Psychological methods最新文献

Social network analysis provides an important framework for studying the causes, consequences, and structure of social ties. However, standard self-report measures-for example, as collected through the popular "name-generator" method-do not provide an impartial representation of such ties, be they transfers, interactions, or social relationships. At best, they represent perceptions filtered through the cognitive biases of respondents. Individuals may, for example, report transfers that did not really occur, or forget to mention transfers that really did. The propensity to make such reporting inaccuracies is both an individual-level and item-level characteristic-variable across members of any given group. Past research has highlighted that many network-level properties are highly sensitive to such reporting inaccuracies. However, there remains a dearth of easily deployed statistical tools that account for such biases. To address this issue, we provide a latent network model that allows researchers to jointly estimate parameters measuring both reporting biases and a latent, underlying social network. Building upon past research, we conduct several simulation experiments in which network data are subject to various reporting biases, and find that these reporting biases strongly impact fundamental network properties. These impacts are not adequately remedied using the most frequently deployed approaches for network reconstruction in the social sciences (i.e., treating either the union or the intersection of double-sampled data as the true network), but are appropriately resolved through the use of our latent network models. To make implementation of our models easier for end-users, we provide a fully documented R package, STRAND, and include a tutorial illustrating its functionality when applied to empirical food/money sharing data from a rural Colombian population. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

When preregistered, one-tailed tests control false-positive results at the same rate as two-tailed tests. They are also more powerful, provided the researcher correctly identified the direction of the effect. So it is surprising that they are not more common in psychology. Here I make an argument in favor of one-tailed tests and address common mistaken objections that researchers may have to using them. The arguments presented here only apply in situations where the test is clearly preregistered. If power is truly as urgent an issue as statistics reformers suggest, then the deliberate and thoughtful use of preregistered one-tailed tests ought to be not only permitted, but encouraged in cases where researchers desire greater power. One-tailed tests are especially well suited for applied questions, replications of previously documented effects, or situations where directionally unexpected effects would be meaningless. Preregistered one-tailed tests can sensibly align the researcher's stated theory with their tested hypothesis, bring a coherence to the practice of null hypothesis statistical testing, and produce generally more persuasive results. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Cross-classified random effects modeling (CCREM) is a common approach for analyzing cross-classified data in psychology, education research, and other fields. However, when the focus of a study is on the regression coefficients at Level 1 rather than on the random effects, ordinary least squares regression with cluster robust variance estimators (OLS-CRVE) or fixed effects regression with CRVE (FE-CRVE) could be appropriate approaches. These alternative methods are potentially advantageous because they rely on weaker assumptions than those required by CCREM. We conducted a Monte Carlo Simulation study to compare the performance of CCREM, OLS-CRVE, and FE-CRVE in models, including conditions where homoscedasticity assumptions and exogeneity assumptions held and conditions where they were violated, as well as conditions with unmodeled random slopes. We found that CCREM out-performed the alternative approaches when its assumptions are all met. However, when homoscedasticity assumptions are violated, OLS-CRVE and FE-CRVE provided similar or better performance than CCREM. When the exogeneity assumption is violated, only FE-CRVE provided adequate performance. Further, OLS-CRVE and FE-CRVE provided more accurate inferences than CCREM in the presence of unmodeled random slopes. Thus, we recommend two-way FE-CRVE as a good alternative to CCREM, particularly if the homoscedasticity or exogeneity assumptions of the CCREM might be in doubt. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Goldberg's (2006) bass-ackward approach to elucidating the hierarchical structure of individual differences data has been used widely to improve our understanding of the relationships among constructs of varying levels of granularity. The traditional approach has been to extract a single component or factor on the first level of the hierarchy, two on the second level, and so on, treating the correlations between adjoining levels akin to path coefficients in a hierarchical structure. This article proposes three modifications to the traditional approach with a particular focus on examining associations among all levels of the hierarchy: (a) identify and remove redundant elements that perpetuate through multiple levels of the hierarchy; (b) (optionally) identify and remove artefactual elements; and (c) plot the strongest correlations among the remaining elements to identify their hierarchical associations. Together these steps can offer a simpler and more complete picture of the underlying hierarchical structure among a set of observed variables. The rationale for each step is described, illustrated in a hypothetical example and three basic simulations, and then applied in real data. The results are compared with the traditional bass-ackward approach together with agglomerative hierarchical cluster analysis, and a basic tutorial with code is provided to apply the extended bass-ackward approach in other data. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Studies of interaction effects are of great interest because they identify crucial interplay between predictors in explaining outcomes. Previous work has considered several potential sources of statistical bias and substantive misinterpretation in the study of interactions, but less attention has been devoted to the role of the outcome variable in such research. Here, we consider bias and false discovery associated with estimates of interaction parameters as a function of the distributional and metric properties of the outcome variable. We begin by illustrating that, for a variety of noncontinuously distributed outcomes (i.e., binary and count outcomes), attempts to use the linear model for recovery leads to catastrophic levels of bias and false discovery. Next, focusing on transformations of normally distributed variables (i.e., censoring and noninterval scaling), we show that linear models again produce spurious interaction effects. We provide explanations offering geometric and algebraic intuition as to why interactions are a challenge for these incorrectly specified models. In light of these findings, we make two specific recommendations. First, a careful consideration of the outcome's distributional properties should be a standard component of interaction studies. Second, researchers should approach research focusing on interactions with heightened levels of scrutiny. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Most scientific disciplines use significance testing to draw conclusions about experimental or observational data. This classical approach provides a theoretical guarantee for controlling the number of false positives across a set of hypothesis tests, making it an appealing framework for scientists seeking to limit the number of false effects or associations that they claim to observe. Unfortunately, this theoretical guarantee applies to few experiments, and the true false positive rate (FPR) is much higher. Scientists have plenty of freedom to choose the error rate to control, the tests to include in the adjustment, and the method of correction, making strong error control difficult to attain. In addition, hypotheses are often tested after finding unexpected relationships or patterns, the data are analyzed in several ways, and analyses may be run repeatedly as data accumulate. As a result, adjusted p values are too small, incorrect conclusions are often reached, and results are harder to reproduce. In the following, I argue why the FPR is rarely controlled meaningfully and why shrinking parameter estimates is preferable to p value adjustments. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Simulation studies are widely used for evaluating the performance of statistical methods in psychology. However, the quality of simulation studies can vary widely in terms of their design, execution, and reporting. In order to assess the quality of typical simulation studies in psychology, we reviewed 321 articles published in Psychological Methods, Behavior Research Methods, and Multivariate Behavioral Research in 2021 and 2022, among which 100/321 = 31.2% report a simulation study. We find that many articles do not provide complete and transparent information about key aspects of the study, such as justifications for the number of simulation repetitions, Monte Carlo uncertainty estimates, or code and data to reproduce the simulation studies. To address this problem, we provide a summary of the ADEMP (aims, data-generating mechanism, estimands and other targets, methods, performance measures) design and reporting framework from Morris et al. (2019) adapted to simulation studies in psychology. Based on this framework, we provide ADEMP-PreReg, a step-by-step template for researchers to use when designing, potentially preregistering, and reporting their simulation studies. We give formulae for estimating common performance measures, their Monte Carlo standard errors, and for calculating the number of simulation repetitions to achieve a desired Monte Carlo standard error. Finally, we give a detailed tutorial on how to apply the ADEMP framework in practice using an example simulation study on the evaluation of methods for the analysis of pre-post measurement experiments. (PsycInfo Database Record (c) 2024 APA, all rights reserved).