Introduction to multiple regression for categorical and limited dependent variables.

The writers explore the multiple regression model for categorical and limited dependent variables (CLDV) in an effort to extend the knowledge of alternative multiple regression models among social workers. The aim is to put social workers in a better position to accurately model important dependent...

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Publicado en:Social Work Research Vol. 25; no. 1; pp. 49 - 62
Autores principales: Orme, John G., Buehler, Cheryl
Formato: Artículo
Publicado: National Association of Social Workers March 2001
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Acceso en línea:Ver este registro en EBSCOhost
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        atl: Introduction to multiple regression for categorical and limited dependent variables.
      aug:
        au:
          Orme, John G.
          Buehler, Cheryl
      su:
        Regression analysis
        Statistics
        Social services -- Research
        Social services
        Methodology
      sug:
        subj:
          Regression analysis
          Statistics
          Social services -- Research
          Social services
          Methodology
      ab: The writers explore the multiple regression model for categorical and limited dependent variables (CLDV) in an effort to extend the knowledge of alternative multiple regression models among social workers. The aim is to put social workers in a better position to accurately model important dependent variables of interest to the profession. The writers define and distinguish among types of CLDVs. They compare linear and CLDV multiple regression models and offer an overview of multiple regression models for CLDVs. They discuss important factors involved in choosing the most appropriate model from those available. In addition, they provide a brief description of the available computer software for estimating CLDV multiple regression models and highlight reference books in the area. Furthermore, they give examples from the social work literature that demonstrate the use of each CLDV model.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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