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Ennio Fioramonti's List: psychometrics


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    • uropsychologist A.R. Luria, as is Kaufman's

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    • The CHC model was expanded by McGrew (1997) and later revised with the help of Flanagan (1998).
    • Recent advances in current theory and research on the structure of human cognitive abilities have resulted in a new empirically derived model commonly referred to as the Cattell-Horn-Carroll theory of cognitive abilities (CHC theory). CHC theory of cognitive abilities is an amalgamation of two similar theories about the content and structure of human cognitive abilities. The first of these two theories is Gf-Gc theory (Cattell, 1941; Horn 1965), and the second is Carroll's (1993) Three-Stratum theory.

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    • Generalizability theory, or G Theory, is a statistical framework for conceptualizing, investigating, and designing reliable observations. It is used to determine the reliability (i.e., reproducibility) of measurements under specific conditions. It is particularly useful for assessing the reliability of performance assessments. It was originally introduced in Cronbach, L.J., Nageswari, R., & Gleser, G.C. (1963).
    • The focus of classical test theory (CTT) is on determining error of the measurement. Perhaps the most famous model of CTT is the equation X = T + e, where X is the observed score, T is the true score, and e is the error involved in measurement. Although e could represent many different types of error, such as rater or instrument error, CTT only allows us to estimate one type of error at a time. Essentially it throws all sources of error into one error term. This may be suitable in the context of highly controlled laboratory conditions, but variance is a part of everyday life. In field research, for example, it is unrealistic to expect that the conditions of measurement will remain constant. Generalizability theory acknowledges and allows for variability in assessment conditions that may affect measurements. The advantage of G theory lies in the fact that researchers can estimate what proportion of the total variance in the results is due to the individual factors that often vary in assessment, such as setting, time, items, and raters.
    • Cronbach's alpha and internal consistency
    • Cronbach's alpha will generally increase as the intercorrelations among test items increase, and is thus known as an internal consistency estimate of reliability of test scores. Because intercorrelations among test items are maximized when all items measure the same construct, Cronbach's alpha is widely believed to indirectly indicate the degree to which a set of items measures a single unidimensional latent construct. However, the average intercorrelation among test items is affected by skew just like any other average. Thus, whereas the modal intercorrelation among test items will equal zero when the set of items measures several unrelated latent constructs, the average intercorrelation among test items will be greater than zero in this case. Indeed, several investigators have shown that alpha can take on quite high values even when the set of items measures several unrelated latent constructs (e.g., Cortina, 1993; Cronbach, 1951; Green, Lissitz & Mulaik, 1977; Revelle, 1979; Schmitt, 1996; Zinbarg, Yovel, Revelle & McDonald, 2006). As a result, alpha is most appropriately used when the items measure different substantive areas within a single construct. When the set of items measures more than one construct, coefficient omega_hierarchical is more appropriate (McDonald, 1999; Zinbarg, Revelle, Yovel & Li, 2005).
    • Summary


      Graphic of person and item distributions for a test produced in RUMM2020 software, RUMMlab, Perth:

    • File:PersItm.PNG - Wikipedia, the free encyclopedia
    • Rasch model
    • Rasch model - Wikipedia, the free encyclopedia
    • Rasch model - Wikipedia, the free encyclopedia

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    • La mesure en psychologie (questionnaires, tests, modèles structuraux)
    • - Comprendre les postulats, concepts et conditions d'application du modèle classique de la mesure (True Score Theory).
       - Comprendre les postulats, concepts et conditions d'application des modèle de la réponse à l'item (MRI), en particulier du modèle de Rasch.
       - Maîtriser procédures de base d'analyse classique et MRI, y compris l'interprétation des résultats.
       - Comprendre les postulats, concepts et conditions d'application de l'analyse factorielle y compris confirmatoire et des modèles structuraux d'équations dans les problèmes de mesure. Maîtriser les procédures de base de l'analyse factorielle et des modèles structuraux d'équations dans ces problèmes.
    • Introduction au modèle de Rasch


      Traduction (partielle) du chapitre 16 de Messen und Testen, Rolf Steyer et Michael Eid. (2001). Berlin : Springer.

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