![]() None - this course will be taught in a lab. generate new variables and change existing values Ĭameron, Adrian Colin and Pravin K.Input data into STATA, basic syntax and mathematical calculations.ĭata visualization (more advanced), analysis and prediction Hypothesis testing/t-test Regression analysis Introduce the STATA working environment Create and save data set Data managementĭescriptive statistics Graphical visualization Measures of association If in doubt, please contact us before registering. The instructor will not teach these prerequisite items. Registered participants will be informed at the time of change.īy registering for this course, you confirm that you possess the knowledge required to follow it. taking into account new developments in the field, participant demands, group size, etc.). This course description may be subject to subsequent adaptations (e.g. In-person courses will consist of daily three-hour classroom sessions, featuring a range of interactive in-class activities including short lectures, peer feedback, group exercises, and presentations. To avoid online fatigue, the course employs a pedagogy that includes small-group work, short andįocused tasks, as well as troubleshooting exercises that utilise a variety of online applications to facilitate collaboration and engagement with the course content. Live classes will be held daily for two hours on a video meeting platform, allowing you to interact with both the instructor and other participants in real-time. Please check your course format before registering. The instructor will conduct live Q&A sessions and offer designated office hours for one-to-one consultations. Thus, students are encouraged to bring their own data which they want to analyze for their research.Įach course includes pre-course assignments, including readings and pre-recorded videos, as well as daily live lectures totalling at least two hours. Like Day 2, the last 90-minute session will be reserved for self-study, practice, clarifying doubts and possibly completing an assignment. Finally, we will learn how to generate beautiful regression tables that look like those in published journal articles. We will then move on with data analysis and prediction, whereby we will learn how to generate summary statistics, how to run an OLS regression, how to conduct a t-test for two sample means and how to perform in-sample predictions. Students will be introduced to more advanced visualization tools and options such as title and subtitle, axis label, marker label, color control and etc. We will learn how to create more sophisticated graphs from STATA. ![]() Statistical software packages often label the y -axis of a histogram by writing 'frequency' or 'percent' by default. However, the label for the y -axis isn't as clear. The last session (90-minutes) of the day will be reserved for students to revise and practice the commands themselves and clarify any doubts they may have.ĭay 3 begins with a continuation of data visualization. Most statistical software packages label the x -axis using the variable name you provided when you entered your data (for example, 'age' or 'weight'). Students will be introduced to various types of graphs STATA can generate, and the formats which these graphs can be exported to or saved as. This will be followed by the basics of visualizaiton. We will learn how to name and label variables and data values, how to generate new variales and replace the values of existing varaibles, how to order observations and variables, how to preserve and restore datasets and how to merge or append different datasets into one. After that, we will briefly look at the type of variables (string, float and etc) stored in STATA and learn some basic syntax and mathematical calculations.ĭay 2 will be dedicated to data manipulation, data cleaning and part of visualization. There are a number of ways to do it, depending on the format of the raw data, including copy and paste. On the first day, we will learn how to get a dataset into STATA for analysis. This course focuses on the latter as that’s the way preferred by almost all STATA experts. Participants will see that there are two ways to use STATA, either through the software’s graphical user interface or through the command lines. ![]() Here's an example where I iterately add the information I would like to plot to the matrices B and V.The guiding logic of the course is to give practical knowledge for the whole data analysis workflow:ĭay 1 will start with a general introduction to STATA. You can fully control what is being plotted using coefplot by adding the point-estimates and standard errors to their respective matrices.
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