We present a revision of latent state-trait (LST-R) theory with new definitions of states and traits. This theory applies whenever we study the consistency of behavior, its variability, and its change over time. States and traits are defined in terms of probability theory. This allows for a seamless transition from theory to statistical modeling of empirical data. LST-R theory not only gives insights into the nature of latent variables but it also takes into account four fundamental facts: Observations are fallible, they never happen in a situational vacuum, they are always made using a specific method of observations, and there is no person without a past. Although the first fact necessitates considering measurement error, the second fact requires allowances for situational fluctuations. The third fact implies that, in the first place, states and traits are method specific. Furthermore, compared to the previous version of LST theory (see, e.g., Steyer et al. 1992 , 1999 ), our revision is based on the notion of a person-at-time-t. The new definitions in LST-R theory have far-reaching implications that not only concern the properties of states, traits, and the associated concepts of measurement errors and state residuals, but also are related to the analysis of states and traits in longitudinal observational and intervention studies.

In this article, an overview is given of four methods to perform Factor Score Regression (FSR), namely regression FSR, Bartlett FSR, the bias avoiding method of Skrondal and Laake (2001) and the bias correcting method of Croon (2002). The bias correcting method is extended to include a reliable standard error. The four methods are compared to each other and to SEM by using analytic calculations and two Monte Carlo simulation studies to examine their finite sample characteristics. Several performance criteria are used, such as the bias using the unstandardized and standardized parameterization, efficiency, mean square error, standard error bias, type I-error rate and power. The results show that the bias correcting method, with the newly developed standard error, is the only suitable alternative for SEM. While it has a higher standard error bias than SEM, it has a comparable bias, efficiency, MSE, power and type I-error rate.

Perceived control and health are closely interrelated in adulthood and old age. However, less is known regarding the differential implications of two facets of perceived control, constraints and mastery, for mental and physical health. Furthermore, a limitation of previous research testing the pathways linking perceived control to mental and physical health is that mediation was tested with cross-sectional designs and not in a longitudinal mediation design that accounts for temporal ordering and prior confounds. Using data from the Health and Retirement Study (HRS; n = 7,612, M age = 68, SD = 10.66; 59% women) we examined the effect of constraints and mastery on 4-year changes in mental and physical health and whether physical activity mediated such effects in a longitudinal mediation design. Using confirmatory factor analysis, we modeled the two-factor structure of perceived control that consisted of constraints and mastery. In our longitudinal mediation model, where we accounted for possible confounders (e.g., age, gender, education, neuroticism, conscientiousness, memory, and health conditions), constraints showed a stronger total effect on mental and physical health, than mastery, such that more constraints were associated with 4-year declines in mental and physical health. Physical activity did not mediate the effect of constraints and mastery on mental and physical health (indirect effect). In order to demonstrate the importance of a longitudinal mediation model that accounts for confounders, we also estimated the mediated effect using two models commonly used in the literature: cross-sectional mediation model and longitudinal mediation model without accounting for confounders. These mediation models indicated a spurious indirect effect that cannot be causally interpreted. Our results showcase that constraints and mastery have differential implications for mental and physical health, as well as how a longitudinal mediation design can illustrate (or not) pathways in developmental processes. Our discussion focuses on the conceptual and methodological implications of a two facet model of perceived control and the strengths of longitudinal mediation designs for testing conceptual models of human development.

The actor–partner interdependence model (APIM) is widely used for analyzing dyadic data. Although dyadic research has become immensely popular, its statistical complexity can be a barrier. To remedy this, a free user‐friendly web application, called APIM_SEM, has been developed. This app automatically performs the statistical analyses (i.e., structural equation modeling) of both simple and complex APIMs. It allows the researcher to analyze distinguishable or indistinguishable dyads, to examine dyadic patterns, to estimate actor and partner effects of one or two predictors, and to control for covariates. Results are provided in software and text format, complemented by summary tables and figures. As an illustration, the effect of perception of the partner on satisfaction is assessed by fitting APIMs with varying complexity.

We present a framework for estimating average and conditional effects of a discrete treatment variable on a continuous outcome variable, conditioning on categorical and continuous covariates. Using the new approach, termed the EffectLiteR approach, researchers can consider conditional treatment effects given values of all covariates in the analysis and various aggregates of these conditional treatment effects such as average effects, effects on the treated, or aggregated conditional effects given values of a subset of covariates. Building on structural equation modeling, key advantages of the new approach are (1) It allows for latent covariates and outcome variables; (2) it permits (higher order) interactions between the treatment variable and categorical and (latent) continuous covariates; and (3) covariates can be treated as stochastic or fixed. The approach is illustrated by an example, and open source software EffectLiteR is provided, which makes a detailed analysis of effects conveniently accessible for applied researchers.