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E-raamat: Using Statistics in the Social and Health Sciences with SPSS (R) and Excel (R) [Wiley Online]

(Seattle Pacific University, USA)
  • Formaat: 592 pages
  • Ilmumisaeg: 11-Nov-2016
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119121078
  • ISBN-13: 9781119121077
  • Wiley Online
  • Hind: 141,63 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
  • Formaat: 592 pages
  • Ilmumisaeg: 11-Nov-2016
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119121078
  • ISBN-13: 9781119121077
Provides a step-by-step approach to statistical procedures to analyze data and conduct research, with detailed sections in each chapter explaining SPSS® and Excel® applications

This book identifies connections between statistical applications and research design using cases, examples, and discussion of specific topics from the social and health sciences. Researched and class-tested to ensure an accessible presentation, the book combines clear, step-by-step explanations for both the novice and professional alike to understand the fundamental statistical practices for organizing, analyzing, and drawing conclusions from research data in their field.

The book begins with an introduction to descriptive and inferential statistics and then acquaints readers with important features of statistical applications (SPSS and Excel) that support statistical analysis and decision making. Subsequent chapters treat the procedures commonly employed when working with data across various fields of social science research. Individual chapters are devoted to specific statistical procedures, each ending with lab application exercises that pose research questions, examine the questions through their application in SPSS and Excel, and conclude with a brief research report that outlines key findings drawn from the results. Real-world examples and data from social and health sciences research are used throughout the book, allowing readers to reinforce their comprehension of the material.

Using Statistics in the Social and Health Sciences with SPSS® and Excel® includes:





Use of straightforward procedures and examples that help students focus on understanding of analysis and interpretation of findings Inclusion of a data lab section in each chapter that provides relevant, clear examples Introduction to advanced statistical procedures in chapter sections (e.g., regression diagnostics) and separate chapters (e.g., multiple linear regression) for greater relevance to real-world research needs

Emphasizing applied statistical analyses, this book can serve as the primary text in undergraduate and graduate university courses within departments of sociology, psychology, urban studies, health sciences, and public health, as well as other related departments. It will also be useful to statistics practitioners through extended sections using SPSS® and Excel® for analyzing data.
Preface xv
Acknowledgments xix
1 Introduction 1(12)
Big Data Analysis
1(1)
Visual Data Analysis
2(1)
Importance of Statistics for the Social and Health Sciences and Medicine
3(1)
Historical Notes: Early Use of Statistics
4(2)
Approach of the Book
6(1)
Cases from Current Research
7(2)
Research Design
9(1)
Focus on Interpretation
9(4)
2 Descriptive Statistics: Central Tendency 13(42)
What is the Whole Truth? Research Applications (Spuriousness)
13(3)
Descriptive and Inferential Statistics
16(1)
The Nature of Data: Scales of Measurement
16(7)
Descriptive Statistics: Central Tendency
23(5)
Using SPSS® and Excel to Understand Central Tendency
28(7)
Distributions
35(2)
Describing the Normal Distribution: Numerical Methods
37(4)
Descriptive Statistics: Using Graphical Methods
41(6)
Terms and Concepts
47(2)
Data Lab and Examples (with Solutions)
49(2)
Data Lab: Solutions
51(4)
3 Descriptive Statistics: Variability 55(22)
Range
55(1)
Percentile
56(1)
Scores Based on Percentiles
57(1)
Using SPSS® and Excel to Identify Percentiles
57(3)
Standard Deviation and Variance
60(1)
Calculating the Variance and Standard Deviation
61(5)
Population SD and Inferential SD
66(1)
Obtaining SD from Excel and SPSS®
67(3)
Terms and Concepts
70(1)
Data Lab and Examples (with Solutions)
71(2)
Data Lab: Solutions
73(4)
4 The Normal Distribution 77(28)
The Nature of the Normal Curve
77(2)
The Standard Normal Score: Z Score
79(1)
The Z Score Table of Values
80(1)
Navigating the Z Score Distribution
81(2)
Calculating Percentiles
83(1)
Creating Rules for Locating Z Scores
84(3)
Calculating Z Scores
87(3)
Working with Raw Score Distributions
90(1)
Using SPSS® to Create Z Scores and Percentiles
90(4)
Using Excel to Create Z Scores
94(3)
Using Excel and SPSS® for Distribution Descriptions
97(2)
Terms and Concepts
99(1)
Data Lab and Examples (with Solutions)
99(2)
Data Lab: Solutions
101(4)
5 Probability And The Z Distribution 105(28)
The Nature of Probability
106(1)
Elements of Probability
106(3)
Combinations and Permutations
109(2)
Conditional Probability: Using Bayes' Theorem
111(1)
Z Score Distribution and Probability
112(5)
Using SPSS® and Excel to Transform Scores
117(2)
Using the Attributes of the Normal Curve to Calculate Probability
119(4)
"Exact" Probability
123(3)
From Sample Values to Sample Distributions
126(1)
Terms and Concepts
127(1)
Data Lab and Examples (with Solutions)
128(1)
Data Lab: Solutions
129(4)
6 Research Design And Inferential Statistics 133(32)
Research Design
133(3)
Experiment
136(4)
Non-Experimental or Post Facto Research Designs
140(3)
Inferential Statistics
143(11)
Z Test
154(1)
The Hypothesis Test
154(2)
Statistical Significance
156(1)
Practical Significance: Effect Size
156(1)
Z Test Elements
156(1)
Using SPSS® and Excel for the Z Test
157(1)
Terms and Concepts
158(3)
Data Lab and Examples (with Solutions)
161(1)
Data Lab: Solutions
162(3)
7 The T Test For Single Samples 165(42)
Introduction
166(1)
Z Versus T: Making Accommodations
166(1)
Research Design
167(2)
Parameter Estimation
169(4)
The T Test
173(3)
The T Test: A Research Example
176(4)
Interpreting the Results of the T Test for a Single Mean
180(1)
The T Distribution
181(1)
The Hypothesis Test for the Single Sample T Test
182(1)
Type I and Type II Errors
183(4)
Effect Size
187(1)
Effect Size for the Single Sample T Test
187(1)
Power, Effect Size, and Beta
188(1)
One- and Two-Tailed Tests
189(3)
Point and Interval Estimates
192(4)
Using SPSS® and Excel with the Single Sample T Test
196(5)
Terms and Concepts
201(1)
Data Lab and Examples (with Solutions)
201(2)
Data Lab: Solutions
203(4)
8 Independent Sample T Test 207(48)
A Lot of "Ts"
207(1)
Research Design
208(1)
Experimental Designs and the Independent T Test
208(1)
Dependent Sample Designs
209(1)
Between and Within Research Designs
210(1)
Using Different T Tests
211(2)
Independent T Test: The Procedure
213(2)
Creating the Sampling Distribution of Differences
215(1)
The Nature of the Sampling Distribution of Differences
216(2)
Calculating the Estimated Standard Error of Difference with Equal Sample Size
218(1)
Using Unequal Sample Sizes
219(2)
The Independent T Ratio
221(1)
Independent T Test Example
222(1)
Hypothesis Test Elements for the Example
222(4)
Before-After Convention with the Independent T Test
226(1)
Confidence Intervals for the Independent T Test
227(1)
Effect Size
228(2)
The Assumptions for the Independent T Test
230(1)
SPSS® Explore for Checking the Normal Distribution Assumption
231(2)
Excel Procedures for Checking the Equal Variance Assumption
233(4)
SPSS® Procedure for Checking the Equal Variance Assumption
237(2)
Using SPSS® and Excel with the Independent T Test
239(1)
SPSS® Procedures for the Independent T Test
239(4)
Excel Procedures for the Independent T Test
243(2)
Effect Size for the Independent T Test Example
245(1)
Parting Comments
245(1)
Nonparametric Statistics: The Mann-Whitney U Test
246(3)
Terms and Concepts
249(1)
Data Lab and Examples (with Solutions)
249(2)
Data Lab: Solutions
251(3)
Graphics in the Data Summary
254(1)
9 Analysis Of Variance 255(42)
A Hypothetical Example of ANOVA
255(2)
The Nature of ANOVA
257(1)
The Components of Variance
258(1)
The Process of ANOVA
259(1)
Calculating ANOVA
260(8)
Effect Size
268(1)
Post Hoc Analyses
269(5)
Assumptions of ANOVA
274(1)
Additional Considerations with ANOVA
275(1)
The Hypothesis Test: Interpreting ANOVA Results
276(1)
Are the Assumptions Met?
276(6)
Using SPSS® and Excel with One-Way ANOVA
282(7)
The Need for Diagnostics
289(1)
Non-Parametric ANOVA Tests: The Kruskal-Wallis Test
289(3)
Terms and Concepts
292(1)
Data Lab and Examples (with Solutions)
293(1)
Data Lab: Solutions
294(3)
10 Factorial Anova 297(32)
Extensions of ANOVA
297(1)
ANCOVA
298(1)
MANOVA
299(1)
MANCOVA
299(1)
Factorial ANOVA
299(1)
Interaction Effects
299(2)
Simple Effects
301(1)
2XANOVA: An Example
302(1)
Calculating Factorial ANOVA
303(3)
The Hypotheses Test: Interpreting Factorial ANOVA Results
306(2)
Effect Size for 2XANOVA: Partial 12
308(1)
Discussing the Results
309(2)
Using SPSS® to Analyze 2XANOVA
311(8)
Summary Chart for 2XANOVA Procedures
319(1)
Terms and Concepts
319(1)
Data Lab and Examples (with Solutions)
320(1)
Data Lab: Solutions
320(9)
11 Correlation 329(42)
The Nature of Correlation
330(1)
The Correlation Design
331(1)
Pearson's Correlation Coefficient
332(2)
Plotting the Correlation: The Scattergram
334(3)
Using SPSS® to Create Scattergrams
337(2)
Using Excel to Create Scattergrams
339(2)
Calculating Pearson's r
341(1)
The Z Score Method
342(2)
The Computation Method
344(1)
The Hypothesis Test for Pearson's r
345(2)
Effect Size: the Coefficient of Determination
347(2)
Diagnostics: Correlation Problems
349(3)
Correlation Using SPSS® and Excel
352(6)
Nonparametric Statistics: Spearman's Rank Order Correlation (r5)
358(5)
Terms and Concepts
363(1)
Data Lab and Examples (with Solutions)
364(1)
Data Lab: Solutions
365(6)
12 Bivariate Regression 371(46)
The Nature of Regression
372(2)
The Regression Line
374(2)
Calculating Regression
376(3)
Effect Size of Regression
379(1)
The Z Score Formula for Regression
380(2)
Testing the Regression Hypotheses
382(1)
The Standard Error of Estimate
383(2)
Confidence Interval
385(1)
Explaining Variance Through Regression
386(3)
A Numerical Example of Partitioning the Variation
389(1)
Using Excel and SPSS® with Bivariate Regression
390(1)
The SPSS® Regression Output
390(6)
The Excel Regression Output
396(2)
Complete Example of Bivariate Linear Regression
398(1)
Assumptions of Bivariate Regression
398(6)
The Omnibus Test Results
404(1)
Effect Size
404(1)
The Model Summary
405(1)
The Regression Equation and Individual Predictor Test of Significance
405(1)
Advanced Regression Procedures
406(2)
Detecting Problems in Bivariate Linear Regression
408(1)
Terms and Concepts
409(1)
Data Lab and Examples (with Solutions)
410(1)
Data Lab: Solutions
411(6)
13 Introduction To Multiple Linear Regression 417(38)
The Elements of Multiple Linear Regression
417(1)
Same Process as Bivariate Regression
418(1)
Some Differences between Bivariate Linear Regression and Multiple Linear Regression
419(1)
Stuff not Covered
420(1)
Assumptions of Multiple Linear Regression
421(1)
Analyzing Residuals to Check MLR Assumptions
422(1)
Diagnostics for MLR: Cleaning and Checking Data
423(1)
Extreme Scores
424(4)
Distance Statistics
428(1)
Influence Statistics
429(1)
MLR Extended Example Data
430(1)
Assumptions Met?
431(2)
Analyzing Residuals: Are Assumptions Met?
433(3)
Interpreting the SPSS® Findings for MLR
436(1)
Entering Predictors Together as a Block
437(5)
Entering Predictors Separately
442(5)
Additional Entry Methods for MLR Analyses
447(1)
Example Study Conclusion
448(1)
Terms and Concepts
448(2)
Data Lab and Example (with Solution)
450(1)
Data Lab: Solution
450(5)
14 Chi-Square And Contingency Table Analysis 455(34)
Contingency Tables
455(1)
The Chi-square Procedure and Research Design
456(1)
Chi-square Design One: Goodness of Fit
457(1)
A Hypothetical Example: Goodness of Fit
458(4)
Effect Size: Goodness of Fit
462(1)
Chi-square Design Two: The Test of Independence
463(1)
A Hypothetical Example: Test of Independence
464(4)
Special 2 x 2 Chi-square
468(2)
Effect Size in 2 x 2 Tables: PHI
470(1)
Cramer's V: Effect Size for the Chi-square Test of Independence
471(1)
Repeated Measures Chi-square: Mcnemar Test
472(2)
Using SPSS® and Excel with Chi-square
474(1)
Using SPSS® for the Chi-square Test of Independence
475(6)
Using Excel for Chi-square Analyses
481(2)
Terms and Concepts
483(1)
Data Lab and Examples (with Solutions)
483(1)
Data Lab: Solutions
484(5)
15 Repeated Measures Procedures: Tdep And ANOVAws 489(20)
Independent and Dependent Samples in Research Designs
490(1)
Using Different T Tests
491(1)
The Dependent T Test Calculation: The "Long" Formula
491(1)
Example: The Long Formula
492(2)
The Dependent T Test Calculation: The "Difference" Formula
494(2)
Tdep and Power
496(1)
Conducting The Tdep Analysis Using SPSS®
496(2)
Conducting The Tdep Analysis Using Excel
498(1)
Within-Subject ANOVA (ANOVAWS)
498(1)
Experimental Designs
499(1)
Post Facto Designs
500(1)
Within-Subject Example
501(1)
Using SPSS® for Within-Subject Data
501(1)
The SPSS® Procedure
502(2)
The SPSS® Output
504(4)
Nonparametric Statistics
508(1)
Terms and Concepts
508(1)
Appendices
Appendix A SPSS® Basics
509(22)
Using SPSS®
509(1)
General Features
510(3)
Management Functions
513(4)
Additional Management Functions
517(14)
Appendix B Excel Basics
531(14)
Data Management
531(2)
The Excel Menus
533(8)
Using Statistical Functions
541(2)
Data Analysis. Procedures
543(1)
Missing Values and "0" Values in Excel Analyses
544(1)
Using Excel with "Real Data"
544(1)
Appendix C Statistical Tables
545(10)
Table C.1: Z-Score Table (Values Shown are Percentages - %)
545(2)
Table C.2: Exclusion Values for the T-Distribution
547(1)
Table C.3: Critical (Exclusion) Values for the Distribution of F
548(3)
Table C.4: Tukey's Range Test (Upper 5% Points)
551(1)
Table C.5: Critical (Exclusion) Values for Pearson's Correlation Coefficient, r
552(1)
Table C.6: Critical Values of the x2 (Chi-Square) Distribution
553(2)
References 555(2)
Index 557
Martin Lee Abbott, PhD, is Professor of Sociology at Seattle Pacific University, where he has served as Executive Director of the Washington School Research Center, an independent research and data analysis center funded by the Bill & Melinda Gates Foundation. Dr. Abbott has held positions in both academia and industry, focusing his consulting and teaching in the areas of statistical procedures, program evaluation, applied sociology, and research methods. He is the author of Understanding Educational Statistics Using Microsoft Excel and SPSS, The Program Evaluation Prism: Using Statistical Methods to Discover Patterns, and Understanding and Applying Research Design, also from Wiley.