This document provides a comprehensive guide on performing meta-analysis using Stata, emphasizing its importance in integrating various study outcomes to derive robust conclusions. It outlines key steps, including data preparation, importing datasets, installing necessary packages, generating visual representations like forest and funnel plots, and testing for heterogeneity and publication bias. Additionally, it promotes a homework help service for students facing challenges in mastering these statistical techniques.

1. HOW TO PERFORM
META-ANALYSIS IN
STATA
PRESENTATION
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2. INTRODUCTION
Meta-analysis is another statistical procedure that integrates
outcomes of various different independent studies so as to
obtain comprehensive conclusions. This approach becomes
useful in subjects like econometrics, statistics, or social
sciences where authors seek to integrate evidence and
determine unexplored patterns within varied data sets. It
acts as a crucial element for resolving discrepancies between
studies, for raising statistical power, and for arriving at robust
conclusions.
To students learning econometrics and statistics, grasping
meta-analysis provides critical insights and knowledge in
synthesizing evidence, as well as helping sharpen their
analytical skills.

3. WHY USE STATA FOR META-
ANALYSIS
STATA is one of the widely used programs for meta-
analysis and it comprises a number of powerful
commands along with flexibility on features. It is easy
to operate, contains features for statistical analysis and
offers a active online user community that is available
to assist students in data analysis, research and
academics. For instance, STATA makes it easy for
pooling effect sizes, heterogeneity testing, or
generating graphical results such as the forest plot or
the funnel plot. Students who are struggling with
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4. Step-by-Step Guide
HOW TO PERFORM META-ANALYSIS IN
STATA
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5. To perform meta-analysis in STATA, you typically need three key
components from the studies you are analyzing:
(e.g., mean differences,
odds ratios).
(sometimes optional,
calculated from
sample size or degree
of precision).
Effect Sizes The Standard Errors or
Confidence Intervals
linking to the effect
sizes.
Study Weights
META ANALAYSIS IN STATA

6. Study_ID Effect_Size Effect_Size Weight
1 0.25 0.05 10
2 0.30 0.04 15
3 0.18 0.06 8
STEP 1: PREPARE YOUR DATA
First, prepare your data into a tabular form. You can save this data in
either a .csv format or an STATA-compatible .dta format. A sample dataset
might look like this:

7. STEP 2: IMPORT DATA INTO STATA
Open STATA and load your dataset:
This should be saved with the name “meta_analysis_data.dta”
before we continue with our analysis.
use "meta_analysis_data.dta", clear
STEP 3: INSTALL META-ANALYSIS PACKAGE
STATA has a meta package to perform meta-analyses. If not
already installed, you can install it using the following
command:
ssc install meta
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8. STEP 4: DECLARE THE META-ANALYSIS VARIABLES
However, before proceeding further you have to decide on the
meta-analysis variables, especially the effect size and standard
error. Use the meta set command:
meta set Effect_Size Std_Error
STEP 5: GENERATE A FOREST PLOT
A forest plot is a graphic display of effect size and confidence
interval for individual studies. Generate one with:
meta forestplot
It will show a visual summary of results including the effect size
of all the studies and the overall effect size of meta-analysis

9. STEP 6: TEST FOR HETEROGENEITY
Heterogeneity is the extent of the variation of different findings of the studies.
STATA provides heterogeneity statistics like Cochran’s Q and I2:
meta summarize, hetstat(Q I2)
Here’s an interpretation:
• I2>50%: moderate to high heterogeneity, indicating the variability between
the studies.
• I2<25%: Low homogeneity, meaning consistency among the results of the
studies.
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10. STEP 7: PERFORM FIXED-EFFECT OR RANDOM-
EFFECTS META-ANALYSIS
Based on heterogeneity, choose the appropriate model:
• Fixed-effect model assumes that all studies estimate the same underlying
effect.
• Random-effects model accounts for variability across studies.
Fixed-effect meta-analysis:
meta summarize, fixed
Random-effects meta-analysis:
meta summarize, random
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11. STEP 8: USE A FUNNEL PLOT AND PERFORM THE
PUBLICATION BIAS TEST
In order to look for publication bias, a funnel plot is useful since studies with
significant results are more likely to be published. Generate a funnel plot with:
meta funnel plot
Perform Egger’s test for publication bias:
meta bias
A statistically significant result shows publication bias and hence should be
interpreted with care.

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13. If you're struggling with your
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FINAL THOUGHTS
The ability to conduct a meta-analysis using STATA
becomes an essential skill for students studying
econometrics and statistics.
Once the students have mastered the meta
commands, they can tackle their assignments and
projects with confidence in finding insights from
multiple studies.
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14. Introduction to Meta-
Analysis by Michael
Borenstein et al.
Meta-Analysis with R
and STATA by Guido
Schwarzer.
Explore the official
STATA meta-analysis
documentation for
detailed instructions.
RECOMMENDED RESOURCES
FOR STUDENTS

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