patterns amongst behavior states, sociocommunicative, and activity variables in educational programs...

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Journal of Developmental and Physical Disabilities [jodd] pp1131-jodd-481633 March 22, 2004 10:56 Style file version June 18th, 2002

Journal of Developmental and Physical Disabilities, Vol. 16, No. 2, June 2004 ( C© 2004)

Patterns Amongst Behavior States,Sociocommunicative, and Activity Variablesin Educational Programs for StudentsWith Profound and Multiple Disabilities

Michael Arthur1,2

Recent investigations into the behavior states of students with profound andmultiple disabilities have underlined the importance of better understandingthe part that educational variables play in relation to levels of individual alert-ness and involvement. In the study reported here, 10 students in this populationwere observed for a full day each and detailed, noncontinuous informationwas collected on several contextual factors and student behavior states, usingan interval recording technique. This paper examines transitional probabili-ties for observed student behavior states over time and also explores potentialrelationships between states, communicative conditions and activities takingplace in educational settings. These sequential estimates provide evidence ofstate stability and positive relationships amongst student engagement and ac-tive communicative and social learning environments. Implications for prac-tice and further research are discussed.

KEY WORDS: profound and multiple disability; behavior states; transitional probabilities;social; communication.

INTRODUCTION

Although not without controversy on methodological grounds (seeGuess et al., 1998; Mudford et al., 1997), the systematic assessment of be-havior states in students who experience profound and multiple disabilities

1Special Education Centre, The University of Newcastle, Callaghan, NSW, Australia.2To whom correspondence should be addressed at Special Education Centre, TheUniversity of Newcastle, University Drive, Callaghan, 2308, NSW, Australia; e-mail:[email protected].

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126 Arthur

has emerged over the past decade as an important development in the ed-ucation and support of this population. Within this database, the nature ofsequences amongst and within variables has been identified as an importantarea for attention by researchers and practitioners alike. Guess et al. (1993a),for example, investigated several facets of behavior state conditions and thenature of the relationship between these data and a number of environmen-tal variables (Guess et al., 1993a). The behavior state definitions Guess andhis team had developed and reliably implemented in earlier studies (Guesset al., 1988, 1990) were again used in this investigation. In addition, a compre-hensive set of environmental codes was introduced. The codes were basedon a variety of sources and were grounded in the events and conditions oc-curring in typical classrooms and schools that serve students with the mostsevere and multiple disabilities.

The resultant data set was subjected to lag sequential analysis to exam-ine behavior state sequences and more specifically, the probability of par-ticular states following each other (transitional probabilities) in a mannerother than that expected by chance. Two particular patterns were identified,both comprising sequences that were statistically significant (p < .01). Thefirst of these was the relationship between the states of asleep–active (AA),asleep–inactive (AI), and drowsy (DR), with each of these states likely tobe followed by either of the others. While not particularly surprising, thisfinding does clearly demonstrate the interactive power of the sleep and DRstates in relation to each other.

The second pattern involved the awake–inactive–alert (AWIA) stateand its role “as a strong pivotal behavior” (Guess et al., 1993a, p. 640) inrelation to four other states; namely, crying (CR)/agitation, awake–active–alert (AWAA)/stereotypy, daze, and AWAA. As Guess et al. (1993a) havesuggested, a student is likely to move through the awake-inactive-alert stateas part of the process of changing from one of the related states (e.g., daze) toanother. This is an important finding in terms of the extant research knowl-edge pertaining to behavior states demonstrated by students who have themost severe and multiple disabilities. However, it also has particular implica-tions for the design of educational interventions by highlighting the centralplace of the AWIA condition in the process of state change and relatedly,the value of staff training in the identification and support of optimal states(see, e.g., Project ABLE; Ault et al., 1995).

In a second analysis, attention was paid to the question of behavior statecycles over longer periods of time (across the approximately 5-hr observa-tional period). That is, did the data indicate that states were demonstrated inregular, time-dependent cycles? While the researchers concluded that suchtemporal cycles were not evident, this time-series approach yielded a par-ticularly interesting finding in relation to the rate of state change observed

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Patterns Amongst Behavior States, Sociocommunicative, and Activity Variables 127

in the sample of 25 students. Guess et al. (1993a) provided evidence of fourchange periods, namely, 21, 22, 24, and 32 s. In other words, for the studentsobserved in this study, a change in state could be expected within these pe-riodicity classes. This significant finding, which contrasts with the findings ofRichards and Sternberg (1992), has a number of implications for those pro-viding educational support for members of this group (Guess et al., 1993a).In positive terms this may mean that movement from less desirable states(e.g., daze) to more interactive states (e.g., AWAA) will be facilitated by thepassage of time as well as particular intervention strategies. On the otherhand, difficulties in maintaining states that support attentiveness and re-sponsiveness, such as AWAA, may act to undermine attempts to promoteon-task behavior (Guess et al., 1993a). Nevertheless, as one aspect of a com-plex and dynamic process, rate of change in behavior states demonstratedby this population is clearly an important area for further investigation.

In the third and final analysis reported by Guess et al. (1993a) the na-ture of the relationship between observed behavior states and the definedenvironmental variables was explored, using correspondence analysis. In ad-dition to the eight behavior state codes, four environmental categories wereutilized: Interaction/no interaction (and type of activity when an interactiontook place), level of class activity, materials availability, and student position-ing (Guess et al., 1993a, p. 646). Correspondence analysis was used to firstidentify degree of association between various state conditions and environ-mental events, and subsequently to evaluate the effect of grouping particularvariables from the latter category. Nine combinations of environmental vari-ables were shown to be of particular interest, each responsible for at least3% of the nonrandom association with behavioral states. Overall, these nineenvironmental combinations explained 58% of the total association withthe behavior states (Guess et al., 1993a). Four behavior state conditionswere found to account for 79% of the overall association with environmen-tal events in the following proportions: AWAA (29%), AI (25%), awake–active–stereotypy (13%), and CR–agitated (12%; Guess et al., 1993a).

Although there were several very important patterns evident in thisexploratory analysis, only one is considered here. That is, there appeared tobe a definite relationship (.20 of the total association) between the AWAAstate and the first environmental combination (#1) described by Guess et al.(1993a). This grouping of variables was composed of an adult self-help in-teraction, in close proximity with moderate levels of classroom activity, thepresence of materials, and the student in the seated position. This combi-nation was one of only three (of a total of nine that satisfied the minimum3% association criteria) that included adult interaction as a component vari-able. One of these (#7) was identical to the one listed above (#1) exceptwith regard to the level of classroom activity (inactive) and accounted for

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128 Arthur

only .03 of the total association. However, both combinations were asso-ciated with the AWAA state. The other environmental combination thatincluded interaction (#8 in the Guess et al. study) also accounted for a verysmall amount of the total association with behavior states (.03) and differedfrom #7 only in relation to the type of activity taking place when an in-teraction occurred (play/instructional/other). Results of a major replicationstudy (Guess et al., 1993b, 1995; Guess and Siegel-Causey, 1995) were consis-tent with those reported in previous investigations (Guess et al., 1990, 1991,1993a). These findings, along with those of Richards and Sternberg (1992),underline the need for additional investigations into the potential connec-tion between optimal behavior states and aspects of the human ecologies,especially sociocommunicative interactions, experienced by students in ed-ucational programs.

This paper presents one aspect of a study that sought to explore patternsamongst observed student behavior states and several sociocommunicativevariables in a sample of Australian schools (Arthur, 1998, 2003). It is criticalto note that the type of observational recording technique used in this in-vestigation (10-s observation, 10-s recording) does not reflect continuity. Inlight of the amount of data collected for each student (between 870 and 900points for each person), it is most helpful to analyze these data as approxima-tions, or estimates of possible relationships, rather than actual, uninterruptedsequences of information. Specifically, attention was paid to the nature ofpatterns amongst

• behavior states when preceded by the same or another behavior state;• behavior states following a communicative condition;• communicative conditions following a particular behavior state;• behavior states following specific activities.

METHOD

Participants

Detailed information about participants, instrumentation, and proce-dures for this study has been provided elsewhere (Arthur, 1998, 2003). Tenschool-aged students with profound and multiple disabilities (age range 4–18years) were identified, following independent ratings and 80% concurrenceby teachers and researchers on five adaptive criteria listed in the relevantliterature (Guess et al., 1991, 1993a,b,c). The criteria were (a) severe mo-toric difficulties, (b) apparent lack of involvement with the environment,(c) nonverbal, (d) sensory loss, and (e) dependence on others to meet basicdaily needs. In addition, each student had been educationally identified with

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a severe intellectual disability. Specific information about the participantsis provided in Table I. Parental/primary caregiver consents to participationwere obtained, along with written agreement to observation by teachers andparaprofessionals.

Instrumentation

The main instrument used in the study was the Daily ObservationalGrid, developed as part of earlier piloting and minor validation phases ofthe investigation. This grid provided a means of recording detailed informa-tion about student behavior states and aspects of the observed educationalcontext, including setting, with a period of 5 min divided into 15 equal inter-vals (10-s observation, 10-s recording).

Behavior States

A total of nine behavior state codes and definitions (excluding seizures)were used, on the basis of the extant literature and preliminary phases ofthe project (Arthur, 1998; Guess et al., 1991, 1993a). These behavior statesare listed in the Appendix.

Contextual Variables

Several contextual factors were developed over a period of severalyears, reflecting phenomena observed in classrooms serving members ofthe population of students with profound and multiple disabilities. Theseincluded communication indicators, communication partner, activity, posi-tion, an index of social grouping, and the broad setting for the observation(Appendix; Arthur, 1998, 2003). The need for further collection of dataabout the nature of the relationship between student states and factors inthe human ecology of the school and classroom provided the rationale forthe development of these codes, using field observations and the relevantresearch literature as a starting point (see, e.g., Arthur, 1998; Guess et al.,1991; Richards and Richards, 1997).

Procedures

Each student was observed for one entire school day to record behaviorstates, communicative conditions and partners, activity, social grouping, bod-ily positioning and setting. A 10-s observation, 10-s recording rest technique

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131

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132 Arthur

was employed and checks of interobserver agreement levels were conductedduring a randomly distributed 25% of observational intervals throughouteach day. For each variable, a judgment was made about the preceding 10-speriod in terms of the predominant condition observed. For example, if astudent was judged to be in the dazed state for the majority of the 10-sinterval, this was the code noted on the scoring sheet.

This produced between 870 and 900 data points for each student, de-pending on how long the student was at school on that day (up to 5 hr). Theonly exception to this protocol was in relation to communication indicators,with a partner or student cue (SC) scored for an interval when one instancewas observed. Finally, background information on medical conditions, drugdosages, and related characteristics was also collected (Table I).

Preparation of Data for Analysis

A series of steps was followed in the preparation of the observationaldata for analysis (Arthur, 1998). These steps included double entry, the pool-ing of data from the 10 participants into one continuous file, the deletion ofindividual identifying lines, and a process of lagging selected variables. Datawere pooled to analyze emergent patterns across the sample of 10 students,with attention paid to the question of distribution and size of frequenciesamongst participants.

To ensure that small or irrelevant frequencies were not included insubsequent analyzes, the following categories of variables were recoded orexcluded. All of the remaining frequencies were sufficiently large or welldistributed across participants and were therefore retained for analysis.

Behavior States

The category of awake–active self-injury was excluded because of theabsence of any recorded intervals in the final data collection phase. In ad-dition, “seizures” was treated as a nonstate category, consistent with theprocedures described by Guess et al. (1993a). As a result, eight behaviorstates were included in the analysis reported here.

Activity

No instances of vocational activities were recorded across the 10 par-ticipants and thus the category was excluded from further analysis. Lowfrequencies and poor distribution of the conceptual and therapy-relatedcodes resulted in their exclusion from subsequent examination. Last, the

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movement/transition category was removed from the investigation in an ef-fort to focus on definite categories (provision or nonprovision of a particularactivity). These omissions produced three categories within the activity vari-able, namely independent living (IL), leisure (LE), and no activity (NA).

Data Analysis of Broad Patterns Amongst BehaviorStates and Contextual Variables

On the basis of the existing literature in this area and the two concep-tual models for the investigation (Arthur, 1998), a log-linear approach tolag analysis was introduced in relation to patterns within and amongst vari-ables. Four sets of variables were selected for analysis: (a) Behavior state–Behavior state, (b) Behavior state–Communication indicators, (c) Commu-nication indicators–Behavior state, and finally (d) Behavior state–Activity.In each instance, the aim of the analysis was to examine patterns in thecategories of the first variable over six lags as a function of the factors com-prising the second variable. For example, in (b) above, attention was paidto the overall changes in demonstrated student behavior states (throughone to six lags) when specific communicative events occurred. An importantissue at this juncture was the interpretation of statistics produced in suchanalyses.

Interpretation of Log-Linear Statistics

Several writers have argued that in the log-linear model, adjusted resid-uals approximate the z statistic in terms of a normal distribution, thus allow-ing examination of distance from (or deviation) from the mean (Bakemanet al., 1995; Haberman, 1973). In other words, the adjusted residual can behelpful in examining the degree to which observed values are similar to ordifferent from those expected, beyond the level expected by chance. In a lagsequential analysis, the observed values are referred to as transitional prob-abilities and indicate the likelihood of one event (target) following another(antecedent), across specified time lags (Bakeman and Gottman, 1997).

The importance of having sufficient data in each contingency table tojustify conclusions regarding other than chance events cannot be underlinedstrongly enough. Although several categories in the present investigationhave already been excluded because of low frequencies, the interpreta-tion of these data must reflect caution with respect to the overall distri-bution of scores. Furthermore, the possibility of a Type I error, whereby achance finding is treated as statistically significant, along with the exploratorynature of the investigation justifies the introduction of a conservative test of

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134 Arthur

significance. To this end, the Bonferroni adjustment technique was appliedto the analysis of data reported in the following tables.

Bonferroni Adjustment Technique

This approach allows the researcher to reduce the possibility of a Type Ierror by dividing the selected measure of statistical significance by the num-ber of tests or computations to be carried out on a body of data (Hays, 1981;Huck and Cormier, 1996). In the following tables, the significance level of.01 was divided by the particular number of tests carried out as a functionof the dependent variables and categories pertaining to each variable. Theresulting figure was then subjected to a two-tailed analysis of adjusted resid-uals that were above or below the area between the mean and z (Levin andFox, 1994). It is important to note that scores that were above and those thatfell below the computed z score were identified to recognize conditions thatwere observed at levels either in excess of or significantly below the levelsthat could be expected. For example, this meant that a transitional probabil-ity of .000 may be statistically significant at the significance level of .01 (withthe Bonferroni adjustment) due to the observation of fewer than expectedinstances of that event or condition. Conversely, a strong transitional prob-ability of .084 may well be found to be significant because of higher thananticipated frequencies of that particular phenomenon.

RESULTS

Descriptive Data and Levels of Interobserver Agreement

The descriptive findings and the various coefficients for interobserveragreement levels and code–recode reliability estimates for this investiga-tion have been reported elsewhere (Arthur, 1998, 2000, 2003). Briefly, theinterobserver agreement levels, with Cohen’s correction for chance, weregenerally satisfactory, with the lowest corrected kappa score of .71 for com-munication indicators (Arthur, 1998). The descriptive findings for studentbehavior states were consistent with other recent studies (e.g., Guess et al.,1993a), whereas the contextual variables were indicative of several areasof concern for educational programming for this population (Arthur, 1998,2003).

Behavior State Patterns

Table II describes transitional probabilities for the eight behavior states,with a structural zero imposed upon each state following itself at the first

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Table II. Transitional Probabilities for Behavior States at Lags 1–6

Lag

Antecedenta Targets +1 +2 +3 +4 +5 +6 +1b

AI AI — .888(+)c .874(+)c .863(+)c .856(+)c .856(+)c .920(+)c

AA .867(+)c .090(+)c .095(+)c .094(+)c .092(+)c .084(+)c .070(+)c

DR .133 .022(−)c .027 (−)c .038(−)c .041 .047 .011(−)c

DA <.001 <.001(−)c <.001(−)c .002(−)c .006(−)c .006(−)c <.001(−)c

AWIA <.001(−)c <.001(−)c .003(−)c .002(−)c .002(−)c .004(−)c <.001(−)c

AWAA <.001(−)c <.001(−)c <.001(−)c .001(−)c .001(−)c .002(−)c <.001(−)c

AWASS <.001 .001(−)c .001(−)c .001(−)c .002(−)c .002(−)c <.001(−)c

CR <.001 <.001(−)c <.001(−)c <.001(−)c <.001(−)c <.001(−)c <.001(−)c

AA AI .655(+)c .345(+)c .355(+)c .362(+)c .370(+)c .338(+)c .274(=)c

AA — .439(+)c .366(+)c .343(+)c .329(+)c .338(+)c .582(+)c

DR .327(+)c .178(+)c .208(+)c .196(+)c .177(+)c .177(+)c .137(+)c

DA .009 .008 .026 .026 .026 .026 .004AWIA .009(−)c .030(−)c .042(−)c .064(−)c .079(−)c .075(−)c .004(−)c

AWAA <.001(−)c <.001(−)c .004(−)c .004(−)c .015(−)c .019(−)c <.001(−)c

AWASS <.001 <.001(−)c <.001(−)c .004(−)c .011(−)c .026(−)c <.001(−)c

CR <.001 <.001 <.001 <.001 <.001 <.001 <.001DR AI .033 .027(−)c .034(−)c .037(−)c .039(−)c .046(−)c .012(−)c

AA .085 .036 .044 .049 .053 .048 .030DR — .506(+)c .453(+)c .404(+)c .382(+)c .334(+)c .640(+)c

DA .146(+)c .066(+)c .058(+)c .061(+)c .054(+)c .058(+)c .052AWIA .709 .345 .367 .382 .389 .432 .255(−)c

AWAA .009(−)c .009(−)c .024(−)c .037(−)c .049(−)c .048(−)c .003(−)c

AWASS .019 .012(−)c .019(−)c .029(−)c .032(−)c .034(−)c .007(−)c

CR <.001 <.001 .002 <.001 .002 .002 <.001DA AI .007 .008(−)c .004(−)c .008(−)c .012(−)c .008(−)c .004(−)c

AA .022 .012 .016 .020 .032 .036 .012DR .187(+)c .121 .108 .105 .101 .117 .106DA — .282(+)c .285(+)c .254(+)c .234(+)c .211(+)c .435(+)c

AWIA .604 .419 .410 .411 .419 .413 .342AWAA .072 .065(−)c .076(−)c .089(−)c .093(−)c .105(−)c .041(−)c

AWASS .108 .089 .088 .101 .101 .105 .061CR <.001 .004 .012 .012 .008 .004 <.001

AWIA AI .003(−)c .002(−)c .003(−)c .005(−)c .006(−)c .007(−)c .001(−)c

AA .015(−)c .006(−)c .008(−)c .008(−)c .008(−)c .008(−)c .003(−)c

DR .176 .048(−)c .051(−)c .055(−)c .059 .062 .035(−)c

DA .117 .026 .027 .025 .024 .025 .024AWIA — .747(+)c .725(+)c .706(+)c .696(+)c .682(+)c .799(+)c

AWAA .468(+)c .118(−)c .129(−)c .137(−)c .142(−)c .150(−)c .094(−)c

AWASS .180 .043(−)c .047(−)c .053(−)c .053(−)c .054(−)c .036(−)c

CR .041 .011 .011 .013 .013 .013 .008(−)c

AWAA AI .003(−)c .001(−)c .003(−)c .003(−)c .003(−)c .003(−)c .001(−)c

AA .003(−)c .001(−)c .001(−)c .001(−)c .003(−)c .005(−)c .001(−)c

DR .010(−)c .007(−)c .012(−)c .015(−)c .017(−)c .018(−)c .002(−)c

DA .032 .013(−)c .011(−)c .014(−)c .018 .019 .007(−)c

AWIA .827(+)c .218(−)c .241(−)c .258(−)c .269(−)c .284(−)c .178(−)c

AWAA — .723(+)c .692(+)c .666(+)c .647(+)c .623(+)c .785(+)c

AWASS .101 .029(−)c .030(−)c .030(−)c .031(−)c .034(−)c .022(−)c

CR .025 .008 .010 .012 .013 .015 .005(−)c

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136 Arthur

Table II. (Continued.)

Lag

Antecedenta Targets +1 +2 +3 +4 +5 +6 +1b

AWASS AI .005 .003(−)c .005(−)c .006(−)c .006(−)c .009(−)c .001(−)c

AA .010 .002(−)c .003(−)c .005(−)c .005(−)c .004(−)c .002(−)c

DR .036 .007(−)c .007(−)c .007(−)c .005(−)c .007(−)c .007(−)c

DA .057 .016 .019 .021 .019 .018 .011AWIA .691 .167(−)c .180(−)c .202(−)c .213(−)c .214(−)c .138(−)c

AWAA .191 .051(−)c .056(−)c .061(−)c .066(−)c .078(−)c .038(−)c

AWASS — .749(+)c .725(+)c .696(+)c .686(+)c .670(+)c .801(+)c

CR .010 .004 .005 .002 <.001(−)c <.001(−)c .002CR AI <.001 <.001(−)c <.001(−)c <.001(−)c <.001(−)c <.001(−)c <.001(−)c

AA <.001 <.001 <.001 <.001 <.001 <.001 <.001DR <.001 <.001 <.001 <.001 <.001 .014 <.001DA <.001 <.001 <.001 .007 .014 <.001 <.001AWIA .667 .257(−)c .271 .285 .264 .273 .194(−)c

AWAA .310 .139 .167 .188 .201 .217 .090AWASS .024 .007(−)c .035 .035 .042 .028 .007(−)c

CR — .597(+)c .528(+)c .486(+)c .479(+)c .469(+)c .708(+)c

Note. Except in cases of rounding error, each lag column for an antecedent totals 1.0. AI, Asleep–Inactive; AA, Asleep–Active; DR, Drowsy; DA, Daze; AWIA, Awake–Inactive–Alert; AWAA,Awake–Active–Alert; AWASS, Awake–Active–Self-Stimulatory; CR, Crying.aThe antecedent refers to the behavior state observed in real time: the target is the behavior state atlags 1–6.

bTransitional probability at one lag when no structural zero is imposed.cSignificant at p < .0002. Symbols in brackets indicate adjusted residuals significantly above (+) orbelow (−) expected levels.

lag. In addition, the probability for state transitions, with the structural zeroremoved, is also provided. The adjusted residuals used to test the significanceof each transition (following Bonferroni correction) in this and subsequenttables are available on request from the author. However, the symbols ±have been used to identify whether the adjusted residuals are above or belowthe anticipated level for each variable or category.

The large number of statistically significant transitions in Table II in-cludes several that could be expected as a function of the particular behaviorstates observed in the study. For example, both asleep states (AI and AA)demonstrated strong linkage across the six lags when followed by themselvesand each other, a finding that could perhaps be expected. Interestingly, whena student was in the sleep–inactive state, the six awake states (including DRfor several lags) were observed in the following intervals at less than ex-pected levels. In contrast, the AA state was followed by DR at higher thanexpected levels, with AWIA, AWAA, and awake–active–self-stimulatory(AWASS) states noted in fewer instances than expected subsequent to AA.The DR state gave way to daze (DA) at a level above that expected, al-though it is important to note that overall the daze state was observed in

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relatively few instances. AWAA and AWASS states occurred at lower levelsthan anticipated following the DR state.

In the awake states, several patterns are noteworthy. First, like the sleepand DR states, each appeared to be highly likely to follow itself in ensuingtime periods (up to 2 min). Second, the AWIA state appeared likely to serveas an antecedent to AWAA at the first lag. Thereafter, AWIA was followedby AWAA and AWASS at less than expected levels.

A third pattern evident in Table II relates to the AWAA behavior state.Like the AWIA-AWAA sequence, AWAA preceded a shift to AWIA in thefirst lag. At subsequent lags, AWIA followed AWAA at less than expectedlevels, with the same finding for AWASS subsequent to AWAA as an an-tecedent. In other words, in addition to the evidence for state stability (onestate following itself over time), both AWIA and AWAA tended to followeach other at the first lag, after which less than anticipated levels of thesetarget states were observed. The last finding of note relates to the AWASSstate from lag 2 onwards, whereby six states (including AI, DR, AWIA, andAWAA) followed AWASS at lower than expected levels.

Behavior States and Communication Indicators

In the following table (Table III) information relating to observed be-havior states and communication indicators is presented. After a commu-nicative interaction (CI), several broad patterns in the observed behaviorstates of participants in the study can be identified. First, there was evidenceof an inverse relationship between a CI and AI. That is, it was unlikely thata student would be observed in the AI state in the intervals following a CI.The same finding is true for the DR and, perhaps more surprisingly, theAWIA states following a CI. That is, less than expected levels of these con-ditions were observed. In contrast, occurrence of an AWAA state followinga CI above the level that could reasonably be expected is strongly supportedacross six lags. In part this may be explained as an artefact of commonalitiesin the two codes for these phenomena. More specifically, it could be arguedthat to be involved in a CI, a participant must be awake and actively involved.It is interesting to note in this context that the same pattern is in evidence forCR following a CI, and is not apparent in relation to the AWASS behaviorstate.

SCs appeared to be an antecedent to several trends in the behavior statesof participants. Not surprisingly, sleep and DR states were observed at lessthan expected levels following an SC. While the transitional probabilities forDA, AWIA, and AWAA were generally not statistically significant, positivescores for AWASS and CR were computed over the six lags. In other words,these states followed SCs more frequently than expected.

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Table III. Transitional Probabilities for Behavior States and Communication Indicators

Lag

Antecedent Target +1 +2 +3 +4 +5 +6

CI AI <.001(−)a .002(−)a .005(−)a .005(−)a .005(−)a .005(−)a

AA .002 .002 .002 .002 <.001(−)a <.001(−)a

DR .007(−)a .005(−)a .010(−)a .012(−)a .014(−)a .014(−)a

DA .010 .012 .007 .017 .021 .014AWIA .238(−)a .280(−)a .291(−)a .307(−)a .293(−)a .319(−)a

AWAA .579(+)a .527(+)a .516(+)a .502(+)a .514(+)a .500(+)a

AWASS .079 .081 .086 .081 .083 .088CR .086(+)a .090(+)a .084(+)a .074(+)a .069(+)a .060(+)a

SC AI <.001(−)a <.001(−)a <.001(−)a <.001(−)a .002(−)a .003(−)a

AA <.001(−)a <.001(−)a .001(−)a .002(−)a .002(−)a .002(−)a

DR .017(−)a .015(−)a .015(−)a .021(−)a .024(−)a .021(−)a

DA .013 .020 .024 .022 .023 .027AWIA .357(−)a .376 .378 .392 .402 .411AWAA .241 .235 .221 .207 .193 .193AWASS .305(+)a .299(+)a .309(+)a .304(+)a .303(+)a .295(+)a

CR .067(+)a .055(+)a .053(+)a .052(+)a .052(+)a .050(+)a

PC AI .093 .103 .111 .121 .117 .115AA .026 .024 .026 .020 .022 .024DR .038 .040 .038 .038 .044 .051DA .014 .010 .002(−)a .004 .006 .012AWIA .364 .354 .358 .356 .365 .360AWAA .431(+)a .431(+)a .423(+)a .414(+)a .395(+)a .377(+)a

AWASS .028(−)a .028(−)a .032(−)a .036(−)a .034(−)a .034(−)a

CR .006 .010 .010 .010 .016 .026NC AI .160(+)a .160(+)a .159(+)a .158(+)a .158(+)a .158(+)a

AA .038(+)a .038(+)a .037(+)a .038(+)a .038(+)a .038(+)a

DR .082(+)a .082(+)a .082(+)a .080(+)a .079(+)a .080(+)a

DA .033(+)a .032(+)a .032(+)a .031 .031 .030AWIA .432(+)a .427(+)a .425(+)a .422(+)a .421(+)a .418AWAA .168(−)a .173(−)a .177(−)a .180(−)a .184(−)a .186(−)a

AWASS .084(−)a .084(−)a .082(−)a .083(−)a .083(−)a .084(−)a

CR .004(−)a .005(−)a .006(−)a .007(−)a .007(−)a .007(−)a

Note. Except in cases of rounding error, each lag column for an antecedent totals 1.0. AI, Asleep–Inactive; AA, Asleep–Active; DR, Drowsy; DA, Daze; AWIA, Awake–Inactive–Alert; AWAA,Awake–Active–Alert; AWASS, Awake–Active–Self-Stimulatory; CR, Crying. CI, Communica-tive Interaction; SC, Student Cue: No partner response; PC, Partner Cue: No student response;NC, No communication.aSignificant at p < .0004. Symbols in brackets indicate adjusted residuals significantly above(+) or below (−) expected levels.

The major finding of this aspect of the study in relation to partner cues(PCs) pertains to the strong differentiated link with the AWAA and AWASSstates. The first state (AWAA) was observed at higher than expected levelsfollowing a PC. In contrast, AWASS occurred at lower than expected levelsafter a PC.

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The final communication indicator, no communication (NC), was anantecedent to all of the observed behavior states at significant and differen-tiated levels. The finding that sleep and DR states occurred at higher thanexpected levels following NC is not surprising. For lags 1–3, the same pat-tern was demonstrated for daze (DA) following NC. Interestingly, AWIAoccurred at higher than anticipated levels following NC over the first fivelags, whereas the remaining states (AWAA, AWASS, and CR) were observedon fewer occasions than expected following NC.

Communication Indicators and Behavior States

Table IV provides the transitional probabilities for communication in-dicators following behavior states. Although many of the findings are clearlya function of the nature of the variables under scrutiny (e.g., AI followed byhigher than expected levels of NC), several aspects can be highlighted. Thedaze (DA) state appears to predispose students (and their communicationpartners) to high levels of NC (for three lags) and low levels of SC (in thefirst two lags). This short-term effect is an interesting finding. It is also im-portant to note that AWIA is an antecedent for lower than anticipated levelsof CI and higher than expected instances of NC across the six lags. In starkcontrast, AWAA is an antecedent to a positive sequence of CI events andconversely, fewer than anticipated instances of NC. The adjusted residualsfor these probabilities are strong and consistent across the six lags. Relat-edly, while SCs following AWAA are not significant, PCs were observed atconsistently higher than expected levels. AWASS appears to precede higherthan expected levels of SC, although this may be a function of the codingprotocols used. That is, given the state stability observed for AWASS, suchbehaviors may have also been scored as communicative behavior (cueing).Finally, CR appears to be consistently followed by higher than expectedinstances of CI and SC, although the same qualification regarding codingoverlap that applies to AWASS-SC may also apply to the CR-SC finding.

Behavior States and Activities

In the following table (Table V) the transitional probabilities for behav-ior states and activities are provided.

Table V indicates presence of very high levels of AWAA following an ILactivity. In contrast, the remaining behavior states that occur at significantlevels following IL are notable for lower than expected levels, includingAWIA. LE, on the other hand, serves as an antecedent to high levels ofAWIA, AWAA, and, to a lesser degree, CR. NA precedes several passive

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140 Arthur

Table IV. Transitional Probabilities for Communication Indicators and Behavior States

Lag


AI CI <.001(−)a <.001(−)a <.001(−)a .002(−)a .001(−)a .002(−)a

SC <.001(−)a .002(−)a .002(−)a .003(−)a .002(−)a .001(−)a

PC .036 .037 .040 .038 .040 .039NC .965(+)a .961(+)a .958(+)a .957(+)a .957(+)a .958(+)a

AA CI <.001 <.001 .008 <.001 .004 <.001SC .004(−)a <.001(−)a .004(−)a .011(−)a .015(−)a .015(−)a

PC .041 .038 .030 .041 .030 .030NC .955(+)a .963(+)a .959(+)a .948(+)a .951(+)a .955(+)a

DR CI .003(−)a .003(−)a .003(−)a .007(−)a .010(−)a .008(−)a

SC .019(−)a .024(−)a .037(−)a .041(−)a .039(−)a .052(−)a

PC .034 .037 .032 .029 .030 .034NC .944(+)a .936(+)a .927(+)a .924(+)a .921(+)a .905(+)a

DA CI .008 .008 .012 .012 .008 .028SC .016(−)a .052(−)a .084 .088 .108 .096PC .024 .036 .028 .044 .048 .028NC .952(+)a .904(+)a .876(+)a .855 .835 .847

AWIA CI .025(−)a .030(−)a .033(−)a .035(−)a .032(−)a .034(−)a

SC .119 .127 .125 .128 .133 .133PC .049 .049 .048 .051 .051 .050NC .807(+)a .795(+)a .793(+)a .787(+)a .784(+)a .784(+)a

AWAA CI .135(+)a .123(+)a .120(+)a .117(+)a .122(+)a .116(+)a

SC .154 .140 .133 .125 .119 .123PC .119(+)a .118(+)a .119∗(+0 .115(+)a .108(+)a .109(+)a

NC .593(−)a .619(−)a .629(−)a .644(−)a .651(−)a .652(−)a

AWASS CI .033 .037 .032 .035 .035 .036SC .380(+)a .375(+)a .383(+)a .386(+)a .383(+)a .371(+)a

PC .016(−)a .014(−)a .021(−)a .016(−)a .022(−)a .023(−)a

NC .571(−)a .574(−)a .564(−)a .564(−)a .560(−)a .570(−)a

CR CI .285(+)a .285(+)a .250(+)a .215(+)a .215(+)a .222(+)a

SC .549(+)a .479(+)a .438(+)a .451(+)a .396(+)a .403(+)a

PC .014 .035 .028 .021 .042 .056NC .153(−)a .201(−)a .285(−)a .313(−)a .347(−)a .319(−)a

Note. Except in cases of rounding error, each lag column for an antecedent totals 1.0. AI, Asleep–Inactive; AA, Asleep–Active; DR, Drowsy; DA, Daze; AWIA, Awake–Inactive–Alert; AWAA,Awake–Active–Alert; AWASS, Awake–Active–Self-Stimulatory; CR, Crying. CI, Communica-tive Interaction; SC, Student Cue: No partner response; PC, Partner Cue: No student response;NC, No communication.aSignificant at p < .0002. Symbols in brackets indicate adjusted residuals significantly above(+) or below (−) expected levels.

behavior states, including DA for the first two lags. It is interesting to note thatfor the first two lags, AWIA occurs at higher than expected levels followingNA, and AWASS is very likely to be demonstrated at higher than anticipatedlevels over six lags. Conversely, the transitional probabilities for AWAAfollowing NA indicate significantly lower frequencies than expected.

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Table V. Transitional Probabilities for Behavior States and Activities

Lag


IL AI .081(−)a .081(−)a .081(−)a .081(−)a .081(−)a .081(−)a

AA .002(−)a .003(−)a .003(−)a .004(−)a .004(−)a .003(−)a

DR .005(−)a .005(−)a .007(−)a .007(−)a .007(−)a .010(−)a

DA .013(−)a .013(−)a .015(−)a .016 .016 .017AWIA .202(−)a .214(−)a .220(−)a .226(−)a .236(−)a .243(−)a

AWAA .617(+)a .599(+)a .583(+)a .567(+)a .548(+)a .532(+)a

AWASS .058(−)a .061(−)a .066(−)a .070(−)a .075(−)a .078(−)a

CR .023 .025 .026 .030(+)a .033(+)a .036(+)a

LE AI .011(−)a .011(−)a .011(−)a .012(−)a .013(−)a .012(−)a

AA .011(−)a .010(−)a .011(−)a .011(−)a .010(−)a .011(−)a

DR .068 .068 .069 .069 .071 .072DA .026 .027 .028 .028 .028 .025AWIA .522(+)a .523(+)a .525(+)a .530(+)a .535(+)a .539(+)a

AWAA .276(+)a .275(+)a .270(+)a .266(+)a .263(+)a .263(+)a

AWASS .058(−)a .057(−)a .057(−)a .056(−)a .054(−)a .052(−)a

CR .029(+)a .029(+)a .029(+)a .028(+)a .028(+)a .026NA AI .176(+)a .175(+)a .176(+)a .175(+)a .175(+)a .175(+)a

AA .044(+)a .044(+)a .043(+)a .044(+)a .043(+)a .043(+)a

DR .083(+)a .082(+)a .082(+)a .082(+)a .081(+)a .080(+)a

DA .034(+)a .034(+)a .033 .033 .033 .033AWIA .420(+)a .418(+)a .416 .414 .412 .410AWAA .084(−)a .089(−)a .096(−)a .101(−)a .106(−)a .110(−)a

AWASS .148(+)a .147(+)a .144(+)a .142(+)a .142(+)a .140(+)a

CR .011(−)a .011(−)a .010(−)a .010(−)a .010(−)a .009(−)a

Note. Except in cases of rounding error, each lag column for an antecedent totals 1.0. AI, Asleep–Inactive; AA, Asleep–Active; DR, Drowsy; DA, Daze; AWIA, Awake–Inactive–Alert; AWAA,Awake–Active–Alert; AWASS, Awake–Active–Self-Stimulatory; CR, Crying; IL, Independentliving; LE, Leisure; NA, No activity.aSignificant at p < .0005. Symbols in brackets indicate adjusted residuals significantly above(+) or below (−) expected levels.

DISCUSSION

In this section, the educational and clinical implications of the study areconsidered in light of the literature base and rationale for the investigation.

Patterns in Behavior States

The transitional probabilities generated for the behavior states of the10 participants in the study produced several interesting findings (Table II).First, the two sleep states (AI and AA) demonstrated an interactive patternthroughout the six lags. This is consistent with findings of Guess et al. (1993a),

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who also found, in contrast to the present study, that the DR state was partof this interrelationship. Second, the finding that both AWAA and AWIApreceded shifts to each other at the first lag, followed by a return to theoriginal state in subsequent lags, complements the data published by Guesset al. (1995). However, the present study found that from Lag 2 onwards,the AWASS state did not serve as a conduit to more desirable states such asAWIA and AWAA. Rather, the AWASS state could be expected to continue.This has important implications for educators who may need to intervene toredirect self-stimulatory behavior states before they become entrenched inthe repertoire of the individual.

The findings in relation to the AWIA state are difficult to interpret,especially with regard to shifts toward more active states. Specifically, af-ter the first lag it was unlikely that AWIA would precede a move to AWAA(or AWASS). These data contrast with that reported by Guess et al.(1993a), who demonstrated that the AWIA state interacted with a rangeof other awake states including CR/agitation and AWAA (p. 642). Clearly,further detailed analysis of the AWIA state is warranted, both in behav-ior state sequences and in relation to behavior states and other relevantvariables.

Finally, it is interesting to note the evidence for the relative stabilityof each behavior state, in relation to lags 2–6 when the structural zero wasimposed in lag 1 as well as when the structural zero in lag 1 was not im-posed. That is, the data in Table II suggest that the probability of a partic-ular state following itself in subsequent and later intervals was uniformlyhigh.

While such an analysis is beyond the scope of the present investigation,future studies (involving a higher number of participants) could usefullyexplore the time-series issues of periodicity and behavior state cycles (cf.Guess et al., 1993a). In this respect, identification of profile group differences,of the type explored by Guess et al. (1993b, 1995) in recent research programs,will contribute to the more effective design of individualized interventionstrategies for members of this population.

Behavior States Following Communication Indicators

In this study, the condition of CI was observed for generally low amountsof time (Arthur, 1998, 2003). However, when it did occur, it was accompa-nied by strong evidence for a sustained positive effect upon the ensuingbehavior states of students. Specifically, for the students observed in thisstudy there was a strong likelihood of the AWAA state being observedin the 2 min following a CI. A similar finding emerged for CR following

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a CI. While the issues of coding overlap (between the characteristics ofCI and AWAA) and the relatively short-time period during which the ef-fects of an antecedent were observed (six lags, 2 min) must be acknowl-edged, this is an important finding in terms of the sociocommunicativeecology of programs for students with the most severe and multipledisabilities.

These data complement the directions suggested by Guess et al. (1993a,b,1995) and Richards and Sternberg (1992) in relation to the potential interre-lationship between desirable behavior states and human interactions involv-ing members of this population. Additionally, following the individualizedapproach reflected in Project Able (Ault et al., 1995), it may be helpful forpractitioners to analyze the types of exchanges and interactions they facil-itate with their students and concomitantly, the effects of such processesupon student behavior states. In this context, it is also important to note thatthe AWIA state was observed at lower levels than expected following a CI.This may be explained by the fact that the level of arousal and involvementimplicit in a CI, as defined for this study, militated against this more passivebut alert state (AWIA). That is, it could be argued that the motivationalor physiological effects of active engagement in an interaction function tosupport a continued active state, as evidenced by the sustained AWAA andCR behavior states following CIs.

The varying roles of SC/PC in relation to ensuing behavior states arealso noteworthy, especially when it is considered that in each condition, nopartner response was observed. That is, for an SC, no partner involvementwas in evidence. Similarly, for a PC, the student did not appear to respond.Interestingly, following an SC, participants were observed (in subsequent in-tervals) in the AWASS and CR state more frequently than expected. It couldbe argued that this was a reflection of commonality between the AWASSand SC codes and the tendency for states to repeat themselves. Alternatively,such behavior states may indicate some degree of student frustration whenno partner response was forthcoming.

PCs, when they were provided, resulted in several interesting patternsin the subsequent behavior states observed in students. First, there was ev-idence for lower than expected levels of AWASS, in contrast to events af-ter an SC. Second, participants demonstrated higher than anticipated num-bers of shifts to the AWAA state. Overall, these findings suggest that PCsmay play an important part in supporting changes to more desirable states,even though a student response at the time of the cue may not beforthcoming.

In light of the rational for this investigation (Arthur, 1998), the datagathered in relation to extant behavior states, CIs, PCs, and SCs, and the NCcondition underline the interrelatedness of these phenomena. This concept

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is further explored in the next section in relation to the evidence for com-municative events following particular behavior states.

Communication Indicators and Behavior States

In the same way that behavior states appear to be selectively influencedby particular communicative antecedents, such states may also act as inde-pendent variables that impact upon communicative conditions (Table IV).For example, the daze (DA) state precedes higher than expected levels ofNC for several lags and conversely, a low probability of SC (for two lags). It isparticularly interesting to note the different roles that AWAA and AWIA ap-pear to play in relation to subsequent communicative activity. While AWAAprecedes CI at higher than expected levels, AWIA is the antecedent to CI onfewer occasions than anticipated. Despite the fact that AWIA is a more pas-sive state than AWAA, this is a disturbing finding insofar as it suggests thatpartners are not involving students in interactions when they are observed inthis state. Indeed, when students were in the AWIA state, subsequent levelsof NC were above those expected, at statistically significant levels. One areain which efforts could be made to link CI and behavior states is the activitydomain.

Behavior States and Activities

The finding that an AWAA state is highly likely in the period imme-diately following an IL activity is a particularly important and encouragingone for educators. Likewise, in the present study LE preceded higher thananticipated levels of AWAA, AWIA, and CR states. These data (Table V)suggest that such activities are particularly strategic in terms of involvingand engaging students. However, the generally low proportion of the dayduring which activities are taking place is an issue of major concern andhas been discussed elsewhere (Arthur, 2003). Along with increasing activitylevels in programs serving this population, it may also be appropriate forteachers and others working directly with students to focus upon the typeof activity that is taking place. Following Ault et al. (1995) and Richardsand Richards (1997), it seems eminently sensible for practitioners to ex-plore individual changes in student states as a function of particular activ-ities, especially those considered to comprise the IL and LE domains. Inthis regard it is also imperative to note the strong possibility of the AWASSstate following the NA condition. This is consistent with the wider literatureon challenging behaviors, including self-stimulatory repertoires, in which a

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variety of communicative functions (including those which are attention-seeking or sensory-motivated) may be attributed to the behavior (see Guessand Carr, 1991; Reese, 1997).

Limitations to This Study

The contribution of the data reported in this paper is tempered by sev-eral limitations that must be recognized and discussed. The first of these is thesmall sample size and intact nature of the subjects in the main data collectionphase. Although participant selection was based on criteria and proceduresreported in similar, earlier studies (e.g., Guess et al., 1990, 1993a), it wasnot possible to randomly select the number of individuals who met theserequirements from a larger pool of eligible students. Similarly, for practicalreasons and as a function of the low incidence of individuals who comprisethe population of interest, only 10 students were observed.

The findings of the study, therefore, need to be interpreted with cau-tion, especially in terms of their generalization to the larger population ofstudents with profound and multiple disabilities. This is particularly true withregard to the analysis of patterns in the pooled data base (n = 10), especiallygiven the range of scores across individuals and in some variables, the lowfrequencies of phenomena.

A second limitation pertains to the potential for altered behavioral pat-terns in students and staff as a consequence of being observed. Although in-troduction of an orientation and desensitization phase in the study (Arthur,1998) may have reduced the effect of this phenomenon, it is neverthelessimportant to acknowledge the possible impact of a “Hawthorne” effect forthe data collected. Third, it should be noted that the data reported and ana-lyzed in the investigation characterized one entire day in the life of each ofthe 10 students, and the educational context in which they were functioning.The representative strength of these findings is therefore quite limited. Asseveral leading writers have noted, future work could usefully focus uponlongitudinal behavior state patterns and related variables, as well as explor-ing settings other than those incorporated in educational programs (Guesset al., 1996; Richards and Richards, 1997). For example, observations at homeas well as at school and in the community may allow useful comparisons to bemade in relation to ecological variations and further expand our knowledgeof important factors and relationships between factors in this area.

Perhaps most importantly, the data collected in this study were notcontinuous and can therefore only assist in the analysis of broad observedpatterns in and amongst variables. The collection of ongoing streams ofuninterrupted event data using the state and context codes will provide

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accurate information about the nature of sequences in the experiences ofstudents as they participate in educational contexts.

Implications for Future Research

Several areas for further research have already been identified in ear-lier sections of this discussion, including the importance of the longitudi-nal analysis of behavior states in this population and a better understand-ing of the nature of state patterns in diverse situations (especially homecontexts).

In addition to descriptive findings, intervention studies will also makean invaluable contribution to the research base. The provision of basic staffand family training in the identification of student behavior states is one areathat should yield a wealth of data in the future. Issues that could be studiedinclude participant concerns in relation to the practical use of behavior stateinformation, differences amongst groups (e.g., classroom teachers and aides,related professionals, and family members working with a child at home) inthe implementation of behavior state procedures, the potential impact ofstudent enrolment in segregated or inclusive settings on observed states,and the nature of the relationship between the collection of this informationand individualized educational planning processes.

SYNTHESIS OF EDUCATIONAL AND CLINICAL IMPLICATIONS

The findings of this study underline the importance of further staff sup-port and development in relation to the provision of learning environmentsthat maximize student involvement, with particular emphasis upon the num-ber and quality of interactions with students and the place of daily activitiesas a vehicle for such processes. Programs of staff support will do well to em-phasize the interrelatedness of factors that are relevant to this population,including behavior states, contextual and endogenous factors (Arthur, 1998;Guess et al., 1993c), as opposed to the use of behavior state information indiscrete aspects of the educational program.

Perhaps most importantly, the data reported in this paper contributesto an improved understanding of the complex interplay between aspects ofthe educational ecology and student behavior states. It is imperative in lightof this and the wider empirical literature that further studies be conductedin which specific contextual variables are manipulated to examine the pos-sible impacts for student behavior states (Ault et al., 1995; Richards andRichards, 1997). However, it will also be important to recognize the strength

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of behavior states as an independent variable and examine in more detail thedifferential effect of state changes (or lack thereof) in students in relationto the environments in which they participate.

APPENDIX

Behavior state codesAI: Asleep–InactiveAA: Asleep–ActiveDR: DrowsyDA: DazeAWIA: Awake–Inactive–AlertAWAA: Awake–Active–AlertAWASS: Awake–Active–Self-StimulatoryAWASI: Awake–Active–Self-InjuryCR: CryingZ: Seizures

Communication indicatorsCommunicative interaction (CI)Student communicative cue: no partner response (SC)Partner communicative cue: no student response (PC)No communication behaviors (NC)ActivityIndependent living (IL)Leisure (LE)Vocational (VC)Conceptual (CO)Movement/transition (MT)Therapy related (TR)No activity (NA)

Social context experienced by studentSolitary (SO)Small group (SG)Close proximity (CP)Large group (LG)

Communication partnerNo partner (NP)Teacher (T)Aide (A)Peer (P)

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148 Arthur

Other (O)

PositionSeated (SE)Standing (ST)Prone (PR)Supine (SP)Side-lying (SI)Repositioning (RP)

ACKNOWLEDGMENT

The author wishes to express sincere thanks to Dr. Magdalena Mak,Hong Kong Institute of Education, for her expert assistance and support inthe statistical analyses reported in this article.

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