论文信息 - The SAS Macro-Program %AnaQol to Estimate the Parameters of Item Responses Theory Models

The SAS Macro-Program %AnaQol to Estimate the Parameters of Item Responses Theory Models

The analysis of quality of life questionnaires is taking a great importance in clinical research. Usual and general statistical packages like SAS do not allow users to perform classical analysis of items or to estimate parameters of most used models in this specific field: the practitioners must use various specific software to analyze a quality of life scale. In this article, we present an easy to use SAS macro-program that enables SAS users to obtain classical indices, usual graphical representations, and estimation of parameters of five usual Item Response models. We illustrate capabilities of our macro-program by presenting some practical real Quality of Life examples.

M. Mesbah | J.-B. Hardouin | M. Mesbah | J. Hardouin

[1] K. Christensen. SAS macros for Rasch based latent variable modelling , 2003 .

[2] Arnold L. van den Wollenberg,et al. Two new test statistics for the rasch model , 1982 .

[3] Georg Rasch,et al. Probabilistic Models for Some Intelligence and Attainment Tests , 1981, The SAGE Encyclopedia of Research Design.

[4] Benjamin D. Wright,et al. A Procedure for Sample-Free Item Analysis , 1969 .

[5] Mark D. Reckase,et al. Item Response Theory: Parameter Estimation Techniques , 1998 .

[6] J R Morris,et al. The Sickness Impact Profile: Conceptual Formulation and Methodology for the Development of a Health Status Measure , 1976, International journal of health services : planning, administration, evaluation.

[7] R. Terry,et al. IRT-FIT : SAS ® Macros for Fitting Item Response Theory ( IRT ) Models , 2005 .

[8] R. Hambleton,et al. Handbook of Modern Item Response Theory , 1997 .

[9] I. W. Molenaar,et al. Rasch models: foundations, recent developments and applications , 1995 .

[10] L. Cronbach. Coefficient alpha and the internal structure of tests , 1951 .

[11] Deniz Senturk-Doganaksoy,et al. Explanatory Item Response Models: A Generalized Linear and Nonlinear Approach , 2006, Technometrics.