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Understanding Statistical Analysis and Modeling [Pehme köide]

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  • Formaat: Paperback / softback, 440 pages, kõrgus x laius: 254x177 mm, kaal: 740 g
  • Ilmumisaeg: 15-Feb-2018
  • Kirjastus: SAGE Publications Inc
  • ISBN-10: 1506317413
  • ISBN-13: 9781506317410
Teised raamatud teemal:
Understanding Statistical Analysis and ModelingSuurem pilt
  • Formaat: Paperback / softback, 440 pages, kõrgus x laius: 254x177 mm, kaal: 740 g
  • Ilmumisaeg: 15-Feb-2018
  • Kirjastus: SAGE Publications Inc
  • ISBN-10: 1506317413
  • ISBN-13: 9781506317410
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781506317410.html
Märksõnad:

Understanding Statistical Analysis and Modeling is for readers in the social, behavioral, or managerial sciences mathematics to understand the logic of statistical analysis. Robert Bruhl covers all the basic methods of descriptive and inferential statistics in an accessible manner by way of asking and answering research questions. Concepts are discussed in the context of a specific research project and the book includes probability theory as the basis for understanding statistical inference. Instructions on using SPSS® are included so that readers focus on interpreting statistical analysis rather than calculations. Tables are used, rather than formulas, to describe the various calculations involved with statistical analysis and the exercises in the book are intended to encourage students to formulate and execute their own empirical investigations.

Arvustused

"This is a well-thought out and designed text that gives students an open and accessible introduction to the concepts and techniques necessary for conducting social science research." -- Scott Comparato "This book presents the opportunity for those teaching statistics to present probability theory in a non-intrusive manner, allowing students to move beyond their fears of probability theory and access one of the most important aspects of really understanding statistics." -- Robert J. Eger III "This text takes a refreshing approach to presenting statistical concepts in a methodologically rigorous yet meaningful way that students will intuitively grasp." -- Brian Frederick "This text has a competitive edge over similar textbooks. I strongly recommend it to students who want to have a clear understanding of how to develop good research questions and select statistical techniques appropriate in answering the research questions." -- Benjamin C. Ngwudike "Readers will be surprised how much they are learning about statistics and statistical analysis as they read this book. The author presents mathematical concepts by first starting with the familiar and gently guiding the reader in more unfamiliar territory." -- John David Rausch, Jr. "This book provides a thorough introduction to statistics. End-of-chapter exercises and SPSS® tutorials will greatly enhance students abilities to transfer skills learned in the classroom to real-world problems." -- Christopher Larimer "Functional and straightforward. A comprehensive introduction to statistics!" -- Derrick Bryan "This is a remarkable book that integrates examples, SPSS® tutorials, and exercises. The chapters provide an in-depth analysis of the key concepts. This book is an essential resource for advanced-level undergraduate students and graduate students in the study of statistical analysis." -- Prachi Kene "An enjoyable read. The book has the potential to promote numeracy among the general public, and serve as a resource in statistics education." -- Michael Raisinghani

Introduction xvii
Acknowledgments xxi
About the Author xxiii
PART I RESEARCH DESIGN
1(40)
Purpose: Making Sense of What We Observe
1(1)
Deciding How to Represent Properties of a Phenomenon
2(1)
Describing Differences or Explaining Differences Between Phenomena?
2(1)
Deciding How to Collect Observations
3(2)
Chapter 1 "Why" Conduct Research, and "Why" Use Statistics?
5(22)
1.0 Learning Objectives
5(1)
1.1 Motivation
5(2)
1.2 Representation and Modeling
7(5)
Differentiation and Variability
7(1)
Observation Is an Active Process of Cognition
7(2)
Quantitative or "Scale" Assessments
9(1)
Ordinal Scale Assessments
10(1)
Qualitative Assessments
11(1)
1.3 A Special Case: Investigating Subjective Behavior
12(1)
1.4 Reasons for an Empirical Investigation
13(7)
Descriptive Studies
13(1)
Explanatory Studies
14(2)
The Role of Theory in an Explanatory Investigation
16(1)
Some Concluding Remarks on Explanatory Research
17(3)
1.5 Summary
20(3)
1.6 Exercises
23(1)
1.7 Some Formal Terminology (Optional)
23(4)
Chapter 2 Methods of Quantitative Empirical Investigation
27(14)
2.0 Learning Objectives
27(1)
2.1 Motivation
27(1)
2.2 Instrumentation: Choosing a Tool to Assess a Property of Interest
28(2)
2.3 Limited Focus or Intent to Generalize
30(2)
Case Studies
30(1)
Estimation Studies
31(1)
2.4 Controlled or Natural Observations
32(3)
Experimental Studies
32(3)
Observational Studies
35(1)
2.5 Applied Versus Pure Research
35(2)
2.6 Summary
37(1)
2.7 Exercises
38(3)
PART II DESCRIPTIVE STATISTICS
41(136)
Organizing and Describing a Set of Observations
41(1)
Measuring the Variability in a Set of Observations
42(1)
Describing a Set of Observations in Terms of Their Variability
42(1)
Chapter 3 The Frequency Distribution Report: Organizing a Set of Observations
43(38)
3.0 Learning Objectives
43(1)
3.1 Motivation: Comparing, Sorting, and Counting
44(1)
3.2 Constructing a Sample Frequency Distribution for a "Qualitative" Property
45(7)
The Frequency Distribution Report
46(1)
The Relative Frequency Distribution Report
47(2)
Pictorial Presentations of the Relative Frequency Distribution
49(2)
Interpreting the Analysis
51(1)
3.3 Constructing a Sample Frequency Distribution for an "Ordinal" Property
52(6)
The Frequency Distribution Report
54(1)
The Relative Frequency Distribution Report
54(1)
Pictorial Presentations of the Relative Frequency Distribution
55(2)
Interpreting the Results
57(1)
3.4 Some Important Technical Notes
58(2)
Categories, Values, and Counts
58(1)
One Phenomenon---One Value
58(1)
Central Tendencies, Averages, and Norms
58(1)
Precision and Rounding
59(1)
3.5 Summary
60(1)
3.6 SPSS Tutorial
61(17)
Coding
62(2)
Data Entry
64(7)
Data Analysis
71(7)
3.7 Exercises
78(3)
Chapter 4 The Mode, the Median, and the Mean: Describing a Typical Value of a Quantitative Property Observed for a Set of Phenomena
81(30)
4.0 Learning Objectives
81(1)
4.1 Motivation
82(1)
4.2 A Cautionary Note Regarding Quantitatively Assessed Properties
83(3)
4.3 Constructing a Sample Frequency Distribution for a Quantitative Property
86(7)
The Frequency Distribution Report
87(1)
The Relative Frequency Distribution Report
88(1)
Pictorial Representations of the Relative Frequency Distribution
89(2)
The Informational Content of the Relative Frequency Distribution Report
91(2)
4.4 Identifying a Typical Phenomenon from a Set of Phenomena
93(1)
4.5 Assessing and Using the Median of a Set of Observations
94(5)
The Cumulative Relative Frequency Distribution Report
95(2)
Pictorial Depictions of the Cumulative Relative Frequency Distribution Report
97(2)
4.6 Assessing and Using the Mean of a Set of Observations
99(6)
The Mean of a Set of Observations and the Method of Moments
100(3)
A Technical Note on Assessing the Mean for a Set of Observations
103(2)
4.7 Interpreting and Comparing the Mode, the Median, and the Mean
105(1)
4.8 Summary
106(1)
4.9 SPSS Tutorial
107(1)
4.10 Exercises
108(3)
Chapter 5 The Variance and the Standard Deviation: Describing the Variability Observed for a Quantitative Property of a Set of Phenomena
111(48)
5.0 Learning Objectives
111(1)
5.1 Motivation
112(3)
Comparing the Highest and Lowest Observed Values
112(1)
Variation Assessed Through One-To-One Comparisons
113(1)
Variation Assessed Through Comparison with a Reference Point
113(2)
5.2 A Case Example: The Frequency Distribution Report
115(2)
5.3 The Range of a Set of Observations
117(1)
5.4 The Mean Absolute Difference
118(3)
5.5 The Variance and the Standard Deviation
121(7)
A Model of Natural Variation
121(2)
Variation Versus Differentiation
123(1)
The Method of Moments and the Variance
124(1)
The Standard Deviation
125(1)
Assessing the Standard Deviation
126(2)
5.6 Interpreting the Variance and the Standard Deviation
128(3)
Variance or Standard Deviation?
128(1)
Normal Variation or Differentiation?
128(3)
5.7 Comparing the Mean Absolute Difference and the Standard Deviation
131(1)
5.8 A Useful Note on Calculating the Variance
131(4)
5.9 A Note on Modeling and the Assumption of Variability
135(1)
5.10 Summary
135(1)
5.11 SPSS Tutorial
136(13)
5.12 Exercises
149(3)
5.13 The Method of Moments (Optional)
152(5)
Comparing the Different Moments: An Example
153(4)
5.14 A Distribution of "Squared Differences from a Mean" (Optional)
157(2)
Chapter 6 The z-Transformation and Standardization: Using the Standard Deviation to Compare Observations
159(18)
6.0 Learning Objectives
159(1)
6.1 Motivation
159(3)
Comparing Two Phenomena Using the Standard Deviation
160(1)
Comparing Each Phenomenon with the "Typical" Phenomenon
160(1)
The Standardized Frequency Distribution
161(1)
6.2 Executing the z-Transformation
162(3)
6.3 An Example
165(6)
6.4 Summary
171(1)
6.5 An Exercise
172(5)
PART III STATISTICAL INFERENCE AND PROBABILITY
177(94)
Why Probability Theory?
177(1)
The Concept of a Probability
178(1)
Predicting Events Involving Two Coexisting Properties
179(1)
Sampling and the Normal Probability Model
179(2)
Chapter 7 The Concept of a Probability
181(28)
7.0 Learning Objectives
181(1)
7.1 Motivation
181(2)
An Estimation Study
182(1)
An Association Study
182(1)
7.2 Uncertainty, Chance, and Probability
183(1)
7.3 Selection Outcomes and Probabilities
183(1)
7.4 Events and Probabilities
184(4)
7.5 Describing a Probability Model for a Quantitative Property
188(18)
The Probability Polygon
190(3)
The Expected Value of a Random Variable
193(3)
A Note on Calculating the Expected Value
196(1)
The Expected Variation in a Random Variable
196(6)
A Note on Calculating the Variance
202(2)
The z-Transformation and Standardization
204(2)
7.6 Summary
206(2)
7.7 Exercises
208(1)
Chapter 8 Coexisting Properties and Joint Probability Models
209(24)
8.0 Learning Objectives
209(1)
8.1 Motivation
209(1)
8.2 Probability Models Involving Coexisting Properties
210(3)
8.3 Models of Association, Conditional Probabilities, and Stochastic Independence
213(3)
8.4 Covariability in Two Quantitative Properties
216(9)
Representing Coexisting Properties of a Phenomenon as an Interaction
218(1)
The Covariance
219(6)
8.5 Importance of Stochastic Independence and Covariance in Statistical Inference
225(2)
8.6 Summary
227(2)
8.7 Exercises
229(4)
Calculating a Covariance
229(2)
Constructing a Probability Model of a Non-Association
231(2)
Chapter 9 Sampling and the Normal Probability Model
233(38)
9.0 Learning Objectives
233(1)
9.1 Motivation
233(1)
9.2 Samples and Sampling
234(6)
9.3 Bernoulli Trials and the Binomial Distribution
240(13)
In a Single Trial
241(1)
Two Trials
242(2)
Three Trials
244(2)
A Family of Binomial Distributions
246(7)
9.4 Representing the Character of a Population
253(1)
9.5 Predicting Potential Samples from a Known Population
253(6)
9.6 The Normal Distribution
259(3)
9.7 The Central Limit Theorem
262(2)
9.8 Normal Sampling Variability and Statistical Significance
264(2)
9.9 Summary
266(1)
9.10 Exercises
267(4)
PART IV TOOLS FOR MAKING STATISTICAL INFERENCES
271(136)
Estimation Studies
271(1)
Association Studies
271(4)
Chapter 10 Estimation Studies: Inferring the Parameters of a Population from the Statistics of a Sample
275(24)
10.0 Learning Objectives
275(1)
10.1 Motivation
275(2)
10.2 Estimating the Occurrence of a Qualitative Property for a Population
277(6)
10.3 Estimating the Occurrence of a Quantitative Property for a Population
283(8)
10.4 Some Notes on Sampling
291(1)
Selection Bias
291(1)
Response Bias
292(1)
10.5 SPSS Tutorial
292(1)
10.6 Summary
293(1)
10.7 Exercises
294(5)
Chapter 11 Chi-Square Analysis: Investigating a Suspected Association Between Two Qualitative Properties
299(28)
11.0 Learning Objectives
299(1)
11.1 Motivation
299(1)
11.2 An Example
300(11)
Establishing Whether a Relationship Exists Between Two Properties
301(2)
Determining Whether a Relationship Suggested by a Sample Is Significant
303(8)
11.3 An Extension: Testing the Statistical Significance of Population Proportions
311(1)
11.4 Summary
312(2)
11.5 SPSS Tutorial
314(11)
11.6 Exercises
325(2)
Chapter 12 The t-Test of Statistical Significance: Comparing a Quantitative Property Assessed for Two Different Groups
327(20)
12.0 Learning Objectives
327(1)
12.1 Motivation
328(1)
12.2 An Example
328(2)
12.3 Comparing Sample Means Using the Central Limit Theorem (Optional)
330(2)
12.4 Comparing Sample Means Using the t-Test
332(4)
12.5 Summary
336(2)
12.6 SPSS Tutorial
338(6)
12.7 Exercises
344(3)
Chapter 13 Analysis of Variance: Comparing a Quantitative Property Assessed for Several Different Groups
347(20)
13.0 Learning Objectives
347(1)
13.1 Motivation
348(1)
13.2 An Example
349(1)
13.3 The F-Test
350(6)
13.4 A Note on Sampling Distributions (Optional)
356(2)
13.5 Summary
358(1)
13.6 SPSS Tutorial
359(5)
13.7 Exercises
364(3)
Chapter 14 Correlation Analysis and Linear Regression: Assessing the Covariability of Two Quantitative Properties
367(40)
14.0 Learning Objectives
367(1)
14.1 Motivation
368(2)
14.2 An Example
370(1)
14.3 Visual Interpretation with a Scatter Plot (Optional]
371(2)
14.4 Assessing an Association as a Covariance
373(6)
14.5 Regression Analysis: Representing a Correlation as a Linear Mathematical Model
379(4)
14.6 Assessing the Explanatory Value of the Model
383(6)
14.7 Summary
389(3)
14.8 SPSS Tutorial
392(12)
14.9 Exercises
404(3)
Index 407
Robert Bruhl is the author of the statistics textbook Understanding Statistical Analysis and Modeling for Sage (2018).  He is currently Clinical Professor in the Department of Political Science, where his primary focus is on policy analysis and research design.  Included in his specialties are economic history, voter behavior, and Congressional behavior.  Most recently, he has focused his attention on political marketing and campaigns, and has presented papers on this subject both nationally and internationally.  He has also contributed his expertise to both local, national, and international media.  Prior to his appointment at UIC, he taught at DePaul University in their Public Service Graduate Program, and before entering the academic world, he enjoyed a fifteen-year career in the private sector, and held various positions in management consulting, marketing, and business planning.  He has a B.A. degree (Phi Beta Kappa) in Mathematics from Northwestern University, an M.S. degree in Computer and Communication Sciences from the University of Michigan, an M.B.A. in Business Economics from the University of Chicago, and a Ph.D. (Phi Kappa Phi) in Public Policy Analysis from the University of Illinois at Chicago.  He is currently at work on a book describing the political and economic development of the English-American Colonies, with a focus on the effect this development had on the U.S. Constitution.