Sonntag, 22. Januar 2017


  • Pressemitteilung BoxID 68048

Psychology Science Quarterly Nr. 3/08: The use of LLTM - Cognitive modelling and item-technology analyses

(lifePR) (Lengerich, ) "The "linear logistic test model" (LLTM) breaks down the item parameter of the Rasch model into a linear combination of certain hypothesized elementary parameters. Apart from the originally intended primary application of generating an indefinite number of items composed of whichever item difficulties the examiner chooses, there are many other potential applications. They all deal with measuring certain item administration effects", Klaus Kubinger (Vienna) reports (Psych Sci Q 3/2008).

The author "illustrates several of these approaches as well as how to design data sampling using the respective LLTM's structure matrix. These approaches deal with:

- Rasch model item calibration using data sampled consecutively in time but partly from the same examinees;
- measuring position effects of item presentation, in particular, learning and fatigue effects - specific for each position, as well as linear or non-linear
- measuring content-specific learning effects
- measuring warming-up effects
- measuring effects of speeded item presentation
- measuring effects of different item response formats. It is pointed out that the given LLTM approaches have the advantage of "elegance," as a hierarchical system of concurrent (alternative) hypotheses can be tested."

The Contents of Psych Sci Q (formerly Psychologische Beiträge) 3/2008:

Klaus D. Kubinger
On the revival of the Rasch model-based LLTM: From constructing tests using item generating rules to measuring item administration effects

Susan E. Embretson & Robert C. Daniel
Understanding and quantifying cognitive complexity level in mathematical problem solving items

Philipp Sonnleitner
Using the LLTM to evaluate an item-generating system for reading comprehension

Heinz Holling, Helen Blank, Karoline Kuchenbäcker & Jörg-Tobias Kuhn
Rule-based item design of statistical word problems: A review and first implementation

Julia Hahne
Analyzing position effects within reasoning items using the LLTM for structurally incomplete data

Christine Hohensinn, Klaus D. Kubinger, Manuel Reif, Stefana Holocher-Ertl, Lale Khorramdel & Martina Frebort
Examining item-position effects in large-scale assessment using the Linear Logistic Test Model

Yiyu Xie & Mark Wilson
Investigating DIF and extensions using an LLTM approach and also an individual differences approach: an international testing context

Karen Draney & Mark Wilson
A LLTM approach to the examination of teachers’ ratings of classroom assessment tasks

Renato Miceli, Michele Settanni & Giulio Vidotto
Measuring change in training programs: An empirical illustration

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