Regression analysis: a practical introduction
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
London ; New York, NY
Routledge, Taylor & Francis Group
[2023]
|
| Ausgabe: | Second edition |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | First edition published by Routledge 2019 |
| Beschreibung: | xx, 392 Seiten Diagramme |
| ISBN: | 9781032257846 9781032257839 |
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| adam_text | Contents List offigures List of tables About the author Preface Acknowledgments List of abbreviations xi xiii xv xvi xviii xix 1 Introduction 1.1 The problem 1.2 The purpose of research 1.3 What causes problems in the research process? 1.4 About this book 1.5 Quantitative vs. qualitative research 1.6 Stata and R code 1.7 Chapter summary 1 2 3 4 7 10 10 11 2 Regression analysis basics 2.1 What is a regression? 2.2 The four main objectives for regression analysis 2.3 The Simple Regression Model 2.4 How are regression lines determined? 2.5 The explanatory power of the regression 2.6 What contributes to slopes of regression lines? 2.7 Using residuals to gauge relative performance 2.8 Correlation vs. causation 2.9 The Multiple Regression Model 2.10 Assumptions of regression models 2.11 Everyone has their own effect 2.12 Causal effects can change over time 2.13 Why regression results might be wrong: inaccuracy and imprecision 12 13 15 17 21 26 28 30 32 33 36 38 39 40
Contents viii 2.14 2.15 2.16 2.17 The use of regression flowcharts The underlying Linear Algebra in regression equations Definitions and key concepts Chapter summary 42 43 45 47 3 Essential tools for regression analysis 3.1 Using dummy (binary) variables 3.2 Non-linear functional forms using Ordinary Least Squares 3.3 Weighted regression models 3.4 Calculating standardized coefficient estimates to allow comparisons 3.5 Chapter summary 51 51 54 62 63 64 4 What does “holding other factors constant” mean? 4.1 Why do we want to “hold other factors constant”? 4.2 Operative-vs-“held constant” and good-vs-bad variation in a key-explanatory variable 4.3 How “holding other factors constant” works when done cleanly 4.4 Why is it difficult to “hold a factor constant”? 4.5 When you do not want to hold a factor constant 4.6 Proper terminology for controlling for a variable 4.7 Chapter summary 67 68 5 Standard errors, hypothesis tests, p-values, and aliens 5.1 Standard errors 5.2 How the standard error determines the likelihood of various values of the true coefficient 5.3 Hypothesis testing in regression analysis 5.4 Problems with standard errors (multicollinearity, heteroskedasticity, and clustering) and how to fix them 5.5 The Bayesian critique of p-values (and statistical significance) 5.6 What model diagnostics should you do? 5.7 What the research on the hot hand in basketball tells us about the existence of other life in the universe 5.8 What does an insignificant estimate tell you? 5.9 Statistical significance is not the goal 5.10 Why I believe we should scrap hypothesis tests
5.11 Chapter summary 90 91 113 119 122 6 What could go wrong when estimating causal effects? 6.1 Setting up the problem for estimating a causal effect 6.2 Good variation vs. bad variation in the key-explanatory variable 6.3 An introduction to the PITFALLS 6.4 PITFALL #1: Reverse causality 6.5 PITFALL #2: Omitted-factors bias 6.6 PITFALL #3: Self-selection bias 132 135 137 140 141 146 157 68 72 78 81 88 88 97 99 123 124 126 127 128
Contents 6.7 6.8 6.9 6.10 6.11 6.12 6.13 6.14 6.15 PITFALL #4: Measurement error PITFALL #5: Using mediating factors oroutcomes as control variables PITFALL #6: Improper reference groups PITFALL #7: Over-weighting groups (when usingfixed effects or dummyvariables) How to choose the best set of control variables (model selection) What could affect the validity of the sample? Applying the PITFALLS to studies on estimating divorce effects on children Applying the PITFALLS to nutritional studies Chapter summary 7 Strategies for other regression objectives 7.1 Strategies and PITFALLS for forecasting/predicting an outcome 7.2 Strategies and PITFALLS for determining predictors of an outcome 7.3 Strategies and PITFALLS for adjusting outcomes for various factors and anomaly detection 7.4 Summary of the strategies and PITFALLS for each regression objective ix 162 168 176 182 190 196 198 200 201 208 209 213 217 222 8 Methods to address biases 8.1 Fixed effects 8.2 Correcting for over-weighted groups (PITFALL #7) using fixed effects 8.3 Random effects 8.4 First-differences 8.5 Difference-in-differences 8.6 Two-stage least squares (instrumental-variables) 8.7 Regression discontinuities 8.8 Knowing when to punt 8.9 Summary 225 227 238 240 242 246 251 257 260 261 9 Other methods besides Ordinary Least Squares 9.1 Types of outcome variables 9.2 Dichotomous outcomes 9.3 Ordinal outcomes - ordered models 9.4 Categorical outcomes - Multinomial Logit Model 9.5 Censored outcomes—Tobit models 9.6 Count variables - Negative Binomial and Poisson models 9.7 Duration models 9.8 Summary 266 267 268
274 276 279 280 282 285 10 Time-series models 10.1 The components of a time-series variable 10.2 Autocorrelation 10.3 Autoregressive models 10.4 Distributed-lag models 10.5 Consequences of and tests for autocorrelation 10.6 Stationarity 287 288 289 291 297 299 302
x Contents 10.7 Vector Autoregression 10.8 Forecasting with time series 10.9 Summary 11 Some really interesting research 11.1 Can discrimination be a self-fulfilling prophecy? 11.2 Does Medicaid participation improve health outcomes? 11.3 Estimating peer effects on academic outcomes 11.4 How much does a GED improve labor-market outcomes? 11.5 How female integration in the Norwegian military affects gender attitudes among males 307 308 313 315 315 321 322 325 327 12 How to conduct a researchproject 12.1 Choosing a topic 12.2 Conducting the empirical part of the study 12.3 Writing the report 331 332 334 336 13 The ethics of regressionanalysis 13.1 What do we hope to see and not to see in others research? 13.2 The incentives that could lead to unethical practices 13.3 P-hacking and other unethical practices 13.4 How to be ethical in your research 13.5 Examples of how studies could have been improved under the ethical guidelines I describe 13.6 Summary 343 344 344 345 347 349 351 14 Summarizing thoughts 14.1 Be aware of your cognitive biases 14.2 What betrays trust in published studies 14.3 How to do a referee report responsibly 14.4 Summary of the most important points and interpretations 14.5 Final words of wisdom (and one finalYogi quote) 352 352 354 359 360 362 Appendix of background statistical tools A.l Random variables and probabihty distributions A.2 The normal distribution and other important distributions A.3 Sampling distributions A.4 Desired properties of estimators 364 365 371 373 377 Glossary Index 379 389
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| adam_txt |
Contents List offigures List of tables About the author Preface Acknowledgments List of abbreviations xi xiii xv xvi xviii xix 1 Introduction 1.1 The problem 1.2 The purpose of research 1.3 What causes problems in the research process? 1.4 About this book 1.5 Quantitative vs. qualitative research 1.6 Stata and R code 1.7 Chapter summary 1 2 3 4 7 10 10 11 2 Regression analysis basics 2.1 What is a regression? 2.2 The four main objectives for regression analysis 2.3 The Simple Regression Model 2.4 How are regression lines determined? 2.5 The explanatory power of the regression 2.6 What contributes to slopes of regression lines? 2.7 Using residuals to gauge relative performance 2.8 Correlation vs. causation 2.9 The Multiple Regression Model 2.10 Assumptions of regression models 2.11 Everyone has their own effect 2.12 Causal effects can change over time 2.13 Why regression results might be wrong: inaccuracy and imprecision 12 13 15 17 21 26 28 30 32 33 36 38 39 40
Contents viii 2.14 2.15 2.16 2.17 The use of regression flowcharts The underlying Linear Algebra in regression equations Definitions and key concepts Chapter summary 42 43 45 47 3 Essential tools for regression analysis 3.1 Using dummy (binary) variables 3.2 Non-linear functional forms using Ordinary Least Squares 3.3 Weighted regression models 3.4 Calculating standardized coefficient estimates to allow comparisons 3.5 Chapter summary 51 51 54 62 63 64 4 What does “holding other factors constant” mean? 4.1 Why do we want to “hold other factors constant”? 4.2 Operative-vs-“held constant” and good-vs-bad variation in a key-explanatory variable 4.3 How “holding other factors constant” works when done cleanly 4.4 Why is it difficult to “hold a factor constant”? 4.5 When you do not want to hold a factor constant 4.6 Proper terminology for controlling for a variable 4.7 Chapter summary 67 68 5 Standard errors, hypothesis tests, p-values, and aliens 5.1 Standard errors 5.2 How the standard error determines the likelihood of various values of the true coefficient 5.3 Hypothesis testing in regression analysis 5.4 Problems with standard errors (multicollinearity, heteroskedasticity, and clustering) and how to fix them 5.5 The Bayesian critique of p-values (and statistical significance) 5.6 What model diagnostics should you do? 5.7 What the research on the hot hand in basketball tells us about the existence of other life in the universe 5.8 What does an insignificant estimate tell you? 5.9 Statistical significance is not the goal 5.10 Why I believe we should scrap hypothesis tests
5.11 Chapter summary 90 91 113 119 122 6 What could go wrong when estimating causal effects? 6.1 Setting up the problem for estimating a causal effect 6.2 Good variation vs. bad variation in the key-explanatory variable 6.3 An introduction to the PITFALLS 6.4 PITFALL #1: Reverse causality 6.5 PITFALL #2: Omitted-factors bias 6.6 PITFALL #3: Self-selection bias 132 135 137 140 141 146 157 68 72 78 81 88 88 97 99 123 124 126 127 128
Contents 6.7 6.8 6.9 6.10 6.11 6.12 6.13 6.14 6.15 PITFALL #4: Measurement error PITFALL #5: Using mediating factors oroutcomes as control variables PITFALL #6: Improper reference groups PITFALL #7: Over-weighting groups (when usingfixed effects or dummyvariables) How to choose the best set of control variables (model selection) What could affect the validity of the sample? Applying the PITFALLS to studies on estimating divorce effects on children Applying the PITFALLS to nutritional studies Chapter summary 7 Strategies for other regression objectives 7.1 Strategies and PITFALLS for forecasting/predicting an outcome 7.2 Strategies and PITFALLS for determining predictors of an outcome 7.3 Strategies and PITFALLS for adjusting outcomes for various factors and anomaly detection 7.4 Summary of the strategies and PITFALLS for each regression objective ix 162 168 176 182 190 196 198 200 201 208 209 213 217 222 8 Methods to address biases 8.1 Fixed effects 8.2 Correcting for over-weighted groups (PITFALL #7) using fixed effects 8.3 Random effects 8.4 First-differences 8.5 Difference-in-differences 8.6 Two-stage least squares (instrumental-variables) 8.7 Regression discontinuities 8.8 Knowing when to punt 8.9 Summary 225 227 238 240 242 246 251 257 260 261 9 Other methods besides Ordinary Least Squares 9.1 Types of outcome variables 9.2 Dichotomous outcomes 9.3 Ordinal outcomes - ordered models 9.4 Categorical outcomes - Multinomial Logit Model 9.5 Censored outcomes—Tobit models 9.6 Count variables - Negative Binomial and Poisson models 9.7 Duration models 9.8 Summary 266 267 268
274 276 279 280 282 285 10 Time-series models 10.1 The components of a time-series variable 10.2 Autocorrelation 10.3 Autoregressive models 10.4 Distributed-lag models 10.5 Consequences of and tests for autocorrelation 10.6 Stationarity 287 288 289 291 297 299 302
x Contents 10.7 Vector Autoregression 10.8 Forecasting with time series 10.9 Summary 11 Some really interesting research 11.1 Can discrimination be a self-fulfilling prophecy? 11.2 Does Medicaid participation improve health outcomes? 11.3 Estimating peer effects on academic outcomes 11.4 How much does a GED improve labor-market outcomes? 11.5 How female integration in the Norwegian military affects gender attitudes among males 307 308 313 315 315 321 322 325 327 12 How to conduct a researchproject 12.1 Choosing a topic 12.2 Conducting the empirical part of the study 12.3 Writing the report 331 332 334 336 13 The ethics of regressionanalysis 13.1 What do we hope to see and not to see in others' research? 13.2 The incentives that could lead to unethical practices 13.3 P-hacking and other unethical practices 13.4 How to be ethical in your research 13.5 Examples of how studies could have been improved under the ethical guidelines I describe 13.6 Summary 343 344 344 345 347 349 351 14 Summarizing thoughts 14.1 Be aware of your cognitive biases 14.2 What betrays trust in published studies 14.3 How to do a referee report responsibly 14.4 Summary of the most important points and interpretations 14.5 Final words of wisdom (and one finalYogi quote) 352 352 354 359 360 362 Appendix of background statistical tools A.l Random variables and probabihty distributions A.2 The normal distribution and other important distributions A.3 Sampling distributions A.4 Desired properties of estimators 364 365 371 373 377 Glossary Index 379 389 |
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| spelling | Arkes, Jeremy Verfasser (DE-588)171535065 aut Regression analysis a practical introduction Jeremy Arkes Second edition London ; New York, NY Routledge, Taylor & Francis Group [2023] © 2023 xx, 392 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier First edition published by Routledge 2019 Regressionsanalyse (DE-588)4129903-6 gnd rswk-swf Regression analysis Regressionsanalyse (DE-588)4129903-6 s DE-604 Erscheint auch als Online-Ausgabe 978-1-003-28500-7 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=034310235&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
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