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This [...] text provides a state-of the-art introduction to educational and psychological testing and measurement theory that reflects many intellectual developments of the past two decades. The book introduces psychometric theory using a latent variable modeling (LVM) framework and emphasizes interval estimation throughout, so as to better prepare readers for studying more advanced topics later in their careers. Featuring numerous examples, it presents an applied approach to conducting testing and measurement in the behavioral, social, and educational sciences. (DIPF/Verlag).

In international achievement studies, questionnaires typically ask about the presence of particular household assets in students' homes. Responses to the assets questions are used to compute a total score, which is intended to represent household wealth in models of test performance. This study uses item analysis and confirmatory factor analysis to assess the validity of assets indices as wealth measures in three educational surveys of primary or secondary school students. We find that scores generated from binary household asset items can reasonably be interpreted as predominantly measuring a single wealth factor, but some scores are imprecise, with between one-third and one-half of their variability attributable to measurement error. Our study illustrates the potential of psychometric analyses to contribute to improvement of the household wealth items administered in similar international achievement assessments.

In this book, authors Tenko Raykov and George A. Marcoulides introduce students to the basics of structural equation modeling (SEM) through a conceptual, nonmathematical approach. For ease of understanding, the few mathematical formulas presented are used in a conceptual or illustrative nature, rather than a computational one.Featuring examples from EQS, LISREL, and Mplus, A First Course in Structural Equation Modeling is an excellent beginner's guide to learning how to set up input files to fit the most commonly used types of structural equation models with these programs. The basic ideas and methods for conducting SEM are independent of any particular software.Highlights of the Second Edition include:* Review of latent change (growth) analysis models at an introductory level* Coverage of the popular Mplus program* Updated examples of LISREL and EQS* Downloadable resources that contains all of the text's LISREL, EQS, and Mplus examples.A First Course in Structural Equation Modeling is intended as an introductory book for students and researchers in psychology, education, business, medicine, and other applied social, behavioral, and health sciences with limited or no previous exposure to SEM. A prerequisite of basic statistics through regression analysis is recommended. The book frequently draws parallels between SEM and regression, making this prior knowledge helpful.

In this book, authors Tenko Raykov and George A. Marcoulides introduce students to the basics of structural equation modeling (SEM) through a conceptual, nonmathematical approach. For ease of understanding, the few mathematical formulas presented are used in a conceptual or illustrative nature, rather than a computational one. Featuring examples from EQS, LISREL, and Mplus, A First Course in Structural Equation Modeling is an excellent beginner’s guide to learning how to set up input files to fit the most commonly used types of structural equation models with these programs. The basic ideas and methods for conducting SEM are independent of any particular software. Highlights of the Second Edition include: • Review of latent change (growth) analysis models at an introductory level • Coverage of the popular Mplus program • Updated examples of LISREL and EQS • A CD that contains all of the text’s LISREL, EQS, and Mplus examples. A First Course in Structural Equation Modeling is intended as an introductory book for students and researchers in psychology, education, business, medicine, and other applied social, behavioral, and health sciences with limited or no previous exposure to SEM. A prerequisite of basic statistics through regression analysis is recommended. The book frequently draws parallels between SEM and regression, making this prior knowledge helpful.

Purpose The present study aimed to examine (a) how fatigue severity changes during the course of chemotherapy in patients with hematologic cancer and (b) whether cytokines (IL-1 alpha, IL-1 beta, IL-6) are associated with fatigue change after controlling for demographic and clinical factors (e.g., hemoglobin/hematocrit, medications, comorbid conditions). Methods This observational cohort study used data from 148 hematological cancer patients four times: prior to chemotherapy, on the last day of chemotherapy, 1 week after the chemotherapy completion, and 1 month after baseline assessment. Latent growth curve modeling was used to examine the longitudinal association of fatigue severity with cytokines and hemoglobin. Results A quadratic growth curve model fit the data well, indicating model tenability, and explained a large amount of variance in fatigue across measurement time points. Fatigue slightly increased toward the end of chemotherapy and decreased with time after chemotherapy completion. The influence of IL-6 on fatigue was significant at all time points except at the last assessment occasion (i.e., 1 month after the baseline assessment). The influence of IL-6 on fatigue was independent (unique) from the impact of hemoglobin level. Age and chemotherapy given for the first line of treatment significantly influenced the rate of fatigue change over time. Age also influenced the change pattern’s shape. Conclusions Fatigue severity changes across the course of chemotherapy within the context of IL-6 activity, not the hemoglobin level. The influence of IL-6 may be limited during and shortly after chemotherapy. These findings inform the development of new symptom management strategies.

An increasing number of research projects and infrastructure services involve pooling data across different survey programs. Creating a homogenous integrated dataset from heterogeneous source data is the domain of ex-post harmonization. The harmonization process involves various considerations. However, chief among them is whether two survey measurement instruments have captured the same concept. This issue of conceptual comparability is a fundamental precondition for pooling different source variables to form a harmonized target variable. Our paper explores this issue with a mixed-methods approach. On the one hand, we use psychometric latent variable modeling by presenting several single-item wordings for social trust to respondents and then performing factor analytic procedures. On the other hand, we complement and contrast these quantitative findings with qualitative findings gained with an open-ended web probe. The combined approach gave valuable insights into the conceptual comparability of the eleven social-trust-related single-item wordings. For example, we find that negative, distrust-related wordings and positive, trust-related wordings should not be pooled into an integrated variable. However, the paper will also illustrate and discuss why it is easier to disprove conceptual comparability than fully prove it.

Item response modeling (IRM/IRT) has been known to marketing scholars for a number of years. However, with the exception of some notable and important applications in international (cross-cultural) marketing and consumer behavior, even a cursory reading of marketing journals reveals a general lack of interest in applying IRM, despite its ability to provide highly useful measurement-related information. To address and hopefully remedy the paucity of adoption, we offer an application-oriented discussion of the utility of IRM for marketing and related business research to enable researchers to utilize the strengths and realize the benefits of this methodology in their empirical work. After a short discussion of the history of IRM, we focus on its fundamentals within a modern statistical framework based on the generalized linear model and closely related non-linear factor analysis. We then engage major concepts of IRM, including item characteristic curve, local independence, and dimensionality, as well as parameter estimation and information functions. The popular one- and two-parameter logistic models are next discussed, as is the issue of model selection. Several polytomous item response models are subsequently dealt with, followed by a discussion of multidimensional IRM and data illustrations of item response models using widely available software. References to exemplar marketing applications are provided along the way, and a discussion of limitations of IRM concludes the article.

A method of composite reliability estimation using covariance structure analysis with nonlinear constraints is outlined. To motivate the developments, initially a short overview of research is presented, demonstrating that in many cases the widely used coefficient alpha is an unsatisfactory index of scale reliability already at the population level. As an alternative, the proposed covariance structure analysis procedure is based on the theoretical formula of the scale reliability coefficient in terms of parameters pertaining to a given set of congeneric components. The described approach is illustrated with several numerical examples and its performance compared with that of coefficient alpha.

This article describes the development and evaluation of a short form of the 24-item Adaptation to Age-Related Vision Loss (AVL) scale. The evaluation provided evidence of the reliability and validity of the short form (the AVL12), for significant interindividual differences at the baseline and for individual-level change in AVL scores over time. Thus, the AVL12 maintains strong psychometric properties and is a shorter, more efficient measure for assessing adaptation to age-related vision loss in both research and clinical settings.

Although neurocognition is commonly described in terms of different functional domains, some factor analytic studies have suggested a simpler dimensional structure for neuropsychological (NP) tests in patients with schizophrenia. Standardized tasks of everyday functioning, or tests of “functional capacity” (FC), are viewed differently from traditional NP tests, and are hence used as a co-primary measure in treatment studies. However, FC and NP tests have been found to be highly correlated. In fact, a recent study of ours suggested that performances on these different types of tasks constituted a single latent trait in a cross-sectional analysis. The current study examined the longitudinal factor structure of a combined set of NP and FC tests. Patients with schizophrenia (n = 195) were examined at two assessment occasions separated by periods ranging from 6 weeks to 6 months. Participants were assessed with the MATRICS Consensus Cognitive Battery (MCCB) and two performance-based assessments of FC. A single latent trait was extracted using full information maximum likelihood procedures, and its temporal stability was examined in terms of: stability of the latent trait scores, the inter-correlations of the three indicators of the latent trait, and the stability of loadings for the FC and NP items underlying the latent trait at the two measurement occasions. All indices of temporal stability were confirmed, with stability not related to follow-up duration. Variation in clinical symptoms and treatments across the measurement occasions was negligible. These findings raise the question of whether cognitive abilities measured by NP tests and FC instruments are tapping a single ability construct, which might have shared causal influences as well. (JINS, 2013, 19, 1–8)