Psychological methods最新文献

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).

Polynomial regression is an old and commonly discussed modeling technique, though recommendations for its usage are widely variable. Here, we make the case that polynomial regression with second- and third-order terms should be part of every applied practitioners standard model-building toolbox, and should be taught to new students of the subject as the default technique to model nonlinearity. We argue that polynomial regression is superior to nonparametric alternatives for nonstatisticians due to its ease of interpretation, flexibility, and its nonreliance on sophisticated mathematics, like knots and kernel smoothing. This makes it the ideal default for nonstatisticians interested in building realistic models that can capture global as well as local effects of predictors on a response variable. Low-order polynomial regression can effectively model compact floor and ceiling effects, local linearity, and prevent inferring the presence of spurious interaction effects between distinct predictors when none are present. We also argue that the case against polynomial regression is largely specious, relying on either misconceptions around the method, strawman arguments, or historical artifacts. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Joint modeling of decisions and neural activation poses the potential to provide significant advances in linking brain and behavior. However, methods of joint modeling have been limited by difficulties in estimation, often due to high dimensionality and simultaneous estimation challenges. In the current article, we propose a method of model estimation that draws on state-of-the-art Bayesian hierarchical modeling techniques and uses factor analysis as a means of dimensionality reduction and inference at the group level. This hierarchical factor approach can adopt any model for the individual and distill the relationships of its parameters across individuals through a factor structure. We demonstrate the significant dimensionality reduction gained by factor analysis and good parameter recovery, and illustrate a variety of factor loading constraints that can be used for different purposes and research questions, as well as three applications of the method to previously analyzed data. We conclude that this method provides a flexible and usable approach with interpretable outcomes that are primarily data-driven, in contrast to the largely hypothesis-driven methods often used in joint modeling. Although we focus on joint modeling methods, this model-based estimation approach could be used for any high dimensional modeling problem. We provide open-source code and accompanying tutorial documentation to make the method accessible to any researchers. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Bots (i.e., automated software programs that perform various tasks) and fraudulent responders pose a growing and costly threat to psychological research as well as affect data integrity. However, few studies have been published on this topic. (a) Describe our experience with bots and fraudulent responders using a case study, (b) present various bot and fraud detection tactics (BFDTs) and identify the number of suspected bot and fraudulent respondents removed, (c) propose a consensus confidence system for eliminating bots and fraudulent responders to determine the number of BFDTs researchers should use, and (d) examine the initial effectiveness of dynamic versus static BFDT protocols. This study is part of a larger 14-day experience sampling method study with trauma-exposed sexual minority cisgender women and transgender and/or nonbinary people. Faced with several bot and fraudulent responder infiltrations during data collection, we developed an evolving BFDT protocol to eliminate bots and fraudulent responders. Throughout this study, we received 24,053 responses on our baseline survey. After applying our BFDT protocols, we eliminated 99.75% of respondents that were likely bots or fraudulent responders. Some BFDTs seemed to be more effective and afford higher confidence than others, dynamic protocols seemed to be more effective than static protocols, and bots and fraudulent responders introduced significant bias in the results. This study advances online psychological research by curating one of the largest samples of bot and fraudulent respondents and pilot testing the largest number of BFDTs to date. Recommendations for future research are provided. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Repeated measure data design has been used extensively in a wide range of fields, such as brain aging or developmental psychology, to answer important research questions exploring relationships between trajectory of change and external variables. In many cases, such data may be collected from multiple study cohorts and harmonized, with the intention of gaining higher statistical power and enhanced external validity. When psychological constructs are measured using survey scales, a fundamental psychometric challenge for data harmonization is to create commensurate measures for the constructs of interest across studies. Traditional analysis may fit a unidimensional item response theory model to data from one time point and one cohort to obtain item parameters and fix the same parameters in subsequent analyses. Such a simplified approach ignores item residual dependencies in the repeated measure design on one hand, and on the other hand, it does not exploit accumulated information from different cohorts. Instead, two alternative approaches should serve such data designs much better: an integrative approach using multiple-group two-tier model via concurrent calibration, and if such calibration fails to converge, a Bayesian sequential calibration approach that uses informative priors on common items to establish the scale. Both approaches use a Markov chain Monte Carlo algorithm that handles computational complexity well. Through a simulation study and an empirical study using Alzheimer's diseases neuroimage initiative cognitive battery data (i.e., language and executive functioning), we conclude that latent change scores obtained from these two alternative approaches are more precisely recovered. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Many statistical models have been proposed to examine reciprocal cross-lagged causal effects from panel data. The present article aims to clarify how these various statistical models control for unmeasured time-invariant confounders, helping researchers understand the differences in the statistical models from a causal inference perspective. Assuming that the true data generation model (i.e., causal model) has time-invariant confounders that were not measured, we compared different statistical models (e.g., dynamic panel model and random-intercept cross-lagged panel model) in terms of the conditions under which they can provide a relatively accurate estimate of the target causal estimand. Based on the comparisons and realistic plausibility of these conditions, we made some practical suggestions for researchers to select a statistical model when they are interested in causal inference. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

The depth of information collected in participants' daily lives with active (e.g., experience sampling surveys) and passive (e.g., smartphone sensors) ambulatory measurement methods is immense. When measuring participants' behaviors in daily life, the timing of particular events-such as social interactions-is often recorded. These data facilitate the investigation of new types of research questions about the timing of those events, including whether individuals' affective state is associated with the rate of social interactions (binary event occurrence) and what types of social interactions are likely to occur (multicategory event occurrences, e.g., interactions with friends or family). Although survival analysis methods have been used to analyze time-to-event data in longitudinal settings for several decades, these methods have not yet been incorporated into ambulatory assessment research. This article illustrates how multilevel and multistate survival analysis methods can be used to model the social interaction dynamics captured in intensive longitudinal data, specifically when individuals exhibit particular categories of behavior. We provide an introduction to these models and a tutorial on how the timing and type of social interactions can be modeled using the R statistical programming language. Using event-contingent reports (N = 150, N_events = 64,112) obtained in an ambulatory study of interpersonal interactions, we further exemplify an empirical application case. In sum, this article demonstrates how survival models can advance the understanding of (social interaction) dynamics that unfold in daily life. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

A critical assumption in exploratory factor analysis (EFA) is that manifest variables are no longer correlated after the influences of the common factors are controlled. The assumption may not be valid in some EFA applications; for example, questionnaire items share other characteristics in addition to their relations to common factors. We present a computationally efficient and robust method to estimate EFA with correlated residuals. We provide details on the implementation of the method with both ordinary least squares estimation and maximum likelihood estimation. We demonstrate the method using empirical data and conduct a simulation study to explore its statistical properties. The results are (a) that the new method encountered much fewer convergence problems than the existing method; (b) that the EFA model with correlated residuals produced a more satisfactory model fit than the conventional EFA model; and (c) that the EFA model with correlated residuals and the conventional EFA model produced very similar estimates for factor loadings. (PsycInfo Database Record (c) 2025 APA, all rights reserved).