Author: LaKendra Miranda Peoples-McAfee Publisher: ISBN: Category : Dropouts Languages : en Pages : 0
Book Description
Attrition is an issue for colleges and universities, and attempts to retain students are becoming more and more difficult. This study focuses on predicting student attrition of first time incoming (FTIC) students over a long time period. The population of this study consists of all FTIC students from Fall 2001. The students were followed 3.5 academic years to observe whether they experienced attrition. Exploratory data analysis was conducted to examine existing independent variables and some variables that were created to determine their contribution to the model. A discrete time hazard method was used to measure the timing of event occurrence. Cumulative GPA after one semester, number of major changes, major type, and minority status were selected to be included the model. Cross-validation was performed on Fall 2002 FTIC students to assess model fit. Overall, the model did a great job of predicting attrition of students over the long term.
Author: Lesa Hoffman Publisher: Routledge ISBN: 1317591089 Category : Psychology Languages : en Pages : 867
Book Description
Longitudinal Analysis provides an accessible, application-oriented treatment of introductory and advanced linear models for within-person fluctuation and change. Organized by research design and data type, the text uses in-depth examples to provide a complete description of the model-building process. The core longitudinal models and their extensions are presented within a multilevel modeling framework, paying careful attention to the modeling concerns that are unique to longitudinal data. Written in a conversational style, the text provides verbal and visual interpretation of model equations to aid in their translation to empirical research results. Overviews and summaries, boldfaced key terms, and review questions will help readers synthesize the key concepts in each chapter. Written for non-mathematically-oriented readers, this text features: A description of the data manipulation steps required prior to model estimation so readers can more easily apply the steps to their own data An emphasis on how the terminology, interpretation, and estimation of familiar general linear models relates to those of more complex models for longitudinal data Integrated model comparisons, effect sizes, and statistical inference in each example to strengthen readers’ understanding of the overall model-building process Sample results sections for each example to provide useful templates for published reports Examples using both real and simulated data in the text, along with syntax and output for SPSS, SAS, STATA, and Mplus at www.PilesOfVariance.com to help readers apply the models to their own data The book opens with the building blocks of longitudinal analysis—general ideas, the general linear model for between-person analysis, and between- and within-person models for the variance and the options within repeated measures analysis of variance. Section 2 introduces unconditional longitudinal models including alternative covariance structure models to describe within-person fluctuation over time and random effects models for within-person change. Conditional longitudinal models are presented in section 3, including both time-invariant and time-varying predictors. Section 4 reviews advanced applications, including alternative metrics of time in accelerated longitudinal designs, three-level models for multiple dimensions of within-person time, the analysis of individuals in groups over time, and repeated measures designs not involving time. The book concludes with additional considerations and future directions, including an overview of sample size planning and other model extensions for non-normal outcomes and intensive longitudinal data. Class-tested at the University of Nebraska-Lincoln and in intensive summer workshops, this is an ideal text for graduate-level courses on longitudinal analysis or general multilevel modeling taught in psychology, human development and family studies, education, business, and other behavioral, social, and health sciences. The book’s accessible approach will also help those trying to learn on their own. Only familiarity with general linear models (regression, analysis of variance) is needed for this text.
Author: Jason Newsom Publisher: Routledge ISBN: 1136705473 Category : Psychology Languages : en Pages : 407
Book Description
This book provides accessible treatment to state-of-the-art approaches to analyzing longitudinal studies. Comprehensive coverage of the most popular analysis tools allows readers to pick and choose the techniques that best fit their research. The analyses are illustrated with examples from major longitudinal data sets including practical information about their content and design. Illustrations from popular software packages offer tips on how to interpret the results. Each chapter features suggested readings for additional study and a list of articles that further illustrate how to implement the analysis and report the results. Syntax examples for several software packages for each of the chapter examples are provided at www.psypress.com/longitudinal-data-analysis. Although many of the examples address health or social science questions related to aging, readers from other disciplines will find the analyses relevant to their work. In addition to demonstrating statistical analysis of longitudinal data, the book shows how to interpret and analyze the results within the context of the research design. The methods covered in this book are applicable to a range of applied problems including short- to long-term longitudinal studies using a range of sample sizes. The book provides non-technical, practical introductions to the concepts and issues relevant to longitudinal analysis. Topics include use of publicly available data sets, weighting and adjusting for complex sampling designs with longitudinal studies, missing data and attrition, measurement issues related to longitudinal research, the use of ANOVA and regression for average change over time, mediation analysis, growth curve models, basic and advanced structural equation models, and survival analysis. An ideal supplement for graduate level courses on data analysis and/or longitudinal modeling taught in psychology, gerontology, public health, human development, family studies, medicine, sociology, social work, and other behavioral, social, and health sciences, this multidisciplinary book will also appeal to researchers in these fields.
Author: Tyler Hallmark Publisher: ISBN: Category : College dropouts Languages : en Pages : 306
Book Description
This study draws on theories that seek to explain factors that impact college student retention/attrition for understanding any possible differences between individuals from different type of neighborhoods. Additionally, sociological theories pertaining to segregation and capital accumulation underlie key assumptions of this study. The site for this study included a large, public, four-year state flagship institution, referred to as Midwest University. This study utilizes the incoming Autumn 2012, in-state undergraduate cohort -- a sample of nearly 6,000 individuals -- and a series of analyses -- including binomial regression and survival analyses -- in order to examine how students' neighborhood-based socioeconomic variables may be correlated with their retention, success, and status changes over a six-year period. As a result of these analyses, I demonstrate neighborhood socioeconomic context (as measured at the census tract and block group levels) to be a valuable indicator of students' retention and success after enrolling in college and provide implications for future research, policy, and practice.
Author: Jeffrey D. Long Publisher: SAGE ISBN: 1412982685 Category : Mathematics Languages : en Pages : 569
Book Description
This book is a practical guide for the analysis of longitudinal behavioural data. Longitudinal data consist of repeated measures collected on the same subjects over time.
Author: Jason T. Newsom Publisher: Taylor & Francis ISBN: 1000905977 Category : Psychology Languages : en Pages : 522
Book Description
Longitudinal Structural Equation Modeling is a comprehensive resource that reviews structural equation modeling (SEM) strategies for longitudinal data to help readers determine which modeling options are available for which hypotheses. This accessibly written book explores a range of models, from basic to sophisticated, including the statistical and conceptual underpinnings that are the building blocks of the analyses. By exploring connections between models, it demonstrates how SEM is related to other longitudinal data techniques and shows when to choose one analysis over another. Newsom emphasizes concepts and practical guidance for applied research rather than focusing on mathematical proofs, and new terms are highlighted and defined in the glossary. Figures are included for every model along with detailed discussions of model specification and implementation issues and each chapter also includes examples of each model type, descriptions of model extensions, comment sections that provide practical guidance, and recommended readings. Expanded with new and updated material, this edition includes many recent developments, a new chapter on growth mixture modeling, and new examples. Ideal for graduate courses on longitudinal (data) analysis, advanced SEM, longitudinal SEM, and/or advanced data (quantitative) analysis taught in the behavioral, social, and health sciences, this new edition will continue to appeal to researchers in these fields.
Author: Jing Xie Publisher: ISBN: Category : Languages : en Pages : 112
Book Description
"This preliminary longitudinal statistical study analyzes demographic, retention, and GPA achievement data for the 2014 EMERGE Summer Program cohort at Northeastern Illinois University. In particular, this study investigates (1) characteristics of EMERGE participants relative to non-EMERGE participants, (2) the impact of EMERGE participation on first-year GPA relative to non-EMERGE participants, (3) the impact of EMERGE participation on first-year retention relative to non-EMERGE participants, (4) how GPA and retention outcomes relate to demographics, high school attended, high school GPA and ACT, and college major, and (5) findings for the first four research questions for underrepresented students, including low-income, first generation, African-American, and Latina/o students, (6) What impact does EMERGE participation have on first-year math GPA relative to that of non-EMERGE participants? Findings indicate that the EMERGE Summer Program has a significant and positive impact on student retention rate within the first academic year. The impact of the program on student GPA within the first academic year is not significant; however, when controlling for student characteristics such as low-income, first-generation or the underrepresented minority, the impact from the Summer EMERGE Program on GPA becomes significant. In this paper, we demonstrates that program impact on student GPA and retention is associated with student characteristics such as low-income, first-generation, underrepresented minority, gender, age, intended major, student high school performance. On the other hand, it also reveals the need of assessing program longitudinally in improving student GPA including math grade and retention with respect to the EMERGE participants and nonparticipants, low-income students, first-generation students and underrepresented minority students."--
Author: LaKendra Miranda Peoples-McAfee
Author: Lesa Hoffman
Author: Jason Newsom
Author: Tyler Hallmark
Author: Jeffrey D. Long
Author: Jason T. Newsom
Author: Jing Xie
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