Providing a comprehensive review of pressing issues roiling American college campuses today, this book is a valuable resource for students and scholars alike. People often refer to America's colleges and universities as "Ivory Towers," a term that implies that campuses are innocent places of study largely insulated from wider societal concerns. In actuality, our nation's universities are hotbeds of controversy. Some of these sources of heated debate relate directly to access to the college experience, such as the rising cost of tuition and admission policies related to student diversity. Others reflect wider societal schisms, such as divisions over sexual assault (both causes and responses) and "political correctness." Controversies on Campus: Debating the Issues Confronting American Universities in the 21st Century examines the myriad controversies regarding today's college campuses and student bodies, such as tuition costs, campus rape, academic freedom/free speech, gun policies, binge drinking, "hook-up" culture, corporatization of academic research, poverty-level wages of adjunct faculty, and student-athletes in the era of big-money amateur sports. The book objectively examines these issues and others, taking care to not only present up-to-date quantifiable data to help readers understand the controversy but also to provide a fair and impartial summary of perspectives on the issue in question. It is a one-stop resource for learning about a wide range of issues and controversies confronting American colleges and universities and the people—students, professors, and administrators—who comprise those communities.

Multilevel and Longitudinal Modeling with IBM SPSS, Third Edition, demonstrates how to use the multilevel and longitudinal modeling techniques available in IBM SPSS Versions 25-27. Annotated screenshots with all relevant output provide readers with a step-by-step understanding of each technique as they are shown how to navigate the program. Throughout, diagnostic tools, data management issues, and related graphics are introduced. SPSS commands show the flow of the menu structure and how to facilitate model building, while annotated syntax is also available for those who prefer this approach. Extended examples illustrating the logic of model development and evaluation are included throughout the book, demonstrating the context and rationale of the research questions and the steps around which the analyses are structured. The book opens with the conceptual and methodological issues associated with multilevel and longitudinal modeling, followed by a discussion of SPSS data management techniques that facilitate working with multilevel, longitudinal, or cross-classified data sets. The next few chapters introduce the basics of multilevel modeling, developing a multilevel model, extensions of the basic two-level model (e.g., three-level models, models for binary and ordinal outcomes), and troubleshooting techniques for everyday-use programming and modeling problems along with potential solutions. Models for investigating individual and organizational change are next developed, followed by models with multivariate outcomes and, finally, models with cross-classified and multiple membership data structures. The book concludes with thoughts about ways to expand on the various multilevel and longitudinal modeling techniques introduced and issues (e.g., missing data, sample weights) to keep in mind in conducting multilevel analyses. Key features of the third edition: Thoroughly updated throughout to reflect IBM SPSS Versions 26-27. Introduction to fixed-effects regression for examining change over time where random-effects modeling may not be an optimal choice. Additional treatment of key topics specifically aligned with multilevel modeling (e.g., models with binary and ordinal outcomes). Expanded coverage of models with cross-classified and multiple membership data structures. Added discussion on model checking for improvement (e.g., examining residuals, locating outliers). Further discussion of alternatives for dealing with missing data and the use of sample weights within multilevel data structures. Supported by online data sets, the book's practical approach makes it an essential text for graduate-level courses on multilevel, longitudinal, latent variable modeling, multivariate statistics, or advanced quantitative techniques taught in departments of business, education, health, psychology, and sociology. The book will also prove appealing to researchers in these fields. The book is designed to provide an excellent supplement to Heck and Thomas's An Introduction to Multilevel Modeling Techniques, Fourth Edition; however, it can also be used with any multilevel or longitudinal modeling book or as a stand-alone text.

Among other commonly held beliefs about the drivers of postsecondary policy and financing, this book questions the current use of funding per student as the dominant indicator of the adequacy of state investment in higher education, and as a key driver of the costs of college. State legislators balance the competing needs of education, health, transportation, and public safety budgets, and increasingly ask what return (ROI) they get for the funding they provide, including from higher education. This question means the forty-year era of advocating for higher education by comparing funding per student to a national average or last year's result is no longer an effective gauge of funding adequacy for state policymakers. The Science of Higher Education is an introduction to a new paradigm that explores state higher education funding, enrollment, completion, and supply (the number and type of institutions in a state) through the lens of what are commonly known as power laws. Power laws explain patterns in biological systems and characteristics of cities. Like cities, state higher education systems are complex adaptive systems, so it is little surprise that power laws also explain funding, enrollment, completion and supply. The scale relationships uncovered in The Science of Higher Education suggest the potential benefits state policymakers could derive by emphasizing enrollment, completion or capacity policies, based on economies of scale, marginal benefits, and the return state's get on enrollment and completion for the funding they provide. The various features of state higher education systems that conform to scale patterns do not alone provide definitive answers for appropriate funding levels, however. As this book addresses, policy makers need to take into account the macro forces, from demography to geography and the economy, that situate the system, as well the interactions between government and market actors that are at the core of every state higher education system and influence the outcomes it achieves.

Multilevel and Longitudinal Modeling with IBM SPSS, Third Edition, demonstrates how to use the multilevel and longitudinal modeling techniques available in IBM SPSS Versions 25-27. Annotated screenshots with all relevant output provide readers with a step-by-step understanding of each technique as they are shown how to navigate the program. Throughout, diagnostic tools, data management issues, and related graphics are introduced. SPSS commands show the flow of the menu structure and how to facilitate model building, while annotated syntax is also available for those who prefer this approach. Extended examples illustrating the logic of model development and evaluation are included throughout the book, demonstrating the context and rationale of the research questions and the steps around which the analyses are structured. The book opens with the conceptual and methodological issues associated with multilevel and longitudinal modeling, followed by a discussion of SPSS data management techniques that facilitate working with multilevel, longitudinal, or cross-classified data sets. The next few chapters introduce the basics of multilevel modeling, developing a multilevel model, extensions of the basic two-level model (e.g., three-level models, models for binary and ordinal outcomes), and troubleshooting techniques for everyday-use programming and modeling problems along with potential solutions. Models for investigating individual and organizational change are next developed, followed by models with multivariate outcomes and, finally, models with cross-classified and multiple membership data structures. The book concludes with thoughts about ways to expand on the various multilevel and longitudinal modeling techniques introduced and issues (e.g., missing data, sample weights) to keep in mind in conducting multilevel analyses. Key features of the third edition: Thoroughly updated throughout to reflect IBM SPSS Versions 26-27. Introduction to fixed-effects regression for examining change over time where random-effects modeling may not be an optimal choice. Additional treatment of key topics specifically aligned with multilevel modeling (e.g., models with binary and ordinal outcomes). Expanded coverage of models with cross-classified and multiple membership data structures. Added discussion on model checking for improvement (e.g., examining residuals, locating outliers). Further discussion of alternatives for dealing with missing data and the use of sample weights within multilevel data structures. Supported by online data sets, the book's practical approach makes it an essential text for graduate-level courses on multilevel, longitudinal, latent variable modeling, multivariate statistics, or advanced quantitative techniques taught in departments of business, education, health, psychology, and sociology. The book will also prove appealing to researchers in these fields. The book is designed to provide an excellent supplement to Heck and Thomas's An Introduction to Multilevel Modeling Techniques, Fourth Edition; however, it can also be used with any multilevel or longitudinal modeling book or as a stand-alone text.

Among other commonly held beliefs about the drivers of postsecondary policy and financing, this book questions the current use of funding per student as the dominant indicator of the adequacy of state investment in higher education, and as a key driver of the costs of college. State legislators balance the competing needs of education, health, transportation, and public safety budgets, and increasingly ask what return (ROI) they get for the funding they provide, including from higher education. This question means the forty-year era of advocating for higher education by comparing funding per student to a national average or last year's result is no longer an effective gauge of funding adequacy for state policymakers. The Science of Higher Education is an introduction to a new paradigm that explores state higher education funding, enrollment, completion, and supply (the number and type of institutions in a state) through the lens of what are commonly known as power laws. Power laws explain patterns in biological systems and characteristics of cities. Like cities, state higher education systems are complex adaptive systems, so it is little surprise that power laws also explain funding, enrollment, completion and supply. The scale relationships uncovered in The Science of Higher Education suggest the potential benefits state policymakers could derive by emphasizing enrollment, completion or capacity policies, based on economies of scale, marginal benefits, and the return state's get on enrollment and completion for the funding they provide. The various features of state higher education systems that conform to scale patterns do not alone provide definitive answers for appropriate funding levels, however. As this book addresses, policy makers need to take into account the macro forces, from demography to geography and the economy, that situate the system, as well the interactions between government and market actors that are at the core of every state higher education system and influence the outcomes it achieves.

Multilevel modelling is a data analysis method that is frequently used to investigate hierarchal data structures in educational, behavioural, health, and social sciences disciplines. Multilevel data analysis exploits data structures that cannot be adequately investigated using single-level analytic methods such as multiple regression, path analysis, and structural modelling. This text offers a comprehensive treatment of multilevel models for univariate and multivariate outcomes. It explores their similarities and differences and demonstrates why one model may be more appropriate than another, given the research objectives. New to this edition: An expanded focus on the nature of different types of multilevel data structures (e.g., cross-sectional, longitudinal, cross-classified, etc.) for addressing specific research goals; Varied modelling methods for examining longitudinal data including random-effect and fixed-effect approaches; Expanded coverage illustrating different model-building sequences and how to use results to identify possible model improvements; An expanded set of applied examples used throughout the text; Use of four different software packages (i.e., Mplus, R, SPSS, Stata), with selected examples of model-building input files included in the chapter appendices and a more complete set of files available online. This is an ideal text for graduate courses on multilevel, longitudinal, latent variable modelling, multivariate statistics, or advanced quantitative techniques taught in psychology, business, education, health, and sociology. Recommended prerequisites are introductory univariate and multivariate statistics.

Multilevel modelling is a data analysis method that is frequently used to investigate hierarchal data structures in educational, behavioural, health, and social sciences disciplines. Multilevel data analysis exploits data structures that cannot be adequately investigated using single-level analytic methods such as multiple regression, path analysis, and structural modelling. This text offers a comprehensive treatment of multilevel models for univariate and multivariate outcomes. It explores their similarities and differences and demonstrates why one model may be more appropriate than another, given the research objectives. New to this edition: An expanded focus on the nature of different types of multilevel data structures (e.g., cross-sectional, longitudinal, cross-classified, etc.) for addressing specific research goals; Varied modelling methods for examining longitudinal data including random-effect and fixed-effect approaches; Expanded coverage illustrating different model-building sequences and how to use results to identify possible model improvements; An expanded set of applied examples used throughout the text; Use of four different software packages (i.e., Mplus, R, SPSS, Stata), with selected examples of model-building input files included in the chapter appendices and a more complete set of files available online. This is an ideal text for graduate courses on multilevel, longitudinal, latent variable modelling, multivariate statistics, or advanced quantitative techniques taught in psychology, business, education, health, and sociology. Recommended prerequisites are introductory univariate and multivariate statistics.