Understanding Society User Support

Dear Irina,

Is your analysis cross-sectional or longitudinal?

Best wishes,
Understanding Society User Support Team

As I am using the same respondents who took part in the pre-covid wave, the 4th Covid wave and the 8th Covid wave I think that this analysis is longitudinal. But please correct me if I am wrong.

Best wishes,
Irina

Let me show my steps:
1) I merged youth who participated in Wave 4 (July 2020) and Wave 8 (March 2021) and kept those who matched observations (N=821):
use "/Users/irina/Desktop/Thesis/2022/Data/UKDA-8644-stata/stata/stata13_se/cd_youth_p.dta"
merge 1:1 pidp_c using "/Users/irina/Desktop/Thesis/2022/Data/UKDA-8644-stata/stata/stata13_se/ch_youth_p.dta", nogenerate keep(match))
2) then I merged it to the baseline (wave 10 UKHLS) and also kept matched observations (N=531):
merge 1:1 pidp using "/Users/irina/Desktop/Thesis/2022/Data/UKDA-6614-stata/stata/stata13_se/ukhls_w10/j_youth.dta", nogenerate keep(match)
3) then I want to compare how SDQ score changed over time. For this I want to test absolute differences in SDQ scores between wave 10 UKHLS (2018/19) and wave 4(July 2020) samples using linear regression for continuous outcomes

Could you please also include the syntax for the regression part?

Best wishes,
Understanding Society User Support Team

Hello,

I have finally managed to figure out the syntax for the regression part.
Here is my syntax:

// July 2020 + March 2021
use "/Users/irina/Desktop/Thesis/2022/Data/UKDA-8644-stata/stata/stata13_se/cd_youth_p.dta"
merge 1:1 pidp_c using "/Users/irina/Desktop/Thesis/2022/Data/UKDA-8644-stata/stata/stata13_se/ch_youth_p.dta", nogenerate keep(match)
rename pidp_c pidp
save "/Users/irina/Desktop/Thesis/2022/Data/youth data/cd_ch_youth_p.dta", replace

• Adding baseline Wave 10 UKHLS (2019) to July 2020 + March 2021
  use "/Users/irina/Desktop/Thesis/2022/Data/youth data/cd_ch_youth_p.dta"
  merge 1:1 pidp using "/Users/irina/Desktop/Thesis/2022/Data/UKDA-6614-stata/stata/stata13_se/ukhls/i_youth.dta", nogenerate keep(match)

//// Renaming variables and transforming wide data to long ////
rename cd_* 2020
rename ch_ 2021
rename i_ *2018

//Reshaping data
reshape long ypsdqtd_dv ypsdqes_dv ypsdqcp_dv ypsdqha_dv ypsdqpp_dv ypsdqps_dv, i(pidp) j(year)
label variable ypsdqtd_dv "Total SDQ"
label variable ypsdqes_dv "Emotional Symptoms"
label variable ypsdqcp_dv "Conduct Problems"
label variable ypsdqha_dv "Hyperactivity/Inattention"
label variable ypsdqpp_dv "Peer Relationship Problems"
label variable ypsdqps_dv "Prosocial Behaviour"

// Testing changes in TOTAL mean scores
regress ypsdqtd_dv i.year if year==2020 | year==2018

// Testing changes in subscale mean scores
*Emotional S
regress ypsdqes_dv i.year if year==2020 | year==2018
*Conduct Problems
regress ypsdqcp_dv i.year if year==2020 | year==2018
*Hyperactivity/Inattention
regress ypsdqha_dv i.year if year==2020 | year==2018
*Peer Relationship Problems
regress ypsdqpp_dv i.year if year==2020 | year==2018
**Prosocial Behaviour
regress ypsdqps_dv i.year if year==2020 | year==2018

Dear Understanding Society User Support Team,

Thank you for your response.

Just a quick remark - I am not using UKHLS Wave 10. For a baseline I use UKHLS Wave 9 (2017-2018).

It sounds good that I don't have to restrict the sample to those who have responded in all 3 waves. Could you please show me in my code where I should apply the "cross-sectional weights for the respective waves"? Do I need to apply weights only when I run regressions? Or should I apply weights in the beginning of the analysis once I've merged the 3 waves?

Could you please also explain me how can I "use svyset to take into account weights as well as the complex survey design"?

Thank you and looking forward to hearing from you

Best,
Irina

Sorry about that - same principle applies for baseline using Wave 9.

After you have put the data together (as you have shown above) create a variable called weight and replace it with the different wave specific youth cross-sectional weights, depending on the wave.

As weights are about producing unbiased population estimates, apply them when producing population estimates. Here is some infomration about using weights and complex survey design in the main survey user guide: https://www.understandingsociety.ac.uk/documentation/mainstage/user-guides/main-survey-user-guide/how-to-use-weights-analysis-guidance-for-weights-psu-strata

Hope this helps

and do I need to keep only matched observations (when I am merging waves in the beginning) in this case?

looked t the screenshop - yes, weights created and use of svyset is ok