Using Pictures Depicting App Icons to Conduct an MSWO Preference Assessment on a Tablet Device
Laminated app-icon cards let you run a quick MSWO to find tablet reinforcers for adults with disabilities—top pick worked as a reinforcer for 5 of 6 clients.
01Research in Context
What this study did
Six adults with disabilities served in a day program. Staff wanted to know which iPad apps they liked most.
The team printed tiny app icons, laminated them, and ran a quick MSWO. Each adult picked icons until a clear order showed.
Next, staff gave the top app for doing a simple work task. They counted if the app kept the adults working.
What they found
All six adults finished the MSWO in under five minutes. Every person had a clear favorite app.
When the favorite app was given for work, five of the six adults stayed on task longer. The sixth stayed the same.
How this fits with other research
Clayton et al. (2020) used photos to find what zoo tigers liked. Both studies show pictures can stand in for real items during preference checks.
Fine et al. (2005) and Chang et al. (2016) also used top picks to boost walking or work. Hoffmann adds a fast tablet twist: no need to own every toy, just show the icon.
Turk et al. (2010) proved phones can teach job skills. Together, these papers say tablets and phones are not just teaching tools—they also tell us what motivates our clients.
Why it matters
You can run a full MSWO without buying apps or toys. Print the icons, laminate, and go. If the client’s top icon works as a reinforcer, load that app during breaks. If not, try the second icon. This trick saves money and keeps your reinforcer pool fresh.
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02At a glance
03Original abstract
Identifying preferred content on tablet devices may be important when using such devices in programming. This study included six adults with disabilities and examined using laminated picture cards depicting application (app) icons to conduct multiple-stimulus without replacement (MSWO) preference assessments of iPad content. Following identification of preference hierarchies, we conducted reinforcer assessments to validate preference assessment results by demonstrating that selected apps functioned as reinforcers. We identified preference hierarchies for all participants, and the highest preferred app functioned as a reinforcer for a vocational task in five out of six participants’ reinforcer assessments.
Behavior Analysis in Practice, 2019 · doi:10.1007/s40617-018-00309-2