevertheless, any interpretation based on observed correlations, regardless of the p level obtained, must be validated through the manipulation of controlling variables. These cautions notwithstanding, the present study leads to several plausible interpretations of the classroom’s behavioral ecology. First, several of the relationships in Table 5 help to identify some possible effects of teacher attention. For example the positive relationship between Sue’s stereotypy and Tim’s self-injurious behavior suggests that either the behaviors of the two children were directly related or that Sue’s stereotypy was controlled by adult responses to Tim’s SIB, since SIB was the more serious behavior and more likely to command teacher attention. In general, since Roy and Ed showed the highest rates of disruptive behavior (severe SIB) it is most likely that they, rather than the other two children, exerted the most control over adults and others in the room, and the overall pattern of that control is revealed in the present analysis. Tim’s behavior is related only to Sue’s, and Roy’s behavior is correlated with both Ed’s and Sue’s. A second important result involves the positive relationship between Tim’s two most disruptive behaviors (self-injury and destruction) and noncompliance. This pattern suggests that Tim’s SIB may be maintained by escape from academic demands. Nothing in the present analysis has revealed the contingencies maintaining Roy’s SIB, but the escape hypothesis is strongly supported for Tim and could easily be tested within the classroom. This interpretation is consistent with Carr’s (1977) theoretical review of self-injurious behavior which identifies escape as an important reinforcement contingency in some individuals. Finally a third valuable finding is the frequency and nature of correlations involving the category social interaction (SI). Of particular interest is the relationship between Ed and Sue with regard to SI. In both children this appropriate behavior was positively related with a maladaptive behavior in the other. This pattern in the data suggests an interesting view of the classroom ecology. Since social interaction in this classroom was almost exclusively childto-adult, rather than child-to-child, this relationship shows another possible effect of adult attention in the classroom: additional social interaction with one child occurred at the expense of increased maladaptive behavior in another. Once again the actual causal relationships involving social interaction are not established by the present analysis; however, the specific correlations
Ecobehavioral
407
Assessment
suggest that maladaptive behaviors increased in response to the shift of adult attention towards the student who was initiating social interaction. In conclusion, the present study has shown strong evidence for the value of an assessment instrument which combines an ecological data system with a relatively simple correlational analysis procedure. This method gives a much more complete view of classroom behavioral interactions than separate histograms such as those used by Rojahn et al. (1980) and Schroeder et al. (1978); yet it avoids the complicated cluster analysis and MATPAR procedures (Lichstein & Wahler, 1976; Schroeder et al., 1982). In addition, while the present study used the SAS package on a mainframe computer, this same approach could easily be performed using a microcomputer and any one of several commercially available statistical software packages. As these small computers appear in special education classrooms throughout the country, they can thus be used as behavior analysis tools in collaborative efforts by teachers and school psychologists as well as in direct applications with students in computer assisted instruction. Often the most salient stimuli in any environment are the people inhabiting it. Their interrelationships may have a marked effect on educational or therapeutic remedial strategies, in some cases facilitating planned outcomes and in others impeding them. As a result, a valuable aid to the planning of interventions will be practical assessment tools which reveal the nature of these interrelationships. An important future step in our examination of this correlational approach to ecological data will be the use of this system of analysis when observations are made of both clients and teachers. The addition of adult behavioral categories will make the important child-adult interactions more obvious than they are in the present study. Yet it seems clear that the methodological expansion suggested by Willems (1974) and others could be facilitated by similar forms of ecological-correlational analysis.
REFERENCES Carr,
E. G. The motivation Bulletin,
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of self-injurious
behavior:
A review
of some hypotheses.
Psychological
800-816.
Johnson, R. E.. & Quade, D. PROC MATPAR: a SAS procedure for calculating matched-pair partial correlations. Proceedings of the Fifth Annual SAS Users Group Conference, 1980, 188-193. Lichstein, K., & Wahler, R. The ecological assessment of an autistic child. Journal ofAbnormat
Child
Psychology,
1976,
4, 31-54.
Maerov, S. L., Brummett, B., Patterson, G. R., & Reid, J. B. Coding of family interactions. In J. B. Reid (Ed.) A Social Learning Approach to Family Intervention Volume2: Observation in Home Settings. Eugene, Oregon: Castalia Publishing Company, 1978. Quade, D. Nonparametric partial correlation. In H. M. Blalock (Ed.), Measurement in the Social Sciences. New York: Macmillan Press, 1974.
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S. Vyse, J. A. Mulick,
and B. M. Thayer
Risley, T. The effects and side effects of punishing the autistic behaviors of a deviant child. Journal of Applied Behavior Analysis, 1968, 1, 21-34. Rojahn, J., Schroeder, S. R., & Mulick, J. A. Ecological assessment of self-protective devices in three profoundly retarded adults. Journal of Autism and Development Disorders, 1980, 10, 59-66. Sajwaj, T., Twardosz, S., & Burke, M. Side effects of extinction procedures in remedial preschool. Journal of Applied Behavior Analysis, 1972, 5, 163-176. Sall, J. P., & Delong, D. M. PROC CORR. In SAS User’s Guide: Basics. Cary, N. C.: SAS Institute, Inc., 1982. Schroeder, S. R., Kanoy, J. R., Mulick, J. A., Rojahn, J., Thios, S. J., Stephens, M., & Hawk, B. The effects of the environment on programs for self-injurious behavior. In J. H. Hollis &C. E. Meyers (Eds.), Life Threatening Behavior: Analysis and Intervention. Washington, D.C.: American Association on Mental Deficiency, 1982. Schroeder, S. R., Mulick, J. A., & Rojahn, J. The definition, taxonomy, epidemiology, and ecology of self-injurious behavior. Journal of Autism and Development Disorders, 1980, 10,417-431. Schroeder, S. R., Rojahn, J., & Mulick, J. A. Ecobehavioral organization of developmental day care for chronically self-injurious. Journal of Pediatric Psychology, 1978, 3, 81-88. Wahler, R. G., & Fox, J. Setting events in applied behavior analysis: toward a conceptual and methodological expansion. Journal of Applied Behavior Analysis, 1981, 14, 327-338. Wahler, R. G.. House, A. E. & Stambaugh, E. E. Ecological Assessment of Child Problem Behavior. New York: Pergamon Press, 1976. Willems, E. P. Behavioral technology and behavioral ecology. Journal of Applied Behavior Analysis, 1974, 7, 151-165. Willems, E. P. Steps toward an ecobehavioral technology. In A. Rogers-Warren & S. Warren (Eds.), Ecological Perspectives in Behavior Analysis. Baltimore: University Park Press, 1977.
Research in Menfol Relordadon. in the USA. All rights reserved.
Vol. 5. pp. 395-408.
1984
0270.30!92/84
Copyright
TRAINING
AND
0
1984 Pcrgamon
$3.00+ .Oil Press Ltd.
DEMONSTRATION
An Ecobehavioral Assessment of a Special Education Classroom Stuart University
Vyse
of Rhode
/s/and
James A. Mulick Development
Child
Center,
Rhode
Hospital
Island
and
Brown
University
Program
in Medicine
and Brooks Dr.
/oseph
Exeter,
The presenr a correlarional behavioral
study
combined
analysis assessmen
in order insrtutnenr
a muliiple
M. Thayer H. Ladd Center Rhode
/s/and
caregory
lo [es1 rhe value and derermine
observarional
of such whether
data
a combination rhis package
sysrem
with
as an ecowould reveal
Requests for reprints may be addressed to: Stuart Vyse, Dept. of Psychology, University of Rhode Island, Kingston, RI 02881. This paper was presented in a somewhat different form at the 16th Annual Gatlinburg Conference on Research in Mental Retardation and Developmental Disabilities, March 16, 1983, Gatlinburg, Tennessee. James A. Mulick is now at Department of Pediatrics, Ohio State Univ., 700 Children’s Drive, Columbus, OH 43205. This research was supported in part by Maternal and Child Health Grant No. 01-H-000-109-09-0 to the Child Development Center and Title I Project No. 1430-519-901-7-07-72 to the Dr. Joseph H. Ladd Center. The authors are indebted to George Gunther, Lynda Kahn, and B. .I. Whaley for assistance in obtaining parental consent and institutional approval for this research, and to Siegfried M. Pueschel of the Child Development Center of Rhode Island Hospital for his support of this project. 395
396
S. Vyse, J. A. Mulick,
and B. M. Thayer
relationships both between and within the behavioral repetoires of several children. Four severely and profoundly mentally retarded children between the ages of 9 and 12 years were observed in their self-contained special education classroom, and the reacher collected the data over a 20-day period using an interval recording system. Bivariate correlational analysis of the 38 observed categories with the school-day as the unit of analysis revealed significant relationships both between and within children. Two children showed a pattern of relationships in which social interaction in either child was associated with maladaptive behavior in the other, and another child’s destructive and self-injurious behaviors were positively associated with his noncompliance. Patterns of related child behaviors suggested ways in which they may have been affected by the behavior of adults in the classroom. The value of such an instrument for ecological assessment is discussed.
A few early behavior analysis studies (Risley, 1968; Sajwaj, Twardosz, & Burke, 1972) demonstrated that interventions often have important “side effects” on non-target behaviors. This research, a concern for the adequate planning of interventions, and a desire to determine the etiologies of behavior problems have led some behavior analysts to advocate an “ecological psychology” or an “ecobehavioral” approach (Schroeder, Mulick, & Rojahn, 1980; Wahler & Fox, 1981; Willems, 1974, 1977) which is characterized by a conceptual and methodological expansion designed to more thoroughly assess the subject’s interaction with the environment. One of the important ingredients in this ecological approach is the observation of multiple response categories for both target and non-target behaviors and, in some cases, the recording of important setting events (Willems, 1977). A number of multicategory data systems have been developed (Maerov, Brummett, Patterson, & Reid, 1978; Wahler, House, & Stambaugh, 1976); however, while these coding systems provide for the collection of much valuable data, the appropriate method of analysis of this information is not always clear. In the field of mental retardation, a number of studies have employed multiple category data systems for recording several behaviors of a single subject or a group of subjects, but the methods of data analysis have varied widely. Lichstein and Wahler (1976) observed 16 categories of behavior in a single autistic child as well as six categories related to important setting characteristics. Observations were made in a structured school environment, an unstructured school environment, and at home. A cluster analysis revealed that the subject exhibited different response patterns in each of the three settings. This result suggests that patterns of responding within an individual change as a result of changes in the environmental conditions. In another study, Schroeder, Rojahn, and Mulick (1978) employed a data collection system consisting of 13 behavioral categories and six social stimulus codes to assessthe effect of various dosages of psychotropic medications on the behavior of a 16-year-old mentally retarded boy. In this case, ecobehavioral data were reported as multiple histograms indicating the percent occurrence of each category under each of the drug conditions. The results indicated that
Ecobehavioral Assessment
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the drugs used had a general suppressive effect which was not correlated with the effects of behavioral procedures on the target response. In a similar study (Rojahn, Schroeder, & Mulick, 1980), seven behavioral categories and three social stimulus categories were scored in an analysis of the effects of two types of self-protective devices (SPDs) on three profoundly mentally retarded adults. In this case, the use of SPDs was associated with reduction of the target behaviors of pica and rectal digging; however, they also produced a reduction of such behaviors as work and appropriate play and an increase in stereotypy. As in the earlier study (Schroeder et al., 1978), ecobehavioral data were presented in several histograms indicating the percent occurrence of each category under each SPD condition. Finally, Schroeder, Kanoy, Mulick, Rojahn, Thios, Stephens, and Hawk (1982) used a 21 category data collection system in a day care unit for selfinjurious clients to assess the effects of a number of environmental conditions, such as high vs. low staff ratio, time of day, and the presence or absence of toys. In this case, data analysis involved a nonparametric partial correlation technique available as the MATPAR procedure in the Statistical Analysis System (SAS) (Johnson & Quade, 1980; Quade, 1974) which provided for an assessment of the effect of a single environmental variable upon each behavioral category. While a number of significant effects were found, the only environmental condition which produced a significant reduction in selfinjurious behavior (SIB) was the presence or absence of toys. In short, applied research in ecobehavioral analysis suggests that data collection systems employing multiple categories of behavior and setting characteristics show tremendous promise for the assessment of the multiple effects of behavioral interventions and the impact of broad environmental variables on adaptive and maladaptive behaviors. However, if ecobehavioral analysis is to have practical value in applied settings, several obstacles must be overcome. First, each of the studies above employed at least one non-participant observer, a resource which is not often available to schools and institutions. In order to be of wider value, ecological data systems must be adapted for use by teachers, teacher aides, school psychologists, and other support staff. Second, sophisticated statistical procedures such as cluster analysis and MATPAR are too unwieldly for applied use. The techniques for analysis of ecological data must be simple enough to be employed by clinical professionals, yet powerful enough to give a useful picture of classroom and residential environments. The goal of the present study was, therefore, to evaluate a behavioral assessment package consisting of a multicategory data system adapted for use by direct care staff and a relatively simple correlational analysis procedure. A second important goal was directed at a conceptual question within ecological psychology. The studies cited above used ecological coding systems either summed across several subjects or reported for a single subject under different treatment or environmental conditions. While these approaches represent major methodological advances, they do not address an important
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and B. M. Thayer
aspect of the conceptual expansion urged by Willems (1974) and Wahler and Fox (1981). When individuals share a common environment, such as a special education classroom, it follows that they will be confronted with many common stimuli. The manner in which they respond to these stimuli may be very different, but some behavioral covariation is to be expected. In addition, given the likely assumption that the other people in an environment represent especially salient stimuli, the behavior of one student may set the occasion for behavior in another. This view of the interplay of individual subjects within a common environment, if proven valid, would represent a fuller acknowledgement of the multiple determination of behavior implicit in the ecological approach. As a result, the question posed by this study can be summarized as follows: If this kind of behavioral covariation is present in classrooms for the severely and profoundly mentally retarded, is the present package sensitive enough to detect it? METHOD Subjects
The subjects were four members of a self-contained special education classroom within a state-operated residential facility for the mentally retarded. The class roster included a fifth client who was excluded from the study due to her frequent absences. The names used in this report are not the children’s actual names. Roy was a 12-year-old profoundly mentally retarded boy (Vineland Social Age Equivalent = 1.9 years) who had been institutionalized for five years. He exhibited a high frequency of self-injurious behaviors which typically involved rapidly hitting or slapping his face and other areas of his head. This behavior appeared to be equally probable in any setting and was frequently accompanied by tantrum-like outbursts which included screaming and out-of-seat behavior. Tim was a lo-year-old profoundly mentally retarded boy (Vineland Social Age Equivalent = 1.9 years) who had been institutionalized for six years. He exhibited a number of self-injurious behaviors including biting his fingers, hands, and wrists and pinching himself in the stomach, back, and face. He spent much of his time engaged in various forms of self-restraint which he accomplished by twisting his arms through and under his shirt or sweater or by wrapping a towel around his neck. Ed was a lo-year-old severely mentally retarded boy (Vineland Social Age Equivalent = 2.3 years) who had been institutionalized for 11 months. He ex-
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399
hibited infrequent tantrum-like outbursts which included screaming, stamping his feet, and pulling his hair. Occasionally he would aggressively grab staff members or other children. In a nonacademic situation, Ed spent the majority of his time playing with toys or engaged in stereotyped tapping of objects, flapping of his hands, and rocking. Sue was a 9-year-old severely mentally retarded girl (Vineland Social Age Equivalent = 2.3) who had been institutionalized for six months. She exhibited a variety of disruptive behaviors including wandering around the classroom, throwing objects, spitting, and aggressively grabbing other students and staff. Setting All observations were conducted entirely within the classroom in the morning between the hours of 9:30 a.m. and 12:00 p.m. and in the afternoon between 1:30 p.m. and 3:00 p.m. The room was approximately 9.0 m x 9.0 m and was divided roughly in half by partitions which allowed one side of the classroom to be designated as a play area and the other to be arranged with tables and chairs as the primary teaching area. Adjacent to the teaching area and behind another partition were a toilet and sink. Throughout the day the children were involved in a number of activities in all areas of the room. Often while some children were involved in group or individual instruction, others were engaged in free play or toileting. The teacher and the classroom aide spent most of their time in the teaching area of the classroom, from which vantage point students in both the teaching and play areas could be easily supervised; however, the arrangement of the room made it necessary for one of the adults to withdraw from the teaching area in order to monitor children who were in the toilet area. Roy’s disruptive SIB and stereotypy made it necessary for the classroom aide to spend the majority of her time in one-to-one instruction or supervision, while the teacher circulated among the other students providing instruction and reinforcing appropriate behavior. Observational
Procedures and Data Analysis
All data were collected by the teacher during the course of normal classroom operations. An adapted interval recording system was employed in which once every 10 min the teacher would observe and score the behavior of each child in turn. Ten-second observe and record intervals were employed in combination with a partial interval time sampling system in which all behaviors observed for any part of the 10 set period were scored. The 10 min periods between observations were timed on a kitchen timer, and 10 set observe and record intervals were timed on the classroom wall clock. Thus, each child was scored once in a fixed sequence at the end of each 10 min interval.
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S. Vyse, J. A. Mulick.
and B. M. Thayer
A 10 category coding system was employed with each child, and for Tim and Sue an additional personal behavioral category was recorded. The coding system, which was a modified form of the instrument designed by Wahler, House, and Stambaugh (1976), is presented in Table 1. The frequency of each category of response was tallied for each child daily, and the results were divided by the total number of intervals scored and multiplied by 100 to produce the percent of occurrences per day. Since children were present for varying periods of the day, an additional variable (PR) was added to the analysis which reflected the percent of the total daily intervals during which each child was present. Finally, the percent occurrence of all 46 possible variables was submitted to analysis by bivariate correlation (Sal1 & Delong, 1982) to produce a 46 x 46 matrix of all possible subject/category correlations. Reliability
In order to assessthe reliability of teacher observations, a second observer scored a number of intervals on 11 of the 20 days of the study. On these days both the teacher and the observer scored the same children simultaneously using the same 10 set observe and 10 set record procedure. Percent agreement was calculated by the occurrence, nonoccurrence, and total agreement methods using the formula agreements + (agreements + disagreements) x 100.
Codes
and Definitions
TABLE 1. of Observational
Categories
Code
Definition
AGA AGC D ST
Aggression towards adults. Aggression towards children. Destructive behavior, including banging toys and tasks or tearing paper. Stereotypy: rocking, waving arms, flicking objects, staring at objects, making noises, etc. Self-injurious behavior, such as hitting self and biting self. Personal category (self restraint for Tim and spitting for Sue). Appropriate academic work. Appropriate social interaction, including hugs and physical affection. Time off: play time or time during which no structured activity was scheduled. Toileting. Noncompliance. This category could only be scored if academic work or toileting were the scheduled activities. Presence code.
SIB PC WP SI TO SL NC PR
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401
RESULTS
Table 2 shows the mean number and range of intervals scored per day for each child, as well as the number of days each child was present during the 20 days of the study. The number of intervals scored per day for an individual child varied as a function of the special services or other activities he or she was involved in outside of the classroom. Reliability was assessed for at least 18% of the total intervals for each child, and inter-observer agreement calculated across all behaviors was above 80% for all four children under the occurrence method and above 90% for the nonoccurrence and total agreement methods. An examination of individual behavioral categories for each child reveals the variability of percent of occurrence as well as inter-observer agreement across children and categories (see Table 3). Two low frequency categories (AGC and SI) were not scored by either observer during reliability sessions, and two others (AGA and D) were scored so infrequently that relatively few absolute disagreements produced lower occurrence agreement; however the reliability data calculated by the total agreement and nonoccurrence methods, in combination with the data in Table 2, suggests that inter-observer agreement is within acceptable limits for multiple category data systems. Substantial amounts of stereotypy were observed in all four children, and self-injury was observed in everyone but Sue, with Roy exhibiting SIB in 17% of all intervals. Only Ed and Sue exhibited social interaction (SI) and aggressive behaviors (AGA and AGC); however, for both children SI was observed in less than one percent of all intervals. Correlational
Analysis
Data collection yielded a total of 34 observed behavioral categories across all four children, which, with the four presence codes, produced a bivariate correlation matrix that was 38 x 38 variables. An initial concern in the analysis of a matrix of this size is the potential of such a large number of correlation coefficients (703) producing many significant correlations purely by chance. In order to assess this possibility, the number of observed correlations at various significance levels was compared to the number expected by chance alone. Table 4 shows that at the p < .05 level the number of observed correlations is only slightly higher than that expected by chance alone; however, as the p values decrease the observed correlations occur at greater relative frequencies until at thep < .OOl level 7.11 times as many correlations are observed than would be expected by chance. Based on the results summarized in Table 4, a critical value of )r 1 > .60 was chosen for acceptance of a correlation coefficient as clinically valid. This
S. Vyse, J. A. Mulick,
402
and B. M. Thayer
TABLE 2. Mean number and range of intervals scored per child per day, child attendance and percent of intervals during which interobserver agreement was assessed. Reliability data across all behaviors also is shown for each child. Data
Reliability
Collection
Child
Mean Intervals Per Day
Range
ROY Tim Ed Sue
12.1 13.1 13.7 13.0
7-17 8-17 9-20 7-17
Attendance (in days)
Percent of Overall Intervals Agreement
17 20 20 19
27 18 18 24
Data
Occurrence Agreement
95.0 93.1 95.1 98.2
Nonoccurrence Agreement
85.7 81.0 81.6 87.1
92.8 90.2 94.8 97.6
value was chosen because it was equivalent to ap value of < .Ol for TSbased on 17 days of data and p< .005 for 20 days of data. Correlation coefficients with values greater than .60 for each child are shown in Figure 1. Each child’s results are presented as a bar graph indicating the percentage of correlations between that child’s behaviors and behaviors of each of the other children. The results reveal some interesting differences in patterns of relationships for the four children. Ed and Sue showed correlations with three children, while Roy and Tim showed correlations with only two children. Differences in the patterns of correlated behaviors are further shown by
Reliability data for individual for each child.
categories Reliability
Category AGA AGC D ST SIB Pea WP Sl TO SL NC PR
Data
Overall Agreement
Occurrence Agreement
99. I 100.0 97.4 84.0 96.2 97.1 96.2 100.0 96.2 100.0 94.3 -
SO.0
*
33.3 13.9 78.3 93.5 90.0 * 90.0 100.0 88.4 -
aPC (Personal Category) was only *No occurrences of these categories
TABLE 3. and the mean percentages
Nonoccurrence Agreement 99.1 100.0 97.4 70.8 95.6 95.1 94.2 100.0 94.2 100.0 89.8 -
of occurrence
of each category
Mean
Percentage
ROY
Tim
Ed
Sue
0.0 0.0 0.0 69.1 17.5 21.5 0.0 54.3 12.6 18.4 69.4
0.0 0.0 1.0 42.4 4.6 83.6 15.4 0.0 65.2 II.3 9.6 88.4
0.4 0.7 0.4 75.2 3.1 4.4 0.8 61.0 14.6 21.4 92.1
4.5 I.2 1.9 29.8 0.0 4.2 27.8 0.4 48.9 8.3 12.1 83.3
employed for Tim and Sue. were scored by either observer
during
of Occurrence
reliability
sessions.
Ecobehavioral Assessment
Mean
number
TABLE 4. and observed correlations of observed to expected
of expected The ratio
at various significance is also shown.
Significance
Expected
aBased on a total
levels.
level
.05
.Ol
,005
.OOl
70.30 90 1.28
14.06 40 2.84
7.03 22 3.14
1.41 10 7.11
Source Expecteda Observed Observed:
403
of 38 observed
categories.
the striped bars, which indicate the percentage of each child’s “selfcorrelations.” “Other-correlations” are represented by the solid bars. Both Roy and Sue showed all of their correlations as relationships between their own behavior and that of another child and exhibited no correlations between different behaviors in their own repetoires. Ed showed 20% self- and 80% othercorrelations. In contrast to the other children, Tim’s correlations were evenly divided between self and other. Table 5 shows the specific correlations observed for each child (I rl > .60) and the observed r values. Note that the negative or positive r value is important to an understanding of the behavioral relationships. For example, the negative correlation between ST-R and SI-S indicates that on days when Roy showed less stereotypy, Sue had more social interaction, and vice versa. Conversely, the positive correlation between SIB-T and NC-T indicates that on days when Tim had more self-injurious behavior he also had more noncompliance. DISCUSSION
While the present study was exploratory in nature, it establishes that significant behavioral covariation within and between children existed in this classroom and that the data system was usable in the context of a functioning special education classroom. In addition, the types of relationships which have been revealed could be of great value in the functional analysis of behavior problems, as well as, in suggesting the possible effects of various interventions. However, the relationships between behaviors represented in Table 5 are correlational and not causal; and their interpretation must be approached with care. For instance, a positive relationship between a behavior of Roy’s and another behavior of Sue’s could have three possible explanations: 1) some aspect of Roy’s behavior may influence that of Sue’s; 2) some aspect of Sue’s behavior may influence that of Roy’s; or 3) some third stimulus (such as
404
S. Vyse, J. A. Mulick, and B. M. Thayer
ROY
TIM 60 40 20
i
0
‘RTES
I
1 RTES
SUE
ED 60
SELF IXSJ
OTHER -
FIGURE 1. Four histograms showing the percentages of each child’s total correlations which were shared with each of the other children in the study. The letters below each bar correspond to the initial of the child with whom correlations were shared. Striped bars represent self-correlations, and solid bars represent other-correlations. For all children, only correlations with an absolute value of .60 or greater are presented.
teacher behavior or another environmental condition) may influence the relative frequency of both behaviors. All of these explanations are possible with each of the correlations in the present study, making it important to use this method of analysis as a technique for selecting hypotheses regarding functional relationships which can then be tested through direct manipulation of environmental variables. It should also be mentioned that the criterion for accepting an r value has been based on the ratio of the number of observed correlations at a particular
- .646 - .647 ,605 ,612 - ,656 ,649
r D-T/NC-T D-T/SI-S SIB-T/NC-T SIB-T/ST-S WP-T/TO-T WP-T/PC-S
correlated behaviors
Tim
correlated
TABLE
,632 ,687’ .644 .682* - ,764’ ,615
r
behaviors
D-E/ST-R D-E/SL-R D-E/SI-S SI-E/NC-S TO-E/NC-E
Ed
observed
correlated behaviors
and
5.
- .646 ,605 1.000* ,624 - .736*
r
R values.*
AGC-S/NC-R ST-S/SIB-T PC-S/WI’-T SI-S/ST-R SI-S/SL-R SI-S/D-T SI-S/D-E NC-S/SLR NC-S/SI-E
correlated behaviors
Sue
,649 ,682 ,615 .647* .612 ,687’ 1.000* ,656 ,624
r
Nore: The letter following each behavioral category code is the initial of the child who showed that behavior, e.g. ST-R refers to stereotypy for Roy. aeach correlation coefficient involving the categories for two different children is listed for both children. *p< ,001
ST-R/D-E ST-R/SI-S SL-R/D-E SL-R/SI-S SL-R/NC-S NC-R/AGC-S
correlated behaviors
ROY
Individual
406
S. Vyse, J. A. Mulick, and B. M. Thayer
p value to the number expected by chance alone. A more conservative method
would be to divide thep level by the number of variables (38), which for these data would mean accepting only rs with p< .OOl (indicated by asterisks in Table 5). However the purpose of this technique is not to establish the statistical significance of any individual correlation coefficient, but rather to aid the planning of behavioral interventions. Therefore setting a criterion value based on the data in Table 2 is appropriate. Nevertheless, any interpretation based on observed correlations, regardless of the p level obtained, must be validated through the manipulation of controlling variables. These cautions notwithstanding, the present study leads to several plausible interpretations of the classroom’s behavioral ecology. First, several of the relationships in Table 5 help to identify some possible effects of teacher attention. For example the positive relationship between Sue’s stereotypy and Tim’s self-injurious behavior suggests that either the behaviors of the two children were directly related or that Sue’s stereotypy was controlled by adult responses to Tim’s SIB, since SIB was the more serious behavior and more likely to command teacher attention. In general, since Roy and Ed showed the highest rates of disruptive behavior (severe SIB) it is most likely that they, rather than the other two children, exerted the most control over adults and others in the room, and the overall pattern of that control is revealed in the present analysis. Tim’s behavior is related only to Sue’s, and Roy’s behavior is correlated with both Ed’s and Sue’s. A second important result involves the positive relationship between Tim’s two most disruptive behaviors (self-injury and destruction) and noncompliance. This pattern suggests that Tim’s SIB may be maintained by escape from academic demands. Nothing in the present analysis has revealed the contingencies maintaining Roy’s SIB, but the escape hypothesis is strongly supported for Tim and could easily be tested within the classroom. This interpretation is consistent with Carr’s (1977) theoretical review of self-injurious behavior which identifies escape as an important reinforcement contingency in some individuals. Finally a third valuable finding is the frequency and nature of correlations involving the category social interaction (SI). Of particular interest is the relationship between Ed and Sue with regard to SI. In both children this appropriate behavior was positively related with a maladaptive behavior in the other. This pattern in the data suggests an interesting view of the classroom ecology. Since social interaction in this classroom was almost exclusively childto-adult, rather than child-to-child, this relationship shows another possible effect of adult attention in the classroom: additional social interaction with one child occurred at the expense of increased maladaptive behavior in another. Once again the actual causal relationships involving social interaction are not established by the present analysis; however, the specific correlations
Ecobehavioral
407
Assessment
suggest that maladaptive behaviors increased in response to the shift of adult attention towards the student who was initiating social interaction. In conclusion, the present study has shown strong evidence for the value of an assessment instrument which combines an ecological data system with a relatively simple correlational analysis procedure. This method gives a much more complete view of classroom behavioral interactions than separate histograms such as those used by Rojahn et al. (1980) and Schroeder et al. (1978); yet it avoids the complicated cluster analysis and MATPAR procedures (Lichstein & Wahler, 1976; Schroeder et al., 1982). In addition, while the present study used the SAS package on a mainframe computer, this same approach could easily be performed using a microcomputer and any one of several commercially available statistical software packages. As these small computers appear in special education classrooms throughout the country, they can thus be used as behavior analysis tools in collaborative efforts by teachers and school psychologists as well as in direct applications with students in computer assisted instruction. Often the most salient stimuli in any environment are the people inhabiting it. Their interrelationships may have a marked effect on educational or therapeutic remedial strategies, in some cases facilitating planned outcomes and in others impeding them. As a result, a valuable aid to the planning of interventions will be practical assessment tools which reveal the nature of these interrelationships. An important future step in our examination of this correlational approach to ecological data will be the use of this system of analysis when observations are made of both clients and teachers. The addition of adult behavioral categories will make the important child-adult interactions more obvious than they are in the present study. Yet it seems clear that the methodological expansion suggested by Willems (1974) and others could be facilitated by similar forms of ecological-correlational analysis.
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Johnson, R. E.. & Quade, D. PROC MATPAR: a SAS procedure for calculating matched-pair partial correlations. Proceedings of the Fifth Annual SAS Users Group Conference, 1980, 188-193. Lichstein, K., & Wahler, R. The ecological assessment of an autistic child. Journal ofAbnormat
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Maerov, S. L., Brummett, B., Patterson, G. R., & Reid, J. B. Coding of family interactions. In J. B. Reid (Ed.) A Social Learning Approach to Family Intervention Volume2: Observation in Home Settings. Eugene, Oregon: Castalia Publishing Company, 1978. Quade, D. Nonparametric partial correlation. In H. M. Blalock (Ed.), Measurement in the Social Sciences. New York: Macmillan Press, 1974.
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Risley, T. The effects and side effects of punishing the autistic behaviors of a deviant child. Journal of Applied Behavior Analysis, 1968, 1, 21-34. Rojahn, J., Schroeder, S. R., & Mulick, J. A. Ecological assessment of self-protective devices in three profoundly retarded adults. Journal of Autism and Development Disorders, 1980, 10, 59-66. Sajwaj, T., Twardosz, S., & Burke, M. Side effects of extinction procedures in remedial preschool. Journal of Applied Behavior Analysis, 1972, 5, 163-176. Sall, J. P., & Delong, D. M. PROC CORR. In SAS User’s Guide: Basics. Cary, N. C.: SAS Institute, Inc., 1982. Schroeder, S. R., Kanoy, J. R., Mulick, J. A., Rojahn, J., Thios, S. J., Stephens, M., & Hawk, B. The effects of the environment on programs for self-injurious behavior. In J. H. Hollis &C. E. Meyers (Eds.), Life Threatening Behavior: Analysis and Intervention. Washington, D.C.: American Association on Mental Deficiency, 1982. Schroeder, S. R., Mulick, J. A., & Rojahn, J. The definition, taxonomy, epidemiology, and ecology of self-injurious behavior. Journal of Autism and Development Disorders, 1980, 10,417-431. Schroeder, S. R., Rojahn, J., & Mulick, J. A. Ecobehavioral organization of developmental day care for chronically self-injurious. Journal of Pediatric Psychology, 1978, 3, 81-88. Wahler, R. G., & Fox, J. Setting events in applied behavior analysis: toward a conceptual and methodological expansion. Journal of Applied Behavior Analysis, 1981, 14, 327-338. Wahler, R. G.. House, A. E. & Stambaugh, E. E. Ecological Assessment of Child Problem Behavior. New York: Pergamon Press, 1976. Willems, E. P. Behavioral technology and behavioral ecology. Journal of Applied Behavior Analysis, 1974, 7, 151-165. Willems, E. P. Steps toward an ecobehavioral technology. In A. Rogers-Warren & S. Warren (Eds.), Ecological Perspectives in Behavior Analysis. Baltimore: University Park Press, 1977.