In analyzing longitudinal data with growth curve models, a critical assumption is that changes in the observed measures reflect construct changes and not changes in the manifestation of the construct over time. However, growth curve models are often fit to a repeated measure constructed as a sum or mean of scale items, making an implicit assumption of constancy of measurement. This practice risks confounding

The coefficient of determination, R², also called the explained variance, is often taken as a proportional measure of the relative determination of model on outcome. However, while R² has some attractive statistical properties, its reliance on squared variations (variances) may limit its use as an easily interpretable descriptive statistic of that determination. Here, the properties of this coefficient

The interpretation of cross-effects from vector autoregressive models to infer structure and causality among constructs is widespread and sometimes problematic. I describe problems in the interpretation of cross-effects when processes that are thought to fluctuate continuously in time are, as is typically done, modeled as changing only in discrete steps (as in e.g., structural equation modeling)-zeroes

Psychologists tend to rely on verbal descriptions of the environment over time, using terms like "unpredictable," "variable," and "unstable." These terms are often open to different interpretations. This ambiguity blurs the match between constructs and measures, which creates confusion and inconsistency across studies. To better characterize the environment, the field needs a shared framework that

Propensity score analysis (PSA) is a prominent method to alleviate selection bias in observational studies, but missing data in covariates is prevalent and must be dealt with during propensity score estimation. Through Monte Carlo simulations, this study evaluates the use of imputation methods based on multiple random forests algorithms to handle missing data in covariates: multivariate imputation

We demonstrate that all conventional meta-analyses of correlation coefficients are biased, explain why, and offer solutions. Because the standard errors of the correlation coefficients depend on the size of the coefficient, inverse-variance weighted averages will be biased even under ideal meta-analytical conditions (i.e., absence of publication bias, p-hacking, or other biases). Transformation to

A currently overlooked application of the latent curve model (LCM) is its use in assessing the consequences of development patterns of change-that is as a predictor of distal outcomes. However, there are additional complications for appropriately specifying and interpreting the distal outcome LCM. Here, we develop a general framework for understanding the sensitivity of the distal outcome LCM to the

This article considers identification, estimation, and model fit issues for models with contemporaneous and reciprocal effects. It explores how well the models work in practice using Monte Carlo studies as well as real-data examples. Furthermore, by using models that allow contemporaneous and reciprocal effects, the paper raises a fundamental question about current practice for cross-lagged panel modeling

The use of ambulatory assessments (AAs) as an approach to gather self-reported questionnaires or self-collected biochemical data is constantly increasing to investigate the experiences, states, and behaviors of individuals and their interaction with external situational factors during everyday life. It is often implicitly assumed that data from different sampling protocols can be used interchangeably

The Bayesian highest-density interval plus region of practical equivalence (HDI + ROPE) decision rule is an increasingly common approach to testing null parameter values. The decision procedure involves a comparison between a posterior highest-density interval (HDI) and a prespecified region of practical equivalence. One then accepts or rejects the null parameter value depending on the overlap (or

Testing the presence of mediation effects is important in social science research. Recently, Bayesian hypothesis testing with Bayes factors (BFs) has become increasingly popular. However, the use of BFs for testing mediation effects is still under-studied, despite the growing literature on Bayesian mediation analysis. In this study, we systematically examine the performance of the BF for testing the

Latent change score (LCS) models within a continuous-time state-space modeling framework provide a convenient statistical approach for analyzing developmental data. In this study, we evaluate the robustness of such an approach in the context of accelerated longitudinal designs (ALDs). ALDs are especially interesting because they imply a very high rate of planned data missingness. Additionally, most

Much research in psychology relies on data from observational studies that traditionally do not allow for causal interpretation. However, a range of approaches in statistics and computational sciences have been developed to infer causality from correlational data. Based on conceptual and theoretical considerations on the integration of interventional and time-restrainment notions of causality, we set

Increasingly, psychologists make use of modern configurational comparative methods (CCMs), such as qualitative comparative analysis (QCA) and coincidence analysis (CNA), to infer regularity-theoretic causal structures from psychological data. At the same time, existing CCMs remain unable to reveal such structures in the presence of complex effects. Given the strong emphasis configurational methodology

Dishop (see record 2022-78260-001) identifies the consensus emergence model (CEM) as a useful tool for future research on emergence but argues that autoregressive models with positive autoregressive effects are an important alternative data-generating mechanism that researchers need to rule out. Here, we acknowledge that alternative data-generating mechanisms are possibility for most, if not all, nonexperimental

The comparison of idiographic network structures to determine the presence of heterogeneity is a challenging endeavor in many applied settings. Previously, researchers eyeballed idiographic networks, computed correlations, and used techniques that make use of the multilevel structure of the data (e.g., group iterative multiple model estimation and multilevel vector autoregressive) to investigate individual

Some researchers and clinicians may feel hesitant to assess sexual orientation and gender-related characteristics in youth surveys because they are unsure if youth will respond to these questions or are concerned the questions will cause discomfort or offense. This can result in missed opportunities to identify LGBTQ+ youth and address health inequities among this population. The aim of this study

Human decision behavior entails a graded awareness of its certainty, known as a feeling of confidence. Until now, considerable interest has been paid to behavioral and computational dissociations of decision and confidence, which has raised an urgent need for measurement frameworks that can quantify the efficiency of confidence rating relative to decision accuracy (metacognitive efficiency). As a unique

Latent moderated structural equation (LMS) is one of the most common techniques for estimating interaction effects involving latent variables (i.e., XWITH command in Mplus). However, empirical applications of LMS often overlook that this estimation technique assumes normally distributed variables and that violations of this assumption may lead to seriously biased parameter estimates. Against this backdrop

Methods that measure the association between two intensively measured time series are of interest to researchers studying the symmetry of behaviors during social interaction. Such methods have historically focused on aggregating the amount of symmetry across all measurement occasions. However, it is rarely expected that symmetry is present at all measurement occasions. The current method, the pairwise

Measurement invariance is an assumption underlying the regression of a latent variable on a background variable. It requires the measurement model parameters of the latent variable to be equal across the levels of the background variable. Item-specific violations of this assumption are referred to as differential item functioning and are ideally substantively explainable to warrant theoretically valid

Colliders, variables that serve as a common outcome of an independent and dependent variable, pose a major challenge in psychological research. Collider variables can induce bias in the estimation of a population relationship of interest when (a) the composition of a research sample is restricted by scores on a collider variable or (b) researchers adjust for a collider variable in their statistical

Following an extensive simulation study comparing the operating characteristics of three different procedures used for establishing equivalence (the frequentist "TOST," the Bayesian "HDI-ROPE," and the Bayes factor interval null procedure), Linde et al. (2021) conclude with the recommendation that "researchers rely more on the Bayes factor interval null approach for quantifying evidence for equivalence"

Individual differences are studied with a multitude of test instruments. Meta-analysis of tests is useful to understand whether individual differences in certain populations can be detected with the help of a class of tests. A method for the quantitative meta-analytical evaluation of test instruments with dichotomous items is introduced. The method assumes beta-binomially distributed test scores, an

Loftus (1978) highlighted the distinction between a theoretical concept such as memory or attention, and its observed measure such as hit rate or percent correct. If the functional relationship between the concept and its measure is nonlinear then only some interaction effects are interpretable. This is an example of the wider "problem of coordination" which pervades scientific measurement. Loftus

Ecological momentary assessment (EMA) involves repeated real-time sampling of respondents' current behaviors and experiences. The intensive repeated assessment imposes an increased burden on respondents, rendering EMAs vulnerable to respondent noncompliance and/or careless and insufficient effort responding (C/IER). We developed a mixture modeling approach that equips researchers with a tool for (a)

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

The linear mixed model (LMM) and latent growth model (LGM) are frequently applied to within-subject two-group comparison studies to investigate group differences in the time effect, supposedly due to differential group treatments. Yet, research about LMM and LGM in the presence of outliers (defined as observations with a very low probability of occurrence if assumed from a given distribution) is scarce

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

A fundamental part of experimental design is to determine the sample size of a study. However, sparse information about population parameters and effect sizes before data collection renders effective sample size planning challenging. Specifically, sparse information may lead research designs to be based on inaccurate a priori assumptions, causing studies to use resources inefficiently or to produce

Multilevel models allow researchers to test hypotheses at multiple levels of analysis-for example, assessing the effects of both individual-level and school-level predictors on a target outcome. To assess these effects with the greatest clarity, researchers are well-advised to cluster mean center all Level 1 predictors and explicitly incorporate the cluster means into the model at Level 2. When an

Bayesian linear mixed-effects models (LMMs) and Bayesian analysis of variance (ANOVA) are increasingly being used in the cognitive sciences to perform null hypothesis tests, where a null hypothesis that an effect is zero is compared with an alternative hypothesis that the effect exists and is different from zero. While software tools for Bayes factor null hypothesis tests are easily accessible, how

Self-report scales are widely used in psychology to compare means in latent constructs across groups, experimental conditions, or time points. However, for these comparisons to be meaningful and unbiased, the scales must demonstrate measurement invariance (MI) across compared time points or (experimental) groups. MI testing determines whether the latent constructs are measured equivalently across groups

While a great deal of thought, planning, and money goes into the design of multisite randomized control trials (RCTs) that are used to evaluate the effectiveness of interventions in fields like education and psychology, relatively little thought is often paid to the measurement choices made in such evaluations. In this study, we conduct a series of simulation studies that consider a wide range of options

Factor mixture modeling (FMM) incorporates both continuous latent variables and categorical latent variables in a single analytic model clustering items and observations simultaneously. After two decades since the introduction of FMM to psychological and behavioral science research, it is an opportune time to review FMM applications to understand how these applications are utilized in real-world research

Network psychometrics leverages pairwise Markov random fields to depict conditional dependencies among a set of psychological variables as undirected edge-weighted graphs. Researchers often intend to compare such psychometric networks across subpopulations, and recent methodological advances provide invariance tests of differences in subpopulation networks. What remains missing, though, is an analogue

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

In observational data, understanding the causal link when estimating the causal effect of an independent variable (x) on a dependent variable (y) often requires researchers to identify the role of a third variable in the x → y relationship. Mediation, confounding, and colliding are three key third-variable effects that yield different theoretical and methodological implications for drawing causal conclusions

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

The availability of smart devices has made it possible to collect intensive longitudinal data (ILD) from individuals, providing a unique opportunity to study the complex dynamics of psychological systems. Existing time-series methods often have limitations, such as assuming linear interactions or having restricted forms, leading to difficulties in capturing the complex nature of these systems. To address

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

The planning of adequately powered research designs increasingly goes beyond determining a suitable sample size. More challenging scenarios demand simultaneous tuning of multiple design parameter dimensions and can only be addressed using Monte Carlo simulation if no analytical approach is available. In addition, cost considerations, for example, in terms of monetary costs, are a relevant target for

This study proposes a Bayesian approach to testing informative hypotheses in confirmatory factor analysis (CFA) models. The informative hypothesis, which is formulated by the constrained loadings, can directly represent researchers' theories or expectations about the tau equivalence in reliability analysis, item-level discriminant validity, and relative importance of indicators. Support for the informative

Meta-d'/d' has become the quasi-gold standard to quantify metacognitive efficiency because meta-d'/d' was developed to control for discrimination performance, discrimination criteria, and confidence criteria even without the assumption of a specific generative model underlying confidence judgments. Using simulations, we demonstrate that meta-d'/d' is not free from assumptions about confidence models:

Intervention studies in psychology often focus on identifying mechanisms that explain change over time. Cross-lagged panel models (CLPMs) are well suited to study mechanisms, but there is a controversy regarding the importance of detrending-defined here as separating longer-term time trends from cross-lagged effects-when modeling these change processes. The aim of this study was to present and test

Data sets with missing observations are common in psychology research. One typically analyzes such data by applying statistical methods that rely on the assumption that the missing observations are missing at random (MAR). This assumption greatly simplifies analysis but is unverifiable from the data at hand, and assuming it incorrectly may lead to bias. Thus we often wish to conduct sensitivity analyses

Psychological data are often clustered within organizational units, which violates the independence assumption in standard regression models. Clustered errors, multilevel models, and fixed-effects models all address this issue, but in different ways. Disciplinary preferences for approaching clustered data are strong, which can restrict questions researchers ask because certain approaches are better

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

Estimating power for multilevel models is complex because there are many moving parts, several sources of variation to consider, and unique sample sizes at Level 1 and Level 2. Monte Carlo computer simulation is a flexible tool that has received considerable attention in the literature. However, much of the work to date has focused on very simple models with one predictor at each level and one cross-level

The fulfillment of measurement invariance/equivalence is considered a prerequisite for meaningfully proceeding with substantive cross-group comparisons. In the multiple-group confirmatory factor analysis approach, one model identification issue has unfortunately received little attention: the specification of a referent variable in the test of measurement invariance. A multiple-indicator multiple-cause

Partially clustered designs are widely used in psychological research, especially in randomized controlled trials that examine the effectiveness of prevention or intervention strategies. In a partially clustered trial, individuals are clustered into intervention groups in one or more study arms, for the purpose of intervention delivery, whereas individuals in other arms (e.g., the waitlist control

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

How to model cross-lagged relations in panel data continues to be a source of disagreement in psychological research. While the cross-lagged panel model (CLPM) was the modeling approach of choice for many years, it has also been criticized repeatedly for its inability to separate within-person dynamics from stable between-person differences. Hence, various alternative models that disentangle these

People show stable differences in the way their affect fluctuates over time. Within the general framework of dynamical systems, the damped linear oscillator (DLO) model has been proposed as a useful approach to study affect dynamics. The DLO model can be applied to repeated measures provided by a single individual, and the resulting parameters can capture relevant features of the person's affect dynamics

Multivariate generalizability theory (GT) represents a comprehensive framework for quantifying score consistency, separating multiple sources contributing to measurement error, correcting correlation coefficients for such error, assessing subscale viability, and determining the best ways to change measurement procedures at different levels of score aggregation. Despite such desirable attributes, multivariate

To establish a theory one needs cleverly designed and well-executed studies with appropriate and correctly interpreted statistical analyses. Equally important, one also needs replications of such studies and a way to combine the results of several replications into an accumulated state of knowledge. An approach that provides an appropriate and powerful analysis for studies targeting prespecified theories

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

The outcomes in single-case experimental designs (SCEDs) are often counts or proportions. In our study, we provided a colloquial illustration for a new class of generalized linear mixed models (GLMMs) to fit count and proportion data from SCEDs. We also addressed important aspects in the GLMM framework including overdispersion, estimation methods, statistical inferences, model selection methods by

Decades of published methodological research have shown the chi-square test of model fit performs inconsistently and unreliably as a determinant of structural equation model (SEM) fit. Likewise, SEM indices of model fit, such as comparative fit index (CFI) and root-mean-square error of approximation (RMSEA) also perform inconsistently and unreliably. Despite rather unreliable ways to statistically

Psychology has seen an increase in the use of machine learning (ML) methods. In many applications, observations are classified into one of two groups (binary classification). Off-the-shelf classification algorithms assume that the costs of a misclassification (false positive or false negative) are equal. Because this is often not reasonable (e.g., in clinical psychology), cost-sensitive machine learning