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Presenting Statistical Results Effectively
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Presenting Statistical Results Effectively



December 2021 | 456 pages | SAGE Publications Ltd
Perfect for any statistics student or researcher, this book offers hands-on guidance on how to interpret and discuss your results in a way that not only gives them meaning, but also achieves maximum impact on your target audience. No matter what variables your data involves, it offers a roadmap for analysis and presentation that can be extended to other models and contexts.

Focused on best practices for building statistical models and effectively communicating their results, this book helps you:
-        Find the right analytic and presentation techniques for your type of data
-        Understand the cognitive processes involved in decoding information
-        Assess distributions and relationships among variables
-        Know when and how to choose tables or graphs
-        Build, compare, and present results for linear and non-linear models
-        Work with univariate, bivariate, and multivariate distributions
-        Communicate the processes involved in and importance of your 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

 

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
Albert J Weatherhead III University Professor and director of the Institute for Quantitative Social Science, Harvard University

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