Table 2.

Overview of practical considerations in application of the SEM approach for the detection of response shift

	Decisions to be made	Recommendations
Know your measures
Step 1: establishing a measurement model	• Choose one of two procedures (i.e., model all measurement occasions simultaneously or separately) to arrive at a longitudinal measurement model • Modify the measurement model when model fit is not adequate, in order to obtain a well-fitting model • Decide which and how many modifications are necessary to obtain a substantively meaningful measurement model	• Specify the measurement model based on the structure of the questionnaire, previous research, and/or theory • In case of unclear or unknown structure, use exploratory analyses and substantive considerations to arrive at an interpretable and well-fitting measurement model • Combine substantive and statistical criteria to guide (re)specification to arrive at the most parsimonious, most reasonable, and best-fitting measurement model
Identification of possible response shift
Step 2: overall test of response shift  Step 3: detection of response shift	• Choose statistical criteria to guide response shift detection • Choose between competing response shifts • Decide when to stop searching for response shift	• Use the overall test of response shift to protect against false detection (i.e., type I error) • When possible, use an iterative procedure (where all possible response shifts are considered one at a time) to identify specific response shift effects • Alternatively, use statistical indices such as modification indices, expected parameter change, inspection of residuals, and/or Wald tests to guide response shift detection • Evaluate each possible response shift statistically (i.e., difference in model fit) and substantively (i.e., interpretation) in order to identify all meaningful effects • For more robust stopping criteria, use overall model-fit evaluation and evaluation of difference in model fit of the measurement model • Use different sequential decision-making practices in order to find confidence in robustness of results
Interpretation of response shift and ‘true’ change
Step 3: detection of response shift  Step 4: assessment of ‘true’ change	• Can the detected violations of invariance of model parameters be interpreted as response shifts? • Is there ‘true’ change? • What is the impact of response shift on the assessment of change?	• Detected effects can be substantively interpreted as response shift using substantive knowledge of the patient group, treatment, or disease trajectory • Compare common factor means across occasions to assess ‘true’ change in the target construct • To understand both ‘true’ change and response shift, consider possible other explanations for the detected effects and include—when available—possible explanatory variables (e.g., coping, adaptation, social comparison) • To understand the impact of response shift on change assessment, evaluate (1) the impact of response shift on observed change in the indicator variable using the decomposition of change [12], and (2) the impact of response shift on ‘true’ change by comparing the common factor means from the final model of Step 3 (while taking into account response shift) with the common factor means from Step 2 (under no response shift)