Conducting a Cross Sectional study

[Daniel Kosoko]

I am conducting a cross-sectional study of 99 UK firms in 2017. I aim to find a positive/negative relationship between a board characteristic (diversity) and performance variables (i.e. ROA, ROA, EBITDA, Tobin's Q) while controlling for firm and board characteristics (firm size, board size, average age, average directorships). Previous studies use panel data methodology but due to data limitations, I only have values for my main independent variable for 2017 hence the choice of a cross-sectional study. I want to explore the differences between performance across various industries as well. By simply running reg or rreg my results pose high p values and very low r-squared values but these are more suited for panel data. What commands can I use specifically for cross sectional data?

[Phil Bromiley]

regress

[Phil Bromiley]

You'll increase your chances of a useful answer by following the FAQ on asking questions - provide Stata code in code delimiters, readable Stata output and sample data using dataex. We don't even know exactly what you ran.

"my results pose high p values and very low r-squared values but these are more suited for panel data" - I don't know where you got this idea, but regress is the standard regression used for cross sectional data. High p's and low r-squares don't necessarily indicate a problem although you may have a lot of variables relative to your sample size.