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  • Interpretation of Interaction effects

    Hello,

    I am trying to estimate the impact on working in partime vs full time (jbft_dv=1 FT employee and jbft_dv=2 PT employee) on job satistaction (jbsat that goes from 1 completelety dissatisfied to 7 completely satisfied).
    I regress a xtoprobit model with vce(robust) and I found that working in part-time increases the odd of being in a higher level of job satisfaction. All the other variables in the example are control variables:
    Commands that I used:
    xtoprobit jbsat i.abused i.sex dvage i.urban i.h_educ i.jbft_dv i.jbmngr i.ma_status i.scropenup, vce(robust)
    xtoprobit jbsat i.abused i.sex dvage i.urban i.h_educ i.jbft_dv i.jbmngr i.ma_status i.scropenup i.jbft_dv#i.sex, vce(robust)
    xtoprobit jbsat i.abused dvage i.urban i.h_educ i.jbft_dv i.jbmngr i.ma_status i.scropenup if sex==1, vce(robust)
    xtoprobit jbsat i.abused dvage i.urban i.h_educ i.jbft_dv i.jbmngr i.ma_status i.scropenup if sex==2, vce(robust)I would like to know if there are differences by sex. I regress a model with an interaction effect i.jbft_dv#i.sex and the coefficient are not significant. When I regress thwo models (one for female and other for male) the coefficients for bft_dv are significant.
    Questions:
    1 - How can I interpret these results?
    2 - Looking for the results of the estimations by sex, can I say that female that work in part-time have lower odds than men of being in a higher level of job satisfaction?
    Thank you very much in advance.
    * Example generated by -dataex-. For more info, type help dataex
    clear
    input byte jbsat float abused byte sex int dvage byte(urban_dv h_educ jbft_dv jbmngr ma_status scropenup)
    . 0 2 73 2 0 . . 1 4
    . 0 2 20 1 0 . . 1 .
    5 0 2 44 1 1 1 3 1 .
    5 0 2 22 1 0 1 3 1 .
    6 0 2 18 1 0 1 3 1 .
    . 0 1 61 1 0 . . 1 .
    . 0 2 19 1 0 . . 1 .
    6 0 2 49 1 1 1 2 1 .
    5 0 1 30 1 0 1 3 1 2
    5 0 2 34 1 0 2 3 1 .
    . 0 1 17 1 0 . . 1 3
    6 0 2 47 2 0 2 3 1 .
    . 0 1 27 1 1 . 3 1 .
    . 0 2 41 1 0 . . 1 4
    6 0 1 20 1 0 1 3 1 2
    . 0 2 18 1 0 . . 1 .
    6 0 2 27 1 1 1 1 1 1
    6 0 2 18 1 0 2 3 1 1
    . 0 1 18 2 0 . . 1 1
    5 0 2 50 2 . 1 2 1 .
    . 0 1 32 1 1 . 3 1 .
    6 0 2 33 1 . 1 . 1 .
    . 0 2 16 1 0 . . 1 .
    7 0 2 42 1 0 1 3 1 .
    7 0 1 47 1 1 1 2 1 2
    5 0 2 62 1 0 2 2 1 .
    5 0 2 29 1 1 1 2 1 .
    . 0 1 20 1 0 . . 1 .
    . 1 2 17 1 0 . . 1 .
    . 0 1 26 1 1 . 3 1 .
    5 0 2 32 1 0 1 1 1 3
    . 0 2 21 1 . . . 1 .
    7 0 1 45 1 1 1 1 1 .
    . 0 2 28 1 0 . . 1 .
    2 0 2 24 1 1 1 3 1 .
    . 0 1 57 1 1 . . 1 .
    . 0 2 24 1 0 . . 1 1
    4 0 1 51 1 . 1 3 1 .
    . 0 1 72 2 0 . . 1 3
    . 0 2 83 1 0 . . 1 .
    . 0 2 46 2 0 . 3 1 .
    . 0 1 17 1 0 . . 1 .
    7 0 2 57 1 0 2 3 1 .
    . 0 1 43 1 0 . . 1 4
    . 0 1 19 1 0 . 3 1 .
    . 0 2 59 1 1 . . 1 .
    4 0 1 53 1 0 1 1 1 .
    7 0 2 44 1 1 1 2 1 .
    5 0 1 20 2 0 1 3 1 2
    . 0 2 27 1 . . . 1 3
    . 0 2 16 2 0 . . 1 .
    . 0 2 16 1 0 . . 1 1
    5 0 1 17 1 0 2 3 1 .
    . 0 1 20 2 . . . 1 3
    7 0 2 37 1 . 2 2 1 .
    . 0 2 16 2 0 . . 1 .
    5 0 2 28 1 . 1 2 1 1
    . 0 1 54 2 0 . . 1 1
    . 0 2 30 1 1 . . 1 1
    . 0 1 55 1 0 . . 1 .
    . 0 2 27 1 1 . . 1 3
    6 0 2 44 1 1 2 3 1 .
    . 0 1 17 1 0 . . 1 3
    5 0 2 33 1 1 1 1 1 .
    6 0 2 23 1 0 1 1 1 2
    . 0 2 16 1 0 . . 1 .
    3 0 2 19 1 0 2 3 1 .
    5 0 1 52 1 1 1 2 1 .
    . 0 2 65 1 1 . . 1 .
    7 0 1 27 1 1 1 3 1 2
    6 0 1 22 1 0 1 3 1 .
    5 0 2 21 2 0 1 3 1 .
    . 0 2 36 1 0 . 1 1 .
    6 0 1 21 1 0 1 3 1 2
    . 0 1 58 1 . . . 1 .
    . 0 1 18 2 0 . . 1 .
    5 0 2 29 1 0 2 3 1 1
    6 0 1 33 1 0 1 3 1 .
    . 0 2 19 1 0 . . 1 3
    . 0 1 66 2 0 . . 1 .
    . 0 1 20 2 0 . 3 1 .
    7 0 1 65 2 . 2 3 1 1
    . 0 2 18 1 0 . . 1 .
    . 0 2 86 2 . . . 1 4
    5 0 2 21 1 0 2 3 1 2
    . 0 1 30 1 0 . . 1 .
    . 0 2 18 1 0 . . 1 .
    . 0 2 34 1 0 . . 1 .
    . 0 2 36 1 0 . . 1 .
    2 0 1 30 1 1 1 3 1 .
    . 0 2 17 2 . . . 1 .
    . 0 1 17 2 0 . . 1 .
    . 0 2 72 1 0 . . 1 .
    7 0 2 50 1 0 2 1 1 1
    5 0 2 52 1 0 1 2 1 .
    . 0 1 16 2 0 . . 1 3
    . 0 2 19 1 0 . . 1 .
    5 0 1 75 1 0 1 3 1 3
    . 0 2 25 1 0 . . 1 .
    5 0 2 40 1 0 1 2 1 .
    end
    label values jbsat c_jbsat
    label def c_jbsat 2 "mostly dissatisfied", modify
    label def c_jbsat 3 "somewhat dissatisfied", modify
    label def c_jbsat 4 "neither satisfied or dissatisfied", modify
    label def c_jbsat 5 "somewhat satisfied", modify
    label def c_jbsat 6 "mostly satisfied", modify
    label def c_jbsat 7 "completely satisfied", modify
    label values abused abc
    label def abc 0 "not abused at work", modify
    label def abc 1 "abused at work", modify
    label values sex c_sex
    label def c_sex 1 "male", modify
    label def c_sex 2 "female", modify
    label values dvage c_dvage
    label values urban_dv c_urban_dv
    label def c_urban_dv 1 "urban area", modify
    label def c_urban_dv 2 "rural area", modify
    label values h_educ h_educ
    label def h_educ 0 "all except higher education", modify
    label def h_educ 1 "higher education", modify
    label values jbft_dv c_jbft_dv
    label def c_jbft_dv 1 "FT employee", modify
    label def c_jbft_dv 2 "PT employee", modify
    label values jbmngr c_jbmngr
    label def c_jbmngr 1 "manager", modify
    label def c_jbmngr 2 "foreman/supervisor", modify
    label def c_jbmngr 3 "not manager or supervisor", modify
    label values ma_status ma
    label def ma 1 "single", modify
    label values scropenup e_scropenup
    label def e_scropenup 1 "a lot", modify
    label def e_scropenup 2 "somewhat", modify
    label def e_scropenup 3 "a little", modify
    label def e_scropenup 4 "not at all", modify
    [/CODE]
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