Goodness-of-fit tests for logistic regression models when data are collected using a complex sampling design

2007 ◽  
Vol 51 (9) ◽  
pp. 4450-4464 ◽  
Author(s):  
Kellie J. Archer ◽  
Stanley Lemeshow ◽  
David W. Hosmer
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Goodness Of Fit ◽  
Sampling Design ◽  
Logistic Regression Models ◽  
Complex Sampling ◽  
Goodness Of Fit Tests ◽  
Complex Sampling Design

Multinomial goodness-of-fit tests for logistic regression models

2008 ◽  
Vol 27 (21) ◽  
pp. 4238-4253 ◽  
Author(s):  
Morten W. Fagerland ◽  
David W. Hosmer ◽  
Anna M. Bofin
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Goodness Of Fit ◽  
Logistic Regression Models ◽  
Goodness Of Fit Tests

On Graphically Checking Goodness-of-fit of Binary Logistic Regression Models

2009 ◽  
Vol 48 (03) ◽  
pp. 306-310 ◽  
Author(s):  
C. E. Minder ◽  
G. Gillmann
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Regression Models ◽  
Goodness Of Fit ◽  
Graphical Method ◽  
Logistic Models ◽  
Binary Logistic Regression ◽  
Logistic Regression Models ◽  
Logistic Analysis ◽  
Viable Solution

Summary Objectives: This paper is concerned with checking goodness-of-fit of binary logistic regression models. For the practitioners of data analysis, the broad classes of procedures for checking goodness-of-fit available in the literature are described. The challenges of model checking in the context of binary logistic regression are reviewed. As a viable solution, a simple graphical procedure for checking goodness-of-fit is proposed. Methods: The graphical procedure proposed relies on pieces of information available from any logistic analysis; the focus is on combining and presenting these in an informative way. Results: The information gained using this approach is presented with three examples. In the discussion, the proposed method is put into context and compared with other graphical procedures for checking goodness-of-fit of binary logistic models available in the literature. Conclusion: A simple graphical method can significantly improve the understanding of any logistic regression analysis and help to prevent faulty conclusions.

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