Applying cluster analysis in counseling psychology research.

Multilevel modeling (MLM) is rapidly becoming the standard method of analyzing nested data, for example, data from students within multiple schools, data on multiple clients seen by a smaller number of therapists, and even longitudinal data. Although MLM analyses are likely to increase in frequency in counseling psychology research, many readers of counseling psychology journals have had only limited exposure to MLM concepts. This paper provides an overview of MLM that blends mathematical concepts with examples drawn from counseling psychology. This tutorial is intended to be a first step in learning about MLM; readers are referred to other sources for more advanced explorations of MLM. In addition to being a tutorial for understanding and perhaps even conducting MLM analyses, this paper reviews recent research in counseling psychology that has adopted a multilevel framework, and it provides ideas for MLM approaches to future research in counseling psychology.

This article presents an overview of philosophy of science and research paradigms. The philosophy of science parameters of ontology, epistemology, axiology, rhetorical structure, and methodology are discussed across the research paradigms of positivism, postpositivism, constructivism-interpretivism, and the critical-ideological perspective. Counseling researchers are urged to locate their inquiry approaches within identifiable research paradigms, and examples of locating 2 popular inquiry approaches-consensual qualitative research and grounded theory-are provided. Examples of how counseling research would proceed from varying paradigms are explored, and a call is made for expanding the training students receive in philosophy of science and qualitative approaches to inquiry.

Content & FocusCounselling, positive and community psychologies have a lot in common and overlapping values and goals. I outline here the focus of community psychology on those marginalised by the social system and the values and key concepts that underline community psychological praxis. The values of stewardship, justice and community are outlined and their implications for practice drawn out. Key community concepts are discussed, including the emphasis on social change and an action orientation, working with the ecological metaphor, the psychological sense of community, and the idea of new social settings. The four key strategies for change that follow are: the furtherance of critical consciousness; the creation of new forms of social relations or settings; the development of alliances and accompaniment; and advocacy and policy analysis.ConclusionI argue that central to all community psychological work is critical reflection and that synergies can be found in research and practice with greater integration of community, counselling and positive psychology.

Clustering analysis is widely used to group objects by similarity, but for complex datasets such as those produced by single-cell analysis, the currently available clustering methods are limited by accuracy, robustness, ease of use, and interpretability. To address these limitations, we developed an ensemble clustering method with hyperparameter randomization that outperforms other methods across a broad range of single-cell and synthetic datasets, without the need for manual hyperparameter selection. In addition to hard cluster labels, it also outputs soft cluster memberships to characterize continuum-like regions and per cell overlap scores to quantify the uncertainty in cluster assignment. We demonstrate the improved clustering interpretability from these features by tracing the intermediate stages between handwritten digits in the MNIST dataset, and between tanycyte subpopulations in the hypothalamus. This approach improves the quality of clustering and subsequent downstream analyses for single-cell datasets, and may also prove useful in other fields of data analysis.

How significant is a significant difference? Average effect size of research in counseling psychology.

MR diffusion tensor imaging (DTI) can measure and visualize organization of white matter fibre tracts in vivo. DTI is a relatively new imaging technique, and new tools developed for quantifying fibre tracts require evaluation. The purpose of this study was to compare the reliability of a novel clustering approach with a multiple region of interest (MROI) approach in both healthy and disease (schizophrenia) populations.DTI images were acquired in 20 participants (n=10 patients with schizophrenia: 56±15 years; n=10 controls: 51±20 years) (1.5 T GE system) with diffusion gradients applied in 23 non-collinear directions, repeated three times. Whole brain seeding and creation of fibre tracts were then performed. Interrater reliability of the clustering approach, and the MROI approach, were each evaluated and the methods compared.There was high spatial (voxel-based) agreement within and between the clustering and MROI methods. Fractional anisotropy, trace, and radial and axial diffusivity values showed high intraclass correlation (p<0.001 for all tracts) for each approach. Differences in scalar indices of diffusion between the clustering and MROI approach were minimal.The excellent interrater reliability of the clustering method and high agreement with the MROI method, quantitatively and spatially, indicates that the clustering method can be used with confidence. The clustering method avoids biases of ROI drawing and placement, and, not limited by a priori predictions, may be a more robust and efficient way to identify and measure white matter tracts of interest.

CONTENTS GENDER ISSUES: RESEARCH RELEVANT TO COUNSELING WOMEN 346 Career Development 346 Eating Disorders 350 Sexual Violence 352 Therapist Attitudes and Behaviors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . .. . . . . . . . . . . . . . . 355 GENDER ISSUES: RESEARCH RELEVANT TO COUNSELING MEN 357 Male Socialization: The Hazards of Being Male . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357 Men in Therapy: A Difficult Relationship 358 Assessment: The Measure of Man 360 Male Violence: The Missing Variable? 361 RACIAL, ETHNIC, AND CULTURALLY DIVERSE POPULATIONS 362 Counseling and Psychotherapy with Diverse Populations 362 Multiculturallssues in Training 364 Research Comment 366 RESEARCH ON COUNSELING ISSUES WITH GAY, LESBIAN, AND BISEXUAL INDIVIDUALS . . ... . . . . ....... . . . . . ....... . . . . . ....... . . . . . ... ... . . . . .. ...... . . .. 367 From Pathulogy to Diversity . . . . .. . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367 Defining Sexual Orientation 368 Therapist and Client Attitudes 369 Research on Gay and Lesbian Lifestyles 371 POSTSCRIPT , ........ . . . . . . . . . . . . . ... . ...... . ..... . . . . . . . . . ...... . . . . . . . . ....... 373

Clustering is a popular technique for discovering groups of similar objects in large datasets. It is nowadays applied in all areas of life sciences, from biomedicine to physics. However, designing high-quality cluster analyses is a tedious and complicated task with manifold choices along the way. As a cluster analysis is often the first step of a succeeding downstream analysis, the clustering must be reliable, reproducible, and of the highest quality. To address these challenges, we recently developed ClustEval, an integrated and extensible platform for the automated and standardized design and execution of complex cluster analyses. It allows researchers to design and carry out cluster analyses involving a large number of clustering methods applied to many, large datasets. ClustEval helps to shed light on all major aspects of cluster analysis, from choosing the right similarity function to using validity indices and data preprocessing protocols. Only this high degree of automation allows the researcher to easily run a clustering task with many different tools, parameters, and settings in order to gain the best possible outcome. In this paper, we guide the user step by step through three fundamentally important and widely applicable use cases: (i) identification of the best clustering method for a new, user-given protein sequence similarity dataset; (ii) evaluation of the performance of a new, user-given clustering method (densityCut) against the state of the art; and (iii) prediction of the best method for a new protein sequence similarity dataset. This protocol guides the user through the most important features of ClustEval and takes ∼4 h to complete.

Inferences made from summary indices of psychometric scales must be reliable and valid to advance scientific knowledge in counseling psychology. This article illustrates traditional and innovative uses of the Rasch model in the development and validation of small- and large-scale psychological instruments. Discussion of traditional uses of the Rasch model included reliability and validity of scale scores, whereas discussion of innovative uses included (a) determining the optimal number of response categories for Likert-type items and (b) obtaining a measure of response style for each person. Both mathematical and conceptual explanations are provided, and counseling psychology data are used to illustrate examples.

Multilevel modeling (MLM) should be used when a researcher has collected hierarchical data. For example, when a researcher investigates an outcome variable (e.g., depression) with several clients drawn from different clinicians, the data set has a hierarchical structure. Herein, we describe the use of MLM in counseling research. The goals include the following: (a) to specify research contexts where MLM may be applied, (b) to describe how to conduct data analyses using MLM, and (c) to highlight key statistical and design issues encountered when analyzing hierarchical data. We also highlight how MLM can be used (a) to provide valid statistical inference in the presence of hierarchical data structure, (b) to separate the within-group effects from between-group effects for predictor variables, and (c) to study the interactions among predictor variables drawn from different levels (e.g., variables drawn from both clients and their clinicians).

High-throughput sequencing technologies have greatly facilitated omics research in the past two decades. An enormous volume of omics data have been generated, with the potential to answer various key biological questions. Unsupervised learning technologies such as cluster analysis serve as a powerful tool to uncover hidden patterns and associations in omics data. However, considering the complex natural structure and special characteristics of omics data, it is usually inappropriate or inefficient to directly apply clustering methods designed for general purposes. To address these issues, we develop several clustering and biclustering algorithms specifically designed for omics data. %In this dissertation, we tried to address these concerns from multiple different aspects: The first project is about Poisson hurdle model-based clustering, which aims to deal with extra sparsity in microbiome count data. Extra sparsity is a huge problem when dealing with microbiome data and some other omics data. In this study, we present a novel model-based clustering framework based on Poisson hurdle models, designed specifically for the intricacies of sparse microbiome data. We describe an expectation-maximization algorithm and a modified version using simulated annealing to conduct the cluster analysis. Additionally, we offer algorithms for intuitive initialization and determining the optimal number of clusters. Simulation results demonstrate that our proposed methods provide better clustering results than alternative methods under a variety of settings. A practical application of our method to a sorghum rhizosphere microbiome dataset yielded intriguing biological insights. The second project is a Dirichlet-Multinomial biclustering algorithm for compositional microbiome data. Similar to (one-way) cluster analysis, biclustering is a popular tool to study the interactions between microbial sub-communities and environmental samples. In this project, we introduce a Dirichlet-Multinomial (DM) model-based checkerboard biclustering technique tailored for the simultaneous clustering of microbiome features and samples. This approach assumes the presence of a DM distribution mixture within each microbiome sample, and utilizes a combination of Expectation-Maximization and coordinate descent algorithm to solve for optimal parameters. Compared to conventional, general-purpose biclustering algorithms, our method consistently demonstrated superior clustering accuracy and enriched biological interpretation across diverse simulation settings and multiple real datasets. The third project studied and compared the effect of several feature selection techniques implemented in the biclustering framework. When dealing with biological data, usually tens of thousands of features are present in a single dataset, but most of them are likely non-informative and do not benefit our analysis. Including them in the analysis can be computationally inefficient and lead to overfitting issues. This project gives a review of implementing penalization methods in the biclustering framework to do feature selection. Penalization/regularization are broadly used in regression analysis, and have also been applied to one-way clustering. This project focuses on extending and applying penalization methods to bicluster analysis of biological data. We show two different classes of penalization methods, one on the with-cluster sum of squares (WCSS) and the other on the feature weights. By introducing sparsity into the feature space, we are able to simultaneously find feature groups/sample subtypes and identify non-informative features. The fourth project focused on clustering analysis using an over-clustering and merging structure. This idea originates from a problem we encountered in the third project: if the true feature classes show a highly imbalanced structure, K-means type of clustering or biclustering methods usually lose power. This is due to the spherical nature of K-means clusters, which lets it perform poorly when the class sizes differ a lot. Motivated by this drawback of K-means, and also by the simple fact that K-means can only produce convex clusters, we developed a novel K-means merging algorithm \emph{CavMerge} that is suitable for producing general-sized and imbalanced clustering results. This method is intuitive, free of parameter tuning, and computationally efficient. It starts from an over-clustered K-means result, and utilizes a property called log-concavity to perform merging. Operating under minimal local distributional assumptions, our algorithm demonstrates strong consistency and guarantees rapid convergence. Empirical studies on various simulated and real datasets demonstrate that our method yields more reliable clusters in comparison to current state-of-the-art algorithms.

Building a background radiation field on the ground on the basis of measurement data taken at a finite number of points is one of the most important tasks of radiation monitoring. The aim of the work: to study the possibility of applying cluster analysis for the tasks of radiation monitoring of the environment. Cluster analysis is a multidimensional statistical analysis. Its main purpose is to split the set of objects under study (observation points) into homogeneous groups or clusters, that is, the task of classifying data and identifying the corresponding structure in them is solved. Methods of research: the measurements of the power of the ambient dose of continuous X-ray and gamma radiation on the terrain by using the MKS-05 dosimeter "TERRA-0"; processing of the obtained data by cluster analysis methods using the computer program "Statistics-10", wherein each cluster point is characterized by three coordinates: two coordinates on the ground and the power of the ambient dose of radiation at a given point; Euclidean distance was chosen as the distance between two points. Results: after processing data using various clustering methods: the method of Complete Linkage, the method of Weighted pair-group average and the Ward's method, it was found that the results of the analysis practically coincide with each other, that proves the reliability of the application of cluster analysis for the tasks of radiation monitoring of the environment and mapping of radiation pollution. Conclusions: the concept of a "radiation cluster" was first formulated in this work, combining coordinates on a plane with an ambient dose rate;the possibility of using cluster analysis to construct a map of radiation pollution of the environment has been proved by sequential projectionfrom more connected to less connected radiation clusters onto the plane of the controlled zone. In this sense, cluster analysis is similar to the operator approach to the construction of the radiation field. For further research, it is of some interest to study the issues of integration of cluster analysis with geographic information systems.

This article builds on earlier conceptual models of identity development to propose a model of heterosexual identity development. A review of the existing literature on majority and minority group identity development, heterosexuality, and other relevant foundational literature is provided as a rationale for the proposed model. Based on an analysis of the strengths and limitations of the existing literature, the authors propose a preliminary model of heterosexual identity development. The article concludes with implications for research, practice, training, and measurement in counseling psychology.

In this paper we review methods of cluster analysis in the context of classifying patients on the basis of clinical and/or laboratory type observations. Both hierarchical and non-hierarchical methods of clustering are considered, although the emphasis is on the latter type, with particular attention devoted to the mixture likelihood-based approach. For the purposes of dividing a given data set into g clusters, this approach fits a mixture model of g components, using the method of maximum likelihood. It thus provides a sound statistical basis for clustering. The important but difficult question of how many clusters are there in the data can be addressed within the framework of standard statistical theory, although theoretical and computational difficulties still remain. Two case studies, involving the cluster analysis of some haemophilia and diabetes data respectively, are reported to demonstrate the mixture likelihood-based approach to clustering.

A robust adaptive clustering analysis method for automatic identification of clusters