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Probing When is it acceptable to probe an interaction effect between categorical variables--when is it warranted? (Regression vs. anova)

(3) Say the moderator and DV are flipped (categorical by continuous predicting binary) so you are now running a binary logistic regression. P-value for the contrast of interaction effect for 2 dummy classes is still significant. Does it now make sense to probe the interaction solely within a regression framework? Would you not if the anova output is still saying the whole interaction term is NOT significant?

Regression output:

Probing an interaction effect between categorical variables--when is it warranted? (Regression vs. anova)

Regression output:

When is it acceptable to probe an interaction effect between categorical variables? (Regression vs. anova)

(3) Say the moderator and DV are flipped (categorical by continuous predicting binary) so you are now running a binary logistic regression. P-value for the contrast of interaction effect for 2 dummy classes is still significant. Does it now make sense to probe the interaction solely within a regression framework? Would you not if the anova output is still saying the whole interaction term is NOT significant?

Regression output:

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This question seems to be an overly simple one but I would still appreciate an informed response.

I am testing the interaction effect between a categorical variable (5 levels) and a binary variable. Outcome variable is continuous.

I have completed by regression and anova to test for a possible interaction effect. Results of regression indicates a significant interaction effect between two groups (EX: class 1 and 2).

In short, the regression output suggests a significant interaction effect (Class=3 * BIN=1) and the anova output suggests that the SSQ of the interaction term is non-significant (CLASS:BIN).

(1) Does the significant interaction term (Class=3 * BIN=1) in the regression output justify "probing" of the significant interaction effect?

(2) What is the significance of the (non-significant) interaction term (CLASS:BIN) in anova?

Regression output:

 Coef              S.E.   t       Pr(>|t|)
 Intercept         0.6533 0.2402  2.72 0.0068  
 Class=2          -0.6955 0.2997 -2.32 0.0208  
 Class=3          -1.0229 0.2617 -3.91 0.0001  
 Class=4          -0.8226 0.2891 -2.84 0.0047  
 Class=5          -0.6086 0.3250 -1.87 0.0619  
 BIN=1             -0.3671 0.3082 -1.19 0.2343  
 Class=2 * BIN=1  0.7025 0.3955  1.78 0.0765  
 Class=3 * BIN=1  0.8602 0.3561  2.42 0.0162 
 Class=4 * BIN=1  0.6066 0.3743  1.62 0.1058  
 Class=5 * BIN=1  0.5961 0.4285  1.39 0.1649

Anova Table:

            SSQ     df1   df2     F value  Pr(>F)    eta2    partial.eta2
CLASS       19.04070  4  621.7598 4.0077   0.00322  0.04562   0.04726
BIN         7.11855   1 1014.6402 6.2523   0.01256  0.01706   0.01821
CLASS:BIN   7.40183   4  479.6875 1.3864   0.23751  0.01773   0.01892
Residual    383.82114  NA    NA      NA      NA      NA           NA

This question seems to be an overly simple one but I would still appreciate an informed response.

I am testing the interaction effect between a categorical variable (5 levels) and a binary variable. Outcome variable is continuous.

I have completed by regression and anova to test for a possible interaction effect. Results of regression indicates a significant interaction effect between two groups (EX: class 1 and 2).

In short, the regression output suggests a significant interaction effect (Class=3 * BIN=1) and the anova output suggests that the SSQ of the interaction term is non-significant (CLASS:BIN).

(1) Does the significant interaction term in regression output justify "probing" of the significant interaction effect?

(2) What is the significance of the (non-significant) interaction term in anova?

Regression output:

 Coef              S.E.   t       Pr(>|t|)
 Intercept         0.6533 0.2402  2.72 0.0068  
 Class=2          -0.6955 0.2997 -2.32 0.0208  
 Class=3          -1.0229 0.2617 -3.91 0.0001  
 Class=4          -0.8226 0.2891 -2.84 0.0047  
 Class=5          -0.6086 0.3250 -1.87 0.0619  
 BIN=1             -0.3671 0.3082 -1.19 0.2343  
 Class=2 * BIN=1  0.7025 0.3955  1.78 0.0765  
 Class=3 * BIN=1  0.8602 0.3561  2.42 0.0162 
 Class=4 * BIN=1  0.6066 0.3743  1.62 0.1058  
 Class=5 * BIN=1  0.5961 0.4285  1.39 0.1649

Anova Table:

            SSQ     df1   df2     F value  Pr(>F)    eta2    partial.eta2
CLASS       19.04070  4  621.7598 4.0077   0.00322  0.04562   0.04726
BIN         7.11855   1 1014.6402 6.2523   0.01256  0.01706   0.01821
CLASS:BIN   7.40183   4  479.6875 1.3864   0.23751  0.01773   0.01892
Residual    383.82114  NA    NA      NA      NA      NA           NA

This question seems to be an overly simple one but I would still appreciate an informed response.

I am testing the interaction effect between a categorical variable (5 levels) and a binary variable. Outcome variable is continuous.

I have completed by regression and anova to test for a possible interaction effect. Results of regression indicates a significant interaction effect between two groups (EX: class 1 and 2).

In short, the regression output suggests a significant interaction effect (Class=3 * BIN=1) and the anova output suggests that the SSQ of the interaction term is non-significant (CLASS:BIN).

(1) Does the significant interaction term (Class=3 * BIN=1) in the regression output justify "probing" of the significant interaction effect?

(2) What is the significance of the (non-significant) interaction term (CLASS:BIN) in anova?

Regression output:

 Coef              S.E.   t       Pr(>|t|)
 Intercept         0.6533 0.2402  2.72 0.0068  
 Class=2          -0.6955 0.2997 -2.32 0.0208  
 Class=3          -1.0229 0.2617 -3.91 0.0001  
 Class=4          -0.8226 0.2891 -2.84 0.0047  
 Class=5          -0.6086 0.3250 -1.87 0.0619  
 BIN=1             -0.3671 0.3082 -1.19 0.2343  
 Class=2 * BIN=1  0.7025 0.3955  1.78 0.0765  
 Class=3 * BIN=1  0.8602 0.3561  2.42 0.0162 
 Class=4 * BIN=1  0.6066 0.3743  1.62 0.1058  
 Class=5 * BIN=1  0.5961 0.4285  1.39 0.1649

Anova Table:

            SSQ     df1   df2     F value  Pr(>F)    eta2    partial.eta2
CLASS       19.04070  4  621.7598 4.0077   0.00322  0.04562   0.04726
BIN         7.11855   1 1014.6402 6.2523   0.01256  0.01706   0.01821
CLASS:BIN   7.40183   4  479.6875 1.3864   0.23751  0.01773   0.01892
Residual    383.82114  NA    NA      NA      NA      NA           NA
1
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Probing an interaction effect between categorical variables--when is it warranted? (Regression vs. anova)

This question seems to be an overly simple one but I would still appreciate an informed response.

I am testing the interaction effect between a categorical variable (5 levels) and a binary variable. Outcome variable is continuous.

I have completed by regression and anova to test for a possible interaction effect. Results of regression indicates a significant interaction effect between two groups (EX: class 1 and 2).

In short, the regression output suggests a significant interaction effect (Class=3 * BIN=1) and the anova output suggests that the SSQ of the interaction term is non-significant (CLASS:BIN).

(1) Does the significant interaction term in regression output justify "probing" of the significant interaction effect?

(2) What is the significance of the (non-significant) interaction term in anova?

Regression output:

 Coef              S.E.   t       Pr(>|t|)
 Intercept         0.6533 0.2402  2.72 0.0068  
 Class=2          -0.6955 0.2997 -2.32 0.0208  
 Class=3          -1.0229 0.2617 -3.91 0.0001  
 Class=4          -0.8226 0.2891 -2.84 0.0047  
 Class=5          -0.6086 0.3250 -1.87 0.0619  
 BIN=1             -0.3671 0.3082 -1.19 0.2343  
 Class=2 * BIN=1  0.7025 0.3955  1.78 0.0765  
 Class=3 * BIN=1  0.8602 0.3561  2.42 0.0162 
 Class=4 * BIN=1  0.6066 0.3743  1.62 0.1058  
 Class=5 * BIN=1  0.5961 0.4285  1.39 0.1649

Anova Table:

            SSQ     df1   df2     F value  Pr(>F)    eta2    partial.eta2
CLASS       19.04070  4  621.7598 4.0077   0.00322  0.04562   0.04726
BIN         7.11855   1 1014.6402 6.2523   0.01256  0.01706   0.01821
CLASS:BIN   7.40183   4  479.6875 1.3864   0.23751  0.01773   0.01892
Residual    383.82114  NA    NA      NA      NA      NA           NA