Statistical Analysis for the Social Sciences

ISBN-10: 020528972X
ISBN-13: 9780205289721
Edition: 1999
Authors: Norman R. Kurtz
List price: $250.40
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Description: This book will prepare students to use and understand basic statistical applications and get them ready to move into more advanced statistical studies. Written at a level appropriate for undergraduates, the book gives careful attention to the flow  More...

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Book details

List price: $250.40
Copyright year: 1999
Publisher: Allyn & Bacon, Incorporated
Publication date: 9/11/1998
Binding: Hardcover
Pages: 379
Size: 7.50" wide x 9.75" long x 1.00" tall
Weight: 1.628

This book will prepare students to use and understand basic statistical applications and get them ready to move into more advanced statistical studies. Written at a level appropriate for undergraduates, the book gives careful attention to the flow of ideas and concepts so there is a stream of logic internal to each chapter as well as one that links chapters together. Statistical Analysis for the Social Sciences provides a simple to understand but sophisticated introduction to statistics. The book begins with a discussion of methods for describing the distribution of a variable. The discussion of measures of central tendency and variance is followed by a presentation of continuous and discrete probability distributions. The introduction of probability avoids the traditional discussion of the basic laws of probability in that they are not directly applied in the everyday use of statistical probability. Instead, the discussion is focused on the relationship of probability to outcomes for values in distributions, and the value of probability in statistical decision making. The discussion of probability is followed by a chapter on the logic underlying statistical inference. The concept of the standard error, and confidence limits link the discussion back to probability. Hypothesis testing is discussed, followed by tests of significance for one sample and two sample tests for various types of statistical measures. Student's t-Test not only illustrates significance testing, but presents an opportunity to introduce the idea of explained variance that is the emphasis of analysis of variance and regression. The discussion of regression and correlation concludes with a demonstration of how correlation analysis provides the basis for evaluating explained variance.

Chapters conclude with Summary, Key Terms, Symbols, Formulas, and Exercises and/or Problem Set
Preface
Social Statistics and Measurement: The Purpose of Social Statistics
What Is Statistics?
Measurement
Displaying Data
Frequencies and Frequency Distributions
Relative Frequencies
Using Tables for Display
Using Graphic Displays
Numerical Summaries of Data
Measures of Central Tendency
Measures of Variation
A Continuous Probability Distribution: The Normal Distribution
The Role of Probability
The Normal Probability Distribution
Skewness and Kurtosis
A Discrete Probability Distribution: The Binomial Distribution
Characteristics of the Binomial Probability Distribution
The Binomial Model and Its Application
Transforming Binomial Data to Continuous Form with Z Scores
The Basis of Statistical Inference
Random Sampling
Accuracy and Error in Sampling
The Logic of Statistical Inference
Point Estimates for Population Means
Confidence Intervals and Population Means
Confidence Limits and Sample Size
Confidence Limits and Proportions
Testing Hypotheses: One-Sample Tests
What Is a Hypothesis?
Hypotheses Testing: The Standard-Error-of-the-Mean Test
The Risk of Error
Student's t Test: One Sample
The Standard-Error-of-Proportions Test: One Sample
Chi-Square: A One-Sample Test for Nominal Data
Comparing Two Continuous Measures: Parametric Tests
Group Comparisons
Two Independent Samples for Interval Measures
Two Independent Samples with Dichotomous Measures
Comparing Two Dependent Samples with Interval Measures
Comparing Two Discrete Measures: Nonparametric Tests
Comparing Two Independent Samples: Ordinal Measures
Comparing Two Dependent Samples: Ordinal Measures
Comparing Two Independent Samples: Nominal Measures
Comparing Two Dependent Samples: Nominal Measures
Comparing More Than Two Populations: Analysis of Variance
Parametric ANOVA Models
Nonparametric ANOVA
Linear Regression and Correlation
Regression Analysis
Correlation
The Association of Discrete Data
The Concept of Association
The Association of Ordinal Measures
The Association of Nominal Measures
Basic Mathematical Procedures
Statistical Tables
Selected Answers to Exercises
References
Index

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