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E-raamat: Applied Statistics Using Stata: A Guide for the Social Sciences

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  • Formaat: 488 pages
  • Ilmumisaeg: 26-Apr-2022
  • Kirjastus: Sage Publications Ltd
  • Keel: eng
  • ISBN-13: 9781529786484
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Applied Statistics Using Stata: A Guide for the Social SciencesSuurem pilt
  • Formaat: 488 pages
  • Ilmumisaeg: 26-Apr-2022
  • Kirjastus: Sage Publications Ltd
  • Keel: eng
  • ISBN-13: 9781529786484
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Straightforward, clear, and applied, this book will give you the theoretical and practical basis you need to apply data analysis techniques to real data.

Combining key statistical concepts with detailed technical advice, it addresses common themes and problems presented by real research, and shows you how to adjust your techniques and apply your statistical knowledge to a range of datasets. It also embeds code and software output throughout and is supported by online resources to enable practice and safe experimentation.

The book includes:
·       Original case studies and data sets
·       Practical exercises and lists of commands for each chapter
·       Downloadable Stata programmes created to work alongside chapters
·       A wide range of detailed applications using Stata
·       Step-by-step guidance on writing the relevant code.

This is the perfect text for anyone doing statistical research in the social sciences getting started using Stata for data analysis. 



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.

Arvustused

Newly updated, now with more advanced content, this book remains a must have for those studying applied statistics. The book is practically orientated with intuitive theoretical explanations, a wide array  "how-to-do-it" examples and an engaging narrative.  You wont be sorry! -- Franz Buscha This is a most impressive teaching and learning resource. Mehmetoglu and Jakobsen expertly introduce introductory to advanced social science data analysis skills in a clear and engaging manner. This text teaches students how to do data analysis in a transparent and principled manner. -- Roxanne Connelly Mehmetoglu and Jakobsens book offers a concise, yet comprehensive, introduction to the statistical methods that are widely used in data analysis. In addition to presenting a thorough overview of the basics of conducting empirical research, the book also emphasizes how to use Stata to analyze data in practice. This book is an excellent starting point for those who are interested in empirical work. -- Hector H. Sandoval

Companion Website xv
About the Authors xvii
Preface xix
Acknowledgements xxi
1 Research and statistics 1(22)
1.1 The methodology of statistical research
2(1)
1.2 The statistical method
3(2)
1.3 The logic behind statistical inference
5(9)
1.3.1 Central limit theorem
5(2)
1.3.2 t-distribution
7(1)
1.3.3 Degrees of freedom
8(3)
1.3.4 Probability theory
11(1)
1.3.5 Population size
11(2)
1.3.6 Why do I need significance levels if I am investigating the whole population?
13(1)
1.4 General laws and theories
14(1)
1.4.1 Objectivity and critical realism
14(1)
1.5 Survey data
15(1)
1.6 Quantitative research papers
16(3)
1.6.1 p-hacking
18(1)
1.7 Concluding remarks
19(1)
Key terms
19(1)
Questions
20(1)
Practical exercises
20(1)
List of commands
20(1)
Further reading
20(1)
References
21(2)
2 Introduction to stata 23(30)
2.1 What is Stata?
24(4)
2.1.1 The Stata interface
24(1)
2.1.2 How to use Stata
25(3)
2.2 Entering and importing data into Stata
28(1)
2.2.1 Entering data
28(1)
2.2.2 Importing data
28(1)
2.3 Data management
29(10)
2.3.1 Opening data
30(1)
2.3.2 Examining data
31(2)
2.3.3 Making changes to variables
33(2)
2.3.4 Generating variables
35(3)
2.3.5 Subsetting data
38(1)
2.3.6 Labelling variables
38(1)
2.4 Descriptive statistics and graphs
39(7)
2.4.1 Frequency distributions
39(2)
2.4.2 Summary statistics
41(3)
2.4.3 Appending data
44(1)
2.4.4 Merging data
45(1)
2.4.5 Reshaping data
46(1)
2.5 Bivariate inferential statistics
46(4)
2.5.1 Correlation
47(1)
2.5.2 Independent t-test
47(1)
2.5.3 Analysis of variance (ANOVA)
48(1)
2.5.4 Chi-squared test
49(1)
2.6 Conclusion
50(1)
Key terms
50(1)
Questions
51(1)
Practical exercises
51(1)
List of commands
51(1)
Further reading
52(1)
3 Simple (bivariate) regression 53(28)
3.1 What is regression analysis?
54(1)
3.2 Simple linear regression analysis
55(12)
3.2.1 Ordinary least squares
58(2)
3.2.2 Goodness of fit
60(3)
3.2.3 Hypothesis test for slope coefficient
63(3)
3.2.4 Prediction in linear regression
66(1)
3.3 Example in Stata
67(4)
3.4 Conclusion
71(1)
Key terms
71(1)
Questions
72(1)
Practical exercises
72(1)
List of commands
72(1)
Further reading
72(1)
References
73(1)
Supplemental Appendix
74(7)
A3.1 Calculating a bivariate regression
74(4)
A3.2 Calculating standard errors
78(3)
4 Multiple regression 81(16)
4.1 Multiple linear regression analysis
82(7)
4.1.1 Estimation
83(1)
4.1.2 Goodness of fit and the F-test
84(1)
4.1.3 Adjusted R2
85(1)
4.1.4 Partial slope coefficients
86(1)
4.1.5 Prediction in multiple regression
87(1)
4.1.6 Standardization and relative importance
88(1)
4.2 Example in Stata
89(6)
4.3 Conclusion
95(1)
Key terms
95(1)
Questions
95(1)
Practical exercises
96(1)
List of commands
96(1)
Further reading
96(1)
References
96(1)
5 Dummy-variable regression 97(24)
5.1 Why dummy-variable regression?
98(3)
5.1.1 Creating dummy variables
98(2)
5.1.2 The logic behind dummy-variable regression
100(1)
5.2 Regression with one dummy variable
101(2)
5.2.1 Example in Stata
102(1)
5.3 Regression with one dummy variable and a covariate
103(3)
5.3.1 Example in Stata
105(1)
5.4 Regression with more than one dummy variable
106(6)
5.4.1 Example in Stata
108(1)
5.4.2 Comparing the included groups
109(3)
5.5 Regression with more than one dummy variable and a covariate
112(3)
5.5.1 Example in Stata
113(2)
5.6 Regression with two separate sets of dummy variables
115(4)
5.6.1 Example in Stata
117(2)
5.7 Conclusion
119(1)
Key terms
119(1)
Questions
119(1)
Practical exercises
119(1)
List of commands
120(1)
Further reading
120(1)
References
120(1)
6 Interaction/moderation effects using regression 121(24)
6.1 Interaction/moderation effect
122(2)
6.2 Product-term approach
124(18)
6.2.1 Interaction between a continuous predictor and a continuous moderator
126(4)
6.2.2 Interaction between a continuous predictor and a dummy moderator
130(3)
6.2.3 Interaction between a dummy predictor and a dummy moderator
133(3)
6.2.4 Interaction between a continuous predictor and a polytomous moderator
136(6)
6.3 Conclusion
142(1)
Key terms
142(1)
Questions
143(1)
Practical exercises
143(1)
List of commands
143(1)
Further reading
143(1)
References
143(2)
7 Linear regression assumptions and diagnostics 145(32)
7.1 Correct specification of the model
147(13)
7.1.1 All relevant and no irrelevant X-variables
147(2)
7.1.2 Linearity and polynomial regression
149(9)
7.1.3 Additivity
158(1)
7.1.4 Absence of multicollinearity
158(2)
7.2 Assumptions about residuals
160(10)
7.2.1 The error term has a conditional mean of zero
160(1)
7.2.2 Homoscedasticity
161(6)
7.2.3 Uncorrelated errors
167(1)
7.2.4 Normally distributed errors
168(2)
7.3 Influential observations
170(4)
7.3.1 Leverage
170(1)
7.3.2 DFBETA
171(1)
7.3.3 Cook's distance
172(2)
7.4 Conclusion
174(1)
Key terms
174(1)
Questions
174(1)
Practical exercises
175(1)
List of commands
175(1)
Further reading
175(1)
References
175(2)
8 Logistic regression 177(34)
8.1 What is logistic regression?
179(5)
8.1.1 Tests of significance
182(2)
8.2 Assumptions of logistic regression
184(9)
8.2.1 Example in Stata
185(8)
8.3 Conditional effects
193(2)
8.4 Diagnostics
195(3)
8.5 Probit models
198(1)
8.6 Multinomial logistic regression
199(5)
8.7 Ordered logistic regression
204(3)
8.8 Conclusion
207(1)
Key terms
207(1)
Questions
208(1)
Practical exercises
208(1)
List of commands
208(1)
Further reading
209(1)
References
209(2)
9 Survival analysis 211(20)
9.1 Data structure
213(1)
9.2 Censoring
214(1)
9.3 Life table
215(1)
9.4 Hazard function
216(1)
9.5 Survival function
217(2)
9.6 Example in Stata: Life tables and Kaplan-Meier
219(3)
9.6.1 Kaplan-Meier estimator
219(2)
9.6.2 Hazard function
221(1)
9.7 Proportional hazard models (Cox regression)
222(5)
9.7.1 Assumption of proportional hazard model
223(1)
9.7.2 Extending the Cox regression model (time-varying covariates)
224(1)
9.7.3 Multiple events
225(1)
9.7.4 Competing risks
225(2)
9.8 Conclusion
227(1)
Key terms
228(1)
Questions
228(1)
Practical exercises
228(1)
List of commands
228(1)
Further reading
229(1)
References
229(2)
10 Multilevel analysis 231(40)
10.1 Multilevel data
233(4)
10.1.1 Statistical reasons for using multilevel analysis
236(1)
10.2 Empty or intercept-only model
237(4)
10.2.1 Example in Stata
239(2)
10.3 Variance partition (intraclass correlation)
241(1)
10.4 Random intercept model
242(2)
10.5 Level-2 explanatory variables
244(3)
10.5.1 How much of the dependent variable is explained?
246(1)
10.6 Logistic multilevel model
247(1)
10.7 Random coefficient (slope) model
248(3)
10.8 Interaction effects
251(3)
10.9 Three-level models
254(5)
10.9.1 Cross-classified multilevel model
258(1)
10.10 Weighting
259(1)
10.11 Post-estimation
260(7)
10.11.1 Deviation from intercept and random slope regression line
263(2)
10.11.2 Level-2 outliers
265(2)
10.12 Conclusion
267(1)
Key terms
267(1)
Questions
268(1)
Practical exercises
268(1)
List of commands
268(1)
Further reading
268(1)
References
269(2)
11 Panel data analysis 271(44)
11.1 Panel data
272(3)
11.2 Pooled OLS
275(5)
11.3 Between effects
280(3)
11.4 Fixed effects (within estimator)
283(10)
11.4.1 Explaining fixed effects
285(6)
11.4.2 Summary of fixed effects
291(1)
11.4.3 Time-fixed effects
292(1)
11.5 Random effects
293(2)
11.6 Time-series cross-section methods
295(9)
11.6.1 Testing for non-stationarity
299(3)
11.6.2 Lag selection
302(1)
11.6.3 The TSCS model
303(1)
11.7 Binary dependent variables
304(4)
11.8 Arellano-Bond estimator
308(3)
11.9 Conclusion
311(1)
Key terms
311(1)
Questions
311(1)
Practical exercises
311(1)
List of commands
312(1)
Further reading
312(1)
References
312(3)
12 Time-series analysis 315(34)
12.1 Time series
316(9)
12.1.1 Trends and smoothing
317(3)
12.1.2 Cycles
320(1)
12.1.3 Seasonality
320(3)
12.1.4 A prelude to forecasting
323(2)
12.2 Autocorrelation
325(3)
12.2.1 Testing for autocorrelation
326(1)
12.2.2 How to cope with first-order autocorrelation
327(1)
12.3 Stationarity
328(6)
12.3.1 Unit roots: testing for non-stationarity
329(3)
12.3.2 First difference
332(2)
12.4 Time-series models
334(12)
12.4.1 Autoregressive models
335(1)
12.4.2 ARIMA model (single time series)
336(5)
12.4.3 Vector autoregression (multiple time series)
341(5)
12.5 Conclusion
346(1)
Key terms
346(1)
Questions
346(1)
Practical exercises
346(1)
List of commands
347(1)
Further reading
347(1)
References
347(2)
13 Exploratory factor analysis 349(22)
13.1 What is factor analysis?
350(2)
13.1.1 What is factor analysis used for?
352(1)
13.2 The factor analysis process
352(9)
13.2.1 Extracting the factors
353(3)
13.2.2 Determining the number of factors
356(1)
13.2.3 Rotating the factors
357(3)
13.2.4 Refining and interpreting the factors
360(1)
13.3 Composite scores and reliability testing
361(2)
13.4 Example in Stata
363(5)
13.5 Conclusion
368(1)
Key terms
368(1)
Questions
369(1)
Practical exercises
369(1)
List of commands
369(1)
Further reading
369(1)
References
370(1)
14 Structural equation modelling and confirmatory factor analysis 371(28)
14.1 What is structural equation modelling?
372(2)
14.1.1 Types of structural equation modelling
373(1)
14.2 Confirmatory factor analysis
374(13)
14.2.1 Model specification
375(1)
14.2.2 Model identification
376(2)
14.2.3 Parameter estimation
378(1)
14.2.4 Model assessment
379(7)
14.2.5 Model modification
386(1)
14.3 Latent path analysis
387(8)
14.3.1 Specification of the LPA model
388(1)
14.3.2 Measurement part
389(3)
14.3.3 Structural part
392(3)
14.4 Conclusion
395(1)
Key terms
396(1)
Questions
396(1)
Practical exercises
396(1)
List of commands
397(1)
Further reading
397(1)
References
397(2)
15 Advanced statistical techniques 399(36)
15.1 Count data
400(7)
15.1.1 Poisson regression
400(4)
15.1.2 Negative binomial regression
404(3)
15.2 Instrumental regression
407(6)
15.2.1 Two-stage estimation
407(2)
15.2.2 Example in Stata
409(4)
15.3 Transformation of variables
413(6)
15.3.1 Skewness and kurtosis
413(3)
15.3.2 Transformations
416(3)
15.4 Weighting cases
419(2)
15.5 Missing data
421(10)
15.5.1 Traditional methods for handling missing data
422(3)
15.5.2 Multiple imputation
425(6)
15.6 Conclusion
431(1)
Key terms
431(1)
Questions
431(1)
Practical exercises
431(1)
List of commands
432(1)
Further reading
432(1)
References
432(3)
16 Programming and dynamic reporting using Stata 435(22)
16.1 Programming features of Stata
436(11)
16.1.1 Macros
436(3)
16.1.2 Loops
439(3)
16.1.3 If statements
442(1)
16.1.4 Stored r- and e-class objects
443(2)
16.1.5 Creating your own Stata command
445(2)
16.2 Reproducible and dynamic reporting
447(8)
16.2.1 Dynamic reporting using dyndoc
449(4)
16.2.2 Dynamic reporting using putdocx
453(1)
16.2.3 dyndoc versus putdocx
454(1)
16.3 Conclusion
455(1)
Key terms
455(1)
Questions
455(1)
Practical exercises
455(1)
List of commands
456(1)
Further reading
456(1)
Index 457
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.
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