{"id":28201,"date":"2023-11-13T16:42:57","date_gmt":"2023-11-13T07:42:57","guid":{"rendered":"https:\/\/www.waseda.jp\/inst\/sgu\/?p=28201"},"modified":"2023-11-16T14:11:47","modified_gmt":"2023-11-16T05:11:47","slug":"sgu%e7%89%b9%e5%88%a5%e3%82%bb%e3%83%9f%e3%83%8a%e3%83%bc%e3%80%8cempirical-strategies-toward-external-vallidity%e3%80%8d%e9%96%8b%e5%82%ac%e3%81%ae%e3%81%8a%e7%9f%a5%e3%82%89%e3%81%9b%e3%80%80","status":"publish","type":"post","link":"https:\/\/www.waseda.jp\/inst\/sgu\/news-through-2023\/2023\/11\/13\/28201\/","title":{"rendered":"SGU\u7279\u5225\u30bb\u30df\u30ca\u30fc\u300cEmpirical Strategies Toward External Vallidity\u300d\u958b\u50ac\u306e\u304a\u77e5\u3089\u305b\u3000"},"content":{"rendered":"

\u65e9\u7a32\u7530\u5927\u5b66\u30b9\u30fc\u30d1\u30fc\u30b0\u30ed\u30fc\u30d0\u30eb\u5927\u5b66\u5275\u6210\u652f\u63f4\u4e8b\u696d \u5b9f\u8a3c\u653f\u6cbb\u7d4c\u6e08\u5b66\u62e0\u70b9\u306f\u3001<\/span>\u30b3\u30ed\u30f3\u30d3\u30a2\u5927\u5b66<\/a>\u653f\u6cbb\u5b66\u90e8Assistant Professor\u306e\u6c5f\u4e0a\u5c1a\u8f1d\u5148\u751f<\/a>\u3092\u304a\u62db\u304d\u3057\u3066\u30011<\/span>2<\/span><\/span>\u6708<\/span><\/span>7<\/span><\/span>\u65e5<\/span>\u6728<\/span>\u66dc\u65e5(<\/span>\uff13<\/span>\u9650)\u306b\u7279\u5225\u30bb\u30df\u30ca\u30fc\u3092\u958b\u50ac\u3044\u305f\u3057\u307e\u3059\u3002\u6c5f\u4e0a\u5148\u751f\u306f\u3001\u653f\u6cbb\u5b66\u65b9\u6cd5\u8ad6\u3092\u5c02\u9580\u3068\u3055\u308c\u3066\u3044\u308b\u3001\u7c73\u56fd\u653f\u6cbb\u5b66\u3067\u5927\u6d3b\u8e8d\u4e2d\u306e\u82e5\u624b\u7814\u7a76\u8005\u3067\u3044\u3089\u3063\u3057\u3083\u3044\u307e\u3059\u3002\u4ee5\u524d\u3001\u30d7\u30ea\u30f3\u30b9\u30c8\u30f3\u5927\u5b66\u3067\u30dd\u30b9\u30c9\u30af\u3092\u3055\u308c\u3066\u3044\u308b\u969b\u306b\u3082\u3001\u672c\u5b66\u3067\u30c8\u30fc\u30af\u3092\u884c\u3063\u3066\u3044\u305f\u3060\u304d\u307e\u3057\u305f\u3002\u3053\u306e\u6a5f\u4f1a\u306b\u3001\u662f\u975e\u3054\u53c2\u52a0\u304f\u3060\u3055\u3044\u3002<\/span><\/span>\u00a0<\/span><\/p>\n

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\u3010\u30bf\u30a4\u30c8\u30eb\u3011<\/span>Empirical Strategies Toward External Validity<\/span>\u00a0<\/span><\/p>\n

\u3010\u65e5\u6642\u3011<\/span>12<\/span>\u6708<\/span>7<\/span>\u65e5(\u6728<\/span>) 13<\/span>:<\/span>10-14<\/span>:<\/span>50<\/span>\u00a0<\/span><\/p>\n

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\u3010\u8a00\u8a9e\u3011 \u82f1\u8a9e<\/span>\u00a0<\/span><\/p>\n

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\u3010\u4e8b\u524d\u7533\u8fbc\u3011\u4e0d\u8981<\/span>\u00a0<\/span><\/p>\n

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\u3010\u8981\u65e8\u3011<\/span>\u00a0<\/span><\/p>\n

Over the last few decades, social scientists have developed and applied a host of statistical methods to make valid causal inferences, known as the credibility revolution. This trend has primarily focused on\u202f<\/span>internal validity<\/span><\/i>\u2014researchers aim to unbiasedly estimate causal effects within a study. However, one of the most important long-standing methodological debates is about\u202f<\/span>external validity<\/span><\/i>\u2014how scientists can generalize causal findings beyond a specific study.\u202fThis question of external validity has a long history in the social sciences, going back to at least the 1960s, and it has recently become even more essential, given that huge opportunities and challenges of accumulating causal knowledge have become evident.\u202f<\/span>\u00a0<\/span><\/p>\n

In this talk, I will discuss a unified pipeline for external validity, consisting of a framework, study design, and data analysis.\u202fFirst, I will introduce\u202fa framework of external validity\u202f<\/span>(Egami and Hartman, 2022; APSR)<\/span><\/a>\u202fthat\u202f<\/span>synthesizes diverse external validity concerns. Then,\u202fI will discuss how to design studies for external validity (<\/span>Egami and Lee, 2023<\/span><\/a>).\u202f<\/span>In particular, I examine a question of the site\/case selection, e.g., where should we run experiments, and which cases should we examine? I propose a general method to systematically select diverse study sites for external\u202fvalidity.\u202f<\/span>This new approach, which I call\u202f<\/span>synthetic purposive sampling,\u202f<\/span><\/i>combines ideas from the synthetic control method in the causal inference literature and purposive sampling in the research design\u202fliterature. It offers\u202f<\/span>a new statistical foundation to design causal studies for external validity<\/span>. I introduce our companion R package spsR<\/span> (<\/span>https:\/\/naokiegami.com\/spsR<\/span><\/a>).\u202fFinally, if time permits, I will also discuss how to assess\u202fthe robustness of causal findings to external validity bias\u202f<\/span>(Devaux and Egami, 2022)<\/span><\/a>.\u202fThis measure of\u202f<\/span>external robustness\u202f<\/span><\/i>is particularly useful when researchers analyze randomized experiments that were designed without explicit external validity consideration.\u202f<\/span>\u00a0<\/span><\/p>\n

\u00a0<\/span><\/p>\n

\u3010\u554f\u5408\u305b\u3011\u591a\u6e56\u6df3\uff08<\/span>tago@waseda.jp<\/span>\uff09\u30fb\u9ad8\u6a4b\u767e\u5408\u5b50\uff08<\/span>ytakahashi@waseda.jp<\/span>\uff09<\/span>\u00a0<\/span><\/p>\n

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