Tenko Raykov’s research while affiliated with Michigan State University and other places

A procedure for interval estimation of the difference in the adjusted R -square index for nested linear models is discussed. The method yields as a byproduct confidence intervals for their standard R -square difference, as well as for the adjusted and standard R -squares associated with each model. The resulting interval estimate of the difference in adjusted R -square represents a useful and informative complement to the commonly used R -square change statistic and its significance test in model selection and contains substantially more information than that test. The outlined procedure is readily employed with popular software in empirical educational and psychological studies and is illustrated with numerical data.

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 index extending the widely used omega-hierarchical coefficient is discussed, which can be used for evaluating the influence of a second-order factor on the interrelationships among the components of a hierarchical measuring instrument. The index represents a useful and informative complement to the traditional omega-hierarchical measure of explained overall scale score variance by that underlying construct. A point and interval estimation procedure is outlined for the described index, which is based on model reparameterization and is developed within the latent variable modeling framework. The method is readily applicable with popular software and is illustrated with examples.

In a time where new research methods are constantly being developed and science is evolving, researchers must continually educate themselves on cutting-edge methods and best practices related to their field. The second of three volumes, this Handbook provides comprehensive and up-to-date coverage of a variety of issues important in developing, designing, and collecting data to produce high-quality research efforts. First, leading scholars from around the world provide an in depth explanation of various advanced methodological techniques. In section two, chapters cover general important methodological considerations across all types of data collection. In the third section, the chapters cover self-report and behavioral measures and their considerations for use. In the fourth section, various psychological measures are covered. The final section of the handbook covers issues that directly concern qualitative data collection approaches. Throughout the book, examples and real-world research efforts from dozens of different disciplines are discussed.

A multiple-step procedure is outlined that can be used for examining the latent structure of behavior measurement instruments in complex empirical settings. The method permits one to study their latent structure after assessing the need to account for clustering effects and the necessity of its examination within individual levels of fixed factors, such as gender or group membership of substantive relevance. The approach is readily applicable with binary or binary-scored items using popular and widely available software. The described procedure is illustrated with empirical data from a student behavior screening instrument

We outline a procedure for examining collapsibility over site in multiple-location settings that are frequently utilized in contemporary educational and behavioral research. The method is based on a test of cross-site identity of the response distributions of polytomous items in multi-component measuring instruments, which implies the possibility to pool over study location. The approach is readily applicable in empirical studies using popular and widely circulated software and is generalizable to various types of items. The described procedure is illustrated with data from a child development survey.

We outline a procedure for examining collapsibility over site in multiple-location settings that are frequently utilized in contemporary educational and behavioral research. The method is based on a test of cross-site identity of the response distributions of polytomous items in multi-component measuring instruments, which implies the possibility to pool over study location. The approach is readily applicable in empirical studies using popular and widely circulated software and is generalizable to various types of items. The described procedure is illustrated with data from a child development survey.

A latent variable modeling procedure for studying factorial invariance and differential item functioning for multi-component measuring instruments with nominal items is discussed. The method is based on a multiple testing approach utilizing the false discovery rate concept and likelihood ratio tests. The procedure complements the Revuelta, Franco-Martinez, and Ximenez approach to factorial invariance examination, and permits localization of individual invariance violations. The outlined method does not require the selection of a reference observed variable and is illustrated with empirical data.