Wolfgang Wiedermann
Biographic Data
| ID | 4095294 |
|---|---|
| NAME | Wolfgang Wiedermann |
| GIVEN NAMES | Wolfgang |
| FAMILY NAME | Wiedermann |
| SIGNATURE | WIEDERMANN W |
| AFFILIATIONS | University of Missouri |
| ORCID | 0000-0002-6468-905X |
| VERIFIED | Yes |
| TOTAL WORKS | 27 |
| TOTAL CITATIONS | 5 |
| AUTHOR COUNT | 26 |
| EDITOR COUNT | 1 |
| FIRST PUBLICATION YEAR | 2011 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Confirmatory Person-Oriented Mediation Analysis in Categorical Variables-Options and Limitations
In this article, we propose three new approaches to analyzing person-oriented mediation hypotheses. First, we propose using relevance logic instead of classical logic. Relevance logic (1) rejects the ex falso sequitur quodlibet (“from a falsehood anything follows”) rule and (2) requires that the premise and the outcome of an implication exhibit variable sharing, that is, possess a substantive link. Propositions are declared irrelevant when the pr…
X implies Y – Testing Hypotheses of Direction of Effect Using Configural Frequency Analysis
This article proposes using formal theory to specify models for confirmatory Configural Frequency Analysis (CFA). Statement calculus is used from the perspective of relevance logic. Under this perspective, patterns are identified that are ‘true’, that is, that are conform with a priori made statements. Among the true patterns, there also are patterns that reflect true statements although the premise is false – the well known ‘ex falso sequitur qu…
Cumulant-Based Approaches for Testing the Assumption of Independent Errors in Non-Gaussian Parallel and Congeneric Measures
In classical test theory, independence of measurement errors constitutes a central assumption when estimating the reliability of measures. Furthermore, it is well known that this assumption cannot be tested with standard methods that rely on second-order moments (variances, covariances). The present study, therefore, explores properties of non-Gaussian parallel and congeneric measures (i.e., when observed scores deviate from the Gaussian distribu…
Distributional moderation analysis: Unpacking moderation effects in intervention research
Conceptual and methodological advances for understanding contextual, identity, and cultural effects in intervention research: The contextually informed research model
Moving From Statistical to Hypothesis-driven Outliers
Log-Linear and Configural Analysis of Intra-Individual Time Series under Consideration of Serial Dependence
Equal Precision Measurement Structural Models
Error variance in structural models is often specified as a conditional variance associated with a manifest variable or as a regression path from a standardized error variance. This paper describes scenarios in which specification of both terms is useful: (1) Corrections for attenuation in single indicator factor models; (2) Tests of the equality in the proportion of explained variance across multiple dependent variables in regression models; (3)…
Accounting for Heteroskedasticity Resulting from Between-Group Differences in Multilevel Models
Homogeneity of variance (HOV) is a well-known but often untested assumption in the context of multilevel models (MLMs). However, depending on how large the violation is, how different group sizes are, and the variance pairing, standard errors can be over or underestimated even when using MLMs, resulting in questionable inferential tests. We evaluate several tests (e.g., the H statistic, Breusch Pagan, Levene’s test) that can be used with MLMs to …
Conditional Direction of Dependence Modeling: Application and Implementation in SPSS
Conditional Direction Dependence Analysis (CDDA) has recently been proposed as a statistical framework to test reverse causation ( x → y vs. y → x) and potential of confounding ( x ← c → y) of variable relations in linear models when moderation is present. Similar to standard DDA, CDDA assumes that the “true” predictor is a continuous, non-normal, exogenous variable. Under non-normality, a conditional causal effect of one variable does not only c…
Base Models for Configural Frequency Analysis – Data Generation Processes
A Simple Configural Approach for Testing Person-Oriented Mediation Hypotheses
Conditional Direction Dependence Analysis: Evaluating the Causal Direction of Effects in Linear Models with Interaction Terms
Direction dependence analysis (DDA) makes use of higher than second moment information of variables (x and y) to detect potential confounding and to probe the causal direction of linear variable relations (i.e., whether x → y or y → x better approximates the underlying causal mechanism). The “true” predictor is assumed to be a continuous nonnormal exogenous variable. Existing methods compatible with DDA, however, are of limited use when the relat…
Sensitivity Analysis and Extensions of Testing the Causal Direction of Dependence: A Rejoinder to Thoemmes (2019)
A commentary by Thoemmes on Wiedermann and Sebastian's introductory article on Direction Dependence Analysis (DDA) is responded to in the interest of elaborating and extending direction dependence principles to evaluate causal effect directionality. Considering Thoemmes' observation that some DDA principles are already well-established in machine learning, we argue that several other connections between DDA and research lines in theoretical stati…
Direction Dependence Analysis in the Presence of Confounders: Applications to Linear Mediation Models Using Observational Data
Statistical methods to identify mis-specifications of linear regression models with respect to the direction of dependence (i.e. whether x→y or y→x better approximates the data-generating mechanism) have received considerable attention. Direction dependence analysis (DDA) constitutes such a statistical tool and makes use of higher-moment information of variables to derive statements concerning directional model mis-specifications in observational…
Prosocial skills causally mediate the relation between effective classroom management and academic competence: An application of direction dependence analysis
Direction dependence analysis (DDA) is a recently developed method that addresses the need for more sophisticated tools to evaluate causal mechanisms of developmental processes and interventions. The present study applied DDA to evaluate the hypothesized mediators of a classroom behavior management training program on student academic competence. The study involved a group randomized controlled trial with 105 teachers and 1,818 students (K-3rd gr…
Locating Event-Based Causal Effects: A Configural Perspective
Heteroscedasticity as a Basis of Direction Dependence in Reversible Linear Regression Models
Heteroscedasticity is a well-known issue in linear regression modeling. When heteroscedasticity is observed, researchers are advised to remedy possible model misspecification of the explanatory part of the model (e.g., considering alternative functional forms and/or omitted variables). The present contribution discusses another source of heteroscedasticity in observational data: Directional model misspecifications in the case of nonnormal variabl…
Testing Event-Based Forms of Causality
Person‐Centered Analysis
The majority of data analyses in the empirical sciences that are concerned with humans proceeds at the level of variables. Typical results relate variables to each other, for example, in correlational or regression‐type statements. In these analyses, individuals are considered random data carriers, replaceable without damage by other individuals, also random data carriers. This type of research is known as variable‐oriented . It has been shown th…
Dependent Data in Social Sciences Research: Forms, Issues, and Methods of Analysis
Direction of Effects in Multiple Linear Regression Models
Previous studies analyzed asymmetric properties of the Pearson correlation coefficient using higher than second order moments. These asymmetric properties can be used to determine the direction of dependence in a linear regression setting (i.e., establish which of two variables is more likely to be on the outcome side) within the framework of cross-sectional observational data. Extant approaches are restricted to the bivariate regression case. Th…
Manifest Variable Granger Causality Models for Developmental Research: A Taxonomy
Granger models are popular when it comes to testing hypotheses that relate series of measures causally to each other. In this article, we propose a taxonomy of Granger causality models. The taxonomy results from crossing the four variables Order of Lag, Type of (Contemporaneous) Effect, Direction of Effect, and Segment of Dependent Series Targeted. Among the uses of such a taxonomy are that existing models can be embedded in the context of possib…
The Lemming-effect: Harm perception of psychotropic substances among music festival visitors
Previous authors have recognised the need for a re-characterisation of risk assessment as a lived experience which is constructed in, and influenced by, the social context. In this article, we examine the impact of perceived drug consumption norms on perceived drug-related harm in a social context encouraging drug use. We hypothesised that cognitive accessibility of perceived peer behaviour leads to a trivialisation of perceived harm. To test thi…
On Direction of Dependence in Latent Variable Contexts
Approaches to determining direction of dependence in nonexperimental data are based on the relation between higher-than second-order moments on one side and correlation and regression models on the other. These approaches have experienced rapid development and are being applied in contexts such as research on partner violence, attention deficit hyperactivity disorder, and currency exchange rates. In this article, we propose using these methods in…
Prosocial skills causally mediate the relation between effective classroom management and academic competence: An application of direction dependence analysis
Direction dependence analysis (DDA) is a recently developed method that addresses the need for more sophisticated tools to evaluate causal mechanisms of developmental processes and interventions. The present study applied DDA to evaluate the hypothesized mediators of a classroom behavior management training program on student academic competence. The study involved a group randomized controlled trial with 105 teachers and 1,818 students (K-3rd gr…
A Simple Configural Approach for Testing Person-Oriented Mediation Hypotheses
Testing Event-Based Forms of Causality
Granger Causality—Statistical Analysis Under a Configural Perspective
Correcting Overestimated Effect Size Estimates in Multiple Trials
In a simulation study, Brand, Bradley, Best, and Stoica (2011) have shown that Cohen's d is notably overestimated if computed for data aggregated over multiple trials. Although the phenomenon is highly important for studies and meta-analyses of studies structurally similar to the simulated scenario, the authors do not comprehensively address how the problem could be handled. In this comment, we first suggest a corrective term d(')c that includes …
The Lemming-effect: Harm perception of psychotropic substances among music festival visitors
Previous authors have recognised the need for a re-characterisation of risk assessment as a lived experience which is constructed in, and influenced by, the social context. In this article, we examine the impact of perceived drug consumption norms on perceived drug-related harm in a social context encouraging drug use. We hypothesised that cognitive accessibility of perceived peer behaviour leads to a trivialisation of perceived harm. To test thi…
On Direction of Dependence in Latent Variable Contexts
Approaches to determining direction of dependence in nonexperimental data are based on the relation between higher-than second-order moments on one side and correlation and regression models on the other. These approaches have experienced rapid development and are being applied in contexts such as research on partner violence, attention deficit hyperactivity disorder, and currency exchange rates. In this article, we propose using these methods in…
Granger Causality—Statistical Analysis Under a Configural Perspective
Person‐Centered Analysis
The majority of data analyses in the empirical sciences that are concerned with humans proceeds at the level of variables. Typical results relate variables to each other, for example, in correlational or regression‐type statements. In these analyses, individuals are considered random data carriers, replaceable without damage by other individuals, also random data carriers. This type of research is known as variable‐oriented . It has been shown th…
Dependent Data in Social Sciences Research: Forms, Issues, and Methods of Analysis
Direction of Effects in Multiple Linear Regression Models
Previous studies analyzed asymmetric properties of the Pearson correlation coefficient using higher than second order moments. These asymmetric properties can be used to determine the direction of dependence in a linear regression setting (i.e., establish which of two variables is more likely to be on the outcome side) within the framework of cross-sectional observational data. Extant approaches are restricted to the bivariate regression case. Th…
Manifest Variable Granger Causality Models for Developmental Research: A Taxonomy
Granger models are popular when it comes to testing hypotheses that relate series of measures causally to each other. In this article, we propose a taxonomy of Granger causality models. The taxonomy results from crossing the four variables Order of Lag, Type of (Contemporaneous) Effect, Direction of Effect, and Segment of Dependent Series Targeted. Among the uses of such a taxonomy are that existing models can be embedded in the context of possib…
Heteroscedasticity as a Basis of Direction Dependence in Reversible Linear Regression Models
Heteroscedasticity is a well-known issue in linear regression modeling. When heteroscedasticity is observed, researchers are advised to remedy possible model misspecification of the explanatory part of the model (e.g., considering alternative functional forms and/or omitted variables). The present contribution discusses another source of heteroscedasticity in observational data: Directional model misspecifications in the case of nonnormal variabl…
Testing Event-Based Forms of Causality
Locating Event-Based Causal Effects: A Configural Perspective
Conditional Direction Dependence Analysis: Evaluating the Causal Direction of Effects in Linear Models with Interaction Terms
Direction dependence analysis (DDA) makes use of higher than second moment information of variables (x and y) to detect potential confounding and to probe the causal direction of linear variable relations (i.e., whether x → y or y → x better approximates the underlying causal mechanism). The “true” predictor is assumed to be a continuous nonnormal exogenous variable. Existing methods compatible with DDA, however, are of limited use when the relat…
Sensitivity Analysis and Extensions of Testing the Causal Direction of Dependence: A Rejoinder to Thoemmes (2019)
A commentary by Thoemmes on Wiedermann and Sebastian's introductory article on Direction Dependence Analysis (DDA) is responded to in the interest of elaborating and extending direction dependence principles to evaluate causal effect directionality. Considering Thoemmes' observation that some DDA principles are already well-established in machine learning, we argue that several other connections between DDA and research lines in theoretical stati…
Direction Dependence Analysis in the Presence of Confounders: Applications to Linear Mediation Models Using Observational Data
Statistical methods to identify mis-specifications of linear regression models with respect to the direction of dependence (i.e. whether x→y or y→x better approximates the data-generating mechanism) have received considerable attention. Direction dependence analysis (DDA) constitutes such a statistical tool and makes use of higher-moment information of variables to derive statements concerning directional model mis-specifications in observational…
Prosocial skills causally mediate the relation between effective classroom management and academic competence: An application of direction dependence analysis
Direction dependence analysis (DDA) is a recently developed method that addresses the need for more sophisticated tools to evaluate causal mechanisms of developmental processes and interventions. The present study applied DDA to evaluate the hypothesized mediators of a classroom behavior management training program on student academic competence. The study involved a group randomized controlled trial with 105 teachers and 1,818 students (K-3rd gr…
A Simple Configural Approach for Testing Person-Oriented Mediation Hypotheses
Conditional Direction of Dependence Modeling: Application and Implementation in SPSS
Conditional Direction Dependence Analysis (CDDA) has recently been proposed as a statistical framework to test reverse causation ( x → y vs. y → x) and potential of confounding ( x ← c → y) of variable relations in linear models when moderation is present. Similar to standard DDA, CDDA assumes that the “true” predictor is a continuous, non-normal, exogenous variable. Under non-normality, a conditional causal effect of one variable does not only c…
Base Models for Configural Frequency Analysis – Data Generation Processes
Equal Precision Measurement Structural Models
Error variance in structural models is often specified as a conditional variance associated with a manifest variable or as a regression path from a standardized error variance. This paper describes scenarios in which specification of both terms is useful: (1) Corrections for attenuation in single indicator factor models; (2) Tests of the equality in the proportion of explained variance across multiple dependent variables in regression models; (3)…
Accounting for Heteroskedasticity Resulting from Between-Group Differences in Multilevel Models
Homogeneity of variance (HOV) is a well-known but often untested assumption in the context of multilevel models (MLMs). However, depending on how large the violation is, how different group sizes are, and the variance pairing, standard errors can be over or underestimated even when using MLMs, resulting in questionable inferential tests. We evaluate several tests (e.g., the H statistic, Breusch Pagan, Levene’s test) that can be used with MLMs to …
Log-Linear and Configural Analysis of Intra-Individual Time Series under Consideration of Serial Dependence
Distributional moderation analysis: Unpacking moderation effects in intervention research
Conceptual and methodological advances for understanding contextual, identity, and cultural effects in intervention research: The contextually informed research model
Moving From Statistical to Hypothesis-driven Outliers
Confirmatory Person-Oriented Mediation Analysis in Categorical Variables-Options and Limitations
In this article, we propose three new approaches to analyzing person-oriented mediation hypotheses. First, we propose using relevance logic instead of classical logic. Relevance logic (1) rejects the ex falso sequitur quodlibet (“from a falsehood anything follows”) rule and (2) requires that the premise and the outcome of an implication exhibit variable sharing, that is, possess a substantive link. Propositions are declared irrelevant when the pr…
Mathematics (20 works) · Econometrics (19 works) · Statistics (19 works) · Computer Science (17 works) · Advanced Statistical Modeling Techniques (15 works) · Psychology (11 works) · Advanced Statistical Methods and Models (10 works) · Statistical hypothesis testing (8 works) · Artificial Intelligence (7 works) · Regression analysis (7 works)