مدیریت داده‌ها با استفاده از استاتا: یک راهنمای عملی

Using simple language and illustrative examples, this book comprehensively covers data management tasks that bridge the gap between raw data and statistical analysis. Rather than focus on clusters of commands, the author takes a modular approach that enables readers to quickly identify and implement the necessary task without having to access background information first. Each section in the chapters presents a self-contained lesson that illustrates a particular data management task via examples, such as creating data variables and automating error checking. The text also discusses common pitfalls and how to avoid them and provides strategic data management advice. Ideal for both beginning statisticians and experienced users, this handy book helps readers solve problems and learn comprehensive data management skills. Acknowledgements Contents List of Tables List of Figures Preface 1 Introduction 1.1 Using this book 1.2 Overview of this book 1.3 Listing observations in this book 2 Reading and writing datasets 2.1 Introduction 2.2 Reading Stata datasets 2.3 Saving Stata datasets 2.4 Reading comma-separated and tab-separated files 2.5 Reading space-separated files 2.6 Reading fixed-column files 2.7 Reading fixed-column files with multiple lines of raw data per observation 2.8 Reading SAS XPORT files 2.9 Common errors reading files 2.10 Entering data directly into the Stata Data Editor 2.11 Saving comma-separated and tab-separated files 2.12 Saving space-separated files 2.13 Saving SAS XPORT files 3 Data cleaning 3.1 Introduction 3.2 Double data entry 3.3 Checking individual variables 3.4 Checking categorical by categorical variables 3.5 Checking categorical by continuous variables 3.6 Checking continuous by continuous variables 3.7 Correcting errors in data 3.8 Identifying duplicates 3.9 Final thoughts on data cleaning 4 Labeling datasets 4.1 Introduction 4.2 Describing datasets 4.3 Labeling variables 4.4 Labeling values 4.5 Labeling utilities 4.6 Labeling variables and values in different languages 4.7 Adding comments to your dataset using notes 4.8 Formatting the display of variables 4.9 Changing the order of variables in a dataset 5 Creating variables 5.1 Introduction 5.2 Creating and changing variables 5.3 Numeric expressions and functions 5.4 String expressions and functions 5.5 Recoding 5.6 Coding missing values 5.7 Dummy variables 5.8 Date variables 5.9 Date-and-time variables 5.10 Computations across variables 5.11 Computations across observations 5.12 More examples using the egen command 5.13 Converting string variables to numeric variables 5.14 Converting numeric variables to string variables 5.15 Renaming and ordering variables 6 Combining datasets 6.1 Introduction 6.2 Appending: Appending datasets 6.3 Appending: Problems 6.4 Merging: One-to-one match-merging 6.5 Merging: One-to-many match-merging 6.6 Merging: Merging multiple datasets 6.7 Merging: Update merges 6.8 Merging: Additional options when merging datasets 6.9 Merging: Problems merging datasets 6.10 Joining datasets 6.11 Crossing datasets 7 Processing observations across subgroups 7.1 Introduction 7.2 Obtaining separate results for subgroups 7.3 Computing values separately by subgroups 7.4 Computing values within subgroups: Subscripting observations 7.5 Computing values within subgroups: Computations across observations 7.6 Computing values within subgroups: Running sums 7.7 Computing values within subgroups: More examples 7.8 Comparing the by and tsset commands 8 Changing the shape of your data 8.1 Introduction 8.2 Wide and long datasets 8.3 Introduction to reshaping long to wide 8.4 Reshaping long to wide: Problems 8.5 Introduction to reshaping wide to long 8.6 Reshaping wide to long: Problems 8.7 Multilevel datasets 8.8 Collapsing datasets 9 Programming for data management 9.1 Introduction 9.2 Tips on long-term goals in data management 9.3 Executing do-files and making log files 9.4 Automating data checking 9.5 Combining do-files 9.6 Introducing Stata macros 9.7 Manipulating Stata macros 9.8 Repeating commands by looping over variables 9.9 Repeating commands by looping over numbers 9.10 Repeating commands by looping over anything 9.11 Accessing results saved from Stata commands 9.12 Saving results of estimation commands as data 9.13 Writing Stata programs 10 Additional resources 10.1 Online resources for this book 10.2 Finding and installing additional programs 10.3 More online resources Appendix A Common elements A.1 Introduction A.2 Overview of Stata syntax A.3 Working across groups of observations with by A.4 Comments A.5 Data types A.6 Logical expressions A.7 Functions A.B Subsetting observations with if and in A.9 Subsetting observations and variables with keep and drop A.10 Missing values A.11 Referring to variable lists Subject index Using illustrative examples, this book comprehensively covers data management tasks that bridge the gap between raw data and statistical analysis. Rather than focus on clusters of commands, it takes a modular approach that enables readers to quickly identify and implement the necessary task without having to access background information first. Reading And Writing Datasets -- Data Cleaning -- Labeling Datasets -- Creating Variables -- Combining Datasets -- Processing Observations Across Subgroups -- Changing The Shape Of Your Data -- Programming For Data Management -- Additional Resources -- Common Elements -- Subject Index. Michael N. Mitchell. Includes Index.