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This is a really good question. You need to think about what the two groups are. You have everyone who did not serve, but only a subset of those that did. Therefore you cannot generalize to the full population or those that did serve. Here is a GPT4 generater explanation: The correct answer is c) "The average earnings effect of serving in the army for those who did not serve." This might initially seem counterintuitive, but it's based on a fundamental concept in econometrics known as the "Local Average Treatment Effect" (LATE). In the scenario described, there is a random assignment of military service exemptions through a lottery. This randomization ensures that, on average, the groups of exempted (treatment group) and non-exempted (control group) applicants are similar in all respects except for the treatment-here, serving in the military. This similarity is crucial because it mimics the conditions of a randomized controlled trial, allowing us to infer causality from the comparison between the two groups. Now, let's break down why each answer choice is what it is: a) "The average earnings effect of serving in the army" suggests we are looking at the impact on all individuals, regardless of their inclination or circumstances that led them to apply for the exemption. This is not what the comparison would reveal since the analysis only includes individuals who applied for the exemption and were subjected to the lottery system. b) "The average earnings effect of serving in the army for those who served" might seem like a reasonable answer, but it's not the focus of this comparison. This is because the analysis isn't solely focused on those who served; it also includes those who were exempted. The aim is to understand the impact of not serving (being exempted) versus serving. c) "The average earnings effect of serving in the army for those who did not serve" is correct because the analysis effectively compares individuals who wanted to be exempted (and thus, by extension, did not want to serve). Those who win the lottery (and are exempted) serve as the treatment group, and those who lose (and thus serve) are the control group. The comparison then reveals the effect of not serving on the group that applied for exemptions but had to serve due to losing the lottery. In essence, this setup allows us to estimate the impact of military service on those who would have preferred not to serve but were compelled to do so because they did not win the exemption lottery. It's a subtle but important distinction in understanding the causal effects in this scenario.

First differencing does eliminate beta_0, but there is an error in my slides. My video on first differencing has the correct equation ua-cam.com/video/hQWSh_j3Oy0/v-deo.html

I do not understand the question. What call are you referring to?

You could use t for testing a single constraint. But in practice we use F because it can handle both single constraint and multiple constraints. A single parameter F distribution is simply a square of the corresponding t distribution.

@@mronkko thanks!! Then why in some cases we should use a wald test instead of a t test like in this video?

@@junbeombahk3668 I am not using a t test in the video. The test is a single parameter Wald test. See this for an explanation of the tests ua-cam.com/video/AbhwpFX2Xdw/v-deo.html

No, I meant that we measure the characteristic of each individual after the treatment. We cannot measure the effects of treatment. We can only measure the characteristics of interest and then use these measured characteristics to calculate an estimate for the average treatment effect.

Alpha does not overestimate reliability in the example that you mention, but you are correct that alpha can be misleading. The problem with alpha is that it does not quantify whether the variables measure one thing or multiple things. If you have a score generated from scales measuring two orthogonal factors, the scale score is almost certainly meaningless. Nevertheless, we can estimate the reliability of that scale score (think e.g. test-retest reliability). Alpha underestimates this reliability. The problem is that an acceptable alpha does not guarantee that you have a useful scale score. At minimum, you should also do a factor analysis as well.

You are welcome. It is surprising that very few people teach how to actually use the analyses in empirical research practice.

@@mronkko that's true. Most videos cover only interpretation of results or are focused on let's say one part of the analysis but no one covers the whole process in a single video, with a single dataset. Just an idea - You could maybe consider doing a workflow series focusing on how to do analysis with different combinations of explanatory/response variables? Let's say one categorical explanatory variable, 1 exp and 1 quantitative, 2 categorical exp var, and so on. And the same logic with explanatory - quantitative vs qualitative. I'm not sure if you've done it already, but it'd be so so helpful! Thanks again, keep up the good work. I wish you good luck!

You can use factor scores or take a mean of all items to get a score for G. However, make sure you understand the challenges in using scores to represent factors: I have a video on factor scores that explains this issue. Whether you take the mean of all items or the mean of two subsets and then a meanwill not make a of these means meaningful difference. This is because taking a mean of all item vs the mean of subscale means approaches are just two different ways of calculating a weighted average. In practice, indicator weights rarely make a difference as long as the items are at least moderately correlated. Thanks for the compliments! These videos are actually a part of my doctoral level course and most of the content is something that I have also presented in class..

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Thanks. I am glad that the videos are helpful.

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Thanks!

SEM does not use factor scores but estimates the relationships between latent variables without estimating latent variable values. " factor scores are passed into the structural part of an SEM" is thus not what SEM does.

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Here you go: osf.io/mjzyw I actually recorded these screencasts specifically for that paper. I will link it to the video description after it is published. (Or if it is published)

1) I do not understand the first question. SEs are calculated based on the simulated datasets. 2) You can get convergence status from e(converged). For example. run webuse census13 sem ( <- mrgrate dvcrate medage), standardized ereturn list

@@mronkkoThanks. I meant that instead of getting the standard deviation of estimates by the command summarize, we directly simulate the standard error for beta from each sample and store them. That is instead of (simulate _b) and using the summarize command to get the standard deviation of bs, we directly simulate the standard errors for each sample (simulate _b _se). Do they differ?

@@Rezayyyyyyyyy It depends on what you want to study with the Monte Carlo simulation. If you want to study the precision of estimate, you look at the SD of the estimates and ignored the SEs. If you want to study whether the SEs are unbiased, you store the SEs and compare the average SE against the SD of the estimates.

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I assume GAN refers to some kind of neural network. Imputation works regardless of the amount of missing data, under these three conditions: 1) You are doing multiple imputation and not single imputation so that you can quantify the uncertainty introduced by the imputation process. 2) The imputation model contains all features of your data that are relevant for the analysis. 3) The missingness does not depend on the missing value itself. (i.e. data are MAR or MCAR) I do not really see what neural nets would add over throughfully developed imputation model but they are likely to increase sample size requirements.

@@mronkko "Hi again Mikko, I'm tackling a unique challenge with my dataset and believe your insights could greatly help. Could you share any contact info for more brief discussion? Thanks!"

@@mohamadmatinhavaei9859 I take consulting orders through instats.org/expert/mikko--rönkkö-829.

Thanks!

Rainfall affects agricultural value, but it does not measure agricultural value. You could use rainfall as an instrumental variable. If aggricultural value is the dependent variable, then I do not see how rainfall would be useful for its measurement.

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Sorry for a delayed response. In your case, you need to clearly define what the causal effect of interest is conceptually. For example, if you are assuming that X causes M with some delay and M cause Y with some delay, you could be intersted in the total time for Y to occur and how it depends on the value of X. I would start by predicting the time to Y with different levels of X and M and then comparing. But this really depends on your research question.

No. F tests is designed to be used with nested models. This means that they 1) use the same observations and 2) the same variables, but differ in parameters.

BBEdit. Mutta toi tekstieditorin kautta käyttäminen on turha välivaihe koska nykyään nämä työkalut ymmärtää suoraan PDF:ää.

What do you mean by predictive modeling? Like neural networks?

Thank you for your reply! So I'm working on a project where I would like to predict the outcome of a certain variable based on specific patient characteristics, i have been using linear and logistic regression to check for predictive power of the patient characterisitcs i am interested in. What i know about this subject is from googling and reading about it independently, i would appreciate if you could walk me through the details. Neural networking is another technique i would like to learn about, but if I had to choose, i would like to master the classical way using regressions.

@@alikh.3225 I do not teach or do predictive analytics myself. ( I have reviewed a few prediction papers, though). For these reasons, it is unlikely that I would be posting anything about that on the channel. But I can recommend a couple of books on prediction Hastie, T., Tibshirani, R., & Friedman, J. H. (2013). The elements of statistical learning: Data mining, inference, and prediction (2nd ed. 2009. Corr. 10th printing 2013 edition). Springer. statweb.stanford.edu/~tibs/ElemStatLearn/printings/ESLII_print10.pdf James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning (Vol. 103). Springer New York. doi.org/10.1007/978-1-4614-7138-7

I see, thank you for the resources! Keep up the good work 👍🏼