Robert Andersen & David A. Armstrong II 
Presenting Statistical Results Effectively [EPUB ebook] 

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.

Daftar Isi

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

Tentang Penulis

Dave Armstrong is the Canada Research Chair in Political Methodology and Associate Professor of Political Science at Western University and is cross-appointed in the Department of Statistics and Actuarial Sciences.  Professor Armstrong earned a Ph.D. in Government and Politics from the University of Maryland in 2009.  Prior to arriving at Western, he had a post-doctoral position at Oxford University after which he taught in the Political Science department at the University of Wisconsin-Milwaukee.  He has been a faculty member at the Inter-university Consortium for Political and Social Research Summer Program at the University of Michigan since 2006 and has taught multiple courses at the Essex Summer School in Social Science Data Analysis at the University of Essex and the Oxford University Spring School in Quantitative Methods for Social Research.  His current work focuses on the use of non-parametric models in conventional social scientific inference.  His work has been published in such journals as The American Political Science Review, The American Journal of Political Science, The American Sociological Review, The Annual Review of Political Science, The Journal of Peace Research, The Canadian Journal of Political Science and The R Journal.  His most recent book is Analyzing Spatial Models of Choice and Judgement with R, with Ryan Bakker, Royce Carroll, Chris Hare, Keith Poole and Howard Rosenthal (2nd ed. 2021)