A Causal View on Bias in Missing Data Imputation: The Impact of Evil Auxiliary Variables on Norming of Test Scores.

Among the most important merits of modern missing data techniques such as multiple imputation (MI) and full-information maximum likelihood estimation is the possibility to include additional information about the missingness process via auxiliary variables. During the past decade, the choice of auxiliary variables has been investigated under a variety of different conditions and more recent research points to the potentially biasing effect of certain auxiliary variables, particularly colliders (Thoemmes & Rose, 2014). In this article, we further extend biasing mechanisms of certain auxiliary variables considered in previous research and thereby focus on their effects on individual diagnosis based on norming, in which the whole distribution of a variable is of interest rather than average coefficients (e.

Studying item-effect variables and their correlation patterns with multi-construct multi-state models.

Method effects on the item level can be modeled as latent difference variables in longitudinal data. These item-effect variables represent interindividual differences associated with responses to a specific item when assessing a common construct with multi-item scales. In latent variable analyses, their inclusion substantially improves model fits in comparison to classical unidimensional measurement models.

The Predictive Validity of Item Effect Variables in the for Psychological and Physical Health.

Although the strives to capture a single dimension, describing respondents' satisfaction with life as a whole, individual items might also capture unique aspects of life satisfaction leading to some form of multidimensionality. Such systematic item-specific variance can be viewed as a content-laden secondary trait. Information on the nomological net and predictive validity can be useful to aid the interpretation of these item-specific effects.

Causal effect analysis in nonrandomized data with latent variables and categorical indicators: The implementation and benefits of EffectLiteR.

Instead of using manifest proxies for a latent outcome or latent covariates in a causal effect analysis, the R package EffectLiteR facilitates a direct integration of latent variables based on structural equation models (SEM). The corresponding framework considers latent interactions and provides various effect estimates for evaluating the differential effectiveness of treatments. In addition, a user-friendly graphical interface customizes the implementation of the complex models.

Compensation and Amplification of Attenuation Bias in Causal Effect Estimates.

Covariate-adjusted treatment effects are commonly estimated in non-randomized studies. It has been shown that measurement error in covariates can bias treatment effect estimates when not appropriately accounted for. So far, these delineations primarily assumed a true data generating model that included just one single covariate.

When does measurement error in covariates impact causal effect estimates? Analytic derivations of different scenarios and an empirical illustration.

The average causal treatment effect (ATE) can be estimated from observational data based on covariate adjustment. Even if all confounding covariates are observed, they might not necessarily be reliably measured and may fail to obtain an unbiased ATE estimate. Instead of fallible covariates, the respective latent covariates can be used for covariate adjustment.

Evaluative conditioning increases with temporal contiguity. The influence of stimulus order and stimulus interval on evaluative conditioning.

Evaluative conditioning (EC) is a change in valence that is due to pairing a conditioned stimulus (CS) with another, typically valent, unconditioned stimulus (US). This paper investigates how basic presentation parameters moderate EC effects. In two studies we tested the effectiveness of different temporal relations of the CS and the US, that is, the order in which the stimuli were presented and the temporal distance between them.