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Identifying and measuring cognitive aspects of a mathematics achievement testLutz, Megan E. 16 March 2012 (has links)
Cognitive Diagnostic Models (CDMs) are a useful way to identify potential areas of intervention for students who may not have mastered various skills and abilities at the same time as their peers. Traditionally, CDMs have been used on narrowly defined classroom tests, such as those for determining whether students are able to use different algebraic principles correctly. In the current study, the Deterministic Input, Noisy "And" Gate model (DINA; Haertel, 1989; Junker&Sijtsma, 2001) and the Compensatory Reparameterized Unified Model (CRUM; Hartz, 2002), as parameterized by the log-linear cognitive diagnosis model (LCDM; Henson, Templin,&Willse, 2009), were used to analyze the utility of pre-defined cognitive components in estimating students' abilities in a broadly defined, standardized mathematics achievement test. The attribute mastery profile distributions were compared; the majority of students was classified into the extremes of no mastery or complete mastery for both the CRUM and DINA models, though greater variability among attribute mastery classifications was obtained by the CRUM.
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Consumer Choice of Hotel Experiences: The Effects of Cognitive, Affective, and Sensory AttributesKim, Dohee 02 August 2011 (has links)
Understanding the choice behavior of customers is crucial for effective service management and marketing in the hospitality industry. The first purpose of this dissertation is to examine the differential effects that cognitive, affective, and sensory attributes have on consumer hotel choice. The second purpose is to examine the moderating effects of consumer choice context on the relationship between the cognitive, affective, and sensory attributes and hotel choice.
To achieve these two purposes, this dissertation includes the design of a choice experiment to examine how cognitive, affective, and sensory attributes predict consumer hotel choice using multinomial logit (MNL) and random parameter (or mixed) logit (RPL) models. For choice experiments, the main objectives are to determine the choice attributes and attribute levels to be used for the choice modeling and to create an optimal choice design. I used a Bayesian D-optimal design for the choice experiment, which I assess from the DOE (design of experiment) procedure outlined in JMP 8.0. The primary analysis associated with discrete choice analysis is the log-likelihood ratio (LR) test and the estimation of the parameters (known as part-worth utilities), using LIMDEP 9.0. The results showed that the addition of affective and sensory attributes to the choice model better explained hotel choice compared to the model with only cognitive attributes.
The second purpose is to examine the moderating effects of choice context on the relationship between cognitive, affective, and sensory attributes and hotel choice. Using a stated choice model, respondents were randomly divided into two different groups and asked to evaluate their preference for two differently manipulated choice sets. For this purpose, it is necessary to include interaction effects in the choice model. This study identified the differences among choice criteria based on two different contexts. Among eight interaction effects, four interaction effects with the contexts -- price, comfortable, room quality, and atmosphere -- were statistically significant on hotel choice. The findings provide hotel managers with important insights and implications in terms of target segmentation, product development, and marketing communication strategy. / Ph. D.
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The Use of Preprogram and Within-Program Cognitive Attributes to Predict Midprogram Outcomes in Baccalaureate Nursing EducationBishop, Patricia Jean 12 August 2013 (has links)
No description available.
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