Mijke Rhemtulla
Biographic Data
| ID | 1464788 |
|---|---|
| NAME | Mijke Rhemtulla |
| GIVEN NAMES | Mijke |
| FAMILY NAME | Rhemtulla |
| SIGNATURE | RHEMTULLA M |
| AFFILIATIONS | University of California, Davis |
| ORCID | 0000-0003-2572-2424 |
| VERIFIED | Yes |
| TOTAL WORKS | 25 |
| TOTAL CITATIONS | 24 |
| AUTHOR COUNT | 25 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2012 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 1 |
Estimated Factor Scores Are Not True Factor Scores
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 type of factor score estimates, regression factor scores, computed from single-indicator and multip…
Nodewise Parameter Aggregation for Psychometric Networks
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, which need to be aggregated to obtain the edge weight (i.e., the conditional association). The nodewise …
Exploring the Effects of Sampling Variability, Scale Variability, and Node Aggregation on the Consistency of Estimated Networks
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 measurement tools. The current study used a resampling approach to disentangle the effects of sampl…
Modeling and interpretation of personality and individual differences constructs
The prevalence of direct replication articles in top-ranking psychology journals
Despite lip service about replication being a cornerstone of science, replications have historically received little real estate in the published literature. Following psychology's recent replication crisis, we assessed the prevalence of one type of replication contribution: direct replication articles-articles where a direct or close replication of a previously published study is one of the main contributions of the article. This prevalence prov…
Pay Attention to the Ignorable Missing Data Mechanisms! An Exploration of Their Impact on the Efficiency of Regression Coefficients
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 estimates can differ for different MAR variations, even when the amount of missing data is held constant; …
Statistical Control Requires Causal Justification
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 true causal effect. But statistical control works only under ideal circumstances. When the selected co…
Incorporating Stability Information into Cross-Sectional Estimates
How education impacts income, how conscientiousness impacts work outcomes, and how relationship satisfaction impacts time spent together are psychological research questions about longitudinal proc
Network analysis of multivariate data in psychological science
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. This Primer provides an anatomy of these techniques, describes the current state of the art and discu…
Power Analysis for Parameter Estimation in Structural Equation Modeling
Despite the widespread and rising popularity of structural equation modeling (SEM) in psychology, there is still much confusion surrounding how to choose an appropriate sample size for SEM. Currently available guidance primarily consists of sample-size rules of thumb that are not backed up by research and power analyses for detecting model misspecification. Missing from most current practices is power analysis for detecting a target effect (e.g.,…
Eavesdropping on Missing Data
Participants in experience sampling method (ESM) studies are “beeped” several times per day to report on their momentary experiences—but participants do not always answer the beep. Knowing whether there are systematic predictors of missing a report is critical for understanding the extent to which missing data threatens the validity of inferences from ESM studies. Here, 228 university students completed up to four ESM reports per day while wearin…
Investigating the Utility of Fixed-margin Sampling in Network Psychometrics
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 parameters are not distinguishable from those that would be expected if the data were generated by chance. W…
On Penalty Parameter Selection for Estimating Network Models
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 precision matrix of Gaussian variables using an l1-penalty to push small values to zero. A tuning param…
Latent Variable Models and Networks
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 radically different understandings of the psychological constructs of interest, recent articles have e…
Robust data and power in infant research
As in many areas of science, infant research suffers from low power. The problem is further compounded in infant research because of the difficulty in recruiting and testing large numbers of infant participants. Researchers have been searching for a solution and, as illustrated by this special section, have been focused on getting the most out of infant data. We illustrate one solution by showing how we can increase power in visual preference tas…
On Nonregularized Estimation of Psychological Networks
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 proposed as an alternative approach for characterizing relationships among variables through off-di…
Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits
Generalized Network Psychometrics
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 more common latent variables. Here, we present two generalizations of the network model that encompass…
What is the p -factor of psychopathology? Some risks of general factor modeling
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 factors have been proposed. We point out some of the risks of modeling and interpreting general factors, …
Using Principal Components as Auxiliary Variables in Missing Data Estimation
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) to reduce the number of possible auxiliary variables to a manageable number. A series of Monte Carlo…
The application of a network approach to Health-Related Quality of Life (HRQoL)
We concluded that the NM provides a fruitful alternative to classical approaches used in the psychometric analysis of HRQoL data
Planned Missing Data Designs for Developmental Researchers
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-method measurement designs). When these designs are implemented correctly and when missingness is dea…
Why the items versus parcels controversy needn’t be one.
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 discuss the many advantages of parcels that some researchers find attractive and highlight, too, …
When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions.
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 thresholds. Results revealed that factor loadings and robust standard errors were generally most accura…
Children’s acquisition of word order depends on syntactic/semantic role
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 order and semantic/syntactic roles. To clarify the nature of children’s abstract knowledge, we taugh…
When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions.
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 thresholds. Results revealed that factor loadings and robust standard errors were generally most accura…
Children’s acquisition of word order depends on syntactic/semantic role
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 order and semantic/syntactic roles. To clarify the nature of children’s abstract knowledge, we taugh…
Planned Missing Data Designs for Developmental Researchers
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-method measurement designs). When these designs are implemented correctly and when missingness is dea…
Why the items versus parcels controversy needn’t be one.
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 discuss the many advantages of parcels that some researchers find attractive and highlight, too, …
Using Principal Components as Auxiliary Variables in Missing Data Estimation
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) to reduce the number of possible auxiliary variables to a manageable number. A series of Monte Carlo…
The application of a network approach to Health-Related Quality of Life (HRQoL)
We concluded that the NM provides a fruitful alternative to classical approaches used in the psychometric analysis of HRQoL data
Generalized Network Psychometrics
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 more common latent variables. Here, we present two generalizations of the network model that encompass…
What is the p -factor of psychopathology? Some risks of general factor modeling
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 factors have been proposed. We point out some of the risks of modeling and interpreting general factors, …
On Nonregularized Estimation of Psychological Networks
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 proposed as an alternative approach for characterizing relationships among variables through off-di…
Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits
Robust data and power in infant research
As in many areas of science, infant research suffers from low power. The problem is further compounded in infant research because of the difficulty in recruiting and testing large numbers of infant participants. Researchers have been searching for a solution and, as illustrated by this special section, have been focused on getting the most out of infant data. We illustrate one solution by showing how we can increase power in visual preference tas…
Network analysis of multivariate data in psychological science
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. This Primer provides an anatomy of these techniques, describes the current state of the art and discu…
Power Analysis for Parameter Estimation in Structural Equation Modeling
Despite the widespread and rising popularity of structural equation modeling (SEM) in psychology, there is still much confusion surrounding how to choose an appropriate sample size for SEM. Currently available guidance primarily consists of sample-size rules of thumb that are not backed up by research and power analyses for detecting model misspecification. Missing from most current practices is power analysis for detecting a target effect (e.g.,…
Eavesdropping on Missing Data
Participants in experience sampling method (ESM) studies are “beeped” several times per day to report on their momentary experiences—but participants do not always answer the beep. Knowing whether there are systematic predictors of missing a report is critical for understanding the extent to which missing data threatens the validity of inferences from ESM studies. Here, 228 university students completed up to four ESM reports per day while wearin…
Investigating the Utility of Fixed-margin Sampling in Network Psychometrics
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 parameters are not distinguishable from those that would be expected if the data were generated by chance. W…
On Penalty Parameter Selection for Estimating Network Models
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 precision matrix of Gaussian variables using an l1-penalty to push small values to zero. A tuning param…
Latent Variable Models and Networks
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 radically different understandings of the psychological constructs of interest, recent articles have e…
Statistical Control Requires Causal Justification
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 true causal effect. But statistical control works only under ideal circumstances. When the selected co…
Incorporating Stability Information into Cross-Sectional Estimates
How education impacts income, how conscientiousness impacts work outcomes, and how relationship satisfaction impacts time spent together are psychological research questions about longitudinal proc
Pay Attention to the Ignorable Missing Data Mechanisms! An Exploration of Their Impact on the Efficiency of Regression Coefficients
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 estimates can differ for different MAR variations, even when the amount of missing data is held constant; …
The prevalence of direct replication articles in top-ranking psychology journals
Despite lip service about replication being a cornerstone of science, replications have historically received little real estate in the published literature. Following psychology's recent replication crisis, we assessed the prevalence of one type of replication contribution: direct replication articles-articles where a direct or close replication of a previously published study is one of the main contributions of the article. This prevalence prov…
Estimated Factor Scores Are Not True Factor Scores
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 type of factor score estimates, regression factor scores, computed from single-indicator and multip…
Nodewise Parameter Aggregation for Psychometric Networks
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, which need to be aggregated to obtain the edge weight (i.e., the conditional association). The nodewise …
Exploring the Effects of Sampling Variability, Scale Variability, and Node Aggregation on the Consistency of Estimated Networks
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 measurement tools. The current study used a resampling approach to disentangle the effects of sampl…
Modeling and interpretation of personality and individual differences constructs
Computer Science (18 works) · Mathematics (18 works) · Statistics (17 works) · Econometrics (13 works) · Mental Health Research Topics (11 works) · Artificial Intelligence (9 works) · Machine learning (9 works) · Psychology (9 works) · Data mining (8 works) · Functional Brain Connectivity Studies (8 works)