Atjaunināt sīkdatņu piekrišanu

Using and Interpreting Statistics in the Social, Behavioral, and Health Sciences [Mīkstie vāki]

4.00/5 (6 ratings by Goodreads)
,
  • Formāts: Paperback / softback, 232 pages, height x width: 228x152 mm, weight: 350 g
  • Izdošanas datums: 30-Jul-2018
  • Izdevniecība: Sage Publications Ltd
  • ISBN-10: 1526402491
  • ISBN-13: 9781526402493
Citas grāmatas par šo tēmu:
  • Mīkstie vāki
  • Cena: 80,72 €
  • Grāmatu piegādes laiks ir 3-4 nedēļas, ja grāmata ir uz vietas izdevniecības noliktavā. Ja izdevējam nepieciešams publicēt jaunu tirāžu, grāmatas piegāde var aizkavēties.
  • Daudzums:
  • Ielikt grozā
  • Piegādes laiks - 4-6 nedēļas
  • Pievienot vēlmju sarakstam
Using and Interpreting Statistics in the Social, Behavioral, and Health SciencesPalielināt
  • Formāts: Paperback / softback, 232 pages, height x width: 228x152 mm, weight: 350 g
  • Izdošanas datums: 30-Jul-2018
  • Izdevniecība: Sage Publications Ltd
  • ISBN-10: 1526402491
  • ISBN-13: 9781526402493
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781526402493.html
Keywords:
In an auxiliary textbook to use with any undergraduate introduction to research methods by students in a number of disciplines, Wagner and Gillespie introduce students to statistics at the conceptual rather than computational level, examining the meaning of statistics and why researchers use a particular statistical technique. Among their topics are variables with measurement, how to sample and collect data for analysis, using and interpreting univariate and bivariate visualizations, what z scores are and why they are important, how to measure the relationship between nominal and ordinal variables, and how to interpret and report regression results. Annotation ©2018 Ringgold, Inc., Portland, OR (protoview.com)

Using and Interpreting Statistics in the Social, Behavioral, and Health Sciences by William E. Wagner, III and Brian J. Gillespie is designed to be paired with any undergraduate introduction to research methods text used by students in a variety of disciplines. It introduces students to statistics at the conceptual level—examining the meaning of statistics, and why researchers use a particular statistical technique, rather than computational skills. Focusing on descriptive statistics, and some more advanced topics such as tests of significance, measures of association, and regression analysis, this brief, inexpensive text is the perfect companion to help students who have not yet taken an introductory statistics course or are confused by the statistics used in the articles they are reading.


Acknowledgments xiii
Chapter 1 Brief Introduction to Research in the Social, Behavioral, and Health Sciences
1(8)
What Is the Purpose of Research?
1(1)
How Is Research Done?
1(1)
Scientific Method and Hypothesis Testing
2(1)
Inductive Research
3(1)
Deductive Research
4(1)
Research Designs
5(3)
Cross-Sectional Research Design
5(1)
Longitudinal Research Design
5(1)
Repeated Cross-Sectional [ Longitudinal] Research Design
6(1)
Fixed Sample Panel [ Longitudinal] Research Design
6(1)
Cohort [ Longitudinal] Research Design
7(1)
Terms
8(1)
Reference
8(1)
Chapter 2 Variables and Measurement
9(18)
Variables and Data
9(3)
Variables and Hypotheses
9(1)
Independent and Dependent Variables
10(1)
Directional Relationships
11(1)
Levels of Variable Measurement
12(5)
Categorical Variables
12(1)
Nominal Variables
12(1)
Dichotomous Variables
13(1)
Ordinal Variables
13(1)
Quantitative [ Interval/Ratio) Variables
14(1)
The Zero-Point
15(1)
Continuous and Discrete Quantitative Variables
15(1)
Transforming Variable Types
16(1)
Types of Relationships
17(1)
Causal Relationships
17(1)
Correlational Relationships
18(1)
Research Design and Measurement Quality
18(7)
Operationalization and Conceptualization
19(1)
Internal and External Validity
19(1)
Internal Validity
20(3)
External Validity
23(1)
Measurement Validity and Reliability
23(1)
Measurement Validity
23(1)
Reliability
24(1)
Testing for Reliability
25(1)
Conclusion
25(1)
Terms
26(1)
References
26(1)
Chapter 3 How to Sample and Collect Data for Analysis
27(10)
Why Use a Sample?
27(1)
Probability Sampling Methods
27(4)
Simple Random Sample
27(1)
Systematic Random Sample
28(1)
Systematic Random Sampling: Periodicity
28(1)
Stratified Random Sample
29(1)
Proportionate Stratified Random Sample
30(1)
Disproportionate Stratified Random Sample
30(1)
Cluster and Multistage Cluster Samples
30(1)
Nonprobability Sampling Methods
31(3)
Why Would Anyone Choose to Obtain a Nonprobability Sample Over a Probability Sample?
32(1)
Convenience
32(1)
Snowball
32(1)
Respondent-Driven Sampling
33(1)
Quota Sampling
34(1)
Validating a Sample
34(1)
Split Ballot Designs
35(1)
How and Where Are Data Collected Today?
35(1)
Terms
36(1)
References
36(1)
Chapter 4 Data Frequencies and Distributions
37(22)
Univariate Frequencies and Relative Frequencies
37(3)
Cumulative Percentages and Percentiles
40(2)
Frequencies for Quantitative Data
42(2)
Univariate Distributions
44(1)
The Normal Distribution
45(3)
Characteristics of the Normal Distribution
45(2)
Normal Quantile-Quantile Plots
47(1)
Non-Normal Distribution Characteristics
48(2)
Bimodal and Multimodal Distributions
48(1)
Skewness
48(1)
Kurtosis
49(1)
Data Transformations for Dealing With Non-Normal Distributions
50(2)
Bivariate Frequencies
52(5)
Conclusion
57(1)
Terms
57(2)
Chapter 5 Using and Interpreting Univariate and Bivariate Visualizations
59(26)
Univariate Data Visualization
60(12)
Bar Graphs
60(1)
Presentation and Interpretation Issues
61(1)
Pie Charts
61(3)
Presentation and Interpretation Issues
64(2)
Cumulative Frequency Polygons
66(1)
Presentation and Interpretation Issues
67(1)
Boxplots
67(2)
Presentation and Interpretation Issues
69(1)
Stem and Leaf Plots
70(1)
Presentation and Interpretation Issues
71(1)
Bivariate Data Visualization
72(11)
Clustered Bar Graphs
73(1)
Presentation and Interpretation Issues
74(1)
Stacked Bar Graphs
75(2)
Presentation and Interpretation Issues
77(1)
Time Series Graphs
78(1)
Presentation and Interpretation Issues
78(1)
Scatterplots
79(3)
Presentation and Interpretation Issues
82(1)
Visual Presentation
83(1)
Conclusion
83(1)
Terms
84(1)
Chapter 6 Central Tendency and Variability
85(12)
Understanding How to Calculate and Interpret Measures of Central Tendency
85(7)
What Is a Measure of Central Tendency?
85(1)
Variables Measured at the Nominal Level
86(2)
Variables Measured at the Ordinal Level
88(1)
Variables Measured at the Interval or Ratio Level
88(4)
Understanding How Individuals in a Distribution Vary Around a Central Tendency
92(4)
Index of Qualitative Variation: Appropriateness, Calculation, Interpretation
92(2)
Range: Appropriateness, Calculation, Interpretation
94(1)
Interquartile Range: Appropriateness, Calculation, Interpretation
94(1)
Variance: Appropriateness, Calculation, Interpretation
94(1)
Standard Deviation: Appropriateness, Calculation, Interpretation
95(1)
Answer to
Chapter 6 Learning Check
96(1)
Terms
96(1)
Reference
96(1)
Chapter 7 What Are z Scores, and Why Are They Important?
97(16)
What Is a z Score?
97(1)
Why Are z Scores Important?
97(1)
How to Calculate a z Score
98(2)
How to Calculate a Raw Score From a z Score
99(1)
The Standard Normal Table
100(6)
Areas Under the Curve (Probabilities)
103(3)
Working With the Standard Normal Distribution to Calculate z Scores, Raw Scores, and Percentiles
106(1)
z Scores, Raw Scores
106(1)
Percentiles
107(1)
Confidence Intervals
107(4)
Constructing a Confidence Interval
108(1)
Standard Error
109(1)
Calculating a Confidence Interval
109(1)
Confidence Interval Width
110(1)
Confidence Intervals for Proportions
110(1)
Answer to Quick Learning Check
111(1)
Terms
111(2)
Chapter 8 Hypothesis Testing and Statistical Significance
113(34)
Null and Alternative Hypotheses
113(2)
Determination About the Null Hypothesis
114(1)
Statistical Significance
115(2)
Type I and Type II Errors
115(1)
Alpha
116(1)
Test Statistic Distributions
117(1)
Choosing a Test of Statistical Significance
118(1)
The Chi-Square Test of Independence (Χ2)
119(8)
Observed and Expected Frequencies
120(1)
Chi-Square Test Statistic
121(1)
Chi-Square Distribution and Degrees of Freedom
122(1)
Chi-Square Table
122(1)
Chi-Square Output and Interpretation
123(1)
Example 1 Gender and Family Stress
123(2)
Example 2 Gender and Chore Stress
125(1)
Example 3 Gender and Chore Stress by Age Group
125(2)
Chi-Square Overview
127(1)
The Independent Samples t Test
127(8)
Independent Samples t-Test Notation and Hypotheses
128(1)
The Logic of the t-Test Statistic
129(1)
One-Tailed and Two-Tailed t Tests
130(1)
The t Distribution and Degrees of Freedom
131(1)
The t Table
131(1)
Independent Samples t-Test Output and Interpretation
132(1)
Example 1 Gender and Number of Friends (Nondirectional)
132(1)
Example 2 Children and Friends {Directional]
133(2)
The t-Test Overview
135(1)
One-Way Analysis of Variance
135(9)
ANOVA Assumptions and Notation
135(1)
ANOVA Hypotheses
136(1)
ANOVA Test Statistic and Degrees of Freedom
136(3)
ANOVA Table
139(2)
ANOVA Output and Interpretation
141(1)
Example 1 Level of Education and Number of Close Friends
141(1)
Example 2 Age Group and Number of Close Friends
142(2)
Post Hoc Analysis
144(1)
ANOVA Overview
144(1)
Conclusion
144(1)
Terms
145(2)
Chapter 9 How to Measure the Relationship Between Nominal and Ordinal Variables
147(10)
Choosing the Correct Measure of Association
147(1)
Trying to Reduce Error (PRE Statistics)
147(1)
Calculating and Interpreting Lambda
148(1)
Calculating and Interpreting Gamma
149(2)
In General
150(1)
For This Specific Example
150(1)
Calculating and Interpreting Somers' d
151(1)
In General
151(1)
For This Specific Example
151(1)
Calculating and Interpreting Kendall's Tau-b
152(1)
In General
152(1)
For This Specific Example
153(1)
Interpreting PRE Statistics Overview
153(2)
Multivariate Analysis With PRE Statistics for Nominal and Ordinal Variables
154(1)
Terms
155(1)
References
156(1)
Chapter 10 Effect Size
157(8)
Effect Size
158(1)
Choosing an Effect Size
158(5)
Effect Sizes for Chi-Square: φ and Cramer's V
159(2)
Effect Size for f Test: Cohen's d
161(1)
Effect Size for One-Way ANOVA: η2
162(1)
Conclusion
163(1)
Terms
164(1)
References
164(1)
Chapter 11 How to Interpret and Report Regression Results
165(12)
What Is a Regression?
165(1)
Correlation
166(1)
Bivariate Regression
166(2)
Coefficient of Determination (r2)
168(2)
Multiple Regression
170(3)
Variables That Can Be Analyzed in Regression Analysis
170(2)
Interaction Effects
172(1)
Mediating and Moderating Effects
172(1)
Logistic Regression
173(2)
Preparing Variables for Logistic Regression Analysis
173(1)
Creating a Set of Dummy Variables
173(1)
Interpreting Odds Ratios
174(1)
Step Models
175(1)
Terms
175(2)
Chapter 12 Indices, Typologies, and Scales
177(4)
Indices, Typologies, and Scales Defined and Explained
177(2)
Indices
177(1)
Typologies
178(1)
Scales
178(1)
Terms
179(1)
References
180(1)
Appendix A The Standard Normal Table 181(8)
Appendix B Critical Values for (Statistic 189(2)
Appendix C Critical Values for Chi-Square 191(2)
Appendix D Critical Values for F Statistic 193(6)
Appendix E Glossary 199(14)
About the Authors 213
William E. Wagner, III,  PhD, is Chair of the Department of Sociology at California State University, Dominguez Hills and Executive Director of the Social Science Research & Instructional Council of the CSU. He is co-author of Adventures in Social Research, 11th edition (SAGE, 2022), The Practice of Survey Research (SAGE, 2016), and A Guide to R for Social and Behavioral Sciences (SAGE, 2020) and author of Using IBM® SPSS® Statistics for Research Methods and Social Science Statistics, 7th edition (SAGE, 2019).







Brian Joseph Gillespie, Ph.D. is a researcher in the Faculty of Spatial Sciences at the University of Groningen in the Netherlands. He is the author of Household Mobility in America: Patterns, Processes, and Outcomes (Palgrave, 2017) and coauthor of The Practice of Survey Research: Theory and Applications (Sage, 2016) and Using and Interpreting Statistics in the Social, Behavioral, and Health Sciences (Sage, 2018). He has also published research in a variety of social science journals on topics related to family, migration, the life course, and interpersonal relationships.