Longitudinal processes rarely occur in isolation; often the growth curves of 2 or more variables are interdependent. Moreover, growth curves rarely exhibit a constant pattern of change. Many educational and psychological phenomena are comprised of different developmental phases (segments). Bivariate piecewise linear mixed-effects models (BPLMEM) are a useful and flexible statistical framework that

We propose a new sequential hypothesis testing procedure in which data are collected and analyzed in a series of independent segments. As in fixed-sample hypothesis testing and in previous sequential procedures, the overall α level can be set to any desired value. Like other sequential procedures, the independent segments procedure generally requires smaller samples than fixed-sample procedures-often

Research on individual variation has received increased attention. The bulk of the models discussed in psychological research so far, focus mainly on the temporal development of the mean structure. We expand the view on within-person residual variability and present a new model parameterization derived from classic multivariate GARCH models used to predict and forecast volatility in financial time-series

Social influence occurs when an individual's outcome is affected by another individual's actions. Current approaches in psychology that seek to examine social influence have focused on settings where individuals are nested in predefined groups and do not interact across groups. Such study designs permit using standard estimation methods such as multilevel models for estimating treatment effects but

Many approaches in the item response theory (IRT) literature have incorporated response styles to control for potential biases. However, the specific assumptions about response styles are often not made explicit. Having integrated different IRT modeling variants into a superordinate framework, we highlighted assumptions and restrictions of the models (Henninger & Meiser, 2020). In this article, we

The p value is still misinterpreted as the probability that the null hypothesis is true. Even psychologists who correctly understand that p values do not provide this probability may not realize the degree to which p values differ from the probability that the null hypothesis is true. Importantly, previous research on this topic has not addressed the influence of multiple testing, often a reality in

A large variety of item response theory (IRT) modeling approaches aim at measuring and correcting for response styles in rating data. Here, we integrate response style models of the divide-by-total model family into one superordinate framework that parameterizes response styles as person-specific shifts in threshold parameters. This superordinate framework allows us to structure and compare existing

Reports an error in "Do simple slopes follow-up tests lead us astray? Advancements in the visualization and reporting of interactions" by Megan C. Finsaas and Brandon L. Goldstein (Psychological Methods, Advanced Online Publication, Apr 20, 2020, np). In the article, Figure 5 contained an error. The second sentence of the caption of Figure 5 should read: "The left plot depicts the region of significance

The Bayesian statistical framework requires the specification of prior distributions, which reflect predata knowledge about the relative plausibility of different parameter values. As prior distributions influence the results of Bayesian analyses, it is important to specify them with care. Prior elicitation has frequently been proposed as a principled method for deriving prior distributions based on

Randomized response models (RRMs) aim at increasing the validity of measuring sensitive attributes by eliciting more honest responses through anonymity protection of respondents. This anonymity protection is achieved by implementing randomization in the questioning procedure. On the other hand, this randomization increases the sampling variance and, therefore, increases sample size requirements. The

Psychology researchers are rapidly adopting open science practices, yet clear guidelines on how to apply these practices to meta-analysis remain lacking. In this tutorial, we describe why open science is important in the context of meta-analysis in psychology, and suggest how to adopt the 3 main components of open science: preregistration, open materials, and open data. We first describe how to make

Numerous psychological and educational researchers are concerned with the moderating effect of a categorical variable on the relationship between a continuous predictor and a continuous response. Despite the great interest in moderation analyses, there exist few studies that explicitly address the essential issues of effect size measure, power calculation, and sample size determination under heterogeneity

Numerous tutorial publications are available to researchers seeking the procedures needed to analyze longitudinal count response variable data. However, most of the available tutorial publications have drawbacks that limit their usefulness to applied researchers, and to the best of our knowledge, very few publications make both the sample data and the data analysis syntax scripts available to readers

When a person takes alternative forms of the same test across replications of the testing procedure, the test taker's observed scores on the alternative forms are rarely identical. In educational and psychological measurement, inconsistencies in a test taker's scores that are irrelevant to the construct being measured are attributed to errors of measurement. Typically, errors of measurement are summarized

A norm-referenced score expresses the position of an individual test taker in the reference population, thereby enabling a proper interpretation of the test score. Such normed scores are derived from test scores obtained from a sample of the reference population. Typically, multiple reference populations exist for a test, namely when the norm-referenced scores depend on individual characteristic(s)

We studied three models for longitudinal mediation analysis: the autoregressive mediation model (AMM) using composite scores (uncorrected composite model, UCM), AMM using composite scores with correction for measurement error (corrected composite model, CCM), and AMM using latent variables with multiple indicators (latent variable model, LVM). Under the condition of unidimensional measurement model

Randomized control trials (RCTs) often use clustered designs, where intact clusters (such as classroom, schools, or treatment centers) are randomly assigned to treatment and control conditions. Hierarchical linear models (HLMs) are used almost universally to estimate the effects in such experiments. While study designs that utilize intact clusters have many potential advantages, there is little guidance

Mediation analysis is an important approach for investigating causal pathways. One approach used in mediation analysis is the test of an indirect effect, which seeks to measure how the effect of an independent variable impacts an outcome variable through 1 or more mediators. However, in many situations the proposed tests of indirect effects, including popular confidence interval-based methods, tend

Science depends on trustworthy evidence. Thus, a biased scientific record is of questionable value because it impedes scientific progress, and the public receives advice on the basis of unreliable evidence that has the potential to have far-reaching detrimental consequences. Meta-analysis is a technique that can be used to summarize research evidence. However, meta-analytic effect size estimates may

Meta-analytic structural equation modeling (MASEM) is an increasingly popular meta-analytic technique that combines the strengths of meta-analysis and structural equation modeling. MASEM facilitates the evaluation of complete theoretical models (e.g., path models or factor analytic models), accounts for sampling covariance between effect sizes, and provides measures of overall fit of the hypothesized

The geographic distribution of psychological constructs has long been an area of focus for psychological researchers. Recently, however, there has been increased interest in investigations of the so-called subnational distribution of psychological variables, which focus on localized groupings of individuals within spatial units, such as counties or states. By estimating the subnational distribution

Structural equation modeling (SEM) applications routinely employ a trilogy of significance tests that includes the likelihood ratio test, Wald test, and score test or modification index. Researchers use these tests to assess global model fit, evaluate whether individual estimates differ from zero, and identify potential sources of local misfit, respectively. This full cadre of significance testing

Intraindividual patterns or configurations are intuitive explanations for phenomena, and popular in both lay and research contexts. Criterion profile analysis (CPA; Davison & Davenport, 2002) is a well-established, regression-based pattern matching procedure that identifies a pattern of predictors that optimally relate to a criterion of interest and quantifies the strength of that association. Existing

To make valid statistical inferences from mediation analysis, a number of assumptions need to be assessed. Among the assumptions, 2 frequently discussed ones are (a) the independent variable, mediator, and outcome variables are measured without error; and (b) no confounders of the effects in the mediation model are omitted. The impact of violating either assumption alone on statistical inference of

When designing experimental studies with human participants, experimenters must decide how many trials each participant will complete, as well as how many participants to test. Most discussion of statistical power (the ability of a study design to detect an effect) has focused on sample size, and assumed sufficient trials. Here we explore the influence of both factors on statistical power, represented

In this article, we propose integrated generalized structured component analysis (IGSCA), which is a general statistical approach for analyzing data with both components and factors in the same model, simultaneously. This approach combines generalized structured component analysis (GSCA) and generalized structured component analysis with measurement errors incorporated (GSCAM) in a unified manner and

This article shows how the concept of reliability of composite scores, as defined in classical test theory, can be extended to the context of multilevel modeling. In particular, it discusses the contributions and limitations of the various level-specific reliability indices proposed by Geldhof, Preacher, and Zyphur (2014), denoted as ω̃b and ω̃w (and also α̃b and α̃w). One major limitation of those

Selective reporting of results based on their statistical significance threatens the validity of meta-analytic findings. A variety of techniques for detecting selective reporting, publication bias, or small-study effects are available and are routinely used in research syntheses. Most such techniques are univariate, in that they assume that each study contributes a single, independent effect size estimate

The incorporation of causal inference in mediation analysis has led to theoretical and methodological advancements-effect definitions with causal interpretation, clarification of assumptions required for effect identification, and an expanding array of options for effect estimation. However, the literature on these results is fast-growing and complex, which may be confusing to researchers unfamiliar

The exploratory and confirmatory approaches of factor analysis lie on two ends of a continuum of substantive input for scale development. Recent advancements in Bayesian regularization methods enable more flexibility in covering a wide range of the substantive continuum. Based on the Bayesian Lasso (least absolute shrinkage and selection operator) methods for the regression model and covariance matrix

Assessments are commonly used to make a decision about an individual, such as grade placement, treatment assignment, job selection, or to inform a diagnosis. A psychometric approach to classify respondents based on the assessment would aggregate items into a score, and then each respondent's score is compared to a cut score. In contrast, a machine learning approach to classify respondents would build

With the advent of online and app-based studies, researchers in psychology are making increasing use of repeated subjective reports. The new methods open up opportunities to study behavior in the field and to map causal processes, but they also pose new challenges. Recent work has added initial elevation bias to the list of common pitfalls; here, higher negative states (i.e., thoughts and feelings)

Numerous applications in psychological research require that a pool of elements is partitioned into multiple parts. While many applications seek groups that are well-separated, that is, dissimilar from each other, others require the different groups to be as similar as possible. Examples include the assignment of students to parallel courses, assembling stimulus sets in experimental psychology, splitting

Experiments in research on memory, language, and in other areas of cognitive science are increasingly being analyzed using Bayesian methods. This has been facilitated by the development of probabilistic programming languages such as Stan, and easily accessible front-end packages such as brms. The utility of Bayesian methods, however, ultimately depends on the relevance of the Bayesian model, in particular

Exploratory graph analysis (EGA) is a new technique that was recently proposed within the framework of network psychometrics to estimate the number of factors underlying multivariate data. Unlike other methods, EGA produces a visual guide-network plot-that not only indicates the number of dimensions to retain, but also which items cluster together and their level of association. Although previous studies

Estimation methods for structural equation models with interactions of latent variables were compared in several studies. Yet none of these studies examined models that were structurally misspecified. Here, the model-implied instrumental variable 2-stage least square estimator (MIIV-2SLS; Bollen, 1995; Bollen & Paxton, 1998), the 2-stage method of moments estimator (2SMM; Wall & Amemiya, 2003), the

In many disciplines researchers use longitudinal panel data to investigate the potentially causal relationship between 2 variables. However, the conventions and concerns vary widely across disciplines. Here we focus on 2 concerns, that is: (a) the concern about random effects versus fixed effects, which is central in the (micro)econometrics/sociology literature; and (b) the concern about grand mean

Psychological measurement and theory are afflicted with an ongoing proliferation of new constructs and scales. Given the often redundant nature of new scales, psychological science is struggling with arbitrary measurement, construct dilution, and disconnection between research groups. To address these issues, we introduce an easy-to-use online application: the Semantic Scale Network. The purpose of

Meta-analyses are conducted to synthesize the quantitative results of related studies. The random-effects meta-analysis model is based on the assumption that a distribution of true effects exists in the population. This distribution is often assumed to be normal with a mean and variance. The population variance, also called heterogeneity, can be estimated numerous ways. Research exists comparing subsets

This study investigates the impact of the time interval (the time passed between 2 consecutive measurements) selection on autoregressive mediation modeling (AMM). For a widely used autoregressive mediation model, via analytical derivations, we explained why and how the conventionally reported time-specific coefficient estimates (e.g., âb̂ and ĉ' ) and inference results in AMM provide limited information

Scale builders strive to maximize 2 priorities: validity and reliability. While the literature is full of tips for increasing 1, the other, or both simultaneously, how to navigate tensions between them is less clear. Confusion shrouds the nature, prevalence, and practical implications of trade-offs between validity and reliability-formerly called paradoxes. This confusion results in most trade-offs

Mixed-effects models are becoming common in psychological science. Although they have many desirable features, there is still untapped potential. It is customary to view homogeneous variance as an assumption to satisfy. We argue to move beyond that perspective, and to view modeling within-person variance as an opportunity to gain a richer understanding of psychological processes. The technique to do

A first step when fitting multilevel models to continuous responses is to explore the degree of clustering in the data. Researchers fit variance-component models and then report the proportion of variation in the response that is due to systematic differences between clusters. Equally they report the response correlation between units within a cluster. These statistics are popularly referred to as

Statistical interactions between two continuous variables in linear regression are common in psychological science. As a follow-up analysis of how the moderator impacts the predictor-outcome relationship, researchers often use the pick-a-point simple slopes method. The simple slopes method requires researchers to make two decisions: (a) which moderator values should be used for plotting and testing

An information criterion (IC) like the Akaike IC (AIC), can be used to select the best hypothesis from a set of competing theory-based hypotheses. An IC developed to evaluate theory-based order-restricted hypotheses is the Generalized Order-Restricted Information Criterion (GORIC). Like for any IC, the values themselves are not interpretable but only comparable. To improve the interpretation regarding

Adaptive interventions (AIs) are increasingly popular in the behavioral sciences. An AI is a sequence of decision rules that specify for whom and under what conditions different intervention options should be offered, in order to address the changing needs of individuals as they progress over time. The sequential, multiple assignment, randomized trial (SMART) is a novel trial design that was developed

For several years, the public debate in psychological science has been dominated by what is referred to as the reproducibility crisis. This crisis has, inter alia, drawn attention to the need for proper control of statistical decision errors in testing psychological hypotheses. However, conventional methods of error probability control often require fairly large samples. Sequential statistical tests

When estimating multiple regression models with incomplete predictor variables, it is necessary to specify a joint distribution for the predictor variables. A convenient assumption is that this distribution is a joint normal distribution, the default in many statistical software packages. This distribution will in general be misspecified if the predictors with missing data have nonlinear effects (e

The last decade has witnessed a resurgence of interest in exploratory bifactor analysis models and the concomitant development of new methods to estimate these models. Understandably, due to the rapid pace of developments in this area, existing Monte Carlo comparisons of bifactor analysis have not included the newest methods. To address this issue, we compared the model recovery capabilities of 5 existing

Instrumental variable methods are an underutilized tool to enhance causal inference in psychology. By way of incorporating predictors of the predictors (called "instruments" in the econometrics literature) into the model, instrumental variable regression (IVR) is able to draw causal inferences of a predictor on an outcome. We show that by regressing the outcome y on the predictors x and the predictors

Researchers across many domains of psychology increasingly wish to arrive at personalized and generalizable dynamic models of individuals' processes. This is seen in psychophysiological, behavioral, and emotional research paradigms, across a range of data types. Errors of measurement are inherent in most data. For this reason, researchers typically gather multiple indicators of the same latent construct

Though consent forms include important information, those experienced with behavioral research often observe that participants do not carefully read consent forms. Three studies examined participants' reading of consent forms for in-person experiments. In each study, we inserted the phrase "some researchers wear yellow pants" into sections of the consent form and measured participants' reading of the

In psychology, the causal process between 2 variables can be studied with statistical mediation analysis. To make a causal interpretation about the relation between variables, researchers who use the statistical mediation model make many assumptions about the variables in the model, among which are measurement assumptions about the mediator. For example, researchers often assume that the measure of

This study uses a within study comparison design (WSC) to conduct a novel test of how much causal bias results when researchers use a nonequivalent comparison group design type (NECGD) that combines: (a) a comparison group local to the treatment group; (b) a pretest measure of the study outcome; and (c) a rich set of 19 other multidimensional covariates. Most prior WSCs have dealt with the bias consequences

Reporting the reliability of the scores obtained from a scale or test is part of the standard repertoire of empirical studies in psychology. With reliability being a key concept in psychometrics, researchers have become more and more interested in evaluating reliability coefficients across studies and, ultimately, quantify and explain possible between-study variation. This approach-commonly known as

Neurocognitive tasks are frequently used to assess disordered decision making, and cognitive models of these tasks can quantify performance in terms related to decision makers' underlying cognitive processes. In many cases, multiple cognitive models purport to describe similar processes, but it is difficult to evaluate whether they measure the same latent traits or processes. In this article, we develop

Determining the number of factors is one of the most crucial decisions a researcher has to face when conducting an exploratory factor analysis. As no common factor retention criterion can be seen as generally superior, a new approach is proposed-combining extensive data simulation with state-of-the-art machine learning algorithms. First, data was simulated under a broad range of realistic conditions

The analysis of reciprocal relations in categorical variables poses methodological challenges. Effects that go in opposite causal directions must be integrated into the same model, and parameters must be interpretable. In this article, we propose taking an event-based perspective and present a new approach to the analysis of reciprocal relations in manifest categorical variables. Instead of asking

Gaussian graphical models are commonly used to characterize conditional (in)dependence structures (i.e., partial correlation networks) of psychological constructs. Recently attention has shifted from estimating single networks to those from various subpopulations. The focus is primarily to detect differences or demonstrate replicability. We introduce two novel Bayesian methods for comparing networks

Prediction rule ensembles (PREs) are a relatively new statistical learning method, which aim to strike a balance between predictive performance and interpretability. Starting from a decision tree ensemble, like a boosted tree ensemble or a random forest, PREs retain a small subset of tree nodes in the final predictive model. These nodes can be written as simple rules of the form if [condition] then