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Statistical Models
This lively and engaging book explains the things you have to know in order to read empirical papers in the social and health sciences, as well as the techniques you need to build statistical models of your own. The discussion in the book is organized around published studies, as are many of the exercises. Relevant journal articles are reprinted at the back of the book. Freedman makes a thorough appraisal of the statistical methods in these papers and in a variety of other examples. He illustrates the principles of modelling, and the pitfalls. The discussion shows you how to think about the critical issues - including the connection (or lack of it) between the statistical models and the real phenomena. The book is written for advanced undergraduates and beginning graduate students in statistics, as well as students and professionals in the social and health sciences.
eBook
Interpretive Research Design
Research design is fundamental to all scientific endeavors, at all levels and in all institutional settings. In many social science disciplines, however, scholars working in an interpretive-qualitative tradition get little guidance on this aspect of research from the positivist-centered training they receive. This book is an authoritative examination of the concepts and processes underlying the design of an interpretive research project. Such an approach to design starts with the recognition that researchers are inevitably embedded in the intersubjective social processes of the worlds they study.
In focusing on researchers' theoretical, ontological, epistemological, and methods choices in designing research projects, Schwartz-Shea and Yanow set the stage for other volumes in the Routledge Series on Interpretive Methods. They also engage some very practical issues, such as ethics reviews and the structure of research proposals. This concise guide explores where research questions come from, criteria for evaluating research designs, how interpretive researchers engage with \"world-making,\" context, systematicity and flexibility, reflexivity and positionality, and such contemporary issues as data archiving and the researcher's body in the field.
eBook
The data detective : ten easy rules to make sense of statistics
\"Today we think statistics are the enemy, numbers used to mislead and confuse us. That's a mistake, Tim Harford says in The Data Detective. We shouldn't be suspicious of statistics-we need to understand what they mean and how they can improve our lives: they are, at heart, human behavior seen through the prism of numbers and are often \"the only way of grasping much of what is going on around us.\" If we can toss aside our fears and learn to approach them clearly-understanding how our own preconceptions lead us astray-statistics can point to ways we can live better and work smarter. As \"perhaps the best popular economics writer in the world\" (New Statesman), Tim Harford is an expert at taking complicated ideas and untangling them for millions of readers. In The Data Detective, he uses new research in science and psychology to set out ten strategies for using statistics to erase our biases and replace them with new ideas that use virtues like patience, curiosity, and good sense to better understand ourselves and the world. As a result, The Data Detective is a big-idea book about statistics and human behavior that is fresh, unexpected, and insightful\"-- Provided by publisher.
Structural Equation Modeling with Mplus
[This book] reviews the basic concepts and applications of SEM using Mplus Version 6. ... The first two chapters introduce the fundamental concepts of SEM and important basics of the Mplus program. The remaining chapters focus on SEM applications and include a variety of SEM models presented within the context of three sections: Single-group analyses, Multiple-group analyses, and other important topics, the latter of which includes the multitrait-multimethod, latent growth curve, and multilevel models. (DIPF/Orig.).
eBook
Counterfactuals and causal inference : methods and principles for social research
\"In this second edition of Counterfactuals and Causal Inference, completely revised and expanded, the essential features of the counterfactual approach to observational data analysis are presented with examples from the social, demographic, and health sciences. Alternative estimation techniques are first introduced using both the potential outcome model and causal graphs; after which, conditioning techniques, such as matching and regression, are presented from a potential outcomes perspective. For research scenarios in which important determinants of causal exposure are unobserved, alternative techniques, such as instrumental variable estimators, longitudinal methods, and estimation via causal mechanisms, are then presented. The importance of causal effect heterogeneity is stressed throughout the book, and the need for deep causal explanation via mechanisms is discussed\"-- Provided by publisher.
Book
Ensuring Positiveness of the Scaled Difference Chi-square Test Statistic
A scaled difference test statistic
that can be computed from standard software of structural equation models (SEM) by hand calculations was proposed in Satorra and Bentler (Psychometrika 66:507–514,
2001
). The statistic
is asymptotically equivalent to the scaled difference test statistic
introduced in Satorra (Innovations in Multivariate Statistical Analysis: A Festschrift for Heinz Neudecker, pp. 233–247,
2000
), which requires more involved computations beyond standard output of SEM software. The test statistic
has been widely used in practice, but in some applications it is negative due to negativity of its associated scaling correction. Using the implicit function theorem, this note develops an improved scaling correction leading to a new scaled difference statistic
that avoids negative chi-square values.
Journal Article
A Nondegenerate Penalized Likelihood Estimator for Variance Parameters in Multilevel Models
Group-level variance estimates of zero often arise when fitting multilevel or hierarchical linear models, especially when the number of groups is small. For situations where zero variances are implausible a priori, we propose a maximum penalized likelihood approach to avoid such boundary estimates. This approach is equivalent to estimating variance parameters by their posterior mode, given a weakly informative prior distribution. By choosing the penalty from the log-gamma family with shape parameter greater than 1, we ensure that the estimated variance will be positive. We suggest a default log-gamma(2,
λ
) penalty with
λ
→0, which ensures that the maximum penalized likelihood estimate is approximately one standard error from zero when the maximum likelihood estimate is zero, thus remaining consistent with the data while being nondegenerate. We also show that the maximum penalized likelihood estimator with this default penalty is a good approximation to the posterior median obtained under a noninformative prior.
Our default method provides better estimates of model parameters and standard errors than the maximum likelihood or the restricted maximum likelihood estimators. The log-gamma family can also be used to convey substantive prior information. In either case—pure penalization or prior information—our recommended procedure gives nondegenerate estimates and in the limit coincides with maximum likelihood as the number of groups increases.
Journal Article