Download Mac Programs, Themes, Skins and Tools for your desktop, school, business and government. Try Parallels Desktop 13 for Mac and experience software that lets you run Windows apps on your Macbook or iMac. You can even run games, transfer files, run programs like Microsoft. Title: Analysis Methods for Clustered and Hierarchical Data Dates: April 20 and 21 Time: 9:00 am – 4:00 pm Location: PAES 143 and PAES 110 Fee: Faculty/Professionals $659 Students/PostDocs $325 Fees are payable using cash, check, or funds transfer Description: This two-day workshop introduces modeling methods for clustered or hierarchically-structured outcomes. O’Connell will focus on fundamental concepts and practical applications, with limited emphasis on statistical theory. Cluster-robust inference will be covered, as well as methods for linear mixed-models. Participants will gain the knowledge and skills necessary to successfully interpret these methods in the literature, and fit and interpret results of these kinds of models for their own research. Participants are encouraged to bring their own laptops, and data will be made available for in-class demonstrations and discussion. Emphasis will be on continuous outcomes with discussion of adaptations for dichotomous, ordinal or count outcomes. Examples will be demonstrated through a variety of software including SPSS (for data manipulation), R (open-source), and HLM (free student version available on the web). Familiarity with ordinary least-squares regression and/or analysis of variance models is sufficient background for this introductory workshop. Examples will be drawn from diverse fields including education and health. Learning Objectives: Following successful completion of this workshop, students will be able to: • Read and critically evaluate literature pertaining to analysis of clustered data in the context of different substantive research areas. • Understand and describe the differences in estimation, analysis, and interpretation of models for clustered data, including cluster-robust standard errors, GEE, and random-effects models; and identify situations in which these alternative approaches might be preferred. • Conduct and interpret results for two-level organizational and simple longitudinal models using a variety of statistical packages including SPSS, HLM and R. • Assess quality of model fit through information criteria (AIC, BIC) and Likelihood Ratio (LR) tests. • Become familiar with extensions of these models for discrete clustered data. Requirements: Attendees should have familiarity with ordinary least-squares regression and/or analysis of variance models. Familiarity with software packages is not required. Computers and required software will be provided. Participants who wish to use their own laptops will need to download the following software. • SPSS version 24 () • HLM version 7.03 (Student version minimum; ) • RStudio and RStudio • Windows users: • R: • RStudio: • Mac users: • R: • RStudio: Facilitator: Ann A. O’Connell, Professor, Educational Studies and Director of the Research Methodology Center Dr. O’Connell has been teaching research methods and statistics for more than 20 years. Among her published works is a book with Sage on Logistic Regression Models for Ordinal Response Variables, and a co-edited volume on Multilevel Modeling of Educational Data. She is a former Fulbright Scholar (2013-14) to Addis Ababa University, Ethiopia, where she taught statistics and research methods to graduate students and faculty at AAU as well as throughout the country. Her areas of specialization include generalized linear and mixed models, and evaluation methods for health and education interventions. The software titles listed in the table below are found on all SSC lab machines. Click on the software name for more information. If you have any problems with any of these programs at SSC, please inform the front desk. Adobe Acrobat DC Reader Plug-in reader to view files that are in PDF format. ArcGIS Program for desktop geographic information analysis and mapping. Data Desk brings fast, easy-to-use visual analysis to your desktop with its powerful tools for data exploration. ENVI is a powerful, easy to use remote sensing software package. A forecasting and econometric analysis software program.
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