Comments for Replicated Typo http://replicatedtypo.com Culture, its evolution and anything inbetween Thu, 17 May 2012 21:53:25 +0000 hourly 1 http://wordpress.org/?v=3.3.2 Comment on Having more children affects your basic word order by Claire http://replicatedtypo.com/having-more-children-affects-your-basic-word-order/5252.html/comment-page-1#comment-31022 Claire Thu, 17 May 2012 21:53:25 +0000 http://www.replicatedtypo.com/?p=5252#comment-31022 You've made my day with this! However, I wonder about your information structure story. Free word order languages (the languages correlated with the fewest number of children here) usually put the new/important information first, whether it's a noun phrase or verb. So your explanation should apply better to non-dominant word order languages than to SOV languages. You’ve made my day with this! However, I wonder about your information structure story. Free word order languages (the languages correlated with the fewest number of children here) usually put the new/important information first, whether it’s a noun phrase or verb. So your explanation should apply better to non-dominant word order languages than to SOV languages.

]]> Comment on Having more children affects your basic word order by John Pate http://replicatedtypo.com/having-more-children-affects-your-basic-word-order/5252.html/comment-page-1#comment-30912 John Pate Tue, 15 May 2012 07:42:37 +0000 http://www.replicatedtypo.com/?p=5252#comment-30912 sigh, I really need to proofread my comments. The second model contains the predictors of interest (and any control random effects), and takes as its response variable the residuals of the control model. sigh, I really need to proofread my comments. The second model contains the predictors of interest (and any control random effects), and takes as its response variable the residuals of the control model.

]]> Comment on Having more children affects your basic word order by John Pate http://replicatedtypo.com/having-more-children-affects-your-basic-word-order/5252.html/comment-page-1#comment-30911 John Pate Tue, 15 May 2012 07:40:04 +0000 http://www.replicatedtypo.com/?p=5252#comment-30911 It has transformed it into many binary variables. As I said before, this is a genuine ambiguity in your data, so there isn't a single straightforward response. One thing you can do is divide your predictors into control predictors (whose values you do not care about) and predictors of interest. Since you don't seem to be especially interested in the effect of religion on word order, it seems more natural to treat religion as a random effect with many levels rather than an explosion of binary variables. This is like using a hierarchical bayesian model; mixed effects regression can be viewed as the frequentist version of a hierarchical bayesian model. Another thing you can do (and this is what I did in my cog-sci paper last year http://homepages.inf.ed.ac.uk/s0930006/pate-goldwater_predictability-effects-IDS-ADS_2011.pdf) is fit two models. The first model contains *only* your control predictors. The parameters of this model will not be stable, but the predicted values will be, which means that the residuals will be. You then fit a second model containing your predictors of interest (and any control random effects). If you have a predictor of interest that is correlated with a control predictor, this approach explains as much variation as possible using the control predictor, and the predictor of interest is significant iff it can explain variation that the control predictor cannot. This is conservative (it's possible that, under the true generative story we're trying to discover, both the response variable and the control variable are influenced by the predictor of interest), but I think it's the best you can do without gathering less ambiguous data. It has transformed it into many binary variables. As I said before, this is a genuine ambiguity in your data, so there isn’t a single straightforward response. One thing you can do is divide your predictors into control predictors (whose values you do not care about) and predictors of interest. Since you don’t seem to be especially interested in the effect of religion on word order, it seems more natural to treat religion as a random effect with many levels rather than an explosion of binary variables. This is like using a hierarchical bayesian model; mixed effects regression can be viewed as the frequentist version of a hierarchical bayesian model.

Another thing you can do (and this is what I did in my cog-sci paper last year http://homepages.inf.ed.ac.uk/s0930006/pate-goldwater_predictability-effects-IDS-ADS_2011.pdf) is fit two models. The first model contains *only* your control predictors. The parameters of this model will not be stable, but the predicted values will be, which means that the residuals will be. You then fit a second model containing your predictors of interest (and any control random effects). If you have a predictor of interest that is correlated with a control predictor, this approach explains as much variation as possible using the control predictor, and the predictor of interest is significant iff it can explain variation that the control predictor cannot. This is conservative (it’s possible that, under the true generative story we’re trying to discover, both the response variable and the control variable are influenced by the predictor of interest), but I think it’s the best you can do without gathering less ambiguous data.

]]> Comment on Having more children affects your basic word order by Sean http://replicatedtypo.com/having-more-children-affects-your-basic-word-order/5252.html/comment-page-1#comment-30902 Sean Mon, 14 May 2012 16:53:51 +0000 http://www.replicatedtypo.com/?p=5252#comment-30902 Ah. The religion variable was entered as a single categorical predictor, the table is how R presents the results, so has it transformed it into many binary variables? But I take your point. How do you get around this? Could you use something like a Bayesian causal graph model? Ah. The religion variable was entered as a single categorical predictor, the table is how R presents the results, so has it transformed it into many binary variables? But I take your point. How do you get around this? Could you use something like a Bayesian causal graph model?

]]> Comment on Having more children affects your basic word order by John Pate http://replicatedtypo.com/having-more-children-affects-your-basic-word-order/5252.html/comment-page-1#comment-30901 John Pate Mon, 14 May 2012 16:42:36 +0000 http://www.replicatedtypo.com/?p=5252#comment-30901 Ack! In the first paragraph, I meant to say the first scenario concludes the second predictor is significant but not the first, the second scenario concludes the first predictor is significant but not the second, and the third scenario could come out either way. Ack! In the first paragraph, I meant to say the first scenario concludes the second predictor is significant but not the first, the second scenario concludes the first predictor is significant but not the second, and the third scenario could come out either way.

]]> Comment on Having more children affects your basic word order by John Pate http://replicatedtypo.com/having-more-children-affects-your-basic-word-order/5252.html/comment-page-1#comment-30900 John Pate Mon, 14 May 2012 16:41:14 +0000 http://www.replicatedtypo.com/?p=5252#comment-30900 interactions are potentially interesting, but not what I was pointing out. Consider a case where you have perfectly correlated predictors. You can obtain exactly the same predicted values by giving the first predictor a coefficient of 0 and giving a very high coefficient to the other, or giving the second predictor a coefficient of 0 and giving very high coefficients to the first, or giving moderate coefficients to either. In the first case, you're likely to conclude the first predictor is significant but the second is not, in the second case you're likely to conclude the second predictor is significant but the first is not, and in the third case you might conclude neither or both of them are significant. When you have highly correlated predictors, you can explain your response variable using any subset of them: it's a genuine ambiguity in your data. Your religion variables, for example, are probably highly (negatively) correlated, since someone is unlikely to be both Bahai and Evangelical Christian. If, in the true generative process we're trying to discover, Bahai religion has a strong positive effect, you could explain that by either giving Bahai a large coefficient *or* by giving Evangelical Christian a smaller coefficient. interactions are potentially interesting, but not what I was pointing out. Consider a case where you have perfectly correlated predictors. You can obtain exactly the same predicted values by giving the first predictor a coefficient of 0 and giving a very high coefficient to the other, or giving the second predictor a coefficient of 0 and giving very high coefficients to the first, or giving moderate coefficients to either. In the first case, you’re likely to conclude the first predictor is significant but the second is not, in the second case you’re likely to conclude the second predictor is significant but the first is not, and in the third case you might conclude neither or both of them are significant. When you have highly correlated predictors, you can explain your response variable using any subset of them: it’s a genuine ambiguity in your data.

Your religion variables, for example, are probably highly (negatively) correlated, since someone is unlikely to be both Bahai and Evangelical Christian. If, in the true generative process we’re trying to discover, Bahai religion has a strong positive effect, you could explain that by either giving Bahai a large coefficient *or* by giving Evangelical Christian a smaller coefficient.

]]> Comment on Having more children affects your basic word order by Sean http://replicatedtypo.com/having-more-children-affects-your-basic-word-order/5252.html/comment-page-1#comment-30899 Sean Mon, 14 May 2012 16:16:35 +0000 http://www.replicatedtypo.com/?p=5252#comment-30899 @John Pate: That's a good point, I didn't explore this too much. The point of the big stepwise regression model was to see whether the linguistic factors would usefully remain in the model. Part of the point is that we should expect colinearity, but it's a good idea to actually quantify it. I could use a better model selection process to find the optimal model that included interactions, but the whole thing would take a while to compute! Another problem is that the sub-sample of the data that each linguistic variable is tested on will be different, due to missing values in the WALS, so the sample run on the stepwise regression was only about 30% of the whole database. @John Pate: That’s a good point, I didn’t explore this too much. The point of the big stepwise regression model was to see whether the linguistic factors would usefully remain in the model. Part of the point is that we should expect colinearity, but it’s a good idea to actually quantify it. I could use a better model selection process to find the optimal model that included interactions, but the whole thing would take a while to compute! Another problem is that the sub-sample of the data that each linguistic variable is tested on will be different, due to missing values in the WALS, so the sample run on the stepwise regression was only about 30% of the whole database.

]]> Comment on Having more children affects your basic word order by John Pate http://replicatedtypo.com/having-more-children-affects-your-basic-word-order/5252.html/comment-page-1#comment-30897 John Pate Mon, 14 May 2012 15:32:38 +0000 http://www.replicatedtypo.com/?p=5252#comment-30897 That's a lot of predictors! How much collinearity exists in your model? Your parameters may not be stable. That’s a lot of predictors! How much collinearity exists in your model? Your parameters may not be stable.

]]> Comment on Spurious correlation bonanza to mark Replicated Typo 2.0 reaching 100,000 hits by Podcast on spurious correlations between social structures and linguistic structures | Replicated Typo http://replicatedtypo.com/spurious-correlation-bonanza-to-mark-replicated-typo-2-0-reaching-100000-hits/4374.html/comment-page-1#comment-30888 Podcast on spurious correlations between social structures and linguistic structures | Replicated Typo Sun, 13 May 2012 20:39:35 +0000 http://www.replicatedtypo.com/?p=4374#comment-30888 [...] me about my work on spurious correlations between social structures and linguistic structures (see my overview post here).  Christos Christodoulopoulos challenges me to find a link between the number of children a [...] [...] me about my work on spurious correlations between social structures and linguistic structures (see my overview post here).  Christos Christodoulopoulos challenges me to find a link between the number of children a [...]

]]> Comment on Having more children affects your basic word order by Sean http://replicatedtypo.com/having-more-children-affects-your-basic-word-order/5252.html/comment-page-1#comment-30887 Sean Sun, 13 May 2012 20:37:51 +0000 http://www.replicatedtypo.com/?p=5252#comment-30887 A longer version of my interview at EU:Sci is now available online: <a href="http://www.eusci.org.uk/podcasts/eusci-podcast-extra-sean-roberts-spurious-correlation" rel="nofollow">Listen here!</a> A longer version of my interview at EU:Sci is now available online:
Listen here!

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