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may be of little use to caregivers or other individuals without specialized training. Lastly, preference and choice methodologies seem to be beneficial for a wide range of individuals with severe to profound disabilities. Implementation of choice and preference technologies is effective for identifying various potentially reinforcing stimuli (e.g., edible items, leisure items, smells). Less apparent are strategies that are more effective with specific populations or individual characteristics. The effectiveness of the most widely used preference assessments (i.e., PC & MSWO) may be influenced by each individual’s behavioral repertoire or physical ability. For example, if an individual has a physical impairment (e.g., cerebral palsy) an MSWO may be ineffective as a means of determining preference due to limited mobility, whereas a PC assessment using eye gaze may be more appropriate (Fleming et al., 2010). Clevenger and Graff (2005) demonstrated that pictorial preference assessments were more appropriate for individuals who engaged in object-topicture matching. The effectiveness of any preference assessment methodology to identify reinforcers may be impacted by individual differences such as behavioral repertoire and physical ability. One method that may be promising is assessing prerequisite skills that may be required for an assessment to be applicable to a given population of individuals. Thomson et al. (2007) tested the efficacy of SS and PC preference assessments with 11 individuals diagnosed with severe disabilities. In the preassessment phase, participants were assessed with the Assessment of Basic Learning Abilities (ABLA; Kerr, Meyerson, & Flora, 1977) to determine individual skill levels. PC and SS assessments were then conducted with the participants. The data indicated that either assessment was effective in identifying highand low-preference items for individuals who had mastered two-choice discriminations on the ABLA. Although these results are positive, Thomson et al. did not conduct reinforcer assessments to determine the predictive validity of the assessments. Additionally the authors did not assess the effectiveness of the preference assessments with individuals that did not demonstrate mastery of two item discriminations on the ABLA. With regard to future research, it may be especially relevant to determine if there are prerequisite skills that influence the results of specific preference assessment methodologies and resulting choice interventions or specific individuals who may benefit from one type of assessment over another. No clear criteria exist to guide the use of one preference assessment over another, to determine who would benefit from a specific type of preference assessment, how much of the stimulus should be available (e.g., duration, magnitude), or what overall method is the most efficient and effective. Graff and Gibson (2003) reported mixed results when assessing preferences using pictures of stimuli and similar results were reported by Cobigo et al. (2009). These results indicate the potential for a requisite skill set for this type of assessment to be accurate. Another area of future research is on the complexity of preference. For example, what magnitude of a preferred stimulus is most effective, or how durable are the results of a preference assessment? Further understanding of preference may be required for assessments and interventions based on choice to be effective in less controlled environments (e.g., home or classroom). Current research supports the efficacy of using highly preferred items as reinforcers under lean schedules of reinforcement and that this effect is persistent in the presence of low preference stimuli (e.g., Glover et al., 2008). It is unknown if response allocation in the presence of other highly preferred items or moderately preferred items would yield the same or similar results. Additionally, methods for increasing the effectiveness of low preference stimuli to function as reinforcers may also be useful. Glover et al. (2008) demonstrated the reinforcing effectiveness of low preference items at a low schedule requirement. Further investigating the mechanisms that underlie preference may also shed light on methods of increasing preference measures for low preference items. This information may also be useful for increasing the effectiveness of lower preferred items to function as reinforcers (Hanley et al., 2003). New research may also seek to determine the extent to which magnitude influences preference for items. Altering the magnitude Review of Choice and Preference Assessment / 591
of a preferred item while continuing to observe an increased response rate may have a great impact on the use of choice interventions in non-clinical settings. If a teacher, parent, or paraprofessional can deliver a small amount of a reinforcer and continue to observe persistent levels of a target behavior, the probability of that intervention continuing may increase. The effects of differing magnitudes may also impact the long-term effectiveness of choice interventions to decrease challenging behavior across settings and individuals. For example, magnitude of reinforcement may be increased or decreased depending on the probability of challenging behavior occurring in a specific environment. Another area of future research that has vast practical implications is training. Of the research currently reviewed, training participants consisted of professionals who were required to implement specific procedures as part of their job. Future research should investigate training packages to determine what type of training is the most efficient in teaching parents or other non-professionals to implement choice interventions and preference assessments. Further research in training professionals is also warranted. Only three studies presented data on skill acquisition of the intervention implementers (Machalicek et al., 2009; Reid et al., 2007; Roscoe & Fisher, 2008; Roscoe et al., 2006). Future research should focus on what aspects of the training procedures were necessary and sufficient for skill acquisition to occur. These procedures should also be implemented with parents, paraprofessionals, teachers, and other direct-care providers to a greater degree to further demonstrate efficacy. The current literature base contains support for choice interventions to be classified as an evidence-based practice for individuals with severe to profound disabilities. One limitation is the lack of data on treatment fidelity. Future researchers should present this data in order to provide more support for a functional relation. Documenting treatment fidelity in a controlled setting may also aid researchers in constructing effective training programs for parents and other caregivers, which would allow for a higher degree of control over training before implementing the procedures in a more naturalistic context. Information from the current review supports the efficacy and utility of preference assessment methodologies and choice interventions for individuals with severe to profound disabilities and extends the findings of Cannella et al. (2005) and Lancioni et al. (1996). Overall, the area of choice and preference research seems to be shifting toward a more complete explanation of the mechanisms of preference, rather than primarily focusing on assessment. As choice and preference methodologies are refined, it will be important to collect data on the integrity of treatment implementation, the generality of methods to natural contexts, and the training of caregivers. Advancements in these areas are needed and would lend further support for choice and preference methodologies as evidence-based practice for individuals with severe to profound disabilities. References Ahearn, W. H., Clark, K. M., DeBar, R., & Florentino, C. (2005). On the role of preference in response competition. Journal of Applied Behavior Analysis, 38, 247–250. Cannella, H. I., O’Reilly, M. F., & Lancioni, G. E. (2005). Choice and preference assessment research with people with severe to profound developmental disabilities: A review of the literature. Research in Developmental Disabilities, 26, 1–15. Cannella-Malone, H. I., DeBar, R. M., & Sigafoos, J. (2009). An examination of preference for augmentative and alternative communication devices with two boys with significant intellectual disabilities. Augmentative and Alternative Communication, 25, 262–273. Carlson, J. I., Luiselli, J. K., Slyman, A., & Markowski, A. (2008). Choice-making as an intervention for public disrobing in children with developmental disabilities. Journal of Positive Behavior Interventions, 10, 86–90. Ciccone, F. J., Graff, R. B., & Ahearn, W. H. (2007). Long-term stability of edible preferences in individuals with developmental disabilities. Behavioral Interventions, 22, 223–228. Clevenger, T. M., & Graff, R. B. (2005). Assessing object-to-picture and picture-to-object matching as prerequisite skills for pictoral preference assessments. Journal of Applied Behavior Analysis, 38, 543–547. Cobigo, V., Morin, D., & Lachapelle, Y. (2009). A method to assess work preferences. Education & Training in Developmental Disabilities, 44, 561–572. DeLeon, I. G., Frank, M. A., Gregory, M. K., & 592 / Education and Training in Autism and Developmental Disabilities-December 2011
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Education and Training in Autism an
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March 2012 Manuscripts Accepted for
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new line of research, the review ho
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TABLE 1—(Continued) Overview of H
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TABLE 1—(Continued) Overview of H
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Figure 1. MP3 Player (Model: SM-320
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Figure 3. Sony-Ericsson K800. Retri
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computer with three middle school s
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Figure 6. Video iPod. Apple, Inc. h
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example, in contrast to previous st
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using public transportation. Device
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accessible software to increase ind
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(14.3%), a unique curriculum (13.8%
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TABLE 1 Student Characteristics Stu
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eos” was highlighted. Students th
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ment was 100% for baseline and main
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Figure 3. Percentage of independent
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how to program and operate the equi
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Individuals with Disabilities Educa
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TABLE 1 Video Prompting Studies Con
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TABLE 1—(Continued) Video Prompti
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Length of video clips. Researchers
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to learn three cooking tasks. Resul
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Van Laarhoven and Van Laarhoven-Mye
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Dogoe, M., & Banda, D. R. (2009). R
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TABLE 1 Summary of Demographic and
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TABLE 2 Summary of Outcome Variable
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with students in preschool (Akmanog
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with MoID acquiring some of their p
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Vocational task. To date one study
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Page 67 and 68: Retardation and Developmental Disab
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Page 71 and 72: TABLE 1 Student Demographic Informa
Page 73 and 74: Figure 1. Example of the mini-poste
Page 75 and 76: Experimental Design This study empl
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Page 85 and 86: Figure 1. Correct responses for Joe
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Page 89 and 90: then be used as a template for quic
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Page 93 and 94: making choices is clearly not in th
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Page 97 and 98: transportation, commitment) to prov
Page 99 and 100: can be made. Last, because consumer
Page 103 and 104: TABLE 1 Studies Listed According to
Page 105 and 106: TABLE 1—(Continued) Studies Liste
Page 111 and 112: (i.e., MSW) and duration-based (i.e
Page 113 and 114: 2008), and increasing adaptive beha
Page 115: port difficulty implementing the in
Page 119 and 120: Kreiner, J., & Flexer, R. (2009). A
Page 123 and 124: autism on the CARS. Regarding socia
Page 125 and 126: other effect size measures and with
Page 127 and 128: quests during baseline sessions. Du
Page 129 and 130: ulation was generally clear and he
Page 131 and 132: communication training as a treatme
Page 133 and 134: encing allergy symptoms (one partic
Page 135 and 136: Assessments Preference assessment.
Page 137 and 138: Interobserver agreement. A second o
Page 139 and 140: challenging behavior in the presess
Page 141 and 142: tion of the preferred item signaled
Page 143 and 144: ior: An analysis of the literature.
Page 145 and 146: moderate intellectual disabilities
Page 147 and 148: Effects of the STAR intervention pr
Page 149 and 150: Copeland, S. R. (2002). See citatio
Page 151 and 152: 126. Ezell, D., & Klein, C. (2003).
Page 153 and 154: computer-based instruction program.
Page 155 and 156: sight words to students with mental
Page 157 and 158: Mastropieri, M. A. (2009). See cita
Page 159 and 160: 262. Noyes, D. A., & Sax, C. L. (20
Page 161 and 162: Rispoli, M. (2009). See citation #1
Page 163 and 164: Sparks, S. (2010). See citation #87
Page 165 and 166: Vlaskamp, C. (2008). See citation #
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INDEX—VOLUME 46,
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Runes, Sandra. See Hausmann-Stabile
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Review of video prompting studies w
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Look! I'm in College! DVD Look, I
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13th International Conference on Au
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