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From |
"Fitzgerald, James" <J.Fitzgerald2@ucc.ie> |

To |
Lisa Marie Yarnell <lisayarnell@yahoo.com>, "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: Comparing coefficients across sub-samples |

Date |
Thu, 2 Aug 2012 23:42:44 +0000 |

Lisa, Do I need to divide the squared standard errors by n of each sample? The formula you provided appears to be Welch's t-test formula, but Welch's formula would be: z = (B1 - B2) / √(seB1^2/n1 + seB2^2/n2) Welch, B. L. (1947). "The generalization of "Student's" problem when several different population variances are involved". Biometrika 34 (1–2): 28–35 Regards James ________________________________________ From: Lisa Marie Yarnell [lisayarnell@yahoo.com] Sent: 01 August 2012 04:29 To: statalist@hsphsun2.harvard.edu; Fitzgerald, James Subject: Re: st: Comparing coefficients across sub-samples Hi James, Typically the effect of a predictor in two different groups can be compared with the unstandardized beta. You can do a statistical test of the difference in the betas using the z-score formula below. I usually just calculate the difference between unstandardized betas from two different models by hand, though Stata might have a command to do this for you. Is that what you are looking for: the Stata command? (b1 – b2) b1 and b2 are the unstandardized regression weights that you want z = -------------------- to test the difference between √(seb12 + seb22) seb1 and seb2are the standard errors of these unstandardized ↑ regression weights, found next to the weights themselves This is a square root sign! in your SPSS output. Remember to square them. Take the square root of the entire value in parentheses. In terms of comparing the *magnitude* of the effect in the two different subsamples, it is more correct to do this qualitatively by comparing the *standardized* beta for the variable of interest against effect size rules of thumb for small/medium/large (which sometimes differ by discipline, such as social sciences/education/engineering). Just report the standardized beta as the effect size in each group; it would be a qualitative statement about the effect in each group. Here are rules that I have: Standardized regression coefficients: * Keith’s (2006) rules for effects on school learning: .05 = too small to be considered meaningful, .above .05 = small but meaningful effect, .10 = moderate effect, .25 = large effect. * Cohen’s (1988) rules of thumb: .10 = small, .30 = medium, > (or equal to) .50 = large Lisa ----- Original Message ----- From: "Fitzgerald, James" <J.Fitzgerald2@ucc.ie> To: "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> Cc: Sent: Tuesday, July 31, 2012 4:14 PM Subject: st: Comparing coefficients across sub-samples Hi Statalisters I am running the same model on two sub-samples as follows: xtreg ltdbv lnta tang itang prof mtb if nolowlntalowtang==1, fe cluster(firm) xtreg ltdbv lnta tang itang prof mtb if nolowlntalowtang==0, fe cluster(firm) I want to compare the explanatory power of lnta across the two sub-samples i.e. in which sub-sample does lnta explain significantly more of the variation in ltdbv? Can anyone give me some advice on how to achieve this? Thanks in advance James * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: Comparing coefficients across sub-samples***From:*David Hoaglin <dchoaglin@gmail.com>

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