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## Contributions: Abstract## How does censoring of continuous parallel measurements bias validity of their common variable and scale reliability? A simulation study
Empirical studies often face data with strong floor or ceiling effects. When data are collected with analogue rating scales, the concept of censored variables seems useful to model these distribution phenomena, invoking thresholds. We assessed to what extent the censoring process may bias (a) the validity of the latent variable built using linear regression of the censored variables, and (b) the scale reliability. The degree of censoring was the independent variable. Moreover, PRELIS 2 allows one to estimate covariance matrices corrected for censoring. We also evaluated to what extent the use of corrected input data could reduce the censoring effect. Multinormal zero-mean data were generated on 5000 cases for 5 parallel variables of unit variance, where the error variance was set at 1/3. Then, 5 below-censoring rules where applied to the data, using threshold values of -1, -0.5, 0, 0.5, and 1. Each of the 5 resulting data sets provided two covariance matrices (raw, corrected), further used as input data for ML estimation. Validity of the modeled true-score variable (h The magnitude of the censoring effect was positively associated with the degree of censoring. Biases became marked before 0, i.e., the theoretical mean of the measurement variables, ranging from .73 to 1.00 for validity. Reliability measures ranged from .79 to .90 (theoretical reliability = .91). With corrected input data, censoring effects were clearly attenuated, but not neutralized. |