Evaluating mixture modeling for clustering: Recommendations and cautions.

This article provides a large-scale investigation into several of the properties of mixture-model clustering techniques (also referred to as latent class cluster analysis, latent profile analysis, model-based clustering, probabilistic clustering, Bayesian classification, unsupervised learning, and finite mixture models; see Vermunt & Magdison, 2002). Focus is given to the multivariate normal distribution, and 9 separate decompositions (i.e., class structures) of the covariance matrix are investigated. To provide a link to the current literature, comparisons are made with K-means clustering in 3 detailed Monte Carlo studies. The findings have implications for applied researchers in that mixture-model clustering techniques performed best when the covariance structure and number of clusters were known. However, as the information about the shape and number of clusters became unknown, degraded performance was observed for both K-means clustering and mixture-model clustering. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Applying cluster analysis in counseling psychology research.

As a research technique that has grown rapidly in applications in many scientific disciplines, cluster analysis has potential for wider use in counseling psychology research. We begin with a simple example illustrating the clustering approach. Topics covered include the variety of approaches in clustering, the times when cluster analysis may be a choice for analysis, the steps in cluster analysis, the data features, such as level, shape, and scatter, that affect cluster results, alternate clustering methods and evidence indicating which are most effective, and examples of clustering applications in counseling research. Although we make an attempt to provide a comprehensive overview of major issues, the reader is encouraged to consult several good recent publications on the topic that are especially relevant for psychologists. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Profiling local optima in K-means clustering: Developing a diagnostic technique.

Using the cluster generation procedure proposed by D. Steinley and R. Henson (2005), the author investigated the performance of K-means clustering under the following scenarios: (a) different probabilities of cluster overlap; (b) different types of cluster overlap; (c) varying samples sizes, clusters, and dimensions; (d) different multivariate distributions of clusters; and (e) various multidimensional data structures. The results are evaluated in terms of the Hubert-Arabie adjusted Rand index, and several observations concerning the performance of K-means clustering are made. Finally, the article concludes with the proposal of a diagnostic technique indicating when the partitioning given by a K-means cluster analysis can be trusted. By combining the information from several observable characteristics of the data (number of clusters, number of variables, sample size, etc.) with the prevalence of unique local optima in several thousand implementations of the K-means algorithm, the author provides a method capable of guiding key data-analysis decisions. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Clustering Binary Data in the Presence of Masking Variables.

A number of important applications require the clustering of binary data sets. Traditional nonhierarchical cluster analysis techniques, such as the popular K-means algorithm, can often be successfully applied to these data sets. However, the presence of masking variables in a data set can impede the ability of the K-means algorithm to recover the true cluster structure. The author presents a heuristic procedure that selects an appropriate subset from among the set of all candidate clustering variables. Specifically, this procedure attempts to select only those variables that contribute to the definition of true cluster structure while eliminating variables that can hide (or mask) that true structure. Experimental testing of the proposed variable-selection procedure reveals that it is extremely successful at accomplishing this goal. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Multivariate statistics for family psychology research.

The application of selected multivariate statistics is illustrated for use in family psychology research. The use of multivariate analysis of variance (MANOVA) and discriminant analysis in factorial designs and profile analysis is discussed. Profile analysis provides a method for dealing with unit of analysis issues in family psychology research. Applications of confirmatory factor analysis are also discussed as useful methods for researchers examining multiple components of families and handling multiple perspectives of various family members. Limitations and applications of these methods in family psychology research are reviewed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

K-means may perform as well as mixture model clustering but may also be much worse: Comment on Steinley and Brusco (2011).

Steinley and Brusco (2011) presented the results of a huge simulation study aimed at evaluating cluster recovery of mixture model clustering (MMC) both for the situation where the number of clusters is known and is unknown. They derived rather strong conclusions on the basis of this study, especially with regard to the good performance of K-means (KM) compared with MMC. I agree with the authors' conclusion that the performance of KM may be equal to MMC in certain situations, which are primarily the situations investigated by Steinley and Brusco. However, a weakness of the paper is the failure to investigate many important real-world situations where theory suggests that MMC should outperform KM. This article elaborates on the KM–MMC comparison in terms of cluster recovery and provides some additional simulation results that show that KM may be much worse than MMC. Moreover, I show that KM is equivalent to a restricted mixture model estimated by maximizing the classification likelihood and comment on Steinley and Brusco's recommendation regarding the use of mixture models for clustering. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Choosing the number of clusters in Κ-means clustering.

Steinley (2007) provided a lower bound for the sum-of-squares error criterion function used in K-means clustering. In this article, on the basis of the lower bound, the authors propose a method to distinguish between 1 cluster (i.e., a single distribution) versus more than 1 cluster. Additionally, conditional on indicating there are multiple clusters, the procedure is extended to determine the number of clusters. Through a series of simulations, the proposed methodology is shown to outperform several other commonly used procedures for determining both the presence of clusters and their number. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

K-means clustering and mixture model clustering: Reply to McLachlan (2011) and Vermunt (2011).

McLachlan (2011) and Vermunt (2011) each provided thoughtful replies to our original article (Steinley & Brusco, 2011). This response serves to incorporate some of their comments while simultaneously clarifying our position. We argue that greater caution against overparamaterization must be taken when assuming that clusters are highly elliptical in nature. Specifically, users of mixture model clustering techniques should be wary of overreliance on fit indices, and the importance of cross-validation is highlighted. Additionally, we note that K-means clustering is part of a larger family of discrete partitioning algorithms, many of which are designed to solve problems identical to those for which mixture modeling approaches are often touted. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Methodological advances in family psychology research: Introduction to the special section.

This article introduces a special section in the Journal of Family Psychology on methodological advances in family psychology research. The need for innovative methodologies to capture the richness and complexity of family relationships and to advance the field is discussed. Articles that address the application of mathematical modeling of couple interactions, methods for analyzing sequential observational data, the application of multivariate analysis of variance and confirmatory factor analysis, the application of hierarchical linear modeling, and the use of experimental methods for the study of family process are included in the special section. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Facilitating rigorous qualitative research in rehabilitation psychology.

Qualitative research methods have much to contribute to theoretical and applied knowledge in rehabilitation psychology. However, as a discipline, rehabilitation psychology has been behind the curve in employing qualitative methods. Objectives: This article is a summary of the state of qualitative research in rehabilitation and an introduction to various methodological dimensions to consider in implementing qualitative rehabilitation psychology research. Types and examples of qualitative rehabilitation research are presented. Criteria for evaluating qualitative research are discussed. Finally, the majority of this article is devoted to presenting the various methodological dimensions on which researchers must make decisions in designing and implementing rigorous qualitative research (e.g., paradigms, methods, data collection strategies, data analysis procedures, reliability/validity). Conclusions: Rehabilitation psychology has much to gain through qualitative research, and success in incorporating qualitative evidence will be ensured by rehabilitation psychologists learning and rigorously implementing qualitative methods. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Commentary on Steinley and Brusco (2011): Recommendations and cautions.

I discuss the recommendations and cautions in Steinley and Brusco's (2011) article on the use of finite models to cluster a data set. In their article, much use is made of comparison with the K-means procedure. As noted by researchers for over 30 years, the K-means procedure can be viewed as a special case of finite mixture modeling in which the components are in equal (fixed) proportions and are taken to be normal with a common spherical covariance matrix. In this commentary, I pay particular attention to this link and to the use of normal mixture models with arbitrary component-covariance matrices. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Mixed Methods Research Designs in Counseling Psychology.

With the increased popularity of qualitative research, researchers in counseling psychology are expanding their methodologies to include mixed methods designs. These designs involve the collection, analysis, and integration of quantitative and qualitative data in a single or multiphase study. This article presents an overview of mixed methods research designs. It defines mixed methods research, discusses its origins and philosophical basis, advances steps and procedures used in these designs, and identifies 6 different types of designs. Important design features are illustrated using studies published in the counseling literature. Finally, the article ends with recommendations for designing, implementing, and reporting mixed methods studies in the literature and for discussing their viability and continued usefulness in the field of counseling psychology. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Neglected psychological issues in population research.

Proposes a new research orientation for population research, a psychology of population. This approach would include elements of social and developmental psychology (e.g., attitudes toward family planning, childhood socialization, sex roles, evaluation of different contraceptive methods, and family and group influences). Population psychology could integrate biological and social studies, provide a framework for studying the entire life cycle, and establish a methodology for evaluating micro- and macrosystems. The overall purpose of a psychology of population is presented as the ability to show (a) how biological variables are expressed in behavior and (b) the effects of basic drives on interpersonal, social, and cultural conditions and vice versa. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Levels of analysis issues in group psychology: Using efficacy as an example of a multilevel model.

Levels of analysis issues have attracted a lot of attention in group psychology research. Despite assertions pertaining to the value of multilevel models, most researchers focus on either the individual within groups or the group as a whole, but seldom on both. A multilevel approach may be helpful to group psychologists. This article addresses levels of analysis issues that are an inherent part of group research, and a number of methods that can be used to analyze multilevel data are presented. The methods fall into 3 categories: (a) assessing the extent of agreement within a single group, (b) contrasting within-group and between-groups variance, and (c) conducting multiple-level analyses. Finally, recommendations are offered for future multilevel research. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Survival Analysis in Family Research.

Within the field of family psychology, questions regarding the risk of event occurrence may be common. Such questions, about whether and when events occur and what predicts these occurrences, pose particular methodological challenges and are often best addressed via a statistical method known as survival analysis. This article provides a brief overview of that method, explicating through a data example the major components of a discrete-time survival analysis. Readers not familiar with this method are encouraged to use this article as an introduction to survival analysis and recognize its potential usefulness within the field of family psychology. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

How to enrich scientific method.

Comments on an article (see record 2007-14606-004) by A. Machado and F. J. Silva, who claimed that psychology's current conception of scientific method comprises two clusters of activities: experimentation and mathematization. They proposed that psychology needs to enrich its accounts of scientific method with a third cluster of activities that they call conceptual analysis. Haig agrees with the authors that psychology needs to improve its understanding of scientific methods, but he believes that giving greater attention to conceptual analysis is not the appropriate way to do this. In this comment, Haig suggests that conceptual analysis as such is not a distinctive feature of scientific method. He maintains instead that the most appropriate way to enrich our understanding of scientific method is to develop better theories of scientific method. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Integrating gay, lesbian, and bisexual issues into mainstream psychology.

Despite the growing clinical and research literature dealing with gay, lesbian, and bisexual (GLB) issues, mainstream psychology has tended to ignore much of the work that has been done in this area. This article illustrates how clinical and research writings on GLB issues continue to remain invisible to mainstream psychology in such areas as life span development and aging, teenage suicide, substance abuse, victimization and abuse, and family and couple relationships. It also deals with some of the determinants of well-being among GLB individuals, such as family support, and notes the benefits accruing to mainstream psychology from studying GLB issues. A network of family members within psychology having GLB relatives has been formed--AFFIRM: Psychologists Affirming their Gay, Lesbian, and Bisexual Family--and is dedicated to supporting its own family members, encouraging other family members to do likewise, supporting research and clinical work on GLB issues, and closing the gap between GLB clinical and research work and mainstream psychology. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Phenomenological Research Methods for Counseling Psychology.

This article familiarizes counseling psychologists with qualitative research methods in psychology developed in the tradition of European phenomenology. A brief history includes some of Edmund Husserl's basic methods and concepts, the adoption of existential-phenomenology among psychologists, and the development and formalization of qualitative research procedures in North America. The choice points and alternatives in phenomenological research in psychology are delineated. The approach is illustrated by a study of a recovery program for persons repeatedly hospitalized for chronic mental illness. Phenomenological research is compared with other qualitative methods, and some of its benefits for counseling psychology are identified. (PsycINFO Database Record (c) 2010 APA, all rights reserved)