Douglas Steinley
Dados Biográficos
| ID | 95358 |
|---|---|
| NOME | Douglas Steinley |
| PRENOMES | Douglas |
| SOBRENOME | Steinley |
| ASSINATURA | STEINLEY D |
| AFILIAÇÕES | University of Missouri |
| ORCID | 0000-0001-9900-5028 |
| VERIFICADO | Sim |
| TOTAL DE OBRAS | 28 |
| TOTAL DE CITAÇÕES | 23 |
| TOTAL COMO AUTOR | 27 |
| TOTAL COMO EDITOR | 1 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 2006 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2024 |
| ÍNDICE H | 3 |
Improving the Walktrap Algorithm Using K -Means Clustering
The walktrap algorithm is one of the most popular community-detection methods in psychological research. Several simulation studies have shown that it is often effective at determining the correct number of communities and assigning items to their proper community. Nevertheless, it is important to recognize that the walktrap algorithm relies on hierarchical clustering because it was originally developed for networks much larger than those encount…
Teaching the Mechanics of Factor Analysis Using Excel Spreadsheets
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)…
Combinatorial Optimization of Clustering Decisions
Using complete enumeration (e.g., generating all possible subsets of item combinations) to evaluate clustering problems has the benefit of locating globally optimal solutions automatically without the concern of sampling variability. The proposed method is meant to combine clustering variables in such a way as to create groups that are maximally different on a theoretically sound derivation variable(s). After the population of all unique sets is …
Recent Advances in (Graphical) Network Models
Recently, (as measured by advances in quantitative methodology) Borsboom (2008 Borsboom, D. (2008). Psychometric perspectives on diagnostic systems. Journal of Clinical Psychology, 64(9), 1089–1099. https://doi.org/10.1002/jclp.20503[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]) has proposed network analysis as an alternative to the latent variable model as a method for understanding the potentially complex relationships among a sys…
On Fixed Marginal Distributions and Psychometric Network Models
This reply addresses the commentary by Epskamp et al. (in press) on our prior work, of using fixed marginals for sampling the data for testing hypothesis in psychometric network application. Mathematical results are presented for expected column (e.g., item prevalence) and row (e.g., subject severity) probabilities under three classical sampling schemes in categorical data analysis: (i) fixing the density, (ii) fixing either the row or column mar…
Detecting Clusters/Communities in Social Networks
Cohen's κ, a similarity measure for categorical data, has since been applied to problems in the data mining field such as cluster analysis and network link prediction. In this paper, a new application is examined: community detection in networks. A new algorithm is proposed that uses Cohen's κ as a similarity measure for each pair of nodes; subsequently, the κ values are then clustered to detect the communities. This paper defines and tests this …
Deterministic Blockmodeling of Two-Mode Binary Networks Using a Two-Mode KL -Median Heuristic
Deterministic blockmodeling of a two-mode binary network matrix based on structural equivalence is a well-known problem in the social network literature. Whether implemented in a standalone fashion, or embedded within a metaheuristic framework, a popular relocation heuristic (RH) has served as the principal solution tool for this problem. In this paper, we establish that a two-mode KL -median heuristic (TMKLMedH) seeks to optimize the same criter…
The analysis of bridging constructs with hierarchical clustering methods
Extracting Spurious Latent Classes in Growth Mixture Modeling With Nonnormal Errors
Growth mixture modeling is generally used for two purposes: (1) to identify mixtures of normal subgroups and (2) to approximate oddly shaped distributions by a mixture of normal components. Often in applied research this methodology is applied to both of these situations indistinctly: using the same fit statistics and likelihood ratio tests. This can lead to the overextraction of latent classes and the attribution of substantive meaning to these …
Local Optima in Mixture Modeling
It is common knowledge that mixture models are prone to arrive at locally optimal solutions. Typically, researchers are directed to utilize several random initializations to ensure that the resulting solution is adequate. However, it is unknown what factors contribute to a large number of local optima and whether these coincide with the factors that reduce the accuracy of a mixture model. A real-data illustration and a series of simulations are p…
Modeling Between‐Subject Variability in Decision Strategies via Statistical Clustering
We present a statistical methodology for clustering decision makers according to similar choice behavior. We apply a p ‐median clustering algorithm that identifies an “exemplar” for each cluster, a decision maker who best represents that cluster. We demonstrate that information about group behavior can be inferred by examining the behavior of each cluster's exemplar. The method is exploratory, providing information about the prevalence of decisio…
Psychometrics
Using Cohen's κ for Community Detection in Social Networks
"Using Cohen's κ for Community Detection in Social Networks." Multivariate Behavioral Research, 50(6), pp. 740–741
Searching for Mr. Hyde
Some individuals "change" more dramatically than others when intoxicated, and the nature and magnitude of these changes can result in harmful outcomes. This study utilized reports (N1⁄4374) of participants' "typical" five-factor model (FFM) characteristics across sober and intoxicated states and assessed the degree to which these reports could be grouped into meaningful clusters, as well as the association of cluster membership with negative alco…
A note on using the adjusted Rand index for link prediction in networks
The Role of Gender and Friends’ Gender on Peer Socialization of Adolescent Drinking
An Exact Algorithm for Blockmodeling of Two-Mode Network Data
We consider problems where relationships between two sets (or modes) of objects are available in the form of a binary matrix with elements of 1 (0) indicating a bond (lack of a bond) between corresponding row and column objects. The goal is to establish a partition of the row objects and, simultaneously, a partition of the column objects to form blocks that consist of either exclusively 1s or exclusively 0s to the greatest extent possible. This t…
A variable neighborhood search method for a two-mode blockmodeling problem in social network analysis
This paper presents a variable neighborhood search (VNS) algorithm that is specially designed for the blockmodeling of two-mode binary network matrices in accordance with structural equivalence. Computational results for 768 synthetic test networks revealed that the VNS heuristic outperformed a relocation heuristic (RH) and a tabu search (TS) method for the same problem. Next, the three heuristics were applied to two-mode network data pertaining …
Principal Cluster Axes
A measure of "clusterability" serves as the basis of a new methodology designed to preserve cluster structure in a reduced dimensional space. Similar to principal component analysis, which finds the direction of maximal variance in multivariate space, principal cluster axes find the direction of maximum clusterability in multivariate space. Furthermore, the principal clustering approach falls into the class of projection pursuit techniques. Compa…
Two Algorithms for Relaxed Structural Balance Partitioning
Understanding social phenomena with the help of mathematical models requires a coherent combination of theory, models, and data together with using valid data analytic methods. The study of social networks through the use of mathematical models is no exception. The intuitions of structural balance were formalized and led to a pair of remarkable theorems giving the nature of partition structures for balanced signed networks. Algorithms for partiti…
How and why terrorism corrupts the consistency principle of organizational justice
We examined the impact of terrorism on the administration of organizational justice. Based on Terror Management Theory (TMT), it was hypothesized that punishment of deviance would change following an act of terrorism. Specifically, deviant individuals who committed an act high in moral severity would receive more extreme punishment after a terrorist attack than they would have received prior to this incident—thereby compromising consistency in th…
Statistics in the social sciences
"Statistics in the Social Sciences: Current Methodological Developments presents new and exciting statistical methodologies to help advance research and data analysis across the many disciplines in the social sciences. Quantitative methods in various subfields, from psychology to economics, are under demand for constant development and refinement. This volume features invited overview papers, as well as original research presented at the Sixth An…
A New Variable Weighting and Selection Procedure for K -means Cluster Analysis
A variance-to-range ratio variable weighting procedure is proposed. We show how this weighting method is theoretically grounded in the inherent variability found in data exhibiting cluster structure. In addition, a variable selection procedure is proposed to operate in conjunction with the variable weighting technique. The performances of these procedures are demonstrated in a simulation study, showing favorable results when compared with existin…
Cautionary Remarks on the Use of Clusterwise Regression
Clusterwise linear regression is a multivariate statistical procedure that attempts to cluster objects with the objective of minimizing the sum of the error sums of squares for the within-cluster regression models. In this article, we show that the minimization of this criterion makes no effort to distinguish the error explained by the within-cluster regression models from the error explained by the clustering process. In some cases, most of the …
A variable neighborhood search method for a two-mode blockmodeling problem in social network analysis
This paper presents a variable neighborhood search (VNS) algorithm that is specially designed for the blockmodeling of two-mode binary network matrices in accordance with structural equivalence. Computational results for 768 synthetic test networks revealed that the VNS heuristic outperformed a relocation heuristic (RH) and a tabu search (TS) method for the same problem. Next, the three heuristics were applied to two-mode network data pertaining …
An Exact Algorithm for Blockmodeling of Two-Mode Network Data
We consider problems where relationships between two sets (or modes) of objects are available in the form of a binary matrix with elements of 1 (0) indicating a bond (lack of a bond) between corresponding row and column objects. The goal is to establish a partition of the row objects and, simultaneously, a partition of the column objects to form blocks that consist of either exclusively 1s or exclusively 0s to the greatest extent possible. This t…
Two Algorithms for Relaxed Structural Balance Partitioning
Understanding social phenomena with the help of mathematical models requires a coherent combination of theory, models, and data together with using valid data analytic methods. The study of social networks through the use of mathematical models is no exception. The intuitions of structural balance were formalized and led to a pair of remarkable theorems giving the nature of partition structures for balanced signed networks. Algorithms for partiti…
The Role of Gender and Friends’ Gender on Peer Socialization of Adolescent Drinking
How and why terrorism corrupts the consistency principle of organizational justice
We examined the impact of terrorism on the administration of organizational justice. Based on Terror Management Theory (TMT), it was hypothesized that punishment of deviance would change following an act of terrorism. Specifically, deviant individuals who committed an act high in moral severity would receive more extreme punishment after a terrorist attack than they would have received prior to this incident—thereby compromising consistency in th…
Deterministic Blockmodeling of Two-Mode Binary Networks Using a Two-Mode KL -Median Heuristic
Deterministic blockmodeling of a two-mode binary network matrix based on structural equivalence is a well-known problem in the social network literature. Whether implemented in a standalone fashion, or embedded within a metaheuristic framework, a popular relocation heuristic (RH) has served as the principal solution tool for this problem. In this paper, we establish that a two-mode KL -median heuristic (TMKLMedH) seeks to optimize the same criter…
A note on using the adjusted Rand index for link prediction in networks
Patients' Health Literacy and Experience With Instructions
The finding that preferences varied with patients' experience using the instructions and cognitive abilities suggests instructions should accommodate diverse patient needs and abilities
K‐means clustering
This paper synthesizes the results, methodology, and research conducted concerning the K ‐means clustering method over the last fifty years. The K ‐means method is first introduced, various formulations of the minimum variance loss function and alternative loss functions within the same class are outlined, and different methods of choosing the number of clusters and initialization, variable preprocessing, and data reduction schemes are discussed.…
Examining Factor Score Distributions to Determine the Nature of Latent Spaces
Similarities between latent class models with K classes and linear factor models with K − 1 factors are investigated. Specifically, the mathematical equivalence between the covariance structure of the two models is discussed, and a Monte Carlo simulation is performed using generated data that represents both latent factors and latent classes with known amounts of overlap. It is shown that, under certain conditions, the distribution of factor scor…
Patients' Health Literacy and Experience With Instructions
The finding that preferences varied with patients' experience using the instructions and cognitive abilities suggests instructions should accommodate diverse patient needs and abilities
A New Variable Weighting and Selection Procedure for K -means Cluster Analysis
A variance-to-range ratio variable weighting procedure is proposed. We show how this weighting method is theoretically grounded in the inherent variability found in data exhibiting cluster structure. In addition, a variable selection procedure is proposed to operate in conjunction with the variable weighting technique. The performances of these procedures are demonstrated in a simulation study, showing favorable results when compared with existin…
Cautionary Remarks on the Use of Clusterwise Regression
Clusterwise linear regression is a multivariate statistical procedure that attempts to cluster objects with the objective of minimizing the sum of the error sums of squares for the within-cluster regression models. In this article, we show that the minimization of this criterion makes no effort to distinguish the error explained by the within-cluster regression models from the error explained by the clustering process. In some cases, most of the …
How and why terrorism corrupts the consistency principle of organizational justice
We examined the impact of terrorism on the administration of organizational justice. Based on Terror Management Theory (TMT), it was hypothesized that punishment of deviance would change following an act of terrorism. Specifically, deviant individuals who committed an act high in moral severity would receive more extreme punishment after a terrorist attack than they would have received prior to this incident—thereby compromising consistency in th…
Statistics in the social sciences
"Statistics in the Social Sciences: Current Methodological Developments presents new and exciting statistical methodologies to help advance research and data analysis across the many disciplines in the social sciences. Quantitative methods in various subfields, from psychology to economics, are under demand for constant development and refinement. This volume features invited overview papers, as well as original research presented at the Sixth An…
Two Algorithms for Relaxed Structural Balance Partitioning
Understanding social phenomena with the help of mathematical models requires a coherent combination of theory, models, and data together with using valid data analytic methods. The study of social networks through the use of mathematical models is no exception. The intuitions of structural balance were formalized and led to a pair of remarkable theorems giving the nature of partition structures for balanced signed networks. Algorithms for partiti…
Principal Cluster Axes
A measure of "clusterability" serves as the basis of a new methodology designed to preserve cluster structure in a reduced dimensional space. Similar to principal component analysis, which finds the direction of maximal variance in multivariate space, principal cluster axes find the direction of maximum clusterability in multivariate space. Furthermore, the principal clustering approach falls into the class of projection pursuit techniques. Compa…
An Exact Algorithm for Blockmodeling of Two-Mode Network Data
We consider problems where relationships between two sets (or modes) of objects are available in the form of a binary matrix with elements of 1 (0) indicating a bond (lack of a bond) between corresponding row and column objects. The goal is to establish a partition of the row objects and, simultaneously, a partition of the column objects to form blocks that consist of either exclusively 1s or exclusively 0s to the greatest extent possible. This t…
A variable neighborhood search method for a two-mode blockmodeling problem in social network analysis
This paper presents a variable neighborhood search (VNS) algorithm that is specially designed for the blockmodeling of two-mode binary network matrices in accordance with structural equivalence. Computational results for 768 synthetic test networks revealed that the VNS heuristic outperformed a relocation heuristic (RH) and a tabu search (TS) method for the same problem. Next, the three heuristics were applied to two-mode network data pertaining …
The Role of Gender and Friends’ Gender on Peer Socialization of Adolescent Drinking
Psychometrics
Using Cohen's κ for Community Detection in Social Networks
"Using Cohen's κ for Community Detection in Social Networks." Multivariate Behavioral Research, 50(6), pp. 740–741
Searching for Mr. Hyde
Some individuals "change" more dramatically than others when intoxicated, and the nature and magnitude of these changes can result in harmful outcomes. This study utilized reports (N1⁄4374) of participants' "typical" five-factor model (FFM) characteristics across sober and intoxicated states and assessed the degree to which these reports could be grouped into meaningful clusters, as well as the association of cluster membership with negative alco…
A note on using the adjusted Rand index for link prediction in networks
Extracting Spurious Latent Classes in Growth Mixture Modeling With Nonnormal Errors
Growth mixture modeling is generally used for two purposes: (1) to identify mixtures of normal subgroups and (2) to approximate oddly shaped distributions by a mixture of normal components. Often in applied research this methodology is applied to both of these situations indistinctly: using the same fit statistics and likelihood ratio tests. This can lead to the overextraction of latent classes and the attribution of substantive meaning to these …
Local Optima in Mixture Modeling
It is common knowledge that mixture models are prone to arrive at locally optimal solutions. Typically, researchers are directed to utilize several random initializations to ensure that the resulting solution is adequate. However, it is unknown what factors contribute to a large number of local optima and whether these coincide with the factors that reduce the accuracy of a mixture model. A real-data illustration and a series of simulations are p…
Modeling Between‐Subject Variability in Decision Strategies via Statistical Clustering
We present a statistical methodology for clustering decision makers according to similar choice behavior. We apply a p ‐median clustering algorithm that identifies an “exemplar” for each cluster, a decision maker who best represents that cluster. We demonstrate that information about group behavior can be inferred by examining the behavior of each cluster's exemplar. The method is exploratory, providing information about the prevalence of decisio…
The analysis of bridging constructs with hierarchical clustering methods
Detecting Clusters/Communities in Social Networks
Cohen's κ, a similarity measure for categorical data, has since been applied to problems in the data mining field such as cluster analysis and network link prediction. In this paper, a new application is examined: community detection in networks. A new algorithm is proposed that uses Cohen's κ as a similarity measure for each pair of nodes; subsequently, the κ values are then clustered to detect the communities. This paper defines and tests this …
Deterministic Blockmodeling of Two-Mode Binary Networks Using a Two-Mode KL -Median Heuristic
Deterministic blockmodeling of a two-mode binary network matrix based on structural equivalence is a well-known problem in the social network literature. Whether implemented in a standalone fashion, or embedded within a metaheuristic framework, a popular relocation heuristic (RH) has served as the principal solution tool for this problem. In this paper, we establish that a two-mode KL -median heuristic (TMKLMedH) seeks to optimize the same criter…
Combinatorial Optimization of Clustering Decisions
Using complete enumeration (e.g., generating all possible subsets of item combinations) to evaluate clustering problems has the benefit of locating globally optimal solutions automatically without the concern of sampling variability. The proposed method is meant to combine clustering variables in such a way as to create groups that are maximally different on a theoretically sound derivation variable(s). After the population of all unique sets is …
Recent Advances in (Graphical) Network Models
Recently, (as measured by advances in quantitative methodology) Borsboom (2008 Borsboom, D. (2008). Psychometric perspectives on diagnostic systems. Journal of Clinical Psychology, 64(9), 1089–1099. https://doi.org/10.1002/jclp.20503[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]) has proposed network analysis as an alternative to the latent variable model as a method for understanding the potentially complex relationships among a sys…
On Fixed Marginal Distributions and Psychometric Network Models
This reply addresses the commentary by Epskamp et al. (in press) on our prior work, of using fixed marginals for sampling the data for testing hypothesis in psychometric network application. Mathematical results are presented for expected column (e.g., item prevalence) and row (e.g., subject severity) probabilities under three classical sampling schemes in categorical data analysis: (i) fixing the density, (ii) fixing either the row or column mar…
Computer Science (23 obras) · Mathematics (21 obras) · Artificial Intelligence (13 obras) · Data mining (12 obras) · Complex Network Analysis Techniques (11 obras) · Artificial Intelligence (10 obras) · Statistics (10 obras) · Cluster analysis (9 obras) · Algorithm (8 obras) · Machine learning (8 obras)