time invariant model - Advanced Quantitative Research...

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Advanced Quantitative Research Methodology, Lecture Notes: Case Control Studies 1 Gary King http://GKing.Harvard.Edu January 29, 2008 1 c ± Copyright 2008 Gary King, All Rights Reserved. Gary King http://GKing.Harvard.Edu () January 29, 2008 1 / 1
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Case-Control Data Collection Designs Readings: 1. King, Gary and Langche Zeng. “Logistic Regression in Rare Events Data,” Political Analysis , Vol. 9, No. 2 (Spring, 2001): Pp. 137–163. 2. King, Gary and Langche Zeng. “Explaining Rare Events in International Relations,” International Organization , Vol. 55, No. 3 (Summer, 2001): Pp. 693–715. [a less technical version of the PA article.] 3. King, Gary and Langche Zeng. 2001. “Estimating Risk and Rate Levels, Ratios, and Differences in Case-Control Studies,” Statistics in Medicine , in press. 4. Tomz, Michael; Gary King; and Langche Zeng. ReLogit: Rare Events Logistic Regression software . for Gauss and Stata. 5. Copies of all are at http://GKing.Harvard.edu . Gary King () Rare Events 2 / 1
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Rare Events Data Gary King () Rare Events 3 / 1
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Rare Events Data 1. Y is binary, with few 1s and many 0s in the population. Gary King () Rare Events 3 / 1
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Rare Events Data 1. Y is binary, with few 1s and many 0s in the population. 2. Traditional prospective data collection designs are grossly inefficient Gary King () Rare Events 3 / 1
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Rare Events Data 1. Y is binary, with few 1s and many 0s in the population. 2. Traditional prospective data collection designs are grossly inefficient (a) Tradeoff: more observations vs better variables leads to huge n ’s with poor X ’s Gary King () Rare Events 3 / 1
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Rare Events Data 1. Y is binary, with few 1s and many 0s in the population. 2. Traditional prospective data collection designs are grossly inefficient (a) Tradeoff: more observations vs better variables leads to huge n ’s with poor X ’s (b) Also called random , exogenous stratified , cohort , or random cross-sectional sampling. Gary King () Rare Events 3 / 1
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Rare Events Data 1. Y is binary, with few 1s and many 0s in the population. 2. Traditional prospective data collection designs are grossly inefficient (a) Tradeoff: more observations vs better variables leads to huge n ’s with poor X ’s (b) Also called random , exogenous stratified , cohort , or random cross-sectional sampling. (c) E.G., n = 303 , 814 nation-year dyads, 1042 at war. X variables are information-poor (e.g., contiguity, allies, lag(y), etc). Most dyads are “nearly irrelevant” (Maoz and Russett, 1993) (such as say Burkina Faso and St. Lucia). Gary King () Rare Events 3 / 1
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Rare Events Data 1. Y is binary, with few 1s and many 0s in the population. 2. Traditional prospective data collection designs are grossly inefficient (a) Tradeoff: more observations vs better variables leads to huge n ’s with poor X ’s (b) Also called random , exogenous stratified , cohort , or random cross-sectional sampling. (c) E.G., n = 303 , 814 nation-year dyads, 1042 at war. X variables are information-poor (e.g., contiguity, allies, lag(y), etc). Most dyads are “nearly irrelevant” (Maoz and Russett, 1993) (such as say Burkina Faso and St. Lucia).
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This note was uploaded on 05/12/2010 for the course APPLIED ST 2010 taught by Professor Various during the Spring '10 term at Universidad Nacional Agraria La Molina.

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time invariant model - Advanced Quantitative Research...

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