SlideShare a Scribd company logo
Introduction to Epi Info
Emiru Merdassa
6 January 2023 1
2
–Overview of EPI-Info
–Data entry using EPI-INFO
–Sample size determination
Outline
6 January 2023
3
• Epi Info is a public domain database and statistics program.
– Develop an electronic data entry form
– Customize the data entry process
– Enter data, and analyze the data
– Statistics, graphs, tables, and maps can be produced with simple commands
– Epi Info is free, downloadable software provided by the CDC
(www.cdc.gov/epiinfo)
Introduction
6 January 2023
4
• Make view
– For designing an electronic data entry form which automatically creates
a data table.
• Enter data
– For entering data into the designed electronic data entry form.
• Analyze Data
– For conducting a relatively wide range of statistical analysis of the data.
Main applications in Epi Info
6 January 2023
The Epi-Info 7 Main Menu
6 January 2023 5
Epi Info – Core Principles
–Free
–Easy to use
–Flexible
–Standards based
–No “IT guys” needed in most cases
6 January 2023 6
Designing a Form
❑ The Form Designer has several “work areas”
▪ The Menu
– The toolbar contains buttons for creating projects, editing the form’s check code,
going to the data entry module, and undo/redo.
▪ The Project Explorer is where you can add and remove forms from your project,
add, edit, and remove pages from individual forms, and work with templates.
▪ The Canvas is where fields are placed, moved, and edited.
Designing a Form
1. The Menu
2. The Project Explorer
3. The Canvas
Creating question/prompt
Choice one
of the
property for
the variable
9
6 January 2023
Sample Size determination using Epi Info
• Step 1: Go to the main menu at the top left corner and click on StatCalc and subsequent
options
6 January 2023 10
Sample size determination
• Fill the components necessary to determine sample size for the three case scenarios:
Population survey, Unmatched case control, and Cross-sectional or Cohort study
designs.
6 January 2023 11
Exercise
Let us assume the population that we want to conduct the study has a target
population of size N=100,000, and the proportion of the variable of interest is not
known which means there is no previous study done and hence we decided to use 50
percent as an estimate of the prevalence for that variable.
a) What would be the sample size required if the sampling technique was simple
random sampling?
b) What will be the sample size if the sampling technique is two stage sampling
and is also expected to have 10% non-response rate?
6 January 2023 12
Exercise
• The prevalence of under weight of newborns is compared between two
regions. In one region, it is estimated that about 30% of them could be under
weight. In other region it is probably 15%. If the required sample is to show
with a 90% likelihood (power) that the percentage of newborns is different in
these two regions at 95% confidence level, what would be the sample size?
Suppose that the current study uses multistage sampling and another similar
study has reported 91.5% response rate.
6 January 2023 13
Thank you
6 January 2023 14

More Related Content

What's hot

Cross sectional study
Cross sectional studyCross sectional study
Cross sectional study
Adugnagirma
 
Data, Distribution Introduction and Types - Biostatistics - Ravinandan A P.pdf
Data, Distribution  Introduction and Types - Biostatistics - Ravinandan A P.pdfData, Distribution  Introduction and Types - Biostatistics - Ravinandan A P.pdf
Data, Distribution Introduction and Types - Biostatistics - Ravinandan A P.pdf
Ravinandan A P
 
Epidata lecture note
Epidata lecture note Epidata lecture note
Epidata lecture note
Bahir Dar Univerisity
 
06 cohort studies
06 cohort studies06 cohort studies
06 cohort studies
Abdiwali Abdullahi Abdiwali
 
When to use, What Statistical Test for data Analysis modified.pptx
When to use, What Statistical Test for data Analysis modified.pptxWhen to use, What Statistical Test for data Analysis modified.pptx
When to use, What Statistical Test for data Analysis modified.pptx
Asokan R
 
Types of Statistics
Types of StatisticsTypes of Statistics
Types of Statistics
loranel
 
Clinical research (study designs)
Clinical research  (study designs)Clinical research  (study designs)
Clinical research (study designs)
Mohamed Fahmy Dehim
 
Protocol writing
Protocol writingProtocol writing
Protocol writing
Dr.Amreen Saba Attariya
 
Introduction to SAS
Introduction to SASIntroduction to SAS
Introduction to SAS
izahn
 
Survival analysis
Survival analysisSurvival analysis
Survival analysis
Har Jindal
 
Categorical data analysis
Categorical data analysisCategorical data analysis
Categorical data analysis
Sumit Das
 
Sas Statistical Analysis System
Sas Statistical Analysis SystemSas Statistical Analysis System
Sas Statistical Analysis System
Sushil kasar
 
Surveillance
SurveillanceSurveillance
Surveillance
anchal arora
 
Chapter 2.2 screening test
Chapter 2.2 screening testChapter 2.2 screening test
Chapter 2.2 screening test
Nilesh Kucha
 
Bias and errors
Bias and errorsBias and errors
Bias and errors
utpal sharma
 
Basic biostatistics dr.eezn
Basic biostatistics dr.eeznBasic biostatistics dr.eezn
Basic biostatistics dr.eezn
EhealthMoHS
 
Nested case control,
Nested case control,Nested case control,
Nested case control,
shefali jain
 
Nested case control study
Nested case control studyNested case control study
Nested case control study
Prayas Gautam
 
Health system research
Health system researchHealth system research
Health system research
Zainab&Sons
 
Inferential statistics
Inferential statisticsInferential statistics
Inferential statistics
Ashok Kulkarni
 

What's hot (20)

Cross sectional study
Cross sectional studyCross sectional study
Cross sectional study
 
Data, Distribution Introduction and Types - Biostatistics - Ravinandan A P.pdf
Data, Distribution  Introduction and Types - Biostatistics - Ravinandan A P.pdfData, Distribution  Introduction and Types - Biostatistics - Ravinandan A P.pdf
Data, Distribution Introduction and Types - Biostatistics - Ravinandan A P.pdf
 
Epidata lecture note
Epidata lecture note Epidata lecture note
Epidata lecture note
 
06 cohort studies
06 cohort studies06 cohort studies
06 cohort studies
 
When to use, What Statistical Test for data Analysis modified.pptx
When to use, What Statistical Test for data Analysis modified.pptxWhen to use, What Statistical Test for data Analysis modified.pptx
When to use, What Statistical Test for data Analysis modified.pptx
 
Types of Statistics
Types of StatisticsTypes of Statistics
Types of Statistics
 
Clinical research (study designs)
Clinical research  (study designs)Clinical research  (study designs)
Clinical research (study designs)
 
Protocol writing
Protocol writingProtocol writing
Protocol writing
 
Introduction to SAS
Introduction to SASIntroduction to SAS
Introduction to SAS
 
Survival analysis
Survival analysisSurvival analysis
Survival analysis
 
Categorical data analysis
Categorical data analysisCategorical data analysis
Categorical data analysis
 
Sas Statistical Analysis System
Sas Statistical Analysis SystemSas Statistical Analysis System
Sas Statistical Analysis System
 
Surveillance
SurveillanceSurveillance
Surveillance
 
Chapter 2.2 screening test
Chapter 2.2 screening testChapter 2.2 screening test
Chapter 2.2 screening test
 
Bias and errors
Bias and errorsBias and errors
Bias and errors
 
Basic biostatistics dr.eezn
Basic biostatistics dr.eeznBasic biostatistics dr.eezn
Basic biostatistics dr.eezn
 
Nested case control,
Nested case control,Nested case control,
Nested case control,
 
Nested case control study
Nested case control studyNested case control study
Nested case control study
 
Health system research
Health system researchHealth system research
Health system research
 
Inferential statistics
Inferential statisticsInferential statistics
Inferential statistics
 

Similar to Introduction to Epi Info.pdf

Data science 101
Data science 101Data science 101
Data science 101
University of West Florida
 
Pertemuan 3 & 4 - Pengendalian Mutu Statistik.pptx
Pertemuan 3 & 4 - Pengendalian Mutu Statistik.pptxPertemuan 3 & 4 - Pengendalian Mutu Statistik.pptx
Pertemuan 3 & 4 - Pengendalian Mutu Statistik.pptx
gigol12808
 
IRJET- Diabetes Diagnosis using Machine Learning Algorithms
IRJET- Diabetes Diagnosis using Machine Learning AlgorithmsIRJET- Diabetes Diagnosis using Machine Learning Algorithms
IRJET- Diabetes Diagnosis using Machine Learning Algorithms
IRJET Journal
 
josirias_IS205_MajorAssignment
josirias_IS205_MajorAssignmentjosirias_IS205_MajorAssignment
josirias_IS205_MajorAssignment
Joshua Sirias
 
Fundamentals of Data science Introduction Unit 1
Fundamentals of Data science Introduction Unit 1Fundamentals of Data science Introduction Unit 1
Fundamentals of Data science Introduction Unit 1
sasi
 
Introduction to data science
Introduction to data scienceIntroduction to data science
Introduction to data science
Subrata Saharia
 
Presentation1.pptx
Presentation1.pptxPresentation1.pptx
Presentation1.pptx
VishalLabde
 
Sampling Technique
Sampling TechniqueSampling Technique
Sampling Technique
Rajesh Narayanan
 
Users Approach on Providing Feedback for Smart Home Devices – Phase II
Users Approach on Providing Feedback for Smart Home Devices – Phase IIUsers Approach on Providing Feedback for Smart Home Devices – Phase II
Users Approach on Providing Feedback for Smart Home Devices – Phase II
ijujournal
 
Users Approach on Providing Feedback for Smart Home Devices – Phase II
Users Approach on Providing Feedback for Smart Home Devices – Phase IIUsers Approach on Providing Feedback for Smart Home Devices – Phase II
Users Approach on Providing Feedback for Smart Home Devices – Phase II
ijujournal
 
Zeroth review major project (1).pptx
Zeroth review major project (1).pptxZeroth review major project (1).pptx
Zeroth review major project (1).pptx
ShreyaBharadwaj7
 
analysis plan.ppt
analysis plan.pptanalysis plan.ppt
analysis plan.ppt
SamsonOlusinaBamiwuy
 
The High Quality Data Gathering System Essay
The High Quality Data Gathering System EssayThe High Quality Data Gathering System Essay
The High Quality Data Gathering System Essay
Divya Watson
 
Statistical analysis and Statistical process in 2023 .pptx
Statistical analysis and Statistical process in 2023 .pptxStatistical analysis and Statistical process in 2023 .pptx
Statistical analysis and Statistical process in 2023 .pptx
Fayaz Ahmad
 
Lecture-1-Introduction to Deep learning.pptx
Lecture-1-Introduction to Deep learning.pptxLecture-1-Introduction to Deep learning.pptx
Lecture-1-Introduction to Deep learning.pptx
JayChauhan100
 
Aed1222 lesson 1 and 3
Aed1222 lesson 1 and 3Aed1222 lesson 1 and 3
Aed1222 lesson 1 and 3
nurun2010
 
Cri big data
Cri big dataCri big data
Cri big data
Putchong Uthayopas
 
Descriptive Statistics and Interpretation Grading GuideQNT5.docx
Descriptive Statistics and Interpretation Grading GuideQNT5.docxDescriptive Statistics and Interpretation Grading GuideQNT5.docx
Descriptive Statistics and Interpretation Grading GuideQNT5.docx
theodorelove43763
 
DESIGN AND IMPLEMENTATION OF CARDIAC DISEASE USING NAIVE BAYES TECHNIQUE
DESIGN AND IMPLEMENTATION OF CARDIAC DISEASE USING NAIVE BAYES TECHNIQUEDESIGN AND IMPLEMENTATION OF CARDIAC DISEASE USING NAIVE BAYES TECHNIQUE
DESIGN AND IMPLEMENTATION OF CARDIAC DISEASE USING NAIVE BAYES TECHNIQUE
IRJET Journal
 
Week_2_Lecture.pdf
Week_2_Lecture.pdfWeek_2_Lecture.pdf
Week_2_Lecture.pdf
AlbertoLugoGonzalez
 

Similar to Introduction to Epi Info.pdf (20)

Data science 101
Data science 101Data science 101
Data science 101
 
Pertemuan 3 & 4 - Pengendalian Mutu Statistik.pptx
Pertemuan 3 & 4 - Pengendalian Mutu Statistik.pptxPertemuan 3 & 4 - Pengendalian Mutu Statistik.pptx
Pertemuan 3 & 4 - Pengendalian Mutu Statistik.pptx
 
IRJET- Diabetes Diagnosis using Machine Learning Algorithms
IRJET- Diabetes Diagnosis using Machine Learning AlgorithmsIRJET- Diabetes Diagnosis using Machine Learning Algorithms
IRJET- Diabetes Diagnosis using Machine Learning Algorithms
 
josirias_IS205_MajorAssignment
josirias_IS205_MajorAssignmentjosirias_IS205_MajorAssignment
josirias_IS205_MajorAssignment
 
Fundamentals of Data science Introduction Unit 1
Fundamentals of Data science Introduction Unit 1Fundamentals of Data science Introduction Unit 1
Fundamentals of Data science Introduction Unit 1
 
Introduction to data science
Introduction to data scienceIntroduction to data science
Introduction to data science
 
Presentation1.pptx
Presentation1.pptxPresentation1.pptx
Presentation1.pptx
 
Sampling Technique
Sampling TechniqueSampling Technique
Sampling Technique
 
Users Approach on Providing Feedback for Smart Home Devices – Phase II
Users Approach on Providing Feedback for Smart Home Devices – Phase IIUsers Approach on Providing Feedback for Smart Home Devices – Phase II
Users Approach on Providing Feedback for Smart Home Devices – Phase II
 
Users Approach on Providing Feedback for Smart Home Devices – Phase II
Users Approach on Providing Feedback for Smart Home Devices – Phase IIUsers Approach on Providing Feedback for Smart Home Devices – Phase II
Users Approach on Providing Feedback for Smart Home Devices – Phase II
 
Zeroth review major project (1).pptx
Zeroth review major project (1).pptxZeroth review major project (1).pptx
Zeroth review major project (1).pptx
 
analysis plan.ppt
analysis plan.pptanalysis plan.ppt
analysis plan.ppt
 
The High Quality Data Gathering System Essay
The High Quality Data Gathering System EssayThe High Quality Data Gathering System Essay
The High Quality Data Gathering System Essay
 
Statistical analysis and Statistical process in 2023 .pptx
Statistical analysis and Statistical process in 2023 .pptxStatistical analysis and Statistical process in 2023 .pptx
Statistical analysis and Statistical process in 2023 .pptx
 
Lecture-1-Introduction to Deep learning.pptx
Lecture-1-Introduction to Deep learning.pptxLecture-1-Introduction to Deep learning.pptx
Lecture-1-Introduction to Deep learning.pptx
 
Aed1222 lesson 1 and 3
Aed1222 lesson 1 and 3Aed1222 lesson 1 and 3
Aed1222 lesson 1 and 3
 
Cri big data
Cri big dataCri big data
Cri big data
 
Descriptive Statistics and Interpretation Grading GuideQNT5.docx
Descriptive Statistics and Interpretation Grading GuideQNT5.docxDescriptive Statistics and Interpretation Grading GuideQNT5.docx
Descriptive Statistics and Interpretation Grading GuideQNT5.docx
 
DESIGN AND IMPLEMENTATION OF CARDIAC DISEASE USING NAIVE BAYES TECHNIQUE
DESIGN AND IMPLEMENTATION OF CARDIAC DISEASE USING NAIVE BAYES TECHNIQUEDESIGN AND IMPLEMENTATION OF CARDIAC DISEASE USING NAIVE BAYES TECHNIQUE
DESIGN AND IMPLEMENTATION OF CARDIAC DISEASE USING NAIVE BAYES TECHNIQUE
 
Week_2_Lecture.pdf
Week_2_Lecture.pdfWeek_2_Lecture.pdf
Week_2_Lecture.pdf
 

More from Yomif3

Neonatal TETANUS (1).pptx
Neonatal TETANUS (1).pptxNeonatal TETANUS (1).pptx
Neonatal TETANUS (1).pptx
Yomif3
 
8. Acute Rheumatic fever (1).ppt
8.  Acute Rheumatic fever (1).ppt8.  Acute Rheumatic fever (1).ppt
8. Acute Rheumatic fever (1).ppt
Yomif3
 
TORCH Infections.pptx
TORCH Infections.pptxTORCH Infections.pptx
TORCH Infections.pptx
Yomif3
 
neurology (1).pptx
neurology (1).pptxneurology (1).pptx
neurology (1).pptx
Yomif3
 
Pedi HIV (1).pptx
Pedi HIV (1).pptxPedi HIV (1).pptx
Pedi HIV (1).pptx
Yomif3
 
Meningitis and Encephalitis.pptx
Meningitis and Encephalitis.pptxMeningitis and Encephalitis.pptx
Meningitis and Encephalitis.pptx
Yomif3
 
Female Reproductive m3.ppt
Female Reproductive m3.pptFemale Reproductive m3.ppt
Female Reproductive m3.ppt
Yomif3
 
Male Reproductive m3 (2).ppt
Male Reproductive m3 (2).pptMale Reproductive m3 (2).ppt
Male Reproductive m3 (2).ppt
Yomif3
 
Tissue renewal and healing.pptx
Tissue renewal and healing.pptxTissue renewal and healing.pptx
Tissue renewal and healing.pptx
Yomif3
 
Introduction to pathology.pptx
Introduction to pathology.pptxIntroduction to pathology.pptx
Introduction to pathology.pptx
Yomif3
 
Inflammation (2).pptx
Inflammation (2).pptxInflammation (2).pptx
Inflammation (2).pptx
Yomif3
 
neoplasia 2 (2).ppt
neoplasia 2 (2).pptneoplasia 2 (2).ppt
neoplasia 2 (2).ppt
Yomif3
 
Introduction to STATA(2).pdf
Introduction to STATA(2).pdfIntroduction to STATA(2).pdf
Introduction to STATA(2).pdf
Yomif3
 
1 Introduction to SPSS.pdf
1 Introduction to SPSS.pdf1 Introduction to SPSS.pdf
1 Introduction to SPSS.pdf
Yomif3
 

More from Yomif3 (14)

Neonatal TETANUS (1).pptx
Neonatal TETANUS (1).pptxNeonatal TETANUS (1).pptx
Neonatal TETANUS (1).pptx
 
8. Acute Rheumatic fever (1).ppt
8.  Acute Rheumatic fever (1).ppt8.  Acute Rheumatic fever (1).ppt
8. Acute Rheumatic fever (1).ppt
 
TORCH Infections.pptx
TORCH Infections.pptxTORCH Infections.pptx
TORCH Infections.pptx
 
neurology (1).pptx
neurology (1).pptxneurology (1).pptx
neurology (1).pptx
 
Pedi HIV (1).pptx
Pedi HIV (1).pptxPedi HIV (1).pptx
Pedi HIV (1).pptx
 
Meningitis and Encephalitis.pptx
Meningitis and Encephalitis.pptxMeningitis and Encephalitis.pptx
Meningitis and Encephalitis.pptx
 
Female Reproductive m3.ppt
Female Reproductive m3.pptFemale Reproductive m3.ppt
Female Reproductive m3.ppt
 
Male Reproductive m3 (2).ppt
Male Reproductive m3 (2).pptMale Reproductive m3 (2).ppt
Male Reproductive m3 (2).ppt
 
Tissue renewal and healing.pptx
Tissue renewal and healing.pptxTissue renewal and healing.pptx
Tissue renewal and healing.pptx
 
Introduction to pathology.pptx
Introduction to pathology.pptxIntroduction to pathology.pptx
Introduction to pathology.pptx
 
Inflammation (2).pptx
Inflammation (2).pptxInflammation (2).pptx
Inflammation (2).pptx
 
neoplasia 2 (2).ppt
neoplasia 2 (2).pptneoplasia 2 (2).ppt
neoplasia 2 (2).ppt
 
Introduction to STATA(2).pdf
Introduction to STATA(2).pdfIntroduction to STATA(2).pdf
Introduction to STATA(2).pdf
 
1 Introduction to SPSS.pdf
1 Introduction to SPSS.pdf1 Introduction to SPSS.pdf
1 Introduction to SPSS.pdf
 

Recently uploaded

Palo Alto Cortex XDR presentation .......
Palo Alto Cortex XDR presentation .......Palo Alto Cortex XDR presentation .......
Palo Alto Cortex XDR presentation .......
Sachin Paul
 
End-to-end pipeline agility - Berlin Buzzwords 2024
End-to-end pipeline agility - Berlin Buzzwords 2024End-to-end pipeline agility - Berlin Buzzwords 2024
End-to-end pipeline agility - Berlin Buzzwords 2024
Lars Albertsson
 
Udemy_2024_Global_Learning_Skills_Trends_Report (1).pdf
Udemy_2024_Global_Learning_Skills_Trends_Report (1).pdfUdemy_2024_Global_Learning_Skills_Trends_Report (1).pdf
Udemy_2024_Global_Learning_Skills_Trends_Report (1).pdf
Fernanda Palhano
 
A presentation that explain the Power BI Licensing
A presentation that explain the Power BI LicensingA presentation that explain the Power BI Licensing
A presentation that explain the Power BI Licensing
AlessioFois2
 
一比一原版(Glasgow毕业证书)格拉斯哥大学毕业证如何办理
一比一原版(Glasgow毕业证书)格拉斯哥大学毕业证如何办理一比一原版(Glasgow毕业证书)格拉斯哥大学毕业证如何办理
一比一原版(Glasgow毕业证书)格拉斯哥大学毕业证如何办理
g4dpvqap0
 
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
Walaa Eldin Moustafa
 
一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理
一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理
一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理
nuttdpt
 
DSSML24_tspann_CodelessGenerativeAIPipelines
DSSML24_tspann_CodelessGenerativeAIPipelinesDSSML24_tspann_CodelessGenerativeAIPipelines
DSSML24_tspann_CodelessGenerativeAIPipelines
Timothy Spann
 
The Building Blocks of QuestDB, a Time Series Database
The Building Blocks of QuestDB, a Time Series DatabaseThe Building Blocks of QuestDB, a Time Series Database
The Building Blocks of QuestDB, a Time Series Database
javier ramirez
 
Experts live - Improving user adoption with AI
Experts live - Improving user adoption with AIExperts live - Improving user adoption with AI
Experts live - Improving user adoption with AI
jitskeb
 
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
Timothy Spann
 
一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理
一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理
一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理
g4dpvqap0
 
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
Aggregage
 
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
Timothy Spann
 
一比一原版(Harvard毕业证书)哈佛大学毕业证如何办理
一比一原版(Harvard毕业证书)哈佛大学毕业证如何办理一比一原版(Harvard毕业证书)哈佛大学毕业证如何办理
一比一原版(Harvard毕业证书)哈佛大学毕业证如何办理
zsjl4mimo
 
一比一原版(UCSF文凭证书)旧金山分校毕业证如何办理
一比一原版(UCSF文凭证书)旧金山分校毕业证如何办理一比一原版(UCSF文凭证书)旧金山分校毕业证如何办理
一比一原版(UCSF文凭证书)旧金山分校毕业证如何办理
nuttdpt
 
Intelligence supported media monitoring in veterinary medicine
Intelligence supported media monitoring in veterinary medicineIntelligence supported media monitoring in veterinary medicine
Intelligence supported media monitoring in veterinary medicine
AndrzejJarynowski
 
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataPredictably Improve Your B2B Tech Company's Performance by Leveraging Data
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
Kiwi Creative
 
Analysis insight about a Flyball dog competition team's performance
Analysis insight about a Flyball dog competition team's performanceAnalysis insight about a Flyball dog competition team's performance
Analysis insight about a Flyball dog competition team's performance
roli9797
 
一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理
一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理
一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理
nyfuhyz
 

Recently uploaded (20)

Palo Alto Cortex XDR presentation .......
Palo Alto Cortex XDR presentation .......Palo Alto Cortex XDR presentation .......
Palo Alto Cortex XDR presentation .......
 
End-to-end pipeline agility - Berlin Buzzwords 2024
End-to-end pipeline agility - Berlin Buzzwords 2024End-to-end pipeline agility - Berlin Buzzwords 2024
End-to-end pipeline agility - Berlin Buzzwords 2024
 
Udemy_2024_Global_Learning_Skills_Trends_Report (1).pdf
Udemy_2024_Global_Learning_Skills_Trends_Report (1).pdfUdemy_2024_Global_Learning_Skills_Trends_Report (1).pdf
Udemy_2024_Global_Learning_Skills_Trends_Report (1).pdf
 
A presentation that explain the Power BI Licensing
A presentation that explain the Power BI LicensingA presentation that explain the Power BI Licensing
A presentation that explain the Power BI Licensing
 
一比一原版(Glasgow毕业证书)格拉斯哥大学毕业证如何办理
一比一原版(Glasgow毕业证书)格拉斯哥大学毕业证如何办理一比一原版(Glasgow毕业证书)格拉斯哥大学毕业证如何办理
一比一原版(Glasgow毕业证书)格拉斯哥大学毕业证如何办理
 
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
 
一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理
一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理
一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理
 
DSSML24_tspann_CodelessGenerativeAIPipelines
DSSML24_tspann_CodelessGenerativeAIPipelinesDSSML24_tspann_CodelessGenerativeAIPipelines
DSSML24_tspann_CodelessGenerativeAIPipelines
 
The Building Blocks of QuestDB, a Time Series Database
The Building Blocks of QuestDB, a Time Series DatabaseThe Building Blocks of QuestDB, a Time Series Database
The Building Blocks of QuestDB, a Time Series Database
 
Experts live - Improving user adoption with AI
Experts live - Improving user adoption with AIExperts live - Improving user adoption with AI
Experts live - Improving user adoption with AI
 
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
 
一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理
一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理
一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理
 
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
 
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
 
一比一原版(Harvard毕业证书)哈佛大学毕业证如何办理
一比一原版(Harvard毕业证书)哈佛大学毕业证如何办理一比一原版(Harvard毕业证书)哈佛大学毕业证如何办理
一比一原版(Harvard毕业证书)哈佛大学毕业证如何办理
 
一比一原版(UCSF文凭证书)旧金山分校毕业证如何办理
一比一原版(UCSF文凭证书)旧金山分校毕业证如何办理一比一原版(UCSF文凭证书)旧金山分校毕业证如何办理
一比一原版(UCSF文凭证书)旧金山分校毕业证如何办理
 
Intelligence supported media monitoring in veterinary medicine
Intelligence supported media monitoring in veterinary medicineIntelligence supported media monitoring in veterinary medicine
Intelligence supported media monitoring in veterinary medicine
 
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataPredictably Improve Your B2B Tech Company's Performance by Leveraging Data
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
 
Analysis insight about a Flyball dog competition team's performance
Analysis insight about a Flyball dog competition team's performanceAnalysis insight about a Flyball dog competition team's performance
Analysis insight about a Flyball dog competition team's performance
 
一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理
一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理
一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理
 

Introduction to Epi Info.pdf

  • 1. Introduction to Epi Info Emiru Merdassa 6 January 2023 1
  • 2. 2 –Overview of EPI-Info –Data entry using EPI-INFO –Sample size determination Outline 6 January 2023
  • 3. 3 • Epi Info is a public domain database and statistics program. – Develop an electronic data entry form – Customize the data entry process – Enter data, and analyze the data – Statistics, graphs, tables, and maps can be produced with simple commands – Epi Info is free, downloadable software provided by the CDC (www.cdc.gov/epiinfo) Introduction 6 January 2023
  • 4. 4 • Make view – For designing an electronic data entry form which automatically creates a data table. • Enter data – For entering data into the designed electronic data entry form. • Analyze Data – For conducting a relatively wide range of statistical analysis of the data. Main applications in Epi Info 6 January 2023
  • 5. The Epi-Info 7 Main Menu 6 January 2023 5
  • 6. Epi Info – Core Principles –Free –Easy to use –Flexible –Standards based –No “IT guys” needed in most cases 6 January 2023 6
  • 7. Designing a Form ❑ The Form Designer has several “work areas” ▪ The Menu – The toolbar contains buttons for creating projects, editing the form’s check code, going to the data entry module, and undo/redo. ▪ The Project Explorer is where you can add and remove forms from your project, add, edit, and remove pages from individual forms, and work with templates. ▪ The Canvas is where fields are placed, moved, and edited.
  • 8. Designing a Form 1. The Menu 2. The Project Explorer 3. The Canvas
  • 9. Creating question/prompt Choice one of the property for the variable 9 6 January 2023
  • 10. Sample Size determination using Epi Info • Step 1: Go to the main menu at the top left corner and click on StatCalc and subsequent options 6 January 2023 10
  • 11. Sample size determination • Fill the components necessary to determine sample size for the three case scenarios: Population survey, Unmatched case control, and Cross-sectional or Cohort study designs. 6 January 2023 11
  • 12. Exercise Let us assume the population that we want to conduct the study has a target population of size N=100,000, and the proportion of the variable of interest is not known which means there is no previous study done and hence we decided to use 50 percent as an estimate of the prevalence for that variable. a) What would be the sample size required if the sampling technique was simple random sampling? b) What will be the sample size if the sampling technique is two stage sampling and is also expected to have 10% non-response rate? 6 January 2023 12
  • 13. Exercise • The prevalence of under weight of newborns is compared between two regions. In one region, it is estimated that about 30% of them could be under weight. In other region it is probably 15%. If the required sample is to show with a 90% likelihood (power) that the percentage of newborns is different in these two regions at 95% confidence level, what would be the sample size? Suppose that the current study uses multistage sampling and another similar study has reported 91.5% response rate. 6 January 2023 13