Mindfulness is an attribute of consciousness long believed to promote well-being. This research provides a theoretical and empirical examination of the role of mindfulness in psychological well-being. The development and psychometric properties of the dispositional Mindful Attention Awareness Scale (MAAS) are described. Correlational, quasi-experimental, and laboratory studies then show that the MAAS measures a unique quality of consciousness that is related to a variety of well-being constructs, that differentiates mindfulness practitioners from others, and that is associated with enhanced selfawareness. An experience-sampling study shows that both dispositional and state mindfulness predict self-regulated behavior and positive emotional states. Finally, a clinical intervention study with cancer patients demonstrates that increases in mindfulness over time relate to declines in mood disturbance and stress. Many philosophical, spiritual, and psychological traditions emphasize the importance of the quality of consciousness for the maintenance and enhancement of well-being (Wilber, 2000). Despite this, it is easy to overlook the importance of consciousness in human well-being because almost everyone exercises its primary capacities, that is, attention and awareness. Indeed, the relation between qualities of consciousness and well-being has received little empirical attention. One attribute of consciousness that has been much-discussed in relation to well-being is mindfulness. The concept of mindfulness has roots in Buddhist and other contemplative traditions where conscious attention and awareness are actively cultivated. It is most commonly defined as the state of being attentive to and aware of what is taking place in the present. For example, Nyanaponika Thera (1972) called mindfulness “the clear and single-minded awareness of what actually happens to us and in us at the successive moments of perception” (p. 5). Hanh (1976) similarly defined mindfulness as “keeping one’s consciousness alive to the present reality” (p. 11). Recent research has shown that the enhancement of mindfulness through training facilitates a variety of well-being outcomes (e.g., Kabat-Zinn, 1990). To date, however, there has been little work examining this attribute as a naturally occurring characteristic. Recognizing that most everyone has the capacity to attend and to be aware, we nonetheless assume (a) that individuals differ in their propensity or willingness to be aware and to sustain attention to what is occurring in the present and (b) that this mindful capacity varies within persons, because it can be sharpened or dulled by a variety of factors. The intent of the present research is to reliably identify these inter- and intrapersonal variations in mindfulness, establish their relations to other relevant psychological constructs, and demonstrate their importance to a variety of forms of psychological well-being.

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The Benefits of Being Present: Mindfulness and Its Role in Psychological Well-Being

For structural equation models, a huge variety of fit indices has been developed. These indices, however, can point to conflicting conclusions about the extent to which a model actually matches the observed data. The present article provides some guidelines that should help applied researchers to evaluate the adequacy of a given structural equation model. First, as goodness-of-fit measures depend on the method used for parameter estimation, maximum likelihood (ML) and weighted least squares (WLS) methods are introduced in the context of structural equation modeling. Then, the most common goodness-of-fit indices are discussed and some recommendations for practitioners given. Finally, we generated an artificial data set according to a "true" model and analyzed two misspecified and two correctly specified models as examples of poor model fit, adequate fit, and good fit.

/pdf/evaluating-the-fit-of-structural-equation-models-tests-of-4lmsoac5b1.pdf

Evaluating the Fit of Structural Equation Models: Tests of Significance and Descriptive Goodness-of-Fit Measures.

The authors provide a basic set of guidelines and recommendations for information that should be included in any manuscript that has confirmatory factor analysis or structural equation modeling as the primary statistical analysis technique. The authors provide an introduction to both techniques, along with sample analyses, recommendations for reporting, evaluation of articles in The Journal of Educational Research using these techniques, and concluding remarks.

Reporting structural equation modeling and confirmatory factor analysis results: A review

Purpose – The authors aim to present partial least squares (PLS) as an evolving approach to structural equation modeling (SEM), highlight its advantages and limitations and provide an overview of recent research on the method across various fields Design/methodology/approach – In this review article, the authors merge literatures from the marketing, management, and management information systems fields to present the state-of-the art of PLS-SEM research Furthermore, the authors meta-analyze recent review studies to shed light on popular reasons for PLS-SEM usage Findings – PLS-SEM has experienced increasing dissemination in a variety of fields in recent years with nonnormal data, small sample sizes and the use of formative indicators being the most prominent reasons for its application Recent methodological research has extended PLS-SEM's methodological toolbox to accommodate more complex model structures or handle data inadequacies such as heterogeneity Research limitations/implications – While rese

Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research

In the past few years, the IS field has seen a substantial increase in the number of submissions and publications using structural equation modeling (SEM) techniques. Part of the reason may be the increase in software packages to perform such covariance-based (e.g., LISREL, EQS, AMOS, SEPATH, RAMONA, MX, and CALIS) and componentbased (e.g., PLS-PC, PLS-Graph) analysis. Viewed as a coupling of two traditions -an econometric perspective focusing on prediction and a psychometric emphasis that models concepts as latent (unobserved) variables that are indirectly inferred from multiple observed measures (alternately termed as indicators or manifest variables) -SEM has allowed social scientists to perform path analytic modeling with latent variables (Lvs), which in turn has led some to describe this approach as an example of “a second generation of multivariate analysis” (Fornell 1987, p. 408).

https://misq.org/misq/downloads/download/editorial/314/

Issues and opinion on structural equation modeling

This book provides the reader with a review of correlation and covariance among variables, followed by multiple regression and path analysis techniques to better understand the building blocks of structural equation modelling. The concepts behind measurement models are introduced to illustrate how measurement error impacts statistical analyses, and structural models are presented that indicate how latent variable relationships can be established. Examples are included throughout to make the concepts clear to the reader. The structural equation modelling examples are presented using either EQS5.0 or LISREL8-SIMPLIS programming language, both of which have an easy-to-use set of commands to specify measurement and strucural models. No complicated programming is required, nor does the reader need an advanced understanding of statistics of matrix algebra. A goal in writing this volume was to focus conceptually on the steps one takes in analyzing theoretical models. These steps encompass: specifying a model based upon theory or prior research; determining whether the model can be identified to have unique estimates for variables in the model; selecting an appropriate estimation method based on the distributional assumptions of variables; testing the model and interpreting fit indices; and finally respecifying a model based on suggested modification indices, which involves adding or dropping paths in the model to obtain a better model fit. The resources and references provided in this book should equip faculty, students and researchers to enhance their working knowledge of structural equation modelling. Not intended as an in-depth presentation of statistics or factor analysis, this text focuses on the basic ideas and principles behind structural equation modelling. Assuming that the reader has a basic understanding of correlation, the authors have built upon this understanding to present these basic ideas and principles.

A beginner's guide to structural equation modeling

A Beginner's Guide to Structural Equation Modeling

A beginner's guide to structural equation modeling, 2nd ed.

This simulation study investigated the robustness of structural equation modeling to different degrees of nonnormality under 2 estimation methods, generalized least squares and maximum likelihood, and 4 sample sizes, 100, 250, 500, and 1,000. Each of the slight and severe nonnormality degrees was comprised of pure skewness, pure kurtosis, and both skewness and kurtosis. Bias and standard errors of parameter estimates were analyzed. In addition, an analysis of variance was conducted to investigate the effects of the 3 factors on several goodness-of-fit indexes. The study found that standard errors of parameter estimates were not significantly affected by estimation methods and nonnormality conditions. As expected, standard errors decreased at larger sample sizes. Parameter estimates were more sensitive to nonnormality than to sample size and estimation method. Chi-square was the least robust model fit index compared with Normed Fit Index, Nonnormed Fit Index, and Comparative Fit Index. Sample sizes of 100 ...

Richard G. Lomax

Papers

A beginner's guide to structural equation modeling

A Beginner's Guide to Structural Equation Modeling

A beginner's guide to structural equation modeling, 2nd ed.

The Effect of Varying Degrees of Nonnormality in Structural Equation Modeling

A beginner's guide to structural equation modeling, 3rd ed.