Dear Statalisters,
I struggle with interpreting my results from multilevel linear regressions, using restricted maximum likelihood and Kenward-Roger correction. I use Stata 15.0 on a Mac (version 10.14), and I hope I’m using the Code-function right.
My problem is that I do not know how to interpret the significance of the coefficients in my multilevel outputs. I’m doing my master thesis, so I will report significance at both the 0.1-, 0.05-, 0.01- and 0.001-level in my regression tables.
For example, from reading off P>|t| in the output below, I immediately thought that x1-x3 are statistically significant (x1 at the 0.1-level, x2 at the 0.01-level, x3 at the 0.001-level). However, is that the correct interpretation? The confusion arise as I do not know how to calculate the critical t-value, so that I can compare t from the output to the critical t-value. I tried using this calculatur (http://www.ttable.org/student-t-value-calculator.html), plotting in df=6, but I'm not sure this yields the right value. The critical value for a two-tail test with significance level 0.01 is calculated to be +/-3.71. However, if x2 is significant at the 0.01-level (which I though from reading its P>|t|), why is the t-value only 3.43, which yields a significance at the 0.05-level if the critical t-value is 3.71? Is the critical t-value in fact something else, or is my interpretation of P>|t| wrong?
All help is very much appreciated.
Kind regards,
Frøydis Jensen
I struggle with interpreting my results from multilevel linear regressions, using restricted maximum likelihood and Kenward-Roger correction. I use Stata 15.0 on a Mac (version 10.14), and I hope I’m using the Code-function right.
My problem is that I do not know how to interpret the significance of the coefficients in my multilevel outputs. I’m doing my master thesis, so I will report significance at both the 0.1-, 0.05-, 0.01- and 0.001-level in my regression tables.
For example, from reading off P>|t| in the output below, I immediately thought that x1-x3 are statistically significant (x1 at the 0.1-level, x2 at the 0.01-level, x3 at the 0.001-level). However, is that the correct interpretation? The confusion arise as I do not know how to calculate the critical t-value, so that I can compare t from the output to the critical t-value. I tried using this calculatur (http://www.ttable.org/student-t-value-calculator.html), plotting in df=6, but I'm not sure this yields the right value. The critical value for a two-tail test with significance level 0.01 is calculated to be +/-3.71. However, if x2 is significant at the 0.01-level (which I though from reading its P>|t|), why is the t-value only 3.43, which yields a significance at the 0.05-level if the critical t-value is 3.71? Is the critical t-value in fact something else, or is my interpretation of P>|t| wrong?
Code:
mixed CHILDREN x1 x2 x3 || COUNTRY2:, reml dfmethod(kroger)
Code:
Mixed-effects REML regression Number of obs = 3,245
Group variable: COUNTRY2 Number of groups = 10
Obs per group:
min = 108
avg = 324.5
max = 466
DF method: Kenward-Roger DF: min = 16.07
avg = 1,561.11
max = 3,240.03
F(3, 98.23) = 21.91
Log restricted-likelihood = -4836.0572 Prob > F = 0.0000
--------------------------------------------------------------------------------
CHILDREN | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------------+----------------------------------------------------------------
x1 | .2339834 .1167206 2.00 0.058 -.0091296 .4770963
x2 | .133747 .0389608 3.43 0.001 .0573566 .2101373
x3 | .0837528 .0124587 6.72 0.000 .0593243 .1081814
_cons | .5332078 .2389675 2.23 0.040 .0268024 1.039613
--------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
COUNTRY2: Identity |
var(_cons) | .1383108 .0711551 .0504601 .3791085
-----------------------------+------------------------------------------------
var(Residual) | 1.136198 .0282628 1.082133 1.192965
------------------------------------------------------------------------------
LR test vs. linear model: chibar2(01) = 124.99 Prob >= chibar2 = 0.0000
. estat ic
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 3,245 . -4836.057 6 9684.114 9720.624
-----------------------------------------------------------------------------
All help is very much appreciated.
Kind regards,
Frøydis Jensen
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