The rcode to load and clean the dataset and to run the analysis is available on my github: gist.github.com/musa5237/2e41fa4ec8fe36b34d374e0879523754 Some additional updates to the video: 03:06 -- To load a csv into your environment, the code should be read.csv(file.choose(), sep= ‘,’, header=T) 03:31 -- Type.1 is the pokemon type (e.g., water, fire, etc.) not the name. I left the name out of the dataset with select(). 29:43 -- I should clarify that the mean decrease in accuracy and mean decrease in gini agreed only on which feature is most important. 41:57 -- The hash notes should read default arguments for regression not classification.

excellent tutorial i watched 3x

thank you sm!!

very informative. Can I use this MIRT for an instrument with more than 2 dimensions? I am working on validating a research instrument that is polytomous with 4 dimensions but 1 latent trait.

Thank you very much for the video. I have a question regarding the ability scale/x-axis in the plots. It is always ranging from -4 to 4. Is there a possibility to change resp. rescale it, e.g., to -3 to 3? So that all values (also the extrimity parameters and the discrimination parameters) correspond to the new scale (-3 to 3).

Thank you for your great video on DIF with ‘mirt’. I managed to do DIF analysis with your detailed instructions. I have a few questions, though. I’ll be very thankful if you answer them. 1. When we examine DIF, we are basically interested in the item difficulties across the two groups. I don’t follow when the manual says, “Determine whether it's a1 or d parameter causing DIF”. How can the slope parameter cause DIF? Isn’t DIF a property of the intercept? 2. What is the ‘RMSD_DIF’ function? How is it used and interpreted? 3. I’m working with large scale international educational data where around 50 countries take part. I need to examine country DIF. I know that in this context we don’t do pairwise comparisons, but we estimate a pooled international ICC (all countries combined) and then compare the country ICCs with the pooled international ICC. Can you please provide the codes for this? Thanks for your help in advance. Afshin

Would it be correct to say that while the distractors should have 0 in at least one of the three attributes, the key (correct option) should have mastery of all three attributes? I don't think that the key should have 0s in any attributes.

I've got nominal data (dependent variable outcome of 0/1/2], how do you run LOOCV on multinom model? Any help is appreciated.

I noticed the [method = "glm" ] was used in the LOOCV method, but what if you have a nominal dependent variable [outcome of 0/1/2], how can we run LOOCV on that? Any help is appreciated.

Very informative video. I am trying to train an RF model where I have 40+ independent variables. I am currently using k-fold CV with 3 repeats. It is taking a lot of time. How can I reduce the model training time? I am afraid if I will use bootstrap method, it may take even longer time.. 2-3days!! Any suggestions??

how about for real data how to plug in real data into the command as q matrix

Hi, Thank you for video. I loaded dataset coll from the link that you have pinned and then ran the script from identify field names to adjust units for continuous variables. After running it makes all values as NULL in coll and makes coll2 as o obs. of 6 variables. what should i do?

this is such a terrific video. thanks. I wish that you would have more similar pieces. I have 2 questions: a) I am trying to examine DIF between 4 latent classes. Can the command: sex_a<-subset(sex_a,(group ==1 | group == 2| group == 3| group == 4), select = c(A1:A11,group)) be followed by: plot(genDIF, labels = c('1', '2', '3', '4')) to give me 4 groups plotted for each item. It seems to limit me to 2 groups (as in your example). b) genDIF <- lordif(sex_a[,1:11],sex_a[,12], criterion = 'Chisqr', alpha = 0.01): Error in collapse(resp.data[, selection[i]], group, minCell) : items must have at least two valid response categories with 5 or more cases. I know that there are more than 5 cases per group. Thanks again

What an amazing explanation!!! Hats off. You even provided the R-script. Super helpful! You saved my thesis, thank you so very much.

genDIF <- lordif(IPC[,1:7],IPC[,'Gender'], criterion ='Chisqr', alpha = 0.01) Iteration: 34, Log-Lik: -5580.797, Max-Change: 0.00009 (mirt) | Iteration: 1, 5 items flagged for DIF (1,2,4,6,7) Error in `vec_equal()`: ! Can't combine `..1` <character> and `..2` <double>. Run `rlang::last_trace()` to see where the error occurred. this happened after running this code can you help?

can you help me interpret the interaction term logit(DEATH_EVENT)=−1.698+0.0385×age+0.8267×serum_creatinine−0.0006520×ejection_fraction×time

please how to do step with glmer? Is there a package?

I did the the first step (design phase: selecting covariates) but only 3 out of 14 are significant. And I want to know if it is considered balanced or not and what to do.

Do you include both the quadratic and non quadratic terms in your propensity match? For example, if my quadratic term had a lower SDM, should I remove the non quadratic term and just include the quadratic one in my final model?

What about SEM ? Can we do it in this package ????

I needed to update the rcode to load and clean the dataset to get the data ready for the analyses. Please use the updated rcode here to follow along: gist.github.com/musa5237/78a694bd6663a92a82e45e684e616724

I've watched many tutorials explaining propensity score matching on KZfaq, and I can tell that this video is the best I've ever seen. Well done, sir. You helped me a lot. ❤❤❤❤

It was super helpful! Truly appreciate your clear demonstration and explanation. I have a quick question: if there are missing data in responses, how CDM package impute these missing data? Looking forward to hearing from you!

The data doesnt work anymore!

when you type IRTpars = TRUE, simplify = TRUE in the coef() command, you will receive the actual threshold parameters b. The intercepts cannot be interpreted properly.

Thank you for the video. Please, I have a question about "factor.scores()", it doesn't work for me because I receive an error of a missing argument "f". Do you have any solutions to solve this problem ?

Thank you very much for a great video! What is the link between the MH chi square statistic and the effect size (A-C)? In my dataset the item with the largest MH-chi square statistic (629,49) has an effect size in the category "B", while another item has a MH-chi square statistic of 56.14 and is in the effect size category "C". I hope you can shed light on this mystery for me :)

Amazing videos! Just a question: I have a questionnaire with a 5likert scale. Shall I use the Rasch or GRM model?

Thank you very much for this clear explanation! I have a small question: would you use PSM to match patients to healthy controls in a cross-sectional case-controled study? I want to look at the difference in physical activity expressed in minutes per day (dependent variable) between these two groups. thank you!

Thank you for a lecture. I would like to ask some additional questions: 1. Model fit - do you recommend to use M2 statistics and global fit indices such as CFI, TLI, RMSEA etc. as a measure of model appropriateness? Or for a model comparison? 2. What measures do you recommend for the decision whether to use a bifactor or a two-dimensional model? 3. How can you in the bfactor function specify the other polytomous IRT models? For example, generalized partial credit model - does it allow the argument itemtype="gpcm" such as in the case of mirt function? Thank you very much!

"uh" (the video is good you need to train to avoiding repeating that 'word')

great video!!! what will you advise I do if I have more 'treated than control' and the matching approach to use if treatment is not randomized; take for example a state legislation

Very helpful, especially the logistic regression section. Thank you.

You have no idea how much you helped me with mirt. I used to play with other irt packages but this one is much more complex than others and often I get error messages that I don't when using other packages. Great thanks to you again, and for including bifactor model example (which I'm studying too). Rewatching your video, I noticed something. You added more d parameters than I typically found in other examples using mirt. You have d1-d4, which I assume is because of your dependent var having 5 categories? On this topic too, I would like to confirm one thing. Because in one psychological paper, I have seen the author testing for DIF using only which.par('c=a1') but not using 'd'. It wasn't explained so I wonder what's the difference? I believe 'a1' tests for only non-uniform DIF, while 'd' tests for uniform DIF, and 'a1'+'d' tests for both types of DIFs. If that's the case, would it be best to test for both types of DIFs by having 'a1'+'d'? I generally see no reason why you would test for just one type of DIF? what's your take on this?

Why do we suppress it to 0.25 when looking at the factors?

Thank you for this! Still I am unsure, which of these omegas should be reported in a methods part of a paper?

while im installing "MatchIt" it shows "There is no package called MatchIt". How to solve it?

Awesome job! Thank you for doing it, it's very helpful.

Thank you so much for this video! I hope I can get your consultation as I work on my analysis

Many thanks for the very instructive video. I am following your lectures. At the end the plot(genDIF, labels, etc etc) seems to automatically plot in a new device, opens new window, however only the 'last' plot is available, as it seems to overwrite all previous ones. I am using Windows 10 and R Studio. Having had a look at stackoverflow I don't seem to be able to find the answer. It is suggested that the plot function is intended to plot automatically in a new device, but this is not clear. Unfortunately, unless fixed this renders the whole plotting useless. Any ideas/suggestions?

Thank you for informative video. I did full matching based on your video, and ran comparisons after propensity matching. But, mean, standard deviations and p score did not change at all compared to unmatched data. How can I solve this problem?

Thank you for the great video.

for how many features logistic regression works well? I have over 300 features, deos logistic regression work or other model is suggested? thank you

This video is pretty informative. I have one question. In cov balancing plot using cobalt, we need to match both mean and variance stats? In my case mean us balanced with in the threshold but variance is not. Can i say that matching is balanced with mean balancing only?

Please validate if this link has same data, which you have posted initially, since your link is no more accessible: LINK: ed-public-download.app.cloud.gov/downloads/CollegeScorecard_Raw_Data_04262022.zip

Amazing and useful video, thank you! One question: why fstruct3 only included items from G and F1 but not F2, when the previously tested bifactor model included items from both F1 and F2?

Thank you, this was really helpful! Do you have any ideas about how I can approach this if I want to match three groups i.e. non-binary??

Thank you for this great video. Could you please share the code and data?

Very logical and lucid explanation. Thank you very much.

do you have any tutorial on how to test irt assumptions using R?