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Latent Change in Discrete Data: Rasch Models Taehoon Kang

Latent Change in Discrete Data: Rasch Models

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Latent Change in Discrete Data: Rasch Models. Taehoon Kang. Item Response Theory. Modern test theory to analyze test result data in item level Basic Assumptions 1) Unidimensionality 2) Local independence. 1) Unidimensionality. Only one latent ability decides item performance of - PowerPoint PPT Presentation

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Page 1: Latent Change in Discrete Data:  Rasch Models

Latent Change in Discrete Data: Rasch Models

Taehoon Kang

Page 2: Latent Change in Discrete Data:  Rasch Models

Item Response Theory Modern test theory to analyze test

result data in item level Basic Assumptions 1) Unidimensionality 2) Local independence

Page 3: Latent Change in Discrete Data:  Rasch Models

1) Unidimensionality Only one latent ability decides item performance of an examinee

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Page 4: Latent Change in Discrete Data:  Rasch Models

2) Local Independence Once the ability influencing item

performance is taken into account, the responses to items are statistically independent.

P(U1 ,U2 ,…, Un|) = P(U1|) P(U2|) … P(Un|)

Page 5: Latent Change in Discrete Data:  Rasch Models

Unidimensional-Dichotomous IRT 1PL model (Rasch Model): item difficulty 2PL model: item difficulty and discrimination 3PL model: item difficulty, discrimination, and guessing

<< Item Characteristic Curve (ICC) of each model >>

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Page 6: Latent Change in Discrete Data:  Rasch Models

Unidimensional-Polytomous IRT Used when items are scored using more than two score

categories (graded responses, ordered categories, partial credits; 0,1,2,…m)

An item has three categories (0,1,2)

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category 0

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category 2

Page 7: Latent Change in Discrete Data:  Rasch Models

Extensions of Unidimensional IRT

1) Multidimensional IRT models : adding continuous latent ability variables

2) Mixture IRT models : latent subgroups (adding categorical

variables)

Page 8: Latent Change in Discrete Data:  Rasch Models

1) Multidimensional IRT models

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Page 9: Latent Change in Discrete Data:  Rasch Models

2) Mixture IRT models When the observed data are generated by two or more latent classes

of individuals so that within each class the unidimensional IRT model holds but with different item parameters between the classes, the unidimensional model is generalized to a mixture IRT model.

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Strategy A class

Stragety B class

Page 10: Latent Change in Discrete Data:  Rasch Models

Rasch Models for ChangeTo apply various IRT models to the analysis of

change,this article introduces three models.

1) The Unidimensional Rasch Model

2) The Two-Dimensional Rasch Model

3) The Mover-Stayer Mixed Rasch Model

Page 11: Latent Change in Discrete Data:  Rasch Models

The data structure used in this article

Three arithmetic word items at second and third grade

T1 T2

I1 I2 I3 I4 I5 I6

N=1,030

0001

0 0 1 1 0 1

0002

1 0 0 1 1 1

… … …

1030

0 0 1 1 0 1

Page 12: Latent Change in Discrete Data:  Rasch Models

1) The Unidimensional Rasch Model This Model assumes the change of all examinee from T1 to T2 are same. Instead of looking at the change of abilities, they say the change of item

difficulties(eta) reflects the individuals’ global amount of change on the latent continuum.

Every item has same eta.

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ICC at T2

ICC at T1

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Page 13: Latent Change in Discrete Data:  Rasch Models

2) The Two-Dimensional Rasch Model In this model, the estimated ability of an examinee at T1 becomes the first

dimension ability and the estimated ability at T2 becomes the second dimension. Through the difference of two estimated abilities, we can get person-specific change. Item difficulty parameters are specified to be invariant over time.

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ICC at Ti and T2

ability at T1 ability at T2

Page 14: Latent Change in Discrete Data:  Rasch Models

3) The Mover-Stayer Mixture Rasch Model

There are two latent subgroups. One is the Mover group (c=1) in which ability change occurs. The other is Stayer group (c=2) in which no change occurs. Here, like the unidimensional Rasch Model, It is assumed that every examinee in Mover group has same change from T1 to T2.

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ICC of Mover and Stayer grops at T1

Page 15: Latent Change in Discrete Data:  Rasch Models

ICC of Mover-Stayer groups at T2

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ICC of Stayer grops at T2

ICC of Mover grops at T2

Page 16: Latent Change in Discrete Data:  Rasch Models

Results (1)

Page 17: Latent Change in Discrete Data:  Rasch Models

Result (2)

Page 18: Latent Change in Discrete Data:  Rasch Models

Questions

1) How can we test the assumption of unidimensionality?

- Factor Analysis : 30 % (?) - parallel analysis

Scree Plot

Analysis weighted by FREQ

Component Number

987654321

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4

3

2

1

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Page 19: Latent Change in Discrete Data:  Rasch Models

Questions

2) In each of three Rasch models for analyzing change introduced in this article, what is going on the individual differences about change?

In the unidimensional Rasch model and the Mover-Stayer mixture Rasch Model, they don’t allow individual differences in change. Only in the two-dimensional Rasch model, we could get the person-specific change.

Page 20: Latent Change in Discrete Data:  Rasch Models

Questions 3) In Mover-Stayer Mixture Rasch model, they

are dealing with only two latent groups. If this model doesn’t fit the data well, what kind of extensions of latent groups could be possible?

Quantitative extensions : stayer/ slow mover /fast mover Qualitative extensions : Stayer who doesn’t have profit from school instruction/ Stayer who, in grade2, can solve items for grade3 well/ Mover whose ability increases well/ Mover who moves backward