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Applied Statistics Using Stata: A Guide for the Social Sciences [Pehme köide]

3.67/5 (18 hinnangut Goodreads-ist)
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  • Formaat: Paperback / softback, 376 pages, kõrgus x laius: 242x170 mm, kaal: 450 g
  • Ilmumisaeg: 22-Nov-2016
  • Kirjastus: Sage Publications Ltd
  • ISBN-10: 1473913233
  • ISBN-13: 9781473913233
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Applied Statistics Using Stata: A Guide for the Social SciencesSuurem pilt
  • Formaat: Paperback / softback, 376 pages, kõrgus x laius: 242x170 mm, kaal: 450 g
  • Ilmumisaeg: 22-Nov-2016
  • Kirjastus: Sage Publications Ltd
  • ISBN-10: 1473913233
  • ISBN-13: 9781473913233
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781473913233.html
Märksõnad:

Assiduously combining theory with plenty of practical, technical advice – and accompanied by original case studies and data sets – this book makes sure that students both understand Stata and know exactly what to do to make it meet their needs .



Clear, intuitive and written with the social science student in mind, this book represents the ideal combination of statistical theory and practice. It focuses on questions that can be answered using statistics and addresses common themes and problems in a straightforward, easy-to-follow manner. The book carefully combines the conceptual aspects of statistics with detailed technical advice providing both the ‘why’ of statistics and the ‘how’.

Built upon a variety of engaging examples from across the social sciences it provides a rich collection of statistical methods and models. Students are encouraged to see the impact of theory whilst simultaneously learning how to manipulate software to meet their needs.

The book also provides:

  • Original case studies and data sets
  • Practical guidance on how to run and test models in Stata
  • Downloadable Stata programmes created to work alongside chapters
  • A wide range of detailed applications using Stata
  • Step-by-step notes on writing the relevant code.

This excellent text will give anyone doing statistical research in the social sciences the theoretical, technical and applied knowledge needed to succeed.

Arvustused

Practically orientated with a plethora of examples and an engaging narrative, this book is a must have for all those studying applied social statistics. -- Franz Buscha This book provides an extraordinary and very readable account of the applied statistics methods needed in the social sciences. With its captivating didactical exposition, the book will be an invaluable resource for the novice as well as the advanced researcher. -- Sergio Venturini Stata users, especially social scientists, will find helpful advice in fitting statistical models to a diverse set of examples encountered when investigating the complexity and subtlety of real data. The authors emphasize the importance of assumptions behind the models and present clear exposition of the tools embedded in Stata to test these assumptions.   -- James L Rosenberger This book addresses in an entertaining and instructive way multivariate analysis in the context of the Stata program. Well structured in relation to the themes and the main techniques of multivariate analysis, it is an easy-to-read book that helps you not only with statistics but also with using stata in investigations.  -- Jorge Omar Cabrera Lavernia I believe this is an excellent textbook for methods at the Masters level. The sample of methods and approaches is very good. In addition to treating the "ordinary" techniques like linear and logistic regression, the book also deals with multilevel analysis, panel data analysis, factor analysis, and structural equation model. This covers the quantitative methods that are relevant for todays Masters students.





The material is presented at an acceptable advanced level where the necessary formulas are presented, and at the same time explained in an accessible manner. The highlighting of how to perform an analysis combined with examples from Stata makes the material easy to access for the students. This is definitely a book that I would like to use in my teaching. -- Per Arne Tufte

Companion Website xiii
About the Authors xiv
Preface xv
1 Research And Statistics
1(14)
1.1 The methodology of statistical research
2(1)
1.2 The statistical method
3(1)
1.3 The logic behind statistical inference
4(4)
1.3.1 Probability theory
5(1)
1.3.2 Population size
6(1)
1.3.3 Why do I need significance levels if I am investigating the whole population?
7(1)
1.4 General laws and theories
8(1)
1.4.1 Objectivity and critical realism
9(1)
1.5 Quantitative research papers
9(2)
1.6 Concluding remarks
11(4)
Questions
12(1)
Further reading
12(1)
References
13(2)
2 Introduction To Stata
15(30)
2.1 What is Stata?
16(4)
2.1.1 The Stata interface
16(2)
2.1.2 How to use Stata
18(2)
2.2 Entering and importing data into Stata
20(1)
2.2.1 Entering data
20(1)
2.2.2 Importing data
20(1)
2.3 Data management
21(10)
2.3.1 Opening data
22(1)
2.3.2 Examining data
23(2)
2.3.3 Making changes to variables
25(2)
2.3.4 Generating variables
27(3)
2.3.5 Subsetting data
30(1)
2.3.6 Labelling variables
30(1)
2.4 Descriptive statistics and graphs
31(8)
2.4.1 Frequency distributions
31(2)
2.4.2 Summary statistics
33(4)
2.4.3 Appending data
37(1)
2.4.4 Merging data
37(1)
2.4.5 Reshaping data
38(1)
2.5 Bivariate inferential statistics
39(3)
2.5.1 Correlation
39(1)
2.5.2 Independent f-test
39(1)
2.5.3 Analysis of variance (ANOVA)
40(1)
2.5.4 Chi-squared test
41(1)
2.6 Conclusion
42(3)
Questions
43(1)
Further reading
43(2)
3 Simple (Bivariate) Regression
45(22)
3.1 What is regression analysis?
46(1)
3.2 Simple linear regression analysis
47(12)
3.2.1 Ordinary least squares
50(2)
3.2.2 Goodness of fit
52(3)
3.2.3 Hypothesis test for slope coefficient
55(3)
3.2.4 Prediction in linear regression
58(1)
3.3 Example in Stata
59(4)
3.4 Conclusion
63(4)
Questions
64(1)
Further reading
64(1)
References
64(3)
4 Multiple Regression
67(18)
4.1 Multiple linear regression analysis
68(7)
4.1.1 Estimation
69(1)
4.1.2 Goodness of fit and the F-test
70(2)
4.1.4 Partial slope coefficients
72(1)
4.1.5 Prediction in multiple regression
73(1)
4.1.6 Standardization and relative importance
74(1)
4.2 Example in Stata
75(6)
4.3 Conclusion
81(4)
Questions
81(1)
Further reading
81(1)
References
82(3)
5 Dummy-Variable Regression
85(24)
5.1 Why dummy-variable regression?
86(3)
5.1.1 Creating dummy variables
86(2)
5.1.2 The logic behind dummy-variable regression
88(1)
5.2 Regression with one dummy variable
89(2)
5.2.1 Example in Stata
90(1)
5.3 Regression with one dummy variable and a covariate
91(3)
5.3.1 Example in Stata
93(1)
5.4 Regression with more than one dummy variable
94(6)
5.4.1 Example in Stata
96(1)
5.4.2 Comparing the included groups
97(3)
5.5 Regression with more than one dummy variable and a covariate
100(3)
5.5.1 Example in Stata
101(2)
5.6 Regression with two separate sets of dummy variables
103(3)
5.6.1 Example in Stata
105(1)
5.7 Conclusion
106(3)
Questions
107(1)
Further reading
107(1)
References
107(2)
6 Interaction/Moderation Effects Using Regression
109(24)
6.1 Interaction/moderation effect
110(2)
6.2 Product-term approach
112(18)
6.2.1 Interaction between a continuous predictor and a continuous moderator
114(4)
6.2.2 Interaction between a continuous predictor and a dummy moderator
118(3)
6.2.3 Interaction between a dummy predictor and a dummy moderator
121(3)
6.2.4 Interaction between a continuous predictor and a polytomous moderator
124(6)
6.3 Conclusion
130(3)
Questions
131(1)
Further reading
131(1)
References
131(2)
7 Linear Regression Assumptions And Diagnostics
133(28)
7.1 Correct specification of the model
135(13)
7.1.1 All X-variables relevant, and none irrelevant
135(2)
7.1.2 Linearity
137(9)
7.1.3 Additivity
146(1)
7.1.4 Absence of multicollinearity
146(2)
7.2 Assumptions about residuals
148(5)
7.2.1 That the error term has a conditional mean of zero
148(1)
7.2.2 Homoscedasticity
149(1)
7.2.3 Uncorrelated errors
150(1)
7.2.4 Normally distributed errors
151(2)
7.3 Influential observations
153(4)
7.3.1 Leverage
153(1)
7.3.2 DFBETA
154(1)
7.3.3 Cook's distance
155(2)
7.4 Conclusion
157(4)
Questions
158(1)
Further reading
158(1)
References
158(3)
8 Logistic Regression
161(32)
8.1 What is logistic regression?
163(4)
8.1.1 Tests of significance
166(1)
8.2 Assumptions of logistic regression
167(8)
8.2.1 Example in Stata
169(6)
8.3 Conditional effects
175(3)
8.4 Diagnostics
178(2)
8.5 Multinomial logistic regression
180(5)
8.6 Ordered logistic regression
185(4)
8.7 Conclusion
189(4)
Questions
190(1)
Further reading
190(1)
References
190(3)
9 Multilevel Analysis
193(34)
9.1 Multilevel data
195(4)
9.1.1 Statistical reasons for using multilevel analysis
198(1)
9.2 Empty or intercept-only model
199(4)
9.2.1 Example in Stata
201(2)
9.3 Variance partition or intraclass correlation
203(1)
9.4 Random intercept model
204(2)
9.5 Level-2 explanatory variables
206(2)
9.5.1 How much of the dependent variable is explained?
208(1)
9.6 Logistic multilevel model
208(2)
9.7 Random coefficient (slope) model
210(3)
9.8 Interaction effects
213(3)
9.9 Three-level models
216(5)
9.9.1 Cross-classified multilevel model
219(2)
9.10 Weighting
221(2)
9.11 Conclusion
223(4)
Questions
223(1)
Further reading
223(1)
References
224(3)
10 Panel Data Analysis
227(42)
10.1 Panel data
228(3)
10.2 Pooled OLS
231(5)
10.3 Between effects
236(4)
10.4 Fixed effects (within estimator)
240(10)
10.4.1 Explaining fixed effects
241(7)
10.4.2 Summary of fixed effects
248(1)
10.4.3 Time-fixed effects
249(1)
10.5 Random effects
250(2)
10.6 Time-series cross-section methods
252(9)
10.6.1 Testing for non-stationarity
256(2)
10.6.2 Lag selection
258(2)
10.6.3 The TSCS model
260(1)
10.7 Binary dependent variables
261(4)
10.8 Conclusion
265(4)
Questions
266(1)
Further reading
266(1)
References
266(3)
11 Exploratory Factor Analysis
269(24)
11.1 What is factor analysis?
270(2)
11.1.1 What is factor analysis used for?
272(1)
11.2 The factor analysis process
272(9)
11.2.1 Extracting the factors
273(3)
11.2.2 Determining the number of factors
276(1)
11.2.3 Rotating the factors
277(3)
11.2.4 Refining and interpreting the factors
280(1)
11.3 Composite scores and reliability test
281(2)
11.4 Example in Stata
283(5)
11.5 Conclusion
288(5)
Questions
288(1)
Further reading
289(1)
References
289(4)
12 Structural Equation Modelling And Confirmatory Factor Analysis
293(32)
12.1 What is structural equation modelling?
294(3)
12.1.1 Types of structural equation modelling
296(1)
12.2 Confirmatory factor analysis
297(14)
12.2.1 Model specification
298(1)
12.2.2 Model identification
299(2)
12.2.3 Parameter estimation
301(1)
12.2.4 Model assessment
302(7)
12.2.5 Model modification
309(2)
12.3 Latent path analysis
311(8)
12.3.1 Specification of the LPA model
312(1)
12.3.2 Measurement part
313(4)
12.3.3 Structural part
317(2)
12.4 Conclusion
319(6)
Questions
320(1)
Further reading
321(1)
References
321(4)
13 Critical Issues
325(26)
13.1 Transformation of variables
326(5)
13.1.1 Skewness and kurtosis
326(3)
13.1.2 Transformations
329(2)
13.2 Weighting cases
331(3)
13.3 Robust regression
334(4)
13.4 Missing data
338(10)
13.4.1 Traditional methods for handling missing data
339(3)
13.4.2 Multiple imputation
342(6)
13.5 Conclusion
348(3)
Questions
348(1)
Further reading
348(1)
References
349(2)
Index 351
Mehmet Mehmetoglu is a Professor of Research Methods in the Department of Psychology at the Norwegian University of Science and Technology (NTNU). His research interests include consumer psychology, evolutionary psychology and statistical methods. Mehmetoglu has co/publications in about 35 different refereed international journals, among which include Personality and Individual Differences, Evolutionary Psychology and the Journal of Statistical Software. 

Tor Georg Jakobsen is professor of political science at NTNU Business School at the Norwegian University of Science and Technology. His research interests include political behavior, peace research and statistical methods. Jakobsen has authored and co-authored articles in, among others, European Sociological Review, Work, Employment and Society and Conflict Management and Peace Science.