AMERICAN JOURNAL ON INTELLECTUAL AND DEVELOPMENTAL DISABILITIES 2014, Vol. 119, No. 2, 151–170

EAAIDD DOI: 10.1352/1944-7558-119.2.151

Clinical Decision Making and Preference Assessment for Individuals With Intellectual and Developmental Disabilities Javier Virue´s-Ortega, Kristen Pritchard, Robin L. Grant, Sebastian North, Camilo Hurtado-Parrado, May S. H. Lee, Bev Temple, Fla´via Julio, and C. T. Yu

Abstract Individuals with intellectual or developmental disabilities are able to reliably express their likes and dislikes through direct preference assessment. Preferred items tend to function as rewards and can therefore be used to facilitate the acquisition of new skills and promote task engagement. A number of preference assessment methods are available and selecting the appropriate method is crucial to provide reliable and meaningful results. The authors conducted a systematic review of the preference assessment literature, and developed an evidence-informed, decision-making model to guide practitioners in the selection of preference assessment methods for a given assessment scenario. The proposed decisionmaking model could be a useful tool to increase the usability and uptake of preference assessment methodology in applied settings. Key Words: knowledge translation; preference assessment; systematic review; clinical decision making

Preference may be defined as the relative strength of behaviors among two or more choice options and it is often measured as a pattern of choosing (Martin, Yu, Martin, & Fazzio, 2006). Assessing the preferences of individuals with intellectual and developmental disabilities (IDD) is important for several reasons. Preferred items often function as reinforcers, and they can be used in intervention programs for establishing new skills and reducing problem behaviors for people with IDD (e.g., Tullis et al., 2011). Moreover, the mere presentation of choices and highly preferred items has been shown to reduce problem behaviors and increase engagement in academic activities (e.g., Dunlap et al., 1994). Also, the noncontingent presentation of highly preferred items (noncontingent reinforcement) may reduce the frequency of problem behavior (Carr, Severtson, & Lepper, 2009). Preference assessment (PA) refers to a collection of procedures that could be used to identify whether one item is more preferred than others. J. Virue´s-Ortega et al.

Since individuals with severe or profound IDD often lack speech to express their preferences, an indirect approach is often used to gather that information, for example, by asking a staff member or caregiver who is familiar with the person. However, methods based on informants are not as accurate as direct PA methods (e.g., Green, Reid, Canipe, & Garner, 1991; Resetar & Noell, 2008). Direct PA involves presenting various items to a person systematically and observing their choices. Five main direct PA procedures have been developed to assess preferences of individuals with IDD: multiple stimulus with or without replacement (MSW, MSWO), paired-stimulus or pairwise preference assessment (PWPA), free operant preference assessment (FOPA), single stimulus preference assessment (SSPA), and response-restriction preference assessment (RRPA). During MSWO, the person is presented with an array of items and is asked to select one. The selected item is removed from the array and the 151

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presentation is repeated until no choice is made or the last two items have been presented. The order in which the items are chosen reflects the preference for each item (DeLeon & Iwata, 1996). In MSW, the same array of items is presented across all trials (Windsor, Piche´, & Lock, 1994). During PWPA, a person is presented with two items on each trial. Across trials, the person is exposed to all possible item pairings in the stimulus array. The proportion of trials an item is selected out of the trials it has been presented reflects the strength of the preference for each item (Fisher et al., 1992). During FOPA, a person is presented with two or more items concurrently and is free to interact with either item. The amount of time spent interacting with an item relative to other items reflects the strength of the preference (Worsdell, Iwata, & Wallace, 2002). During SSPA, a person is presented with only one item on each trial while the duration of engagement is monitored to determine preference (DeLeon, Iwata, Conners, & Wallace, 1999). Finally, during RRPA, a person is free to interact with an array of items while duration of interaction is monitored. Over successive trials, items inducing a high level of engagement are restricted in a stepwise fashion according to a set of procedural rules (Hanley, Iwata, Lindberg, & Conners, 2003). MSWO and PWPA are discretechoice or selection-based procedures in that preference is determined by discrete choice responses. On the other hand, FOPA, SSPA, and RRPA are continuous-choice or engagementbased procedures in that preference is established on the basis of duration of interaction with the leisure items. Tullis et al. (2011) referred to these methods as duration-based procedures. To date, there is ample evidence that direct PA is an effective approach for identifying preference for people with IDD (Tullis et al., 2011). However, selecting the most appropriate PA method requires a sophisticated interpretation of the empirical literature. Recent reviews do not elaborate on the contextual, clinical, and procedural factors that should inform the practitioner’s selection of PA methods. For instance, Tullis et al. (2011) focused on the effect of choice on other behaviors, the effect of choice-based interventions, and summarized the evidence on PA training. By contrast, the present analysis presents a series of quantitative analyses and a broad systematic review to support a clinical decision making model for selecting PA methods. Recently, Karsten, Carr, and Lepper 152

EAAIDD DOI: 10.1352/1944-7558-119.2.151

(2011) developed a decision tree for selecting MSWO and FOPA. However, other contextual and clinical factors such as the assessment duration and prerequisite skills for the required choice responses were not considered. The purpose of this paper was to review the empirical evidence on direct PA with individuals with IDD and develop an evidence-informed, decision-making model to assist teachers and other practitioners to select PA methods that best fit their needs.

Methods Knowledge Translation Process Knowledge translation refers to actions taken to make research results readily available to practitioners and to the public to foster evidence-based practices. The present paper is a result of an integrated knowledge translation initiative that focused on evidence-based practices for individuals with IDD in special education settings. The team included special education teachers, researchers, and students from school psychology, applied behavior analysis, community health sciences, and nursing. Following the knowledge-to-action approach described by Straus, Tetroe, and Graham (2009), we asked special education teachers to identify research topics associated with specific training needs of teachers in an open-ended survey. Seven teachers from St. Amant School (Winnipeg, Canada) indicated ‘‘identifying easily enjoyable activities for students with profound disabilities’’ as their top-ranked training need. Teachers indicated that they were particularly interested in simple and time-efficient methods to assess preferred leisure and educational activities that would engage the student for long periods of time. Furthermore, teachers were concerned about how these methods would adapt to individuals with minimal verbal and motor skills, sensory impairments, and challenging behaviors. A working group was struck to review the relevant literature and develop a decision-making model for selecting PA methods for assessing individuals with IDD.

Search Strategy Empirical studies were included for review based on the following criteria: (a) participants were individuals with IDD; and (b) study focused on a standard or modified version of any of the direct PA procedures (MSWO, PWPA, FOPA, RRPA, and SSPA). A search was conducted using PsycInfo Preference Assessment

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(ProQuest search engine), Medline (PubMed), and Google Scholar (biology, life and environmental sciences, social sciences, arts, and humanities) on June 1, 2012, using the unrestricted search term preference assessment. Our methods were in compliance with the relevant items of the PRISMA statement for reporting systematic reviews (Liberati et al., 2009).

Data Extraction and Interrater Reliability The following information was extracted from each study reviewed: Preference assessment method. MSWO, PWPA, FOPA, RRPA, and SSPA. Participant characteristics. Age, gender, reported diagnoses, sensory and motor impairments. Participants were assigned to one of the following categories: (a) significant motor and sensory impairment with or without IDD, (b) intellectual disability with or without pervasive developmental disorder, and (c) pervasive developmental disorder (usually autism) without intellectual disability. Choice response. We recorded whether the choice response involved: contact (manipulating/ interacting) with the item, pointing to an item, picking up an item, naming an item (using a spoken name), approaching an item (gross motor physical approach towards an item without physical contact), reaching (approach response using the hand or arm), gazing (looking at the item), and consumption (edible items only). Type of stimuli. Academic, leisure, sensory (visual, tactual, auditory, and olfactory stimuli), edible, and social stimuli. There is some potential for overlap between social and leisure stimuli. We drew stimulus categories directly from the selected studies. We also recorded if choice stimuli were (a) actual items; (b) photos, drawings, or pictures; or (c) spoken stimuli (verbal presentation). Time needed to complete the assessment. We recorded or estimated the administration time in minutes and the administration time per item. When not reported, administration time was estimated based on the number of trials and iterations of the procedure, the maximum time allowed per trial, and the maximum item access time after each selection. Setting. Clinic or research facility, home, or school. Specific features of the physical setting were also recorded (e.g., the assessment was conducted in a special chair). J. Virue´s-Ortega et al.

EAAIDD DOI: 10.1352/1944-7558-119.2.151

Preference hierarchy. The study reported a preference hierarchy with high, moderate, and low preference items. Problem behavior. Effect of PA on cooccurring problem behavior. Methodological quality. Procedural integrity, interobserver agreement, experimental evaluation of the rewarding effects of preferred items, outcome of the experimental evaluation (positive, negative, or mixed according to Tullis et al., 2011). A coauthor, not involved in data extraction, independently recoded all of the above information items for each study and an item-by-item comparison was made with the original coder. An item was a disagreement if the coders’ recordings did not match exactly. We found 26 disagreements out of 539 items (49 Studies 3 11 Information Items; see Table 1) or 95% exact agreement. Disagreements were resolved by reexamining the original manuscripts and reaching a consensus.

Data Analysis Study outcomes were summarized using descriptive statistics disaggregated by student characteristic and by PA method. In addition, we used a qualitative approach to answer the following questions based on a detailed analysis of the literature: (a) What choice responses were characteristic of each PA method and clinical population? (b) What presentation format (e.g., pictorial vs. tangible) and PA methods (e.g., selection vs. engagement based) were commonly used with different types of stimuli (e.g., edible, leisure, social)? (c) What duration was characteristic of a given PA method? (d) What aspects of the setting were relevant to the assessment process? (d) What was the relative efficiency of each method to identify distinct preference hierarchies? (e) What level of problem behavior was associated with each PA method? Evidence-based answers to these questions were integrated into a systematic decision-making model.

Results Our search strategy resulted in 177 distinct references. Sixty-nine studies did not focus on PA. Thirty additional studies did not include participants with IDD. Twenty-nine additional studies did not use a standard method of PA. Finally, 49 studies were included in the review totaling 291 distinct participants (70% male; 153

EAAIDD DOI: 10.1352/1944-7558-119.2.151

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Table 1 Characteristics of Participants, Methods, and Procedures Across Preference Assessment Studies Participants Reference Age Multiple stimulus with replacement Kodak, Fisher, Kelley, & 3–10 Kisamore, 2009

Gender (M, F)

Diagnosis

Sensory/motor impairments

4, 0

Autism, PDD

Spina bifida, hydrocephaly

2–7 14–22

2, 1 19

Autism Autism, ID

-

8–25

1, 4

PDD, ID (M-S)

-

7–21

15

Autism, PDD-NOS, ID

-

4, 5

ID (S-P)

-

2, 0

Autism, ID (Mo)

Multiple stimulus without replacement Carr, Nicolson, & Higbee, 2000 Ciccone, Graff, & Ahearn, 2005 DeLeon et al., 2001 Graff & Ciccone, 2002 Higbee, Carr, & Harrison, 2000 Kang et al., 2010

22–53 6

Autism, PDD-NOS

Chiari malformation, thyroid disfunction -

Karsten et al., 2011

3–11

14, 1

Lanner et al., 2009

14–20

4, 0

Autism, ID

McCord et al., 2001 Milo, Mace, & Nevin, 2010 Nuernberger et al., 2012

41–43 6–10 4

0, 2 4, 0 2, 1

Autism, ID (S, P) Autism Autism

Reed et al., 2009

20

1, 0

PDD

-

Steinhilber & Johnson, 2007

11–12

2, 0

PDD

-

Ardoin et al., 2004

12

3, 0

ID (M-Mo)

-

Chappell, Graff, Libby, and Ahern, 2009 Clevenger & Graff, 2005

18–19

3, 0

Autism

-

9–16

5, 1

-

Conyers et al., 2002

17–44

4, 5

Autism, PDD, Fragile X syndrome ID (M-P)

-

8–11

4, 2

Autism, PDD-NOS, ID

-

Visual impairment Deafness -

Pairwise preference assessment

Davis et al., 2009

(Table 1 continued)

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Table 1 Extended Methods IOA, PI design (effect)a

Procedure No. items

Choice response

Stimuli

Timec (min)

b

Settingd

.95%, RV (+)

6

Pointing, contact

L, S, E

Tangible

,23

C

100%, - ME-LP (+) . 95%, . 95% RV (+) . 95%, CO (+) . 95%, 100% RV(+) 100%, RV(+) . 95%, . 95%, . 95% CO-LP(+) 100%, RV(+) . 95%, RV(+) 100%, - CO(+) . 95%, . 95% RV-LP(M) . 95%, ME-LP(+) 100%, RV(+)

8 7

Contact Contact

L, S, E E

Tangible Tangible

,9 -

C S

7-9

Pointing, reaching

E, S, L

Tangible

-

-

7

Picking up

L, E

Tangible

-

S

7

Contact

L, S, E, A

Tangible

-

H, C

-

Contact

-

Tangible

-

-

5–8

Contact

L, S, So, A Tangible

-

H, S

5

Contact, picking up

L, S, E

Tangible

,9

S

Picking up Contact Contact

E E So

Tangible Tangible Pictorial, tangible

,15-21

C S H

6

Consumption

E

Tangible

-

S

7

Contact

L, S, A

Tangible

,17d

H

-

L, S, E

Pictorial, tangible

,15-18

S

4-8

Picking up

E

Tangible

,4-19

H

8

Contact, picking up

E

Tangible, pictorial

10

S, H

6

Contact, pointing

E, L

,5

C, H

8

-

E, So, L

Tangible, pictorial, verbal Tangible Edible Social/leisure Tangible, no access Edible Social/leisure

25 87 20 30

S

100%, 100% RV-LP(+) 100%, 100% ME (M) . 95%, RV (+) 100%, . 95%, - CO-LP (+)

6–7 10 6–7

10–15

(Table 1 continued)

J. Virue´s-Ortega et al.

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Table 1 Continued Participants Reference

Age

Gender (M, F)

Diagnosis

Sensory/motor impairments

DeLeon et al., 2001

8–25

1, 4

PDD, ID (M-S)

DeLeon et al., 2009

9–20

0, 4

Autism, ID (Mo-S)

Cri du chat

DiCarlo, Reid, & Stricklin, 2003

2–3

1, 2

PDD

Cerebral palsy

Didden & de Moor, 2004

2–4

10

ID

Fisher et al., 1992

2–10

2, 2

PDD, ID (Mo-P)

14–21

2, 2

ID (S-P)

6–11

3, 1

Autism

Cerebral palsy, spina bifida, Sotos syndrome Visual impairment, seizures, hemiparesis Seizures, visual impairment, cerebral palsy Muscular dystrophy, seizures

Graff & Gibson, 2003

14–20

4, 0

Autism, PDD, ID (S)

-

Graff & Larsen, 2011

12–15

2, 3

ASD

-

Groskreutz & Graff, 2009e

15–17

5, 0

Autism, PDD

7–20

3, 1

Autism, ID (Mo-S)

13–15

5, 1

Autism, ID

Visual impairment

2, 0

Autism, ID (Mo)

Chiari malformation, thyroid disfunction -

Fleming et al., 2010 Gottschalk, Libby, & Graf, 2000

Hagopian et al., 2001 Horrocks & Higbee, 2008

-

Dandy-Walker syndrome

-

Kang et al., 2010

6

Kenzer & Bishop, 2011

2–9

23, 8

Lanner et al., 2009

14–20

4, 0

Autism, ID

-

Lee et al., 2008

33–50

2, 5

Autism, PDD, ID (Mo-P)

-

Lee et al., 2010

35–37

1, 1

ID (Mo-S)

-

ASD

(Table 1 continued)

156

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Table 1 Extended continued Methods IOA, PI design (effect)a

100%, CO (+) 95%, PR-LP (+) -,MB-LP(M) 100%, 100% . 95%, RV (+) . 95%, 100% RV-LP (+) 100%, 100%, RV (+) 100%, RV-LP (+) . 95%, . 90% RV-LP (+) . 90%, CO-LP(+) . 95%, ME-LP (+) . 95%, . 95%, . 95% 100%, RV (+) . 95%, 100% . 95%, . 95% RV-LP (+)

Procedure No. items

Choice response

Stimuli

b

Timec (min)

Settingd

30 75

7–9

Pointing, reaching

E, S, L

Pictorial Edible Social/leisure Pictorial, no access Edible Social/leisure Tangible

12

Approach, contact, verbal Gaze, reaching

L

Tangible

-

S

L

Tangible

-

S

5–6

15 19 -

-

,5

C

16

Approach, pointing, L Tangible verbal Approach E, S, So, L Tangible

,20

C

6–14

Gaze

E, S, L

Tangible

,8–46

S/H

4

Picking up

E

Tangible

,5

H

8

Contact, picking up

E

Tangible, pictorial

10

S

8

Picking up

E

Tangible

,5

S

8

Contact

E

Approach

20 19 12 49–69

S

8–13

Tangible Pictorial Pictorial, no access E, L, S, So Tangible

H

6

Contact

Au

Tangible

,5

S

-

Contact

-

Tangible

-

-

6

Contact, verbal

E, L, S, So Tangible, pictorial

,21

-

5

-

E, L, S

Tangible

8

S

6

Contact, pointing

L, S

,5

C

6

Contact, pointing

E

Pictorial, tangible, verbal Tangible

,30

-

6

(Table 1 continued)

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Table 1 Continued Participants Reference

Age

Gender (M, F)

Diagnosis

Sensory/motor impairments

Mangum et al., 2012

6–13

2, 1

Autism

Visual impairment, seizures

McCord et al., 2001

25–44

0, 2

Autism, ID (P)

Visual impairment

6

0, 1

Rett syndrome

-

20

1, 0

Autism, PDD

-

Meador, Derby, & McLaughlin, 2007 Reed et al., 2009 Sturmey, Lee, Reyer, & Robek, 2003 Thomson et al., 2007

4–42

7, 0

Autism, ID, PDD

-

26–65

8, 7

Autism, ID (S-P)

-

Wilder et al., 2008

13–38

3, 0

Autism, ID (P)

-

16–20

2, 0

ID

Cerebral palsy

6

2, 0

Autism, ID (M)

Karsten et al., 2011

4–7

5, 0

Autism, PDD-NOS

Chiari malformation, thyroid disfunction -

Keen & Pennell, 2010

4–5

3, 1

Autism, PDD-NOS

Kodak et al., 2009

3–10

4, 0

Autism, PDD

Rapp, Rojas, Colby-Dirksen, 5–11 Swanson, & Marvin, 2010 Reed et al., 2009 19 Reid, DiCarlo, Schepis, 1–3 Hawkins, & Stricklin, 2003 Sautter et al., 2008 3–9 Worsdell et al., 2002 .18

6, 3

Autism, PDD, ADHD

1, 0 5, 2

Autism, PDD Autism, PDD

6, 0 2, 2

Autism ID

-

7–20

3, 1

Autism, ID (M-S)

-

Klatt, Sherman, & Sheldon, 2000 30–49

3, 0

ID (S-P)

-

Spevack et al., 2008

2, 1

PDD, ID

Free operant preference assessment Gutierrez, Vollmer, & Samaha., 2010 Kang et al., 2010

Spina bifida, hydrocephaly Spina bifida

Single stimulus preference assessment Hagopian et al., 2001

5–10

Cortical blindness, spastic quadriparesis, microcephaly, seizures, cerebral palsy (Table 1 continued)

158

Preference Assessment

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Table 1 Extended continued Methods IOA, PI design (effect)a 100%, CO-LP (+) . 95%, RV (+)

Procedure No. items

Choice response

Stimuli

Timec (min)

b

Settingd

5–11

Contact

L, S

Tangible

-

C

6–7

Picking up

E

Tangible

-

C

11

Reaching

A, E, L

Tangible

-

S

. 95%, ME-LP (+) -, RV-LP (+) . 95%, 100%, RV-LP (+)

6

Consumption

E

Tangible

,5

S

-

Approach

So

Tangible

-

S

6

E, L, S

Tangible

,5

H

6

Contact, pointing, reaching Contact

S, O

Tangible

,5

C

-, MB(M) . 95%, . 95%, . 90% CO-LP (+) . 90%, RV(+) . 95%, RV(M) . 95%, . 95%, ME-LP (+)

6

Contact

E, L

Tangible

5–10

S

-

Contact

-

Tangible

25

-

-

Contact

A, L, S

Tangible

-

H, S

6

Contact

L, S

Tangible

20

C

6

Contact, verbal

E, L, S

Tangible

30

C

6–12

Contact

A, L, S

Tangible

60–90

S

6

Consumption

E

Tangible

42

S

6 6 7

Contact Contact Contact

A, L, S L, S V

Tangible Tangible Tangible

25–75 20 5

S C S

. 90%, CO-LP (+)

8–13

E, L, S, So Tangible

,96–156

C

. 95%, 100% ME (+) . 95%, 100% CO-LP(M)

11–14

Approach, consumption, contact Contact

A, L

Tangible

-

H

L

Tangible

,120

C

. 85%, -

12

Approach, contact, emotional, gaze, reaching

(Table 1 continued)

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Table 1 Continued Participants Reference Thomson et al., 2007 Worsdell et al., 2002

Age 26–65 .18

Response-restriction preference assessment Hanley, Iwata, Lindberg, & 34–66 Conners, 2003 25–50 Hanley, Iwata, Roscoe, Thompson, & Lindberg, 2003 Peterson et al., 2012 3–4

Gender (M, F)

Diagnosis

Sensory/motor impairments

8, 7

Autism, ID (S-P)

-

2, 2

ID

-

2, 1

ID (Mo-S)

Seizures

5, 2

Autism, ID (M-P)

Seizures, Prader-Willi syndrome

3, 1

PDD

-

Note. ID 5 intellectual disability (M 5 mild, Mo 5 moderate; S 5 severe; P 5 profound); PDD 5 pervasive developmental disability; PDD-NOS 5 pervasive developmental disability not otherwise specified. a IOA (average interobserver agreement across participant), PI (average procedural integrity across participants), Design (CO 5 concurrent schedules; LP 5 comparison with lower preference items; MB 5 multiple baseline; ME 5 multielement; PR 5 progressive ratio schedule; RV 5 reversal design), Effect (+ 5 positive; M 5 mixed). b L 5 leisure; S 5 sensory; E 5 edible; So 5 social; A 5 academic; V 5 vocational O 5 olfactory; Au 5 auditory. c Average administration time reported or estimated (pairing/trials multiplied by trial duration and access time). Range refers to durations for the smallest and largest array of stimuli in the study. d Only the short-MSWO duration is reported. e Second preference and reinforcer assessments reported.

average age 5 16.3 years, SD 5 12.8; Table 1). Fourteen studies used multiple-stimulus preference assessment and included 91 participants. Among these, 13 studies used MSWO and one used MSW. Twenty-nine studies used PWPA with 163 participants. Ten studies used FOPA with 44 participants. Five studies used SSPA with 29 participants. Finally, three studies used RRPA with 14 participants. Nineteen studies included 79 individuals with either sensory or motor impairments. Twenty-nine studies included 168 individuals with mild to profound intellectual disability. Forty studies included 242 participants with autism or pervasive developmental disorder. Among these 13 studies included 93 individuals with autism or pervasive developmental disability without comorbid intellectual disability or conditions involving significant sensory and motor impairments. Among the studies reviewed, 44 (90%) reported interobserver agreement, which was above 85% in all cases. Fourteen (29%) studies assessed procedural integrity, all of which were above 90%. Thirty-five studies (71%) used an experimental design, such as reversal, multi-element, 160

and multiple baseline, to validate the reinforcing effects of high-preference items, and 30 (86%) studies confirmed the positive reinforcing effects of high preference items in all participants while results were mixed in five studies (14%). Finally, 18 studies (51%) compared the reinforcing effects of high versus moderate/low preference items. See Table 1 for further details.

Descriptive and Qualitative Analyses of the Literature Choice responses. Making contact with an item was the most prevalent target response across populations and PA methods (Figure 1). Eye gaze and emotional behaviors (e.g., smiling/laughing) were used more frequently among individuals with severe or profound intellectual disability and some form of motor or sensory impairment (e.g., Fleming et al., 2010; Spevack, Wright, Yu, Walters, & Holborn, 2008). Contact, choice, and engagement responses were often used in PWPA procedures. By contrast, MSWO, FOPA, SSPA, and RRPA tended to use contact responses to Preference Assessment

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Table 1 Extended continued Methods IOA, PI design (effect)a

Procedure No. items

Choice response

Stimuli

b

Timec (min)

Settingd

E, L, S

Tangible

,30

C

7

Contact, pointing, reaching Contact

V

Tangible

35

C

. 95%, . 95%, RV, MB (+)

7

Contact

V

Tangible

,80

C

7

Contact

L, A, V, Au Tangible

,80

C

. 90%, -

6

Contact

L, S

,53

S

. 95%, . 85%, -

6

the exclusion of other choice and engagement responses. Conclusions based on the empirical literature. (a) Preference can be established on the basis of direct responses to stimuli (selection-based and engagement-based responses) and indirect responses to stimuli (gaze, emotional behavior); (b) indirect responses are more commonly used with individuals with severe or profound intellectual and physical/sensory disabilities; (c) among direct responses, engagement responses, as opposed to choice responses, are more frequently

Tangible

used among lower functioning individuals; and (d) PWPA and MSWO are in general, better suited to pointing responses, while FOPA, SSPA, and RRPA are better suited to engagement responses. Stimuli included in the PA. Types of stimuli were evenly distributed across participants with and without motor/sensory impairments and intellectual disability. As shown in Figure 2 (top graph), there were no strong associations between PA paradigms and categories of stimuli assessed. Activities likely to require engagement for some

Figure 1. Number of studies using a particular choice response across individuals with sensory and/or motor impairments and intellectual disability (S/M), individuals with intellectual disability without sensory/motor impairments (ID), and individuals with autism or other pervasive developmental disability without intellectual disability (ASD). J. Virue´s-Ortega et al.

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Figure 2. Number of participants across reviewed studies using a specific preference assessment method by type of stimuli (upper graph) and population (lower graph). A study may appear in multiple categories if more than one procedure was implemented. Notes. MSPA 5 Multiple stimulus preference assessment; PWPA 5 5 Pairwise preference assessment; FOPA 5 5 Free operant preference assessment; SSPA 5 Single stimulus preference assessment. duration (leisure and academic items) were more often assessed with FOPA and SSPA, while edibles were often assessed with MSWO and PWPA. However, edibles could potentially be assessed in engagement-based methods (FOPA, SSPA), for instance by providing unlimited access to the edibles during the assessment sessions. In general, the types of stimuli evaluated were restricted to edible and leisure items. Individuals with IDD demonstrated a greater preference toward edible items relative to leisure items (Bojak & Carr, 1999; DeLeon & Iwata, 1996; DeLeon, Iwata, & Roscoe, 1997). Sensory (e.g., visual, auditory, olfactory) and social stimuli were used on rare occasions. Social stimuli were never assessed using FOPA and SSPA and were the least frequently assessed stimuli in the other protocols (Figure 2). The studies reviewed suggest that preference toward social stimuli was frequently assessed with pictures (drawings, photos), which are only amenable to MSWO and PWPA methods. For instance, Nuernberger, Smith, Czapar, and Klatt (2012) assessed an array of social stimuli using MSWO and found that preference predicted the reinforcing value of social activities in two of the three 162

participants in the study; i.e., participants complied with task instructions to gain access to the social stimuli. However, the assessment of preference using pictorial stimuli may be amenable only to individuals with picture-tangible matching skills (Conyers et al., 2002; Clevenger & Graff, 2005). PWPA was most frequently used across populations (Figure 2, bottom graph), particularly with lower functioning individuals. Specifically, individuals with intellectual disability were assessed with PWPA more often than individuals with autism (58% vs. 41%), and participants with significant sensory and motor impairments were assessed with PWPA even more often (68%). This finding is consistent with the view that multiplestimulus presentation could hamper choice consistency if the stimulus array is too large. Conversely, by limiting the number of stimuli in each trial, paired- or single-stimulus arrangements may facilitate consistent choosing. Conclusions based on the empirical literature. (a) PA is adaptable to a large variety of stimuli including sensory, leisure, edible, social, and academic stimuli; (b) PWPA and MSWO Preference Assessment

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using pictures may be used to establish preference toward social stimuli among individuals with picture-tangible matching skills; (c) PWPA and SSPA may be better suited for lower functioning individuals that may not be able to choose reliably from an array of more than two items. Assessment time and setting characteristics. All PA protocols showed some level of linear relation between the number of items being assessed and administration time although this relation could not be clearly assessed for MSWO and FOPA (Figure 3). MSWO was the most timeefficient method. Namely, increases in the number of stimuli translated into very small increments in administration time. By contrast, PWPA resulted in greater time increments as the number of items in the array increased (Figure 3). SSPA showed the strongest relation between number of items and assessment time. For example, Worsdell et al. (2002) reported 35 min for assessing seven stimuli using SSPA, while Hagopian, Rush, Lewin, and Long (2001) required an estimated time of 156 min to establish an assessment hierarchy composed of 13 items. When considering the duration per item, MSWO and PWPA were more time efficient than FOPA and SSPA (Figure 3). Differences between MSWO and PWPA arise when a relatively high number of items are assessed. DeLeon and Iwata (1996) found that MSWO and PWPA produced similar results in identifying preferred stimuli, but the MSWO format required less than half the time

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compared to PWPA (means of 22 min vs. 53 min across participants). Another variation that can result in significant timesaving is the reduction or elimination of access time after a choice is made. Davis, Brock, and McNulty (2009) reported over 60% time saving when PA was conducted without access to the preferred items during or after the assessment. Further time saving was reported by using pictures as opposed to tangible items. Similar findings have been reported by Groskreutz and Graff (2009). Conclusions based on the empirical literature. (a) Selection-based PA are more time efficient than engagement-based PA; (b) MSWO is more time efficient than PWPA, particularly when a relatively high number of items are assessed; (c) restricting access time could result in increased time efficiency; and (d) using pictorial stimuli as opposed to tangible may result in modest increases in time efficiency. Setting. Assessments have taken place in school, home, and clinic/research settings (e.g., Fisher et al., 1992; Mangum, Roane, Fredrick, & Pabico, 2012; McCord, Iwata, Galensky, Ellingson, & Thomson, 2001). Most researchers indicated that the assessment took place in a space free from potential distractors (see for instance Fleming et al., 2010). Studies using PWPA with individuals with various degrees of intellectual disability describe the use of a table to present stimuli (Ardoin, Martens, Wolfe, Hilt, & Rosenthal, 2004; Conyers

Figure 3. Administration time by number of items and preference assessment paradigm. Nonparametric correlations (Tau-a) weighted by number of participants and durations reported per study. Separate durations were extracted for the minimum and maximum number of items and for each procedural variation within a study. FOPA 5 Free operant preference assessment; MSWO 5 Multiple-stimulus without replacement; PWPA 5 Pairwise preference assessment; RRPA 5 Response-restriction preference assessment; SSPA 5 Single stimulus preference assessment. J. Virue´s-Ortega et al.

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et al., 2002; DeLeon, Frank, Gregory, & Allman, 2009; Didden & de Moor, 2004; Fleming et al., 2010; Lanner, Nichols, Field, & Hanson, 2009; Lee et al., 2008; Lee, Yu, Martin, & Martin, 2010). By contrast, methods that relied on engagement responses (FOPA and SSPA) frequently required an empty observation room during the assessment (e.g., Sautter, LeBlanc, & Gillett, 2008; Spevack et al., 2008; Thomson, Czarnecki, Martin, Yu, & Martin, 2007; Worsdell et al., 2002). In summary, setting considerations to facilitate choosing responses include minimizing distractions and presenting items on table, tray, or mat when necessary. Preference hierarchies. Kenzer and Bishop (2011) have shown that caregivers provide inaccurate predictions of preference toward unfamiliar items, which could result in a high number of low preference items in the stimuli array. Therefore, caregivers’ report should be used as the basis for prescreening familiar items only. The few comparative studies among PA methods suggest that MSW and FOPA induce engagement with a low number of highly preferred items and are therefore inadequate to detect distinct preference hierarchies (DeLeon & Iwata, 1996; Hanley, Iwata, Lindberg, et al., 2003). Single-stimulus preference assessment generates hierarchies with a limited range in preference values and in general overreports preference (Hagopian, Crockett, van Stone, DeLeon, & Bowman., 2001). By contrast, MSWO, PWPA, and RRPA tend to generate a wider range of preference values and therefore more distinct preference hierarchies (DeLeon & Iwata, 1996; Hanley, Iwata, Lindberg, et al., 2003). Finally, FOPA with sessions of over 20 min can be useful for identifying high preference items that would endure long-term engagement periods. Preference values change when various engagement times are used during the assessment (e.g., 5 min vs. 30 min). Therefore, target engagement time could be integrated in the PA method by modifying the duration of FOPA sessions (Worsdell et al., 2002). Conclusions based on the empirical literature. (a) The identification of a wide range of preference values can be facilitated by the inclusion of a blend of novel items and caregiver-reported familiar items; (b) SSPA, FOPA, and MSW are not adequate to establish distinct preference hierarchies; (c) MSWO, PWPA, and RRPA can effectively identify distinct preference hierarchies; and (d) on ocassions when duration of engagement is an important target of assessment (e.g., academic, leisure items), FOPA and 164

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SSPA may be more valid approaches to the assessment of preference. Problem behavior. Two recent studies have examined the relation between PA method and variations in problem behavior during the assessment. These studies suggest that the function of the behavior mediates the presence of problem behavior during the PA (Iwata, Dorsey, Slifer, Bauman, & Richman, 1994). Specifically, problem behavior motivated by access to tangible items increases during both MSWO and PWPA, and remains low during FOPA. In contrast, problem behavior motivated by social attention increases during FOPA and remains low during MSWO and PWPA (Kang et al., 2010, 2011).

Clinical Decision-Making Model We have integrated the preliminary conclusions based on the empirical literature listed in the preceding section into a decision-making model (Figure 4). The proposed clinical decision-making model incorporates all major features of PA paradigms and allows practitioners and researchers to select the most suitable PA paradigm in a range of assessment scenarios. Specifically, this model incorporates the following considerations: (a) prerequisite skills of participants (e.g., presence of engagement/ choice responses, ability to discriminate among multiple items, ability to match tangible and pictorial stimuli); (b) time constraints of the practitioner (is assessment time a concern?); (c) need to identify distinct preference hierarchies with multiple stimuli as opposed to a single preferred stimulus; (d) need to avoid problem behavior during the assessment; and (e) need to identify long-duration, high-preference items (i.e., items that would endure long periods of engagement). The model includes 12 unambiguously posed questions that would lead the practitioner to the most suitable assessment method based on the practitioner’s assessment priorities (questions starting with ‘‘Do you need …?’’) and the characteristics of the client (questions starting with ‘‘Can the student …?’’). In addition to PWPA, MWPA, SSPA, and FOPA, the model also incorporates variations of these methods such as PA based on indirect or idiosyncratic responses (e.g., gaze, emotional behavior) and PA based on pictorial stimuli. Although not extensively used in the literature, we have also included response-restriction preference assessment due to its value as an engagement-based approach to the identification of preference hierarchies (Hanley, Iwata, Lindberg, et al., 2003). Finally, we have also Preference Assessment

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Figure 4. Decision tree for the selection of preference assessment methods. Notes. FOPA 5 Free operant preference assessment; IRPA 5 Indirect/idiosyncratic response preference assessment; MSWO 5 Multiple-stimulus without replacement; PA 5 Preference assessment; PWPA 5 Pairwise preference assessment; (P) 5 Pictorial stimuli; RA 5 Reinforcer assessment; RRPA 5 Responserestriction preference-assessment; SSPA 5 Single stimulus preference assessment. incorporated reinforcer assessment as a final resource on occasions when major PA methods are not feasible (see a discussion on reinforcer assessment methods by Ivancic, 2000).

Discussion In the present paper, we have systematically reviewed the literature on PA in individuals with IDD in search of the distinctive attributes of PA methods. We have summarized the various attributes of PA methods in terms of: (a) choice responses, (b) stimuli included in the PA, (c) assessment time, (d) setting characteristics, (d) preference hierarchies, and (e) presence of problem behavior. Findings on these attributes were subsequently integrated in a systematic decision-making model or decision tree to provide a user-friendly approach for clinicians to make informed decisions when selecting PA methods (Figure 4). J. Virue´s-Ortega et al.

The following case studies illustrate the use of the proposed decision-making model. Garry is a 25year old male with profound intellectual disability and cortical blindness. He has no instructional control and does not present any choice or engagement responses. He engages almost continuously in various self-stimulatory behaviors including clapping and pounding his chair tray. His therapist aims to identify specific sensory stimuli that could be used as rewards in teaching programs and would have used the decision tree as follows: 1 (‘‘Do you wish to assess preference toward social stimuli?’’ No) R 2 (‘‘Can the student display engagement or choice responses?’’ No) R 8 (‘‘Can the student engage in indirect responses?’’ Yes) R Indirect/idiosyncratic preference assessment. In other words, Garry would be a good candidate for a PA based on indirect responses (e.g., monitoring variations of self-stimulatory behavior as a function of the presentation of various sensory stimuli). 165

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Gloria is 17-year-old girl with moderate intellectual disability who has some speech, and is able to choose among objects by naming them or pointing to photos of the items. Her teacher wants to determine Gloria’s preference toward various leisure and social activities that could be used as rewards in a token economy system in class. Examples of the activities that will be assessed include phoning her mother, drawing in the computer, and buying candy with a friend, among others. The teacher would like to establish a distinct hierarchy including multiple activities, which would increase the likelihood that one or more of these activities would be available when needed. Gloria’s teacher would have proceeded with the decision tree as follows: 1 (‘‘Do you need to establish preference toward social stimuli?’’ Yes) R 7 (‘‘Can the student match reliably pictorial and tangible items?’’ Yes) R 12 (‘‘Can the student choose reliably from more than two stimuli?’’ Yes) R MSWO (P). Therefore, MSWO using pictorial stimuli would have been the most suitable PA method for Gloria. Finally, Billy is a 6-year-old boy with severe intellectual disability and frequent tantrums associated with the transition across leisure activities (e.g., stop playing with crayons and start playing with action figures). His teacher has attempted to assess Billy’s preferences towards various leisure activities, but refrained from doing so owing to his frequent tantrums. The selection of a PA method for Billy would have likely followed this sequence: 1 (‘‘Do you need to assess preference toward social stimuli?’’ No) R 2 (‘‘Can the student display engagement or choice responses?’’ Yes) R 3 (‘‘Do you need to avoid tangible-maintained problem behavior?’’ Yes) R FOPA. Free-operant preference assessment would have allowed the teacher to assess preferences based on engagement toward various items while reducing the likelihood of tantrums. There are several limitations to the present study. First, the proposed model is dependent on the evidence currently available on PA. As such, the proposed model is subject to change. In connection to this, it is possible that PA procedures that have been explored more often in the applied literature are more clearly integrated in our decision tree (e.g., PWPA vs. RRPA). Finally, the proposed decision-making model should be the subject of a detailed content and face validity analyses to establish its usability and 166

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potential for uptake by special education teachers, behavior analysts, and other practitioners.

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Received 4/23/2013, accepted 4/24/2013. Authors: Javier Virue´s-Ortega (e-mail: j.virues-ortega@ auckland.ac.nz, The University of Auckland, School of Psychology, Victoria Street West, Auckland 1142, New Zealand; Kristen Pritchard, St. Amant School; Robin L. Grant, University of Manitoba; Sebastian

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EAAIDD DOI: 10.1352/1944-7558-119.2.151

North, University of Manitoba; Camilo HurtadoParrado, University of Manitoba & Konrad Lorenz Fundacio´n Universitaria; May S. H. Lee, University of Manitoba; Bev Temple, University of Manitoba; Fla´via Julio, University of Manitoba; C. T. Yu, University of Manitoba & St. Amant Research Centre and the Knowledge Translation in Developmental Disabilities (KATYDID) team.

Preference Assessment