Hi Suraiya Yasmin, your question is very much interesting. The residual effect in path analysis, We assume and follow like as:
Residual based indices:
1. When the model fits well, the residuals (difference between the model
implied covariance matrix and the sample covariance matrix) should be small.
2. Standardized root mean square residual (SRMR):
1. It measures the average value across the standardized residuals.
2. It ranges from zero (perfect fit) to one (very poor fit).
3. Rule of thumb: A well-fitting model < .05.
3. Root mean square error of approximation (RMSEA; Steiger & Lind, 1980)
1.Similar to SRMR.
2.Rules of thumb (Browne & Cudeck, 1993):
3.Close fit: < 0.05
4. Reasonable fit: 0.05 – 0.08
5. Inadequate fit: > 0.1
6. Advantage: Confidence intervals on RMSEA are available in most SEM packages.
Does the value of residual also depends on the number of genotypes taken in a study. I mean to say if more number of genotypes (>50) then can high value of residuals be justified or not?
It measure the role of other possible independent variables which were not included in the study on the dependent variable
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