LEADER 00000cam a2200553Mi 4500 
003    OCoLC 
005    20240129213017.0 
006    m     o  d         
007    cr cn||||||||| 
008    210101s2016    xx     eo     000 0 eng d 
020    1466569743 
020    9781466569744 
024 8  9781466569744 
024 8  KE75520 
035    (OCoLC)1228514978 
040    TOH|beng|cTOH|dOCLCF|dOCLCQ|dOCLCO|dOCLCL 
049    INap 
082 04 519.5/36 
082 04 519.5/36|qOCoLC|223/eng/20230216 
099    eBook O'Reilly for Public Libraries 
100 1  Fullerton, Andrew,|eauthor. 
245 10 Ordered Regression Models /|cFullerton, Andrew.|h[O'Reilly
       electronic resource] 
250    1st edition. 
264  1 [Place of publication not identified] :|bChapman and Hall/
       CRC,|c2016. 
300    1 online resource (188 pages). 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
347    text file 
365    |b94.95 
490 1  Chapman & Hall/CRC statistics in the social and behavioral
       sciences 
520    Ordered Regression Models: Parallel, Partial, and Non-
       Parallel Alternatives presents regression models for 
       ordinal outcomes, which are variables that have ordered 
       categories but unknown spacing between the categories. The
       book provides comprehensive coverage of the three major 
       classes of ordered regression models (cumulative, stage, 
       and adjacent) as well as variations based on the 
       application of the parallel regression assumption. The 
       authors first introduce the three "parallel" ordered 
       regression models before covering unconstrained partial, 
       constrained partial, and nonparallel models. They then 
       review existing tests for the parallel regression 
       assumption, propose new variations of several tests, and 
       discuss important practical concerns related to tests of 
       the parallel regression assumption. The book also 
       describes extensions of ordered regression models, 
       including heterogeneous choice models, multilevel ordered 
       models, and the Bayesian approach to ordered regression 
       models. Some chapters include brief examples using Stata 
       and R. This book offers a conceptual framework for 
       understanding ordered regression models based on the 
       probability of interest and the application of the 
       parallel regression assumption. It demonstrates the 
       usefulness of numerous modeling alternatives, showing you 
       how to select the most appropriate model given the type of
       ordinal outcome and restrictiveness of the parallel 
       assumption for each variable. Web Resource More detailed 
       examples are available on a supplementary website. The 
       site also contains JAGS, R, and Stata codes to estimate 
       the models along with syntax to reproduce the results. 
542    |fCopyright © Chapman and Hall/CRC 2016|g2016 
550    Made available through: Safari, an O'Reilly Media Company.
588 0  Online resource; Title from title page (viewed April 21, 
       2016). 
590    O'Reilly|bO'Reilly Online Learning: Academic/Public 
       Library Edition 
650  0 Regression analysis. 
650  6 Analyse de régression. 
650  7 Regression analysis|2fast 
700 1  Xu, Jun|c(Professor of Sociology) 
700 1  Xu, Jun|c(Professor of Sociology),|eeditor. 
700 1  Xu, Jun,|eauthor. 
710 2  O'Reilly for Higher Education (Firm),|edistributor. 
710 2  Safari, an O'Reilly Media Company. 
776 08 |iErscheint auch als:|nDruck-Ausgabe|aFullerton, Andrew S.
       Ordered Regression Models .|tParallel, Partial, and Non-
       Parallel Alternatives 
776 08 |iPrint version :|z9781466569737 
830  0 Chapman & Hall/CRC statistics in the social and behavioral
       sciences. 
856 40 |uhttps://ezproxy.naperville-lib.org/login?url=https://
       learning.oreilly.com/library/view/~/9781466569744/?ar
       |aAvailable on O'Reilly for Public Libraries 
994    92|bJFN