Thinking clearly with data: a guide to quantitative reasoning and analysis
"This is an intro-level text that teaches how to think clearly and conceptually about quantitative information, emphasizing ideas over technicality and assuming no prior exposure to data analysis, statistics, or quantitative methods. The books four parts present the foundation for quantiative r...
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| Main Authors: | , |
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| Format: | Book |
| Language: | English |
| Published: |
Princeton ; Oxford
Princeton University Press
[2021]
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Summary: | "This is an intro-level text that teaches how to think clearly and conceptually about quantitative information, emphasizing ideas over technicality and assuming no prior exposure to data analysis, statistics, or quantitative methods. The books four parts present the foundation for quantiative reasoning: correlation and causation; statistical relationships; causal phenomena; and incorporating quantitative information into decision making. Within these parts it covers the array of tools used by social scientists, including regression, inference, experiments, research design, and more, all by explaining the rationale and logic behind such tools rather than focusing only on the technical calculations used for each. New concepts are presented simply, with the help of copious examples, and the books leans towards graphic rather than mathematical representation of data, with any technical material included in appendices"-- |
| Physical Description: | xxi, 377 Seiten Diagramme 26 cm |
| ISBN: | 9780691214351 9780691214368 |
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| adam_text | Short Contents Preface xvii CHAPTER 1 Thinking Clearly in a Data-Driven Age 1 PARTI ESTABLISHING A COMMON LANGUAGE 11 CHAPTER 2 Correlation: What Is It and What Is It Good For? 13 CHAPTER 3 Causation: What Is It and What Is It Good For? 37 PARTII DOES A RELATIONSHIP EXIST? 53 CHAPTER 4 Correlation Requires Variation 55 CHAPTER 5 Regression for Describing and Forecasting 74 CHAPTER 6 Samples, Uncertainty, and Statistical Inference 94 CHAPTER 7 Over-Comparing, Under-Reporting 113 CHAPTER 8 Reversion to the Mean 138 PARTIU IS THE RELATIONSHIP CAUSAL? 157 CHAPTER 9 Why Correlation Doesn’t Imply Causation 159 CHAPTER 10 Controlling for Confounders 193 CHAPTER 11 Randomized Experiments 218 CHAPTER 12 Regression Discontinuity Designs 243
viii Short Contents CHAPTER 13 Difference-in-Differences Designs 266 CHAPTER 14 Assessing Mechanisms 290 PART IV FROM INFORMATION TO DECISIONS 303 CHAPTER 15 Turn Statistics into Substance 305 CHAPTER 16 Measure Your Mission 336 CHAPTER 17 On the Limits of Quantification 357 Index 371
Contents CHAPTER 1 PART I CHAPTER 2 Preface xvii Organization Who Is This Book For? Acknowledgments xviii xix xx Thinking Clearly in a Data-Driven Age 1 What You’ll Learn Introduction Cautionary Tales Abe’s hasty diagnosis Civil resistance Broken-windows policing Thinking and Data Are Complements, Not Substitutes Readings and References 1 1 2 2 3 5 7 9 ESTABLISHING A COMMON LANGUAGE 11 Correlation: What Is It and What Is It Good For? 13 What You’ll Learn Introduction _ What Is a Correlation? Fact or correlation? What Is a Correlation Good For? Description Forecasting Causal inference Measuring Correlations Mean, variance, and standard deviation Covariance Correlation coefficient Slope of the regression line Populations and samples Straight Talk about Linearity Wrapping Up Key Terms 13 13 13 17 19 19 20 23 24 24 27 28 29 29 30 33 33
x Contents Exercises Readings and References э 36 CHAPTER 3 Causation: What Is It and What Is It Good For? What You’U Learn Introduction What Is Causation? Potential Outcomes and Counterfactuals What Is Causation Good For? The Fundamental Problem of Causal Inference Conceptual Issues What is the cause? Causality and counterexamples Causality and the law Can causality run backward in time? Does causality require a physical connection? Causation need not imply correlation Wrapping Up Key Terms Exercises Readings and References 37 37 37 38 39 40 41 42 42 44 47 47 48 49 49 50 50 52 PART II DOES A RELATIONSHIP EXIST? 53 CHAPTER 4 Correlation Requires Variation What You’ll Learn Introduction Selecting on the Dependent Variable The 10,000-hour rule Corrupting the youth High school dropouts Suicide attacks The World Is Organized to Make Us Select on the Dependent Variable Doctors mostly see sick people Post-mortems The Challenger disaster The financial crisis of 2008 Life advice Wrapping Up Key Term Exercises Readings and References 55 55 55 56 57 59 62 63 64 65 65 67 69 69 70 70 70 72 CHAPTER 5 Regression for Describing and Forecasting What You’ll Learn Introduction Regression Basics Linear Regression, Non-Linear Data 74 74 74 74 79
Contents The Problem of Overfitting Forecasting presidential elections How Regression Is Presented A Brief Intellectual History of Regression Wrapping Up Key Terms Exercises Readings and References CHAPTER 6 Samples, Uncertainty, and Statistical Inference What You’ll Learn Introduction Estimation Why Do Estimates Differ from Estimands? Bias Noise What Makes for a Good Estimator? Quantifying Precision Standard errors Small samples and extreme observations Confidence intervals Statistical Inference and Hypothesis Testing Hypothesis testing Statistical significance Statistical Inference about Relationships What If We Have Data for the Whole Population? Substantive versus Statistical Significance Social media and voting The Second Reform Act Wrapping Up Key Terms Exercises Readings and References CHAPTER 7 Over-Comparing, Under-Reporting What You’ll Learn Introduction Can an octopus be a soccer expert? Publication Bias p-hacking p-screening Are Most Scientific “Facts” False? ESP Get out the vote p-hacking forensics Potential Solutions Reduce the significance threshold Adjust p-values for multiple testing Don’t obsess over statistical significance 87 87 89 89 91 91 92 93 94 94 94 94 96 96 97 98 99 99 101 102 103 103 104 105 106 107 107 108 109 109 110 111 113 113 113 113 118 119 120 122 122 123 124 126 126 127 127 xi
Contents Pre-registration ° ։ Requiring pre-registration in drug trials _ i« . Replication Football and elections Test important and plausible hypotheses The power pose Beyond Science Superstars Wrapping Up Key Terms Exercises Readings and References ļ ՛ԴՈ 128 129 լ3° 131 131 132 134 1 34 ^ CHAPTER 8 Reversion to the Mean What You’ll Learn Introduction Does the truth wear off? Francis Galton and Regression to Mediocrity Reversion to the Mean Is Not a Gravitational Force Seeking Help Does knee surgery work? Reversion to the Mean, the Placebo Effect, and Cosmic Habituation The placebo effect Cosmic habituation explained Cosmic habituation and genetics Beliefs Don’t Revert to the Mean Wrapping Up Keywords Exercises Readings and References 138 *38 *38 138 139 142 145 146 147 147 148 150 150 152 152 152 155 PARTIU IS THE RELATIONSHIP CAUSAL? 157 CHAPTER 9 Why Correlation Doesn’t Imply Causation 159 What You’ll Learn Introduction Charter schools Thinking Clearly about Potential Outcomes Sources of Bias Confounders Reverse causality The 10,000-hour rule, revisited Diet soda How Different Are Confounders and Reverse Causality? Campaign spending Signing the Bias Contraception and HIV Mechanisms versus Confounders Thinking Clearly about Bias and Noise 159 159 160 163 168 168 169 170 173 174 174 176 I79 !8! 183
Contents CHAPTER 10 CHAPTER 11 CHAPTER 12 Wrapping Up Key Terms Exercises Readings and References 186 187 187 191 Controlling for Confounders What You’ll Learn Introduction Party whipping in Congress A note on heterogeneous treatment effects The Anatomy of a Regression How Does Regression Control? Controlling and Causation Is social media bad for you? Reading a Regression Table Controlling for Confounders versus Mechanisms There Is No Magic Wrapping Up Key Terms Exercises Readings and References 193 193 193 193 197 198 201 209 210 211 213 214 215 215 216 217 Randomized Experiments 218 What You’ll Learn Introduction Breastfeeding Randomization and Causal Inference Estimation and Inference in Experiments Standard errors Hypothesis testing Problems That Can Arise with Experiments Noncompliance and instrumental variables Chance imbalance Lack of statistical power Attrition Interference Natural Experiments Military service and fhture earnings Wrapping Up Key Terms Exercises Readings and References 218 218 219 221 224 224 225 225 226 232 234 235 236 237 238 239 239 240 242 Regression Discontinuity Designs 243 What You’ll Learn Introduction How to Implement an RD Design Are extremists or moderates more electable? Continuity at the Threshold Does continuity hold in election RD designs? 243 243 247 249 251 255 xiii
Contents Noncompliance and the Fuzzy RD Bombing in Vietnam Motivation and Success Wrapping Up KeyTerms Ճ=Ծ 257 761 ^ 262 Exercises Readings and References CHAPTER 13 CHAPTER 14 Difference-in-Differences Designs What You’ll Learn Introduction Parallel Trends Two Units and Two Periods Unemployment and the minimum wage N Units and Two Periods Is watching TV bad for kids? N Units and N Periods Contraception and the gender-wage gap Useftil Diagnostics Do newspaper endorsements affect voting decisions? Is obesity contagious? Difference-in-Differences as Gut Check The democratic peace Wrapping Up Key Terms Exercises Readings and References 266 266 2 շ6շ 269 269 222 222 22^ 226 շշ8 278 229 282 շ8շ 285 285 286 288 Assessing Mechanisms 290 What You’ll Learn Introduction Causal Mediation Analysis Intermediate Outcomes Cognitive behavioral therapy and at-risk youths in Liberia Independent Theoretical Predictions Do voters discriminate against women? Testing Mechanisms by Design Social pressure and voting Disentangling Mechanisms Commodity price shocks and violent conflict Wrapping Up Key Terms Exercises Readings and References 290 290 291 292 293 294 294 295 295 296 296 298 299 299 300 PART IV FROM INFORMATION TO DECISIONS 303 CHAPTER 15 Turn Statistics into Substance 305 What You’ll Learn 305
Contents CHAPTER 16 CHAPTER 17 Introduction What’s the Right Scale? Miles-per-gallon versus gallons-per-mile Percent versus percentage point Visual Presentations of Data Policy preferences and the Southern realignment Some rules of thumb for data visualization From Statistics to Beliefs: Bayes’ Rule Bayes’ rule Information, beliefs, priors, and posteriors Abe’s celiac revisited Finding terrorists in an airport Bayes’ rule and quantitative analysis Expected Costs and Benefits Screening frequently or accurately Wrapping Up KeyWords Exercises Readings and References 305 305 306 309 309 311 314 314 317 318 319 322 325 328 329 331 331 332 334 Measure Your Mission 336 What You’ll Learn Introduction Measuring the Wrong Outcome or Treatment Partial measures Metal detectors in airports Intermediate outcomes Blood pressure and heart attacks Ill-defined missions Climate change and economic productivity Do You Have the Right Sample? External validity Malnutrition in India and Bangladesh Selected samples College admissions Why can’t major league pitchers hit? Strategic Adaptation and Changing Relationships The duty on lights and windows The shift in baseball The war on drugs Wrapping Up Key Words Exercises Readings and References 336 336 337 337 337 339 340 341 342 343 343 343 344 345 345 349 349 350 351 353 353 353 355 On the Limits of Quantification 357 What You’ll Learn Introduction Decisions When Evidence Is Limited Cost-benefit analysis and environmental regulation 357 357 358 358 xv
XVI Contents Hoss your teeth and wear a mask Floss your teeth Wear a mask Quantification and Values How quantitative tools sneak in values Algorithms and racial bias in health care How quantification shapes our values Think Clearly and Help Others Do So Too Exercises Readings and References 359 359 360 361 361 361 363 367 367 368 Index 371
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| adam_txt |
Short Contents Preface xvii CHAPTER 1 Thinking Clearly in a Data-Driven Age 1 PARTI ESTABLISHING A COMMON LANGUAGE 11 CHAPTER 2 Correlation: What Is It and What Is It Good For? 13 CHAPTER 3 Causation: What Is It and What Is It Good For? 37 PARTII DOES A RELATIONSHIP EXIST? 53 CHAPTER 4 Correlation Requires Variation 55 CHAPTER 5 Regression for Describing and Forecasting 74 CHAPTER 6 Samples, Uncertainty, and Statistical Inference 94 CHAPTER 7 Over-Comparing, Under-Reporting 113 CHAPTER 8 Reversion to the Mean 138 PARTIU IS THE RELATIONSHIP CAUSAL? 157 CHAPTER 9 Why Correlation Doesn’t Imply Causation 159 CHAPTER 10 Controlling for Confounders 193 CHAPTER 11 Randomized Experiments 218 CHAPTER 12 Regression Discontinuity Designs 243
viii Short Contents CHAPTER 13 Difference-in-Differences Designs 266 CHAPTER 14 Assessing Mechanisms 290 PART IV FROM INFORMATION TO DECISIONS 303 CHAPTER 15 Turn Statistics into Substance 305 CHAPTER 16 Measure Your Mission 336 CHAPTER 17 On the Limits of Quantification 357 Index 371
Contents CHAPTER 1 PART I CHAPTER 2 Preface xvii Organization Who Is This Book For? Acknowledgments xviii xix xx Thinking Clearly in a Data-Driven Age 1 What You’ll Learn Introduction Cautionary Tales Abe’s hasty diagnosis Civil resistance Broken-windows policing Thinking and Data Are Complements, Not Substitutes Readings and References 1 1 2 2 3 5 7 9 ESTABLISHING A COMMON LANGUAGE 11 Correlation: What Is It and What Is It Good For? 13 What You’ll Learn Introduction _ What Is a Correlation? Fact or correlation? What Is a Correlation Good For? Description Forecasting Causal inference Measuring Correlations Mean, variance, and standard deviation Covariance Correlation coefficient Slope of the regression line Populations and samples Straight Talk about Linearity Wrapping Up Key Terms 13 13 13 17 19 19 20 23 24 24 27 28 29 29 30 33 33
x Contents Exercises Readings and References э 36 CHAPTER 3 Causation: What Is It and What Is It Good For? What You’U Learn Introduction What Is Causation? Potential Outcomes and Counterfactuals What Is Causation Good For? The Fundamental Problem of Causal Inference Conceptual Issues What is the cause? Causality and counterexamples Causality and the law Can causality run backward in time? Does causality require a physical connection? Causation need not imply correlation Wrapping Up Key Terms Exercises Readings and References 37 37 37 38 39 40 41 42 42 44 47 47 48 49 49 50 50 52 PART II DOES A RELATIONSHIP EXIST? 53 CHAPTER 4 Correlation Requires Variation What You’ll Learn Introduction Selecting on the Dependent Variable The 10,000-hour rule Corrupting the youth High school dropouts Suicide attacks The World Is Organized to Make Us Select on the Dependent Variable Doctors mostly see sick people Post-mortems The Challenger disaster The financial crisis of 2008 Life advice Wrapping Up Key Term Exercises Readings and References 55 55 55 56 57 59 62 63 64 65 65 67 69 69 70 70 70 72 CHAPTER 5 Regression for Describing and Forecasting What You’ll Learn Introduction Regression Basics Linear Regression, Non-Linear Data 74 74 74 74 79
Contents The Problem of Overfitting Forecasting presidential elections How Regression Is Presented A Brief Intellectual History of Regression Wrapping Up Key Terms Exercises Readings and References CHAPTER 6 Samples, Uncertainty, and Statistical Inference What You’ll Learn Introduction Estimation Why Do Estimates Differ from Estimands? Bias Noise What Makes for a Good Estimator? Quantifying Precision Standard errors Small samples and extreme observations Confidence intervals Statistical Inference and Hypothesis Testing Hypothesis testing Statistical significance Statistical Inference about Relationships What If We Have Data for the Whole Population? Substantive versus Statistical Significance Social media and voting The Second Reform Act Wrapping Up Key Terms Exercises Readings and References CHAPTER 7 Over-Comparing, Under-Reporting What You’ll Learn Introduction Can an octopus be a soccer expert? Publication Bias p-hacking p-screening Are Most Scientific “Facts” False? ESP Get out the vote p-hacking forensics Potential Solutions Reduce the significance threshold Adjust p-values for multiple testing Don’t obsess over statistical significance 87 87 89 89 91 91 92 93 94 94 94 94 96 96 97 98 99 99 101 102 103 103 104 105 106 107 107 108 109 109 110 111 113 113 113 113 118 119 120 122 122 123 124 126 126 127 127 xi
Contents Pre-registration ° ։ Requiring pre-registration in drug trials _ i« . Replication Football and elections Test important and plausible hypotheses The power pose Beyond Science Superstars Wrapping Up Key Terms Exercises Readings and References ļ ՛ԴՈ 128 129 լ3° 131 131 132 134 1 34 ^ CHAPTER 8 Reversion to the Mean What You’ll Learn Introduction Does the truth wear off? Francis Galton and Regression to Mediocrity Reversion to the Mean Is Not a Gravitational Force Seeking Help Does knee surgery work? Reversion to the Mean, the Placebo Effect, and Cosmic Habituation The placebo effect Cosmic habituation explained Cosmic habituation and genetics Beliefs Don’t Revert to the Mean Wrapping Up Keywords Exercises Readings and References 138 *38 *38 138 139 142 145 146 147 147 148 150 150 152 152 152 155 PARTIU IS THE RELATIONSHIP CAUSAL? 157 CHAPTER 9 Why Correlation Doesn’t Imply Causation 159 What You’ll Learn Introduction Charter schools Thinking Clearly about Potential Outcomes Sources of Bias Confounders Reverse causality The 10,000-hour rule, revisited Diet soda How Different Are Confounders and Reverse Causality? Campaign spending Signing the Bias Contraception and HIV Mechanisms versus Confounders Thinking Clearly about Bias and Noise 159 159 160 163 168 168 169 170 173 174 174 176 I79 !8! 183
Contents CHAPTER 10 CHAPTER 11 CHAPTER 12 Wrapping Up Key Terms Exercises Readings and References 186 187 187 191 Controlling for Confounders What You’ll Learn Introduction Party whipping in Congress A note on heterogeneous treatment effects The Anatomy of a Regression How Does Regression Control? Controlling and Causation Is social media bad for you? Reading a Regression Table Controlling for Confounders versus Mechanisms There Is No Magic Wrapping Up Key Terms Exercises Readings and References 193 193 193 193 197 198 201 209 210 211 213 214 215 215 216 217 Randomized Experiments 218 What You’ll Learn Introduction Breastfeeding Randomization and Causal Inference Estimation and Inference in Experiments Standard errors Hypothesis testing Problems That Can Arise with Experiments Noncompliance and instrumental variables Chance imbalance Lack of statistical power Attrition Interference Natural Experiments Military service and fhture earnings Wrapping Up Key Terms Exercises Readings and References 218 218 219 221 224 224 225 225 226 232 234 235 236 237 238 239 239 240 242 Regression Discontinuity Designs 243 What You’ll Learn Introduction How to Implement an RD Design Are extremists or moderates more electable? Continuity at the Threshold Does continuity hold in election RD designs? 243 243 247 249 251 255 xiii
Contents Noncompliance and the Fuzzy RD Bombing in Vietnam Motivation and Success Wrapping Up KeyTerms Ճ=Ծ 257 761 ^ 262 Exercises Readings and References CHAPTER 13 CHAPTER 14 Difference-in-Differences Designs What You’ll Learn Introduction Parallel Trends Two Units and Two Periods Unemployment and the minimum wage N Units and Two Periods Is watching TV bad for kids? N Units and N Periods Contraception and the gender-wage gap Useftil Diagnostics Do newspaper endorsements affect voting decisions? Is obesity contagious? Difference-in-Differences as Gut Check The democratic peace Wrapping Up Key Terms Exercises Readings and References 266 266 2 շ6շ 269 269 222 222 22^ 226 շշ8 278 229 282 շ8շ 285 285 286 288 Assessing Mechanisms 290 What You’ll Learn Introduction Causal Mediation Analysis Intermediate Outcomes Cognitive behavioral therapy and at-risk youths in Liberia Independent Theoretical Predictions Do voters discriminate against women? Testing Mechanisms by Design Social pressure and voting Disentangling Mechanisms Commodity price shocks and violent conflict Wrapping Up Key Terms Exercises Readings and References 290 290 291 292 293 294 294 295 295 296 296 298 299 299 300 PART IV FROM INFORMATION TO DECISIONS 303 CHAPTER 15 Turn Statistics into Substance 305 What You’ll Learn 305
Contents CHAPTER 16 CHAPTER 17 Introduction What’s the Right Scale? Miles-per-gallon versus gallons-per-mile Percent versus percentage point Visual Presentations of Data Policy preferences and the Southern realignment Some rules of thumb for data visualization From Statistics to Beliefs: Bayes’ Rule Bayes’ rule Information, beliefs, priors, and posteriors Abe’s celiac revisited Finding terrorists in an airport Bayes’ rule and quantitative analysis Expected Costs and Benefits Screening frequently or accurately Wrapping Up KeyWords Exercises Readings and References 305 305 306 309 309 311 314 314 317 318 319 322 325 328 329 331 331 332 334 Measure Your Mission 336 What You’ll Learn Introduction Measuring the Wrong Outcome or Treatment Partial measures Metal detectors in airports Intermediate outcomes Blood pressure and heart attacks Ill-defined missions Climate change and economic productivity Do You Have the Right Sample? External validity Malnutrition in India and Bangladesh Selected samples College admissions Why can’t major league pitchers hit? Strategic Adaptation and Changing Relationships The duty on lights and windows The shift in baseball The war on drugs Wrapping Up Key Words Exercises Readings and References 336 336 337 337 337 339 340 341 342 343 343 343 344 345 345 349 349 350 351 353 353 353 355 On the Limits of Quantification 357 What You’ll Learn Introduction Decisions When Evidence Is Limited Cost-benefit analysis and environmental regulation 357 357 358 358 xv
XVI Contents Hoss your teeth and wear a mask Floss your teeth Wear a mask Quantification and Values How quantitative tools sneak in values Algorithms and racial bias in health care How quantification shapes our values Think Clearly and Help Others Do So Too Exercises Readings and References 359 359 360 361 361 361 363 367 367 368 Index 371 |
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| id | DE-604.BV047567604 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T18:29:17Z |
| indexdate | 2024-07-10T09:15:04Z |
| institution | BVB |
| isbn | 9780691214351 9780691214368 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032953290 |
| oclc_num | 1292516985 |
| open_access_boolean | |
| owner | DE-188 DE-11 DE-473 DE-BY-UBG DE-573 DE-739 DE-19 DE-BY-UBM |
| owner_facet | DE-188 DE-11 DE-473 DE-BY-UBG DE-573 DE-739 DE-19 DE-BY-UBM |
| physical | xxi, 377 Seiten Diagramme 26 cm |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | Princeton University Press |
| record_format | marc |
| spelling | Bueno de Mesquita, Ethan 1974- Verfasser (DE-588)1115254561 aut Thinking clearly with data a guide to quantitative reasoning and analysis Ethan Bueno de Mesquita, Anthony Fowler Princeton ; Oxford Princeton University Press [2021] © 2021 xxi, 377 Seiten Diagramme 26 cm txt rdacontent n rdamedia nc rdacarrier "This is an intro-level text that teaches how to think clearly and conceptually about quantitative information, emphasizing ideas over technicality and assuming no prior exposure to data analysis, statistics, or quantitative methods. The books four parts present the foundation for quantiative reasoning: correlation and causation; statistical relationships; causal phenomena; and incorporating quantitative information into decision making. Within these parts it covers the array of tools used by social scientists, including regression, inference, experiments, research design, and more, all by explaining the rationale and logic behind such tools rather than focusing only on the technical calculations used for each. New concepts are presented simply, with the help of copious examples, and the books leans towards graphic rather than mathematical representation of data, with any technical material included in appendices"-- Sociology / Statistical methods Sociology / Methodology Sociology / Methodology fast Sociology / Statistical methods fast Methode (DE-588)4038971-6 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Kritisches Denken (DE-588)4231288-7 gnd rswk-swf Soziologie (DE-588)4077624-4 gnd rswk-swf Soziologie (DE-588)4077624-4 s Methode (DE-588)4038971-6 s Statistik (DE-588)4056995-0 s Kritisches Denken (DE-588)4231288-7 s DE-604 Fowler, Anthony Verfasser (DE-588)1249416116 aut Erscheint auch als Online-Ausgabe, EPUB 978-0-691-21501-3 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032953290&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Bueno de Mesquita, Ethan 1974- Fowler, Anthony Thinking clearly with data a guide to quantitative reasoning and analysis Sociology / Statistical methods Sociology / Methodology Sociology / Methodology fast Sociology / Statistical methods fast Methode (DE-588)4038971-6 gnd Statistik (DE-588)4056995-0 gnd Kritisches Denken (DE-588)4231288-7 gnd Soziologie (DE-588)4077624-4 gnd |
| subject_GND | (DE-588)4038971-6 (DE-588)4056995-0 (DE-588)4231288-7 (DE-588)4077624-4 |
| title | Thinking clearly with data a guide to quantitative reasoning and analysis |
| title_auth | Thinking clearly with data a guide to quantitative reasoning and analysis |
| title_exact_search | Thinking clearly with data a guide to quantitative reasoning and analysis |
| title_exact_search_txtP | Thinking clearly with data a guide to quantitative reasoning and analysis |
| title_full | Thinking clearly with data a guide to quantitative reasoning and analysis Ethan Bueno de Mesquita, Anthony Fowler |
| title_fullStr | Thinking clearly with data a guide to quantitative reasoning and analysis Ethan Bueno de Mesquita, Anthony Fowler |
| title_full_unstemmed | Thinking clearly with data a guide to quantitative reasoning and analysis Ethan Bueno de Mesquita, Anthony Fowler |
| title_short | Thinking clearly with data |
| title_sort | thinking clearly with data a guide to quantitative reasoning and analysis |
| title_sub | a guide to quantitative reasoning and analysis |
| topic | Sociology / Statistical methods Sociology / Methodology Sociology / Methodology fast Sociology / Statistical methods fast Methode (DE-588)4038971-6 gnd Statistik (DE-588)4056995-0 gnd Kritisches Denken (DE-588)4231288-7 gnd Soziologie (DE-588)4077624-4 gnd |
| topic_facet | Sociology / Statistical methods Sociology / Methodology Methode Statistik Kritisches Denken Soziologie |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032953290&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT buenodemesquitaethan thinkingclearlywithdataaguidetoquantitativereasoningandanalysis AT fowleranthony thinkingclearlywithdataaguidetoquantitativereasoningandanalysis |