How To Conduct A Meta-Analysis: A Practical Guide

#1 Meta-Analysis Course for Researchers: A Practical Approach to Synthesizing Data

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