Hi!
I'm a beginner when it comes to both statistics and STATA. This question might be more of a statistics question than a STATA question, but I'm looking for some help to interpret the results of this model and specifically the interaction of two continuous variables.
The following regression is from an assignment I'm working on. The dependent variable is wage with a plethora of explanatory variables. I know interactions can be added by using ##, but we were specifically asked to create the interactions the old-fashioned way and then add them to the model.
I'm a little bit insecure and uncertain in how to interpret the results, but this is how I think it should be:
1. Take the variable Married for example. Holding all other variables constant, then being married (coded as 1) will increase wage by 3.9% compared to not being married.
2. Now, given then interaction Female_Married, it is also possible to say the effect of being married for females is 0.039 + (-0.0479351).
3. The big question I have is how Experience_Education affects the model (both are continuous). If we start by just looking at Education, then I would say that, again all other variables constant at 0, a one unit increase in Education will lead to an increase in wage by 0.066528 or 6.6%. Is this a correct interpretation of just Education?
However, if we start to consider the interaction of Experience_Education, would the effect of Education then be, using the same approach as for the Female_Married interaction, 0.066528 + (-0.0003983)? This seems wrong to me and from what I found using Google it is not possible to interpret a continuous interaction this way, but other commands and plotting is necessary (something we have not covered in my course).
So, just from this model and without doing any other commands, what can possibly be said about Experience_Education? Because the way I'm thinking of it, when interpreting the coefficient of Education as 0.066528, then all other coefficients in the model are 0, including Experience_Education.
What can be said about the coefficient (-0.0003983) of Experience_Education? Would it be correct to say for each unit change in education, the slope of experience vs. wage decreases by -0.0003983? And the same effect on the slope of education vs. wage for each unit change in experience?
Any help or input is much appreciated. Thanks!
I'm a beginner when it comes to both statistics and STATA. This question might be more of a statistics question than a STATA question, but I'm looking for some help to interpret the results of this model and specifically the interaction of two continuous variables.
The following regression is from an assignment I'm working on. The dependent variable is wage with a plethora of explanatory variables. I know interactions can be added by using ##, but we were specifically asked to create the interactions the old-fashioned way and then add them to the model.
Code:
Source | SS df MS Number of obs = 550
-------------+---------------------------------- F(19, 530) = 22.82
Model | 59.3445613 19 3.12339796 Prob > F = 0.0000
Residual | 72.5548162 530 .13689588 R-squared = 0.4499
-------------+---------------------------------- Adj R-squared = 0.4302
Total | 131.899377 549 .240253875 Root MSE = .36999
--------------------------------------------------------------------------------------
ln_Wage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------------------+----------------------------------------------------------------
Education | .066528 .0143397 4.64 0.000 .0383583 .0946977
Experience | .0374092 .0111086 3.37 0.001 .015587 .0592314
Experience2 | -.0004465 .0001462 -3.05 0.002 -.0007337 -.0001593
Female | .0390759 .2135387 0.18 0.855 -.3804102 .4585621
Married | .0391859 .0523632 0.75 0.455 -.063679 .1420507
Union | .2220958 .0450569 4.93 0.000 .1335837 .3106079
Female_Education | -.0083708 .0144876 -0.58 0.564 -.0368309 .0200893
Female_Experience | -.0227384 .0102753 -2.21 0.027 -.0429237 -.0025531
Female_Experience2 | .0004476 .0002256 1.98 0.048 4.35e-06 .0008908
Female_Married | -.0479351 .0755239 -0.63 0.526 -.1962982 .1004279
Female_Union | .0834588 .0767746 1.09 0.278 -.0673611 .2342786
Nonwhite | -.1297967 .054883 -2.36 0.018 -.2376116 -.0219818
Hispanic | .0042826 .0675995 0.06 0.950 -.1285133 .1370785
Experience_Education | -.0003983 .0004914 -0.81 0.418 -.0013636 .000567
Manager | .2656318 .0625375 4.25 0.000 .14278 .3884837
Sales | .0954787 .078817 1.21 0.226 -.0593533 .2503108
Clerical | -.0133465 .050185 -0.27 0.790 -.1119324 .0852394
Service | -.2393058 .0618983 -3.87 0.000 -.360902 -.1177097
Professional | .118085 .0596035 1.98 0.048 .0009969 .2351731
_cons | .4570651 .1980329 2.31 0.021 .0680394 .8460907
I'm a little bit insecure and uncertain in how to interpret the results, but this is how I think it should be:
1. Take the variable Married for example. Holding all other variables constant, then being married (coded as 1) will increase wage by 3.9% compared to not being married.
2. Now, given then interaction Female_Married, it is also possible to say the effect of being married for females is 0.039 + (-0.0479351).
3. The big question I have is how Experience_Education affects the model (both are continuous). If we start by just looking at Education, then I would say that, again all other variables constant at 0, a one unit increase in Education will lead to an increase in wage by 0.066528 or 6.6%. Is this a correct interpretation of just Education?
However, if we start to consider the interaction of Experience_Education, would the effect of Education then be, using the same approach as for the Female_Married interaction, 0.066528 + (-0.0003983)? This seems wrong to me and from what I found using Google it is not possible to interpret a continuous interaction this way, but other commands and plotting is necessary (something we have not covered in my course).
So, just from this model and without doing any other commands, what can possibly be said about Experience_Education? Because the way I'm thinking of it, when interpreting the coefficient of Education as 0.066528, then all other coefficients in the model are 0, including Experience_Education.
What can be said about the coefficient (-0.0003983) of Experience_Education? Would it be correct to say for each unit change in education, the slope of experience vs. wage decreases by -0.0003983? And the same effect on the slope of education vs. wage for each unit change in experience?
Any help or input is much appreciated. Thanks!
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