Add to ORKGOrdinal Logistic Hierarchical Linear Modeling (HLM) is a method that has seen limited use due to its complex nature, and lack of software to properly model it. Recently, modeling capabilities have been added to the HLM software (Raudenbush, Bryk, & Congdon, 2004) which allow easy modeling of ordinal dependent variables in a logistic fashion. I describe the method, convey an interpretation of coefficients from a sample study, and discuss other research which has employed the method
Most data involving organizations are hierarchical in nature and often contain variables measured at...
Most data involving organizations are hierarchical in nature and often contain variables measured at...
Log-Linear Models (LLMs) are important techniques used in categorical data analysis. Though there ar...
Log-linear models are widely used for qualitative data in multidimensional contingency tables. Hier...
In social research work, the structure of the data are often hierarchical. Hierarchical linear model...
This tutorial aims to introduce Hierarchical Linear Modeling (HLM). A simple explanation of HLM is p...
This tutorial aims to introduce Hierarchical Linear Modeling (HLM). A simple explanation of HLM is p...
Abstract Previous publications on hierarchical linear modeling (HLM) have provided guidance on how ...
Log-linear modeling is a popular statistical tool for analysing a contingency table. This presentati...
Recent advances in multilevel modeling software have made it easier to investigate potential bias in...
The paper presents an analysis of association in a contingency table using log-linear models. The fo...
The paper presents an analysis of association in a contingency table using log-linear models. The fo...
Estimating linear-by-linear association has long been an important topic in the analysis of continge...
The recent lirerature oii.log-linear i?todels iticorrecrb implies that the Iterative Proportional Fi...
Most data involving organizations are hierarchical in nature and often contain variables measured at...
Most data involving organizations are hierarchical in nature and often contain variables measured at...
Most data involving organizations are hierarchical in nature and often contain variables measured at...
Log-Linear Models (LLMs) are important techniques used in categorical data analysis. Though there ar...
Log-linear models are widely used for qualitative data in multidimensional contingency tables. Hier...
In social research work, the structure of the data are often hierarchical. Hierarchical linear model...
This tutorial aims to introduce Hierarchical Linear Modeling (HLM). A simple explanation of HLM is p...
This tutorial aims to introduce Hierarchical Linear Modeling (HLM). A simple explanation of HLM is p...
Abstract Previous publications on hierarchical linear modeling (HLM) have provided guidance on how ...
Log-linear modeling is a popular statistical tool for analysing a contingency table. This presentati...
Recent advances in multilevel modeling software have made it easier to investigate potential bias in...
The paper presents an analysis of association in a contingency table using log-linear models. The fo...
The paper presents an analysis of association in a contingency table using log-linear models. The fo...
Estimating linear-by-linear association has long been an important topic in the analysis of continge...
The recent lirerature oii.log-linear i?todels iticorrecrb implies that the Iterative Proportional Fi...
Most data involving organizations are hierarchical in nature and often contain variables measured at...
Most data involving organizations are hierarchical in nature and often contain variables measured at...
Most data involving organizations are hierarchical in nature and often contain variables measured at...
Log-Linear Models (LLMs) are important techniques used in categorical data analysis. Though there ar...