Alan Agresti
Biographic Data
| ID | 273827 |
|---|---|
| NAME | Alan Agresti |
| GIVEN NAMES | Alan |
| FAMILY NAME | Agresti |
| SIGNATURE | AGRESTI A |
| AFFILIATIONS | University of Florida |
| ORCID | 0000-0003-2012-3166 |
| VERIFIED | Yes |
| TOTAL WORKS | 13 |
| TOTAL CITATIONS | 68 |
| AUTHOR COUNT | 13 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 1978 |
| LATEST PUBLICATION YEAR | 2017 |
| H-INDEX | 4 |
Statistical Methods for the Social Sciences
"Statistical methods applied to social sciences, made accessible to all through an emphasis on concepts Statistical Methods for the Social Sciences introduces statistical methods to students majoring in social science disciplines. With an emphasis on concepts and applications, this book assumes you have no previous knowledge of statistics and only a minimal mathematical background. It contains sufficient material for a two-semester course. The 5t…
Multivariate Analysis: Discrete Variables (Overview)
Analysis of Ordinal Categorical Data
Statistical science's first coordinated manual of methods for analyzing ordered categorical data, now fully revised and updated, continues to present applications and case studies in fields as diverse as sociology, public health, ecology, marketing, and pharmacy. Analysis of Ordinal Categorical Data, Second Edition provides an introduction to basic descriptive and inferential methods for categorical data, giving thorough coverage of new developme…
Statistical Methods for the Social Sciences (4th Edition)
Strategies for Modeling a Categorical Variable Allowing Multiple Category Choices
This article discusses strategies for modeling a categorical variable when subjects can select any subset of the categories. With c outcome categories, the models relate to a c- dimensional binary response, with each component indicating whether a particular category is chosen. The strategies are the following: (1) Using logit models directly for the marginal distribution of each component; this accounts for dependence among the component respons…
Random-Effects Modeling of Categorical Response Data
In many applications observations have some type of clustering, with observations within clusters tending to be correlated. A common instance of this occurs when each subject in the sample undergoes repeated measurement, in which case a cluster consists of the set of observations for the subject. One approach to modeling clustered data introduces cluster-level random effects into the model. The use of random effects in linear models for normal re…
Statistical Models for Ordinal Variables
Logit Models and Related Quasi-Symmetric Log-Linear Models for Comparing Responses to Similar Items in a Survey
Suppose that subjects respond to a battery of questions (items) of a similar nature in a survey, with each item having the same categorical scale. This article discusses models that express logits for the response distributions in terms of subject and item effects. The models, which generalize the Rasch model, have interpretations referring to subject-specific comparisons of the items. Recent literature shows that one can estimate item parameters…
Categorical Data Analysis
Tutorial on modeling ordered categorical response data
In the past decade there has been great progress in the development of methodology for analyzing ordered categorical data. Logit and log linear model-building techniques for nominal data have been generalized for use with ordinal data. There are many advantages to using these procedures instead of the Pearson chi-square test of independence to analyze ordered categorical data. These advantages include (a) more complete description of the nature o…
The Analysis of Cross-Classified Data Having Ordered Categories
The Measurement of Classification Agreement: An Adjustment to the Rand Statistic for Chance Agreement
Investigators examining empirically derived classifications are often concerned with the replicability of an obtained classification. However, most available statistics which allow replication comparison suffer from various limitations. This paper proposes an adjustment to one of these statistics, the Rand statistic, which will allow comparison across different levels for number of clusters found within a classification. This adjustment permits t…
Statistical Analysis of Qualitative Variation
Many variables of interest in the social sciences are measurable only at the nominal level. That is, they represent types of phenomena such as race, ethnicity, religious affiliation, or political party preference. It is sometimes of interest to measure the amount of variation, or heterogeneity, within a population with respect to one or more of these variables. By a measure of variation for a qualitative variable, we mean a description of the dis…
Statistical Analysis of Qualitative Variation
Many variables of interest in the social sciences are measurable only at the nominal level. That is, they represent types of phenomena such as race, ethnicity, religious affiliation, or political party preference. It is sometimes of interest to measure the amount of variation, or heterogeneity, within a population with respect to one or more of these variables. By a measure of variation for a qualitative variable, we mean a description of the dis…
Random-Effects Modeling of Categorical Response Data
In many applications observations have some type of clustering, with observations within clusters tending to be correlated. A common instance of this occurs when each subject in the sample undergoes repeated measurement, in which case a cluster consists of the set of observations for the subject. One approach to modeling clustered data introduces cluster-level random effects into the model. The use of random effects in linear models for normal re…
Tutorial on modeling ordered categorical response data
In the past decade there has been great progress in the development of methodology for analyzing ordered categorical data. Logit and log linear model-building techniques for nominal data have been generalized for use with ordinal data. There are many advantages to using these procedures instead of the Pearson chi-square test of independence to analyze ordered categorical data. These advantages include (a) more complete description of the nature o…
Strategies for Modeling a Categorical Variable Allowing Multiple Category Choices
This article discusses strategies for modeling a categorical variable when subjects can select any subset of the categories. With c outcome categories, the models relate to a c- dimensional binary response, with each component indicating whether a particular category is chosen. The strategies are the following: (1) Using logit models directly for the marginal distribution of each component; this accounts for dependence among the component respons…
Logit Models and Related Quasi-Symmetric Log-Linear Models for Comparing Responses to Similar Items in a Survey
Suppose that subjects respond to a battery of questions (items) of a similar nature in a survey, with each item having the same categorical scale. This article discusses models that express logits for the response distributions in terms of subject and item effects. The models, which generalize the Rasch model, have interpretations referring to subject-specific comparisons of the items. Recent literature shows that one can estimate item parameters…
Statistical Analysis of Qualitative Variation
Many variables of interest in the social sciences are measurable only at the nominal level. That is, they represent types of phenomena such as race, ethnicity, religious affiliation, or political party preference. It is sometimes of interest to measure the amount of variation, or heterogeneity, within a population with respect to one or more of these variables. By a measure of variation for a qualitative variable, we mean a description of the dis…
The Measurement of Classification Agreement: An Adjustment to the Rand Statistic for Chance Agreement
Investigators examining empirically derived classifications are often concerned with the replicability of an obtained classification. However, most available statistics which allow replication comparison suffer from various limitations. This paper proposes an adjustment to one of these statistics, the Rand statistic, which will allow comparison across different levels for number of clusters found within a classification. This adjustment permits t…
The Analysis of Cross-Classified Data Having Ordered Categories
Tutorial on modeling ordered categorical response data
In the past decade there has been great progress in the development of methodology for analyzing ordered categorical data. Logit and log linear model-building techniques for nominal data have been generalized for use with ordinal data. There are many advantages to using these procedures instead of the Pearson chi-square test of independence to analyze ordered categorical data. These advantages include (a) more complete description of the nature o…
Categorical Data Analysis
Statistical Models for Ordinal Variables
Logit Models and Related Quasi-Symmetric Log-Linear Models for Comparing Responses to Similar Items in a Survey
Suppose that subjects respond to a battery of questions (items) of a similar nature in a survey, with each item having the same categorical scale. This article discusses models that express logits for the response distributions in terms of subject and item effects. The models, which generalize the Rasch model, have interpretations referring to subject-specific comparisons of the items. Recent literature shows that one can estimate item parameters…
Random-Effects Modeling of Categorical Response Data
In many applications observations have some type of clustering, with observations within clusters tending to be correlated. A common instance of this occurs when each subject in the sample undergoes repeated measurement, in which case a cluster consists of the set of observations for the subject. One approach to modeling clustered data introduces cluster-level random effects into the model. The use of random effects in linear models for normal re…
Strategies for Modeling a Categorical Variable Allowing Multiple Category Choices
This article discusses strategies for modeling a categorical variable when subjects can select any subset of the categories. With c outcome categories, the models relate to a c- dimensional binary response, with each component indicating whether a particular category is chosen. The strategies are the following: (1) Using logit models directly for the marginal distribution of each component; this accounts for dependence among the component respons…
Statistical Methods for the Social Sciences (4th Edition)
Analysis of Ordinal Categorical Data
Statistical science's first coordinated manual of methods for analyzing ordered categorical data, now fully revised and updated, continues to present applications and case studies in fields as diverse as sociology, public health, ecology, marketing, and pharmacy. Analysis of Ordinal Categorical Data, Second Edition provides an introduction to basic descriptive and inferential methods for categorical data, giving thorough coverage of new developme…
Multivariate Analysis: Discrete Variables (Overview)
Statistical Methods for the Social Sciences
"Statistical methods applied to social sciences, made accessible to all through an emphasis on concepts Statistical Methods for the Social Sciences introduces statistical methods to students majoring in social science disciplines. With an emphasis on concepts and applications, this book assumes you have no previous knowledge of statistics and only a minimal mathematical background. It contains sufficient material for a two-semester course. The 5t…
Statistics (12 works) · Mathematics (11 works) · Econometrics (8 works) · Categorical variable (7 works) · Computer Science (7 works) · Ordinal data (5 works) · Psychology (4 works) · Statistical Methods and Bayesian Inference (4 works) · Ordinal regression (3 works) · 519.5 (2 works)