An IRT Approach to Constructing and Scoring Pairwise Preference Items Involving Stimuli on Different Dimensions: The Multi-Unidimensional Pairwise-Preference Model

By Stephen E. Stark, O. Chernyshenko, Fritz Drasgow. 2005

187 citations recorded by the source corpus.

Subfields: Food Science, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c29a3bd4ec6e2a77262b646687b6bc4e86f689b7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Food Science

1. Jesús Salgado
2. Gabriel Táuriz
3. O. Chernyshenko
4. Fritz Drasgow
5. Stephen E. Stark

In Management Science and Operations Research

1. Jesús Salgado
2. Gabriel Táuriz
3. O. Chernyshenko
4. Fritz Drasgow
5. Stephen E. Stark

In Statistics and Probability

1. Jesús Salgado
2. Gabriel Táuriz
3. O. Chernyshenko
4. Fritz Drasgow
5. Stephen E. Stark

Citations in the corpus

• 75 Years After Likert: Thurstone Was Right! (2010, methodology): “Our simulation and empirical studies (Chernyshenko et al., 2009; Stark et al., 2005) have shown that accurate latent trait estimates (i.e., normative scores) can be obtained when analyzing pairwise preference responses with MUPPM.”
• The Five-Factor Model, forced-choice personality inventories and performance: A comprehensive meta-analysis of academic and occupational validity studies (2014, methodology): “The first approach relies on an ideal-point response process (Chernyshenko, Stark, Drasgow, & Roberts, 2007; McCloy et al., 2005; Stark et al., 2005).”