Hi guys! Welcome to the Saturday vlog. I think this is going up Saturday and I'm vlogging because this is my 100th ride. I'm so excited. Hopefully I'll get some footage. I know the last vlog I got like one clip just like walking in. Hopefully I can get a little bit more footage but overall it's just like kind of hard because there's a lot of people and then you're just sitting in a basically a dark room riding a bike. So it's not that deep but hopefully I don't know. I'm just excited because like the 100th ride is kind of a big deal. At least I think it is because in some of the cycle bars they have these little plaques that you can like sign that say 100 on it. Today's going to be a good day. I'm claiming it's like get the energy you know. I am extremely tired but that's okay. Okay so I just got out of spin. I feel like we're reliving the same day because we kind of are. But today was my 100th ride. It was so good. I'll show you guys my little cycle stats here. They email you after the ride. I think I actually like I actually kind of tried on this one.  It was my 100th ride and it was Jen. So I got 6th and all these are all like my little stats if you want to like read it. Yeah no these white, the white bikes are definitely harder to like rank higher in in my opinion than the black ones. I think it's just because they're older. That's how I did. And now I'm gonna go to Starbucks because that's part of the routine. I need to, guys I'm thinking about starting more Instagram reels because what are my reels which if you follow me on Instagram which you should. I posted my mini keychain that I showed you guys in my last video and it has like almost 10,000 views. Guys like what's going on? This like it's crazy. Okay like I haven't gotten that many views on YouTube in like forever let alone Instagram. Like so what I'm thinking about doing is just doing a lot more reels and frankly like I was talking to one of my friends about Instagram like how many times do you guys visit like actual profiles of Instagram? I mean I do sometimes like of an influencer if I want to see their highlights but honestly like I don't visit someone's profile a lot enough to where I'm like oh my god she has such a cute feed anymore because reels have like taken over. So I think I'm gonna drop the feed. I think I'm gonna drop trying to make my feed look good because it's not doing anything for me. I get 30 something likes on my photos. Like my photos don't get a lot of traction unless I'm like showing my butt which one where I was showing my butt got like 40 likes. So I mean it's not like that those are even doing well either.  Like and I know not all of them are gonna be like getting a thousand views. Like some might get 500 but dude that's more than I'm getting on here and now I've made money from Instagram twice and like two months so I made my first paycheck. It was like a hundred something dollars and then a few months go by and then like I make my other another hundred something dollars and even though that's not as much as I get from YouTube it's starting to equate what I get on YouTube now because I feel like after privating my big video that like I've kind of gotten just taken out of the algorithm a little bit which I understand that like shit happens um but I think I'm gonna start focusing on Instagram a little bit more because the views and the luck there for me has been much better lately than YouTube but obviously like YouTube's my passion and like this is what I like to do so obviously like nothing's going to change here but it's just like hmm a frustrating game and I was also telling my friend too we were talking about how Instagram's just changing now it's definitely not like when Instagram first started and it's definitely not like when we uh used to take like well we still take cute pictures and stuff and it's still fun but it's not like focusing on the feed and everything is just not a thing anymore and you don't really visit people's profiles anymore which is true so why even focus on that like what I need to be focusing on is just posting reels posting a picture when I feel like it like you know what I'm saying so it doesn't matter who you're following because like it doesn't matter how many people or who you follow because so that's why I'm just been kind of like saying what the hell with Instagram and being crazy on there like well not going not being crazy but just like not caring as much and I think I'm gonna stop caring about like oh is this like picture gonna go well with the one underneath and the one over okay I'm like going on a tangent but as you can see I've like been thinking about this and I think that I'm just gonna relax about it and have fun with Instagram and not take it so serious and hopefully maybe some success will come my way okay so I think I forgot to mention this I don't know if I did or not but my friend London she has like two names but my friend London um she gave me a little Starbucks gift card for reaching like a hundred good rides so thank you if you watch this video I don't know if she watches my stuff maybe she does um because I remember telling her like oh let's not watch it like together because I always feel like that's super awkward and in my opinion like watching myself like with someone else well my family vlogs are different like because it's like a home video but like just like my daily vlogs like I'm like I don't know but um yeah so I just want to say thank you to her that was like such a surprise to me because um like sometimes she just like randomly does that like sends me little coffee things like coffee gift cards which is like really nice so I don't know it's just one of those little things that it's the little things in life you know so anyway just wanted to include that in this vlog on my way to work I always pass this Krispy Kreme donuts and it's just so tempting but I'm kind of glad that like you know I'm going what like 45 miles an hour and don't want to turn in there because I'm just dead shotting it like down to work okay so I am on my way home I'm almost home but I'm gonna stop by the grocery store because because Valerie wants some chips and I've been craving chip and dip after like eating it one night so I'm gonna get some sour cream so I can make myself some dip because the chips are like almost always like buy one get one so I'm hoping that they'll be on sale I need to get some salad stuff too I've had salad like all this week like at least for one meal which is totally fine um because I like salad like it's not a bad thing but um because Valerie's not cooking we just don't have any ideas and I know Sherry's trying to be like really strict about her diet so I'm gonna run into the store and then I'm gonna come home and we'll make the little dip together I'm thinking I'll have salad and a couple of hot dogs with um the dip that I make because it's so good it's like the french onion dip so hopefully I will get some footage in the grocery store and also too this is kind of like a little celebration for um my hundredth ride so I'm kind of using that as an excuse even though during summer like I crave like the junk food I don't know why it's like summer and winter but a winter makes sense because your your body's like saving energy because it's cold and stuff so it's I don't know I feel like we're like bears in that sense we're like going into hibernation or something and like our bodies want to put on weight but during the summer it's just all about like the chips and the hot dogs and stuff like 4th July like you guys know I don't know it's hard to describe sherry how was work it was fine but she was in lounge today I was as you can see by the outfit say hi to the vlog ralory hello she's cooking herself with some food I brought home sherry some chicken I know I can't wait to try it yeah and then I have my salad here and we got the chips going on we good didn't want to forget to vlog the shipping did the next day good morning guys it's the next day got the coffee it's literally just part of my routine and I made sure that I could get it and still like be on time at work but as you guys can see I kind of did uh light makeup look because I filmed this tiktok um it's the hi I'm dory one and I wanted to film that this morning because I wanted to go up like Saturday it's my post for fabletics because I emailed my contact at fabletics asking like hey can I post a reel instead of an in feed post because my in feed posts of photos are not doing so well um it's super annoying and like one picture that I was like I really loved this picture and I like posted it almost like at at noon you know like a good time to post and it literally got like 20 likes and I was just like oh my goodness it was just really discouraging I really do think I'm gonna push the real side um because it's just where Instagram is going unfortunately I still love taking pictures and everything like that it's one of my true passions like I love doing like little photo shoots and stuff but honestly now I think I'm gonna have to change my passion and like film dancey videos outfit videos but I already filmed videos for YouTube but I think I'm just gonna like try to incorporate both of them I don't know how I'm gonna do that because you film horizontal for YouTube but for all these other platforms you film vertical so yeah I'm just like I'm a little annoyed but I got up today showered washed my hair because it was a hair washing day and then I filmed the reel like I think this is the fastest I've ever filmed a reel but I kind of like mapped out like in my brain what I wanted it to be like so I wanted the first shot of me like saying hi I'm Dory obviously like close up and then I wanted to show my outfit and then I wanted like a full body shot um at the end saying like let's go or something whatever it says so I filmed that in probably 10 minutes which was like crazy because as you guys know I take forever to film TikToks because you had to get it just right and I really hate when my lips are out of sync with the with the audio but um like my one that I did with my moms I literally only did that one take and my lips are a little off on the second like part where she talks and it bothers me so I can't watch that one but again it was like it was last week so it was very difficult for me because I only had time to take one take and I wanted to take it when my hair and makeup was done because honestly let's let's just face it the more like put together you look online the more attention you get so I only had time because my mom was driving me that day to do that take for that part of the TikTok once so I'm like okay it'll do because the first part is like in sync so yeah as you guys can see like my uh situation my ranting and stuff hope this isn't too boring but like as we're seeing with social media it's changing drastically and yeah like I don't know what's going on man like uh but it's okay um I'm glad that you guys are here to listen to me and my thoughts as I go to work and uh but yeah the TikTok came out really cute and I'm happy with it and my lips are in sync both times little dory or whoever's talking is like my mouth matches the sound which is good and then my cuts match the beat because I had to do the self timer thing it was just like a whole whole thing anyway I'm gonna stop rambling now but I thought this would be like a good time to vlog because yeah I'm just in the car driving this is so random but I don't know if you guys can tell like I think I'm in a windstorm like just like so windy all my windshields dirty but still to continue on with the video I got this a fun package it is it is from Janet Gwynn designs and I actually just bought this myself because I needed a new airpod case because um my airpod case I lost the top and it just like really bothered me this is from the same company that my other case was from um my other case was a great Christmas present I loved it so much um but again like I lost the top just ended up getting the same one except it's in a different color the other one kind of came in this little carrying pouch but this one came like this in this little box little key chains kind of uh detached but that's okay then we have this little card it just says to be honest you're the best it was like a little QR code which fun I'm so happy to have gotten this because I really needed another case for my airpods this is what the back of the box looks like so I'm just gonna put my airpod case in this real quick I'm gonna clean them too because like it's a little dirty like my my airpods need a little cleaning so I'm gonna clean those as well excited to have this and then my other one too did not have like the indentation either like as you guys can see this one says Janet Gwynn on it my other one did not have this because I think her shop was still like a little new um but now she's like more established I think okay so I just wanted to show you guys my little case I don't know why my camera's not focusing there we go but I love this color it's very like a muted nude color um they say it's pink but I guess in different lighting it looks different but I just think it looks so cute um I had a like a mauve pink color one before I'm sure you guys seen it um if you watch my what I got for Christmas video I show it there I still have my old keychain too like I kept that but I threw away the plastic part of the old one just because I don't need it this is what it looks like I love it so much and I'm kind of glad that my airpods have a case again because I feel like the case without the the original airpod case is just too tiny and I feel like it will just like slip out and like I'll lose them if I don't have like a more substantial case on them and every time I put on the case or take off the case I feel like I'm gonna break this like lover the lid it freaks me out


Estimation of nonlinear medias and models can be a bit challenging. When we will first learn about mediation, we learn about the linear case. For the linear case, we might define mediation as a product of the coefficients from x to m from m to y, and we also learn that the full mediation, a partial mediation can be differentiated based on this coefficient beta y1 here. If it's zero or non-significant, then we have full mediation, if it's non-zero and significant, then we have partial mediation. In another video I talked about the problems of this approach. The first problem was that this is not applicable to all nonlinear models. It works for some nonlinear models, but not most nonlinear models. There's also a problem of cos-lighter-genet. So if the effect of x to m is not the same for everybody, but it varies between individuals, then this model of product of coefficients approach assumes that the variation of beta m1 is uncorrelated to the variation of beta y2. And I also talked about how we can't use any statistical model as a definition of mediation, because mediation is a causal concept and not tied to any particular model. Now let's start looking at how do we estimate nonlinear models. But before we talk about nonlinear models, it's good to remind that for linear models, the Baron and Kenny or product of coefficient approach is still valid. So this article by Mayan co-authors, which is probably one of the most cited sources on estimating nonlinear models, still recommends the product of coefficient if your model is linear. Of course, how we check assumptions and how we actually go about estimating these models has evolved since 1986 when Baron and Kenny was published, but the basic idea that you multiply two paths together is still the same. But it doesn't work for all nonlinear models. Let's take a look at how nonlinear models are underestimated. We need to understand first the counterfactual model based on nonlinear models. We need to understand first the counterfactual model based definition of mediation effect. So Imai defines mediation as difference between two outcomes only of one of which is observed. So this is the outcome of an individual which is either treated or not, and the mediation is assumed as or mediation is either observed as after mediation or it is a counterfactual outcome, and we compare it against the outcome where that individual has not received or the mediator is not affected by the treatment. That can be either observed or counterfactual depending on what we actually observed. So the general estimation principle is that we record the observed outcomes. We estimate the counterfactual for each individual. So for the cases that are treated we observe the case under treatment and the mediator under treatment we predict or we somehow estimate a counterfactual for that individual where that individual was treated but the mediator was observed as if not treated. We do the same for the untreated cases. For those cases we observe the cases untreated and mediator under the untreated condition and we estimate somehow what the mediator would be in the treatment condition for that person. So we estimate counterfactual for each individual. Then we estimate the individual of a causal effect as the difference between these outcomes and then we take the average of these individual causal effects and that gives us the average causal mediation effect. Now there is still the little complexity on how we choose the counterfactual. So one way is to hold the treatment as it is and simply write the mediation but this article by New England talks about also different options. So which counterfactual do we estimate? So we observe two outcomes. We observe either individual under the treatment and the mediator is measured under treatment for those and then we observe individuals were in the control and then a mediator is observed for the control condition for those individuals and in EMAs approach we generate these counterfactuals. So we generate counterfactual by keeping the treatment where it is either one or zero and flipping the mediator. So we have this T1, mediator zero means that individual receives the treatment and we predict what the moderator would be for that individual if that individual was not treated. For this individual under control we predict what the moderator would be had that individual been treated. So this is one way but this is not the only way and another way that New England talks about which is also at least according to them fairly common is to assume first generate the counterfactual by assigning these two treatments. So the first counterfactual for the treated case would be generated the same way but for this untreated case we would generate the counterfactual not by changing the moderator or mediator but by changing the treatment and we can do of course the opposite so that we change the mediator and adjust the treatment so that all cases are untreated but all cases observe the mediator. I would say that this the first one is probably the safest choice because it's kind of middle ground it's also easiest to implement in your statistical software but if you do this kind of analysis New England's paper that talks about this decision is worth checking out as is my explanation on how to choose between these approaches in another video. So let's take a look at what is the general estimation principle for these. So there are there are estimation strategies. There are two main ways there are more but these are the two main ways of estimating this nonlinear. Mediation effects are in my article 2010 presents one article one approach. This is called the simulation based approach so they do pit models for outcome or mediator then they simulate multiple replications of parameters then apply this kind of formula and then calculate an average that gives you a causal effect. Importantly this does not depend on on the functional form of any of these relationships. Then Wonder Wheel talks about another approach where you have a regression model this is called regression based approach so you estimate two models and then you apply this kind of equation for example for binary mediators and that gives you the natural indirect or the causal mediation effect. Let's take a look at these two approaches. They both start with estimating a model or estimating two models. So if we have this is the Baron Kenney approach and we estimate two models so we estimate a model for mediator we estimate a model for the outcome they can be any model and model for m model for y and then these approaches differ in how we calculate the mediation effect. So we have two models one for m one for y they can be nonlinear and then we have the problem of how do we calculate the mediation effect. I'll take a look at the regression based approach first because it's a bit easier to implement in your statistical software. Both of these approaches is a simulation based approach and the regression based approach are implemented in packages so you can apply these without really understanding the estimation principle or the math or how it's programmed but understanding the principle and the math and how it's programmed will make you a better user of these techniques. So let's start with the regression approach because it's a bit easier to implement. And this is from Vanderbilt's book which is a really great resource for the regression based approach. He talks about different combinations of mediator and outcome and then he goes and proves how they are, they are modeled using regression analysis. And to understand what's the meaning of all this stuff we need to define a couple of things. So we need to define first what A star and A is and A star is the baseline so if we have a treatment control kind of scenario then the baseline is the control A is the treatment and then we need to understand this is the mediation and we need to understand what's this thing here and what's this thing here and what's this multiplier here and well this is just the probability of M under the treatment so this is the probability of a positive outcome calculated from logistic regression analysis so you might not recognize that this is actually the inverse logic that you use in logistic regression but that's what it is. And then we have the probability of M in baseline so we compare the differences in probabilities. So we don't actually use the option of M at all but instead we calculate or we predict what is the probability of M for an individual under treatment what is the probability of M of that individual under control so we do these adjusted predictions we calculate two predictions for each individual one under the treatment, one under the control and then we simply multiply that with the effect of M because this is a linear model for the outcome and this theta 3a is we can ignore it for now it's simply because a van der Wiel considers the case where the mediator and the X variable treatment variable are allowed to interact so this is why there's the second term here so how do you then do this in practice so how do you move from these equations to statistical software I'll show examples using theta and my examples use linear models the reason why I'm using linear models here is that when I use this in a class I give students assignments I don't want to give them the model assignment but you will just replace these equations or these commands with logistic regression and predictions from that to calculate using nonlinear models and the linear model is easier to understand and check so we generate some data so we have a full mediation model T is the treatment, M is the mediator we have a thousand observations from this hypothetical population and we start by regressing M on T so that's the model for M then we generate observations we store what's the original value of T we store what's the original value of M and then we generate MT0 what would be the value or we predict MT0 what would be the value of the mediator for every case if they were not treated then we generate the other outcome which is under treatment so this is another adjusted prediction we adjust everyone to be under the treatment we calculate prediction and then average of those will be the average marginal prediction of under treatment and then we estimate the model for Y and we do two predictions for using Y so we replace first M using the MT0 M outcome we calculate predictions for Y then we calculate another adjusted prediction using M under the treatment condition we calculate another predictions for Y and then the difference between these is the comparisons between Y, T, MT1 and Y, T, MT0 which is EMI's definition of causal mediation effect but of course it can be defined in different ways as well so this is a simple way using simply regression prediction and you can replace this the regress command with some nonlinear function it does not need to be linear it works the same way now there is the problem of how do we get the standard errors so the standard error between these two differences is not valid from summary because they are predicted instead of observed so some gives you the differences but assuming that they are observed there are three main ways of getting the standard errors the first one is delta method this is a large sample analytical approximation so it's basically an equation that you apply to the data and it gives you a result it gives you an approximation of standard errors that works well in large samples then another approach is simulation I will not talk about simulation in detail but the basic idea is that instead of calculating these from M and these models just once you simulate multiple sets of parameters from the sampling distribution of the estimated variance covariance matrix of that model and then you do the calculation based on that I will talk more about that in another video and then there is bootstrapping and I will talk more about that bootstrapping approach in another video too how do we do it in practice so this is a data implementation that gives you the delta method standard errors so we will be using margins here and margins implements delta method which is nice because it's faster to calculate but it might not be the best in very small samples some important things that we do here first the average direct effect is calculated simply by adjusting T so it's adjusting the prediction of T at 0 and 1 because we are predicting at holding M at the observed value so what is the value of T when M does not change that is the average direct effect or natural direct effect calculating the mediation effect is a bit more complicated so we need to first make a system out of these two equations using SWEST and the reason why do we need to make them form a system out of three equations is that we need to use two different sets of coefficients in margins and this is the way to do it so we just need to treat these estimates as estimates so if we store the estimates in a matrix then margins would not know that they are estimates but it would treat them as fixed values so taking the coefficients of this regression here and put it in the matrix taking this coefficients here put it in the matrix and then using the matrices here in margins instead of the estimates the B's would produce incorrect results because they would not be considered in the standard error calculus then this coefficient legend option here in the SWEST command gives you the technical names of the coefficients which you need for writing the margins command so for example my underscore mean column D T is something that the coefficient legend will tell you to use so what is the meaning of these margins how does one come up with this kind of margins command well we need to have a custom prediction equation here so we use the expression command and we are simply writing this regression equation here so we are predicting we have the intercept then we have there are prediction using observed and not adjusted T so we are calculating adjusted predictions by adjusting the T but in the actual predictions we are holding T constant at the observed value and we are adjusting the M based on the T so adjusting T changes M which is this Y but it does not have a direct effect so we need to you predict using the option T instead of the adjusted T then we predict M using adjusted T and we just multiply then this prediction of M using the coefficient of M and that gives us the average causal mediation effect so it's not that complicated to do with status margins if your model is very complex like you have lots of covariates then typing this equation by hand will be tedious so there's another approach that you can apply and here is a different way of specifying the same thing with warmsms so if you have let's say 10 covariates in the equation typing the coefficients of those covariates times the name of the variable is not something that you want to do instead what you can do is that you start with the linear prediction using the original M and adjusted T here and M is at option values and then we subtract the effect of adjusted T and original T here and then we do the same so we add the effect of option T we add the effect of predicted M to the prediction and if you have a non-linear model then how do you do that well instead of using predict XB which gives you the linear prediction you simply apply the link function from your GLM like you would use exponential model of Poisson regression analysis and inverse logic for logistic regression analysis or the cumulative normal distribution for probit regression so limitations of the regression based approach this approach works well when the model for M or model for Y is linear and for non-linear models when both are non-linear special cases where it works and the approach that I talked about also works when there is one mediator when there are more than one mediator if you have like two competing causal paths and you want to understand which one is the one that is more important then it gets more complicated there's a really great book by Tyler J. Wonderveal about this regression approach it's like a few hundred pages of reading and it goes through all the complications on a very detailed level and also how to address those and which at the time of writing in 2015 were still unaddressed by methodological literature other cases except this one linear needs to be proven on case by case basis and this book gives you the proofs so for example if you have a logistic model for for Y logistic model for M then this approach that I just presented wouldn't work directly you can get odds ratios using math but you can't get the predictions like I showed because of if we assume that the probability of Y is 0.5 in one particular case then the inverse logic of 0.5 is not the same as average of these two logits so it works when you have linear because in linear outcome or linear mediator or linear outcome then the outcome is simply if you have two possible linear outcomes 1 and 0 then the average of 1 and 0 is the same as the average of it's the same as the predicts probability of 1 but it doesn't work if you need to use a link function for the outcome as well so this is a simple technique but it does not work in all potential cases EMA's approach on the other hand the simulation based approach is generally it works in pretty much every condition that you can think of but it's more challenging computationally so similarities between the simulation based approach is that they first start with estimating models for y and m and calculating predictions for m so this is the similarities but there are also differences between the simulation based approach and the regression based approach the differences include multiple replications whereas in the regression approach you simply predicted 1 probability for under treatment under control in the binary mediator case in this case you do multiple replications and we don't predict expectations so we don't predict the probability in a binary model like logistic and probid but instead we predict there are some actual values so we generate actual values 1's and 0's instead of predicting a probability and predictions use samples of model estimates instead of actual estimates so let's take a look at why we need to go all this trouble so why do we simulate why do we take samples of estimates and why all this stuff instead of just using the regression one well this is something that I struggle myself to understand but the key to understanding why this happens is in theorem 1 of IMAI's paper so there is some kind of integral over M so integral over M means that we are analyze the distribution of M instead of the expected value like we do in the regression based approach so we need to look at the full distribution instead of just what is the expected value and so where does this integration integral comes from it comes from something called the mediation formula which is produced by I think it was pearl in the literature on causality and the idea here in the mediation formula is that we are not looking at two predicted outcomes for each individual but we are looking at how does the distribution of the moderator mediator, how does the distribution of the mediator change between the treatment and the control those we don't analyze the differences in expectations but we analyze the differences in distributions and then we multiply each value of the distribution with the coefficient or whatever function there is, it's for why and that gives us the causal mediation effect. How this is proven it's not important but to know you just need to know that this is a very general way so instead of looking at particle value of M under treatment particle value of M under control for an individual case basis we look at the distribution of M and under treatment distribution of M under control and then what the simulation does is something called Monte Carlo integration so Monte Carlo integration is a way of calculating an integral so if you want to analyze the full distribution of a variable we need to do integration and Monte Carlo integration is one way of doing it so this is a simple example of Monte Carlo integration let's assume that we have x which is normally distributed mean zero standard deviation 1 and we want to know what is the mean of x square so we know that that's chi-square with one degree of freedom and that's worked out in any business that's book but for a demonstration let's assume we don't know what is the mean of x square and we want to do Monte Carlo integration to find out so the idea of Monte Carlo integration is that we take a random sample so we take one observation of x it's minus 0.7 then we raise it to the second power and then we get that that's about 0.3 or something and we repeat this many many times so we draw samples from the distribution of x we calculate x square and we record these x squares this dashed line here shows what is the average of these now 35 replications and we can see that when we do this kind of simulation we most of the time we get x values that are close to zero sometimes we get x values that are far from zero but they are less common because this is normal distribution and in normal distribution the most of revisions are close to zero and in about 500 replications or so we can see that the expected value or the average of these squares is here at 1 so this is the idea of Monte Carlo simulation Monte Carlo integration we are similar values from the distribution of interest we calculate the function of interest for each of these observations and then we calculate the mean the idea is that we will then have very few occurrences on the tails as we should be and most of revisions are here and this works out pretty well for many many problems so this is the reason why we take multiple simulations we need to calculate the intercroppers and this is another way of showing the integral so we calculate the integral and we calculate actual predictions of m from the distribution like we did the prediction we simulated values for x in the normal distribution in the previous slide we do the same here so this is the reason why we do simulations and why do we do actual draws from random m instead of the expected value because it allows us to do Monte Carlo in the case now there is the problem of why samples of estimates and this is a bit tricky to understand and this is explained really well in the missing data literature and for example my video on data augmentation I talk about this issue in more detail but the idea is that when we want to estimate what is the expected value of x then the linear model or whatever model we have gives us the estimate and that's our best guess but if we want to characterize or estimate how is x distributed then using a linear model or whatever model and then there are the distribution of the variable around the model prediction actually is a biased estimate of the distribution because it doesn't take the estimation error of the linear model into account so we draw these samples of estimates to take the estimation error into account or the estimation uncertainty I will not talk about that in detail here but it's basically the same thing that you do in missing data analysis when you do multiple implementation with the data augmentation algorithm so how do we actually implement this in practice so this is a quick R implementation of my simulation based approach and we generate some data so this is again full media model 1000 observations we estimate two models this time both are probit models so we generate data from probit model 2 and then we draw samples of coefficients from these models so we draw samples the mean of these random number variables are the actual coefficients and the variance covariance matrix of the random numbers is the variance covariance matrix of the estimates and then we simulate t1 and t0 so these are adjusted predictions for each case so we have n is 1000 so the simulated t's are 2000 so we have 1 and 0 for each case 2 times 1000 is 2000 then what we do is that we simulate this one line simulates 1000 replications of m for each case under treatment and under control so this is a matrix product of 2000 by 2 matrix so it is 2000 by 2 1 is the intercept intercept is something times 1 and you add that to the model so this would be called the design matrix and it's 2000 so 1000 observations and 2 adjustments t0, t1 for each case and then we have this 2 times 1000 matrix which is 2 coefficients the intercept and the coefficient of t and this produces 2000 times 10000 matrix so the columns other replications the rows are the cases under these 2 adjustments treated and untreated then we calculate for each combination for each replication for each row of of the mediator we calculate this kind of matrix product again so we have 4000 by 3 matrix so 4000 now is because we have for each observation we have 4 cases we have the cases where t was adjusted at 1 and mediator is predicted under t equals 1 t is adjusted to 0 the mediator is predicted under t is 0 and then we are also predicting to counterfactuals where we use t equals 0 and the prediction of t equals 1 and then we have t equals 0 and the prediction of t of mediator under t equals 0 so we have 4 different cases that we compare we apply to each condition and then we take take sums and we will get these causal average causal effects this runs in maybe less than a second on my computer and if we calculate using EMI software package we will get the same results it takes 2 or 3 seconds on my computer so this is in a nutshell the simulation based approach limitations of the simulation based approach limitations of the simulation based approach I wonder who advocates the recursion based approach says that this takes the main drawback of the simulation based approach is that it takes a lot of computational time if your data set is large I am not sure if that is actually true because you are not really estimating things so if you estimate a GLM model for 26 million like you have here in their example then that takes some time takes a few minutes on a modern computer but this is if you have 26 million observations and then you need to simulate 1000 observations then the matrix products that I showed and that EMI software package uses they will explode in size so this is you will run out of memory on a computer and when your computer runs out of memory then it gets slower because it needs to save results on the hard drive and that several orders of magnitude slower than just using estimates from the memory if you are working with large data in a simulation based approach then your options are basically that don't use the full data take a random sample if you have 26 million observations a random sample of 260,000 observations would be good enough for most purposes so that's one option another option is that then you can go back to matrices and just implement it yourself using R for example and just split the simulation into smaller tasks where you don't have to calculate a big matrix that fills your computer's memory so these techniques can be implemented using the more foundational or primitive tools of your statistical software I demonstrated the regression based approach using Stata I demonstrated the simulation based approach using R both can be done in both software but there are also several packages and these packages are constantly developed this article in structural as a model shows you a review of these six or seven common packages they cover Stata R, SAS and then M+, and the article talks about the differences between these so if you want to do this causal mediation analysis or non-linear mediation analysis then this article is a good source for you because the first thing that you need to do is to pick a tool trying to implement this by hand is a good idea for learning but if you want to use these then relying on your own implementation is problematic for two reasons one, it's a bit tedious to think it through instead of applying these black boxes and two, how do you know that you've implemented it correctly so verifying your implementation that it actually works right is a bit of work so I recommend that you do both so use a package and then you can verify if you understood the package what it does correctly by implementing it yourself using adjusted predictions now this part we have talked about binary treatment so how do we deal with continuous treatment so we don't have zeroes at once but we have for example individuals receive different amounts of medication it's another yes or no or we have let's say a sports example we tell people do running two times more than they would normally but the running baseline varies between different people so continuous treatment addresses these questions the case for continuous treatment is actually not different at all so all these equations as pointed out by Tinglis note in their package documentation is just same math so let's take a look at how you do it in this data so instead of predicting a T01 we predict at T from let's say from normal distribution it's from minus 2 to 2 with increments of 0.5 and what do we actually do here we use linear prediction using the original M and adjusted T this is the same thing that I did before and then we subtract the effect of adjusted T and original M we add the effect of offset T and then we add the effect of predicted M and what these models is not the difference between 0 and 1 but it is the difference from going whatever is your current baseline to a particle of value so if you move from let's say let's use running example and we have treatment would be running once 3 times 4 times 5 times a week then if you adjust your current running level whatever it is to be 5 times a week what's the average effect and we can use margins here and plot the effect so we would see that if we change everybody to be at minus 2 on the treatment without changing the mediator then the expected outcome would be here so if we move on from the current level to 1.5 for example this is the value of the outcome holding mediator and offset values and this red line is the causal mediation effect so what is the effect of changing the mediator based on different predicted levels of T considering the current T and current M Mediation testing is an active topic so if you want to do non-linear mediation you are in for some reading there are quite a few recent articles that talk about problems such as measurement error confounding like omitted variables multi-level stuff and all kinds of things so particularly the regression based approach every case needs to be proven case by case and then the simulation based approach that also has some limitations that need to be studied and they are also because this is a hot topic in some disciplines there are some good commentaries and reviews and guidelines in epidemiology particularly but also in the modeling literature so this is an active topic under research but it's just not something that is active within management so not all management researchers necessarily read all this literature


Talking about contraception, I don't want to take pills or anything that will disturb the hormones. We came across the option of spermicide vaginal suppositories, but the internet says that suppositories have side effects. But we are unaware about the intensity of these side effects. Can you help? If I'm talking about intensity of side effects, of using spermicide over a period of time, to be honest, there is no long-term side effects that a spermicide can cause within your vagina. You will know it if it is irritating you and if you're sensitive to it, if you have an allergic reaction in the beginning. Sometimes using any product for a very long period of time can cause irritation or allergy in the due course. Other than that, the spermicide actually does not affect your fertility or does not go in through your cervix and cause damage to your uterus or your ovaries or anything like that, to be honest. Are there any other non-hormonal contraception options? If you want a non-hormonal contraception, there are other non-hormonal contraception. Like for example, the copper tea is a fantastic contraception and it has copper but not hormones. And it sits in your uterus for about 10 years and is 98% efficient. Copper IUDs are also something that you can use as emergency contraceptives.  So if you want non-hormonal contraception, probably your condoms, your internal condoms, external condoms, they are probably a better contraceptive method to use if you want a non-hormonal choice or you can go with the copper IUD. I hope I've answered the question. Please let me know if I can answer anything else. I hope that helps.


When you enter the management ranks, a big part of your future success will depend on you learning how to influence others at work, how to influence your manager, and potentially how to influence the leadership team. And of course the closer you get to the leadership team, the more important it becomes to work out how to influence the leadership team, especially relating to those projects and activities that you're responsible for delivering. So today I'll be covering three communication factors that you must consider, and seven key influencing tactics to employ, and then at the end I'll explain a practical approach to increasing your influence within groups, such as leadership teams. Being able to influence is a set of skills that anyone can learn, practice and get good at. My name is Jess Coles and I've had a 25 year management career in Innocent Drinks, clusters, EY, peer consulting and many others. My roles required me to know how to influence others at work to be successful. Influencing was a critical part of the job, influencing team members, peers, managers and leaders all the way through to the leadership team itself. And if you're new to this channel, Enhance.Training provides online business courses to help you improve personal performance and that of your team and business. And if you like this video, please give it a thumbs up and subscribe. So let's talk through three important factors when communicating with managers and leaders within your business. The first communication factor to consider when thinking about how to influence your manager or how to influence the leadership team is that their time is precious. Managers are pulled in many directions and have many demands on their time. As those managers get more senior and progress into leadership roles, the competing demands on their time only increases. Therefore, when communicating to managers and leaders, you must be concise. You need to get your message across quickly and clearly. You know, don't go into loads of detail or send lengthy emails or give lengthy presentations. Put the key messages, insights or requirements at the beginning and then justify or provide more detail after delivering the key messages. Managers want to know that you have gone into the detail and know your stuff. They don't often want to go into the detail themselves. Therefore, you must know the detail and be able to demonstrate you know it without necessarily going through it. Less is often more in how you communicate with senior people. The second communication factor to consider when thinking about how to influence leaders and managers is flexing your communication style. To choose the right communication style, you need to get to know the person that you're influencing. You know, how do they like being communicated with? Do they want you to get straight to the point, provide your analysis and then wait their decision? Or would they prefer to ask you how the week has been, you know, catch up on mutual interests and then talk about business? Or do they want to know what actions you're recommending and why? We all respond to certain communication styles better than others and the situation can change which style we prefer to receive as well. So work out which communication style you're going to use for which person and in which situation. The third communication factor to consider when thinking about how to influence your manager and others is which communication method to use in person, via phone or via email. Given a choice, I would always choose the in person first, follow by the phone and then lastly email or messaging. It is much easier to influence a person when you're face to face with them. You know, you can pick up all the non-verbal clues, see how your message lands and adjust it if needed. With phone calls, you only have the tone and pace of voice to get feedback and then with emails you have next to no feedback at all. You know, plus building relationships is much easier face to face, a bit harder on the phone and very hard via email. So try to influence others in person as much as you can. It's a lot easier. Now I'm going to talk you through seven key influencing tactics that marketers, salespeople and business managers and leaders use day in, day out because they work. The first tactic to use when thinking about how to influence your manager is to put yourself in their shoes. You know, what are their goals, ambitions, interests and work objectives? What do they need to achieve to look good, get promoted and get their annual bonus? Think about what you want to achieve and how you can position your project, activity or request with your manager or the leadership team so that you are helping them achieve their goals and objectives. Explaining how you're going to be helping them achieve their goals is going to make your proposal more interesting to them. And because you're highlighting the personal incentives they have, it's going to make getting their help or sign off that much easier as a result. So put yourself in the other person's shoes and work out what they want. How to influence others at work means talking in their terms, not yours. The second tactic to use when thinking about how to influence your manager is to build rapport. Building relationships is equally important when thinking about how to influence the leadership team. We all like to help and support people we like on a personal level. It's hardwired into human social history and skills. So build personal connections. Find out what they like to do, what hobbies they have, what sports team or charity they support, and get the other person talking about themselves and listen. You also need to deliver in your day job. Performing above average, being a contributor, a team player, a problem solver, etc. All help to establish your credibility and increase your influence of others. Likeability and professional competence go a long way to building trust in the workplace. When someone trusts you, you are able to significantly increase your influence with that person. The third tactic to use when thinking about how to influence others is to give before you take. This is often referred to as reciprocity. Again, all cultures are predisposed to help those that have helped us first. So look out for opportunities where you can help others. When other staff members, managers and leaders ask for help, go out of your way to ensure that you deliver what they need quickly. And do this consistently and you become a go-to person with a reputation for helping and delivering. And when you need to influence, ask for favours or get support. You are a lot more likely to get what you want. The fourth tactic to use when thinking about how to influence your manager or how to influence the leadership team is to use data to influence decisions, back up your argument or suggested course of action. Everyone has an opinion. So unfortunately, opinions don't count for a lot, unless you have supporting evidence to back up why you have the opinion you do. This evidence can take several forms and a key one is data. A simple example would be, I think advert A is better than advert B and the higher view rates, higher click-through rates and higher conversion rates all confirm this. This statement is so much more persuasive than, I know advert A is better, trust me, or words of that effect. So take the time to gather the data, analyse it, provide insights and use the data to influence and persuade others to take a particular course of action aligned to what the data is showing. The fifth tactic to use when thinking about how to influence your manager or how to influence others at work is to use third-party authority. Your articles from well-known publications such as Forbes or the Harvard Business Review or YouTube videos from well-known and respected public speakers are great ways to build greater influence for what you want to achieve. Use the third-party information as the centerpiece of, rather than an addition to, your argument. For instance, if an HBR article is explaining why moving away from owned warehouses to using third-party logistics is more cost-effective and provides greater flexibility, then use, refer and show this article and explain why the benefits in the article are relevant to your business and then present your proposal. Respected third-party authorities are seen as experts. They carry weight and will increase your influence when used correctly. A great example of how to influence the leadership team, for example. The sixth tactic to use when thinking about how to influence your manager or how to influence others at work is to use social proof. And using social proof is effectively saying that we should be following the crowd. It is a powerful influencer when used in the right way and in the right circumstances. An example would be to find out what your competitors are doing and if, say, 50% plus of them have already made a specific change that you would like to get signed off for, document your findings and evidence and then use this to influence your manager and the leadership team. Your message might effectively be, we need to do this or we'll be left behind by our competitors. So use what others are doing to increase your influence. The seventh tactic to use when thinking about how to influence your manager or how to influence the leadership team is to use urgency or scarcity. Both tap into the fear of missing out. There are usually time constraints to take advantage of business opportunities or to avoid or reduce risks. Use these constraints to influence getting signed off and taking action sooner rather than later. And resource constraints are also another form of scarcity that every business has to deal with. Resources include people, skills, suppliers, capacity, raw materials, cash, funding, etc. So how can you use these constraints to influence the course of action that you're proposing? So there you have seven key tactics to increase your influence with your manager, the leadership team and anywhere else that you work with. And like all skills, you get better at using all of these with practice. Growing your influence within a business should make it easier for you to be more effective, deliver more and speed up your promotions. Influencing skills are vital for any management or leadership position. And finally, as mentioned at the start, an effective tactic to influence groups is to speak to each person in the group individually first before meeting with a combined group. By meeting each person first, you get to firstly, influence their thinking. Second, answer the questions they have in private. Third, incorporate any feedback or suggestions that they have into your proposals. Fourth, give them a level of data or information that they need to fully understand the benefits of your proposal. Fifth, understand and counter any objections that they might have. And of course, lastly, win support for your proposal. So when you go into the meeting to get a decision, to get sign off, to get resource allocations or whatever you need, you've gone through dry runs with each attendee and hopefully have already won them over before the meeting starts. This approach takes more time up front, but I promise you that you will save that investment many times over by avoiding group arguments and decision delays, refusals, requests for more information, et cetera. So for those important decisions and proposals, approach each individual from the group before approaching the group as a whole. So in summary, they have three communication factors, seven influencing tactics and a way to build an influence support within groups. Developing influencing skills are a key skill group for leadership positions and extremely useful for management positions as well. Keep trying to improve your influencing skills. Your career will thank you for it. And do visit us at enhance.training and take a look at the business courses and resources available to you to help improve your performance. Thanks very much for watching and I look forward to speaking to you again soon.


Happy holidays show y'all this dry erase monthly Calendar that I got from Wayfair just a couple of days ago. This will help me to stay more Organized for a family household stuff and the way I did this my handwriting is horrible The way I did this is that each person is color coordinated. My husband is green or turquoise JB's blue and I am purple Yeah, this is just a quick glance and of course I have my planners. I have these sticky notes for extra stuff This is just a look at the calendar real quick Like when we're leaving out the door or just on a daily basis and then of course I still have My happy planner here That I've been using this is my layout. Isn't this cute my layout for next week. Yeah, I gotta say organized girl It's been creasing my foundation because I haven't been blending my makeup in properly because I've been in a hairy I Am go we're going to this concert. We didn't go take pictures with Santa cuz girl. I'm like what for? and This mom is texting me asked me if we got tickets for the show I'm like, yeah, and I'm wondering if there's something wrong girl Don't make me don't make me get ready and we get up there and there's something wrong. So um, I Apply somebody must see some to my face Just because there's a glitter, you know what and I'm gonna be using this Touch and soil soul touch and soul.  Let me act as poor primer. It's okay now. Um, I Can already see how this is gonna work Okay, when it comes to I don't even need to do this to be quiet on this. I don't cuz I'd every Yeah, I'll be doing the most. I need to take advice from Bobby Brown Bobby Brown the makeup orders not the singer girl She has impeccable makeup. You can afford it, baby I could only afford one time. I bid on a makeup palette Do you? Child, I don't know what she's talking about. This woman is higher energy. Yeah tank. It's on hold on tank is on hold on This is one of my favorite songs by Tang So here we go again JB you need to stop I Gotta do my makeup Yeah, take this sex loving pain album girl Make sure you got your yo, what are your depot shot your ortho trucker your low your low less trim fee Your manero your yes, man.   Anyway, y'all Y'all know how I do Chitchat, and if I ain't this ain't no makeup tutorial. I'm just doing my makeup, you know Um, it's still a little chilly out here. So I'm gonna wear tights. I'm wearing this Yeah, all the sweater dresses I've been wearing have been very affordable. I mean Goodwill Ross the sweater dress. I'm gonna show y'all and I'm wearing tonight or today. I got from fries grocery grocery Okay, I Was trying to find a cute scarf to jazz it up. It's just a huge oversized sweater all of toned. It's six bucks If we do go to church tomorrow, which we probably won't cuz I am I have been ridiculously tired in the morning time I Need something under my eyes cuz they're so dark I'll fix that See that kind of fixed it. I mean I'm gonna do a little bit more. Hold on y'all. What did I do with it? I Showed y'all they're dressing the last vlog. You don't need to see it.  Well my husband I tend to eat the same food JB. It's different. We all eat different snacks I'm trying a JB. Bye You snow you Child I don't even need any foundation on look at look at knowledge goes This oh y'all done. Are y'all ready for Christmas? We'll only a few days away like I told y'all order all of JB stuff on Friday via Amazon You know pencils you stop putting stuff under this door didn't JB For makeup, I'm not sure but we're gonna be going in between different palettes I will be trying this revelant photo ready art because this right here matches Perfectly. I have a good eye y'all That matches my dress perfectly this right here does so What we talking about? Oh, yeah ready for Christmas? I am got everyone's gift Got all the big stuff. I'm gonna be baking. I'm gonna be making She still text me y'all um Y'all this lady is a diehard Prince fan like when I tell you Lisa When she pulls up her phone every now and then she has screensavers of Prince. I mean diehard I think she's from his home state too. She saw him in concert Like a gazillion times. She was a part of the fan club. She meets other fans. Yeah, she's that type of fan She goes on trips with other Prince fans.  You ain't got to tell me. I believe you I know they do But anyway back to Christmas. I've got all the Christmas stuff y'all All right You know how I do acting the flow with this makeup My hair is basically done What else is going on y'all Look, I finished a happy place on Netflix and it was good and I hate that it's not gonna be renewed for a season I really like that show y'all So now I'm not watching anything really. I can't get to true detective because I can't get to my um, I can't get to Show box on my Android. I don't want you in here because you don't know how to behave The art of ignoring I have perfected it All right, y'all, let me see what this hair looks like towards the back now. Yeah, you can see that one You couldn't see it at first. Okay, let me put a Barbie pin right there It's an oversized sweater dress. It's nothing fancy the earrings to go with it I mean get oversized oversized some black boots Put it over it Bring it together very whatever. All right y'all Chia Well, hey y'all just make this real quick I could not film inside of the theater because it was like there's no filming and filming I'm a loud. You can't take any pictures. I'm like cool. So we had a good time. It was sweet It was like a low-budget cute little aunt thing for the community out here up in Anthem So it was really cute. It was kids. So And then JP and I had lunch with her family afterwards. They're really sweethearts. They're really cool people Lisa is cool Out of everybody I have met here. She is one of the coolest people They have the pay stocks Sorry, they have their own business to where they stage houses like million-dollar houses Hey Hey, snap your fingers. Do you still you could do it all by your snappy fingers, JB snappy snappy snappy fingers, JB snappy You don't have to literally snap your finger. He's so cute


Welcome to this video vlog about the process of the IRB. While the previous vlog talked about, well, why is the IRB there? Now we are going more into how to submit a proposal to the IRB. Now, as I mentioned, we are not the Spanish acquisition. We are trying to make the process smooth. And this is also true for submitting a research proposal to the IRB. If you want to submit a research proposal, you first have to think, well, should I do IRB? Which people should do IRB? Well, all faculty, PhD researchers, and research master students that are planning to do research with data related to living human persons, they require IRB review. In a later phase, the master thesis proposals will be added. So important already is this research status and also the aspect of approaching human beings in your research. What do we check? Well, as mentioned, we check the quality checks of participants' privacy data storage consents. We do not check the contribution of your research. We want to have a description of your research to understand, well, what are participants doing and what harm do we do to participants in terms of privacy or what are they protected? That's why we require a description of your research, but we don't evaluate the contribution. Now, what are the ways to see whether you need IRB approval? Well, you can go to this website of the IRB where you can follow the flow charts. And depending on the outcome, you know whether you need IRB approval, yes or no. Most of the time, if you go through these IRB charts, there are two major outcomes. And that's important that I want to stress today in this video. You either have the standardized research protocol or you might have the full review required for your proposal. Now, what do we have? These standardized research protocol are really the way to smooth the research process. These standardized research protocols cover about 90% of the research that we do at Tyson. And so they can get a quick approval. It's important to stress that it all relates to people doing research with human subjects. For example, if you use databases from the Tilburg approved database list and they contain information on CEO compensation, then you're not directly approaching people. And so you might not have to have IRB review. But if you do research with human subjects, the two major outcomes are actually the first category, the standardized research protocol. 90% of the research falls under this. Or full review if you do a little bit more extra procedures that don't follow fall under standardized research. What is standardized research? Well, standardized research is research that we all know in certain departments. For example, SRP-1 are the incentivized experiments. The experiments that typically happen in Tilburg University in the lab or on prolific or M-Turk or other ways of where human subjects can be approached to conduct your research. As long as this research is relatively standard, you can submit under standardized research protocol one. Standardized research protocol two are the studies on judgment and decision-making. Often marketing studies fall under this category. How do you react to an advertisement survey research? These kind of things fall under the idea of SRP-2. SRP-3, we also know that people in our department do qualitative interviews, case studies. Well, they fall under SRP-3 if you are doing standardized recordings of participants. Then you have this SRP-3 that is important for your case study research or interviews. The advantage of the standardized research protocols is that you only have to do limited information provision.  So here it's quite easy. You have to fill out only two aspects of our full form. Why? Well, we already know that people are protected because you fall under the standardized way of doing things. So privacy issues, probably you follow that protocol and it's up to the researcher to declare, yes, I follow that protocol. What is an example here? For example, experiments in center lab or on a crowdsourcing platform. Participants make choices that can determine their money. The choices are fully anonymous and there is no deception, little risk and everything included in the instructions is real. Then you have SRP-1. Why is this standardized?  The data is not sensitive. There is minimal risk. It doesn't involve deception. There is anonymity guaranteed. People participate on a voluntary basis. As long as you have the feeling while all these things that are in my research comply with what is actually here in the bullet points, you can simply go for SRP-1 and you don't need to complete the full form but you only complete the one part, one and part three of the submission form. Now here is another thing, a natural field experiment. An experiment conducted in real life setting in which participants are unaware that they are part of the experiment. Now, if I go back to the standardized research protocol, I see that you have to provide informed consent to participants. Then this research, I cannot ask informed consent. So here we think there might be more risk to participants. And for this proposal, instead of going for a standardized research protocol, you might need to have a full review. Other examples of full review, you might, for example, want to test whether people are stressed. And because of that stress intervention, you got a saliva. Well, probably that's a full review too. Or you do a topic about very sensitive issues like suicide kind of IDs or discrimination. These kind of IDs probably involve more intrusive questions and this might not fall under the standardized research protocol. But the good news is that if we look at TISM, a lot of these proposals fall under the standardized research protocol. This was 2020-2021. And in 2021, we did not have SRP-3 yet fully operationalized. And so a lot of these interviews went for full review, which are now already in a category called standardized research protocol. So the majority are actually exempt via the standardized research and how much time do they take? Well, five-day average we have now, 10 working days, that's the aim, but we are faster than the aim. The full review, the limited proposals that go for full review, well, they are 15 working days, but we are here on a 10-day average. So it's important to know that the majority goes via these SRPs if you do research with human subjects. And this is expected to increase because we have a third one on qualitative research and interviews. So this is how we try to make the process quite smooth. We try to guide you and what do we do? Well, not only guide you, but first of all, make the process simple through these SRPs. And second, we also try to give fast turnaround time so that you can go on with your research. If you have more questions, I suggest you visit our website again where you also see more details about how these SRP works because if you do an interview, it's best to also read the bullet points regarding the two SRP three to see, well, does my standardized interview fall under the standardized research? Or do I do more than the standard which might require full review? So this is in a nutshell the procedure. I wish you good luck with your research. And if you have questions, do not hesitate to visit the website or contact one of us.


Greetings, folks, and welcome. It is time for Mack Geekabb. And listener Ari brings us our quick tip of the week with three finger drag. Says it's an accessibility feature that he turns on immediately when he sets up his laptop. And the reason is clicking with one finger and awkwardly scrabbling with another to move things is no bueno. However, you go into system settings or system preferences, if you're not yet on Ventura, click accessibility, click pointer control, or mouse and trackpad, if you're not on Ventura, click on the trackpad options button, and then turn on use trackpad for dragging or enable dragging. From the pop-up menu, choose three finger drag as the dragging style. I tried this, it is amazing because you just put three fingers down and now you can drag a window.