Kim Mannemar Sønderskov. Stata. A practical introduction. Hans Reitzels Forlag

Transcription

1 Stata

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3 Kim Mannemar Sønderskov Stata A practical introduction Hans Reitzels Forlag

4 Stata: A practical introduction Edition 1, print run 1 The authors and Hans Reitzel Publishers, 2015 Publishing editor: Martin Laurberg Translator: Tam McTurk and the author Cover: Louise Glargaard Perlmutter/Louises design Graphic design: Louise Glargaard Perlmutter/Louises design Layout and typesetting: Lone Bjarkow/Louises design Font: Minion Pro, Taz and ScalaSans Printed by Livonia Press Printed in Latvia 2015 ISBN This book has been subjected to a process of independent peer review at the manuscript stage. All rights reserved. Mechanical, photographic or any other reproduction or copying of this book is permitted only in accordance with the agreement between the Ministry of Education and Copydan. Any other usage without the written consent of the publisher is prohibited by Danish copyright law. The only exception consists of short extracts for use in reviews.

5 Contents 1. Introduction Making the most of the book Online material Notation Getting started with Stata and data: User interface and data inspection The primary user interface Open datasets Commands Inspection of data/descriptive statistics Overview of observations and variables in the dataset with -describe Codebook with -codebook set more off The output from -codebook- and types of variables Codebook information for the selected variables Notes with -notes Notes in the codebook Inspecting the data matrix with -browse Structure of the data matrix...31 Missing values Inspect selected variables with -browse Sort data with -sort- and -gsort Frequency distributions with -tabulate Frequency distributions with -codebook Frequency distributions for multiple variables with -tab Visual inspection of data with -inspect- and -histogram Detailed histogram with the graphics module and -histogram Descriptive statistics with -summarize Using the Review, Variables and Command windows Using do-files and the Do-file Editor Open the Do-file Editor and do-files Ways to use the editor...40 Run commands from the Do-file Editor...41

6 2.4.3 Commenting on commands in the do-file Entering long lines in the do-file Saving datasets and do-files...44 *Auto-save do-files Opening existing do-files Open the do-file with solutions to the exercises in this book Stata's help functions, and external resources...46 External resources Updates Data manipulation 1: Entering, deleting and documenting data Assigning and changing variable labels Assigning labels with the Variables manager...51 Editing variable labels Add and edit notes Assigning existing value labels to variables Data entry Creating new variables with -generate Stata s variable types Entering data in matrices *The commands used to create variables and input data Creating new value labels...58 Enter data for variables with value labels Deleting variables with -drop- and -keep Implicit references to multiple variables (varlist) Use of wildcards: * and?...61 Use of hyphens: Deleting observations with -drop Data manipulation II: Modification of existing variables Navigating (large) datasets Searching datasets...65 Search for specific words in variable names and labels with -lookfor *Advanced searching with -ds *Searching in notes with -notes search Rename or copy variables with -rename-, -generate- and -clonevar *Rearrange the sequence of variables with -order Finding the dialogue box for a command

7 4.3 Recode variables with -recode Simple recoding...70 Checking the recoding: Cross-tabulation with -tabulate Assigning labels and notes to recoded variables Recoding from more than one value...73 Other references to multiple numbers Recode values to missing values Multiple types of missing values Copying and editing value labels when recoding Recoding multiple variables simultaneously with -recode Recoding variables from metric to ordinal scale Recoding with -replace- and -if One or two equals signs?...86 Operators...86 Be careful with missing values!...86 The and operator (&) The or operator ( ) and -inlist recode- and -if Recoding to dummy variable with -tabulate Other types of recoding and creating new variables Tab expansion and variable name abbreviations *Abbreviations of variable names *Abbreviations, varlist and wildcards Univariate analyses Save output to log files...95 The log file s file type File location and Stata s active directory...96 Existing log files...96 Closing log files Recommended use of log files User-written programs Univariate analyses of categorical variables Descriptive analysis with -tabulate Graphic illustration Pie chart with -graph pie Bar chart with -catplot- and -histogram Disappearing graphics...107

8 5.3.3 Inference Confidence interval and hypothesis tests for proportions with -ciand -bitest An alternative for normal sampling distributions: -prtest Univariate analyses of metric variables Descriptive analyses of metric data with -summarize Graphic illustration with -histogram Inference with -ci Inspecting the log file Reporting results: exporting tables and graphics Exporting graphs Manual export of tables *Command-based export of tables with -tabout General command structure Options if- and -in- qualifiers Prefixes Expressions weight- and -using General command syntax Command abbreviations Option abbreviations Common bivariate analyses Selected bivariate analysis techniques Basic table analysis: cross-tabulation with -tabulate Multiple bivariate tables with -tab Comparison of two groups' proportions Strength of the relationship Graphic illustration with -catplot Inference with -prtest *Small samples: Fisher s exact test Bivariate analyses of multinomial variables The correlation measure lambda with -lambda Graphic illustration bivariate relationships with multinomial dependent variables Inference: Pearson's chi-squared *Small samples: Fisher s exact test...135

9 6.3.3 Graphic illustration: Multinomial independent variables and nominal/ordinal dependent variables Bivariate analyses of ordinal-scaled variables Graphic illustration with -catplot Inference for gamma and tau-b Comparison of two groups' means Graphic illustration with -graph dot Inference with -ttest *Heteroscedasticity? ANOVA comparison of means for multiple groups Graphic illustration with -graph dot Inference with -oneway *Heteroscedasticity? Bivariate analyses of metric variables Multiple correlation coefficients/a correlation matrix Inference with -pwcorr Graphic illustration: scatterplot and sunflowerplot A clearer example of a scatterplot Graphs in general: -twoway-, tab display, etc Label observations OLS line/trend line Disappearing graphs Tab display with -set autotabgraphs Recall and save graphs with -graph The graph editor Stata without data Immediate versions of -bitest-, -ci-, etc *Stata as a calculator Using saved results Saved results in r() Individual results Practical use of saved results Towards multivariate analysis Constructing reflective indices Selection of indicators Missing values Replace with neutral category Replace with the variable mean

10 Replace with the observation s mean for other items Changing the range Identical range for discrete variables Identical range for continuous variables *Standardisation/z-transformation with -egen Changing direction Testing measurement validity Item-item analysis Item-scale analysis Test of reliability with -alpha The output from the alpha command The actual index construction Summation with -alpha Use -alpha- to replace missing values with the mean of the other items Rescaling with -generate Distribution analysis Command loops with -foreach Linear regression with OLS Bivariate linear regression Metric independent variable Standardised regression coefficients Dichotomous independent variable: comparison of two groups' means with -regress Categorical independent: comparison of means of multiple groups with -regress Manual dummy regression Dummy regression with factor variables (i.var) Joint F-test of categorical variable with -testparm Change of reference category Multivariate analysis with multiple linear regression Control variables Estimation Heteroscedasticity robust standard errors Multiple models estimated with identical observations Tabulation and export of regression results Export with -esttab

11 8.4 Multiple comparisons with -margins margins-, -pwcompare- and -contrast Comparing and testing multiple differences Summing up differences with the option -groups Adjusted mean Graphic illustration of results from linear regression *Bivariate relationships with -graph dot- and -twoway Multivariate relationships, categorical variables with -marginsplot *Multivariate relationships, metric variables with -marginscontplot *Multivariate relationships metric variables and categorical variables *Illustration of results from multiple models with -coefplot Assumptions and diagnostics for the linear model Linearity in the parameters Multiple acpr plots with -foreach Absence of influential observations Leverage-versus-residual-squared plot with -lvr2plot Cook s distance with -predict Partial regression plot with -avplot- and -avplots DFBETA Normally distributed error terms Histogram of the residuals distribution q-q-plot of the residuals distribution Homoscedasticity Absence of perfect (and strong) multicollinearity Modelling and illustration of non-linear relationships Logarithmic transformations Quadratic and polynomial models Graphic illustration *Use of macros Local and global macros Global macros Interaction in linear models: Estimation, interpretation and illustration A longer example Question, theory, hypothesis and model Operationalisation

12 Estimating the interaction model Estimation with factor variable notation Evaluation of assumptions Reporting of relevant estimates/statistics Interpretation of coefficients and associated P-values from interaction models Interpretation with -margins Illustration of the relationship Plotting marginal effects Plotting the predicted relationships Estimating and modelling other types of interaction Metric primary; dichotomous interacting Dichotomous primary; metric interacting Logistic regression Preparing Stata and data for logistic regression Coding the dependent variable Example: red or blue bloc? Estimation of logistic models with -logit The output from -logit *Alternative measures of fit with -fitstat Interpretation of the results from logistic regression Interpretation with log-odds *Multiple comparisons of log-odds coefficients Interpretation with changes in predicted probabilities and -margins Interpretation with average marginal effects (AME) *Interpretation with marginal effect at the mean *Interpretation with marginal effect for typical/interesting observations Interpretation with changes in predicted probabilities *Interpretation using odds ratios *Inverted odds-ratios Graphic illustration Metric independent variable Categorical independent variable Assumptions and diagnostics Linearity/correct functional form...270

13 Graphic inspection of linearity in bivariate analyses Graphic inspection of linearity in multivariate analyses The distribution of the error terms Absence of perfect (and strong) multicollinearity Number of observations and computational challenges *Lack of variation on the dependent variable within categories of an independent variable *Complete or quasi-complete separation *Lack of convergence Appendix A: More about the Results window Font colours in the Results window Lack of output Delete the content of the Results window Appendix B: Evaluate coder reliability with -datasignature- and merge Procedure 1: Comparison of dataset with -datasignature Procedure 2: Merging datasets with -merge Rename the variables in the control dataset, using names that differ from those in the original dataset Merge the two datasets Determine whether observations have different values and, if so, identify them Correction of errors Appendix C: Vertical bar charts for univariate distribution with -catplot Appendix D: -prtest- to test univariate proportions Appendix E: Multivariate analysis of categorical variables with table analysis Inference and graphic illustration Appendix F: More about index construction Constructing a formative index Item-scale analysis with gamma References Index...299

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15 1. Introduction This book serves as an introduction to the statistics programme Stata. The primary audience is students and others using statistical software for the first time. Experienced users of other statistical programmes who would like to learn Stata or Stata users lacking a comprehensive introduction will probably also find it useful. The book is based on Stata 13 for Windows. Stata for Mac (and for Unix) only differ very slightly from the Windows version, and most of the differences are described in the text. The purpose of the book is to help the reader to perform and reproduce statistical analyses and data processing with Stata as efficiently as possible. Method ological considerations and the statistical background for analyses are sometimes only treated superficially, so the book should probably not be used as the only resource for statistical analyses. The book addresses three main topics: 1) Navigating Stata, 2) Data manipulation and documentation, and 3) Basic univariate, bivariate and multivariate analysis techniques including graphical illustrations. Chapter 2 introduces the most important elements of Stata, including the main user interface, the data matrix, the Do-file Editor and the help system. It also looks at inspecting data using tables, graphics and statistics. Chapters 3 and 4 deal with data processing, i.e. entry, recoding, documentation, etc. The following two chapters (5 and 6) deal with frequently used univariate and bivariate analysis techniques, as well as topics such as graphics. Chapter 7 looks at the construction of reflective indices, whilst chapters 8 11 deal with linear and binary regression analysis, regression diagnostics, and regression interaction models. 1.1 Making the most of the book The book is based on two didactic principles: people learn best by doing; and people learn best if what they are doing makes sense. 1. Introduction 15

16 As a consequence of the first principle, the book contains a number of exercises for the reader. By doing the exercises and using the functions described with real datasets, readers will develop their skills and improve their understanding of Stata. Unlike most textbooks, the exercises are dotted throughout the text, immediately after the descriptions of the functions. The book also builds sequentially upon the exercises in order to derive maximum benefit from the process, readers should do the exercises when they encounter them in the text. The choice of continuous exercises was based on several years experience teaching Stata. 1 As well as clear learning benefits, it also allows readers to produce the screenshots that would otherwise take up so much space in a book like this. Some screenshots will, however, be included. The second principle means that the degree of difficulty increases steadily, and attempts have been made to structure the book so that the functions presented make sense in real-life analysis situations. This, in turn, means that some functions may not be described from start to finish at one single point in the book. Instead, some functions are referred to several times, and picked up again when they will (hopefully) make more sense. However, for the benefit of readers who would like to learn more about specific functions right away, extensive use has been made of cross-referencing throughout the book. The index can also be used for this purpose, of course. References are also made to functions in the statistics program SPSS. These are solely for the benefit of SPSS users and may be ignored by other readers. 1.2 Online material At you will find datasets and solutions to the exercises in the book (see Section 2.5). Any errata and additional material will also be published there. Mac users who attempt to download datasets and do-files may find that the file does not download but instead opens in a new window. This can be overcome by right-clicking on the file, selecting Download Linked File As and then choosing the folder in which to save the file. 1 I should like to take this opportunity to thank the many students who have provided constructive comments on my teaching materials over the years. I should also like to thank Lene Aarøe, Lotte Bøgh Andersen, Mette Bisgaard, Katrine Degn, Camilla Bjarnøe Jensen, Erik Gahner Larsen, Søren Heldgaard Olesen, Rune Stubager, editor Martin Laurberg and three reviewers for their comments on this book. And thank you to Søren Risbjerg Thomsen and Peter Thisted Dinesen for a great deal of insightful input regarding the use of Stata and statistics. Any errors, omissions and ambiguities are, of course, my own responsibility. Finally, I would like to thank Rune Stubager and Jørgen Goul Andersen, who made some of the exercise material available to me. 16 Stata

17 1.3 Notation In the interests of clarity, the book makes use of a very small number of typographical functions. Instructions for using the menu system (which will be introduced in the next section) are written in. As such, means click on in the menu bar, then and then on. Command names (e.g. ), everything that is part of the Stata interface, keyboard combinations (e.g. ) and variable names in the active data set (e.g. ) are also written in. Commands, user interfaces, etc. are introduced in the next section. ## Exercises are marked like this section. The number refers to the numbering in the file containing the solutions. Commands that can be run from the Do-file Editor or command window are in boxes with shaded backgrounds like this. The Do-file Editor and Command window are introduced in the next section. These types of commands will often contain general references to variable names such as var1 in the next line: summarize var1 General names like these, which you have to replace with actual variable names, are in italics. *Entire sections that you can skip without losing the thread are marked with an asterisk, like this section. Box 1.1 Explanatory comments that you can bypass without losing the thread are in boxes like this. 1. Introduction 17