Muutke küpsiste eelistusi

E-raamat: Quick Guide to IBM(R) SPSS(R): Statistical Analysis With Step-by-Step Examples

4.00/5 (2 hinnangut Goodreads-ist)
(Southern Methodist University, Dallas TX), (Southern Methodist University, USA)
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 22-Jul-2019
  • Kirjastus: SAGE Publications Inc
  • Keel: eng
  • ISBN-13: 9781544360447
Teised raamatud teemal:
  • Formaat - EPUB+DRM
  • Hind: 35,80 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
Quick Guide to IBM(R) SPSS(R): Statistical Analysis With Step-by-Step ExamplesSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 22-Jul-2019
  • Kirjastus: SAGE Publications Inc
  • Keel: eng
  • ISBN-13: 9781544360447
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

Alan C. Elliott and Wayne A. Woodward’s Quick Guide to IBM® SPSS®: Statistical Analysis With Step-by-Step Examples gives students the extra guidance with SPSS they need without taking up valuable in-class time. A practical, accessible guide for using software while doing data analysis in the social sciences, students can learn SPSS on their own, allowing instructors to focus on the concepts and calculations in their lectures, rather than SPSS tutorials. Designed to work across disciplines, the authors have provided a number of SPSS "step-by-step" examples in chapters showing the user how to plan a study, prepare data for analysis, perform the analysis and interpret the output from SPSS. 

The new Third Edition covers IBM® SPSS® version 25, includes a new section on Syntax, and all chapters have been updated to reflect current menu options along with many SPSS screenshots,  making  the process much simpler for the user. In addition, helpful hints and insights are provided through the features "Tips and Caveats" and "Sidebars."
 

Arvustused

"Wonderful feedback from my graduate students using this textbook!" -- Shlomo Sawilowsky "This book is an ideal resource and a guide for understanding basic concepts of statistics."  -- Vinayak Nahar "I really appreciate the detailed yet also practical approach. I also appreciate the comprehensive approach to the topics so that students are better grounded in how to think about their statistical analysis." -- Carrie Petrucci

Preface & Acknowledgments xiii
About the Authors xv
Chapter 1 Introduction 1(24)
Getting the Most Out of Quick Guide to IBM SPSS
2(2)
A Brief Overview of the Statistical Process
4(2)
Using Descriptive Statistics
4(1)
Using Comparative Statistics
5(1)
Using Correlational Statistics
5(1)
Understanding Hypothesis Testing, Power, and Sample Size
6(3)
Understanding the p-Value
9(1)
Planning a Successful Analysis
10(3)
Formulate a Testable Research Question (Hypothesis)
10(1)
Collect Data Appropriate to Testing Your Hypotheses
11(1)
Decide on the Type of Analysis Appropriate to Test Your Hypothesis
11(2)
Properly Interpret and Report Your Results
13(1)
Guidelines for Creating Data Sets
13(6)
1 Decide What Variables You Need and Document Them
13(2)
2 Design Your Data Set With One Subject (or Observation) Per Line
15(1)
3 Each Variable Must Have a Properly Designated Name
16(1)
4 Select Descriptive Labels for Each Variable
16(1)
5 Select a Data Type for Each Variable
16(1)
6 Additional Tips for Categorical (String) Variables
17(1)
7 Define Missing Values Codes
17(1)
8 Consider the Need for a Grouping Variable
18(1)
Preparing Excel Data for Import
19(1)
Guidelines for Reporting Results
20(1)
Downloading Sample SPSS Data Files
21(1)
Opening Data Files for Examples
21(1)
Summary
22(1)
References
22(3)
Chapter 2 Describing and Examining Data 25(32)
Example Data Files
26(1)
Describing Quantitative Data
26(16)
Observe the Distribution of Your Data
27(1)
Testing for Normality
27(1)
Tips and Caveats for Quantitative Data
28(1)
How to Use the Information About Normality
28(1)
If Data are Not Normally Distributed, Don't Report the Mean
28(1)
When in Doubt, Report the SD Rather than the SEM
29(1)
Use Tables and Figures to Report Many Descriptive Statistics
29(1)
Break Down Descriptive Statistics by Group
29(1)
Quantitative Data Description Examples
29(13)
Describing Categorical Data
42(13)
Considerations for Examining Categorical Data
43(1)
Tips and Caveats
43(1)
When Should Categorical Variables be Treated as Quantitative Data?
43(1)
Describing Categorical Data Examples
44(11)
Summary
55(1)
References
56(1)
Chapter 3 Creating and Using Graphs 57(30)
Introduction to SPSS Graphs
57(1)
Guidelines for Creating and Using Graphs
57(2)
Chart Builder
59(1)
Graphboard Template Chooser
60(1)
Legacy Plots
61(1)
Scatterplots
61(11)
Appropriate Applications for a Scatterplot
62(1)
Design Considerations for a Scatterplot
62(10)
Histograms
72(5)
Appropriate Applications for a Histogram
73(1)
Design Considerations for a Histogram
73(4)
Bar Charts
77(5)
Appropriate Applications for a Bar Chart
77(1)
Design Considerations for Bar Charts
78(4)
Pie Charts
82(2)
Appropriate Applications for a Pie Chart
82(1)
Design Considerations for Pie Charts
82(2)
Boxplots
84(2)
Appropriate Applications for Boxplots
84(1)
Design Considerations for Boxplots
84(2)
Summary
86(1)
References
86(1)
Chapter 4 Comparing One or Two Means Using the t-Test 87(38)
One-Sample t-Test
88(7)
Appropriate Applications for a One-Sample t-Test
88(1)
Design Considerations for a One-Sample t-Test
89(1)
Hypotheses for a One-Sample t-Test
89(6)
Two-Tailed t-Tests
89(1)
One-Tailed t-Tests
90(5)
Two-Sample t-Test
95(19)
Appropriate Applications for a Two-Sample t-Test
96(1)
Design Considerations for a Two-Sample t-Test
96(2)
A Two-Sample t-Test Compares Means
96(1)
You Are Comparing Independent Samples
97(1)
The t-Test Assumes Normality
97(1)
Are the Variances Equal?
97(1)
Hypotheses fora Two-Sample t-Test
97(1)
Two-Tailed Tests
98(1)
One-Tailed Tests
98(1)
Tips and Caveats for a Two-Sample t-Test
98(16)
Don't Misuse the t-Test
98(1)
Preplan One-Tailed t-Tests
99(1)
Small Sample Sizes Make Normality Difficult to Assess
99(1)
Performing Multiple t-Tests Causes Loss of Control of the Experiment-Wise Significance Level
100(1)
Interpreting Graphs Associated With the Two-Sample t-Test
100(1)
Deciding Which Version of the t-Test to Use
100(2)
Two-Sample t-Test Examples
102(12)
Calculating Effect Size for a Two-Sample t-Test
114(1)
Paired t-Test
114(9)
Associated Confidence Interval
115(1)
Appropriate Applications fora Paired t-Test
115(1)
Design Considerations for a Paired t-Test
116(1)
Pairing Observations May Increase the Ability to Detect Differences
116(1)
Paired t-Test Analysis is Performed on the Difference Scores
116(1)
The Paired t-Test Assumes Normality of the Differences
116(1)
Hypotheses for a Paired t-Test
117(6)
Summary
123(1)
References
124(1)
Chapter 5 Correlation and Regression 125(42)
Correlation Analysis
126(11)
Appropriate Applications for Correlation Analysis
127(1)
Design Considerations for Correlation Analysis
128(1)
Hypotheses for Correlation Analysis
128(1)
Tips and Caveats for Correlation Analysis
129(8)
One-Sided Tests
129(1)
Variables Don't Have to Be on the Same Scale
129(1)
Correlation Does Not Imply Cause and Effect
129(1)
The Effect Size Provides a Description of a Correlation's Strength
129(1)
Correlations Provide an Incomplete Picture of the Relationship
130(1)
Examine Relationship With a Scatterplot and Watch for Outliers
130(2)
Don't Extrapolate
132(1)
If Variables Are Not Normally Distributed
132(5)
Simple Linear Regression
137(11)
Appropriate Applications for Simple Linear Regression
137(1)
Design Considerations for a Simple Linear Regression
138(1)
There Is a Theoretical Regression Line
138(1)
The Observed Regression Equation Is Calculated
From the Data Based on the Least Squares Principle
138(1)
Several Assumptions Are Involved
138(1)
Hypotheses for Simple Linear Regression Analysis
139(1)
Tips and Caveats for Simple Linear Regression
139(2)
Don't Extrapolate
139(1)
Analyze Residual Plots
140(1)
Transformations
141(1)
Additional Tips
141(1)
Interval Estimates
141(7)
Multiple Linear Regression
148(17)
Appropriate Applications of Multiple Linear Regression
149(1)
Design Considerations for Multiple Linear Regression
149(1)
A Theoretical Multiple Regression Equation Exists That Describes the Relationship Between the Dependent Variable and the Independent Variables
149(1)
The Observed Multiple Regression Equation Is Calculated From the Data Based on the Least Squares Principle
150(1)
Several Assumptions Are Involved
150(1)
Hypotheses for Multiple Linear Regression
150(1)
R-Square
151(1)
Model Selection Procedures for Multiple Linear Regression
152(3)
Tips and Caveats for Multiple Linear Regression
155(1)
Using Indicator Variables
155(1)
Don't Extrapolate
155(1)
Too Many Predictors?
155(1)
Model Interpretation and Evaluation for Multiple Linear Regression
156(1)
Effect Size
156(9)
Summary
165(1)
References
165(2)
Chapter 6 Analysis of Categorical Data 167(48)
Contingency Table Analysis (r x c) 168
Appropriate Applications of Contingency Table Analysis
169(1)
Design Considerations for a Contingency Table Analysis
169(1)
Two Sampling Strategies
169(1)
Expected Cell Size Considerations
170(1)
Combining Categories
170(1)
Hypotheses for a Contingency Table Analysis
170(1)
Test of Independence
170(1)
Test for Homogeneity
171(1)
Tips and Caveats for a Contingency Table Analysis
171(1)
Use Counts-Do Not Use Percentages
171(1)
No One-Sided Tests
171(1)
Each Subject is Counted Only Once
171(1)
Using Ordinal Categories
172(1)
Explain Significant Findings
172(1)
Contingency Table Examples
172(18)
Analyzing Risk Ratios in a 2 x 2 Table
184(2)
Appropriate Applications for Retrospective (Case Control) Studies
186(1)
Appropriate Applications for Prospective (Cohort) Studies
186(1)
Program Comments
186(4)
McNemar's Test
190(5)
Appropriate Applications of McNemar's Test
190(1)
Hypotheses or McNemar's Test
191(4)
Mantel-Haenszel Meta-Analysis Comparison
195(6)
Appropriate Applications of the Mantel-Haenszel Procedure
195(1)
Hypotheses Tests Used in Mantel-Haenszel Analysis
195(1)
Design Considerations for a Mantel-Haenszel Test
196(4)
Tips and Caveats for Mantel-Haenszel Analysis
200(1)
Simpson's Paradox
200(1)
Tests of Interrater Reliability
201(4)
Appropriate Applications of Interrater Reliability
201(1)
Effect Size
202(3)
Goodness-of-Fit Test
205(6)
Appropriate Applications of the Goodness-of-Fit Test
205(1)
Design Considerations for a Goodness-of-Fit Test
206(1)
Hypotheses for a Goodness-of-Fit Test
206(1)
Tips and Caveats for a Goodness-of-Fit Test
206(4)
No One-Sided Tests
206(4)
Program Comments
210(1)
Other Measures of Association for Categorical Data
211(2)
Summary
213(1)
References
213(2)
Chapter 7 Analysis of Variance and Covariance 215(56)
One-Way ANOVA
216(21)
Appropriate Applications for a One-Way ANOVA
216(1)
Design Considerations for a One-Way ANOVA
216(2)
The One-Way ANOVA Assumptions
217(1)
Hypotheses fora One-Way ANOVA
218(1)
Tips and Caveats for a One-Way ANOVA
218(12)
Other Comparison Tests for a One-Way ANOVA
230(7)
Two-Way Analysis of Variance
237(12)
Appropriate Applications for a Two-Way ANOVA
237(1)
Design Considerations for a Two-Way ANOVA
238(1)
Two-Way ANOVA Assumptions
238(1)
Hypotheses for a Two-Way ANOVA
239(2)
1 First Test for Interaction
240(1)
2 Test for Main Effects
240(1)
Tips and Caveats for a Two-Way ANOVA
241(8)
Unequal Sample Sizes Within Cells
241(1)
Significant Interactions
241(8)
Repeated-Measures Analysis of Variance
249(10)
Appropriate Applications for a Repeated-Measures ANOVA
249(1)
Design Considerations for a Repeated-Measures ANOVA
250(1)
Repeated Measurements May Increase the Ability to Detect Differences
250(1)
Two Steps in the Analysis
250(1)
Normality and Equal Variance Assumptions
250(1)
Randomization
251(1)
Hypotheses for a Repeated-Measures ANOVA
251(1)
Tips and Caveats for a Repeated-Measures ANOVA
251(8)
Analysis of Covariance
259(11)
Appropriate Applications for Analysis of Covariance
259(1)
Design Considerations for an Analysis of Covariance
260(1)
Hypotheses for an Analysis of Covariance
261(9)
Summary
270(1)
References
270(1)
Chapter 8 Nonparametric Analysis Procedures 271(26)
Spearman's Rho
272(5)
Appropriate Applications for Spearman's Rho
273(1)
Design Considerations for Spearman's Rho
273(1)
Hypotheses for Spearman's Rho
274(1)
Tips and Caveats for Spearman's Rho
274(3)
Mann-Whitney-Wilcoxon (Two Independent Groups Test)
277(3)
Hypotheses for a Mann-Whitney Test
277(3)
Kruskal-Wallis Test
280(5)
Hypotheses for a Kruskal-Wallis Test
281(4)
Sign Test and Wilcoxon Signed-Rank Test for Matched Pairs
285(5)
Hypotheses for a Sign Test
286(1)
Hypotheses for a Wilcoxon Signed-Rank Test
286(4)
Friedman's Test
290(5)
Hypotheses for Friedman's Test
290(5)
Summary
295(1)
Reference
295(2)
Chapter 9 Logistic Regression 297(20)
Appropriate Applications for Logistic Regression
298(1)
Simple Logistic Regression
298(6)
Hypotheses for Simple Logistic Regression
299(1)
Tips and Caveats for Simple Logistic Regression
299(5)
Cause and Effect
299(5)
Multiple Logistic Regression
304(11)
Tips and Caveats for Multiple Logistic Regression
305(10)
Qualitative Predictor Variables
305(1)
Variable Selection
306(1)
Predictor Variables With Large Values
306(9)
Summary
315(1)
References
316(1)
Appendix A: A Brief Tutorial for Using IBM SPSS for Windows 317(30)
Appendix B: Choosing the Right Procedure to Use 347(6)
Index 353
Alan Elliott is the Director of the Statistical Consulting Center at Southern Methodist University within the Department of Statistical Science. Previously he served as a statistical consultant in the Department of Clinical Science at the University of Texas Southwestern Medical Center at Dallas for 30 years. Elliott holds masters degrees in Business Administration (MBA) and Applied Statistics (MAS). He has authored or coauthored over 35 scientific articles and over a dozen books including the Directory of Microcomputer Statistical Software, Microcomputing with Applications, Using Norton Utilities, SAS Essentials, Applied Time Series Analysis, and Statistical Analysis Quick reference Guidebook with SPSS Examples. Elliott has taught university-level courses in statistics, statistical consulting, and statistical computing for over twenty-five years. 

Wayne A. Woodward, Ph.D., is a Professor of Statistics and chair of the Department of Statistical Science at Southern Methodist University. He is a fellow of the American Statistical Association and was the 2004 recipient of the Don Owen award for excellence in research, statistical consulting, and service to the statistical community. In 2007 he received the Outstanding Presentation Award given by the Section on Physical and Engineering Sciences at the 2007 Joint Statistical Meetings in Salt Lake City, Utah. In 2003 he was named a Southern Methodist University Distinguished Teaching Professor by the universitys Center for Teaching Excellence, and he received the 2006-2007 Scholar/Teacher of the Year Award at SMU, an award given by the United Methodist Church. . Over the last 35 years he has served as statistical consultant to a wide variety of clients in the scientific community and has taught statistics courses ranging from introductory undergraduate statistics courses to graduate courses within the Ph.D. program in Statistics at Southern Methodist University. He has been funded on numerous research grants and contracts from government and industry to study such issues as global warming and nuclear monitoring.  He has authored or coauthored over 70 scientific papers and four books. 
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
Püsilink: https://www.kriso.ee/db/97815443604476e.html
Märksõnad: