In this post, we present an introduction guide for beginners of Stata. Over the years, this application has become the leading econometrics data analysis tool. It provides a set of powerful but fast and essay to use features. If you are a Stata beginner, then this guide is for you. So, let us define what is Stata and its features?
Stata is an accurate, intuitive, and sophisticated data analysis and statistical solution available for Linux, Mac, and Windows. It simplifies research processes regardless of a researcher’s specialty or discipline. Stata’s features include data analysis, data management, data modeling, and data visualization tools in one place.
Being a fast and multipurpose statistical application, it contains a broad range of estimation and statistical features. These feature sutilize standard and advanced statistical methods and techniques. Besides that, it is friendly to developers and programmers as well. With it, users can formulate, issue, and execute commands that make inferences on collected data. As one runs Stata, the system stores a list of all commands used in a session. Hence, it is possible to replicate the results of any analysis session for sharing with others.
Stata’s robust data management capabilities enable researchers and business executives to capture, explore, consolidate, and visualize data. In turn, they obtain actionable and compelling business or research insights. Finally, Stata has robust data representation features for printing, publishing, and reproduction of reports,
Here is a summary of Stata’s core features:
Stata’s most exciting feature is it’s easy to use GUI. It is intuitive and straightforward enough for beginners. Regardless of a user’s level of expertise, Stata is a highly customizable application. Indeed, newbies, researchers, programmers, and advanced users will find it an attractive data analysis tool.
Stata’s GUI components include menus and dialog boxes. Its menu and dialog box-based interface lets users access data analysis, data management, and statistical analysis tools. Three of its core menus include the Data, Graphics,and Statistics menus. Users need only click a menu to execute contextually relevant commands and actions. For example, the Statistics menu launches the negative binomial regression tool. Once a user clicks a menu, dialog boxes appear related to the invoked function.
As indicated previously, developers and programmers can depend on Stata’s user-friendly command line. In other words, they can automate routine tasks using its powerful command-line language. For convenience, Stata’s graphical user interface organizes these features are into windows.
In Stata, you use the Command window to enter or issue commands scripts using the appropriate syntax. After command execution, the application displays the results in the Results window. In addition to that, Stata shows a list of executed commands in the Review window. Use the Review window to track your command history.
Stata has a set of advanced components that enhance efficiency. An example is the Data Editor, which provides a live preview of how functions and commands interactwith data. Hence, you can view how data changes over time. Another vital component of Stata is the Variable Manager. This component facilitates the creation and modification ofvariable labels, notes, data types, and names.
Like other statistical data analysis application, Stata has a documentation feature. But how does it work? This feature records all user commands, generated responses,the status of interactive sessions, and changes made to data. Stata uses a log file to store recorded information automatically.
As a beginner, you might ask what is the essence of this feature? With Stata’s documentation feature, you can review and evaluate data analysis results to ensure the accuracy and to eliminate errors. Luckily, you can rerun command scripts or batch files from past sessions. An advantage of this feature is that it streamlines the data analysis process. As a result, you avoid duplicating or replicating data and commands for similar projects.
Stata’s data management features give you complete control over data sets, regardless of their data types. It also links and reshapes data sets. In addition to that, it supports seamless variable declaration, editing, and management. Besides that, this feature lets users simultaneously apply statistical analysis techniques to grouped data sets and collate generated results effortlessly;most importantly, Stata’s inbuilt tools enable the processing of various data types. Examples of unique data types include categorical data, duration/survival data, multilevel data, multiple-imputation data, and survey data.
Need to generate publication-ready graphs? Do not worry! Doing so in Stata is a breeze! As a beginner, you can use its point and click GUI to create custom graphs. Users also can use Stata’s command line feature to generate publication-quality graphs. Publishing graphs via the command line entails writing and running scripts for batch graph generation, printing, publication, or export into other formats(EPS, PNG, TIF, or SVG). If you need to edit a graph, use Stata’s integrated graph editor. With it, you can customize a graphs appearance and modify elements, including annotations, axes, arrows, legends, lines, and markers.
And now let the fun begin!
Neither Stata Corp nor were commend entering your data manually into Stata. Why? Because the program requires you to always hit the Enter key after every cell edit. Entering data in this wayis a time and effort-intensive task. So, what should you do? Preform your initial data entry process using a program such as Excel. Next, import your data into Stata and cleanse it properly. However, if you are working with a small data set, you can enter data manually.
Entering data into Stata is an intuitive process. To create a new spreadsheet, simply type editin the Command window. With Stata, you can generate new variables and change their values. For instance, typing generate newvar1=1, followed by edit creates a blank column to which you can add data. Also, you can use the input command to enter data into Stata.
Most of us consider quantitative data as consisting of numeric values. But that is not always the case. Why? Sometimes you might need to work with alphanumeric data. Secondly, people treat numeric data differently. What is more, computers store data internally using different data types.
Once you begin data entry into Stata, you need to specify an appropriate data type. If you do not, Stata will assign a default data type. For internal data storage and memory management processes, the application stores data using two primary data types, namely string and numeric.
This data type stores non-numeric characters. Note that Stata assigns this data type to data imported from other applications or files. Stata imports numeric data from other apps using the string data type. Even so, the program has functions for converting string variables into numeric variables.
Stata uses five different storage types to represent numeric data internally – byte, int, float, and double as listed below:
In addition to that, Stata encodes time and data values numerically using nine encoding schemes based on the number of digits. It uses the byte for three digits, int for 4 or 5 digits, long for values with up to 10 digits, and double for 318.
Bonus Tip: Use the compress command to compress imported data to the smallest form that still retains your data. This is because Stata assigns variables more bytes than necessary when you import data from other programs or files. But how do I input data into Stata? Here are three approaches for entering data into Stata. Even so, the technique you use depends on your current data format.
Stata allows users to perform various types of regression analysis. In this guide, we focus on using Stata to conduct linear and multiple regression analysis.
Alternatively known as simple linear regression. Researchers use linear regression to predict the value of a dependent variable based on that of an independent variable. Before performing linear or multiple regression analysis of data, you should understand the various assumptions your data must meet.
This regression technique has five key assumptions,including:
Use the steps below to perform a simple linear regression of your data. Note that you can perform this test using Stata code or GUI. As a beginner, we recommend using the latter option.
Step #1: Launch Stata on your computer. Click Statistics> Linear models and related > Linear regression menu. Stata displays the regress – Linear regression dialog box.
Step # 2: Use the Dependent variable and the Independent variable drop down-boxes to define the dependent and independent variables,respectively.
Step # 3: Click the OK command button to generate the output. But how do I interpret results of linear regression analysis?
If your data satisfies the five assumptions listed above, it is time to interpret Stata’s output. Linear regression analysis output consists of four elements: an R2 value (R-squared row); an adjusted R2 value (Adj R-squared); an F value,and; the coefficients of the constant and independent variables.
Your linear regression analysis report should always include:
Consider incorporating a diagram of results in your reports. For example, you could use a scatter-plot with confidence and prediction intervals. In this way, you make it easy for others to understand your analysis.
This technique is an extension of simple linear regression. Use this approach to predict the impact of independent variables on the value of a dependent variable. With this technique, you also can determine the variance of a model and the relative contributions of each independent variable.
Here are the steps to follow to perform multiple regression in Stata:
Step #1: Repeat Step #1 and Step # 2 in the linear regression procedure. Use the regress – Linear regression dialog box to:
Step # 2:Define the independent from the Dependent variable drop-down box. Next,select the continuous independent variables from the Independent variables drop-down box.
Step # 3: Then, define a categorical independent variable from the Independent variables box. To do so, click the button with an ellipsis. The Create var list with factor or time-series variables dialog box appears displaying your continuous independent variables.
Step # 4:Leave the Factor variable option on in the Type of variable section. In the Add factor variable area, select Main effect. Now choose Default in the Base box and click the Add to var list button. Doing so adds your variable to the var list
Step # 5: Clicking the OK button returns you to the regress – Linear regression dialog box. Click the OK button to generate your computed output.
In multiple regression analyses, Stata generates a single output, the R2(coefficient of determination). But you also need to interpret Adj R-squared(adj. R2) to enhance the accuracy of your report.
The F-ratio test determine show the regression model fits your data. Its output indicates the statistical significance of the independent variables.
This coefficient indicates the variations between the dependent variable and independent when all other independent variables remain constant.
This article primarily targets beginners and others who need to learn Stata for data analysis purposes as well. When it comes to learning, you have two different approaches. The first learning pathway entails using the application interactively. In this approach, you launch Stata, load data, and execute commands. For beginners,it is the best way to explore your data, understand data analysis procedures, and validate results. It also can help you learn new concepts and Stata’s data analysis features quickly as you receive instant feedback. Even so, using Stata interactively is not essay nor reliable for learning Stata. Why? Because it is difficult replicating or modifying interactive sessions on the fly. Stata’s lack of an “Undo” command complicates your ability to master the application.
Secondly,you can work with Stata as a programming language. With this pathway, you write programs, known as files, and execute them inside Stata. Technically, one writes a do file consisting of Stata commands in a permanent file. An advantage of this pathway is that you can edit, modify, and debug source files as necessary. Do files also document commands, data manipulation processes, and results.
Regardless of the approach, you decide to use, always remember that Stata is not hard to learn.
But how long does it take to learn Stata?Generally, it is impossible to predict how long it will take you to learn Stata. Why is that so? Because of its extensive library of statistical functions and procedures. Hence, mastering every facet of this application requires tremendous investments of your time and effort.
We recommend splitting your learning process into different skill levels. In this way, you gradually develop the competency you need to exploit the package’s features. So, set realistic goals for mastering the skill level you need.
Even after learning the basics of the Stata programming language, solving real-world business or data analysis problems requires lots of experience and advanced Stata programming skills. Hence, we suggest arming yourself with a good Stata tutorial, practice exercises, and video tutorials as well.
If you are wondering which application between R and Stata is best for statistical data analysis, the wonder no more. In this section, we compare both statistical packages.This comparison evaluates R and Stata across four factors
R is difficult to learn, especially for beginners than Stata is. Besides, R is more of a programming and scripting language.
On the other hand, Stata is easy to learn from scratch. Even so, both applications maintain repositories of free learning resources and community forums. You will also find free or paid Stata and R courses, tutorials, webinars, journals, and training online.
R is open-source, which means it is free for anyone to use. As an R student, then, you might not receive technical support from its team. Getting help involves scouring its documentation, online community, journals, and manuals. As a paid application, Stata offers users and learners an online support service. Access to Stata’s online support team can significantly accelerate your learning curve.
R is free to download, install, and use. What is more, you can even customize it to suit your unique needs at no price. With Stata, you must pay a licensing fee to use its features after the trial expires. A Stata license costs $179.00 per year per user and has three categories, namely single-user,multi-user, or site license. Also, Stata has no free edition like R Studio. Besides, Stata Corp offers different versions of the application to students, education institutions, businesses, and governments.
With R, you receive updates regularly. You can always download the latest version of R Studio from the official website. Apart from that, R updates its library of packages several times a year. Regular updates ensure you remain current with developments in the data science industry. Conversely, Stata updates only once in a year. Even so, you need a licensed copy of Stata to obtain the latest version.
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