Applied regression analysis: doing, interpreting and reporting
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Abingdon, Oxon ; New York, NY
Routledge
2020
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | viii, 191 Seiten |
| ISBN: | 9781138335486 9781138335479 1138335487 |
Internformat
MARC
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Datensatz im Suchindex
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| adam_text | Contents Acknowledgements їх PARTI The basics 1 1 3 What is regression analysis? 1.1 1.2 1.3 1.4 Introduction: Sizes and prices of houses 3 What this book is about and why it has been written 5 Some terminology and a word about statistical software 5 Research questions in the social sciences and economics: The link to regression analysis 7 1.5 The book’s intended audience, pedagogical approach and what lies ahead in Part 1 7 Notes 8 Appendix: A short primer on variables and their measurement levels and how this relates to regression analysis 8 2 Linear regression with a single independent variable 2.1 Prices of houses once more 11 2.2 Prices of houses: The regression coefficient (bf and the constant (b0) 19 2.3 Prices of houses: An independent variable with two values (a dummy variable) 20 2.4 From data on houses to data on soccer players: On performance and pay in football 22 2.5 Data from questionnaires: Female students’ exercise hours 25 2.6 (Optional) calculation of the regression coefficient (bx) and the constant (b0) by Ordinary Least Squares (OLS) 27 2.7 A quick word about the error term (e) 30 2.8 Summary, a few words on data types andfurther reading 31 Notes 32 11
vi Contents vii Contents Appendix A: A short introduction to Statas point-and-click routine 34 Appendix B: A short introduction to the point-and-click routine in SPSS 38 3 Linear regression with several independent variables: Multiple regression Exercises with data and solutions 94 Informal glossary to Part 2 96 PART 3 43 3.1 Introduction 1: Prices of houses again 43 3.2 Introduction 2: Social science research questions and the need to controlfor a third variable 46 3.3 Two motivations for multiple regression 50 3.4 A special case: Sets of independent dummy variables 50 3.5 A short summary and the regression model’s explanatory power (R2) 54 3.6 Summary andfurther reading 61 Notes 61 Exercises with data and solutions 62 Informal glossary to Part 1 64 The extensions 6 4 Samples and populations, random variability and testing of statistical significance 67 7 69 4.1 Introduction 69 4.2 Populations, samples and representative samples: The magic of random sampling 70 4.3 The normal distribution and repeated sampling 71 4.4 Confidence intervals for means and regression coefficients 72 4.5 Testing for the significance of a regression coefficient: Putting the confidence interval on its head 74 4.6 Other critical issues regarding significance testing 77 4.7 Summary andfurther reading 79 Notes 79 5 The assumptions of regression analysis 5.1 Introduction 81 5.2 The assumptions you should think about before doing your regression and, quite often, before you choose your data (A) 81 5.3 The testable assumptions not requiring a random sample from a population-framework (B) 82 5.4 The
additional testable assumptions for a random sample from a population-framework (C) 87 5.5 Summing up the regression assumptions, some other issues andfurther reading 92 Notes 93 101 6.1 Introduction 101 6.2 Non-additivity (assumption BÍ): Interaction!moderator regression models 101 6.3 Non-linearity 1 (assumption B2): Quadratic regression models 105 6.4 Non-linearity 2 (assumption B2): Sets of dummy variables 107 6.5 Non-linearity 3 ( assumption B2 ) : Use of logarithms 108 6.6 Mediation models 113 6.7 Summary andfurther reading 116 Notes 116 PART 2 The foundations Beyond linear regression: Non-additivity, non-linearity and mediation 99 A categorical dependent variable: Logistic (logit) regression and related methods 120 7.1 7.2 7.3 7.4 Introduction 120 The linear probability model (LPM) 121 The standard multiple logistic (logit) regression model 123 Interpreting and presenting the results of the logistic (logit) regression model 124 7.5 Interaction models, non-linearity and model fit 127 7.6 Extension: When y has three or more unordered alternatives (multinomial logistic regression) 130 7.7 Other related issues andfurther reading 132 Notes 133 Exercises with data and solutions 134 81 8 An ordered (ordinal) dependent variable: Logistic (logit) regression 8.1 Introduction 136 8.2 The multiple ordinal (logit) regression model 137 8.3 Interpreting andpresenting the results of the ordinal (logit) regression model 139 8.4 The proportional odds assumption, non-linearity and interaction effects 141 8.5 Alternative modeling strategies 144 8.6 Using linear regression on ordered (
ordinal) y-variables: Is it justified? 146 136
viii Contents 8.7 Final thoughts andfurther reading 147 Notes 148 Exercises with data and solutions 148 9 The quest for a causal effect: Instrumental variable (IV) regression 150 9.1 Introduction 150 9.2 A first take on the relationship between exercising ( xj and health (y) 151 9.3 The assumptions of IV regression: Instrument relevance and instrument exogeneity 153 9.4 When x, andior y are dummies 156 9.5 Final thoughts andfurther reading 158 Notes 159 PART 4 Regression purposes, academic regression projects and the way ahead 161 10 Regression purposes in various academic settings and how to perform them 163 10.1 Introduction 163 10.2 Four purposes of regression analysis: (1) prediction, (2) description, (3) making inferences beyond the data and (4) causal analysis 163 10.3 The structure of an academic regression-based study 171 10.4 Some practical advice 173 Notes 174 11 The way ahead: Related techniques 176 11.1 11.2 11.3 11.4 Introduction 176 Related technique 1: Median and quantile regression 176 Related technique 2: Count data regression 177 Related technique 3: Panel data regression and time-series regression (longitudinal data) 180 11.5 Related technique 4: Multi-level regression models 181 11.6 Related technique 5: Matching 182 11.7 Other, related techniques andfurther reading 182 Notes 182 References Index 184 188
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| institution | BVB |
| isbn | 9781138335486 9781138335479 1138335487 |
| language | English |
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| spelling | Thrane, Christer Verfasser (DE-588)1200375068 aut Applied regression analysis doing, interpreting and reporting Christer Thrane Abingdon, Oxon ; New York, NY Routledge 2020 viii, 191 Seiten txt rdacontent n rdamedia nc rdacarrier Regressionsanalyse (DE-588)4129903-6 gnd rswk-swf Regression analysis Social sciences / Statistical methods Regressionsanalyse (DE-588)4129903-6 s DE-604 Erscheint auch als Online-Ausgabe 978-0-429-44375-6 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031629477&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
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| subject_GND | (DE-588)4129903-6 |
| title | Applied regression analysis doing, interpreting and reporting |
| title_auth | Applied regression analysis doing, interpreting and reporting |
| title_exact_search | Applied regression analysis doing, interpreting and reporting |
| title_full | Applied regression analysis doing, interpreting and reporting Christer Thrane |
| title_fullStr | Applied regression analysis doing, interpreting and reporting Christer Thrane |
| title_full_unstemmed | Applied regression analysis doing, interpreting and reporting Christer Thrane |
| title_short | Applied regression analysis |
| title_sort | applied regression analysis doing interpreting and reporting |
| title_sub | doing, interpreting and reporting |
| topic | Regressionsanalyse (DE-588)4129903-6 gnd |
| topic_facet | Regressionsanalyse |
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