How To Conduct A Meta-Analysis A Practical Guide

What you'll learn
Introduction to Meta-Analysis
Data Extraction and Effect Size Calculation
Fixed-Effect and Random-Effects Models
Heterogeneity Assessment and Moderator Analysis
Reporting and Interpretation of Results
Open Science Practices and Data Sharing
Choosing appropriate effect sizes and measures for meta-analysis
Understanding the concept of publication bias and how to assess it
Using software tools for conducting and visualizing meta-analyses, such as SPSS, SAS, R and Comprehensive Meta-Analysis
Requirements
Basic knowledge of statistics
Familiarity with research methodology
Knowledge of statistical software
Good analytical skills
Motivation and commitment
Description
Meta-analysis is a powerful statistical technique that allows researchers to synthesize and integrate findings from multiple studies on a particular topic, providing a more comprehensive and accurate understanding of the research area. Whether you're a graduate student, academic researcher, or industry professional, this course will provide you with a thorough understanding of the principles and practical skills needed to conduct and interpret meta-analyses.This course, "How to Conduct a Meta-analysis: A Practical Guide," is designed to provide a thorough understanding of the principles and practical skills necessary for conducting and interpreting meta-analyses.Through a combination of video lectures, practical exercises, and real-world examples, this course will cover everything you need to know about meta-analysis, including:Understanding the fundamentals of meta-analysis, including its purpose, benefits, and limitationsConducting a systematic literature review and identifying relevant studies for inclusionExtracting data from primary studies and calculating effect sizesPerforming meta-analyses using both fixed-effect and random-effects modelsAssessing heterogeneity and conducting moderator analyses to explore sources of variationReporting meta-analytic results and interpreting their practical and theoretical implicationsIncorporating open science practices and utilizing online resources for data sharing and collaborationWhether you're looking to conduct your own meta-analysis or interpret and evaluate existing ones, this course will equip you with the knowledge and skills needed to confidently navigate the world of meta-analysis and contribute to advancing your field of study. Upon completion of the course, students will be equipped with the knowledge and skills needed to confidently navigate the world of meta-analysis, contribute to advancing their field of study, and make informed decisions based on the results of meta-analyses.
Overview
Section 1: Introduction
Lecture 1 Instructor Introduction
Lecture 2 What is Meta-analysis?
Lecture 3 What is the importance of meta-analysis in Academia?
Lecture 4 Disadvantages of meta-analysis
Lecture 5 Steps in meta-analysis
Section 2: Step-1: Defining Research Questions
Lecture 6 Selecting a Research Topic for meta-analysis
Lecture 7 Main types of review questions
Lecture 8 Components of review questions
Lecture 9 PICO - A quantitative review question
Lecture 10 PEO - A qualitative review question
Lecture 11 SPIDER - A quantitative review question
Section 3: Step-2: Searching Relevant Literature
Lecture 12 Clarifying the preliminaries
Lecture 13 Search strategies
Lecture 14 Boolean operators
Lecture 15 Inclusion-Exclusion criateria
Section 4: Step-3: Choice of the effect size measure
Lecture 16 Types of effect sizes
Lecture 17 Conversion of effect sizes to a common measure
Section 5: Step4: Choice of analytical method
Lecture 18 Univariate meta-analysis
Lecture 19 Meta-regression analysis
Lecture 20 Meta-analysis structural equation modeling (MASEM)
Lecture 21 Qualitative meta-analysis
Section 6: Step-6: Choice of software
Lecture 22 STATA
Lecture 23 SPSS
Lecture 24 SAS
Lecture 25 R
Section 7: Step-7: Coding of effect sizes
Lecture 26 Developing a coding sheet
Lecture 27 Inclusion of moderator or control variables
Lecture 28 Treatment of multiple effect sizes
Section 8: Step-8: Analysis of Data
Lecture 29 Outlier Analysis
Lecture 30 Tests for publication bias
Lecture 31 Fixed and random effect
Section 9: Step-9: Reporting Results
Lecture 32 Reporting in the article
Lecture 33 Open-science practices
Graduate students,Academicians,Researchers,Industry professionals