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Biostatistics & Public Health Research
Registrace 12. 08. 2021
Stata learning for beginners: “for var” command in Stata
“for var” command in Stata
Data Source:
github.com/ahshanulhaque/MyData/raw/main/MyData.dta
In this video, we explore the powerful "for var" command in Stata, a versatile tool that allows you to efficiently loop through multiple variables and apply the same operation to each one. Whether you're managing large datasets, generating repetitive outputs, or streamlining your data analysis process, the "for var" command can save you valuable time and effort. Join us as we demonstrate how to use this command with practical examples, making your Stata programming more efficient and effective.
The main topics of this channel are given below:
Sample size calculation
Data management in STATA and SPSS
Advance data analysis
Epidemiology
Data Source:
github.com/ahshanulhaque/MyData/raw/main/MyData.dta
In this video, we explore the powerful "for var" command in Stata, a versatile tool that allows you to efficiently loop through multiple variables and apply the same operation to each one. Whether you're managing large datasets, generating repetitive outputs, or streamlining your data analysis process, the "for var" command can save you valuable time and effort. Join us as we demonstrate how to use this command with practical examples, making your Stata programming more efficient and effective.
The main topics of this channel are given below:
Sample size calculation
Data management in STATA and SPSS
Advance data analysis
Epidemiology
zhlédnutí: 19
Video
Stata Learning for beginners: How to rename variables in Stata
zhlédnutí 14Před 21 hodinou
How to rename variables in Stata Data Source: github.com/ahshanulhaque/MyData/raw/main/MyData.dta The main topics of this channel are given below: Sample size calculation Data management in STATA and SPSS Advance data analysis Epidemiology
What is a 95% confidence interval #95%CI
zhlédnutí 5Před 21 hodinou
A 95% confidence interval is an interval estimate that has a probability of 0.95 of containing the true value of the population The main topics of this channel are given below: Sample size calculation Data management in STATA and SPSS Advance data analysis Epidemiology
Stata learning for beginners: 'Set More Off' || How to View all Stata results/outputs at Once
zhlédnutí 30Před dnem
'Set More Off' in Stata | How to View all Stata results/outputs at Once In Stata, the command set more off is a handy feature that allows you to view all of your output without interruption. Normally, Stata pauses the display of results after a certain number of lines, prompting you to press a key to continue. This can be useful for step-by-step analysis but can also slow you down when you're r...
How open SPSS data in Stata || convert SPSS data into Stata | Episode - 10
zhlédnutí 76Před 14 dny
The main topics of this channel are given below: Sample size calculation Data management in STATA and SPSS Advance data analysis Epidemiology
How to make asset index /wealth index in Stata using polychoricpca Command
zhlédnutí 130Před 21 dnem
How to make asset index /wealth index in Stata using #polychoricpca Command github.com/ahshanulhaque/MyData/raw/main/MyAsset.dta use "github.com/ahshanulhaque/MyData/raw/main/MyAsset.dta", clear The main topics of this channel are given below: Sample size calculation Data management in STATA and SPSS Advance data analysis Epidemiology
String to date or numeric, encode, split variable in Stata || Episode-9
zhlédnutí 60Před 21 dnem
String to date or numeric, encode, split variable in Stata || Episode-9 Data Source: github.com/ahshanulhaque/MyData/raw/main/DateData.dta In Stata, generating variables, creating composite variables, and transforming categorical variables from other variables are essential tasks for data analysis. To generate a new variable, you can use the generate command followed by the new variable name an...
How to calculate Minimum dietary diversity for women in Stata
zhlédnutí 72Před 28 dny
How to calculate Minimum dietary diversity for women in Stata #MDDW MDD-w pdf: openknowledge.fao.org/server/api/core/bitstreams/3d973c7c-ed82-4ace-a15e-6d0c7004ed75/content Data Source: github.com/ahshanulhaque/MyData/raw/main/hfiasmdd.dta The main topics of this channel are given below: Sample size calculation Data management in STATA and SPSS Advance data analysis Epidemiology
Variable generate, composite variable from categorical/quantitative variables in Stata || Episode-8
zhlédnutí 121Před měsícem
Variable generate, composite variable from categorical/quantitative variables in Stata || Episode-8
Data replace or values transform, find missing values in Stata || Episode-7
zhlédnutí 32Před měsícem
Data replace or values transform, find missing values in Stata || Episode-7
What forest plot | How to interpret the forest plot | 95% confidence interval plot
zhlédnutí 49Před měsícem
What forest plot | How to interpret the forest plot | 95% confidence interval plot
Meta-analysis stratified by another variable in Stata #metaanalysis
zhlédnutí 73Před měsícem
Meta-analysis stratified by another variable in Stata #metaanalysis
Meta-analysis in Stata || Funnel Plot || Egger’s Test
zhlédnutí 230Před měsícem
Meta-analysis in Stata || Funnel Plot || Egger’s Test
Meta-Analysis in Stata || English Language #MetaAnalysis #ForestPlot
zhlédnutí 125Před měsícem
Meta-Analysis in Stata || English Language #MetaAnalysis #ForestPlot
Data merge and append in Stata for beginners || Episode-6
zhlédnutí 39Před měsícem
Data merge and append in Stata for beginners || Episode-6
DHS data analysis: composite index of anthropometric failure in Stata #CIAF
zhlédnutí 94Před měsícem
DHS data analysis: composite index of anthropometric failure in Stata #CIAF
Logical operator in Stata for beginners || Episode-5
zhlédnutí 44Před měsícem
Logical operator in Stata for beginners || Episode-5
Nepal Demographic and Health Survey Data Analysis: stunting, wasting and underweight in Stata
zhlédnutí 184Před měsícem
Nepal Demographic and Health Survey Data Analysis: stunting, wasting and underweight in Stata
How to open Stata, Save data, Data view, variable view, DO file for beginners || Episode-1
zhlédnutí 150Před měsícem
How to open Stata, Save data, Data view, variable view, DO file for beginners || Episode-1
How to calculate Household Food Insecurity Access Scale Calculation in Stata
zhlédnutí 698Před 2 měsíci
How to calculate Household Food Insecurity Access Scale Calculation in Stata
Variable order, drop, keep, sort ascending or descending, browse in Stata for beginners || Episode-4
zhlédnutí 44Před 2 měsíci
Variable order, drop, keep, sort ascending or descending, browse in Stata for beginners || Episode-4
How to make a customized table in Stata-18
zhlédnutí 112Před 2 měsíci
How to make a customized table in Stata-18
Variable label and value label in Stata for beginners || Episode-3
zhlédnutí 54Před 2 měsíci
Variable label and value label in Stata for beginners || Episode-3
How to download Stata-18 || Free for only 7 days
zhlédnutí 737Před 2 měsíci
How to download Stata-18 || Free for only 7 days
Data Read in Stata from Excel file for beginners || Episode-2
zhlédnutí 90Před 2 měsíci
Data Read in Stata from Excel file for beginners || Episode-2
Case-Control data analysis in R using gtsummary || Logistic regression analysis
zhlédnutí 861Před 7 měsíci
Case-Control data analysis in R using gtsummary || Logistic regression analysis
Stunting, Wasting and Underweight calculation in R using gtsummary package
zhlédnutí 256Před 7 měsíci
Stunting, Wasting and Underweight calculation in R using gtsummary package
Descriptive Statistic in R using gtsummary package || part 1 || proportion
zhlédnutí 146Před 7 měsíci
Descriptive Statistic in R using gtsummary package || part 1 || proportion
Think Keu Sir
Ma Shallah
salam sir can i find full video in the youtub channel?
@@user-jb3fk6xd5x Walaikumussalam I will make as soon as possible
think Keu Sir
think Keu Sir
মাশাআল্লাহ
Dear sir, would you mind share us a video of HIFAS questionnaire found in excel and how to import it on STATA? Thanks
Think Keu Sir
Think Keu Sir
ভাই খুজতে খুজতে দেখছি fies নিয়ে আপনি ভিডিও বানিয়েছেন। কয়েকদিন আগে বিবিএস থেকে ডেটাসেট পেলাম।
This is example dataset.
Mash alla think Keu Sir
Positivity is contagious. Spread it! 🌈
Excellent
think Keu Sir
Mesmerizing! 👏
ভাইয়া, Epidemiology ও এর স্টাডি ডিজাইনের উপর যদি টিউটোরিয়াল দিতেন তাহলে উপকৃত হতাম।
Sure bhaia
Great video. Thanks so much. My question is why "predict comp1" only? What if comp2 and comp3 have eigen values greater than 1, do we also run the "predict comp2" and "predict comp2" command.
please i have this message : estat kmo correlation matrix is singular
Thanks! This is actually simpler than I anticipated it to be. Great work! I will try it out. However, I was wondering, do you not think that we are losing a nuance in the data by converting it into a binary variable? For example, maybe there's some important information between "having an item (1) and not having that item (0), which is most likely lost when categorising into a binary variable?
First of all, thank you so much for this video!!!, it really helped that it wasn´t edited so I could understand all the process of analyzing case-control data. Im new in the field of data analysis and biostatistics, but I have rather a silly rookie question why OR doesn't appear in No smoking, Rural, Normal BMI, Female and Service holder? Also would you mind showing how to do a OR Forest Plot for this data? It would be really helpful
I love it especially for education purposes
#------------------------------------------------------R Package-------------- library(readxl) #----Import 'Excel' Files library(tidyverse) #--- Several package -- library(expss) # ---Var Label library(gtsummary) #--Descriptive Statistics using Psych Package epid # # Data Source:# github.com/ahshanulhaque/MyData/raw/main/MyData.xlsx # mydata<-read_excel("D:/abc/MyData.xlsx", sheet = "Data1") # MyStat <- list(all_continuous() ~ "{mean} ± {sd}", all_categorical() ~ "{n} ({p})") MyDigit <- list( all_categorical() ~ c(0, 2), all_continuous() ~ c(2,2) ) # A11<- mutate(mydata, bmiCAT=factor(bmiCAT, levels = c(0,1), labels = c("Non-underweight", "Underweight"), exclude=NA), edu = factor(edu, levels = c(0, 1), labels = c("Below secondary", "Secondary and above")), DV = factor(DV, levels = c(0, 1), labels = c("Non-violent", "Violent")), anc2 = factor(anc2, levels = c(0, 1), labels = c("Less than 4", "At least 4")), b4 = factor(b4, levels = c(1, 2), labels = c("Male", "Female")), stunting = factor(stunting, levels = c(0, 1), labels = c("Non-stunted", "Stunted")), v024 = factor(v024, levels = 1:8, labels = c("Barisal", "Chittagong", "Dhaka", "Khulna", "Mymensingh", "Rajshahi", "Rangpur", "Sylhet")), # v025 = factor(v025, levels = c(1, 2), labels = c("Urban", "Rural")), v190 = factor(v190, levels = 1:5, labels = c("Poorest", "Poorer", "Middle", "Richer", "Richest")), toilet2 = factor(toilet2, levels = c(0, 1), labels = c("Improved", "Unimproved")) )%>% apply_labels( main_id="Study ID by SRL", v012="Respondent's current age", mH = "Maternal Height in cm", mW = "Maternal Weight in kg", bmiCAT = "Maternal underweight(BMI<18.5)", edu = "Education", DV = "Attitudes to domestic Violence", anc2 = "At least 4 ANC from Medically trained", b4 = "Sex of child", ChildAge = "Child's Age in Months", stunting = "Childhood stunting", v024 = "Division", v025 = "Type of place of residence", v025 = c("Urban" = 1, "Rural"=2), v190 = "Wealth index", toilet2 = "Type of toilet facility", v021 = "Primary sampling unit" ) A11%>% filter(!is.na(v025))%>% select(-main_id)%>% tbl_summary(by = v025, missing = "no",statistic = MyStat, digits = MyDigit)%>% bold_labels()
#------------------------------------------------------R Package-------------- library(readxl) #----Import 'Excel' Files library(tidyverse) #--- Several package -- library(expss) # ---Var Label library(gtsummary) #--Descriptive Statistics using Psych Package epid # # Data Source:# github.com/ahshanulhaque/MyData/raw/main/MyData.xlsx # mydata<-read_excel("D:/abc/MyData.xlsx", sheet = "Data1") # MyStat <- list(all_continuous() ~ "{mean} ± {sd}", all_categorical() ~ "{n} ({p})") MyDigit <- list( all_categorical() ~ c(0, 2), all_continuous() ~ c(2,2) ) # A11<- mutate(mydata, bmiCAT=factor(bmiCAT, levels = c(0,1), labels = c("Non-underweight", "Underweight"), exclude=NA), edu = factor(edu, levels = c(0, 1), labels = c("Below secondary", "Secondary and above")), DV = factor(DV, levels = c(0, 1), labels = c("Non-violent", "Violent")), anc2 = factor(anc2, levels = c(0, 1), labels = c("Less than 4", "At least 4")), b4 = factor(b4, levels = c(1, 2), labels = c("Male", "Female")), stunting = factor(stunting, levels = c(0, 1), labels = c("Non-stunted", "Stunted")), v024 = factor(v024, levels = 1:8, labels = c("Barisal", "Chittagong", "Dhaka", "Khulna", "Mymensingh", "Rajshahi", "Rangpur", "Sylhet")), # v025 = factor(v025, levels = c(1, 2), labels = c("Urban", "Rural")), v190 = factor(v190, levels = 1:5, labels = c("Poorest", "Poorer", "Middle", "Richer", "Richest")), toilet2 = factor(toilet2, levels = c(0, 1), labels = c("Improved", "Unimproved")) )%>% apply_labels( main_id="Study ID by SRL", v012="Respondent's current age", mH = "Maternal Height in cm", mW = "Maternal Weight in kg", bmiCAT = "Maternal underweight(BMI<18.5)", edu = "Education", DV = "Attitudes to domestic Violence", anc2 = "At least 4 ANC from Medically trained", b4 = "Sex of child", ChildAge = "Child's Age in Months", stunting = "Childhood stunting", v024 = "Division", v025 = "Type of place of residence", v025 = c("Urban" = 1, "Rural"=2), v190 = "Wealth index", toilet2 = "Type of toilet facility", v021 = "Primary sampling unit" ) A11%>% filter(!is.na(v025))%>% select(-main_id)%>% tbl_summary(by = v025, missing = "no",statistic = MyStat, digits = MyDigit)%>% bold_labels()
Good Morning Sir @amalsedekah
* Set variable labels label variable main_id "Study ID by SRL" label variable v012 "Respondent's current age" label variable mH "Maternal Height in cm" label variable mW "Maternal Weight in kg" label variable bmiCAT "Maternal underweight (BMI<18.5)" label variable edu "Education" label variable DV "Attitudes to domestic Violence" label variable anc2 "At least 4 ANC from Medically trained" label variable b4 "Sex of child" label variable ChildAge "Child's Age in Months" label variable stunting "Childhood stunting" label variable v024 "Division" label variable v025 "Type of place of residence" label variable v190 "Wealth index" label variable toilet2 "Type of toilet facility" label variable v021 "Primary sampling unit" * Set value labels label define bmiCAT_lbl 0 "Non-underweight" 1 "Underweight" label define edu_lbl 0 "Below secondary" 1 "Secondary and above" label define DV_lbl 0 "Non-violent" 1 "Violent" label define anc2_lbl 0 "Less than 4" 1 "At least 4" label define b4_lbl 1 "Male" 2 "Female" label define stunting_lbl 0 "Non-stunted" 1 "Stunted" label define v024_lbl 1 "Barisal" 2 "Chittagong" 3 "Dhaka" 4 "Khulna" 5 "Mymensingh" 6 "Rajshahi" 7 "Rangpur" 8 "Sylhet" label define v025_lbl 1 "Urban" 2 "Rural" label define v190_lbl 1 "Poorest" 2 "Poorer" 3 "Middle" 4 "Richer" 5 "Richest" label define toilet2_lbl 0 "Improved" 1 "Unimproved" * Apply value labels label values bmiCAT bmiCAT_lbl label values edu edu_lbl label values DV DV_lbl label values anc2 anc2_lbl label values b4 b4_lbl label values stunting stunting_lbl label values v024 v024_lbl label values v025 v025_lbl label values v190 v190_lbl label values toilet2 toilet2_lbl
does coefplot work also with HR from cox regression?
Yes, it be worked
Saved me a lot of stress. thank you so much
Very helpful. thanks
thank you
Thank you ☺️ for this video
Sir,I need the reference source of the formula for my research study..can you pls help?
woow this was perfect
Learn to speak English first then make videos.. Waste of my time
There is no link in the description
Sorry, Now the link has been given. Thank you.
@@biostatbd Many thanks.
this is gold mine for me! thanks
Thank you very much for your video. It was very helpful. Can i get the do file? Thanks