andrew_gelman_stats andrew_gelman_stats-2011 andrew_gelman_stats-2011-1022 knowledge-graph by maker-knowledge-mining
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Introduction: Lane Kenworthy writes : The book is full of graphs that support the above claims. One thing I like about Kenworthy’s approach is that he performs a separate analysis to examine each of his hypotheses. A lot of social scientists seem to think that the ideal analysis will conclude with a big regression where each coefficient tells a story and you can address all your hypotheses by looking at which predictors and interactions have statistically significant coefficients. Really, though, I think you need a separate analysis for each causal question (see chapters 9 and 10 of my book with Jennifer, follow this link ). Kenworthy’s overall recommendation is to increase transfer payments to low-income families and to increase overall government spending on social services, and to fund this through general tax increases. What will it take for this to happen? After a review of the evidence from economic trends and opinion polls, Kenworthy writes, “Americans are potentially recepti
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1 Lane Kenworthy writes : The book is full of graphs that support the above claims. [sent-1, score-0.143]
2 One thing I like about Kenworthy’s approach is that he performs a separate analysis to examine each of his hypotheses. [sent-2, score-0.467]
3 A lot of social scientists seem to think that the ideal analysis will conclude with a big regression where each coefficient tells a story and you can address all your hypotheses by looking at which predictors and interactions have statistically significant coefficients. [sent-3, score-1.101]
4 Really, though, I think you need a separate analysis for each causal question (see chapters 9 and 10 of my book with Jennifer, follow this link ). [sent-4, score-0.558]
5 Kenworthy’s overall recommendation is to increase transfer payments to low-income families and to increase overall government spending on social services, and to fund this through general tax increases. [sent-5, score-1.506]
6 After a review of the evidence from economic trends and opinion polls, Kenworthy writes, “Americans are potentially receptive to a more generous set of social programs, but their demand for it is far from overwhelming. [sent-7, score-0.753]
7 See Kenworthy’s blog for related material and here for our paper on income inequality and partisan voting. [sent-10, score-0.349]
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Introduction: Lane Kenworthy writes : The book is full of graphs that support the above claims. One thing I like about Kenworthy’s approach is that he performs a separate analysis to examine each of his hypotheses. A lot of social scientists seem to think that the ideal analysis will conclude with a big regression where each coefficient tells a story and you can address all your hypotheses by looking at which predictors and interactions have statistically significant coefficients. Really, though, I think you need a separate analysis for each causal question (see chapters 9 and 10 of my book with Jennifer, follow this link ). Kenworthy’s overall recommendation is to increase transfer payments to low-income families and to increase overall government spending on social services, and to fund this through general tax increases. What will it take for this to happen? After a review of the evidence from economic trends and opinion polls, Kenworthy writes, “Americans are potentially recepti
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Introduction: Several people asked me for my thoughts on Richard Wilkinson and Kate Pickett’s book, “The Spirit Level: Why Greater Equality Makes Societies Stronger.” I’ve outsourced my thinking on the topic to Lane Kenworthy .
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Introduction: From my New York Times blog today, here’s an example of how contemporaneous poll results can be given exactly opposite interpretations. Recently in the New Republic, William Galston shared some recent findings from Gallup: Respondents were asked to categorize three economic objectives as extremely important, very important, somewhat important, or not important. Here’s what they said: Extremely/very important Somewhat/not important Grow and expand the economy 82 18 Increase equality of opportunity for people to get ahead 70 30 Reduce the income and wealth gap between the rich and the poor 46 54 When Gallup asked a sample of Americans in 1998 whether the gap between the rich and the poor was a problem that needed t
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Introduction: Fabio Rojas writes: In much of the social sciences outside economics, it’s very common for people to take a regression course or two in graduate school and then stop their statistical education. This creates a situation where you have a large pool of people who have some knowledge, but not a lot of knowledge. As a result, you have a pretty big gap between people like yourself, who are heavily invested in the cutting edge of applied statistics, and other folks. So here is the question: What are the major lessons about good statistical practice that “rank and file” social scientists should know? Sure, most people can recite “Correlation is not causation” or “statistical significance is not substantive significance.” But what are the other big lessons? This question comes from my own experience. I have a math degree and took regression analysis in graduate school, but I definitely do not have the level of knowledge of a statistician. I also do mixed method research, and field wor
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Introduction: Lane Kenworthy writes : The book is full of graphs that support the above claims. One thing I like about Kenworthy’s approach is that he performs a separate analysis to examine each of his hypotheses. A lot of social scientists seem to think that the ideal analysis will conclude with a big regression where each coefficient tells a story and you can address all your hypotheses by looking at which predictors and interactions have statistically significant coefficients. Really, though, I think you need a separate analysis for each causal question (see chapters 9 and 10 of my book with Jennifer, follow this link ). Kenworthy’s overall recommendation is to increase transfer payments to low-income families and to increase overall government spending on social services, and to fund this through general tax increases. What will it take for this to happen? After a review of the evidence from economic trends and opinion polls, Kenworthy writes, “Americans are potentially recepti
Introduction: Antonio Rinaldi writes: Here in Italy an “hype” topic is the “staffetta tra generazioni”, handover between generations: since unemployment rate in young people is very high, someone in the government is thinking to encourage older people to anticipate their retirement to make more jobs available for youngs. I am not an economist and I don’t want to discuss the economic plausibility of such a possible measure. However, some economists deny its success simply on the basis of a correlation and regression analysis. You can see the charts used as “evidence” here: http://www.lavoce.info/per-una-vera-staffetta-generazionale/ and here: http://noisefromamerika.org/articolo/occupazione-ricambio-intergenerazionale. The first chart shows that there is no correlation between activity rate in older people and unemployment rate in younger people. The second chart shows that there is a positive linear regression (even if the author doesn’t speak of a casual relationship) between activity rate in
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Introduction: In a discussion of workplace segregation, Philip Cohen posts some graphs that led me to a statistical question. I’ll pose my question below, but first the graphs: In a world of zero segregation of jobs by sex, the top graph above would have a spike at 50% (or, whatever the actual percentage is of women in the labor force) and, in the bottom graph, the pink and blue lines would be in the same place and would look like very steep S curves. The difference between the pink and blue lines represents segregation by job. One thing I wonder is how these graphs would change if we redefine occupation. (For example, is my occupation “mathematical scientist,” “statistician,” “teacher,” “university professor,” “statistics professor,” or “tenured statistics professor”?) Finer or coarser classification would give different results, and I wonder how this would work. This is not at all meant as a criticism of Cohen’s claims, it’s just a statistical question. I’m guessing that
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