andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-1933 knowledge-graph by maker-knowledge-mining
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Introduction: Trey Causey asks , Has R-help gotten meaner over time?: I began by using Scrapy to download all the e-mails sent to R-help between April 1997 (the earliest available archive) and December 2012. . . . We each read 500 messages and coded them in the following categories: -2 Negative and unhelpful -1 Negative but helpful 0 No obviously valence or request for additional information 1 Positive or helpful 2 Not a response An example of a response coded -2 would be responses that do not answer the question, along with simply telling the user to RTFM, that they have violated the posting guidelines, or offer “?lm” as the only text when the question is about lm(). . . . Proportions of emails in each category in the test set were estimated on a monthly basis. Much to my surprise, R-help appears to be getting less mean over time! The proportion of “negative and unhelpful” messages has fallen steadily over time, from a high of 0.20 in October of 1997 to a low of 0.015 in January
sentIndex sentText sentNum sentScore
1 The proportion of “negative and unhelpful” messages has fallen steadily over time, from a high of 0. [sent-13, score-0.316]
2 Let’s return to the puzzle of falling meanness, somewhat stable helpfulness, and growing numbers of unanswered questions. [sent-19, score-0.173]
3 Anyone can free ride by reading the archives or asking a question with minimal effort. [sent-24, score-0.176]
4 It is up to the users to overcome the collective action problem and contribute to this public good. [sent-25, score-0.498]
5 As the size of the group grows, it becomes harder and harder to overcome the collective action problem . [sent-26, score-0.544]
6 Maintaining the quality of the public good requires individuals willing to sanction rule-breakers. [sent-29, score-0.413]
7 This was accomplished in early days by chiding people about the content of their posts or generally being unpleasant enough to keep free-riders away. [sent-30, score-0.15]
8 As the R user base grew, however, it became more and more costly to sanction those diluting the quality of the public good and marginal returns to sanctioning decreased. [sent-31, score-0.851]
9 Thus, we get free-riders (question askers) leading to unanswered question and a decline in sanctioning without a concomitant increase in quality of the good. [sent-32, score-0.661]
10 I just have a few thoughts: - It would make sense to check the performance of the classifier by taking a random sample of a couple hundred messages, hand-coding them, and comparing to their classifications. [sent-35, score-0.156]
11 - It might also make sense to break up the data into two or three periods and run the classifier separately on each, just in case other aspects of the messages are changing which could confuse the classification algorithm. [sent-36, score-0.447]
12 This would be the case, for example, if just one or two of the list participants are providing all the negative feedback. [sent-39, score-0.287]
13 These posters have some finite amount of time they can spend on this, so when the list gets larger they represent a smaller proportion of the whole. [sent-40, score-0.383]
14 Being mean isn’t so nice, but it can be an effective way of helping the list function better, thus serving the public good. [sent-42, score-0.592]
15 Contributors to lists can often seem snappy and downright nasty, but they’re really being altruistic. [sent-43, score-0.151]
16 They may be a bit mean because they’re tired after answering the same questions over and over for a decade, but they’re ultimately helping people out. [sent-44, score-0.346]
17 The downside of these mean commenters is sometimes they seem sooooo eager to give snappy answers to stupid questions, that I fear that they go out of their way to answer the easy questions and skip out on the toughies. [sent-45, score-0.465]
18 I’ll read a serious comment and respond only with a +1 or not even that, whereas I’ll go endlessly back and forth with trolls. [sent-47, score-0.148]
19 There’s some way in which correcting error seems so urgent , while more serious exploration can wait. [sent-48, score-0.153]
20 I hope we can work out a better system so they can be even more effective and so that they don’t feel they need to waste so much of their time on the easy questions. [sent-50, score-0.156]
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same-blog 1 1.0 1933 andrew gelman stats-2013-07-10-Please send all comments to -dev-ripley
Introduction: Trey Causey asks , Has R-help gotten meaner over time?: I began by using Scrapy to download all the e-mails sent to R-help between April 1997 (the earliest available archive) and December 2012. . . . We each read 500 messages and coded them in the following categories: -2 Negative and unhelpful -1 Negative but helpful 0 No obviously valence or request for additional information 1 Positive or helpful 2 Not a response An example of a response coded -2 would be responses that do not answer the question, along with simply telling the user to RTFM, that they have violated the posting guidelines, or offer “?lm” as the only text when the question is about lm(). . . . Proportions of emails in each category in the test set were estimated on a monthly basis. Much to my surprise, R-help appears to be getting less mean over time! The proportion of “negative and unhelpful” messages has fallen steadily over time, from a high of 0.20 in October of 1997 to a low of 0.015 in January
Introduction: Someone wrote in: We are about to conduct a voting list experiment. We came across your comment recommending that each item be removed from the list. Would greatly appreciate it if you take a few minutes to spell out your recommendation in a little more detail. In particular: (a) Why are you “uneasy” about list experiments? What would strengthen your confidence in list experiments? (b) What do you mean by “each item be removed”? As you know, there are several non-sensitive items and one sensitive item in a list experiment. Do you mean that the non-sensitive items should be removed one-by-one for the control group or are you suggesting a multiple arm design in which each arm of the experiment has one non-sensitive item removed. What would be achieved by this design? I replied: I’ve always been a bit skeptical about list experiments, partly because I worry that the absolute number of items on the list could itself affect the response. For example, someone might not want to che
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Introduction: The Difficulty with Difficult Questions Andrew’s commented during our Stan meetings that he’s observed that when a user sends an easy question to a mailing list, it gets answered right away, whereas difficult questions often languish with no answers. These difficult questions usually come from power users with real issues, whereas the simple questions are often ill-formulated or already answered in the top-level doc. So we’re arguably devoting our energy to the wrong users by adopting this strategy. Of course, this is related to Andrew’s suggestion that this whole blog be called “tl;dr” (i.e., too long, didn’t read). An Example On the Stan Users Group , we often get very complex models with a simple accompanying question such as “How can I make my model faster or mix better?” An example is this recent query , which involves a difficult multivariate multilevel model. Such questions require a lot of work on our part to answer. Model fitting is hard and often very pr
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Introduction: For background, you can start by reading my recent article, Is It Possible to Be an Ethicist Without Being Mean to People? and then a blog post, Quality over Quantity , by John Cook, who writes: At one point [Ed] Tufte spoke more generally and more personally about pursuing quality over quantity. He said most papers are not worth reading and that he learned early on to concentrate on the great papers, maybe one in 500, that are worth reading and rereading rather than trying to “keep up with the literature.” He also explained how over time he has concentrated more on showcasing excellent work than on criticizing bad work. You can see this in the progression from his first book to his latest. (Criticizing bad work is important too, but you’ll have to read his early books to find more of that. He won’t spend as much time talking about it in his course.) That reminded me of Jesse Robbins’ line: “Don’t fight stupid. You are better than that. Make more awesome.” This made me stop an
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Introduction: Trey Causey writes: Do you have suggestions as to model selection strategies akin to Bayesian model averaging for multilevel models when level-2 inputs are of substantive interest? I [Causey] have seen plenty of R packages and procedures for non-multilevel models, and tried the glmulti package but found that it did not perform well with more than a few level-2 variables. My quick answer is: with a name like that, you should really be fitting three-level models! My longer answer is: regular readers will be unsurprised to hear that I’m no fan of Bayesian model averaging . Instead I’d prefer to bite the bullet and assign an informative prior distribution on these coefficients. I don’t have a great example of such an analysis but I’m more and more thinking that this is the way to go. I don’t see the point in aiming for the intermediate goal of pruning the predictors; I’d rather have a procedure that includes prior information on the predictors and their interactions.
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Introduction: Trey Causey asks , Has R-help gotten meaner over time?: I began by using Scrapy to download all the e-mails sent to R-help between April 1997 (the earliest available archive) and December 2012. . . . We each read 500 messages and coded them in the following categories: -2 Negative and unhelpful -1 Negative but helpful 0 No obviously valence or request for additional information 1 Positive or helpful 2 Not a response An example of a response coded -2 would be responses that do not answer the question, along with simply telling the user to RTFM, that they have violated the posting guidelines, or offer “?lm” as the only text when the question is about lm(). . . . Proportions of emails in each category in the test set were estimated on a monthly basis. Much to my surprise, R-help appears to be getting less mean over time! The proportion of “negative and unhelpful” messages has fallen steadily over time, from a high of 0.20 in October of 1997 to a low of 0.015 in January
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Introduction: Please answer the above question before reading on . . . I’m curious after reading Leif Nelson’s report that, based on research with Minah Jung, approximately 42% of the people they surveyed said they bought laundry detergent on their most recent trip to the store. I’m stunned that the number is so high. 42%??? That’s almost half the time. If we bought laundry detergent half the time we went to the store, our apartment would be stacked so full with the stuff, we wouldn’t be able to enter the door. I think we buy laundry detergent . . . ummm, how often? There are 40 of those little laundry packets in the box, we do laundry once a day, sometimes twice, let’s say 10 times a week, so this means we buy detergent about once every 4 weeks. We go to the store, hmmm, about once a day, let’s say 5 times a week to put our guess on the conservative side. So, 20 trips to the store for each purchase of detergent, that’s 5% of the time. Compared to us, lots of people must (a) go to
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Introduction: For awhile I’ve been curious (see also here ) about the U-shaped relation between happiness and age (with people least happy, on average, in their forties, and happier before and after). But when I tried to demonstrate it to me intro statistics course, using the General Social Survey, I couldn’t find the famed U, or anything like it. Using pooled GSS data mixes age, period, and cohort, so I tried throwing in some cohort effects (indicators for decades) and a couple other variables, but still couldn’t find that U. So I was intrigued when I came across this paper by Paul Frijters and Tony Beatton , who write: Whilst the majority of psychologists have concluded there is not much of a relationship at all, the economic literature has unearthed a possible U-shape relationship. In this paper we [Frijters and Beatton] replicate the U-shape for the German SocioEconomic Panel (GSOEP), and we investigate several possible explanations for it. They write: What is the relationship
Introduction: This is hilarious ( link from a completely deadpan Tyler Cowen). I’d call it “unintentionally hilarious” but I’m pretty sure that rms knew this was funny when he was writing it. It’s sort of like when you write a top 10 list—it’s hard to resist getting silly and going over the top. It’s only near the end that we get to the bit about the parrots. All joking aside, the most interesting part of the email was this: I [rms] have to spend 6 to 8 hours *every day* doing my usual work, which is responding to email about the GNU Project and the Free Software Movement. I’d wondered for awhile what is it that Richard Stallman actually does, that is how does he spend his time (aside from giving lectures to promote his ideas and pay the bills). Emailing –> Blogging I too spend a lot of time on email, but a few years ago I consciously tried to shift a bunch of my email exchanges to the blog. I found that I was sending out a lot of information to an audience of one, information
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Introduction: Trey Causey asks , Has R-help gotten meaner over time?: I began by using Scrapy to download all the e-mails sent to R-help between April 1997 (the earliest available archive) and December 2012. . . . We each read 500 messages and coded them in the following categories: -2 Negative and unhelpful -1 Negative but helpful 0 No obviously valence or request for additional information 1 Positive or helpful 2 Not a response An example of a response coded -2 would be responses that do not answer the question, along with simply telling the user to RTFM, that they have violated the posting guidelines, or offer “?lm” as the only text when the question is about lm(). . . . Proportions of emails in each category in the test set were estimated on a monthly basis. Much to my surprise, R-help appears to be getting less mean over time! The proportion of “negative and unhelpful” messages has fallen steadily over time, from a high of 0.20 in October of 1997 to a low of 0.015 in January
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