Applied Statistics for the Social and Health Sciences

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Covering basic univariate and bivariate statistics and regression models for nominal, ordinal, and interval outcomes, this textbook provides graduate students in the social and health sciences with fundamental skills to estimate, interpret, and publish quantitative research using contemporary standards.

Covering basic univariate and bivariate statistics and regression models for nominal, ordinal, and interval outcomes, Applied Statistics for the Social and Health Sciences provides graduate students in the social and health sciences with fundamental skills to estimate, interpret, and publish quantitative research using contemporary standards.

Reflecting the growing importance of "Big Data" in the social and health sciences, this thoroughly revised and streamlined new edition covers best practice in the use of statistics in social and health sciences, draws upon new literatures and empirical examples, and highlights the importance of statistical programming, including coding, reproducibility, transparency, and open science.

Key features of the book include:

• interweaving the teaching of statistical concepts with examples from publicly available social and health science data and literature excerpts;
• thoroughly integrating the teaching of statistical theory with the teaching of data access, processing, and analysis in Stata;
• recognizing debates and critiques of the origins and uses of quantitative methods.

Obsah

Part I: Getting ready; 1 Considering Examples of Scholarly Publications Modeling Social and Health Variables; 2 Planning and Starting a Quantitative Research Project with Existing Data;; Part II: Describing the data; 3 Graphing and Summarizing Individual Variables; 4 Introducing Population Estimation and Hypothesis Testing; 5 Estimating and Testing the Association between Two Variables; ; Part III: Estimating and presenting linear regression models; 6 Introducing the Linear Regression Model with Two Continuous Variables; 7 Considering Nonlinearity and Nonconstant Variance; 8 Including Categorical Predictor Variables; 9 Including More Than One Predictor Variable in the Model; 10 Considering Interactions among Predictor Variables; ; Part IV: Estimating and presenting generalized linear models; 11 Introducing the Generalized Linear Regression Model; 12 Analyzing Dichotomous Outcomes; 13 Analyzing Multi-Category Outcomes and Offering a Roadmap to Additional Models