LLTMLong Lead Time Material
LLTMLinear Logistic Test Model
LLTMLink Layer Tunneling Mechanism
LLTMLow Land Tiger Meet
LLTMLaughing Loudly to Myself
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References in periodicals archive ?
The closer the Rasch model based item parameters and the LLTM reconstructed item parameters (cf.
LLTM assumes that the difficulty of this item is the sum of the difficulty of these three basic operations.
These last studies have primarily used a quantitative approach, making use of traditional statistical techniques such as multiple regression and ANOVA; however, various studies can also be found where the LLTM is used to estimate the predictive power of certain characteristics of logical reasoning and reading comprehension items (Embretson & Wetzel, 1987; Sheehan & Mislevy, 1990; Gorin, 2005; Sonnleitner, 2008; Attoresi, Picon, Abal, Aguerri & Galibert, 2009; Gilbert, Picon, Lozzia, Aguerri & Attorresi, 2010; Poinstingl, 2009).
The different studies that have used the LLTM for this purpose, along with those that used classical quantitative data analysis techniques, have served to justify the sources of difficulty that will be explained below.
In section 1.3, the appropriateness of the model is checked with the help of Log-Linear Transformation Model (LLTM) and Log-Exponential Transformation Model (LETM).
In this section we will test the appropriateness of the reverse and direct methods by using Log-Linear Transformation Model (LLTM) and Log-ExponentialTransformation Model (LETM).
Palabras clave: modelo LLTM, modelo de Rasch, dimensionalidad
In this section we demonstrate how the DA-T Gibbs sampler for the 2PL is adapted to estimate the parameters of the Linear Logistic Test Model (LLTM) (Fischer, 1995 and references therein).