Search

Your search keyword '"Steinley, Douglas"' showing total 24 results
Start Over Author "Steinley, Douglas" Search Limiters Full Text
24 results on '"Steinley, Douglas"'

1. Benchmarking in cluster analysis: A white paper

Author

Van Mechelen, Iven, Boulesteix, Anne-Laure, Dangl, Rainer, Dean, Nema, Guyon, Isabelle, Hennig, Christian, Leisch, Friedrich, and Steinley, Douglas

Subjects
Statistics - Other Statistics, 62H30
Abstract

Note: A revised version of this is now published. Please cite and read (it's open access): Van Mechelen, I., Boulesteix, A.-L., Dangl, R., Dean, N., Hennig, C., Leisch, F., Steinley, D., Warrens, M. J. (2023). A white paper on good research practices in benchmarking: The case of cluster analysis. WIREs Data Mining and Knowledge Discovery, e1511. https://doi.org/10.1002/widm.1511 To achieve scientific progress in terms of building a cumulative body of knowledge, careful attention to benchmarking is of the utmost importance. This means that proposals of new methods of data pre-processing, new data-analytic techniques, and new methods of output post-processing, should be extensively and carefully compared with existing alternatives, and that existing methods should be subjected to neutral comparison studies. To date, benchmarking and recommendations for benchmarking have been frequently seen in the context of supervised learning. Unfortunately, there has been a dearth of guidelines for benchmarking in an unsupervised setting, with the area of clustering as an important subdomain. To address this problem, discussion is given to the theoretical conceptual underpinnings of benchmarking in the field of cluster analysis by means of simulated as well as empirical data. Subsequently, the practicalities of how to address benchmarking questions in clustering are dealt with, and foundational recommendations are made.

Published
2018
Full Text
View/download PDF

2. Extending Growth Mixture Models Using Continuous Non-Elliptical Distributions

Author

Wei, Yuhong, Tang, Yang, Shireman, Emilie, McNicholas, Paul D., and Steinley, Douglas L.

Subjects
Statistics - Methodology, Statistics - Applications, Statistics - Computation
Abstract

Growth mixture models (GMMs) incorporate both conventional random effects growth modeling and latent trajectory classes as in finite mixture modeling; therefore, they offer a way to handle the unobserved heterogeneity between subjects in their development. GMMs with Gaussian random effects dominate the literature. When the data are asymmetric and/or have heavier tails, more than one latent class is required to capture the observed variable distribution. Therefore, a GMM with continuous non-elliptical distributions is proposed to capture skewness and heavier tails in the data set. Specifically, multivariate skew-t distributions and generalized hyperbolic distributions are introduced to extend GMMs. When extending GMMs, four statistical models are considered with differing distributions of measurement errors and random effects. The mathematical development of GMMs with non-elliptical distributions relies on their expression as normal variance-mean mixtures and the resultant relationship with the generalized inverse Gaussian distribution. Parameter estimation is outlined within the expectation-maximization framework before the performance of our GMMs with non-elliptical distributions is illustrated on simulated and real data.

Published
2017

3. Modeling Heterogeneous Peer Assortment Effects using Finite Mixture Exponential Random Graph Models

Author

Henry, Teague R, Gates, Kathleen M, Prinstein, Mitchell J, and Steinley, Douglas

Subjects
Statistics - Methodology
Abstract

This article develops a class of models called Sender/Receiver Finite Mixture Exponential Random Graph Models (SRFM-ERGMs) that enables inference on networks. This class of models extends the existing Exponential Random Graph Modeling framework to allow analysts to model unobserved heterogeneity in the effects of nodal covariates and network features. An empirical example regarding substance use among adolescents is presented. Simulations across a variety of conditions are used to evaluate the performance of this technique. We conclude that that unobserved heterogeneity in effects of nodal covariates can be a major cause of mis-fit in network models, and the SRFM-ERGM approach can alleviate this misfit. Implications for the analysis of social networks in psychological science are discussed.

Published
2016
Full Text
View/download PDF

4. Extracting Spurious Latent Classes in Growth Mixture Modeling with Nonnormal Errors

Author

Guerra-Peña, Kiero and Steinley, Douglas

Abstract

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 spurious classes. The goals of this study are (1) to explore the performance of the Bayesian information criterion, sample-adjusted BIC, and bootstrap likelihood ratio test in growth mixture modeling analysis with nonnormal distributed outcome variables and (2) to examine the effects of nonnormal time invariant covariates in the estimation of the number of latent classes when outcome variables are normally distributed. For both of these goals, we will include nonnormal conditions not considered previously in the literature. Two simulation studies were conducted. Results show that spurious classes may be selected and optimal solutions obtained in the data analysis when the population departs from normality even when the nonnormality is only present in time invariant covariates.

Published
2016
Full Text
View/download PDF

18. Motivational typologies of drinkers: do enhancement and coping drinkers form two distinct groups?

Author

Littlefield, Andrew K., Vergés, Alvaro, Rosinski, Jenny M., Steinley, Douglas, and Sher, Kenneth J.

Subjects
Male, Motivation, Cross-Sectional Studies, Adolescent, Alcohol Drinking, Surveys and Questionnaires, Adaptation, Psychological, Humans, Female, Longitudinal Studies, Students, Article, United States
Abstract

This study used a person-centered approach to test whether drinking motive typologies could be identified.Longitudinal study of college students within the intensive multivariate prospective alcohol college-transitions (IMPACTS) data set.University campus in the United States.University students (baseline n reporting alcohol motives = 2158; baseline age = 18.60 years old).The drinking motives questionnaire-revised (DMQ-R).Using SteinleyBrusco's cluster analysis approach [based on the theoretical ratio expected between the within sum of squares and the total sum of squares when the data are divided into two clusters when no cluster structure is present; the cut-off for the ratio is 0.25 for uniform (multivariate uniform) distributions and 0.36 for normal (multivariate normal) distributions], we examined whether there was evidence for distinct clusters of individuals that differed on their overall level of motives to drink. We tested the fit of a one-group (cluster) solution compared to multi-cluster solutions. Both cross-sectionally and prospectively, the data could not be partitioned into two or more clusters [regardless of whether the cut-off assuming a multivariate uniform distribution (i.e. 0.25) or the more liberal multivariate normal distribution (i.e., 0.36) was used]. These findings showed that enhancement and coping drinkers do not form two distinct groups but, rather, these motives exist on a continuum such that individuals who are high in one internal motive tend to be high in the other motive.Coping and enhancement drinkers do not form two distinct groups. Variable-centered approaches to drinking motives may be a better alternative to classifying all drinkers as either enhancement or coping drinkers for both clinical and research endeavors.

Published
2012

19. Deliberate induction of alcohol tolerance: empirical introduction to a novel health risk

Author

Martinez, Julia A., Steinley, Douglas, and Sher, Kenneth J.

Subjects
Male, Adolescent, Alcohol Drinking, Ethanol, Universities, Drug Tolerance, Health Surveys, Article, Peer Group, United States, Young Adult, Cross-Sectional Studies, Logistic Models, Risk-Taking, Humans, Female, Students, Alcohol-Related Disorders, Attitude to Health
Abstract

Alcohol tolerance is a hallmark indicator of alcohol dependence. Even so, the allure of peers' admiration for having the ability to drink heavily may lead some adolescents and young adults to practice, or 'train', to increase their tolerance (particularly at US colleges, where heavy drinking is highly prevalent and central to the social culture). This is a potential health hazard that has not been documented empirically. Thus, we initiated a study of tolerance 'training' and its association to risky and heavy drinking.A cross-sectional online survey of 990 college student life-time drinkers at a large Midwestern US university.Of the sample, 9.9% (n = 97) reported deliberately 'training' to increase tolerance. On average, they reported increasing from approximately seven to 10 US standard drinks in a night prior to 'training' to 12-15 drinks at the end of 'training,' over approximately 2-3 weeks' duration. Although the proportion of frequent binge drinking among 'non-trainers' (34.4%) was similar to national rates, 'trainers' were much more likely to be frequent bingers (76.3%; OR = 6.15).A number of students report deliberately inducing alcohol tolerance, probably directly increasing the risk for alcohol poisoning and other acute harms and/or dependence. This phenomenon might additionally be applicable to other populations, and deserves further study and attention as a potential personal and public health risk. Prevention efforts might aim to reduce the perceived importance of heavy-drinking abilities.

Published
2010

24. The analysis of bridging constructs with hierarchical clustering methods: An application to identity

Author

Colleen M. Farrelly, Seth J. Schwartz, Douglas Steinley, Simona Picariello, Anna Lisa Amodeo, Alan Meca, Daniel J. Feaster, Farrelly, Colleen M., Schwartz, Seth J., Lisa Amodeo, Anna, Feaster, Daniel J., Steinley, Douglas L., Meca, Alan, and Picariello, Simona

Subjects
Measurement, Psychology (all), Social Psychology, Bridging construct, 05 social sciences, Dendrogram, Nonparametric statistics, 050401 social sciences methods, 050109 social psychology, computer.software_genre, Exploratory factor analysis, Hierarchical clustering, Bridging (programming), 0504 sociology, Sample size determination, Identity, Survey data collection, 0501 psychology and cognitive sciences, Data mining, Cluster analysi, Graphics, Psychology, computer, Social psychology, General Psychology
Abstract

When analyzing psychometric surveys, some design and sample size limitations challenge existing approaches. Hierarchical clustering, with its graphics (heat maps, dendrograms, means plots), provides a nonparametric method for analyzing factorially-designed survey data, and small samples data. In the present study, we demonstrated the advantages of using hierarchical clustering (HC) for the analysis of non-higher-order measures, comparing the results of HC against those of exploratory factor analysis. As a factorially-designed survey, we used the Identity Labels and Life Contexts Questionnaire (ILLCQ), a novel measure to assess identity as a bridging construct for the intersection of identity domains and life contexts. Results suggest that, when used to validate factorially-designed measures, HC and its graphics are more stable and consistent compared to EFA.

Published
2017

Catalog

Books, media, physical & digital resources
See catalog results