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Developing a
CAT
What is CAT?
CAT is an algorithm
We need to break down and specify all
aspects
Choice of major algorithms
Subalgorithms
Input parameters
Item bank needs
CAT Components
1. Calibrated item bank
2. Starting rule
3. Item selection rule
4. Scoring rule
5. Stopping rule
We must provide validity documentation on
each
Algorithms
inside your
testing
engine
Test development
side
Background
CAT remains underutilized. What are the
barriers?
What do you think?
Cost
Complexity
Few guidelines on how to develop one
Background
We have approximately 4 decades of technical
research on CAT
Numerous books and other resources
(Rudner’s tutorial) on what CAT is and how it
works
Discussions of issues (Wise & Kingsbury,
2000)
Very few resources on how to develop a CAT
Background
Best existing resource: descriptions of
current CAT programs
Sands, Waters, & McBride (1997): ASVAB
Elements of Adaptive Testing: Part 2 = 5
examples
JATT issue on CAT
Background
Framework, not complete recipe
Identify choices for your org and best way
to investigate/decide
Leads to better quality in the end
Also the foundation for validity arguments
Why did you choose certain things?
Seq. Stage Primary work
1 Feasibility, applicability, and
planning studies
Monte carlo simulation;
business case evaluation
2 Develop item bank content or
utilize existing bank
Item writing and review
3 Pretest and calibrate item
bank
Pretesting; item analysis
4 Determine specifications for
final CAT
Post-hoc or hybrid
simulations
5 Publish live CAT Publishing and
distribution; software
development
The 5 step model
1. Feasibility, applicability, planning
Big question: is CAT worth
the investment?
If so, how can we develop a
project plan and timeline?
1. Feasibility, applicability, planning
Answer: simulations
Simulate how a CAT would operate under
specified conditions
IVs
 Item bank size
 Item quality
 Desired precision
DVs
 Average test length
 Accuracy: CAT θ vs. true θ (or full bank)
1. Feasibility, applicability, planning
For those newer to CAT…
Three types of simulations
Monte Carlo
Post hoc (real data)
Hybrid
1. Feasibility, applicability, planning
At this point, real data not likely, so Monte
Carlo
Generate plausible situations
Item bank: 100, 200, 300…
Item quality: a = 0.7, 0.8…; spread of b
Desired precision: SEM = 0.2, 0.3, 0.4…
Compare results to each other and fixed forms
Base values on reality (e.g., mean a)
1. Feasibility, applicability, planning
Think of the results table you want to see
Bank size Target SEM Mean test length Mean SEM
(current test) - 100 .32
200 0.30 ? ?
200 0.40 ? ?
300 0.30 ? ?
300 0.40 ? ?
1. Feasibility, applicability, planning
Software will do this for you, allowing you to
simulate CATs for thousands of examinees in
seconds
CATSim (ASC)
WinGen (Han)
FireStar (Choi)
You can then easily set up an experiment with
a wide range of conditions, and run a
simulation for each
Workshop by Cito on this
1. Feasibility, applicability, planning
1. Feasibility, applicability, planning
Example takeaway:
CAT with bank of 300 items and SEM=0.25
has average of 53 items
Current fixed test has 100 items, SEM=0.23
in middle and 0.35+ beyond θ of ±1.5
CAT will make test more accurate for
extreme examinees, about same accuracy
for middle, but with 50% reduction
1. Feasibility, applicability, planning
Another question: Business Case Evaluation
Example:
You deliver 100,000 tests per year
You estimate $20/hour seat time
Reducing a test from 2 hours to 1 hour then saves
$2 million
More difficult to estimate for K-12 – cost is not seat
time but time away from instruction
2. Develop item bank
Now that we have an idea what we need, we
need to build it
CAT-based considerations:
Difficulty spread
Anticipated exposure/security issues
TIF adequacy
Normal considerations
Content blueprints
Cognitive level
3. Pretesting and analysis
Must pretest items to obtain bank
calibration
Two situations
New test, new scale: present large amounts of
items to examinees
Existing test, old scale: seed items
Obviously will take longer time to pilot
Requires a linking study
3. Pretesting and analysis
Then calibrate, usually IRT
Also perform other due diligence
Dimensionality
DIF
Model fit
Distractor analysis
Remove/revise items based on stats?
Etc.
4. Determine final specifications
To publish a CAT, we need to specify
algorithms
Starting point
Item selection
Scoring
Termination criterion
Also subalgorithms, such as item exposure,
content, test length constraints
4. Determine final specifications
But we must have a reason for selecting
specifications
Validity documentation
Defensibility
Again, we turn to simulation studies
Define competing conditions
Big difference now: we have real data!
Post Hoc or Hybrid simulations
Example sim study
4. Determine final specifications
After determining psychometric
specifications, evaluate more practical
issues
For example, time limits; can’t really set
until you know how many items
CAT-ASVAB approach: set limits for 90-95% of
population
5. Publish live CAT
Once you have finalized your item bank and
CAT design, time to publish
Need to put everything into item banker and
CAT engine
First: obtain the item banker and CAT engine
If developing your own, this can be the biggest step
If purchasing, this is the easiest step
Epilogue: Maintaining CAT
Like fixed form testing, maintenance is
usually necessary
Check that performing as expected
Is termination criterion being satisfied?
Examinees hitting test length or other
constraints?
Average test length what you expected?
Exposure or security issues?
Thank you!
nthompson@assess.com
See PARE, Volume 16, #1

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Developing a Computerized Adaptive Test

  • 2. What is CAT? CAT is an algorithm We need to break down and specify all aspects Choice of major algorithms Subalgorithms Input parameters Item bank needs
  • 3. CAT Components 1. Calibrated item bank 2. Starting rule 3. Item selection rule 4. Scoring rule 5. Stopping rule We must provide validity documentation on each Algorithms inside your testing engine Test development side
  • 4. Background CAT remains underutilized. What are the barriers? What do you think? Cost Complexity Few guidelines on how to develop one
  • 5. Background We have approximately 4 decades of technical research on CAT Numerous books and other resources (Rudner’s tutorial) on what CAT is and how it works Discussions of issues (Wise & Kingsbury, 2000) Very few resources on how to develop a CAT
  • 6. Background Best existing resource: descriptions of current CAT programs Sands, Waters, & McBride (1997): ASVAB Elements of Adaptive Testing: Part 2 = 5 examples JATT issue on CAT
  • 7. Background Framework, not complete recipe Identify choices for your org and best way to investigate/decide Leads to better quality in the end Also the foundation for validity arguments Why did you choose certain things?
  • 8. Seq. Stage Primary work 1 Feasibility, applicability, and planning studies Monte carlo simulation; business case evaluation 2 Develop item bank content or utilize existing bank Item writing and review 3 Pretest and calibrate item bank Pretesting; item analysis 4 Determine specifications for final CAT Post-hoc or hybrid simulations 5 Publish live CAT Publishing and distribution; software development The 5 step model
  • 9. 1. Feasibility, applicability, planning Big question: is CAT worth the investment? If so, how can we develop a project plan and timeline?
  • 10. 1. Feasibility, applicability, planning Answer: simulations Simulate how a CAT would operate under specified conditions IVs  Item bank size  Item quality  Desired precision DVs  Average test length  Accuracy: CAT θ vs. true θ (or full bank)
  • 11. 1. Feasibility, applicability, planning For those newer to CAT… Three types of simulations Monte Carlo Post hoc (real data) Hybrid
  • 12. 1. Feasibility, applicability, planning At this point, real data not likely, so Monte Carlo Generate plausible situations Item bank: 100, 200, 300… Item quality: a = 0.7, 0.8…; spread of b Desired precision: SEM = 0.2, 0.3, 0.4… Compare results to each other and fixed forms Base values on reality (e.g., mean a)
  • 13. 1. Feasibility, applicability, planning Think of the results table you want to see Bank size Target SEM Mean test length Mean SEM (current test) - 100 .32 200 0.30 ? ? 200 0.40 ? ? 300 0.30 ? ? 300 0.40 ? ?
  • 14. 1. Feasibility, applicability, planning Software will do this for you, allowing you to simulate CATs for thousands of examinees in seconds CATSim (ASC) WinGen (Han) FireStar (Choi) You can then easily set up an experiment with a wide range of conditions, and run a simulation for each Workshop by Cito on this
  • 16. 1. Feasibility, applicability, planning Example takeaway: CAT with bank of 300 items and SEM=0.25 has average of 53 items Current fixed test has 100 items, SEM=0.23 in middle and 0.35+ beyond θ of ±1.5 CAT will make test more accurate for extreme examinees, about same accuracy for middle, but with 50% reduction
  • 17. 1. Feasibility, applicability, planning Another question: Business Case Evaluation Example: You deliver 100,000 tests per year You estimate $20/hour seat time Reducing a test from 2 hours to 1 hour then saves $2 million More difficult to estimate for K-12 – cost is not seat time but time away from instruction
  • 18. 2. Develop item bank Now that we have an idea what we need, we need to build it CAT-based considerations: Difficulty spread Anticipated exposure/security issues TIF adequacy Normal considerations Content blueprints Cognitive level
  • 19. 3. Pretesting and analysis Must pretest items to obtain bank calibration Two situations New test, new scale: present large amounts of items to examinees Existing test, old scale: seed items Obviously will take longer time to pilot Requires a linking study
  • 20. 3. Pretesting and analysis Then calibrate, usually IRT Also perform other due diligence Dimensionality DIF Model fit Distractor analysis Remove/revise items based on stats? Etc.
  • 21. 4. Determine final specifications To publish a CAT, we need to specify algorithms Starting point Item selection Scoring Termination criterion Also subalgorithms, such as item exposure, content, test length constraints
  • 22. 4. Determine final specifications But we must have a reason for selecting specifications Validity documentation Defensibility Again, we turn to simulation studies Define competing conditions Big difference now: we have real data! Post Hoc or Hybrid simulations
  • 24. 4. Determine final specifications After determining psychometric specifications, evaluate more practical issues For example, time limits; can’t really set until you know how many items CAT-ASVAB approach: set limits for 90-95% of population
  • 25. 5. Publish live CAT Once you have finalized your item bank and CAT design, time to publish Need to put everything into item banker and CAT engine First: obtain the item banker and CAT engine If developing your own, this can be the biggest step If purchasing, this is the easiest step
  • 26. Epilogue: Maintaining CAT Like fixed form testing, maintenance is usually necessary Check that performing as expected Is termination criterion being satisfied? Examinees hitting test length or other constraints? Average test length what you expected? Exposure or security issues?