Hi All,

I am trying to determine if a two-stage intervention for a medical centre affected the number of appointments it was able to book. I am interested in determining specifically if the regression line predicting the number of appointments changes as a function of what stage of the intervention the clinic was in. I had read through similar threads in Statalist (e.g., https://www.statalist.org/forums/for...ent-subsamples) and I *think* I understand the logic of using using interaction terms, but would like to make sure that I'm not misunderstanding my model (or made some either grevious error).

The variables in my model are:

avg_apps - the average number of new appointments per month

month - starts in January of 2016 and goes to April of 2018 (28 entries)

int3 - the stage of intervention (0 = pre-intervention (Jan 2016/Jan 2017; 1 = stage 1 of intervention (Feb 2017/July 2017; 2 = stage 2 of intervention (Aug 2017/Apr 2018)

I also include two lagged variables for avg_apps (lagged for 1 month and two months respectively) because they have large zero-order correlations with avg_apps.

I used the interaction term to investigate whether the predicted number of appointments per month, changed as a function for which stage of the intervention the clinic was in.

From this model I am concluding that:

1. There were no differences in the predicted DV for pre-intervention and Stage 1 of the intervention (B = -.18, p = .259).

2. The was a difference in the predicted DV for the pre-intervention and Stage 2 of the intervention (B = -.28, p = .016).

3. Once in Stage 2 of the intervention, moving from one month to an adjacent month, is associated with a decline of .32 appointments (-.04 + -.28 = -.32).

Is this a reasonable interpretation of the findings?

Thanks everyone!

David.

I am trying to determine if a two-stage intervention for a medical centre affected the number of appointments it was able to book. I am interested in determining specifically if the regression line predicting the number of appointments changes as a function of what stage of the intervention the clinic was in. I had read through similar threads in Statalist (e.g., https://www.statalist.org/forums/for...ent-subsamples) and I *think* I understand the logic of using using interaction terms, but would like to make sure that I'm not misunderstanding my model (or made some either grevious error).

The variables in my model are:

avg_apps - the average number of new appointments per month

month - starts in January of 2016 and goes to April of 2018 (28 entries)

int3 - the stage of intervention (0 = pre-intervention (Jan 2016/Jan 2017; 1 = stage 1 of intervention (Feb 2017/July 2017; 2 = stage 2 of intervention (Aug 2017/Apr 2018)

I also include two lagged variables for avg_apps (lagged for 1 month and two months respectively) because they have large zero-order correlations with avg_apps.

Code:

regress avg_apps c.month##i.int3 L1.avg_apps L2.avg_apps Source | SS df MS Number of obs = 26 -------------+---------------------------------- F(7, 18) = 8.99 Model | 23.0524598 7 3.29320854 Prob > F = 0.0001 Residual | 6.5918093 18 .366211628 R-squared = 0.7776 -------------+---------------------------------- Adj R-squared = 0.6912 Total | 29.6442691 25 1.18577076 Root MSE = .60515 ------------------------------------------------------------------------------------- avg_apps | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------------+---------------------------------------------------------------- month | -.04377 .0579524 -0.76 0.460 -.1655235 .0779836 | int3 | Feb 2017/July 2017 | 3.109226 2.460196 1.26 0.222 -2.059455 8.277907 Aug 2017/May 2018 | 6.354542 2.091319 3.04 0.007 1.960845 10.74824 | int3#c.month | Feb 2017/July 2017 | -.1820653 .156301 -1.16 0.259 -.5104416 .146311 Aug 2017/May 2018 | -.2822999 .1062098 -2.66 0.016 -.5054384 -.0591614 | avg_apps | L1. | .400261 .2264945 1.77 0.094 -.0755862 .8761081 L2. | -.1321656 .2767065 -0.48 0.639 -.7135045 .4491732 | _cons | 3.914906 1.795642 2.18 0.043 .1424022 7.68741 -------------------------------------------------------------------------------------

From this model I am concluding that:

1. There were no differences in the predicted DV for pre-intervention and Stage 1 of the intervention (B = -.18, p = .259).

2. The was a difference in the predicted DV for the pre-intervention and Stage 2 of the intervention (B = -.28, p = .016).

3. Once in Stage 2 of the intervention, moving from one month to an adjacent month, is associated with a decline of .32 appointments (-.04 + -.28 = -.32).

Is this a reasonable interpretation of the findings?

Thanks everyone!

David.

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