Robert B. Smith – författare

Millions of years ago, the North American continent was dragged over the world''s largest continental hotspot, a huge column of hot and molten rock rising from the Earth''s interior that traced a 50-mile wide, 500-mile-long path northeastward across Idaho. Generating cataclysmic volcanic eruptions and large earthquakes, the hotspot helped lift the Yellowstone Plateau to more than 7,000 feet and pushed the northern Rockies to new heights, forming unusually large glaciers to carve the landscape. It also created the jewel of the U.S. national park system: Yellowstone. Meanwhile, forces stretching apart the western U.S. created the mountainous glory of Grand Teton National Park. These two parks, with their majestic mountains, dazzling geysers, and picturesque hot springs, are windows into the Earth''s interior, revealing the violent power of the dynamic processes within. Smith and Siegel offer expert guidance through this awe-inspiring terrain, bringing to life the grandeur of these geologic phenomena as they reveal the forces that have shaped--and continue to shape--the greater Yellowstone-Teton region. Over seventy illustrations--including fifty-two in full color--illuminate the breathtaking beauty of the landscape, while two final chapters provide driving tours of the parks to help visitors enjoy and understand the regions wonders. Fascinating and informative, this book affords us a striking new perspective on Earth''s creative forces.

Millions of years ago, the North American continent was dragged over the world''s largest continental hotspot, a huge column of hot and molten rock rising from the Earth''s interior that traced a 50-mile wide, 500-mile-long path northeastward across Idaho. Generating cataclysmic volcanic eruptions and large earthquakes, the hotspot helped lift the Yellowstone Plateau to more than 7,000 feet and pushed the northern Rockies to new heights, forming unusually large glaciers to carve the landscape. It also created the jewel of the U.S. national park system: Yellowstone. Meanwhile, forces stretching apart the western U.S. created the mountainous glory of Grand Teton National Park. These two parks, with their majestic mountains, dazzling geysers, and picturesque hot springs, are windows into the Earth''s interior, revealing the violent power of the dynamic processes within. Smith and Siegel offer expert guidance through this awe-inspiring terrain, bringing to life the grandeur of these geologic phenomena as they reveal the forces that have shaped--and continue to shape--the greater Yellowstone-Teton region. Over seventy illustrations--including fifty-two in full color--illuminate the breathtaking beauty of the landscape, while two final chapters provide driving tours of the parks to help visitors enjoy and understand the regions wonders. Fascinating and informative, this book affords us a striking new perspective on Earth''s creative forces.

The death of George Floyd in Minneapolis, MN, while he was in the custody of law enforcement, combined with several other recent high-profile deaths of African Americans at the hands of the police, have generated congressional interest in legislation to reform state and local policing practices, and to require law enforcement agencies to collect more data on law enforcement actions and activities and share these data with the public. Two major pieces of legislation that contain police reform proposals are before Congress: H.R. 7120, the George Floyd Justice in Policing Act of 2020—passed by the House on June 25, 2020—and S. 3985, the Just and Unifying Solutions to Invigorate Communities Everywhere (JUSTICE) Act. This book discusses the current issues associated with police reform.

The Frankstein Condition is an engaging, self-deprecating and often funny look at our modern day tendency to construct our identities and expectations around what we see as the best of others. Much like Victor Frankenstein, we create our monsters from pieces of the people around us. People who we admire: celebrities, family, friends and social media. As with Victors creation, our monster is destructive and brings with it anxiety and depression.This book explores topics like: envy, jealousy, self-image, peer influence, drug shaming, self-medication and of course, monsters.This edition of The Frankenstein Condition includes the incredible illustrations of legendary artist Bernie Wrightson. They are taken from his 1983 Frankenstein adaptation of the original Frankenstein, written by Mary Shelley. It included an introduction by Stephen King and approximately 50 detailed pen-and-ink illustrations. Wrightson spent seven years on the illustrations for this passion project. He is also remembered for his work on illustrated versions of Stephen King books, creature design for Ghostbusters and the co-creation of the Swamp Thing.

Uniquely focusing on intersections of social problems, multilevel statistical modeling, and causality; the substantively and methodologically integrated chapters of this book clarify basic strategies for developing and testing multilevel linear models (MLMs), and drawing casual inferences from such models. These models are also referred to as hierarchical linear models (HLMs) or mixed models. The statistical modeling of multilevel data structures enables researchers to combine contextual and longitudinal analyses appropriately. But researchers working on social problems seldom apply these methods, even though the topics they are studying and the empirical data call for their use. By applying multilevel modeling to hierarchical data structures, this book illustrates how the use of these methods can facilitate social problems research and the formulation of social policies. It gives the reader access to working data sets, computer code, and analytic techniques, while at the same time carefully discussing issues of causality in such models.This book innovatively:•Develops procedures for studying social, economic, and human development.• Uses typologies to group (i.e., classify or nest) the level of random macro-level factors.• Estimates models with Poisson, binomial, and Gaussian end points using SAS's generalized linear mixed models (GLIMMIX) procedure.• Selects appropriate covariance structures for generalized linear mixed models.• Applies difference-in-differences study designs in the multilevel modeling of intervention studies.•Calculates propensity scores by applying Firth logistic regression to Goldberger-corrected data.• Uses the Kenward-Rogers correction in mixed models of repeated measures.• Explicates differences between associational and causal analysis of multilevel models.• Consolidates research findings via meta-analysis and methodological critique.•Develops criteria for assessing a study's validity and zone ofcausality.Because of its social problems focus, clarity of exposition, and use of state-of-the-art procedures; policy researchers, methodologists, and applied statisticians in the social sciences (specifically, sociology, social psychology, political science, education, and public health) will find this book of great interest. It can be used as a primary text in courses on multilevel modeling or as a primer for more advanced texts.

Uniquely focusing on intersections of social problems, multilevel statistical modeling, and causality; the substantively and methodologically integrated chapters of this book clarify basic strategies for developing and testing multilevel linear models (MLMs), and drawing casual inferences from such models. These models are also referred to as hierarchical linear models (HLMs) or mixed models. The statistical modeling of multilevel data structures enables researchers to combine contextual and longitudinal analyses appropriately. But researchers working on social problems seldom apply these methods, even though the topics they are studying and the empirical data call for their use. By applying multilevel modeling to hierarchical data structures, this book illustrates how the use of these methods can facilitate social problems research and the formulation of social policies. It gives the reader access to working data sets, computer code, and analytic techniques, while at the same time carefully discussing issues of causality in such models.This book innovatively:•Develops procedures for studying social, economic, and human development.• Uses typologies to group (i.e., classify or nest) the level of random macro-level factors.• Estimates models with Poisson, binomial, and Gaussian end points using SAS''s generalized linear mixed models (GLIMMIX) procedure.• Selects appropriate covariance structures for generalized linear mixed models.• Applies difference-in-differences study designs in the multilevel modeling of intervention studies.•Calculates propensity scores by applying Firth logistic regression to Goldberger-corrected data.• Uses the Kenward-Rogers correction in mixed models of repeated measures.• Explicates differences between associational and causal analysis of multilevel models.• Consolidates research findings via meta-analysis and methodological critique.•Develops criteria for assessing a study''s validity and zone ofcausality.Because of its social problems focus, clarity of exposition, and use of state-of-the-art procedures; policy researchers, methodologists, and applied statisticians in the social sciences (specifically, sociology, social psychology, political science, education, and public health) will find this book of great interest. It can be used as a primary text in courses on multilevel modeling or as a primer for more advanced texts.

This bookanalyzes practical and moral influences on voting decisions. Undermining the widespreadassumption that economic self-interest is the key determinant of voting choices,it discovers that moral considerations rooted in religious traditions are oftenthe more decisive. This finding is confirmed through a close analysis oftangible problems, such as child neglect and crime, problems which one wouldexpect to trouble practical voters. Further, this book suggests that politicalideologies influence party affiliation, rather than the other way around. It definesfour categories of states in terms of human development and income equality—South,Heartland, postindustrial, and “balanced.” It then explains why politicalcolor (red, purple, or blue) and societal problems vary across thesecategories.  Voters’ moral ideologies, itshows, combine with a state’s measure of income equality and human development toshape a state’s readiness to pursue practical solutions to societal problems. Finally, it shows that moral ideologiesof the religious right and authoritarianism, two very different concepts, are in fact intertwined empirically.   This book thus suggests that education—a keydriver of human development, anti-authoritarianism, and deliberative voting—shouldbegin in preschools that are both nurturant and instructive.

Uniquely focusing on intersections of social problems, multilevel statistical modeling, and causality; the substantively and methodologically integrated chapters of this book clarify basic strategies for developing and testing multilevel linear models (MLMs), and drawing casual inferences from such models. These models are also referred to as hierarchical linear models (HLMs) or mixed models. The statistical modeling of multilevel data structures enables researchers to combine contextual and longitudinal analyses appropriately. But researchers working on social problems seldom apply these methods, even though the topics they are studying and the empirical data call for their use. By applying multilevel modeling to hierarchical data structures, this book illustrates how the use of these methods can facilitate social problems research and the formulation of social policies. It gives the reader access to working data sets, computer code, and analytic techniques, while at the same time carefully discussing issues of causality in such models.This book innovatively:•Develops procedures for studying social, economic, and human development.• Uses typologies to group (i.e., classify or nest) the level of random macro-level factors.• Estimates models with Poisson, binomial, and Gaussian end points using SAS's generalized linear mixed models (GLIMMIX) procedure.• Selects appropriate covariance structures for generalized linear mixed models.• Applies difference-in-differences study designs in the multilevel modeling of intervention studies.•Calculates propensity scores by applying Firth logistic regression to Goldberger-corrected data.• Uses the Kenward-Rogers correction in mixed models of repeated measures.• Explicates differences between associational and causal analysis of multilevel models.• Consolidates research findings via meta-analysis and methodological critique.•Develops criteria for assessing a study's validity and zone ofcausality.Because of its social problems focus, clarity of exposition, and use of state-of-the-art procedures; policy researchers, methodologists, and applied statisticians in the social sciences (specifically, sociology, social psychology, political science, education, and public health) will find this book of great interest. It can be used as a primary text in courses on multilevel modeling or as a primer for more advanced texts.