Mijke Rhemtulla

• Professor (Associate) at University of California, Davis

In psychological research, the common factor model is the most popular measurement model for scale items. However, there is increasing awareness that alternative measurement models, such as formative models, may make more theoretical sense for many kinds of psychological data. We demonstrate the nesting structure of three models specified in a stru...

Work surrounding the replicability and generalizability of network models has increased in recent years, prompting debate on whether network properties can be expected to be consistent across samples. To date, certain methodological practices may have contributed to observed inconsistencies, including use of single-item indicators and non-identical...

Differences in the patterning of genetic sharing between groups of individuals may arise from biological pathways, social mechanisms, phenotyping and ascertainment. We expand genomic structural equation modeling to allow for testing genomic structural invariance (GSI), that is, the formal comparison of multivariate genetic architecture across group...

Psychometric networks can be estimated using nodewise regression to estimate edge weights when the joint distribution is analytically difficult to derive or the estimation is too computationally intensive. The nodewise approach runs generalized linear models with each node as the outcome. Two regression coefficients are obtained for each link, whic...

In this tutorial, we clarify the distinction between estimated factor scores, which are weighted composites of observed variables, and true factor scores, which are unobservable values of the underlying latent variable. Using an analogy with linear regression, we show how predicted values in linear regression share the properties of the most common...

Differences in the patterning of genetic sharing and differentiation between groups may arise from differences in biological pathways, social mechanisms, phenotyping and ascertainment. We expand the Genomic Structural Equation Modeling framework to allow for testing Genomic Structural Invariance (GSI): the formal comparison of multivariate genetic...

Graph-theoretic metrics derived from neuroimaging data have been heralded as powerful tools for uncovering neural mechanisms of psychological traits, psychiatric disorders, and neurodegenerative diseases. In N = 8,185 human structural connectomes from UK Biobank, we examined the extent to which 11 commonly-used global graph-theoretic metrics index...

The use of modern missing data techniques has become more prevalent with their increasing accessibility in statistical software. These techniques focus on handling data that are missing at random (MAR). Although all MAR mechanisms are routinely treated as the same, they are not equal. The impact of missing data on the efficiency of parameter estima...

Network theory and accompanying methodology are becoming increasingly popular as an alternative to latent variable models for representing and, ultimately, understanding psychological constructs. The core feature of network models is that individual observed items (e.g., symptoms of depression) are allowed to directly influence each other, resultin...

Psychological researchers are often in a position to use data collected at a singletime point (cross-sectional data) to make inferences about processes that unfoldover time (longitudinal processes). It has been well documented that regression andcorrelation coefficients based on cross-sectional data are not unbiased estimates oftheir corresponding...

In modern test theory, response variables are a function of a common latent variable that represents the measured attribute, and error variables that are unique to the response variables. While considerable thought goes into the interpretation of latent variables in these models (e.g., validity research), the interpretation of error variables is ty...

It is common practice in correlational or quasiexperimental studies to use statistical control to remove confounding effects from a regression coefficient. Controlling for relevant confounders can debias the estimated causal effect of a predictor on an outcome; that is, it can bring the estimated regression coefficient closer to the value of the tr...

Editors’ introduction to the special issue “Network psychometrics in action”: Methodological innovations inspired by empirical problems.

Random effects in longitudinal multilevel models represent individuals’ deviations from population means and are indicators of individual differences. Researchers are often interested in examining how these random effects predict outcome variables that vary across individuals. This can be done via a two‐step approach in which empirical Bayes (EB) e...

In recent years, network analysis has been applied to identify and analyse patterns of statistical association in multivariate psychological data. In these approaches, network nodes represent variables in a data set, and edges represent pairwise conditional associations between variables in the data, while conditioning on the remaining variables. T...

In Modern Test Theory, response variables are a function of a common latent variable that represents the measured attribute, and error variables that are unique to the response variables. While considerable thought goes into the interpretation of latent variables in these models (e.g., validity research), the interpretation of error variables is ty...

It is common practice in psychological research to use statistical control to remove the effect of third variables in correlational or quasi-experimental studies. Controlling for relevant confounders can improve estimates of the causal path from a predictor to the outcome, but this only works under ideal circumstances. When the selected control var...

Psychologists use scales comprised of multiple items to measure underlying constructs. Missing data on such scales often occur at the item level, whereas the model of interest to the researcher is at the composite (scale score) level. Existing analytic approaches cannot easily accommodate item-level missing data when models involve composites. A ve...

Observing exclusively positive associations among a set of variables (i.e., a positive manifold) is a robust finding in many areas in psychology. These positive associations can be explained by positing an underlying common cause or, alternatively, through positive direct effects among the variables. Recently, the Kruis-Maris model has been propose...

Network models are gaining popularity as a way to estimate direct effects among psychological variables and investigate the structure of constructs. A key feature of network estimation is determining which edges are likely to be non-zero. In psychology, this is commonly achieved through the graphical lasso regularization method that estimates a pre...

Networks are gaining popularity as an alternative to latent variable models for representing psychological constructs. Whereas latent variable approaches introduce unobserved common causes to explain the relations among observed variables, network approaches posit direct causal relations between observed variables. While these approaches lead to ra...

Previous research and methodological advice has focused on the importance of accounting for measurement error in psychological data. That perspective assumes that psychological variables conform to a common factor model. We explore what happens when data that are not generated from a common factor model are nonetheless modeled as reflecting a commo...

Comorbidity is pervasive across psychopathological symptoms, diagnoses, and domains. Network analysis is a method for investigating symptom-level associations that underlie comorbidity, particularly through bridge symptoms connecting diagnostic syndromes. We applied network analyses of comorbidity to data from a population-based sample of adolescen...

Genetic correlations estimated from genome-wide association studies (GWASs) reveal pervasive pleiotropy across a wide variety of phenotypes. We introduce genomic structural equation modelling (genomic SEM): a multivariate method for analysing the joint genetic architecture of complex traits. Genomic SEM synthesizes genetic correlations and single-n...

An important goal for psychological science is developing methods to characterize relationships between variables. Customary approaches use structural equation models to connect latent factors to a number of observed measurements, or test causal hypotheses between observed variables. More recently, regularized partial correlation networks have been...

Steinley, Hoffman, Brusco, and Sher (2017) proposed a new method for evaluating the performance of psychological network models: fixed-margin sampling. The authors investigated LASSO regularized Ising models (eLasso) by generating random datasets with the same margins as the original binary dataset, and concluded that many estimated eLasso paramete...

Female parenting is obligate in mammals, but fathering behavior among mammals is rare. Only 3–5% of mammalian species exhibit biparental care, including humans, and mechanisms of fathering behavior remain sparsely studied. However, in species where it does exist, paternal care is often crucial to the survivorship of offspring. The present study is...

Female parenting is obligate in mammals, but fathering behavior among mammals is rare. Only 3–5% of mammalian species exhibit biparental care, including humans, and mechanisms of fathering behavior remain sparsely studied. However, in species where it does exist, paternal care is often crucial to the survivorship of offspring. The present study is...

Previous research and methodological advice has focused on the importance of accounting for measurement error in psychological data. That perspective assumes that psychological variables conform to a common factor model, such that they consist of construct variance plus error. In this paper, we explore what happens when a set of items that are not...

For altricial mammalian species, early life social bonds are constructed principally between offspring and their mothers, and the mother-offspring relationship sets the trajectory for offspring bio-behavioral development. In the rare subset of monogamous and biparental species, offspring experience an expanded social network which includes a father...

Although active travel to school for primary school students has been widely studied, research into the determinants of teenage active travel to school is noticeably lacking. Understanding the determinants of teen active travel to school is important given that teenage travel may have implications for the formation of habits that carry over to adul...

Female parenting is obligate in mammals, but fathering behavior among mammals is rare. Only 3–5% of mammalian species exhibit biparental care, including humans, and mechanisms of fathering behavior remain sparsely studied. However, in species where it does exist, paternal care is often crucial to the survivorship of offspring. The present study is...

Methods for using GWAS to estimate genetic correlations between pairwise combinations of traits have produced “atlases” of genetic architecture. Genetic atlases reveal pervasive pleiotropy, and genome-wide significant loci are often shared across different phenotypes. We introduce genomic structural equation modeling (Genomic SEM), a multivariate m...

An important goal for psychological science is developing methods to characterize relationships between variables. The customary approach uses structural equation models to connect latent factors to a number of observed measurements. More recently, regularized partial correlation networks have been proposed as an alternative approach for characteri...

Steinley, Hoffman, Brusco and Sher (2017) proposed a new method for evaluating the performance of psychological network models: fixed-margin sampling. The authors investigated LASSO regularized Ising models (eLasso) by generating random datasets with the same margins as the original binary dataset, and concluded that many estimated eLasso parameter...

Recent research has suggested that a range of psychological disorders may stem from a single underlying common factor, which has been dubbed the p-factor. This finding may spur a line of research in psychopathology very similar to the history of factor modeling in intelligence and, more recently, personality research, in which similar general facto...

Psychological constructs such as personality dimensions or cognitive traits are typically unobserved and are therefore measured by observing so-called indicators of the latent construct (e.g., responses to questionnaire items or observed behavior). The Common Factor Model (CFM) models the relations between the observed indicators and the latent var...

Structural equation models (SEMs) can be estimated using a variety of methods. For complete normally distributed data, two asymptotically efficient estimation methods exist: maximum likelihood (ML) and generalized least squares (GLS). With incomplete normally distributed data, an extension of ML called “full information” ML (FIML), is often the est...

In planned missingness (PM) designs, certain data are set a priori to be missing. PM designs can increase validity and reduce cost; however, little is known about the loss of efficiency that accompanies these designs. The present paper compares PM designs to reduced sample (RN) designs that have the same total number of data points concentrated in...

We present an overview of the issues surrounding and remedies for missing data in life sciences, particularly as these topics relate to field of developmental psychopathology. We focus on the advances in modern treatments for missing data, namely multiple imputation and full information maximum likelihood estimation. Using either of these modern tr...

We introduce the network model as a formal psychometric model, conceptualizing the covariance between psychometric indicators as resulting from pairwise interactions between observable variables in a network structure. This contrasts with standard psychometric models, in which the covariance between test items arises from the influence of one or mo...

Health-Related Quality of Life (HRQoL) research has typically adopted either a formative approach, in which HRQoL is the common effect of its observables, or a reflective approach — defining HRQoL as a latent variable that determines observable characteristics of HRQoL. Both approaches, however, do not take into account the complex organization of...

Purpose Health-Related Quality of Life (HRQoL) research has typically adopted either a formative approach, in which HRQoL is the common effect of its observables, or a reflective approach—defining HRQoL as a latent variable that determines observable characteristics of HRQoL. Both approaches, however, do not take into account the complex organizati...

Although current classification systems have greatly contributed to the reliability of psychiatric diagnoses, they ignore the unique role of individual symptoms and, consequently, potentially important information is lost. The network approach, in contrast, assumes that psychopathology results from the causal interplay between psychiatric symptoms...

Because indirect measures of personality self-concepts such as the Implicit Association Test (IAT) allow tapping into automatic processes, they can offer advantages over self-report measures. However, prior investigations have led to mixed results regarding the validity of indirect measures of conscientiousness. We suggest that these results might...

The design of longitudinal data collection is an essential component of any study of change. A well-designed study will maximize the efficiency of statistical tests and minimize the cost of available resources (e.g., budget). Two families of designs have been used to collect longitudinal data: complete data (CD) and planned missing (PM) designs. Th...

To deal with missing data that arise due to participant nonresponse or attrition, methodologists have recommended an “inclusive” strategy where a large set of auxiliary variables are used to inform the missing data process. In practice, the set of possible auxiliary variables is often too large. We propose using principal components analysis (PCA)...

We are delighted to see Bainter and Bollen’s excellent paper as a focus article in Measurement. In our view, psychological researchers who use SEM rely too reflexively on reflective measurement, without sufficiently considering whether their indicators are likely to be caused by the latent construct. When causality flows from indicators to the cons...

We examine the performance of planned missing (PM) designs for correlated latent growth curve models. Using simulated data from a model where latent growth curves are fitted to two constructs over five time points, we apply three kinds of planned missingness. The first is item-level planned missingness using a three-form design at each wave such th...

Planned missing designs are becoming increasingly popular, but because there is no consensus on how to implement them in longitudinal research, we simulated longitudinal data to distinguish between strategies of assigning items to forms and of assigning forms to participants across measurement occasions. Using relative efficiency as the criterion,...

Planned missing data designs allow researchers to collect incomplete data from participants by randomly assigning participants to have missing items on a survey (multiform designs) or missing measurement occasions in a longitudinal design (wave missing designs) or by administering an intensive measure to a small subsample of a larger dataset (two-m...

Salivary cortisol is often used as an index of physiological and psychological stress in exercise science and psychoneuroendocrine research. A primary concern when designing research studies examining cortisol stems from the high cost of analysis. Planned missing data designs involve intentionally omitting a random subset of observations from data...

The use of item parcels has been a matter of debate since the earliest use of factor analysis and structural equation modeling. Here, we review the arguments that have been levied both for and against the use of parcels and discuss the relevance of these arguments in light of the building body of empirical evidence investigating their performance....

We examine longitudinal extensions of the two-method measurement design, which uses planned missingness to optimize cost-efficiency and validity of hard-to-measure constructs. These designs use a combination of two measures: a gold standard that is highly valid but expensive to administer, and an inexpensive (e.g., survey-based) measure that contai...

A series of Monte Carlo simulations were used to compare the relative performance of the inclusive strategy, where as many auxiliary variables are included as possible, with typical auxiliary variables (AUX) and a smaller set of auxiliary variables derived from principal component analysis (PCAAUX). We examined the influence of seven independent...

On the asymptotic relative efficiency of planned missingness designs. Psychometrika. Abstract In planned missingness (PM) designs, certain data are set a priori to be missing. PM designs can increase validity and reduce cost; however, little is known about the loss of efficiency that accompanies these designs. The present paper compares PM designs...

Based on research on children’s verb production, Usage-Based theorists have argued that children learn grammatical abstractions in the preschool years. The fact that, in English verb clauses, word order determines semantic/syntactic roles leaves open the possibility that children are learning not just syntactic frames, but the relationship between...

Data collection can be the most time- and cost-intensive part of developmental research. This article describes some long-proposed but little-used research designs that have the potential to maximize data quality (reliability and validity) while minimizing research cost. In planned missing data designs, missing data are used strategically to improv...

Fraction of missing information λj is a useful measure of the impact of missing data on the quality of estimation of a particular parameter. This measure can be computed for all parameters in the model, and it communicates the relative loss of efficiency in the estimation of a particular parameter due to missing data. It has been recommended that r...

A simulation study compared the performance of robust normal theory maximum likelihood (ML) and robust categorical least squares (cat-LS) methodology for estimating confirmatory factor analysis models with ordinal variables. Data were generated from 2 models with 2-7 categories, 4 sample sizes, 2 latent distributions, and 5 patterns of category thr...

The roles of linguistic, cognitive, and social-pragmatic processes in word learning are well established. If statistical mechanisms also contribute to word learning, they must interact with these processes; however, there exists little evidence for such mechanistic synergy. Adults use co-occurrence statistics to encode speech-object pairings with d...

Though religion has been shown to have generally positive effects on normative 'prosocial' behavior, recent laboratory research suggests that these effects may be driven primarily by supernatural punishment. Supernatural benevolence, on the other hand, may actually be associated with less prosocial behavior. Here, we investigate these effects at th...

This paper reports on a simulation study that evaluated the performance of five structural equation model test statistics appropriate for categorical data. Both Type I error rate and power were investigated. Different model sizes, sample sizes, numbers of categories, and threshold distributions were considered. Statistics associated with both the d...

In previous work with a nationally representative sample of over 1,400 monozygotic and dizygotic twins born in the US, Tucker-Drob et al. (Psychological Science, 22, 125-133, 2011) uncovered a gene × environment interaction on scores on the Bayley Short Form test of mental ability (MA) at 2 years of age-higher socioeconomic status (SES) was associa...

An important question within developmental psychology concerns the extent to which the maturational gains that children make across multiple diverse domains of functioning can be attributed to global (domain-general) developmental processes. The present study investigated this question by examining the extent to which individual differences in chan...

Recent research in behavioral genetics has found evidence for a Gene × Environment interaction on cognitive ability: Individual differences in cognitive ability among children raised in socioeconomically advantaged homes are primarily due to genes, whereas environmental factors are more influential for children from disadvantaged homes. We investig...

Children's toys provide a rich arena for investigating conceptual flexibility, because they often can be understood as possessing an individual identity at multiple levels of abstraction. For example, many dolls (e.g., Winnie-the-Pooh) and action figures (e.g., Batman) can be construed either as characters from a fictional world or as physical obje...

In three experiments, we explored the basis of adults' judgments of individual object persistence through transformation. Participants watched scenarios in which an object underwent a transformation into an object belonging to the same or a different basic-level kind. Participants were queried about the object's persistence through the transformati...

Infants watched an experimenter retrieve a stuffed animal from an opaque box and then return it. This happened twice, consistent with either 1 animal appearing on 2 occasions or 2 identical-looking animals each appearing once. The experimenter labeled each object appearance with a different novel label. After infants retrieved 1 object from the box...

L. J. Rips, S. Blok, and G. Newman (2006) proposed that singular concepts, which support the tracing of individual objects across their existence, are governed by a principle of causal continuity. They purported to show that causal continuity is better than existing theories at explaining judgments of the persistence of individual objects. This art...

Our discussion with Rips, Blok, and Newman (2006; Blok, Newman, & Rips, in press; Rhemtulla & Xu, in press) has brought together theory and evidence from researchers in adult cognition and those in infant development. By this point, researchers all recognize that any theory of concepts of individuals must consider the evidence from infants' first c...

This paper attempts another defense of psychological essentialism (Strevens, 2000, 2001; Ahn, Kalish, Gelman, Medin, Luhman, Atran, Coley & Shafto, 2001). Using evidence from adults' and children's understanding of artifact concepts, we argue that the notion of essence does play a role in everyday reasoning and inference. Furthermore, there is also...

Psychology has to deal with many interacting variables. The analyses usually used to uncover such relationships have many constraints that limit their utility. We briefly discuss these and describe recent work that uses genetic programming to evolve equations to combine variables in nonlinear ways in a number of different domains. We focus on four...

Usage-Based theoreticians have argued that children make the biggest strides in learning to use many adult-like grammatical rules in the preschool years. This argument is based on how children use novel verbs in verb clauses: many English- speaking 2-year olds are willing to use novel verbs in ungrammatical order; by 4, few children are willing to...