Presenting Statistical Results Effectively provides an overview on the key methods for analysing advanced statistical data and then systematically tackles the topic of how to effectively present results from statistical models. It is unique in its emphasis on understanding and communication while at the same time considering important statistical theory, and in so doing provides an essential resource for advanced students and researchers across the social and behavioural sciences working with statistical data. Throughout the book, generalized linear models and various extensions (e.g., mixed…mehr
Presenting Statistical Results Effectively provides an overview on the key methods for analysing advanced statistical data and then systematically tackles the topic of how to effectively present results from statistical models. It is unique in its emphasis on understanding and communication while at the same time considering important statistical theory, and in so doing provides an essential resource for advanced students and researchers across the social and behavioural sciences working with statistical data. Throughout the book, generalized linear models and various extensions (e.g., mixed models) are given a major emphasis. The authors then demonstrate how relationships, differences, and effects can be clearly communicated using tables and graphs of fitted values derived from these statistical models. All of the examples and analyses presented in the book are done using three statistical software packages: R, Stata and SPSS. The authors give attention to effective presentation both for publication in academic journals and oral presentations. Presenting Statistical Results Effectively takes a very different approach to understanding statistical theory and presenting statistical data and will be a very useful text for advanced students and researchers, across the social and behavioural sciences, throughout the world.
Robert Andersen is Professor of Business, Economics and Public Policy, and Professor of Strategy at the Ivey Business School, Western Univeristy. He is also cross-appointed in the Departments of Sociology, Political Science, and Statistics and Actuarial Science. His previous appointments include Distinguished Professor of Social Science at the University of Toronto, Senator William McMaster Chair in Political Sociology at McMaster University, and Senior Research Fellow at the University of Oxford. Andersen's research expertise is in social statistics, social stratification, and political economy. Much of his recent research has explored the cross-national relationships between economic conditions, especially income inequality, and a wide array of attitudes and behaviours important for liberal democracy and a successful business environment, including social trust, tolerance, civic participation, support for democracy and attitudes toward public policy. His published research includes Modern Methods for Robust Regression (Sage, 2008), and more than 70 academic papers including articles in the Annual Review of Sociology, American Journal of Political Science, American Sociological Review, British Journal of Political Science, British Journal of Sociology, Journal of Politics, Journal of the Royal Statistical Society, and Sociological Methodology. Andersen has provided consulting for the United Nations, the European Commission, the Canadian Government and the Council of Ministers of Education, Canada.
Inhaltsangabe
Chapter 1: Some Foundation What is a 'Model'? Statistical Inference Part A: General Principles of Effective Presentation Chapter 2: Best Practices for Graphs and Tables When to use Tables and Graphs Constructing Effective Tables Constructing Clear and Informative Graphs Chapter 3: Methods for Visualizing Distributions Displaying the Distributions of Categorical Variables Displaying Distributions of Quantitative Variables Transformations Chapter 4: Exploring and Describing Relationships Two Categorical Variables Categorical Explanatory Variable and Quantitative Dependent Variable Two quantitative Variables Multivariate Displays Part B: The Linear Model Chapter 5: The Linear Regression Model Ordinary Least Squares Regression Hypothesis tests and confidence intervals Assessing and Comparing Model Fit Relative Importance of Predictors Interpreting and presenting OLS models: Some empirical examples Linear Probability Model Chapter 6: Assessing the Impact and Importance of Multi-category Explanatory Variables Coding Multi-category Explanatory Variables Revisiting Statistical Significance: Multi-category Predictors Relative importance of sets of regressors Graphical Presentation of Additive Effects Chapter 7: Identifying and Handling Problems in Linear Models Nonlinearity Influential Observations Heteroskedasticity Nonnormality Chapter 8: Modelling and Presentation of Curvilinear Effects Curvilinearity in the Linear Model Framework Nonlinear Transformations Polynomial Regression Regression Splines Nonparametric Regression Generalized Additive Models Chapter 9: Interaction Effects in Linear Models Understanding Interaction Effects Interactions Between Two Categorical Variables Interactions Between One Categorical Variable and One Quantitative Variable Interactions Between Two Continuous Variables Interaction Effects: Some Cautions and Recommendations Part C: The Generalized Linear Model and Extensions Chapter 10: Generalized Linear Models Basics of the Generalized Linear Model Maximum Likelihood Estimation Hypothesis tests and confidence intervals Assessing Model Fit Empirical Example: Using Poisson Regression to Predict Counts Understanding Effects of Variables Measuring Variable Importance Model Diagnostics Chapter 11: Categorical Dependent Variables Regression Models for Binary Outcomes Interpreting Effects in Logit and Probit Models Model Fit for Binary Regression Models Diagnostics Specific to Binary Regression Models Extending the Binary Regression Model - Ordered and Multinomial Models Chapter 12: Conclusions and Recommendations Choosing the Right Estimator Research Design and Measurement Issues Evaluating the Model Effective Presentation of Results
Chapter 1: Some Foundation What is a 'Model'? Statistical Inference Part A: General Principles of Effective Presentation Chapter 2: Best Practices for Graphs and Tables When to use Tables and Graphs Constructing Effective Tables Constructing Clear and Informative Graphs Chapter 3: Methods for Visualizing Distributions Displaying the Distributions of Categorical Variables Displaying Distributions of Quantitative Variables Transformations Chapter 4: Exploring and Describing Relationships Two Categorical Variables Categorical Explanatory Variable and Quantitative Dependent Variable Two quantitative Variables Multivariate Displays Part B: The Linear Model Chapter 5: The Linear Regression Model Ordinary Least Squares Regression Hypothesis tests and confidence intervals Assessing and Comparing Model Fit Relative Importance of Predictors Interpreting and presenting OLS models: Some empirical examples Linear Probability Model Chapter 6: Assessing the Impact and Importance of Multi-category Explanatory Variables Coding Multi-category Explanatory Variables Revisiting Statistical Significance: Multi-category Predictors Relative importance of sets of regressors Graphical Presentation of Additive Effects Chapter 7: Identifying and Handling Problems in Linear Models Nonlinearity Influential Observations Heteroskedasticity Nonnormality Chapter 8: Modelling and Presentation of Curvilinear Effects Curvilinearity in the Linear Model Framework Nonlinear Transformations Polynomial Regression Regression Splines Nonparametric Regression Generalized Additive Models Chapter 9: Interaction Effects in Linear Models Understanding Interaction Effects Interactions Between Two Categorical Variables Interactions Between One Categorical Variable and One Quantitative Variable Interactions Between Two Continuous Variables Interaction Effects: Some Cautions and Recommendations Part C: The Generalized Linear Model and Extensions Chapter 10: Generalized Linear Models Basics of the Generalized Linear Model Maximum Likelihood Estimation Hypothesis tests and confidence intervals Assessing Model Fit Empirical Example: Using Poisson Regression to Predict Counts Understanding Effects of Variables Measuring Variable Importance Model Diagnostics Chapter 11: Categorical Dependent Variables Regression Models for Binary Outcomes Interpreting Effects in Logit and Probit Models Model Fit for Binary Regression Models Diagnostics Specific to Binary Regression Models Extending the Binary Regression Model - Ordered and Multinomial Models Chapter 12: Conclusions and Recommendations Choosing the Right Estimator Research Design and Measurement Issues Evaluating the Model Effective Presentation of Results
Chapter 1: Some Foundation What is a 'Model'? Statistical Inference Part A: General Principles of Effective Presentation Chapter 2: Best Practices for Graphs and Tables When to use Tables and Graphs Constructing Effective Tables Constructing Clear and Informative Graphs Chapter 3: Methods for Visualizing Distributions Displaying the Distributions of Categorical Variables Displaying Distributions of Quantitative Variables Transformations Chapter 4: Exploring and Describing Relationships Two Categorical Variables Categorical Explanatory Variable and Quantitative Dependent Variable Two quantitative Variables Multivariate Displays Part B: The Linear Model Chapter 5: The Linear Regression Model Ordinary Least Squares Regression Hypothesis tests and confidence intervals Assessing and Comparing Model Fit Relative Importance of Predictors Interpreting and presenting OLS models: Some empirical examples Linear Probability Model Chapter 6: Assessing the Impact and Importance of Multi-category Explanatory Variables Coding Multi-category Explanatory Variables Revisiting Statistical Significance: Multi-category Predictors Relative importance of sets of regressors Graphical Presentation of Additive Effects Chapter 7: Identifying and Handling Problems in Linear Models Nonlinearity Influential Observations Heteroskedasticity Nonnormality Chapter 8: Modelling and Presentation of Curvilinear Effects Curvilinearity in the Linear Model Framework Nonlinear Transformations Polynomial Regression Regression Splines Nonparametric Regression Generalized Additive Models Chapter 9: Interaction Effects in Linear Models Understanding Interaction Effects Interactions Between Two Categorical Variables Interactions Between One Categorical Variable and One Quantitative Variable Interactions Between Two Continuous Variables Interaction Effects: Some Cautions and Recommendations Part C: The Generalized Linear Model and Extensions Chapter 10: Generalized Linear Models Basics of the Generalized Linear Model Maximum Likelihood Estimation Hypothesis tests and confidence intervals Assessing Model Fit Empirical Example: Using Poisson Regression to Predict Counts Understanding Effects of Variables Measuring Variable Importance Model Diagnostics Chapter 11: Categorical Dependent Variables Regression Models for Binary Outcomes Interpreting Effects in Logit and Probit Models Model Fit for Binary Regression Models Diagnostics Specific to Binary Regression Models Extending the Binary Regression Model - Ordered and Multinomial Models Chapter 12: Conclusions and Recommendations Choosing the Right Estimator Research Design and Measurement Issues Evaluating the Model Effective Presentation of Results
Chapter 1: Some Foundation What is a 'Model'? Statistical Inference Part A: General Principles of Effective Presentation Chapter 2: Best Practices for Graphs and Tables When to use Tables and Graphs Constructing Effective Tables Constructing Clear and Informative Graphs Chapter 3: Methods for Visualizing Distributions Displaying the Distributions of Categorical Variables Displaying Distributions of Quantitative Variables Transformations Chapter 4: Exploring and Describing Relationships Two Categorical Variables Categorical Explanatory Variable and Quantitative Dependent Variable Two quantitative Variables Multivariate Displays Part B: The Linear Model Chapter 5: The Linear Regression Model Ordinary Least Squares Regression Hypothesis tests and confidence intervals Assessing and Comparing Model Fit Relative Importance of Predictors Interpreting and presenting OLS models: Some empirical examples Linear Probability Model Chapter 6: Assessing the Impact and Importance of Multi-category Explanatory Variables Coding Multi-category Explanatory Variables Revisiting Statistical Significance: Multi-category Predictors Relative importance of sets of regressors Graphical Presentation of Additive Effects Chapter 7: Identifying and Handling Problems in Linear Models Nonlinearity Influential Observations Heteroskedasticity Nonnormality Chapter 8: Modelling and Presentation of Curvilinear Effects Curvilinearity in the Linear Model Framework Nonlinear Transformations Polynomial Regression Regression Splines Nonparametric Regression Generalized Additive Models Chapter 9: Interaction Effects in Linear Models Understanding Interaction Effects Interactions Between Two Categorical Variables Interactions Between One Categorical Variable and One Quantitative Variable Interactions Between Two Continuous Variables Interaction Effects: Some Cautions and Recommendations Part C: The Generalized Linear Model and Extensions Chapter 10: Generalized Linear Models Basics of the Generalized Linear Model Maximum Likelihood Estimation Hypothesis tests and confidence intervals Assessing Model Fit Empirical Example: Using Poisson Regression to Predict Counts Understanding Effects of Variables Measuring Variable Importance Model Diagnostics Chapter 11: Categorical Dependent Variables Regression Models for Binary Outcomes Interpreting Effects in Logit and Probit Models Model Fit for Binary Regression Models Diagnostics Specific to Binary Regression Models Extending the Binary Regression Model - Ordered and Multinomial Models Chapter 12: Conclusions and Recommendations Choosing the Right Estimator Research Design and Measurement Issues Evaluating the Model Effective Presentation of Results
Rezensionen
Is your quantitative work so screamingly clear that your readers never misunderstand your figures, misread your tables, or get confused by your prose? If so, then don't waste your time with Andersen and Armstrong's thoughtful book about the effective presentation and interpretation of statistical results. Gary King 20210627
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