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- 1. The Application of IRT using the Rasch Model in Constructing Cognitive Measures Carlo Magno, PhD De La Salle University-Manila 1
- 2. Outline• Psychometric Theory• Classical test Theory (CTT)• Item Response Theory (IRT)• Approaches in IRT• Issues in CTT• Advantages of the Rasch Model• Assumptions of the Rasch Model• Uses of the Rasch Model• Procedure in Rasch Model• Workshop 2
- 3. Psychometrics• Psychometrics concerns itself with the science of measuring psychological constructs such as ability, personality, affect and skills.• Research in psychology involves the measurement of variables in order to conduct further analysis. 3
- 4. Branches of Psychometric Theory• Classical Test Theory• Item Response Theory 4
- 5. Classical Test Theory (CTT)• Regarded as the “True Score Theory”• Responses of examinees are due only to variation in ability of interest• All other potential sources of variation existing in the testing materials such as external conditions or internal conditions of examinees are assumed either to be constant through rigorous standardization or to have an effect that is nonsystematic or random by nature. 5
- 6. Classical Test Theory (CTT) TO = T + E• The implication of the classical test theory for test takers is that test are fallible imprecise tools• Error = standard error of measurement Sm = S√ 1 - r• True score = M +- Sm = 68% of the normal curve 6
- 7. Normal curve One SE Range of True mean Scores 7
- 8. Focus of Analysis in CTT• frequency of correct responses (to indicate question difficulty);• frequency of responses (to examine distracters);• reliability of the test and item-total correlation (to evaluate discrimination at the item level) 8
- 9. Item Response Theory• Synonymous with latent trait theory, strong true score theory or modern mental test theory• More applicable for tests with right and wrong (dichotomous) responses• An approach to testing based on item analysis considering the chance of getting particular items right or wrong• Each item on a test has its own item characteristic curve that describes the probability of getting each particular item right or wrong given the ability of the test takers (Kaplan & Saccuzzo, 1997) 9
- 10. Logistic Item Characteristic Curve– A function of ability (θ) – latent trait– Forms the boundary between the probability areas of answering an item incorrectly and answering the item correctly 10
- 11. Approaches of IRT• One dimension (Rasch)→ One parameter model = uses only the difficulty parameter• Two dimension → Two parameter Model = difficulty and ability parameter• Three dimension (Logistic Model)→ Three Parameter Model = ability, correct response, item discrimination 11
- 12. Issues in CTT• A score is dependent on the performance of the group tested (Norm referenced)• The group on which the test has been scaled has outlived has usefulness across time – Changes in the defined population – Changes in educational emphasis• There is a need to rapidly make new norms to adopt to the changing times• If the characteristics of a person changes and does not fit s specified norm then a norm for that person needs to be created.• Each collection of norms has an ability of its own = rubber yardstick 12
- 13. Advantages of the Rasch Model• The calibration of test item difficulty is independent of the person used for the calibration.• The method of test calibration does not matter whose responses to these items use for comparison• It gives the same results regardless on who takes the test• The scores a person obtain on the test can be used to remove the influence of their abilities from the estimation of their difficulty. The result is a sample free item calibration. 13
- 14. Rasch Model• Rasch’s (1960) main motivation for his model was to eliminate references to populations of examinees in analyses of tests.• According to him that test analysis would only be worthwhile if it were individual centered with separate parameters for the items and the examinees (van der Linden & Hambleton, 2004). 14
- 15. Rasch Model• The Rasch model is a probabilistic unidimensional model which asserts that: (1) the easier the question the more likely the student will respond correctly to it, and (2) the more able the student, the more likely he/she will pass the question compared to a less able student. 15
- 16. Rasch Model• The model was enhanced to assume that the probability that a student will correctly answer a question is a logistic function of the difference between the students ability [θ] and the difficulty of the question [β] (i.e. the ability required to answer the question correctly), and only a function of that difference giving way to the Rasch model• Thus, when data fit the model, the relative difficulties of the questions are independent of the relative abilities of the students, and vice versa (Rasch, 1977). 16
- 17. Assumptions of the Rasch Model According to Fisher (1974)• (1) Unidimensionality. All items are functionally dependent upon only one underlying continuum.• (2) Monotonicity. All item characteristic functions are strictly monotonic in the latent trait. The item characteristic function describes the probability of a predefined response as a function of the latent trait.• (3) Local stochastic independence. Every person has a certain probability of giving a predefined response to each item and this probability is independent of the answers given to the preceding items. 17
- 18. Assumptions of the Rasch Model According to Fisher (1974)• (4) Sufficiency of a simple sum statistic. The number of predefined responses is a sufficient statistic for the latent parameter.• (5) Dichotomy of the items. For each item there are only two different responses, for example positive and negative. The Rasch model requires that an additive structure underlies the observed data. This additive structure applies to the logit of Pij, where Pij is the probability that subject i will give a predefined response to item j, being the sum of a subject scale value ui and an item scale value vj, i.e. In (Pij/1 - Pij) = ui + vj 18
- 19. Uses of the Rasch Model• Identifies items that are acceptable – items that are significantly different from 0 are good items• Indicates whether an item is extremely difficult or easy 19

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