https://fanghuiz.github.io/ps0700/tutorial_stata/ Recent content in Overview on Political Science Research Methods Hugo -- gohugo.io en-us &copy; Fanghui Zhao 2019 <i class="fas fa-tree" style="color: #40a990;"></i> Tue, 29 Jan 2019 00:00:00 -0500 https://fanghuiz.github.io/ps0700/tutorial_stata/2_2_data_manipulation/ Wed, 20 Mar 2019 00:00:00 +0000 https://fanghuiz.github.io/ps0700/tutorial_stata/2_2_data_manipulation/ Cloning existing variables I prefer to keep the orignal dataset untouched, so I would usually create a copy of the variables that I&rsquo;m interested in, and work with the copy. There are two ways to do this: clonevar orignal_varName clone_varName (preferred) Exact clone, including data values, labels etc. gen orignal_varName clone_varName or generate Only clones the data, not labels Let&rsquo;s try using the World Value Survey (Wave 6) data. https://fanghuiz.github.io/ps0700/tutorial_stata/2_3_data_manipulation/ Wed, 20 Mar 2019 00:00:00 +0000 https://fanghuiz.github.io/ps0700/tutorial_stata/2_3_data_manipulation/ Variable label Variable label helps us to know what the variable is about. This label will also conviently shows up as axis name if we were to draw a graph, describe happiness storage display value variable name type format label variable label --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- happiness float %9.0g We can create labels to describe what the variable is measuring using label variable var_name. label variable happiness &quot;Feelings of happiness&quot; describe happiness storage display value variable name type format label variable label --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- happiness float %9. https://fanghuiz.github.io/ps0700/tutorial_stata/2_1_data_manipulation/ Wed, 20 Mar 2019 00:00:00 +0000 https://fanghuiz.github.io/ps0700/tutorial_stata/2_1_data_manipulation/ Using recode The most frequent use of recode is to recode the numbers that represent missing values to proper &ldquo;missing value&rdquo; as understood by Stata. Very often at the coding stage, missing values (e.g. non-response, no available data) are coded as extreme numbers such as 99, -99. However, without telling Stata those numbers represent missing data, Stata will treat them as numerical values, which will create problems in analysis. So we need to recode those values as . https://fanghuiz.github.io/ps0700/tutorial_stata/3_2_eda_univariate_graph/ Tue, 19 Mar 2019 00:00:00 +0000 https://fanghuiz.github.io/ps0700/tutorial_stata/3_2_eda_univariate_graph/ Bar plot To draw a bar plot, we simply use the command graph bar var. The default setting for graph bar is to set y-axis as percent. The full command behind the scene is in fact graph bar (percent), where the percent option is omitted by default. // Default bar plot, percent graph bar, over(Cheibub4Type) We can change the default setting, and change the y-axis to frequency / count.