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Straightforward Statistics: Understanding the Tools of Research [Mīkstie vāki]

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(Professor and Chair of Psychology, State University of New York at New Paltz),
  • Formāts: Paperback / softback, 416 pages, height x width x depth: 251x175x18 mm, weight: 748 g
  • Izdošanas datums: 15-Oct-2015
  • Izdevniecība: Oxford University Press Inc
  • ISBN-10: 0190276959
  • ISBN-13: 9780190276959
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  • Cena: 100,23 €
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Straightforward Statistics: Understanding the Tools of ResearchPalielināt
  • Formāts: Paperback / softback, 416 pages, height x width x depth: 251x175x18 mm, weight: 748 g
  • Izdošanas datums: 15-Oct-2015
  • Izdevniecība: Oxford University Press Inc
  • ISBN-10: 0190276959
  • ISBN-13: 9780190276959
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9780190276959.html
Keywords:
Straightforward Statistics: Understanding the Tools of Research is a clear and direct introduction to statistics for the social, behavioral, and life sciences. Based on Glenn Geher's extensive experience teaching undergraduate statistics, this book provides a narrative presentation of the core principles that provide the foundation for modern-day statistics. With step-by-step guidance on the nuts and bolts of computing these statistics, the book includes detailed tutorials how to use state-of-the-art software, SPSS, to compute the basic statistics employed in modern academic and applied research. Across 13 succinct chapters, this text presents statistics using a conceptual approach along with information on the relevance of the different tools in different contexts and summaries of current research examples.

Students should find this book easy useful and engaging in its presentation while instructors should find it detailed, comprehensive, accessible, and helpful in complementing a basic course in statistics.
Preface xiii
Acknowledgements xvii
1 Prelude: Why Do I Need to Learn Statistics?
1(14)
The Nature of Findings and Facts in the Behavioral Sciences
3(1)
Statistical Significance and Effect Size
4(2)
Descriptive and Inferential Statistics
6(1)
A Conceptual Approach to Teaching and Learning Statistics
7(1)
The Nature of this Book
8(1)
How to Approach this Class and What You Should Get Out of It
8(1)
Key Terms
9(6)
2 Describing a Single Variable
15(34)
Variables, Values, and Scores
15(1)
Types of Variables
16(2)
Describing Scores for a Single Variable
18(1)
Indices of Central Tendency
18(3)
Indices of Variability (and the Sheer Beauty of Standard Deviation!)
21(4)
Rounding
25(1)
Describing Frequencies of Values for a Single Variable
26(2)
Representing Frequency Data Graphically
28(4)
Describing Data for a Categorical Variable
32(1)
A Real Research Example
33(2)
Summary
35(1)
Key Terms
35(14)
3 Standardized Scores
49(16)
When a Z-Score Equals 0, the Raw Score It Corresponds to Must Equal the Mean
51(2)
Verbal Scores for the Madupistan Aptitude Measure
53(1)
Quantitative Scores for the Madupistan Aptitude Measure
54(1)
Every Raw Score for Any Variable Corresponds to a Particular Z-Score
54(1)
Computing Z-Scores for All Students for the Madupistan Verbal Test
55(1)
Computing Raw Scores from Z-Scores
56(1)
Comparing Your GPA of 3.10 from Solid State University with Pat's GPA of 1.95 from Advanced Technical University
57(1)
Each Z-Score for Any Variable Corresponds to a Particular Raw Score
58(1)
Converting Z-Scores to Raw Scores (The Dorm Resident Example)
58(1)
A Real Research Example
59(1)
Summary
59(1)
Key Terms
60(5)
4 Correlation
65(26)
Correlations Are Summaries
66(1)
Representing a Correlation Graphically
66(4)
Representing a Correlation Mathematically
70(5)
Return to Madupistan
75(3)
Correlation Does Not Imply Causation
78(1)
A Real Research Example
79(1)
Summary
80(1)
Key Terms
80(11)
5 Statistical Prediction and Regression
91(24)
Standardized Regression
92(1)
Predicting Scores on Y with Different Amounts of Information
93(1)
Beta Weight
94(3)
Unstandardized Regression Equation
97(2)
The Regression Line
99(3)
Quantitatively Estimating the Predictive Power of Your Regression Model
102(1)
Interpreting r2
103(1)
A Real Research Example
104(2)
Conclusion
106(1)
Key Terms
106(9)
6 The Basic Elements of Hypothesis Testing
115(24)
The Basic Elements of Inferential Statistics
116(4)
The Normal Distribution
120(9)
A Real Research Example
129(1)
Summary
130(1)
Key Terms
131(8)
7 Introduction to Hypothesis Testing
139(28)
The Basic Rationale of Hypothesis Testing
140(1)
Understanding the Broader Population of Interest
141(1)
Population versus Sample Parameters
141(1)
The Five Basic Steps of Hypothesis Testing
142(14)
A Real Research Example
156(1)
Summary
157(1)
Key Terms
157(10)
8 Hypothesis Testing if N > 1
167(18)
The Distribution of Means
167(5)
Steps in Hypothesis Testing if N > 1
172(4)
Confidence Intervals
176(1)
Real Research Example
177(1)
Summary
178(1)
Key Terms
178(7)
9 Statistical Power
185(34)
What Is Statistical Power?
186(1)
An Example of Statistical Power
186(19)
Factors that Affect Statistical Power
205(3)
A Real Research Example
208(1)
Summary
208(1)
Key Terms
208(11)
10 t-tests (One-Sample and Within-Groups)
219(28)
One-Sample t-test
220(1)
Steps for Hypothesis Testing with a One-Sample t-test
221(4)
Here Are Some Simple Rules to Determine the Sign of tcrit with a One-Sample t-Test
225(3)
Computing Effect Size with a One-Sample t-Test
228(1)
How the t-Test Is Biased Against Small Samples
228(1)
The Within-Group t-Test
229(1)
Steps in Computing the Within-Group t-Test
230(4)
Computing Effect Size with a Within-Group t-test
234(1)
A Real Research Example
234(1)
Summary
235(1)
Key Terms
235(12)
11 The Between-Groups t-test
247(30)
The Elements of the Between-Groups t-test
248(9)
Effect Size with the Between-Groups t-test
257(1)
Another Example
258(6)
Real Research Example
264(1)
Summary
264(1)
Key Terms
265(12)
12 Analysis of Variance
277(28)
ANOVA as a Signal-Detection Statistic
278(2)
An Example of the One-Way ANOVA
280(7)
What Can and Cannot Be Inferred from ANOVA (The Importance of Follow-Up Tests)
287(1)
Estimating Effect Size with the One-Way ANOVA
288(1)
Real Research Example
289(1)
Summary
289(1)
Key Terms
290(15)
13 Chi Square and Hypothesis-Testing with Categorical Variables
305(20)
Chi Square Test of Goodness of Fit
306(1)
Steps in Hypothesis Testing with Chi Square Goodness of Fit
306(3)
What Can and Cannot Be Inferred from a Significant Chi Square
309(1)
Chi Square Goodness of Fit Testing for Equality across Categories
310(4)
Chi Square Test of Independence
314(3)
Real Research Example
317(1)
Summary
318(1)
Key Terms
318(7)
Appendix A Cumulative Standardized Normal Distribution
325(4)
Appendix B T cal Values of t
329(2)
Appendix C F Distribution: Critical Values of F
331(4)
Appendix D Chi Square Distribution: Critical Values of Χ2
335(2)
Appendix E Advanced Statistics to Be Aware of
337(8)
Advanced Forms of ANOVA
337(6)
Summary
343(1)
Key Terms
343(2)
Appendix F Using SPSS
345(38)
Activity 1 SPSS Data Entry Lab
345(2)
Activity 2 Working with SPSS Syntax Files
347(1)
Syntax Files, Recoding Variables, Compute Statements, Out Files, and the Computation of Variables in SPSS
347(1)
Recoding Variables
347(1)
Computing New Variables
348(1)
Output Files
348(1)
Example: How to Recode Items for the Jealousy Data and Compute Composite Variables
348(1)
Activity 3 Descriptive Statistics
349(1)
Frequencies, Descriptives, and Histograms
349(1)
Frequencies, Descriptives, and Histograms for Data Measured in Class
350(1)
The Continuous Variable
351(1)
The Categorical Variable
351(1)
Activity 4 Correlations
351(3)
Activity 5 Regression
354(5)
Activity 6 t-tests
359(1)
Independent Samples Test
360(3)
Activity 7 ANOVA with SPSS
363(2)
Post Hoc Tests
365(1)
Homogeneous Subsets
366(2)
Activity 8 Factorial ANOVA
368(1)
Recomputing Variables so as to Be Able to Conduct a One-Way ANOVA to Examine Specific Differences Between Means
369(10)
Activity 9 Chi Square
379(1)
Crosstabs
380(3)
Glossary 383(6)
References 389(2)
Index 391
Glenn Geher is Professor and Chair of Psychology at the State University of New York at New Paltz, where he has taught Statistics and various other courses related to psychology and evolution since 2000. He also is the founding director of New Paltz's Evolutionary Studies Program, which has been awarded thousands of dollars from the National Science Foundation to help advance evolution's place in higher education. He has over 60 publications including several books and articles on various topics related to evolution and social psychology. His work has been covered in several media outlets including the New York Times, Chronicle of Higher Education, Redbook, and Cosmopolitan. He lives with his wife Kathy and two children, Megan and Andrew, in rural upstate New York.

Sara Hall has earned degrees in both Psychology and Criminology. She lives in Oregon with her husband, Benjamin, and their four children, Jackson, Stella, Susanna, and Sailor.