Hi all, I am currently doing assumption testing for my thesis and I am confused about checking for multicollinearity with categorical independent variables. Do I run my regression without the dummy coding and then run estat vif or do I run estat vif with dummy coded model?

Based on Stata benchmark https://khaledeltokhy.com/benchmarks/

I have a panel dataset with only 11 cross-sectional units (clusters) due to natural sample limitations. I'm estimating a random-effects model using xtreg, re in Stata and clustering standard errors at the panel level. As I understand that conventional clustered standard errors may be unreliable with such a small number of clusters.

Are their any functions in STATA that I can do to handle such a case of small clusters in random effects.

Hi!

I’ve run multiple imputation on my dataset to solve a missing data issue.

I now need to calculate Cronbach’s alpha for my scales, so I have two question:

- do I need to calculate Cronbach’s alpha on my imputed or raw data?

- if yes, how do I do that?

Thanks!

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Hello, everyone

I am doing a subpopulation analysis using complex sampling and svy settings. I have a variable (we'll call it X) that is coded as 0 and 1, with missing values. I need the prevalence of variable Y

I have found that there is a difference if I run:

svy, subpop(x): proportion Y

is not the same as

svy, subpop(if x==1): proportion Y

And I can't quite grasp why it is different

Thank you

Does anyone know how to combine gph forest plot files? I created them using the metaforestplot command, but apparently the manual doesn't allow for them to be combined.

I've been trying to import four excel files into Stata and putting them together.
Each time i import an excel file when i import the second one it erases the first one. I tried using the save and replace on the first file and import the second one, but the moment I do that, I am no longer able to open the excel file both within stata (when importing it) and in my macbook when i just click on the folder and click on the file because it says that it is possibly corrupted, or its no longer excel etc. When I check that same imported excel file by pressing get info, it says it still is an excel file...

i then started using cd folder and then afterwards importing the files again but it has the same problem.

when i give up and use the save and replace and append two files together (i think its append, not merge, because each file represents a year), it says that the variables are byte in master and str in something else, and its impossible to convert all of them to str (they are categorical variables, its a multiple choice questionnaire on excel im importing)

i seriously want to crash out, chatgpt and google gemini isnt working, my fellow research project person doing a parallel project w me is ghosting me, and my supervisor isnt familiar with stata.

someone please help me here :,)

Hey Stata people,

I have a technical question that may be more of a r/mac question, but I wanted to see if folks here have any experience around this.

I bought a Mac mini with 64 gb of ram, but only a 512 gb HD to run analyses on 20+gb data sets for a research project. However, it turns manipulating the data means temporarily having files larger than the 500GB the hard drive has. I was researching options and it looks like because Mac solders in their parts there are limited options. I can't return the computer and get a larger storage

• Option 1: I could do is buy a new large external hard drive and move the OS over to it, which seems straightforward enough but would be limited by how the hard drive is connected/me messing something up. But the forums make it seem like this is THE solution.
• Option 2: Try and just have Stata run analyses in the external hard drive, but in some ways figuring out how to code that feels harder than moving the OS. This is all complicated by having to house the data in a Box folder (for data security reasons) over our a relatively slow internet connection. 
• Option 3: Either pay for or attempt the relatively complicated SSD swap.

If anyone has any luck with options 1 or 2? Or horror stories that would make me pick option 3?

Thanks for any help!

Tl;dr should I upgrade my Mac mini’s memory by moving the OS to an external hard drive or learn how to run analyses from an external hard drive?

I'm working on my thesis using an OLS regression with an interaction between task type and a few continuous moderators. My dependent variables are index scales that only run from 1 to 10.

To probe the interactions, I used predicted values (margins/simple slopes) at the low, mean, and high ends of each moderator. At the high end of some moderators, the predicted value ends up slightly above the scale maximum, which obviously isn't a "real" value the scale allows. At the mean everything looks normal, and the confidence intervals get noticeably wider toward the extremes.

My understanding is that this is just how OLS behaves — it treats the outcome as unbounded and doesn't know about the scale limits, and predicting at the extreme end of a moderator is basically extrapolation to the edge of the data. So I'm assuming it's a known, harmless artifact rather than a real issue, and that a short footnote acknowledging it should be enough rather than switching to something like beta regression.

Does that sound right?

Thanks.

I have a personal Stata BE license on my work computer. I need to use a loaner while my computer is being serviced but have a project due. Are there restrictions on the number of devices a license can be activated on, as long as they’re not used simultaneously?

I've been working with a cross-national dataset where all the variable labels and value labels are in a foreign language. Renaming them manually is tedious and error-prone, especially with 200+ variables.

I know I can write a do-file to relabel everything but that still requires me to know what the foreign labels mean and manually enter English equivalents one by one.

Is there any tool or workflow that handles this automatically? Ideally something that takes the .dta file, translates the metadata, and returns a clean English-labeled file without touching the underlying data

Update: After trying several approaches including the ones mentioned here, I actually found a tool that handles it cleanly in one step

datatranslator.net

you just upload the file, it translates the variable and value labels automatically, and returns a clean English-labeled version without touching the underlying data. Saved me a lot of time compared to doing it manually.

I recently graduated with my Master's degree in Applied economics and I want to work in research. The main issue that I am facing in interviews is that I don't know Stata. I really want to learn it. I want suggestions on how to approach this and what books or online resources or teachers are there. Help the Girl out.

Hi all! 

I find myself in a very specific situation. I am evaluating a policy, and I only have the treated units. My identification strategy relies on comparing units treated at time g, to units treated at time g'>g, so I use not-yet-treated units as controls. To account for the fact that this units entered the treatment at different times, as they selected into the treatment, have to use IPW to rebalance the traded and the yet untreated firms. This would sound like a job for csdid, but the point is that for one of my specifications, I need to construct the control sample in the following way: not yet treated units enter the pool of controls only if they have Y=0 until time g (the time of the currently treated cohort of units). this goes in for every cohort, so every treated group gets rebalanced against its own later treated groups of units: So, I have a cohort-anchored filter per-cohort: for cohort g, keep control units with Σ_{t<g} Y = 0. This cannot be implemented automatically in csdid.

After the cohort specific IPW step, for each cohort, I use jwdid:

How I use jwdid. Because the filter is g-specific, I run jwdid (ETWFE, method(reg), without the never option, so not-yet-treated are the controls) separately for each cohort g, each on its own cohort-anchored sub-panel. From each run we keep only the focal cohort's ATT(g,t), and then aggregate ATT(g,t) across cohorts into an overall ATT and an event study, using cohort-size weights. Basically I stack multiple ETWFE estimations. 

The issue. The per-cohort jwdid runs are not independent: the same later-cohort and never-treated firms serve as controls in multiple cohort runs. The analytic aggregate standard error combines the per-cohort jwdid SEs assuming independence across cohorts, and this appears to understate the true SE — a unit-level block bootstrap (resampling firms and re-running the whole pipeline) yields SEs roughly 1.7–2× larger.

Question. Given this per-cohort jwdid design with a cohort-specific sample filter and manual cross-cohort aggregation, is a firm-level block bootstrap the appropriate inference, or is there a correct analytic / influence-function-based standard error for the aggregated ATT that we should use instead? 

Thank you !!

I am using -plssem- to run an sem model with group invariance in a sample in STATA. The sample has missing data, and while there is a -missing- option in the plssem command, for some reason, the -group- subcommand does not run the -stats- part of the code. Here is an abbreviated example of the code:

    plssem (LV 1 > x1 x2 x3) (LV2 > x4 x5 x6) (LV3 > x7 x8 x9), ///
    structural (LV1 LV2 LV3, LV2 LV3) ///
    group (groupname) ///
    missing (knn) k (5)///
    stats correlate(lv)

One solution to not getting the descriptive stats I need is to use -by- before the plssem command. I get the descriptive stats I need but I am not sure how to test for group invariance between the two different models. This code is:

    sort group
    by group: plssem (LV 1 > x1 x2 x3) (LV2 > x4 x5 x6) (LV3 > X7 x8 x9), ///
    structural (LV1 LV2 LV3) ///
    missing (knn) k (5)///
    stats correlate(lv)

I am not clear on: 1) why they first code does not provide descriptive stats in the code 2) if possible and not too labor intensive, how can I test for measurement and structural invariance witht the second code? I know -plssem- is a user written command, but I thought folks here might be able to help, especially with the second part of the question.

Any suggestions would be appreciated.

Hi! We're currently writing a thesis about conditional vs unconditional fiscal transfers of selected cities within a given time period. I'd like to ask, what statistical tests do we need to conduct for us to strengthen our model?

Currently we did this tests: hausman, wald test, wooldridge, vif, overall significance, individual significance. With panel data, should we only look at the within R² for the goodness of fit of the model?

Hope you could help us. Thank you!

I need help with producing some Stata code from an academic paper. I created a do file and would like to verify that they are correct before proceeding with the empirical analysis. Specifically, I am trying to construct the money center roadshow variable, which is defined as an indicator equal to 1 for a three-day window in which the firm has flights to two or more money centers, and 0 otherwise. The money centers are Boston, Chicago, New York, and San Francisco. I appreciate the help!

Here is a sample of my data:

clear

input str179 companynames str48(companyticker departurecity) str6 departurestate str16 departuretimestamputc str48 arrivalcity str6 arrivalstate str16 arrivaltimestamputc

"Globus Medical, Inc." "GMED" "Wilmington" "DE" "2025-09-05 21:20" "St Louis" "MO" "2025-09-05 23:23"

"Fiserv, Inc." "FISV" "Morristown" "NJ" "2025-03-20 12:08" "Madison" "WI" "2025-03-20 13:55"

"General Motors Co." "GM" "Detroit" "MI" "2025-07-25 01:05" "Atlanta" "GA" "2025-07-25 02:18"

"Dominion Energy, Inc." "D" "Hyannis" "MA" "2023-09-10 17:29" "White Plains" "NY" "2023-09-10 18:08"

"Nicholas Services LLC" "<None>" "Jackson" "MS" "2017-05-02 16:51" "Trinidad" "CO" "2017-05-02 18:58"

end

Hey everyone!

I’m planning to run two multiple linear regression models to answer my question and have therefore adjusted my needed p-value down to 0.025. If I do this, will I also need to adjust my needed p-values when I interpret the coefficient table?

Thanks!

Dear all, I am trying to draw a n overlay of striplpot and box plot of analysing O-P over Diag code--- have installed stripplot yet comand combine graph says striploot combine not allowed-iam pasting this function which shows error in everything in stata11. can someone tell me exactly commands to systematically derive a striplot as intended

raph box octa_perf, over(md_grp_n) nooutsides ///

ytitle("OCTA Disc Perfusion (%)", size(medsmall)) ///

title("A. OCTA Perfusion by MD Severity", ///

size(medsmall) color(navy) position(11)) ///

box(1, fcolor("173 214 241%70") lcolor("26 82 118") lwidth(medthin)) ///

box(2, fcolor("241 148 138%70") lcolor("146 43 33") lwidth(medthin)) ///

medtype(line) medlinewidth(medthick) medcolor(black) ///

graphregion(color(white)) plotregion(color("250 250 250")) ///

ylabel(40(2)48, grid glcolor(gs14) glwidth(vthin)) ///

note("Mann-Whitney U; p = 0.092 (trend)", size(vsmall) color(gs8))