A gentle introduction to Stata:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
College Station, Texas
Stata Press
2023
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| Ausgabe: | Revised sixth edition |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xxxvii, 548 Seiten, 9 Blätter Illustrationen, Diagramme 1270 grams |
| ISBN: | 9781597183673 |
Internformat
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| 100 | 1 | |a Acock, Alan C. |d 1944- |e Verfasser |0 (DE-588)1037993705 |4 aut | |
| 245 | 1 | 0 | |a A gentle introduction to Stata |c Alan C. Acock, Oregon State University |
| 250 | |a Revised sixth edition | ||
| 264 | 1 | |a College Station, Texas |b Stata Press |c 2023 | |
| 300 | |a xxxvii, 548 Seiten, 9 Blätter |b Illustrationen, Diagramme |c 1270 grams | ||
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Datensatz im Suchindex
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Contents List of figures xv List of tables xxiii xxv List of boxed tips xxix Preface Acknowledgments xxxiii Support materials for thebook 1 2 xxxv Getting started 1 1.1 Conventions. 1 1.2 Introduction. 4 1.3 The Stata screen. 7 1.4 Using an existingdataset 1.5 An example of a short Statasession . 11 1.6 Video aids to learningStata. 18 1.7 Summary. 19 1.8 Exercises. 19 . 10 Entering data 21 2.1 Creating a dataset. ·. 21 2.2 An example questionnaire. 24 2.3 Developing a coding system. 25 2.4 Entering data using the DataEditor. 29 2.4.1 Value labels. 33 2.5 The Variables Manager. 34 2.6 The Data Editor (Browse)
view. 40 2.7 Saving your dataset. 41
Contents vi Checking the data . „ . 2.9 Summary. „ . 2.10 Exercises. 2.8 3 4 5 43 50 50 51 Preparing data for analysis 51 3.1 Introduction. 3.2 Planning your work. 3.3 Creating value labels. . $? 3.4 Reverse-code variables. θθ 3.5 Creating and modifying variables. 65 3.6 Creating scales. 70 3.7 Saving some of your data . 74 3.8 Summary. 75 3.9 Exercises. 76 Working with commands, do-files, and results 77 4.1 Introduction. 77 4.2 How Stata commands are
constructed. 78 4.3 Creating a do-file. 83 4.4 Copying your results to a word processor. 88 4.5 Logging your command file . 90 4.6 Summary. 91 4.7 Exercises. 92 Descriptive statistics and graphs for one variable 93 5.1 Descriptive statistics and graphs . 93 5.2 Where is the center of a distribution?. 94 5.3 How dispersed is the distribution? 98 5.4 Statistics and graphs—unordered categories. . 100 5.5 Statistics and graphs—ordered categories and variables. 110 5.6 Statistics and graphs—quantitative variables Ц2 . 5.7 Summary. լշ0 5.8 Exercises. 120
Contents Statistics and graphs for two categorical variables 123 6.1 Relationship between categorical variables. 123 6 7 vii 6.2 Cross-tabulation . 124 6.3 Chi-squared test . 127 6.3.1 Degrees of freedom. 129 6.3.2 Probability tables. 129 6.4 Percentages and measures of association. 132 6.5 Odds ratios when dependent variable has two categories. 135 6.6 Ordered categorical variables. 137 6.7 Interactive tables. 140 6.8 Tables—linking categorical and quantitative variables 142 6.9 Power analysis when using a chi-squared test of significance. 145 6.10 Summary. 148 6.11 Exercises. 148 . Tests for one or two means 151 Introduction to tests for one or two means. 151 7.2 Randomization. 154 7.3 Random
sampling. 156 7.1 7.4 Hypotheses. 156 7.5 One-sample test of a proportion. 158 7.6 Two-sample test of a proportion. 161 7.7 One-sample test of means. 165 7.8 Two-sample test of group means . 167 Testing for unequal variances. · · 176 7.8.1 177 7.9 Repeated-measures t test 7.10 Power analysis 7.11 Nonparametric alternatives . ^? 7.11.1 Mann-Whitney two-sample rank-sum test. 187 7.11.2 Nonparametric alternative: Median test . 188 Video tutorial related to this chapter. 18$ 7.12 . .
Contents viii 7.13 189 Summary. 190 7.14 Exercises. 8 Bivariate correlation and regression 8.1 Introduction to bivariate correlation and regression. 193 1$$ 8.2 8.3 . 194 Scattergrams . Plotting the regression line. $θθ 8.4 An alternative to producing a scattergram, binscatter 8.5 Correlation. 8.6 Regression. 8.7 Spearman’s rho: Rank-order correlationfor ordinal data.215 8.8 Power analysis with correlation. 216 8.9 Summary.2^8 . 201 8.10 Exercises. 218 9 Analysis of variance 221 9.1 The logic of one-way analysis of variance. 221 9.2 ANOVA example. 222 9.3 ANOVA example with nonexperimental
data.231 9.4 Power analysis for one-way ANOVA 9.5 A nonparametric alternative to ANOVA. 236 9.6 Analysis of covariance. 238 9.7 Two-way ANOVA.249 9.8 Repeated-measures design. 255 9.9 Intraclass correlation—measuring agreement. 260 9.10 Power analysis with ANOVA .234 . 252 9.10.1 Power analysis for one-way ANOVA. 263 9.10.2 Power analysis for two-way ANOVA 9.10.3 Power analysis for repeated-measures ANOVA. 267 9.10.4 Summary of power analysis for ANOVA. 269 9.11 Summary. 9.12 Exercises. 265 27Q
Contents 10 ix Multiple regression 10.1 273 Introduction to multiple regression. 273 10.2 What is multiple regression?. 10.3 The basic multiple regression command 274 . 275 10.4 Increment in R-squared: Semipartial correlations. 279 10.5 Is the dependent variable normally distributed?. 281 10.6 Are the residuals normally distributed?. 284 10.7 Regression diagnostic statistics. 290 10.7.1 Outliers and influential cases. 290 10.7.2 Influential observations: DFbeta. 10.7.3 Combinations of variables may cause problems.294 292 10.8 Weighted data. 10.9 Categorical predictors and hierarchical regression. 298 10.10 A shortcut for working with a categorical variable. 296 307 10.11 Fundamentals of interaction. 308 10.12 Nonlinear relations. 315 10.12.1 Fitting a quadratic model. 317 10.12.2 Centering when using a quadratic
term. 323 10.12.3 Do we need to add a quadratic component?. 325 10.13 Power analysis in multiple regression. 327 10.14 Summary. 332 11 10.15 Exercises. 333 Logistic regression 337 11.1 Introduction to logistic regression. 337 11.2 An example. 338 11.3 What is an odds ratio and a logit?. 342 11.3.1 The odds ratio 11.3.2 The logit transformation . 344 . 344 11.4 Data used in the rest of the chapter 11.5 Logistic regression . 345 . 347
Contents x 11.6 Hypothesis testing. 11.6.1 Testing individual coefficients. 357 358 11.6.2 Testing sets of coefficients. 11.7 Margins: More on interpreting results from logistic regression . 361 11.8 Nested logistic regressions. 11.9 Power analysis when doing logistic regression. 371 11.10 Next steps for using logistic regression and its extensions. 374 11.11 Summary. 11.12 Exercises. 375 12 Measurement, reliability, and validity 377 12.1 Overview of reliability and validity. 377 12.2 Constructing a scale. 378 12.2.1 12.3 Generating a mean score for each person.379 Reliability. 381 12.3.1 Stability and test-retest reliability. 382 12.3.2
Equivalence.383 12.3.3 Split-half and alpha reliability—internal consistency . 383 12.3.4 Kuder-Richardson reliability for dichotomous items. 386 12.3.5 Rater agreement—kappa (к.) 12.4 . 387 Validity.389 12.4.1 Expert judgment. 390 12.4.2 Criterion-related validity. 391 12.4.3 Construct validity. 39I 12.5 Factor analysis. 395 12.6 PCF analysis. 499 12.6.1 Orthogonal rotation: Varimax. 404 12.6.2 Oblique rotation: Promax. 499 12.7 But we wanted one scale, not four scales. 407 12.7.1 Scoring our variable. 49g
Contents xi 12.8 Summary. 499 12.9 Exercises. 13 Structural equation and generalized structural equation modeling 411 13.1 Linear regression using sem.412 13.1.1 Using the sem command directly. 413 13.1.2 SEM and working with missing values. 414 13.1.3 Exploring missing values and auxiliary variables. 419 13.1.4 Getting auxiliary variables into your SEM command . 421 13.2 A quick way to draw a regression model. 421 13.3 The gsem command for logistic regression. 425 13.3.1 Fitting the model using the logit command 13.3.2 Fitting the model using the gsem command.428 .425 13.4 Path analysis and mediation. 432 13.5 Conclusions and what is next for the sem command.437 13.6 Exercises. 439 14 Working with missing values—multiple imputation 441 14.1 Working with missing values—multiple imputation 14.2 What variables do we include when doing imputations? 14.3 The nature of the
problem. 444 . 441 . 442 14.4 Multiple imputation and its assumptions about the mechanism for missingness. 445 14.5 Multiple imputation.447 14.6 A detailed example . . . 448 14.6.1 Preliminary analysis. 449 14.6.2 Setup and multiple-imputation stage. 452 14.6.3 The analysis stage 14.6.4 For those who want an R2 and standardized ßs. 456 14.6.5 When impossible values are imputed. 458 . л o 14.7 Summary. .Ί o ր 14.8 Exercises. 454 460 461
Contents xii 15 An introduction to multilevel analysis 15.1 Questions and data for groups of individuals. 463 15.2 Questions and data for a longitudinal multilevel application. 464 15.3 Fixed-effects regression models. 15.4 Random-effects regression models.4θθ 15.5 An applied example 4θ$ . 468 15.5.1 Research questions. 15.5.2 Reshaping data to do multilevel analysis. 469 4$$ 15.6 A quick visualization of our data. 4?2 15.7 Random-intercept model. 4?3 15.7.1 Random intercept—linear model 15.7.2 Random-intercept model—quadratic term.476 15.7.3 Treating time as a categorical variable. 480 . 473 15.8 Random-coefficients model. 483 15.9 Including a time-invariant covariate. 486 15.10 Summary. 491 15.11
Exercises. 492 16 Item response theory (IRT) 493 16.1 How are IRT measures of variables different from summated scales? . 494 16.2 Overview of three IRT models for dichotomous items. 496 16.2.1 The one-parameter logistic (1PL) model. 496 16.2.2 The two-parameter logistic (2PL) model.498 16.2.3 The three-parameter logistic (3PL) model.499 16.3 Fitting the 1PL model using Stata.500 16.3.1 The estimation . . 16.3.2 How important is each of the items? 16.3.3 An overall evaluation of our scale. 506 16.3.4 Estimating the latent score. 507 16.4 Fitting a 2PL IRT model 16.4.1 . Fitting the 2PL model . . 502 . 504 5θ8 րՈօ
Contents xiii 16.5 The graded response model—IRT for Likert-type items. 515 A 16.5.1 The data. 515 16.5.2 Fitting our graded response model . 517 16.5.3 Estimating a person’s score . . . . 522 16.6 Reliability of the fitted IRT model. 522 16.7 Using the Stata menu system.525 16.8 Extensions of IRT 16.9 Exercises. 529 . What’s next? 528 531 A.l Introduction to the appendix. A.2 Resources . 531 A.3 531 A.2.1 Web resources. 532 A.2.2 Books about Stata. 534 A.2.3 Short courses. 536 A.2.4 Acquiring data A.2.5 Learning from thepostestimation methods. 538 . 537 Summary. 539 Glossary of acronyms 541 Glossary of
mathematical and statistical symbols 543 References ^^ Author index 551 Subject index 553 |
| adam_txt |
Contents List of figures xv List of tables xxiii xxv List of boxed tips xxix Preface Acknowledgments xxxiii Support materials for thebook 1 2 xxxv Getting started 1 1.1 Conventions. 1 1.2 Introduction. 4 1.3 The Stata screen. 7 1.4 Using an existingdataset 1.5 An example of a short Statasession . 11 1.6 Video aids to learningStata. 18 1.7 Summary. 19 1.8 Exercises. 19 . 10 Entering data 21 2.1 Creating a dataset. ·. 21 2.2 An example questionnaire. 24 2.3 Developing a coding system. 25 2.4 Entering data using the DataEditor. 29 2.4.1 Value labels. 33 2.5 The Variables Manager. 34 2.6 The Data Editor (Browse)
view. 40 2.7 Saving your dataset. 41
Contents vi Checking the data . „ . 2.9 Summary. „ . 2.10 Exercises. 2.8 3 4 5 43 50 50 51 Preparing data for analysis 51 3.1 Introduction. 3.2 Planning your work. 3.3 Creating value labels. . $? 3.4 Reverse-code variables. θθ 3.5 Creating and modifying variables. 65 3.6 Creating scales. 70 3.7 Saving some of your data . 74 3.8 Summary. 75 3.9 Exercises. 76 Working with commands, do-files, and results 77 4.1 Introduction. 77 4.2 How Stata commands are
constructed. 78 4.3 Creating a do-file. 83 4.4 Copying your results to a word processor. 88 4.5 Logging your command file . 90 4.6 Summary. 91 4.7 Exercises. 92 Descriptive statistics and graphs for one variable 93 5.1 Descriptive statistics and graphs . 93 5.2 Where is the center of a distribution?. 94 5.3 How dispersed is the distribution? 98 5.4 Statistics and graphs—unordered categories. . 100 5.5 Statistics and graphs—ordered categories and variables. 110 5.6 Statistics and graphs—quantitative variables Ц2 . 5.7 Summary. լշ0 5.8 Exercises. 120
Contents Statistics and graphs for two categorical variables 123 6.1 Relationship between categorical variables. 123 6 7 vii 6.2 Cross-tabulation . 124 6.3 Chi-squared test . 127 6.3.1 Degrees of freedom. 129 6.3.2 Probability tables. 129 6.4 Percentages and measures of association. 132 6.5 Odds ratios when dependent variable has two categories. 135 6.6 Ordered categorical variables. 137 6.7 Interactive tables. 140 6.8 Tables—linking categorical and quantitative variables 142 6.9 Power analysis when using a chi-squared test of significance. 145 6.10 Summary. 148 6.11 Exercises. 148 . Tests for one or two means 151 Introduction to tests for one or two means. 151 7.2 Randomization. 154 7.3 Random
sampling. 156 7.1 7.4 Hypotheses. 156 7.5 One-sample test of a proportion. 158 7.6 Two-sample test of a proportion. 161 7.7 One-sample test of means. 165 7.8 Two-sample test of group means . 167 Testing for unequal variances. · · 176 7.8.1 177 7.9 Repeated-measures t test 7.10 Power analysis 7.11 Nonparametric alternatives . ^? 7.11.1 Mann-Whitney two-sample rank-sum test. 187 7.11.2 Nonparametric alternative: Median test . 188 Video tutorial related to this chapter. 18$ 7.12 . .
Contents viii 7.13 189 Summary. 190 7.14 Exercises. 8 Bivariate correlation and regression 8.1 Introduction to bivariate correlation and regression. 193 1$$ 8.2 8.3 . 194 Scattergrams . Plotting the regression line. $θθ 8.4 An alternative to producing a scattergram, binscatter 8.5 Correlation. 8.6 Regression. 8.7 Spearman’s rho: Rank-order correlationfor ordinal data.215 8.8 Power analysis with correlation. 216 8.9 Summary.2^8 . 201 8.10 Exercises. 218 9 Analysis of variance 221 9.1 The logic of one-way analysis of variance. 221 9.2 ANOVA example. 222 9.3 ANOVA example with nonexperimental
data.231 9.4 Power analysis for one-way ANOVA 9.5 A nonparametric alternative to ANOVA. 236 9.6 Analysis of covariance. 238 9.7 Two-way ANOVA.249 9.8 Repeated-measures design. 255 9.9 Intraclass correlation—measuring agreement. 260 9.10 Power analysis with ANOVA .234 . 252 9.10.1 Power analysis for one-way ANOVA. 263 9.10.2 Power analysis for two-way ANOVA 9.10.3 Power analysis for repeated-measures ANOVA. 267 9.10.4 Summary of power analysis for ANOVA. 269 9.11 Summary. 9.12 Exercises. 265 27Q
Contents 10 ix Multiple regression 10.1 273 Introduction to multiple regression. 273 10.2 What is multiple regression?. 10.3 The basic multiple regression command 274 . 275 10.4 Increment in R-squared: Semipartial correlations. 279 10.5 Is the dependent variable normally distributed?. 281 10.6 Are the residuals normally distributed?. 284 10.7 Regression diagnostic statistics. 290 10.7.1 Outliers and influential cases. 290 10.7.2 Influential observations: DFbeta. 10.7.3 Combinations of variables may cause problems.294 292 10.8 Weighted data. 10.9 Categorical predictors and hierarchical regression. 298 10.10 A shortcut for working with a categorical variable. 296 307 10.11 Fundamentals of interaction. 308 10.12 Nonlinear relations. 315 10.12.1 Fitting a quadratic model. 317 10.12.2 Centering when using a quadratic
term. 323 10.12.3 Do we need to add a quadratic component?. 325 10.13 Power analysis in multiple regression. 327 10.14 Summary. 332 11 10.15 Exercises. 333 Logistic regression 337 11.1 Introduction to logistic regression. 337 11.2 An example. 338 11.3 What is an odds ratio and a logit?. 342 11.3.1 The odds ratio 11.3.2 The logit transformation . 344 . 344 11.4 Data used in the rest of the chapter 11.5 Logistic regression . 345 . 347
Contents x 11.6 Hypothesis testing. 11.6.1 Testing individual coefficients. 357 358 11.6.2 Testing sets of coefficients. 11.7 Margins: More on interpreting results from logistic regression . 361 11.8 Nested logistic regressions. 11.9 Power analysis when doing logistic regression. 371 11.10 Next steps for using logistic regression and its extensions. 374 11.11 Summary. 11.12 Exercises. 375 12 Measurement, reliability, and validity 377 12.1 Overview of reliability and validity. 377 12.2 Constructing a scale. 378 12.2.1 12.3 Generating a mean score for each person.379 Reliability. 381 12.3.1 Stability and test-retest reliability. 382 12.3.2
Equivalence.383 12.3.3 Split-half and alpha reliability—internal consistency . 383 12.3.4 Kuder-Richardson reliability for dichotomous items. 386 12.3.5 Rater agreement—kappa (к.) 12.4 . 387 Validity.389 12.4.1 Expert judgment. 390 12.4.2 Criterion-related validity. 391 12.4.3 Construct validity. 39I 12.5 Factor analysis. 395 12.6 PCF analysis. 499 12.6.1 Orthogonal rotation: Varimax. 404 12.6.2 Oblique rotation: Promax. 499 12.7 But we wanted one scale, not four scales. 407 12.7.1 Scoring our variable. 49g
Contents xi 12.8 Summary. 499 12.9 Exercises. 13 Structural equation and generalized structural equation modeling 411 13.1 Linear regression using sem.412 13.1.1 Using the sem command directly. 413 13.1.2 SEM and working with missing values. 414 13.1.3 Exploring missing values and auxiliary variables. 419 13.1.4 Getting auxiliary variables into your SEM command . 421 13.2 A quick way to draw a regression model. 421 13.3 The gsem command for logistic regression. 425 13.3.1 Fitting the model using the logit command 13.3.2 Fitting the model using the gsem command.428 .425 13.4 Path analysis and mediation. 432 13.5 Conclusions and what is next for the sem command.437 13.6 Exercises. 439 14 Working with missing values—multiple imputation 441 14.1 Working with missing values—multiple imputation 14.2 What variables do we include when doing imputations? 14.3 The nature of the
problem. 444 . 441 . 442 14.4 Multiple imputation and its assumptions about the mechanism for missingness. 445 14.5 Multiple imputation.447 14.6 A detailed example . . . 448 14.6.1 Preliminary analysis. 449 14.6.2 Setup and multiple-imputation stage. 452 14.6.3 The analysis stage 14.6.4 For those who want an R2 and standardized ßs. 456 14.6.5 When impossible values are imputed. 458 . л o 14.7 Summary. .Ί o ր 14.8 Exercises. 454 460 461
Contents xii 15 An introduction to multilevel analysis 15.1 Questions and data for groups of individuals. 463 15.2 Questions and data for a longitudinal multilevel application. 464 15.3 Fixed-effects regression models. 15.4 Random-effects regression models.4θθ 15.5 An applied example 4θ$ . 468 15.5.1 Research questions. 15.5.2 Reshaping data to do multilevel analysis. 469 4$$ 15.6 A quick visualization of our data. 4?2 15.7 Random-intercept model. 4?3 15.7.1 Random intercept—linear model 15.7.2 Random-intercept model—quadratic term.476 15.7.3 Treating time as a categorical variable. 480 . 473 15.8 Random-coefficients model. 483 15.9 Including a time-invariant covariate. 486 15.10 Summary. 491 15.11
Exercises. 492 16 Item response theory (IRT) 493 16.1 How are IRT measures of variables different from summated scales? . 494 16.2 Overview of three IRT models for dichotomous items. 496 16.2.1 The one-parameter logistic (1PL) model. 496 16.2.2 The two-parameter logistic (2PL) model.498 16.2.3 The three-parameter logistic (3PL) model.499 16.3 Fitting the 1PL model using Stata.500 16.3.1 The estimation . . 16.3.2 How important is each of the items? 16.3.3 An overall evaluation of our scale. 506 16.3.4 Estimating the latent score. 507 16.4 Fitting a 2PL IRT model 16.4.1 . Fitting the 2PL model . . 502 . 504 5θ8 րՈօ
Contents xiii 16.5 The graded response model—IRT for Likert-type items. 515 A 16.5.1 The data. 515 16.5.2 Fitting our graded response model . 517 16.5.3 Estimating a person’s score . . . . 522 16.6 Reliability of the fitted IRT model. 522 16.7 Using the Stata menu system.525 16.8 Extensions of IRT 16.9 Exercises. 529 . What’s next? 528 531 A.l Introduction to the appendix. A.2 Resources . 531 A.3 531 A.2.1 Web resources. 532 A.2.2 Books about Stata. 534 A.2.3 Short courses. 536 A.2.4 Acquiring data A.2.5 Learning from thepostestimation methods. 538 . 537 Summary. 539 Glossary of acronyms 541 Glossary of
mathematical and statistical symbols 543 References ^^ Author index 551 Subject index 553 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Acock, Alan C. 1944- |
| author_GND | (DE-588)1037993705 |
| author_facet | Acock, Alan C. 1944- |
| author_role | aut |
| author_sort | Acock, Alan C. 1944- |
| author_variant | a c a ac aca |
| building | Verbundindex |
| bvnumber | BV048808028 |
| classification_rvk | MR 2100 ST 601 ST 600 QH 212 QH 234 ST 273 |
| ctrlnum | (OCoLC)1359918837 (DE-599)BVBBV048808028 |
| discipline | Informatik Soziologie Wirtschaftswissenschaften |
| discipline_str_mv | Informatik Soziologie Wirtschaftswissenschaften |
| edition | Revised sixth edition |
| format | Book |
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| genre | (DE-588)4151278-9 Einführung gnd-content |
| genre_facet | Einführung |
| id | DE-604.BV048808028 |
| illustrated | Illustrated |
| index_date | 2024-07-03T21:29:28Z |
| indexdate | 2024-11-28T11:01:53Z |
| institution | BVB |
| isbn | 9781597183673 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034074032 |
| oclc_num | 1359918837 |
| open_access_boolean | |
| owner | DE-473 DE-BY-UBG DE-29T DE-739 DE-N2 DE-824 DE-945 |
| owner_facet | DE-473 DE-BY-UBG DE-29T DE-739 DE-N2 DE-824 DE-945 |
| physical | xxxvii, 548 Seiten, 9 Blätter Illustrationen, Diagramme 1270 grams |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Stata Press |
| record_format | marc |
| spelling | Acock, Alan C. 1944- Verfasser (DE-588)1037993705 aut A gentle introduction to Stata Alan C. Acock, Oregon State University Revised sixth edition College Station, Texas Stata Press 2023 xxxvii, 548 Seiten, 9 Blätter Illustrationen, Diagramme 1270 grams txt rdacontent n rdamedia nc rdacarrier Stata (DE-588)4617285-3 gnd rswk-swf (DE-588)4151278-9 Einführung gnd-content Stata (DE-588)4617285-3 s DE-604 Erscheint auch als Online-Ausgabe, ePub 978-1-59718-368-0 Vorangegangen ist 978-1-59718-269-0 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=034074032&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Acock, Alan C. 1944- A gentle introduction to Stata Stata (DE-588)4617285-3 gnd |
| subject_GND | (DE-588)4617285-3 (DE-588)4151278-9 |
| title | A gentle introduction to Stata |
| title_auth | A gentle introduction to Stata |
| title_exact_search | A gentle introduction to Stata |
| title_exact_search_txtP | A gentle introduction to Stata |
| title_full | A gentle introduction to Stata Alan C. Acock, Oregon State University |
| title_fullStr | A gentle introduction to Stata Alan C. Acock, Oregon State University |
| title_full_unstemmed | A gentle introduction to Stata Alan C. Acock, Oregon State University |
| title_short | A gentle introduction to Stata |
| title_sort | a gentle introduction to stata |
| topic | Stata (DE-588)4617285-3 gnd |
| topic_facet | Stata Einführung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034074032&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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