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Essentials of Social Statistics for a Diverse Society 4th ed. [Mīkstie vāki]

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(Pacific Lutheran University), (University of Wisconsin, Milwaukee, USA), (University of New Mexico, USA)
  • Formāts: Paperback / softback, 456 pages, height x width x depth: 229x178x15 mm, weight: 680 g
  • Izdošanas datums: 16-Oct-2020
  • Izdevniecība: Sage Publications, Inc
  • ISBN-10: 1544372507
  • ISBN-13: 9781544372501
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Essentials of Social Statistics for a Diverse Society 4th ed.Palielināt
  • Formāts: Paperback / softback, 456 pages, height x width x depth: 229x178x15 mm, weight: 680 g
  • Izdošanas datums: 16-Oct-2020
  • Izdevniecība: Sage Publications, Inc
  • ISBN-10: 1544372507
  • ISBN-13: 9781544372501
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781544372501.html
Keywords:
Essentials of Social Statistics for a Diverse Society, by Anna Leon-Guerrero, Chava Frankfort-Nachmias, and Georgiann Davis, is a briefer version of the successful Social Statistics for a Diverse Society, and as in the parent text, the use of real data about contemporary social issues sets this book apart from others in the field. The text explains how to compute and interpret basic descriptive and inferential statistics while teaching and reinforcing important sociological concepts. In every chapter, the authors demonstrate how statistics is an important tool for studying and understanding the role of race, class, gender, and other statuses in a pluralistic society like the United States.

Preface xv
Acknowledgments xix
About the Authors xxi
Chapter 1 The What and the Why of Statistics
1(26)
The Research Process
2(1)
Asking Research Questions
3(1)
The Role of Theory
4(1)
Formulating the Hypotheses
5(5)
Independent and Dependent Variables: Causality
8(1)
Independent and Dependent Variables: Guidelines
9(1)
Collecting Data
10(7)
Levels of Measurement
10(1)
Nominal Level of Measurement
11(1)
Ordinal Level of Measurement
12(1)
Interval-Ratio Level of Measurement
13(1)
Cumulative Property of Levels of Measurement
13(1)
Levels of Measurement of Dichotomous Variables
14(2)
Discrete and Continuous Variables
16(1)
A Closer Look 1.1 A Cautionary Note: Measurement Error
17(1)
Analyzing Data and Evaluating the Hypotheses
17(2)
Descriptive and Inferential Statistics
18(1)
Evaluating the Hypotheses
19(1)
Examining a Diverse Society
19(8)
Data at Work
21(6)
Chapter 2 The Organization and Graphic Presentation of Data
27(42)
Frequency Distributions
27(2)
Proportions and Percentages
29(1)
Percentage Distributions
30(1)
The Construction of Frequency Distributions
31(7)
Frequency Distributions for Nominal Variables
34(1)
Frequency Distributions for Ordinal Variables
34(1)
Frequency Distributions for Interval-Ratio Variables
35(3)
Cumulative Distributions
38(2)
Rates
40(1)
Bivariate Tables
41(5)
How to Construct a Bivariate Table
41(3)
How to Compute Percentages in a Bivariate Table
44(1)
Calculating Percentages Within Each Category of the Independent Variable
44(1)
Comparing the Percentages Across Different Categories of the Independent Variable
45(1)
Graphic Presentation of Data
46(1)
The Pie Chart
47(2)
The Bar Graph
49(1)
The Histogram
50(2)
The Line Graph
52(1)
The Time-Series Chart
53(1)
Statistics in Practice: Foreign-Born Population 65 Years and Over
54(15)
A Closer Look 2.1 A Cautionary Note: Distortions in Graphs
56(1)
Data at Work: Spencer Westby: Senior Editorial Analyst
57(12)
Chapter 3 Measures of Central Tendency and Variability
69(54)
Measures of Central Tendency
69(23)
The Mode
70(2)
The Median
72(1)
Finding the Median in Sorted Data
72(4)
Finding the Median in Frequency Distributions
76(1)
Locating Percentiles in a Frequency Distribution
77(1)
The Mean
78(3)
A Closer Look 3.1 Finding the Mean in a Frequency Distribution
81(1)
Understanding Some Important Properties of the Arithmetic Mean
82(3)
Reading the Research Literature: The Case of Reporting Income
85(1)
Statistics in Practice: The Shape of the Distribution
86(1)
The Symmetrical Distribution
86(1)
The Positively Skewed Distribution
87(1)
The Negatively Skewed Distribution
88(2)
Guidelines for Identifying the Shape of a Distribution
90(1)
Considerations for Choosing a Measure of Central Tendency
90(1)
Level of Measurement
90(1)
Skewed Distribution
90(1)
A Closer Look 3.2 A Cautionary Note: Representing Income
91(1)
Symmetrical Distribution
92(1)
Measures of Variability
92(31)
The Importance of Measuring Variability
93(1)
The Range
94(1)
The Interquartile Range
95(3)
The Box Plot
98(3)
The Variance and the Standard Deviation
101(1)
Calculating the Deviation From the Mean
102(2)
Calculating the Variance and the Standard Deviation
104(2)
Considerations for Choosing a Measure of Variation
106(2)
A Closer Look 3.3 More on Interpreting the Standard Deviation
108(2)
Reading the Research Literature: Community College Mentoring
110(1)
Data at Work: Sruthi Chandrasekaran: Senior Research Associate
111(12)
Chapter 4 The Normal Distribution
123(26)
Properties of the Normal Distribution
123(3)
Empirical Distributions Approximating the Normal Distribution
124(1)
Areas Under the Normal Curve
124(1)
Interpreting the Standard Deviation
125(1)
An Application of the Normal Curve
126(1)
Transforming a Raw Score Into a Z Score
127(1)
The Standard Normal Distribution
127(1)
The Standard Normal Table
128(12)
1 Finding the Area Between the Mean and a Positive or Negative Z Score
130(1)
2 Finding the Area Above a Positive Z Score or Below a Negative Z Score
131(2)
3 Transforming Proportions and Percentages Into Z Scores
133(1)
Finding a Z Score That Bounds an Area Above It
133(1)
Finding a Z Score That Bounds an Area Below It
134(1)
4 Working With Percentiles in a Normal Distribution
135(1)
Finding the Percentile Rank of a Score Higher Than the Mean
135(1)
Finding the Percentile Rank of a Score Lower Than the Mean
136(1)
Finding the Raw Score Associated With a Percentile Higher Than 50
137(1)
Finding the Raw Score Associated With a Percentile Lower Than 50
138(2)
Reading the Research Literature: Child Health and Academic Achievement
140(9)
A Closer Look 4.1 Percentages, Proportions, and Probabilities
140(2)
Data at Work: Claire Wulf Winiarek: Director of Collaborative Policy Engagement
142(7)
Chapter 5 Sampling and Sampling Distributions
149(26)
Aims of Sampling
149(2)
Basic Probability Principles
151(2)
Probability Sampling
153(1)
The Simple Random Sample
154(1)
The Concept of the Sampling Distribution
154(4)
The Population
155(1)
The Sample
156(1)
The Dilemma
157(1)
The Sampling Distribution
157(1)
The Sampling Distribution of the Mean
158(4)
An Illustration
158(2)
Review
160(1)
The Mean of the Sampling Distribution
161(1)
The Standard Error of the Mean
162(1)
The Central Limit Theorem
162(5)
The Size of the Sample
165(1)
The Significance of the Sampling Distribution and the Central Limit Theorem
165(2)
Statistics in Practice: The 2016 U.S. Presidential Election
167(8)
Data at Work: Emily Treichler: Postdoctoral Fellow
168(7)
Chapter 6 Estimation
175(26)
Point and Interval Estimation
176(1)
Confidence Intervals for Means
177(9)
A Closer Look 6.1 Estimation as a Type Inference
178(2)
Determining the Confidence Interval
180(1)
Calculating the Standard Error of the Mean
180(1)
Deciding on the Level of Confidence and Finding the Corresponding Z Value
180(1)
Calculating the Confidence Interval
180(1)
Interpreting the Results
181(1)
Reducing Risk
182(2)
Estimating Sigma
184(1)
Calculating the Estimated Standard Error of the Mean
184(1)
Deciding on the Leuel of Confidence and Finding the Corresponding Z Value
184(1)
Calculating the Confidence Interval
184(1)
Interpreting the Results
185(1)
Sample Size and Confidence Intervals
185(1)
Statistics in Practice: Hispanic Migration and Earnings
186(4)
A Closer Look 6.2 What Affects Confidence Interval Width?
189(1)
Confidence Intervals for Proportions
190(4)
Determining the Confidence Interval
192(1)
Calculating the Estimated Standard Error of the Proportion
192(1)
Deciding on the Desired Level of Confidence and Finding the Corresponding Z Value
193(1)
Calculating the Confidence Interval
193(1)
Interpreting the Results
193(1)
Reading the Research Literature: Women Victims of Intimate Violence
194(7)
Data at Work: Laurel Person Mecca: Research Specialist
196(5)
Chapter 7 Testing Hypotheses
201(36)
Assumptions of Statistical Hypothesis Testing
202(1)
Stating the Research and Null Hypotheses
202(3)
The Research Hypothesis (H1)
203(1)
The Null Hypothesis (H0)
204(1)
Probability Values and Alpha
205(4)
A Closer Look 7.1 More About Significance
208(1)
The Five Steps in Hypothesis Testing: A Summary
209(4)
Errors in Hypothesis Testing
210(1)
The f Statistic and Estimating the Standard Error
211(1)
The t Distribution and Degrees of Freedom
212(1)
Comparing the t and Z Statistics
212(1)
Hypothesis Testing With One Sample and Population Variance Unknown
213(2)
Hypothesis Testing With Two Sample Means
215(2)
The Assumption of Independent Samples
216(1)
Stating the Research and Null Hypotheses
216(1)
The Sampling Distribution of the Difference Between Means
217(2)
Estimating the Standard Error
218(1)
Calculating the Estimated Standard Error
218(1)
The r Statistic
218(1)
Calculating the Degrees of Freedom for a Difference Between Means Test
219(1)
The Five Steps in Hypothesis Testing About Difference Between Means: A Summary
219(2)
A Closer Look 7.2 Calculating the Estimated Standard Error and the Degrees of Freedom (df) When the Population Variances Are Assumed to Be Unequal
220(1)
Statistics in Practice: Vape Use Among Teens
221(2)
Hypothesis Testing With Two Sample Proportions
223(4)
Reading the Research Literature: Reporting the Results of Hypothesis Testing
227(10)
Data at Work: Stephanie Wood: Campus Visit Coordinator
229(8)
Chapter 8 The Chi-Square Test and Measures of Association
237(42)
The Concept of Chi-Square as a Statistical Test
239(1)
The Concept of Statistical Independence
240(1)
The Structure of Hypothesis Testing With Chi-Square
241(9)
The Assumptions
241(1)
Stating the Research and the Null Hypotheses
241(1)
The Concept of Expected Frequencies
241(1)
Calculating the Expected Frequencies
242(2)
Calculating the Obtained Chi-Square
244(1)
The Sampling Distribution of Chi-Square
245(1)
Determining the Degrees of Freedom
246(2)
Making a Final Decision
248(1)
Review
248(2)
Statistics in Practice: Respondent and Mother Education
250(3)
A Closer Look 8.1 A Cautionary Note: Sample Size and Statistical Significance for Chi-Square
252(1)
Proportional Reduction of Error
253(3)
A Closer Look 8.2 What Is Strong? What Is Weak? A Guide to Interpretation
254(2)
Lambda: A Measure of Association for Nominal Variables
256(3)
Cramer's V: A Chi-Square-Related Measure of Association for Nominal Variables
259(1)
Gamma and Kendall's Tau-b: Symmetrical Measures of Association for Ordinal Variables
259(3)
Reading the Research Literature: India's Internet-Using Population
262(17)
Data at Work: Patricio Cumsille: Professor
264(15)
Chapter 9 Analysis of Variance
279(24)
Understanding Analysis of Variance
280(2)
The Structure of Hypothesis Testing With ANOVA
282(6)
The Assumptions
282(1)
Stating the Research and the Null Hypotheses and Setting Alpha
283(1)
The Concepts of Between and Within Total Variance
283(2)
The F Statistic
285(2)
A Closer Look 9.1 Decomposition of SST
287(1)
Making a Decision
288(1)
The Five Steps in Hypothesis Testing: A Summary
288(2)
Statistics in Practice: The Ethical Consumer
290(1)
A Closer Look 9.2 Assessing the Relationship Between Variables
291(1)
Reading the Research Literature: College Satisfaction Among Latino Students
291(12)
Data at Work: Kevin Hemminger: Sales Support Manager/Graduate Program in Research Methods and Statistics
293(10)
Chapter 10 Regression and Correlation
303(46)
The Scatter Diagram
304(1)
Linear Relationships and Prediction Rules
305(9)
Finding the Best-Fitting Line
306(1)
Defining Error
307(1)
A Closer Look 10.1 Other Regression Techniques
308(1)
The Residual Sum of Squares (Le2)
309(1)
The Least Squares Line
309(1)
Computing a and b
309(3)
A Closer Look 10.2 Understanding the Covariance
312(1)
Interpreting a and b
313(1)
A Negative Relationship: Age and Internet Hours per Week
314(3)
Methods for Assessing the Accuracy of Predictions
317(6)
Calculating Prediction Errors
318(4)
Calculating r2
322(1)
Testing the Significance of r2 Using ANOVA
323(4)
Making a Decision
325(1)
Pearson's Correlation Coefficient (r)
326(1)
Characteristics of Pearson's r
326(1)
Statistics in Practice: Multiple Regression and ANOVA
327(5)
A Closer Look 10.3 Spurious Correlations and Confounding Effects
332(1)
Reading the Research Literature: Academic Intentions and Support
332(17)
Data at Work: Shinichi Mizokami: Professor
334(15)
Appendix A Table of Random Numbers 349(4)
Appendix B The Standard Normal Table 353(6)
Appendix C Distribution of t 359(2)
Appendix D Distribution of Chi-Square 361(2)
Appendix E Distribution of F 363(4)
Appendix F Basic Math Review (on the website*) Learning Check Solutions 367(14)
Answers to Odd-Numbered Exercises 381(28)
Glossary 409(6)
Notes 415(6)
Index 421