Carl F Falk
Biographic Data
| ID | 118073 |
|---|---|
| NAME | Carl F Falk |
| GIVEN NAMES | Carl F |
| FAMILY NAME | Falk |
| SIGNATURE | FALK C F |
| AFFILIATIONS | McGill University |
| ORCID | 0000-0002-4788-7206 |
| VERIFIED | Yes |
| TOTAL WORKS | 22 |
| TOTAL CITATIONS | 21 |
| AUTHOR COUNT | 22 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2010 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 3 |
Evaluating Approaches for the Handling of Sign Reflection in Bayesian Latent Variable Models
In Markov chain Monte Carlo estimation of Bayesian latent variable models, sign reflection can cause multiple chains to settle onto equivalent but numerically different solutions, resulting in poorly mixed chains and nonconvergence. Sign reflection can be handled using various methods, such as adopting unit loading identification (ULI), assigning range restricted prior distributions, or using a relabeling algorithm. Some statistical software auto…
A Comparison of Regularization, Alignment, and a Traditional Method for Estimating Structural Relationships Across Two Groups
Establishing the correct partial measurement invariance model is crucial for ensuring unbiased comparisons of relationships between latent variables across multiple groups. While traditional approaches rely on detecting noninvariant items followed by estimation of structural relationships, more recently, approaches that estimate latent parameters without prior knowledge of anchor items have been developed. Specifically, regularization and alignme…
Regularized Cross-Sectional Network Modeling with Missing Data: A Comparison of Methods
Many applications of network modeling involve cross-sectional data of psychological variables (e.g., symptoms for psychological disorders), and analyses are often conducted using a regularized Gaussian graphical model (GGM) employing a lasso, also known as the graphical lasso or glasso. Appropriate methodology for handling missing data is underdeveloped while using glasso, precluding the use of planned missing data designs to reduce participant f…
A Comparison of Scaled Difference Tests for Forming Confidence Intervals in SEM
Latent Variable Interactions with Categorical Indicators: Continuous and Categorical Latent Moderated Structural Equations Approaches
Social science phenomena are often predicted by interactions between variables. When these variables cannot be directly observed, one option is to model them as latent variables that are measured by multiple indicators. When indicators are continuous, latent interactions can be modeled and estimated using the latent moderated structural equations (LMS) approach. A categorical LMS (LMS-cat) approach with full information estimation was more recent…
Evaluation of Generative Adversarial Imputation Nets’ Performance in Handling Missing Data in Structural Equation Modeling
Comparing Factor Score Approaches to SEM in Multigroup Models with Small Samples
Factor Score Regression (FSR) is increasingly employed as an alternative to structural equation modeling (SEM) in small samples. Despite its popularity in psychology, the performance of FSR in multigroup models with small samples remains relatively unknown. The goal of this study was to examine the performance of FSR, namely Croon’s correction and the bias avoiding method, for multigroup models with small samples and compare the methods to SEM. W…
Tackling Challenges in Data Pooling: Missing Data Handling in Latent Variable Models with Continuous and Categorical Indicators
Data pooling is a powerful strategy in empirical research. However, combining multiple datasets often results in a large amount of missing data, as variables that are not present in some datasets effectively contain missing values for all participants in those datasets. Furthermore, data pooling typically leads to a mix of continuous and categorical items with nonnormal multivariate distributions. We investigated two popular approaches to handle …
The Accuracy of Bayesian Model Fit Indices in Selecting Among Multidimensional Item Response Theory Models
Item response theory (IRT) models are often compared with respect to predictive performance to determine the dimensionality of rating scale data. However, such model comparisons could be biased toward nested-dimensionality IRT models (e.g., the bifactor model) when comparing those models with non-nested-dimensionality IRT models (e.g., a unidimensional or a between-item-dimensionality model). The reason is that, compared with non-nested-dimension…
Supervised Classes, Unsupervised Mixing Proportions: Detection of Bots in a Likert-Type Questionnaire
Administering Likert-type questionnaires to online samples risks contamination of the data by malicious computer-generated random responses, also known as bots. Although nonresponsivity indices (NRIs) such as person-total correlations or Mahalanobis distance have shown great promise to detect bots, universal cutoff values are elusive. An initial calibration sample constructed via stratified sampling of bots and humans—real or simulated under a me…
Comparison of latent variable and psychological network models in PROMIS data: Output metrics and factor structure
On the Performance of Semi- and Nonparametric Item Response Functions in Computer Adaptive Tests
Large-scale assessments often use a computer adaptive test (CAT) for selection of items and for scoring respondents. Such tests often assume a parametric form for the relationship between item responses and the underlying construct. Although semi- and nonparametric response functions could be used, there is scant research on their performance in a CAT. In this work, we compare parametric response functions versus those estimated using kernel smoo…
Development and Validation of the Four Facet Mindful Eating Scale (FFaMES)
A Comparison of Limited-Information Test Statistics for a Response Style Mirt Model
An increased use of models for measuring response styles is apparent in recent years with the multidimensional nominal response model (MNRM) as one prominent example. Inclusion of latent constructs representing extreme (ERS) or midpoint response style (MRS) often improves model fit according to information criteria. However, a test of absolute model fit is often not reported even though it could comprise an important piece of validity evidence. L…
On Lagrange Multiplier Tests in Multidimensional Item Response Theory: Information Matrices and Model Misspecification
Lagrange multiplier (LM) or score tests have seen renewed interest for the purpose of diagnosing misspecification in item response theory (IRT) models. LM tests can also be used to test whether parameters differ from a fixed value. We argue that the utility of LM tests depends on both the method used to compute the test and the degree of misspecification in the initially fitted model. We demonstrate both of these points in the context of a multid…
Two Cross-Platform Programs for Inferences and Interval Estimation About Indirect Effects in Mediational Models
In this article, we describe two new programs that compute both p-values and confidence intervals (CI) for the indirect effect in mediational models, including (a) a p-value based on the partial posterior method, which we refer to as p3 computed across the posterior distribution of the regression coefficients; (b) a variant of p3 that uses a normal approximation for the posterior distributions, p3N; (c) Hierarchical Bayesian CIs (CIHB) based on t…
Are Implicit Self‐Esteem Measures Valid for Assessing Individual and Cultural Differences
OBJECTIVE: Our research utilized two popular theoretical conceptualizations of implicit self-esteem: 1) implicit self-esteem as a global automatic reaction to the self; and 2) implicit self-esteem as a context/domain specific construct. Under this framework, we present an extensive search for implicit self-esteem measure validity among different cultural groups (Study 1) and under several experimental manipulations (Study 2). METHOD: In Study 1, …
Recovering Substantive Factor Loadings in the Presence of Acquiescence Bias: A Comparison of Three Approaches
Researchers are often advised to write balanced scales (containing an equal number of positively and negatively worded items) when measuring psychological attributes. This practice is recommended to control for acquiescence bias (ACQ). However, little advice has been given on what to do with such data if the researcher subsequently wants to evaluate a 1-factor model for the scale. This article compares 3 approaches for dealing with the presence o…
Cultural Variation in the Minimal Group Effect
The minimal group effect (MGE) is one of the most robust psychological findings in studies of intergroup conflict, yet there is little evidence comparing its magnitude across cultures. Recent evidence suggests that the MGE is due in part to a projection of one’s own perceived characteristics onto the novel in-group. Because of cultural variability in self-enhancement motivations, we thus expected that those from East Asian cultures would exhibit …
Not all collectivisms are equal: Opposing preferences for ideal affect between East Asians and Mexicans
Previous research has revealed differences in how people value and pursue positive affect in individualistic and collectivistic cultural contexts. Whereas Euro-Americans place greater value on high activation positive affect (HAP; e.g., excitement, enthusiasm, elation) than do Asian Americans and Hong Kong Chinese, the opposite is true for low activation positive affect (LAP; e.g., calmness, serenity, tranquility). Although the form of collectivi…
Unpacking Cultural Differences in Alexithymia: The Role of Cultural Values Among Euro-Canadian and Chinese-Canadian Students
The current study provides a cultural examination of alexithymia, a multifaceted personality construct that refers to a general deficit in the ability to identify and describe emotional states, and that has been linked to a number of psychiatric illnesses. Though this construct has been critiqued as heavily rooted in "Western" norms of emotional expression, it has not received much empirical attention from a cultural perspective. Recently, Ryder …
Assessing Mediational Models: Testing and Interval Estimation for Indirect Effects
Theoretical models specifying indirect or mediated effects are common in the social sciences. An indirect effect exists when an independent variable's influence on the dependent variable is mediated through an intervening variable. Classic approaches to assessing such mediational hypotheses (Baron & Kenny, 1986 Baron, R. M. and Kenny, D. A. 1986. The moderator-mediator variable distinction in social psychological research: Conceptual, strategic a…
Two Cross-Platform Programs for Inferences and Interval Estimation About Indirect Effects in Mediational Models
In this article, we describe two new programs that compute both p-values and confidence intervals (CI) for the indirect effect in mediational models, including (a) a p-value based on the partial posterior method, which we refer to as p3 computed across the posterior distribution of the regression coefficients; (b) a variant of p3 that uses a normal approximation for the posterior distributions, p3N; (c) Hierarchical Bayesian CIs (CIHB) based on t…
Unpacking Cultural Differences in Alexithymia: The Role of Cultural Values Among Euro-Canadian and Chinese-Canadian Students
The current study provides a cultural examination of alexithymia, a multifaceted personality construct that refers to a general deficit in the ability to identify and describe emotional states, and that has been linked to a number of psychiatric illnesses. Though this construct has been critiqued as heavily rooted in "Western" norms of emotional expression, it has not received much empirical attention from a cultural perspective. Recently, Ryder …
Cultural Variation in the Minimal Group Effect
The minimal group effect (MGE) is one of the most robust psychological findings in studies of intergroup conflict, yet there is little evidence comparing its magnitude across cultures. Recent evidence suggests that the MGE is due in part to a projection of one’s own perceived characteristics onto the novel in-group. Because of cultural variability in self-enhancement motivations, we thus expected that those from East Asian cultures would exhibit …
Development and Validation of the Four Facet Mindful Eating Scale (FFaMES)
Assessing Mediational Models: Testing and Interval Estimation for Indirect Effects
Theoretical models specifying indirect or mediated effects are common in the social sciences. An indirect effect exists when an independent variable's influence on the dependent variable is mediated through an intervening variable. Classic approaches to assessing such mediational hypotheses (Baron & Kenny, 1986 Baron, R. M. and Kenny, D. A. 1986. The moderator-mediator variable distinction in social psychological research: Conceptual, strategic a…
Not all collectivisms are equal: Opposing preferences for ideal affect between East Asians and Mexicans
Previous research has revealed differences in how people value and pursue positive affect in individualistic and collectivistic cultural contexts. Whereas Euro-Americans place greater value on high activation positive affect (HAP; e.g., excitement, enthusiasm, elation) than do Asian Americans and Hong Kong Chinese, the opposite is true for low activation positive affect (LAP; e.g., calmness, serenity, tranquility). Although the form of collectivi…
Unpacking Cultural Differences in Alexithymia: The Role of Cultural Values Among Euro-Canadian and Chinese-Canadian Students
The current study provides a cultural examination of alexithymia, a multifaceted personality construct that refers to a general deficit in the ability to identify and describe emotional states, and that has been linked to a number of psychiatric illnesses. Though this construct has been critiqued as heavily rooted in "Western" norms of emotional expression, it has not received much empirical attention from a cultural perspective. Recently, Ryder …
Recovering Substantive Factor Loadings in the Presence of Acquiescence Bias: A Comparison of Three Approaches
Researchers are often advised to write balanced scales (containing an equal number of positively and negatively worded items) when measuring psychological attributes. This practice is recommended to control for acquiescence bias (ACQ). However, little advice has been given on what to do with such data if the researcher subsequently wants to evaluate a 1-factor model for the scale. This article compares 3 approaches for dealing with the presence o…
Cultural Variation in the Minimal Group Effect
The minimal group effect (MGE) is one of the most robust psychological findings in studies of intergroup conflict, yet there is little evidence comparing its magnitude across cultures. Recent evidence suggests that the MGE is due in part to a projection of one’s own perceived characteristics onto the novel in-group. Because of cultural variability in self-enhancement motivations, we thus expected that those from East Asian cultures would exhibit …
Are Implicit Self‐Esteem Measures Valid for Assessing Individual and Cultural Differences
OBJECTIVE: Our research utilized two popular theoretical conceptualizations of implicit self-esteem: 1) implicit self-esteem as a global automatic reaction to the self; and 2) implicit self-esteem as a context/domain specific construct. Under this framework, we present an extensive search for implicit self-esteem measure validity among different cultural groups (Study 1) and under several experimental manipulations (Study 2). METHOD: In Study 1, …
Two Cross-Platform Programs for Inferences and Interval Estimation About Indirect Effects in Mediational Models
In this article, we describe two new programs that compute both p-values and confidence intervals (CI) for the indirect effect in mediational models, including (a) a p-value based on the partial posterior method, which we refer to as p3 computed across the posterior distribution of the regression coefficients; (b) a variant of p3 that uses a normal approximation for the posterior distributions, p3N; (c) Hierarchical Bayesian CIs (CIHB) based on t…
On Lagrange Multiplier Tests in Multidimensional Item Response Theory: Information Matrices and Model Misspecification
Lagrange multiplier (LM) or score tests have seen renewed interest for the purpose of diagnosing misspecification in item response theory (IRT) models. LM tests can also be used to test whether parameters differ from a fixed value. We argue that the utility of LM tests depends on both the method used to compute the test and the degree of misspecification in the initially fitted model. We demonstrate both of these points in the context of a multid…
A Comparison of Limited-Information Test Statistics for a Response Style Mirt Model
An increased use of models for measuring response styles is apparent in recent years with the multidimensional nominal response model (MNRM) as one prominent example. Inclusion of latent constructs representing extreme (ERS) or midpoint response style (MRS) often improves model fit according to information criteria. However, a test of absolute model fit is often not reported even though it could comprise an important piece of validity evidence. L…
On the Performance of Semi- and Nonparametric Item Response Functions in Computer Adaptive Tests
Large-scale assessments often use a computer adaptive test (CAT) for selection of items and for scoring respondents. Such tests often assume a parametric form for the relationship between item responses and the underlying construct. Although semi- and nonparametric response functions could be used, there is scant research on their performance in a CAT. In this work, we compare parametric response functions versus those estimated using kernel smoo…
Development and Validation of the Four Facet Mindful Eating Scale (FFaMES)
Supervised Classes, Unsupervised Mixing Proportions: Detection of Bots in a Likert-Type Questionnaire
Administering Likert-type questionnaires to online samples risks contamination of the data by malicious computer-generated random responses, also known as bots. Although nonresponsivity indices (NRIs) such as person-total correlations or Mahalanobis distance have shown great promise to detect bots, universal cutoff values are elusive. An initial calibration sample constructed via stratified sampling of bots and humans—real or simulated under a me…
Comparison of latent variable and psychological network models in PROMIS data: Output metrics and factor structure
Comparing Factor Score Approaches to SEM in Multigroup Models with Small Samples
Factor Score Regression (FSR) is increasingly employed as an alternative to structural equation modeling (SEM) in small samples. Despite its popularity in psychology, the performance of FSR in multigroup models with small samples remains relatively unknown. The goal of this study was to examine the performance of FSR, namely Croon’s correction and the bias avoiding method, for multigroup models with small samples and compare the methods to SEM. W…
Tackling Challenges in Data Pooling: Missing Data Handling in Latent Variable Models with Continuous and Categorical Indicators
Data pooling is a powerful strategy in empirical research. However, combining multiple datasets often results in a large amount of missing data, as variables that are not present in some datasets effectively contain missing values for all participants in those datasets. Furthermore, data pooling typically leads to a mix of continuous and categorical items with nonnormal multivariate distributions. We investigated two popular approaches to handle …
The Accuracy of Bayesian Model Fit Indices in Selecting Among Multidimensional Item Response Theory Models
Item response theory (IRT) models are often compared with respect to predictive performance to determine the dimensionality of rating scale data. However, such model comparisons could be biased toward nested-dimensionality IRT models (e.g., the bifactor model) when comparing those models with non-nested-dimensionality IRT models (e.g., a unidimensional or a between-item-dimensionality model). The reason is that, compared with non-nested-dimension…
Regularized Cross-Sectional Network Modeling with Missing Data: A Comparison of Methods
Many applications of network modeling involve cross-sectional data of psychological variables (e.g., symptoms for psychological disorders), and analyses are often conducted using a regularized Gaussian graphical model (GGM) employing a lasso, also known as the graphical lasso or glasso. Appropriate methodology for handling missing data is underdeveloped while using glasso, precluding the use of planned missing data designs to reduce participant f…
A Comparison of Scaled Difference Tests for Forming Confidence Intervals in SEM
Latent Variable Interactions with Categorical Indicators: Continuous and Categorical Latent Moderated Structural Equations Approaches
Social science phenomena are often predicted by interactions between variables. When these variables cannot be directly observed, one option is to model them as latent variables that are measured by multiple indicators. When indicators are continuous, latent interactions can be modeled and estimated using the latent moderated structural equations (LMS) approach. A categorical LMS (LMS-cat) approach with full information estimation was more recent…
Evaluation of Generative Adversarial Imputation Nets’ Performance in Handling Missing Data in Structural Equation Modeling
Evaluating Approaches for the Handling of Sign Reflection in Bayesian Latent Variable Models
In Markov chain Monte Carlo estimation of Bayesian latent variable models, sign reflection can cause multiple chains to settle onto equivalent but numerically different solutions, resulting in poorly mixed chains and nonconvergence. Sign reflection can be handled using various methods, such as adopting unit loading identification (ULI), assigning range restricted prior distributions, or using a relabeling algorithm. Some statistical software auto…
A Comparison of Regularization, Alignment, and a Traditional Method for Estimating Structural Relationships Across Two Groups
Establishing the correct partial measurement invariance model is crucial for ensuring unbiased comparisons of relationships between latent variables across multiple groups. While traditional approaches rely on detecting noninvariant items followed by estimation of structural relationships, more recently, approaches that estimate latent parameters without prior knowledge of anchor items have been developed. Specifically, regularization and alignme…
Mathematics (14 works) · Statistics (13 works) · Computer Science (12 works) · Econometrics (9 works) · Psychology (9 works) · Psychometric Methodologies and Testing (9 works) · Advanced Statistical Modeling Techniques (6 works) · Structural equation modeling (6 works) · Artificial Intelligence (5 works) · Latent variable (5 works)