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Informative hypotheses : theory and practice for behavioral and social scientists
\"When scientists formulate their theories, expectations, and hypotheses, they often use statements like: \"I expect mean A to be bigger than means B and C\"; \"I expect that the relation between Y and both X1 and X2 is positive\"; and \"I expect the relation between Y and X1 to be stronger than the relation between Y and X2\". Stated otherwise, they formulate their expectations in terms of inequality constraints among the parameters in which they are interested, that is, they formulate Informative Hypotheses.There is currently a sound theoretical foundation for the evaluation of informative hypotheses using Bayes factors, p-values and the generalized order restricted information criterion. Furthermore, software that is often free is available to enable researchers to evaluate the informative hypotheses using their own data. The road is open to challenge the dominance of the null hypothesis for contemporary research in behavioral, social, and other sciences\"-- Provided by publisher.
Book
Understanding The New Statistics
This is the first book to introduce the new statistics - effect sizes, confidence intervals, and meta-analysis - in an accessible way. It is chock full of practical examples and tips on how to analyze and report research results using these techniques. The book is invaluable to readers interested in meeting the new APA Publication Manual guidelines by adopting the new statistics - which are more informative than null hypothesis significance testing, and becoming widely used in many disciplines.
Accompanying the book is the Exploratory Software for Confidence Intervals (ESCI) package, free software that runs under Excel and is accessible at www.thenewstatistics.com. The book's exercises use ESCI's simulations, which are highly visual and interactive, to engage users and encourage exploration. Working with the simulations strengthens understanding of key statistical ideas. There are also many examples, and detailed guidance to show readers how to analyze their own data using the new statistics, and practical strategies for interpreting the results. A particular strength of the book is its explanation of meta-analysis, using simple diagrams and examples. Understanding meta-analysis is increasingly important, even at undergraduate levels, because medicine, psychology and many other disciplines now use meta-analysis to assemble the evidence needed for evidence-based practice.
The book's pedagogical program, built on cognitive science principles, reinforces learning:
Boxes provide \"evidence-based\" advice on the most effective statistical techniques.
Numerous examples reinforce learning, and show that many disciplines are using the new statistics.
Graphs are tied in with ESCI to make important concepts vividly clear and memorable.
Opening overviews and end of chapter take-home messages summarize key points.
Exercises encourage exploration, deep understanding, and practical app
eBook
Applying the Rasch Model
Recognised as the most influential publication in the field, ARM facilitates deep understanding of the Rasch model and its practical applications. The authors review the crucial properties of the model and demonstrate its use with examples across the human sciences. Readers will be able to understand and critically evaluate Rasch measurement research, perform their own Rasch analyses and interpret their results. The glossary and illustrations support that understanding, and the accessible approach means that it is ideal for readers without a mathematical background.
Intended as a text for graduate courses in measurement, item response theory, (advanced) research methods or quantitative analysis taught in psychology, education, human development, business and other social and health sciences. Professionals in these areas will also appreciate the book’s accessible introduction.
Highlights of the new edition include:
More learning tools to strengthen readers’ understanding including chapter introductions, boldfaced key terms, chapter summaries, activities and suggested readings.
Greater emphasis on the use of R packages; readers can download the R code from the Routledge website.
Explores the distinction between numerical values, quantity and units, to understand the measurement and the role of the Rasch logit scale (Chapter 4).
A new four-option data set from the IASQ (Instrumental Attitude toward Self-assessment Questionnaire) for the Rating Scale Model (RSM) analysis exemplar (Chapter 6).
Clarifies the relationship between Rasch measurement, path analysis and SEM, with a host of new examples of Rasch measurement applied across health sciences, education and psychology (Chapter 10).
eBook
Invariant measurement with raters and rating scales : Rasch models for rater-mediated assessments
\"The purpose of this book is to present methods for developing, evaluating and maintaining rater-mediated assessment systems. Rater-mediated assessments involve ratings that are assigned by raters to persons responding to constructed-response items (e.g., written essays and teacher portfolios) and other types of performance assessments. This book addresses the following topics: (1) introduction to the principles of invariant measurement, (2) application of the principles of invariant measurement to rater-mediated assessments, (3) description of the lens model for rater judgments, (4) integration of principles of invariant measurement with the lens model of cognitive processes of raters, (5) illustration of substantive and psychometric issues related to rater-mediated assessments in terms of validity, reliability, and fairness, and (6) discussion of theoretical and practical issues related to rater-mediated assessment systems. Invariant measurement is fast becoming the dominant paradigm for assessment systems around the world, and this book provides an invaluable resource for graduate students, measurement practitioners, substantive theorists in the human sciences, and other individuals interested in invariant measurement when judgments are obtained with rating scales\"-- Provided by publisher.
Book
Generalized Network Psychometrics: Combining Network and Latent Variable Models
We introduce the network model as a formal psychometric model, conceptualizing the covariance between psychometric indicators as resulting from pairwise interactions between observable variables in a network structure. This contrasts with standard psychometric models, in which the covariance between test items arises from the influence of one or more common latent variables. Here, we present two generalizations of the network model that encompass latent variable structures, establishing network modeling as parts of the more general framework of structural equation modeling (SEM). In the first generalization, we model the covariance structure of latent variables as a network. We term this framework latent network modeling (LNM) and show that, with LNM, a unique structure of conditional independence relationships between latent variables can be obtained in an explorative manner. In the second generalization, the residual variance–covariance structure of indicators is modeled as a network. We term this generalization residual network modeling (RNM) and show that, within this framework, identifiable models can be obtained in which local independence is structurally violated. These generalizations allow for a general modeling framework that can be used to fit, and compare, SEM models, network models, and the RNM and LNM generalizations. This methodology has been implemented in the free-to-use software package
lvnet
, which contains confirmatory model testing as well as two exploratory search algorithms: stepwise search algorithms for low-dimensional datasets and penalized maximum likelihood estimation for larger datasets. We show in simulation studies that these search algorithms perform adequately in identifying the structure of the relevant residual or latent networks. We further demonstrate the utility of these generalizations in an empirical example on a personality inventory dataset.
Journal Article
Psychometric network models from time-series and panel data
Researchers in the field of network psychometrics often focus on the estimation of Gaussian graphical models (GGMs)—an undirected network model of partial correlations—between observed variables of cross-sectional data or single-subject time-series data. This assumes that all variables are measured without measurement error, which may be implausible. In addition, cross-sectional data cannot distinguish between within-subject and between-subject effects. This paper provides a general framework that extends GGM modeling with latent variables, including relationships over time. These relationships can be estimated from time-series data or panel data featuring at least three waves of measurement. The model takes the form of a graphical vector-autoregression model between latent variables and is termed the
ts-lvgvar
when estimated from time-series data and the
panel-lvgvar
when estimated from panel data. These methods have been implemented in the software package
psychonetrics
, which is exemplified in two empirical examples, one using time-series data and one using panel data, and evaluated in two large-scale simulation studies. The paper concludes with a discussion on ergodicity and generalizability. Although within-subject effects may in principle be separated from between-subject effects, the interpretation of these results rests on the intensity and the time interval of measurement and on the plausibility of the assumption of stationarity.
Journal Article
Statistical power analysis for the social and behavioral sciences : basic and advanced techniques
\"This will be the first book to demonstrate the application of power analysis to the newer more advanced techniques such as hierarchical linear modeling, meta-analysis, and structural equation modelling that are increasingly popular in behavioral and social science research\"-- Provided by publisher.
Book
A Large-Scale Evaluation of the KiVa Antibullying Program: Grades 4-6
This study demonstrates the effectiveness of the KiVa antibullying program using a large sample of 8,237 youth from Grades 4-6 (10-12 years). Altogether, 78 schools were randomly assigned to intervention (39 schools, 4,207 students) and control conditions (39 schools, 4,030 students). Multilevel regression analyses revealed that after 9 months of implementation, the intervention had consistent beneficial effects on 7 of the 11 dependent variables, including self- and peer-reported victimization and self-reported bullying. The results indicate that the KiVa program is effective in reducing school bullying and victimization in Grades 4-6. Despite some evidence against school-based interventions, the results suggest that well-conceived school-based programs can reduce victimization.
Journal Article