000 04265nam a2200361 a 4500
001 8218
003 BD-DhEWU
005 20180212160126.0
008 180212s2016 caua g b 001 0 eng d
010 _a 2016939501
020 _a9781473913233 (pbk)
020 _a1473913233
020 _a9781473913226 (hb)
035 _a(OCLC)993043663
040 _aDLC
_beng
_erda
_cDLC
_dBD-DhEWU
041 _aeng
082 0 4 _a519.50285
_bMEA 2017
100 1 _aMehmetoglu, Mehmet.
_922021
245 1 0 _aApplied statistics using stata /
_cMehmet Mehmetoglu and Tor Georg Jakobsen.
260 _aThousand Oaks :
_bSAGE Publications,
_c2017.
300 _axvi, 356 p. :
_bill. ;
_c24 cm.
504 _aIncludes bibliographical references and index
505 _tTOC
_a Research and statistics 1.1 The methodology of statistical research 1.2 The statistical method 1.3 The logic behind statistical inference 1.4 General laws and theories 1.5 Quantitative research papers2. Introduction to Stata 2.1 What is Stata? 2.2 Entering and importing data into Stata 2.3 Data management 2.4 Descriptive statistics and graphs 2.5 Bivariate inferential statistics3. Simple (bivariate) regression 3.1 What is regression analysis? 3.2 Simple linear regression analysis 3.3 Example in Stata4. Multiple regression 4.1 Multiple linear regression analysis 4.2 Example in Stata5. Dummy-Variable Regression 5.1 Why dummy-variable regression? 5.2 Regression with one dummy variable 5.3 Regression with one dummy variable and a covariate 5.4 Regression with more than one dummy variable 5.5 Regression with more than one dummy variable and a covariate 5.6 Regression with two separate sets of dummy variables6. Interaction/moderation effects using regression 6.1 Interaction/moderation effect 6.2 Product-term approach7. Linear regression assumptions and diagnostics 7.1 Correct specification of the model 7.2 Assumptions about residuals 7.3 Influential observations8. Logistic regression 8.1 What is logistic regression? 8.2 Assumptions of logistic regression 8.3 Conditional effects 8.4 Diagnostics 8.5 Multinomial logistic regression 8.6 Ordered logistic regression9. Multilevel analysis 9.1 Multilevel data 9.2 Empty or intercept-only model 9.3 Variance partition / intraclass correlation 9.4 Random intercept model 9.5 Level-2 explanatory variables 9.6 Logistic multilevel model 9.7 Random coefficient (slope) model 9.8 Interaction effects 9.9 Three-level models10. Panel data analysis 10.1 Panel data 10.2 Pooled OLS 10.3 Between effects 10.4 Fixed effects (within estimator) 10.5 Random effects 10.6 Time-series cross-section methods 10.7 Binary dependent variables11. Exploratory factor analysis 11.1 What is factor analysis? 11.2 Factor analysis process 11.3 Composite scores and reliability test 11.4 Example in Stata12. Structural equation modelling and confirmatory factor analysis 12.1 What is structural equation modelling? 12.2 Confirmatory factor analysis 12.3 Latent path analysis13. Critical issues 13.1 Transformation of variables 13.2 Weighting cases 13.3 Robust regression 13.4 Missing data
520 _a 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. --
526 _aEconomics
_aAS
590 _aSagar Shahanawaz
650 _aStatistics
_xData processing.
_922022
650 _aSocial sciences
_xStatistical methods.
_921237
700 1 _aJakobsen, Tor Georg .
_922023
856 4 2 _3WorldCat details
_uhttps://www.worldcat.org/title/applied-statistics-using-stata-a-guide-for-the-social-sciences/oclc/993043663&referer=brief_results
942 _2ddc
_cTEXT
999 _c8218
_d8218
999 _c8218
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