Social Science Data Analysis: An Introduction

✍ Scribed by Florian G. Hartmann, Johannes Kopp, Daniel Lois

• Publisher: Springer
• Year: 2023
• Tongue: English
• Leaves: 194
• Category: Library

✦ Synopsis

In this book, basic statistical knowledge is conveyed in an understandable and application-oriented manner. The readers should be enabled to carry out their own empirical evaluations and to understand or critically reflect on existing analyses.

The third edition is extended by a detailed chapter on the logic of significance tests.

A replication syntax for the statistical program Stata is provided online as supplementary material.

https://link.springer.com/book/10.1007/978-3-658-41230-2

✦ Table of Contents

Contents
1 Preface to the New Edition
Anchor 2
References
2 On the Task of Empirical Social Research and Data Analysis in the Sociological Research Process
References
3 On the Data Situation: Own Data Collection or Secondary Analysis
References
4 The First Steps of Data Analysis: Preparation, Data Description and Bivariate Relationships
4.1 “A Long and Winding Road”—On the Difficulties of Data Preparation
4.2 On Describing Data: Tables
4.3 Distributions: Graphs and Measures
4.4 Measures: All for One, One for All?
4.5 Measures of Dispersion: “Birds of a Feather Flock Together”?
4.6 Measures of Association
4.7 Afterword
References
5 Significance Test
5.1 Basic Terms
5.2 Statistical Hypotheses
5.3 Types of Errors and Significance Level
5.4 Step by Step using the Example of a Correlation Hypothesis
5.5 Step by Step using the Example of a Difference Hypothesis
5.6 Which Test Fits my Project?
5.7 Effect Size
5.8 Statistical Power
5.9 The P-Value
5.10 The Relationship of Hypothesis Testing to Confidence Intervals
5.11 Afterword
References
6 Linear Regression
6.1 Basic Logic of Bivariate Regression
6.2 Bivariate Regression: An Example from Practice
6.3 “You’ll Never Walk Alone”—Multivariate Regression
6.4 Dummy Variables
6.5 Same Results—Different Presentations
6.6 Afterword: a Small to-do List
References
7 On the Logic of Data Analysis: Which Evaluation Strategy Fits Best to My Research Question?
7.1 The Empirical Example
7.2 The Gross-Net Model
7.3 The Mediation Analysis
7.4 The Moderation Analysis
7.5 Afterword
References
8 Logistic Regression
8.1 Two Basic Concepts of Logistic Regression: Odds and Probability
8.2 How to Interpret the Output of Logistic Regression?
8.3 Probabilities, Odds, Log Odds and Average Marginal Effects: Guidelines for Result Interpretation
8.4 An Example: Which Characteristics Influence the Probability of being Non-Denominational?
8.5 Pitfalls of Logistic Regression
8.6 Final Remarks
References
9 An Outlook on Advanced Statistical Analysis Methods
9.1 Event Data Analysis
9.2 Hierarchically Structured Data: Multilevel Analysis
9.3 Causal Analysis with Panel Data
9.4 Covariance-Based Path and Structural Equation Models
9.5 Meta-Analyses
9.6 Afterword
References