Psychological methods最新文献

Contaminant observations often cause problems when estimating the parameters of cognitive models. In this study, we tested and improved the robustness of parameter estimation using amortized Bayesian inference. We conducted systematic analyses in two settings: a toy example (i.e., a normal distribution with an unknown mean) and a popular cognitive model, the drift diffusion model. First, we studied the stylized sensitivity curve and the breakdown point of the estimators. Next, we proposed a simple data augmentation approach that incorporated a contamination distribution into the data-generating process during training to train robust estimators. We examined several robust estimators with different contamination distributions, and evaluated their performance and cost in terms of accuracy and efficiency loss relative to a standard estimator. Introducing contaminants from a Cauchy distribution during training significantly increases the robustness of the neural density estimator, as measured by bounded sensitivity functions and a substantially higher breakdown point. Overall, the proposed method is straightforward and practical to implement, with broad applicability in fields where outlier detection or removal is challenging. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Coefficient α, although ubiquitous in the research literature, is frequently criticized for being a poor estimate of test reliability. In this note, we consider the range of α and prove that it has no lower bound (i.e., α ∈ ( - ∞, 1]). While outlining our proofs, we present algorithms for generating data sets that will yield any fixed value of α in its range. We also prove that for some data sets-even those with appreciable item correlations-α is undefined. Although α is a putative estimate of the correlation between parallel forms, it is not a correlation as α can assume any value below-1 (and α values below 0 are nonsensical reliability estimates). In the online supplemental materials, we provide R code for replicating our empirical findings and for generating data sets with user-defined α values. We hope that researchers will use this code to better understand the limitations of α as an index of scale reliability. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

We propose a novel method to analyze time-constrained yes/no questions about a target behavior (e.g., "Did you take sleeping pills during the last 12 months?"). A drawback of these questions is that the relative frequency of answering these questions with "yes" does not allow one to draw definite conclusions about the frequency of the target behavior (i.e., how often sleeping pills were taken) nor about the prevalence of trait carriers (i.e., percentage of people that take sleeping pills). Here we show how this information can be extracted from the results of such questions employing a prevalence curve and a Poisson model. The applicability of the method was evaluated with a survey on everyday behavior, which revealed plausible results and reasonable model fit. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

For over three decades, methodologists have cautioned against the use of cross-sectional mediation analyses because they yield biased parameter estimates. Yet, cross-sectional mediation models persist in practice and sometimes represent the only analytic option. We propose a sensitivity analysis procedure to encourage a more principled use of cross-sectional mediation analysis, drawing inspiration from Gollob and Reichardt (1987, 1991). The procedure is based on the two-wave longitudinal mediation model and uses phantom variables for the baseline data. After a researcher provides ranges of possible values for cross-lagged, autoregressive, and baseline Y and M correlations among the phantom and observed variables, they can use the sensitivity analysis to identify longitudinal conditions in which conclusions from a cross-sectional model would differ most from a longitudinal model. To support the procedure, we first show that differences in sign and effect size of the b-path occur most often when the cross-sectional effect size of the b-path is small and the cross-lagged and the autoregressive correlations are equal or similar in magnitude. We then apply the procedure to cross-sectional analyses from real studies and compare the sensitivity analysis results to actual results from a longitudinal mediation analysis. While no statistical procedure can replace longitudinal data, these examples demonstrate that the sensitivity analysis can recover the effect that was actually observed in the longitudinal data if provided with the correct input information. Implications of the routine application of sensitivity analysis to temporal bias are discussed. R code for the procedure is provided in the online supplementary materials. (PsycInfo Database Record (c) 2025 APA, all rights reserved).