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Basic Statistics For Social Research
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丛&书&名：Research Methods for the Social Sciences
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字&&&&数：192901
正文语种：英文
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&&　　This textbook for social sciences students provides an overview of univariate and bivariate descriptive and inferential statistics, and emphasizes the logic of inquiry over mechanical calculations.
　　Basic Statistics for Social Research teaches core general statistical concepts and methods that all social science majors must master to understand (and do) social research. It covers logical and procedural aspects of the application of statistical methods for data- and hypothesis- distributions, tabulations, central tendency, variability, independence, correlation and regression. The book has been designed for students in the social sciences by its choice of topics, examples, and exercises. The book's emphasis is applied. The use of math and theory are deliberately limited, and the authors focus on how the concepts and tools of statistics are used in the analysis of social science data, rather than on the mathematical and computational aspects. The book also emphasizes the use of computer software to calculate statistics.
Each chapter is organized around a general idea or ideas that are explained at the beginning, and then applied in increasing detail in the body of the text. The authors' intent is to convey and develop a conceptual understanding of a core idea (for example, dispersion) and to use the detailed elaboration of procedures and applications to real problems to more deeply ingrain the core concept. The book emphasizes the logic of inquiry over the mechanics of calculation.The logical steps in describing a distribution, testing a hypothesis, or making a prediction are used to organize the discussions of the technical details of applying the processes to different data types. Research questions and applications are taken from a wide variety of sub-fields in sociology. The textbook relies on minimal math, and approach that works well with students of extremely diverse academic and social backgrounds.
Table of Contents:
1. Statistics and the research process 1.1. Introduction: Why are you taking this course? 1.2. What are statistics (examples, quiz) 1.3. Statistics and social science (examples, quiz) 1.4. What you will learn (quiz) 1.5. Review of background ideas (examples, quiz) 1.6. Summary Homework assignments
2.Frequency distribution 2.1. Introduction: summarizing variation with charts and graphs 2.2. Frequency distributions for nominal variables (examples, quiz) 2.3. Frequency distributions for ordinal variables (examples, quiz) 2.4. Frequency distributions for interval/ratio variables (examples, quiz) 2.5. Summary Homework assignments
3. Measures of central tendency 3.1. I the concept of central tendency 3.2. Nominal variables (examples, quiz) 3.3. Ordinal variables (examples, quiz) 3.4. Interval/ratio variables (examples, quiz) 3.5. Summary Homework assignments
4. Measures of Dispersion 4.1. I the concept of dispersion 4.2. Categorical variables (examples, quiz) 4.3. Interval/ratio variables (examples, quiz) 4.4. Summary Homework assignments
5. Distributional shape 5.1. I the concept of distributional shape 5.2. Unimodality (examples, quiz) 5.3. Skewness (examples, quiz) 5.4. Kurtosis (examples, quiz) 5.5. Some common distributional shapes (examples, quiz) 5.6. Summary Homework assignments
6. The normal distribution 6.1. I The special importance of the normal distribution in statistical analysis 6.2. Properties of the normal distribution (examples, quiz) 6.3. The standard or normal distribution (examples, quiz) 6.4. Areas under the normal curve (examples, quiz) 6.5. Summary Homework assignments
7. Basic ideas of statistical inference 7.1. Introduction: What is inference? 7.2. Concepts: Sampling methods, sampling variability, sampling distribution, standard error (examples, quiz) 7.3. Application: Estimating population central tendency (examples, quiz) 7.4. A error levels and confidence intervals (examples, quiz) 7.5. Summary Homework assignments
8. Hypotheses testing for one sample 8.1. I The role of hypothesis testing in scientific research. 8.2. The steps in the hypothesis testing process (examples, quiz) 8.3. Tests about one mean (examples, quiz) 8.4. Tests about one proportion (examples, quiz) 8.5. Summary Homework assignments
9. Hypothesis testing for two samples 9.1. Introduction
Why compare two groups? 9.2. Comparing two independent means (examples, quiz) 9.3. Comparing two independent proportions (examples, quiz) 9.4. Non-independent samples (examples, quiz) 9.5. Tstatistical significance (examples, quiz) 9.6. Summary Homework assignments
10. Multi-sample tests for proportions 10.1. I Comparing multiple groups with chi-square. 10.2. Testing for multiple group differences (examples, quiz) 10.3. Describing multiple group differences (examples, quiz) 10.4. Summary Homework assignments
11. Multi-sample tests for means 11.1. Introduction
Comparing multiple group means with ANOVA 11.2. The logic of comparing multiple group means (examples, quiz) 11.3. Analyzing variance (examples, quiz) 11.4. The overall F-test (examples, quiz) 11.5. Exploring group differences (examples, quiz) 11.6. Summary Homework assignments
12. Association: Categorical variables 12.1. I The concept of association (definitions, visualization, steps in the process of studying association) 12.2. Nominal variables (display, testing for the presence of association, estimating strength, and describing the form) (examples, quiz). 12.3. Odds Ratio and Relative Risk (examples, quiz). 12.4. Ordinal variables (grouped)(display, testing for the presence of association, estimating strength, and describing for) 12.5. Summary Homework assignments
13. A Interval/ratio variables 13.1. I Applying the association concept to continuous variables. 13.2. Full rank-order variables (Spearman's rho) (examples, quiz) 13.2. Interval/ratio variables (Pearson's r) (examples, quiz) 13.3. Summary Homework assignments
14. Prediction:; Interval/ratio outcomes 14.1. I Asymmetric association and prediction 14.2. Classical normal linear regression 14.3. Some simple non-linear models 14.4. Summary Homework assignments
15. Prediction: ; Categorical outcomes 15.1. The logit model (binary logistic regression with one continuous covariate) 15. 2. The log-linear model (binary logistic regression with one categorical covariate) 15. 3. O (ordinal logistic regression with one predictor) 15.4. Multiple outcomes (multinomial logistic regression with on predictor) Homework assignments
　　Robert Hanneman, PhD, is professor of sociology in the College of Humanities, Arts, and Social Sciences at the University of California, Riverside. He is the author of books on welfare state development, centralization in the structure of social service delivery systems, medical care system performance, dynamic models of sociological theories, and social network analysis methods.?His primary areas of research at present are in mathematical models, agent based models, and social network analysis methods. He was responsible for drafting much of the multi-media text from which the current proposed text will be developed.
Augustine Kposowa, PhD, is professor of sociology in the College of Humanities, Arts, and Social Sciences at the University of California, Riverside. He has published numerous quantitative papers on morbidity and mortality that have appeared in public health and sociology journals. Dr. Kposowa teaches courses in demography, especially immigration, political sociology, structural equation models, and categorical and survival data analysis. He has extensive experience in teaching basic statistics. He has broad knowledge of applications across many substantive areas that engage students with diverse interests, and knowledge of what aspects of basic statistics are most important to emphasize for applied statistical work.
Mark Riddle, PhD, is assistant professor of sociology at the University of North Colorado. Dr. Riddle's academic interests include quantitative methodology, social network analysis and the study of educational organizations. He also has extensive experience in teaching basic statistics using the existing and competitor texts.He developed the video tutorials, as well as many of the examples and problems in the current text.
Tables and Figures
About the Authors
Part I: Univariate Description
Chapter 1: Using Statistics
Why Study Statistics?
Tasks for Statistics: Describing, Inferring, Testing, Predicting
Statistics in the Research Process
Basic Elements of Research: Units of Analysis and Variables
Chapter 2: Displaying One Distribution
Summarizing Variation in One Variable
Frequency Distributions for Nominal Variables
Frequency Distributions for Ordinal Variables
Frequency Distributions for Interval/Ratio Variables
Summarizing Data Using Excel
Chapter 3: Central Tendency
The Basic Idea of Central Tendency
The Median
Chapter 4: Dispersion
The Basic Idea of Dispersion
Dispersion of Categorical Data
Dispersion of Interval/Ratio Data
Chapter 5: Describing the Shape of a Distribution
The Basic Ideas of Distributional Shape
The Shape of Nominal and Ordinal Distributions
Unimodality
Some Common Distributional Shapes
Chapter 6: The Normal Distribution
Introduction to the Normal Distribution
Properties of Normal Distributions
The Standard Normal, or Z, Distribution
Working with Standard Normal (Z) Scores
Finding Areas “Under the Curve”
Part II: Inference and Hypothesis Testing
Chapter 7: Basic Ideas of Statistical Inference
Introduction to Statistical Inference
Sampling Concepts
Central Tendency Estimates
Assessing Confidence in Point Estimates
Chapter 8: Hypothesis Testing for One Sample
Hypothesis Testing
The Testing Process
Tests about One Mean
Tests about One Proportion
Chapter 9: Hypothesis Testing for Two Samples
Comparing Two Groups
Comparing Two Groups’ Means
Comparing Two Groups’ Proportions
Nonindependent Samples
Using Excel for Two-Sample Tests
Interpreting Group Differences
Chapter 10: Multiple Sample Tests of Proportions: Chi-Squared
Comparing Proportions across Several Groups
Testing for Multiple Group Differences
Describing Group Differences
Chapter 11: Multiple Sample Tests for Means: One-Way ANOVA
Comparing Several Group Means with Analysis of Variance
Analyzing Variance and the F-Test
Analyzing Variance
The F-Test
Comparing Means
Part III: Association and Prediction
Chapter 12: Association with Categorical Variables
The Concept of Statistical Association
Association with Nominal Variables
Association with Ordinal Variables
Chapter 13: Association of Interval/Ratio Variables
Visualizing Interval/Ratio Association
Significance Testing for Interval/Ratio Association
Chapter 14: Regression Analysis
Predicting Outcomes with Regression
Simple Linear Regression
Applying Simple Regression Analysis
Multiple Regression
Applying Multiple Regression
Chapter 15: Logistic Regression Analysis
Predicting with Nonlinear Relationships
Logistic Regression
The Logistic Regression Model
Interpreting Effects in Logistic Regression
Estimating Logistic Regression Models with Maximum Likelihood
Applying Logistic Regression
Assessing Partial Effects
Extending Logistic Regression
Chi-Squared Distribution: Critical Values for Commonly Used Alpha = 0.05 and Alpha = 0.01
F-Distribution: Critical Values for Commonly Used Alpha = 0.05 and Alpha = 0.01
Standard Normal Scores (Z-Scores), and Cumulative Probabilities (Proportion of Cases Having Scores below Z)
Student’s t-Distribution: Critical Values for Commonly Used Alpha Levels
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