{"id":41256,"date":"2026-06-03T23:27:41","date_gmt":"2026-06-03T21:27:41","guid":{"rendered":"https:\/\/www.graviton.at\/letterswaplibrary\/does-anything-exist-that-can-automatically-translate-variable-and-value-labels-in-a-stata-dataset\/"},"modified":"2026-06-03T23:27:41","modified_gmt":"2026-06-03T21:27:41","slug":"does-anything-exist-that-can-automatically-translate-variable-and-value-labels-in-a-stata-dataset","status":"publish","type":"post","link":"https:\/\/www.graviton.at\/letterswaplibrary\/does-anything-exist-that-can-automatically-translate-variable-and-value-labels-in-a-stata-dataset\/","title":{"rendered":"Does Anything Exist That Can Automatically Translate Variable And Value Labels In A Stata Dataset?"},"content":{"rendered":"<p><!-- SC_OFF --><\/p>\n<div class=\"md\">\n<p>I&#8217;ve been working with a cross-national dataset where all the variable labels and value labels are in a foreign language. Renaming them manually is tedious and error-prone, especially with 200+ variables.<\/p>\n<p>I know I can write a do-file to relabel everything but that still requires me to know what the foreign labels mean and manually enter English equivalents one by one.<\/p>\n<p>Is there any tool or workflow that handles this automatically? Ideally something that takes the .dta file, translates the metadata, and returns a clean English-labeled file without touching the underlying data<\/p>\n<\/div>\n<p><!-- SC_ON -->   submitted by   <a href=\"https:\/\/www.reddit.com\/user\/WordAware2689\"> \/u\/WordAware2689 <\/a> <br \/> <span><a href=\"https:\/\/www.reddit.com\/r\/stata\/comments\/1tw1q0e\/does_anything_exist_that_can_automatically\/\">[link]<\/a><\/span>   <span><a href=\"https:\/\/www.reddit.com\/r\/datasets\/comments\/1tw1swe\/does_anything_exist_that_can_automatically\/\">[comments]<\/a><\/span><\/p><div class='watch-action'><div class='watch-position align-right'><div class='action-like'><a class='lbg-style1 like-41256 jlk' href='javascript:void(0)' data-task='like' data-post_id='41256' data-nonce='72e055e984' rel='nofollow'><img class='wti-pixel' src='https:\/\/www.graviton.at\/letterswaplibrary\/wp-content\/plugins\/wti-like-post\/images\/pixel.gif' title='Like' \/><span class='lc-41256 lc'>0<\/span><\/a><\/div><\/div> <div class='status-41256 status align-right'><\/div><\/div><div class='wti-clear'><\/div>","protected":false},"excerpt":{"rendered":"<p>I&#8217;ve been working with a cross-national dataset where all the variable labels and value labels are in&#8230;<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[85],"tags":[],"class_list":["post-41256","post","type-post","status-publish","format-standard","hentry","category-datatards","wpcat-85-id"],"_links":{"self":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/posts\/41256","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/comments?post=41256"}],"version-history":[{"count":0,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/posts\/41256\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/media?parent=41256"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/categories?post=41256"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/tags?post=41256"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}