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Advanced Statistical Modeling and Programming with Stata

via Coursera

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Details

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

7 weeks, 1 hour a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

Found in

Part of

Applied Statistics Using Stata

Overview

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Advance your expertise with multilevel, panel, and time-series analysis, and explore factor analysis, structural equation modeling, and programming in Stata. This course equips learners to handle sophisticated statistical models and automate analyses for dynamic reporting. This course covers a range of advanced statistical techniques, including multilevel and panel data analysis, time-series modeling, exploratory and confirmatory factor analysis, and structural equation modeling. Learners will also explore specialized methods such as negative binomial regression, handling missing data, and variable transformation. The course concludes with an introduction to programming in Stata and creating reproducible, dynamic reports, empowering participants to streamline and document their analytical workflows. The course integrates detailed explanations of advanced statistical models with practical Stata applications, guiding learners through complex analyses and programming tasks. Emphasis is placed on real-world scenarios and best practices for efficient, reproducible research. This course is part three of a three-course Specialization designed to build a complete and cohesive understanding of the subject. While it offers valuable skills on its own, you'll gain the most benefit by progressing through all three courses as a structured learning journey. This course is based on Applied Statistics Using Stata, by Mehmet Mehmetoglu and Tor Georg Jakobsen. Copyright ©2022 by Sage Publications Limited. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies. Published by Sage Publications Limited, London. Used by arrangement with Sage Publications Limited.

Syllabus

Multilevel Analysis
- This module introduces learners to multilevel analysis techniques for handling hierarchical data structures. It covers essential concepts such as variance partitioning, intraclass correlation, and interaction effects, while demonstrating practical applications using Stata. Learners will gain the skills to analyze data at multiple levels and interpret complex relationships within nested datasets.
Panel Data Analysis
- This module provides an in-depth understanding of panel data analysis techniques, including fixed and random effects models, and their implementation in Stata. Learners will explore various methods for analyzing longitudinal data and handling cross-sectional time series structures. The course covers practical applications and diagnostic tests to ensure accurate modeling of complex datasets.
Time-Series Analysis
- This module covers essential techniques for analyzing time series data, including smoothing trends, detecting and addressing autocorrelation, and applying advanced models like ARIMA and VAR for forecasting. Learners will gain practical skills in identifying patterns, testing for stationarity, and implementing statistical methods to improve predictive accuracy.
Exploratory Factor Analysis
- This module covers the fundamental concepts and practical steps involved in exploratory factor analysis. Learners will gain insights into how to extract, interpret, and refine factors using statistical tools like Stata. The module also explores how to compute composite scores and assess reliability in factor analysis.
Structural Equation Modelling and Confirmatory Factor Analysis
- This module provides an in-depth exploration of structural equation modeling (SEM) and confirmatory factor analysis (CFA), covering model specification, identification, assessment, and modification. Learners will develop skills in interpreting and evaluating both measurement and structural components of SEM models. The content also includes practical applications using Stata and theoretical foundations for latent variable analysis.
Advanced Statistical Techniques
- This module covers advanced statistical methods for handling complex data scenarios, including count data modeling, variable transformation, and robust techniques for missing data. Learners will gain practical skills in applying these methods to improve regression analysis and data interpretation.
Programming and Dynamic Reporting Using Stata
- This module covers essential Stata programming techniques such as macros, loops, and stored objects, along with dynamic reporting tools like dyndoc and putdocx. Learners will gain the ability to create reusable commands and generate reproducible documentation. The focus is on improving efficiency and clarity in data management and reporting.