• Post category:StudyBullet-13
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#1 Meta-Analysis Course for Researchers: A Practical Approach to Synthesizing Data

What you will 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

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.


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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 limitations
  • Conducting a systematic literature review and identifying relevant studies for inclusion
  • Extracting data from primary studies and calculating effect sizes
  • Performing meta-analyses using both fixed-effect and random-effects models
  • Assessing heterogeneity and conducting moderator analyses to explore sources of variation
  • Reporting meta-analytic results and interpreting their practical and theoretical implications
  • Incorporating open science practices and utilizing online resources for data sharing and collaboration

Whether 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.

English
language

Content

Introduction

Instructor Introduction
What is Meta-analysis?
What is the importance of meta-analysis in Academia?
Disadvantages of meta-analysis
Steps in meta-analysis

Step-1: Defining Research Questions

Selecting a Research Topic for meta-analysis
Main types of review questions
Components of review questions
PICO – A quantitative review question
PEO – A qualitative review question
SPIDER – A quantitative review question

Step-2: Searching Relevant Literature

Clarifying the preliminaries
Search strategies
Boolean operators
Inclusion-Exclusion criateria

Step-3: Choice of the effect size measure

Types of effect sizes
Conversion of effect sizes to a common measure

Step4: Choice of analytical method

Univariate meta-analysis
Meta-regression analysis
Meta-analysis structural equation modeling (MASEM)
Qualitative meta-analysis

Step-6: Choice of software

STATA
SPSS
SAS
R

Step-7: Coding of effect sizes

Developing a coding sheet
Inclusion of moderator or control variables
Treatment of multiple effect sizes

Step-8: Analysis of Data

Outlier Analysis
Tests for publication bias
Fixed and random effect

Step-9: Reporting Results

Reporting in the article
Open-science practices