In the previous course, we explained that when the modulator is a continuous variable, we use whether the interaction terms of the independent variable and the modulator are significant to the path coefficient of the dependent variable to determine whether the moderating effect is significant. However, when the moderator is categorical, the interaction term method is completely inapplicable, in which case it is necessary to use AMOS categorical moderator multi-group analysis. Let’s explain it through practical cases.
The following is the data of X and Y indicators for 190 patients, M is sex, and I wanted to study the moderating effect of gender on the influence of X on Y (Figure 1)
Figure 1
AMOS classification moderator multi-cohort analysis procedure
(1) Open the amos and draw the structural model diagram (Figure 2)
Figure 2
(2) Create a new group, double-click the blank space in the figure below, and click “New” (Figure 3)
Figure 3
(3) After importing the data, click Grouping Variable, select Gender T, and click OK (Figure 4)
Figure 4
(4) Click Group Value again, select category 0, and click OK (Figure 5)
Figure 5
(5) Use the same method to set Group number2, that is, after group 2 is set to category 1 of the categorical variable, as shown in Figure 6
Figure 6
(6) Drag the data into the variable box (Figure 7)
Figure 7
(7) Click Multiple-Group Analysis to set it in Figure 8 below
Figure 8
(8) Set the residual item and check the output option setting (Figure 9)
Figure 9
(9) Click to calculate and analyze the results. Click Model Fit under View Text to view the CMIN and DF values of the Unconstrained model and the Structural weights model (Figure 10), and calculate the difference values, △CMIN=13.907, △DF=1, respectively
Figure 10
(10) Calculate the significance level in Excel, apply the CHIDIST function (Figure 11), substitute the above, △CMIN and △DF to calculate the significance level is 0.0001 < 0.05, it is concluded that the modulating effect of M in X →Y is significant.
Figure 11
After obtaining the significant adjustment effect, it is necessary to further analyze the difference between the two categories of the adjustment effect, here you need to return to the output interface settings, check Critical ratios for differences and then perform the calculation (Figure 12)
Figure 12
Finally, click Pairwise Parameter Comparisons under View Text, view the value as shown in the figure (Figure 13), and see that the b1_1 and b1_2, that is, the values of women and men are -3.799, and their absolute value size is greater than 1.96, which concludes that there are significant differences in the regulatory effect between men and women, further indicating that the regulatory effect of gender is significant.
Figure 13
That’s all for this issue, more AMOS statistical analysis courses will be updated in the future, please pay attention! We will continue to launch more practical biomedical statistics courses, covering SPSS, Meta, Stata, GraphPad, SAS, R, NoteExpress, EndNote, Revman and other data statistical analysis software and statistical methods! Stay tuned!
Xinghuakai Biomedical Statistics Customer Service QQ3305200052
Copyright note: This article is an original article of Xinghua Biomedical Statistics.
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