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Hello, Could you please tell me if it is possible to test an interaction between a latent variable (based on continuous indicators) and an observed variable that is categorical (0 = condition not present, 1= condition present)in SEM? Both the continuous latent variable and observed categorical variable are exogenous (not predicted by other variables). If so what is the best way to go about this? I was thinking I would use the MLR estimator. Also, do I specify the categorical indicator as categorical in my syntax? I have a medium sized sample of about 350 participants. Thank you for your help! |
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You would use the XWITH option. You would not put the binary covariates on the CATEGORICAL list. This list is for dependent variables only. |
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Hi Linda, Great, thank-you. So can I use the following analysis syntax: ANALYSIS: ESTIMATOR = MLR; TYPE = RANDOM; ALGORITHM = INTEGRATION; Thank you for your help! |
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Yes. |
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Hi Linda, My sample size is 223 and I have a lot of missing data on the two observed binary variables included in the model. I would like to test the effect of an interaction term in the model. The two terms I am using to create the interaction term are hypothesized mediators. Both terms are continuous latent variables measured by ordinal items (5-item likert scales) with 7 and 8 items each. Most items have high kurtosis (the distribution is very pointy and thin in the tails). Is it appropriate to try to run a model with an interaction term given the small sample size, missing data, and the fact that the items of the continuous latent constructs are not normally distributed? ANALYSIS: type = random; MODEL: x BY C1* C2 C3 C4 C5 C6 C7 C8 C9; x@1; stigma BY S1* S2 S3 S4 S5 S6 S7 S8; stigma@1; stigmasw BY sw191* sw192 sw193 sw194 sw195 sw197 sw198; stigmasw@1; stigma ON x; stigmasw ON x; int | stigma XWITH stigmasw; CCU_1 ON x stigma stigmasw; CCU_1 ON int; CCU_2 ON x stigma stigmasw; CCU_2 ON int; OUTPUT: sampstat tech1 stdyx modindices(all); Many thanks! Maria |
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You don't mention the degree of missingness - the coverage on the diagonal of what we print, but missing data certainly hurts when max n is only 223. I don't know if you treat the 5-category Likert indicators as categorical, but if you do the latent variables can still be approx normal when the indicators are not. I haven't thought through how indirect effects should be evaluated when mediators have an interaction term. |
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