Fig 1.
An example of the measurement model used for response shift investigation.
Notes: The squares represent observed variables, i.e., questionnaire scores (X), measured at both baseline and follow-up assessment. The solid single-headed arrows at the bottom represent the residual factors of each observed variable. The dotted double-headed arrow represents the relations between the residual factors, where the residual factors of the same observed variable are allowed to correlate over time. The circles represent the target construct that the observed variables aim to measure (e.g., insomnia severity, depression, or personal meaning; both at baseline and follow-up assessment). Each arrow from a circle to an observed variable represents a factor loading. The double-headed arrows between the circles represent the correlations between the target construct over time.
Table 1.
Means and standard deviations of the primary outcome and predictors used for response shift analyses of the CBT for insomnia dataset.
Table 2.
Means and standard deviations of the primary outcome and predictors used for response shift analyses of the personal meaning for cancer survivors dataset.
Table 3.
Means and standard deviations of the primary outcome and predictor used for response shift analyses of the CBT for depressive symptoms in patients with diabetes dataset.
Table 4.
Overall goodness of fit of the models in steps 1–3 of the SEM-approach for investigation of response shift (objective 1) for each of the three datasets.