Longitudinal designs can fortify causal inquiries of a focal predictor (i.e., treatment) on an outcome. But valid causal inferences are complicated by causal feedback between confounders and treatment over time. G-estimation of a structural nested mean model (SNMM) is designed to handle the complexities beset by measured time-varying or treatment-dependent confounding in longitudinal data. But valid inference requires correctly specifying the functional form of the SNMM, such as how the effects stay constant or change over time. In this article, we develop a g-estimation strategy for linear structural nested mean models whose causal parameters adopt the form of time-varying coefficient functions. These time-varying coefficient functions are smooth semiparametric functions of time that permit probing how the treatment effects may change curvilinearly. Further effect modification by time-invariant and time-varying covariates can be readily postulated in the SNMM to test fine-grained effect heterogeneity. We then describe a g-estimation strategy for estimating such an SNMM. We utilize the established time-varying effect model (TVEM) approach from the prevention and psychotherapy research literature for modeling flexible changes in covariate-outcome associations over time. Moreover, we exploit a known benefit of g-estimation over routine regression methods: its double robustness conferring protection against biases induced by certain forms of model misspecification. We encourage psychology researchers seeking correct causal conclusions from longitudinal data to use an SNMM with time-varying coefficient functions to assess curvilinear causal effects over time, and to use g-estimation with TVEM to resolve measured treatment-dependent confounding. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Observed scores (e.g., summed scores and estimated factor scores) are assumed to reflect underlying constructs and have many uses in psychological science. Constructs are often operationalized as latent variables (LVs), which are mathematically defined by their relations with manifest variables in an LV measurement model (e.g., common factor model). We examine the performance of several types of observed scores for the purposes of (a) estimating latent scores and classifying people and (b) recovering structural relations among LVs. To better reflect practice, our evaluation takes into account different sources of uncertainty (i.e., sampling error and model error). We review psychometric properties of observed scores based on the classical test theory applied to common factor models, report on a simulation study examining their performance, and provide two empirical examples to illustrate how different scores perform under different conditions of reliability, sample size, and model error. We conclude with general recommendations for using observed scores and discuss future research directions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

In the social sciences, measurement scales often consist of ordinal items and are commonly analyzed using factor analysis. Either data are treated as continuous, or a discretization framework is imposed in order to take the ordinal scale properly into account. Correlational analysis is central in both approaches, and we review recent theory on correlations obtained from ordinal data. To ensure appropriate estimation, the item distributions prior to discretization should be (approximately) known, or the thresholds should be known to be equally spaced. We refer to such knowledge as substantive because it may not be extracted from the data, but must be rooted in expert knowledge about the data-generating process. An illustrative case is presented where absence of substantive knowledge of the item distributions inevitably leads the analyst to conclude that a truly two-dimensional case is perfectly one-dimensional. Additional studies probe the extent to which violation of the standard assumption of underlying normality leads to bias in correlations and factor models. As a remedy, we propose an adjusted polychoric estimator for ordinal factor analysis that takes substantive knowledge into account. Also, we demonstrate how to use the adjusted estimator in sensitivity analysis when the continuous item distributions are known only approximately. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Since the start of the 21st century, few advances have had as far-reaching impact in science as the widespread adoption of artificial neural networks in fields as diverse as fundamental physics, clinical medicine, and psychology. In research methods, one promising area for the adoption of artificial neural networks involves the analysis of single-case experimental designs. Given that these types of networks are not generally part of training in the psychological sciences, the purpose of our article is to provide a step-by-step introduction to using artificial neural networks to analyze single-case designs. To this end, we trained a new model using data from a Monte Carlo simulation to analyze multiple baseline graphs and compared its outcomes with traditional methods of analysis. In addition to showing that artificial neural networks may produce less error than other methods, this tutorial provides information to facilitate the replication and extension of this line of work to other designs and datasets. (PsycInfo Database Record (c) 2024 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).

Multimethod measurement designs with planned missing data (MMM-PMD) aim at combining cheap proxy methods (e.g., self-reports) with an expensive gold standard method (e.g., biomarker) in order to improve the cost-efficiency of research designs. This article presents a comprehensive simulation study investigating whether accurate factor scores with trustworthy confidence or credible intervals for the gold standard method can be obtained in MMM-PMD designs. The results indicate that the Bartlett-FIML, regression-FIML, and fully Bayesian estimator perform equally well in terms of recovering the true rank-order and absolute level of the factor scores for subjects with complete data. However, for subjects with planned missing data the estimated factor scores are considerably biased and cannot be computed with the Bartlett-FIML estimator. The confidence or credible intervals for the estimated factor scores are trustworthy for complete cases. For subjects with planned missing data, the coverage rates vary considerably across conditions. Thus, the present study illustrates that individual scores from MMM-PMD designs should not be used for consequential individual decisions. Still, factor scores from MMM-PMD designs might be useful when used for secondary analyses that rely on group-level statistics like computing propensity scores for matching purposes or for factor score regression. The simulation results underline that multiple reliable proxy methods that are highly correlated with the gold standard method but are not highly correlated with each other should be administered to maximize the accuracy of the estimated factor scores as well as the trustworthiness of the corresponding confidence or credible intervals. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The central limit theorem (CLT) is one of the most important theorems in statistics, and it is often introduced to social sciences researchers in an introductory statistics course. However, the recent replication crisis in the social sciences prompts us to investigate just how common certain misconceptions of statistical concepts are. The main purposes of this article are to investigate the misconceptions of the CLT among social sciences researchers and to address these misconceptions by clarifying the definition and properties of the CLT in a manner that is approachable to social science researchers. As part of our article, we conducted a survey to examine the misconceptions of the CLT among graduate students and researchers in the social sciences. We found that the most common misconception of the CLT is that researchers think the CLT is about the convergence of sample data to the normal distribution. We also found that most researchers did not realize that the CLT applies to both sample means and sample sums, and that the CLT has implications for many common statistical concepts and techniques. Our article addresses these misconceptions of the CLT by explaining the preliminaries needed to understand the CLT, introducing the formal definition of the CLT, and elaborating on the implications of the CLT. We hope that through this article, researchers can obtain a more accurate and nuanced understanding of how the CLT operates as well as its role in a variety of statistical concepts and techniques. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Inferences about hypotheses are ubiquitous in the cognitive sciences. Bayes factors provide one general way to compare different hypotheses by their compatibility with the observed data. Those quantifications can then also be used to choose between hypotheses. While Bayes factors provide an immediate approach to hypothesis testing, they are highly sensitive to details of the data/model assumptions and it's unclear whether the details of the computational implementation (such as bridge sampling) are unbiased for complex analyses. Here, we study how Bayes factors misbehave under different conditions. This includes a study of errors in the estimation of Bayes factors; the first-ever use of simulation-based calibration to test the accuracy and bias of Bayes factor estimates using bridge sampling; a study of the stability of Bayes factors against different MCMC draws and sampling variation in the data; and a look at the variability of decisions based on Bayes factors using a utility function. We outline a Bayes factor workflow that researchers can use to study whether Bayes factors are robust for their individual analysis. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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The causal inference methods of potential outcomes (POs), directed acyclic graphs (DAGs), and structural equation models (SEMs) have contributed much to our understanding of causal effects. Yet the teaching and application of these methods (especially POs and DAGs) have nearly always regarded treatment as binary even when the magnitude of treatment can differ greatly. The two most common types of binary treatments are those from randomized experiments and those that are categorized versions of continuous treatments. Binary treatments via categorization are far more common in observational studies. I derive results showing that binary treatment variables that have different origins should be treated differently. Not doing so makes biased causal inferences more likely. I illustrate the value of combining POs, DAGs, and SEMs perspectives to illuminate potential problems with binary treatments rather than relying only on one perspective. The new analytic results are illustrated with simulations and an empirical example. Finally, I make recommendations on how researchers should analyze binary treatments. (PsycInfo Database Record (c) 2026 APA, all rights reserved).