item response theory. what’s wrong with the old approach? classical test theory –sample...

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  • Slide 1
  • Item Response Theory
  • Slide 2
  • Whats wrong with the old approach? Classical test theory Sample dependent Parallel test form issue Comparing examinee scores Reliability No predictability Error is the same for everybody
  • Slide 3
  • So, what is IRT? A family of mathematical models that describe the interaction between examinees and test items Examinee performance can be predicted in terms of the underlying trait Provides a means for estimating scores for people and characteristics of items Common framework for describing people and items
  • Slide 4
  • Some Terminology Ability We use this as a generic term used to describe the thing that we are trying to measure The thing can be any old thing and we need not concern ourselves with labeling the thing, but examples of the thing include: Reading ability Math performance Depression
  • Slide 5
  • The ogive Natural occurring form that describes something about people Used throughout science, engineering, and the social sciences Also, used in architecture, carpentry, photograph, art, and so forth
  • Slide 6
  • The ogive
  • Slide 7
  • Slide 8
  • The Item Characteristic Curve (ICC) This function really does everything: Scales items & people onto a common metric Helps in standard setting Foundation of equating Some meaning in terms of student ability
  • Slide 9
  • The ICC Any line in a Cartesian system can be defined by a formula The simplest formula for the ogive is the logistic function:
  • Slide 10
  • The ICC Where b is the item parameter, and is the person parameter The equation represents the probability of responding correctly to item i given the ability of person j.
  • Slide 11
  • b is the inflection point Item i b i =0.125
  • Slide 12
  • We can now use the item parameter to calculate p Lets assume we have a student with =1.0, and we have our b = 0.125 Then we can simply plug in the numbers into our formula
  • Slide 13
  • Using the item parameters to calculate p p = 0.705 i =1.00
  • Slide 14
  • Wait a minute What do you mean a student with an ability of 1.0?? Does an ability of 0.0 mean that a student has NO ability? What if my student has a reading ability estimate of -1.2?
  • Slide 15
  • The ability scale Ability is on an arbitrary scale that just so happens to be centered around 0.0 We use arbitrary scales all the time: Fahrenheit Celsius Decibels DJIA
  • Slide 16
  • Scaled Scores Although ability estimates are centered around zero reported scores are not However, scaled scores are typically a linear transformation of ability estimates Example of a linear transformation: (Ability x Slope) + Intercept
  • Slide 17
  • The need for scaled scores the kids will have negative ability estimates
  • Slide 18
  • The Two Scales of Measurement Reporting Scale (Scaled Scores) Student/parent level report School/district report Cross year comparisons Performance level categorization The Psychometric Scale ( ) IRT item and person parameters Equating Standard setting
  • Slide 19
  • Unfortunately, life can get a lot worse Items vary from one another in a variety of ways: Difficulty Discrimination Guessing Item type (MC vs. CR)
  • Slide 20
  • Items can vary in terms of difficulty Ability of a student Easier item Harder item
  • Slide 21
  • Items can vary in terms of discrimination Discrimination is reflected by the pitch in the ICC Thus, we allow the ICCs to vary in terms of their slope
  • Slide 22
  • Good item discrimination 2 close ability levels Noticeable difference in p
  • Slide 23
  • Poor item discrimination smaller difference Same 2 ability levels
  • Slide 24
  • Guessing This item is asymptotically approaching 0.25
  • Slide 25
  • Constructed Response Items
  • Slide 26
  • Items and people Interact in a variety of ways We can use IRT to show that there exists a nice little s-shaped curve that shows this interaction As ability increases the probability of a correct response increases
  • Slide 27
  • Advantages of IRT Because of the stochastic nature of IRT there are many statistical principles we can take advantage of A test is a sum of its parts
  • Slide 28
  • The test characteristic curve A test is made up of many items The TCC can be used to summarize across all of our items The TCC is simply the summation of ICCs along our ability continuum For any ability level we can use the TCC to estimate the overall test score for an examinee
  • Slide 29
  • Several ICCs are on a test
  • Slide 30
  • The test characteristic curve
  • Slide 31
  • From an observed test score (i.e., a students total test score) we can estimate ability The TCC is used in standard setting to establish performance levels The TCC can also be used to equate tests from one year to the next The test characteristic curve
  • Slide 32
  • Estimating Ability Total score = 3 Ability0.175
  • Slide 33
  • Psychometric Information The amount that an item contributes to estimating ability Items that are close to a persons ability provide more information than items that are far away An item is most informative around the point of inflection
  • Slide 34
  • Item Information Item is most informative here because this is where we can discriminate among nearby values
  • Slide 35
  • Item Information Item is much less informative at points along where there is little slope in the ICC
  • Slide 36
  • Test Information Test information is the sum of item information Tests are also most informative where the slope of the TCC is the greatest Information (like everything else in IRT) is a function of ability Test information really is test precision
  • Slide 37
  • Lets start with a TCC
  • Slide 38
  • Information Functions We can evaluate information at a given cutpoint BP/P
  • Slide 39
  • Information and CTT CTT has reliability and of course the famous coefficient IRT has the test information function Test quality can be evaluated conditionally along the performance continuum In IRT information is, conveniently, reciprocally related to standard error
  • Slide 40
  • Standard Error as a function of ability 0.175 SE = 0.25
  • Slide 41
  • Standard Error of Ability Total score = 3 Ability0.175
  • Slide 42
  • Standard Error of Ability Total score = 3 Ability0.175 Confident region of ability estimate
  • Slide 43
  • Item Response Theory A vast kingdom of equations, and dizzying array of complex concepts Ultimately, we use IRT to explain the interaction between students and test items The cornerstone to IRT is the ICC which depicts that as ability increases the chances of getting an item correct increases
  • Slide 44
  • Item Response Theory Everything in IRT can be studied conditionally along the performance continuum The CTT concept of reliability is what we call test information, and we can think of this as being a function of test precision SE is related to information and can also be studied along
  • Slide 45
  • The Utility of Item Response Theory Can be used to estimate characteristics of items and people Can be used in the test development process to maximize information (minimize SE) at critical points along Can even be used for test administration purposes