 Täällä on paljon teknikkoja, jotka voivat olla käyttäneet palvelutelijalle tai erilaisuuteen dataa, perustuttavaa esim. Kun sinä katsot, mitkä teknikkoja voitte käyttää tai mitkä teknikkoja voitte oppia, sinun pitäisi ympäristää huomioon, ja se on tullut tämän videon. Ennen kuin ympäristää kaikki nämä teknikkoja erilaisuuteen, jotka sinun pitäisi ympäristää ja ympäristää teille. Se on usein ympäristää, miten nämä teknikkoja käyttävät ja ajattelen, että se on usein käyttävä, että nämä teknikkoja käyttävät erilaisuuteen, kun ympäristää nämä teknikkoja. Ensimmäinen ympäristö on, mitä on tärkeää kontekstuaalista ja ensimmäinen on, mitä on tärkeää kontekstuaalista perustuttavaa. Kontekstuaalista voidaan ympäristää erilaisuuteen dataa, jossa se on usein erilaisuuteen, että se ei ole usein käyttävä, jossa se on usein erilaisuuteen, tai se on usein erilaisuuteen, jossa se on usein erilaisuuteen. Yksi asia, mitä on tärkeää kontekstuaalista perustuttavaa, on haluaa ympäristää dataa, haluaa ympäristää, tai haluaa asseasti tärkeää tutkia app centrifuidaan During the estimation, The most important of these If you'm Aeros about There are no clustered effects, then the outcome will be that your standards will be incorrect. Jos ajatteluaan, että kontekstuaalifekta ei ole järjestävä, niin asioista on yksityistä. Asioista on enemmän tärkeää, kuin asioista, joilla on tärkeää myös. Mutta asioista on asioista, jotka esimerkiksi voivat olla meta-analyysiöistä. Asioista on tehtävä, jolla on kolme kategoriaa. Meillä on se, että asioista on järjestävä, joka elimenevät kontekstuaalifekta. Asioista on tärkeää, että asioista on järjestävä, joka on hyvin käytävä teknistä, jos asioista on enemmän. Jos ajattelua on 20-luvun, niin ajattelua on 19 asioista, joka on järjestävä. Asioista on yksityistä, joka on yksityistä. Jos ajattelua on 300 asioista, niin asioista ei ole järjestävä. Tässä päivällä on tlaimastanäppir intensoa tasapäivän järjestävä Otas. Vesi on hieman kustannus, joka on juuri asioista järjestävä, joka ei ole lisä CreateKalvelut. Asioista on ensimmäisenä leikkoja järjestöä, joka on lopputta vanhoa, jota haluaa takaisin. Näistä asioista on, joka on järjestävä harjoittavasti, joka on järjestävä. Onneksi on niin kun iloinen tuntuu eri varjauksista, mutta en ole eri varjauksista, ja sen aiheuttaa varjauksista uudestaan iloinen tehtävä, joka on iloinen tuntuu eri varjauksista. Tämä on eri varjauksista, jotta heidät tuntuu eri varjauksista, kaikki kontekstua alkaa dataa, koska heidät kaikki ilointuissa, joilla heidät tuntuu eri varjauksista. Heidät ei ole eri varjauksista, mutta eri varjauksista, but the random effect in these models will take care of that. The third approach is that you don't model unobserved heterogeneity by adding random effects, but you adjust your estimates to take that into all your standards, to take that into account. So unobserved heterogeneity does not influence the bias or consistency of the estimates, but it influences the standards. So a useful strategy and very simple strategy to deal with the standard errors issue is to apply the cluster robot standard errors. So you can even use OLS regression and then just apply cluster robot standard errors and you're going to be fine with that analysis. The advantage of that approach is that simpler techniques are usually better because they are less likely to be misunderstood and less likely to be misused and also, particularly in the case of OLS regression versus these other techniques, the diagnostics for that model provided by most statistical software are much more developed than for other models because that is such a common model. Then we have the random effects approaches, which make the assumption that there are no contextual effects. So only within effects are allowed in the model all between cluster differences are just products of the within effect working in the clusters. And again, in these techniques, you have two different categories. What do you do for unobserved heterogeneity? You can model unobserved heterogeneity, in which case you would go with ML, maximum likelihood random effects model, which is the normal multi-level model estimated, for example, by HLM or status mixed command. And then you have the other techniques, the GLS random effects model, so that's the traditional panel data model. And you can also apply OLS or generalized estimating equations and cluster robust standard errors. Because unobserved heterogeneity does not influence the estimate, it only influences or biases the standard errors. Then the third group of techniques is the correlated or CRE, correlated random effects or CRE approaches. And in these techniques, you model the contextual effect explicitly. So the idea is that in all these techniques, we include the cluster means as controls, and those cluster means give us estimates of the contextual effect. Then we only need to decide on what to do with unobserved heterogeneity. We can either model it by applying GLS random effects estimator or maximum likelihood estimation of a random interest model, or we can just adjust the standard errors and use OLS regression or generalized estimating equations. Our paper provides data code or at least lists commands that you can apply to produce all these estimation results in state or in R. So which technique should you actually apply? This lists the techniques, but it doesn't really tell you what you should think about first when you decide which analysis technique to estimate. So we provide this flow chart in our article, and the most important thing that you need to decide first is which effects you are interested in. And quite often it's within effect, but not always. Sometimes we are interested in the contextual effect, and sometimes we are interested in the between effect, or probably not very often, and even the population average effect could be of interest in sometimes. The within effect is useful because it tells you what is the effect of an individual level variable or an individual level outcome. For example, what can firms do to improve their profitability? What can people do to improve their health? The contextual effect was what is the effect of others around you on your performance? For example, how do the intelligence of other team members influence an individual team member's performance? That kind of thing. So that really comes from your research question. You need to think hard which effect you want to know, and then you pick the right analysis technique. The first thing that you need to know is whether you want to estimate the within effect only, or the within effect and between effect or contextual effect. If you only estimate the within effect, then you must ask the question, does the random effect assumption hold? If the random effect assumption holds, then you must provide evidence in the form of the Hausmann test, Wald test, F test or likelihood ratio test, and just demonstrate that you have no reason to reject the random effect assumption. Even better if you can provide a theoretical justification for the random effect assumption, then that should be added. If that is the case, then you can use a random effect model because it's the most efficient among these modeling techniques for estimating the within effect if the random effect assumption holds. If the random effect assumption doesn't hold, then you should apply either one of the fixed effects approaches or preferably one of the correlated random effect approaches, because these correlated random effect approaches also provide you information about the contextual effect, and that might be interesting to you and perhaps some of the readers of your study. If you are interested on effects on multiple levels, then you need to decide for each variable at the time what kind of effects you want to know. And if you only want to study within effects for a particular variable, then you must test the random effect assumption for that particular variable. If it doesn't hold, if it holds, then you provide evidence and you can apply a random effect model. If there are contextual effects or between effects, then you must make a decision which one you want to study. The contextual effect is probably the more commonly of interest and if you study contextual effect, then you add cluster means to the model. If you want to know the between effect, then you are cluster means centered, the original variables, and then add the cluster means to the model and you apply a theory model. This can be done for one variable at the time, so it is possible to estimate only a within effect for some of the variables, contextual effect for some of the variables and between effect for some of the variables. Typically, most commonly you estimate the same set of effects for all variables, but you are not limited to doing that if there is a good reason to do so. So this flow chart gives you a rough idea on what kind of techniques you should consider based on your research question and our paper provides some details about these techniques as well as some examples on how they are applied to data.