This document provides guidance on data presentation and interpretation for program monitoring and evaluation. It covers choosing appropriate tables and graphs to summarize different types of data, as well as best practices for labeling and interpreting data visually. The key lessons are: use the right type of graph or table to clearly display the data; interpret findings by considering their relevance, potential causes, other relevant data sources, and need for further research; and service data on their own do not show causality, but can help track progress and identify issues.
The document discusses public health data analysis. It defines vital statistics and their importance in public health. Key indicators of vital statistics are described like maternal mortality ratio. Methods of data analysis like descriptive analysis and key concepts such as ratio, proportion, and rates are explained. The importance of properly presenting and communicating data through tables, charts and graphs is highlighted. Interpreting findings and conducting further research is also emphasized. Advances in data collection and analytics can help predict health trends but still has limitations.
A series of modules on project cycle, planning and the logical framework, aimed at team leaders of international NGOs in developing countries.
Part 8 of 11
Statistics is the study of collecting, organizing, summarizing, and interpreting data. Medical statistics applies statistical methods to medical data and research. Biostatistics specifically applies statistical methods to biological data. Statistics is essential for medical research, updating medical knowledge, data management, describing research findings, and evaluating health programs. It allows comparison of populations, risks, treatments, and more.
biostatstics :Type and presentation of datanaresh gill
The document provides an overview of different types of data and methods for presenting data. It discusses qualitative vs quantitative data, primary vs secondary data, and different ways to present data visually including bar charts, histograms, frequency polygons, scatter diagrams, line diagrams and pie charts. Guidelines are provided for tabular presentation of data to make it clear, concise and easy to understand.
This document provides an overview of descriptive statistics as taught in a statistics course (STS 102) at Crescent University, Nigeria. It covers topics like statistical data collection methods, presentation of data through tables and graphs, measures of central tendency and dispersion. The key objectives of descriptive statistics are to summarize and describe characteristics of data through measures, charts and diagrams. Inferential statistics is also introduced as a way to make inferences about populations based on samples.
The document describes a Data Quality Review (DQR) framework for assessing the quality of health facility data. The DQR is a multi-pronged approach that builds on previous data quality assessment tools to evaluate data quality in a more systematic way. It includes a desk review of existing health facility data and indicators as well as facility surveys to verify data accuracy and assess health management information systems. The DQR framework provides a standardized method for stakeholders to evaluate routine health data quality and link these findings to health sector planning activities.
This document discusses indicators, data sources, and data quality for tuberculosis (TB) monitoring and evaluation. It outlines criteria for good indicators, such as being valid, reliable, specific, and sensitive. Both qualitative and quantitative indicators are described. Factors for selecting indicators include what different levels need to know, available data and resources. Routine TB data collection methods discussed include registers, reports, process monitoring, and special surveys. Ensuring high quality data is important for effective decision making, and standards like validity, integrity, precision, reliability and timeliness are outlined. Common impediments to data quality and steps to improve it, like training and supervision, are also reviewed.
The document summarizes key concepts from Chapter 1 of the textbook "Elementary Statistics" including:
- The difference between a population and a sample, and how statistics uses samples to make inferences about populations.
- The different types of data: quantitative, categorical, discrete vs. continuous data.
- The different levels of measurement for data: nominal, ordinal, interval, and ratio.
- The importance of critical thinking when analyzing data and statistics, including considering context, sources, sampling methods, and avoiding misleading graphs, samples, conclusions, or survey questions.
The document discusses public health data analysis. It defines vital statistics and their importance in public health. Key indicators of vital statistics are described like maternal mortality ratio. Methods of data analysis like descriptive analysis and key concepts such as ratio, proportion, and rates are explained. The importance of properly presenting and communicating data through tables, charts and graphs is highlighted. Interpreting findings and conducting further research is also emphasized. Advances in data collection and analytics can help predict health trends but still has limitations.
A series of modules on project cycle, planning and the logical framework, aimed at team leaders of international NGOs in developing countries.
Part 8 of 11
Statistics is the study of collecting, organizing, summarizing, and interpreting data. Medical statistics applies statistical methods to medical data and research. Biostatistics specifically applies statistical methods to biological data. Statistics is essential for medical research, updating medical knowledge, data management, describing research findings, and evaluating health programs. It allows comparison of populations, risks, treatments, and more.
biostatstics :Type and presentation of datanaresh gill
The document provides an overview of different types of data and methods for presenting data. It discusses qualitative vs quantitative data, primary vs secondary data, and different ways to present data visually including bar charts, histograms, frequency polygons, scatter diagrams, line diagrams and pie charts. Guidelines are provided for tabular presentation of data to make it clear, concise and easy to understand.
This document provides an overview of descriptive statistics as taught in a statistics course (STS 102) at Crescent University, Nigeria. It covers topics like statistical data collection methods, presentation of data through tables and graphs, measures of central tendency and dispersion. The key objectives of descriptive statistics are to summarize and describe characteristics of data through measures, charts and diagrams. Inferential statistics is also introduced as a way to make inferences about populations based on samples.
The document describes a Data Quality Review (DQR) framework for assessing the quality of health facility data. The DQR is a multi-pronged approach that builds on previous data quality assessment tools to evaluate data quality in a more systematic way. It includes a desk review of existing health facility data and indicators as well as facility surveys to verify data accuracy and assess health management information systems. The DQR framework provides a standardized method for stakeholders to evaluate routine health data quality and link these findings to health sector planning activities.
This document discusses indicators, data sources, and data quality for tuberculosis (TB) monitoring and evaluation. It outlines criteria for good indicators, such as being valid, reliable, specific, and sensitive. Both qualitative and quantitative indicators are described. Factors for selecting indicators include what different levels need to know, available data and resources. Routine TB data collection methods discussed include registers, reports, process monitoring, and special surveys. Ensuring high quality data is important for effective decision making, and standards like validity, integrity, precision, reliability and timeliness are outlined. Common impediments to data quality and steps to improve it, like training and supervision, are also reviewed.
The document summarizes key concepts from Chapter 1 of the textbook "Elementary Statistics" including:
- The difference between a population and a sample, and how statistics uses samples to make inferences about populations.
- The different types of data: quantitative, categorical, discrete vs. continuous data.
- The different levels of measurement for data: nominal, ordinal, interval, and ratio.
- The importance of critical thinking when analyzing data and statistics, including considering context, sources, sampling methods, and avoiding misleading graphs, samples, conclusions, or survey questions.
This document provides an overview of key concepts from Chapter 1 of the textbook "Elementary Statistics". It defines important statistical terms like population, sample, parameter, and statistic. It also distinguishes between different types of data and levels of measurement. Additionally, it discusses the importance of collecting sample data through appropriate random sampling methods. Critical thinking in statistics is emphasized, highlighting factors like the context, source, and sampling method of data when evaluating statistical claims. Different ways of collecting data through studies and experiments are also introduced.
This document provides an overview of data collection methods for research. It discusses:
1) The importance of data collection as the process of gathering information to answer research questions. Both qualitative and quantitative methods are described.
2) Common qualitative methods include interviews, observations, documents, and focus groups. Quantitative methods involve surveys, questionnaires, and probability sampling.
3) The document outlines different ways to present collected data, including tables, bar charts, pie charts, scatter plots, and line graphs. These visual representations make complex data easier to understand.
4) The final sections discuss how data science can help solve problems and briefly compare social science and science approaches to data collection.
The document provides guidance on effectively summarizing data through tables and graphs. It outlines key principles such as using the appropriate type of graphic for the data, clearly labeling all components, and indicating the source and sample size. The goal is to present information in a clear, concise and visually compelling manner for the intended audience. Examples are given of different types of tables and graphs and how to properly construct and interpret them.
Data:
A set of values recorded on one or more observational units i.e. Object, person etc
Types of data:
Qualitative/ Quantitative data
Discrete/ Continuous data
Primary/ Secondary data
Nominal/ Ordinal data
Session 1 Presentation for Volume 1 Service Providers Manual Introduction HMI...CharanjitBasumatary
HMIS is a tool that helps gather, analyze, and use health information to improve health systems performance. It ensures a continuous flow of quality disaggregated health and healthcare services data to assist in local planning, implementation, management, monitoring and evaluation.
Data should be recorded in primary registers during service delivery and aggregated monthly into reporting formats. Each data element should only be entered once to reduce burden and errors. Data flows from facilities to Block, District, State and National levels. Knowledge is created when information is analyzed, communicated and acted upon. Indicators are used to convert data into meaningful information and measure progress toward targets.
This document provides an introduction to statistics, including definitions, reasons for studying statistics, and the scope and importance of statistics. It discusses how statistics is used in fields like insurance, medicine, administration, banking, agriculture, business, and sciences. It also outlines the main functions of statistics and its branches, including theoretical, descriptive, inferential, and applied statistics. Finally, it covers topics related to data representation, including methods of presenting data through tables, graphs, and diagrams.
The document provides an introduction to statistical concepts, explaining that statistics is used to extract useful information from data to help with decision making. It discusses different types of data, variables, methods of data collection and quality, as well as statistical analysis techniques including descriptive statistics, inferential statistics, frequency distributions, graphs and charts. The goal of statistics is to summarize and analyze data to draw conclusions and make informed business decisions.
This document provides an introduction to quantitative methods and statistics. It defines statistics as the science of collecting, organizing, presenting, analyzing and interpreting data to assist in decision making. It outlines descriptive and inferential statistics, and describes variables, levels of measurement, characteristics of statistical data, uses of statistics, and limitations of statistics. It also discusses topics such as frequency distributions, measures of central tendency including the mean, median and mode, and measures of dispersion.
This document provides an introduction and overview of biostatistics. It defines key biostatistics terms like population, sample, parameter, statistic, quantitative vs. qualitative data, levels of measurement, descriptive vs. inferential biostatistics, and common statistical notations. It also discusses sources of health information and how computerized health management information systems are used to collect, analyze and report data.
This document discusses principles and methods for presenting data. It outlines that data should be arranged concisely to arouse interest while retaining important details. The two basic methods of presentation are tabulation and charts/diagrams. Tabulation involves organizing data in tables which should be clearly labeled and structured. Charts and diagrams provide visual summaries and allow comparisons, though some detail is lost. Common types include bar charts, histograms, scatter plots and cumulative frequency diagrams. Proper formatting and scaling is important to effectively convey patterns and relationships in the data.
This document provides an introduction to statistics and data collection methods. It discusses key concepts such as:
1. The difference between economic and non-economic activities, and definitions of common economic roles like consumers, producers, service holders and service providers.
2. The stages of collecting statistical data, including primary and secondary sources, methods of collecting primary data, and the differences between primary and secondary data.
3. Methods of organizing raw data through classification, frequency distributions, and other statistical techniques. Common approaches to presenting organized data are also outlined, including tables, diagrams and graphs.
4. Sampling methods like census surveys and sample surveys are introduced, along with the differences between them. Key organizations involved in
This document provides an overview of statistics as a field of study. It defines statistics as both the plural and singular form, describing aggregates of numerical data and the science dealing with collecting, organizing, and interpreting numerical data. The two main branches of statistics are described as descriptive statistics, which describes what is occurring in a data set, and inferential statistics, which allows making generalizations about a larger population based on a sample. Key terms like data, variables, population, sample, and parameter are also defined. The stages of a statistical investigation and applications, uses, and limitations of statistics are summarized.
This document provides an introduction to statistics, including definitions, scopes, and types. It discusses descriptive statistics which summarizes data, inferential statistics which draws conclusions from samples, and applied statistics which uses theoretical statistics to solve practical problems. It also outlines scales of measurement, types of data like primary and secondary, and different kinds of data like time series, cross-sectional, and panel data. Finally, it defines population as the total group under study.
Assignment 2 RA Annotated BibliographyIn your final paper for .docxjosephinepaterson7611
This document provides information about descriptive statistics and how to calculate various descriptive statistics measures. It defines four types of measurement data: nominal, ordinal, interval, and ratio data. It then explains how to calculate and interpret the mean, median, mode, variability measures including range, variance and standard deviation. Examples are provided to demonstrate calculating these descriptive statistics on sets of sample data. The document emphasizes that descriptive statistics alone cannot be used to draw conclusions, but rather just describe patterns in the data.
Statistics is the science of collecting, organizing, summarizing, presenting, analyzing, and drawing conclusions from data. It involves methods for planning experiments, obtaining data, and making decisions based on data. There are two main types of statistics: descriptive statistics which summarize and describe data, and inferential statistics which are used to draw conclusions about populations based on sample data. Statistics is widely used in fields like business, engineering, economics, and sports to make data-driven decisions.
Analysis of statistical data in heath information managementSaleh Ahmed
This document discusses analysis of statistical data in health information management. It defines key terms like statistics, descriptive statistics, inferential statistics. It describes the different types of health statistics including vital statistics, morbidity statistics, and health service statistics. It also discusses how to calculate rates like crude rates and specific rates that are important measures for analyzing health data. Finally, it covers different methods for presenting statistical data, including tables, graphs, pie charts and histograms. The overall aim is to emphasize the importance of properly collecting, analyzing and presenting health statistics for effective healthcare planning and decision making.
1) Statistics is the study of collecting, organizing, analyzing, and drawing conclusions from data. It involves sampling, hypothesis testing, and using statistical tests tailored to measurement scales and hypothesis types.
2) Descriptive statistics describe and summarize data quantitatively, while inferential statistics allow generalizing from samples to populations through statistical testing and other methods.
3) The document discusses differences between statistics and statistical data, types of data, levels of measurement, sampling techniques, and uses of statistics.
This chapter introduces the basic concepts and terminology of statistics. It discusses two main branches of statistics - descriptive statistics which involves collecting, organizing and summarizing data, and inferential statistics which allows drawing conclusions about populations from samples. The chapter also covers variables, populations, samples, parameters, statistics and how to organize and visualize data through tables, charts and graphs. It emphasizes that statistics helps turn data into useful information for decision making in business.
This document provides an introduction to statistics and its uses in business. It outlines two main branches of statistics - descriptive statistics which involves collecting, summarizing and presenting data, and inferential statistics which uses data from a sample to draw conclusions about a larger population. The document then discusses key statistical concepts like variables, data, populations, samples, parameters and statistics. It explains how descriptive and inferential statistics are used to summarize data, draw conclusions, make forecasts and improve business processes. Finally, it introduces the DCOVA process for examining and concluding from data which involves defining variables, collecting data, organizing data, visualizing data and analyzing data.
The document discusses methods for assessing the quality of health surveillance data used to monitor disease trends and inform public health programs and policies. It describes key factors that can impact data quality, such as changes in case finding efforts, recording and reporting systems, and case definitions. The document outlines indicators and analytical approaches that can help identify issues with completeness, consistency, and reliability of notification data over time and across regions. This includes checks for unusual fluctuations, variations in notification rates, and consistency of case type proportions. The next steps proposed are to establish data quality review units, conduct in-depth analyses guided by quality checks, and develop online platforms to share best practices.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
This document provides an overview of key concepts from Chapter 1 of the textbook "Elementary Statistics". It defines important statistical terms like population, sample, parameter, and statistic. It also distinguishes between different types of data and levels of measurement. Additionally, it discusses the importance of collecting sample data through appropriate random sampling methods. Critical thinking in statistics is emphasized, highlighting factors like the context, source, and sampling method of data when evaluating statistical claims. Different ways of collecting data through studies and experiments are also introduced.
This document provides an overview of data collection methods for research. It discusses:
1) The importance of data collection as the process of gathering information to answer research questions. Both qualitative and quantitative methods are described.
2) Common qualitative methods include interviews, observations, documents, and focus groups. Quantitative methods involve surveys, questionnaires, and probability sampling.
3) The document outlines different ways to present collected data, including tables, bar charts, pie charts, scatter plots, and line graphs. These visual representations make complex data easier to understand.
4) The final sections discuss how data science can help solve problems and briefly compare social science and science approaches to data collection.
The document provides guidance on effectively summarizing data through tables and graphs. It outlines key principles such as using the appropriate type of graphic for the data, clearly labeling all components, and indicating the source and sample size. The goal is to present information in a clear, concise and visually compelling manner for the intended audience. Examples are given of different types of tables and graphs and how to properly construct and interpret them.
Data:
A set of values recorded on one or more observational units i.e. Object, person etc
Types of data:
Qualitative/ Quantitative data
Discrete/ Continuous data
Primary/ Secondary data
Nominal/ Ordinal data
Session 1 Presentation for Volume 1 Service Providers Manual Introduction HMI...CharanjitBasumatary
HMIS is a tool that helps gather, analyze, and use health information to improve health systems performance. It ensures a continuous flow of quality disaggregated health and healthcare services data to assist in local planning, implementation, management, monitoring and evaluation.
Data should be recorded in primary registers during service delivery and aggregated monthly into reporting formats. Each data element should only be entered once to reduce burden and errors. Data flows from facilities to Block, District, State and National levels. Knowledge is created when information is analyzed, communicated and acted upon. Indicators are used to convert data into meaningful information and measure progress toward targets.
This document provides an introduction to statistics, including definitions, reasons for studying statistics, and the scope and importance of statistics. It discusses how statistics is used in fields like insurance, medicine, administration, banking, agriculture, business, and sciences. It also outlines the main functions of statistics and its branches, including theoretical, descriptive, inferential, and applied statistics. Finally, it covers topics related to data representation, including methods of presenting data through tables, graphs, and diagrams.
The document provides an introduction to statistical concepts, explaining that statistics is used to extract useful information from data to help with decision making. It discusses different types of data, variables, methods of data collection and quality, as well as statistical analysis techniques including descriptive statistics, inferential statistics, frequency distributions, graphs and charts. The goal of statistics is to summarize and analyze data to draw conclusions and make informed business decisions.
This document provides an introduction to quantitative methods and statistics. It defines statistics as the science of collecting, organizing, presenting, analyzing and interpreting data to assist in decision making. It outlines descriptive and inferential statistics, and describes variables, levels of measurement, characteristics of statistical data, uses of statistics, and limitations of statistics. It also discusses topics such as frequency distributions, measures of central tendency including the mean, median and mode, and measures of dispersion.
This document provides an introduction and overview of biostatistics. It defines key biostatistics terms like population, sample, parameter, statistic, quantitative vs. qualitative data, levels of measurement, descriptive vs. inferential biostatistics, and common statistical notations. It also discusses sources of health information and how computerized health management information systems are used to collect, analyze and report data.
This document discusses principles and methods for presenting data. It outlines that data should be arranged concisely to arouse interest while retaining important details. The two basic methods of presentation are tabulation and charts/diagrams. Tabulation involves organizing data in tables which should be clearly labeled and structured. Charts and diagrams provide visual summaries and allow comparisons, though some detail is lost. Common types include bar charts, histograms, scatter plots and cumulative frequency diagrams. Proper formatting and scaling is important to effectively convey patterns and relationships in the data.
This document provides an introduction to statistics and data collection methods. It discusses key concepts such as:
1. The difference between economic and non-economic activities, and definitions of common economic roles like consumers, producers, service holders and service providers.
2. The stages of collecting statistical data, including primary and secondary sources, methods of collecting primary data, and the differences between primary and secondary data.
3. Methods of organizing raw data through classification, frequency distributions, and other statistical techniques. Common approaches to presenting organized data are also outlined, including tables, diagrams and graphs.
4. Sampling methods like census surveys and sample surveys are introduced, along with the differences between them. Key organizations involved in
This document provides an overview of statistics as a field of study. It defines statistics as both the plural and singular form, describing aggregates of numerical data and the science dealing with collecting, organizing, and interpreting numerical data. The two main branches of statistics are described as descriptive statistics, which describes what is occurring in a data set, and inferential statistics, which allows making generalizations about a larger population based on a sample. Key terms like data, variables, population, sample, and parameter are also defined. The stages of a statistical investigation and applications, uses, and limitations of statistics are summarized.
This document provides an introduction to statistics, including definitions, scopes, and types. It discusses descriptive statistics which summarizes data, inferential statistics which draws conclusions from samples, and applied statistics which uses theoretical statistics to solve practical problems. It also outlines scales of measurement, types of data like primary and secondary, and different kinds of data like time series, cross-sectional, and panel data. Finally, it defines population as the total group under study.
Assignment 2 RA Annotated BibliographyIn your final paper for .docxjosephinepaterson7611
This document provides information about descriptive statistics and how to calculate various descriptive statistics measures. It defines four types of measurement data: nominal, ordinal, interval, and ratio data. It then explains how to calculate and interpret the mean, median, mode, variability measures including range, variance and standard deviation. Examples are provided to demonstrate calculating these descriptive statistics on sets of sample data. The document emphasizes that descriptive statistics alone cannot be used to draw conclusions, but rather just describe patterns in the data.
Statistics is the science of collecting, organizing, summarizing, presenting, analyzing, and drawing conclusions from data. It involves methods for planning experiments, obtaining data, and making decisions based on data. There are two main types of statistics: descriptive statistics which summarize and describe data, and inferential statistics which are used to draw conclusions about populations based on sample data. Statistics is widely used in fields like business, engineering, economics, and sports to make data-driven decisions.
Analysis of statistical data in heath information managementSaleh Ahmed
This document discusses analysis of statistical data in health information management. It defines key terms like statistics, descriptive statistics, inferential statistics. It describes the different types of health statistics including vital statistics, morbidity statistics, and health service statistics. It also discusses how to calculate rates like crude rates and specific rates that are important measures for analyzing health data. Finally, it covers different methods for presenting statistical data, including tables, graphs, pie charts and histograms. The overall aim is to emphasize the importance of properly collecting, analyzing and presenting health statistics for effective healthcare planning and decision making.
1) Statistics is the study of collecting, organizing, analyzing, and drawing conclusions from data. It involves sampling, hypothesis testing, and using statistical tests tailored to measurement scales and hypothesis types.
2) Descriptive statistics describe and summarize data quantitatively, while inferential statistics allow generalizing from samples to populations through statistical testing and other methods.
3) The document discusses differences between statistics and statistical data, types of data, levels of measurement, sampling techniques, and uses of statistics.
This chapter introduces the basic concepts and terminology of statistics. It discusses two main branches of statistics - descriptive statistics which involves collecting, organizing and summarizing data, and inferential statistics which allows drawing conclusions about populations from samples. The chapter also covers variables, populations, samples, parameters, statistics and how to organize and visualize data through tables, charts and graphs. It emphasizes that statistics helps turn data into useful information for decision making in business.
This document provides an introduction to statistics and its uses in business. It outlines two main branches of statistics - descriptive statistics which involves collecting, summarizing and presenting data, and inferential statistics which uses data from a sample to draw conclusions about a larger population. The document then discusses key statistical concepts like variables, data, populations, samples, parameters and statistics. It explains how descriptive and inferential statistics are used to summarize data, draw conclusions, make forecasts and improve business processes. Finally, it introduces the DCOVA process for examining and concluding from data which involves defining variables, collecting data, organizing data, visualizing data and analyzing data.
The document discusses methods for assessing the quality of health surveillance data used to monitor disease trends and inform public health programs and policies. It describes key factors that can impact data quality, such as changes in case finding efforts, recording and reporting systems, and case definitions. The document outlines indicators and analytical approaches that can help identify issues with completeness, consistency, and reliability of notification data over time and across regions. This includes checks for unusual fluctuations, variations in notification rates, and consistency of case type proportions. The next steps proposed are to establish data quality review units, conduct in-depth analyses guided by quality checks, and develop online platforms to share best practices.
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it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
2. Module 3: Learning Objectives
Understand different ways to best
summarize data
Choose the right table/graph for the right
data
Interpret data to consider the programmatic
relevance
3. Summarizing data
Tables
Simplest way to summarize data
Data are presented as absolute numbers or
percentages
Charts and graphs
Visual representation of data
Data are presented as absolute numbers or
percentages
4. Basic guidance when
summarizing data
Ensure graphic has a title
Label the components of your graphic
Indicate source of data with date
Provide number of observations (n=xx) as a
reference point
Add footnote if more information is needed
6. Tables: Relative frequency
number of values within an interval
total number of values in the table
Year # births (n) Relative frequency (%)
1900–1909 35 27
1910–1919 46 34
1920–1929 51 39
Total 132 100.0
x 100
7. Tables
Year Number of births
(n)
Relative frequency
(%)
1900–1909 35 27
1910–1919 46 34
1920–1929 51 39
Total 132 100.0
Percentage of births by decade between 1900 and 1929
Source: U.S. Census data, 1900–1929.
8. Charts and graphs
Charts and graphs are used to portray:
Trends, relationships, and comparisons
The most informative are simple and self-
explanatory
9. Use the right type of graphic
Charts and graphs
Bar chart: comparisons, categories of data
Line graph: display trends over time
Pie chart: show percentages or proportional
share
11. Percentage of new enrollees tested for HIV at each
site, by quarter
0
1
2
3
4
5
6
Quarter 1 Quarter 2 Quarter 3 Quarter 4
%
o
f
new
enrollees
tested
for
HIV
Months
Site 1
Site 2
Site 3
Q1 Jan–Mar Q2 Apr–June Q3 July–Sept Q4 Oct–Dec
Data Source: Program records, AIDS Relief, January 2009 – December 2009.rce:
Quarterly Country Summary: Nigeria, 2008
12. Has the program met its goal?
0%
10%
20%
30%
40%
50%
60%
Quarter 1 Quarter 2 Quarter 3 Quarter 4
%
of
new
enrollees
tested
for
HIV
Site 1
Site 2
Site 3
Percentage of new enrollees tested for HIV at each site, by
quarter
Data Source: Program records, AIDS Relief, January 2009 – December 2009.. quarterly
Country Summary: Nigeria, 2008
Target
13. Stacked bar chart
Represent components of whole & compare wholes
3
4
6
10
0 5 10 15
Males
Females
0-14 years
15+ years
Number of months patients have been enrolled in HIV care
Number of Months Female and Male Patients Have Been
Enrolled in HIV Care, by Age Group
Data source: AIDSRelief program records January 2009 - 20011
14. Line graph
0
1
2
3
4
5
6
Year 1 Year 2 Year 3 Year 4
Number
of
clinicians
Clinic 1
Clinic 2
Clinic 3
Number of Clinicians Working in Each Clinic During Years 1–4*
*Includes doctors and nurses
Displays trends over time
15. Line graph
0
1
2
3
4
5
6
Year 1 Year 2 Year3 Year 4
Number
of
clinicians
Clinic1
Clinic2
Clinic3
Number of Clinicians Working in Each Clinic During Years 1-4*
*Includes doctors and nurses
Y1 1995 Y2 1996 Y3 1997 Y4 1998
Zambia Service Provision Assessment, 2007.
16. Pie chart
Contribution to the total = 100%
59%
23%
10%
8%
Percentage of All Patients Enrolled by Quarter
1st Qtr
2nd Qtr
3rd Qtr
4th Qtr
N=150
18. Interpreting data
Adding meaning to information by making
connections and comparisons and exploring
causes and consequences
Relevance
of finding
Reasons
for finding
Consider
other data
Conduct
further
research
19. Interpretation – relevance of finding
Adding meaning to information by making
connections and comparisons and exploring
causes and consequences
Relevance
of finding
Reasons
for finding
Consider
other data
Conduct
further
research
20. Interpretation – relevance of finding
Does the indicator meet the target?
How far from the target is it?
How does it compare (to other time periods,
other facilities)?
Are there any extreme highs and lows in the
data?
22. Relevance
of finding
Reasons
for finding
Consider
other data
Conduct
further
research
Interpretation – consider other data
Use routine service data to clarify questions
• Calculate nurse-to-client ratio, review
commodities data against client load, etc.
Use other data sources
23. Interpretation – other data sources
Situation analyses
Demographic and health surveys
Performance improvement data
Relevance
of finding
Reasons
for finding
Consider
other data
Conduct
further
research
24. Interpretation – conduct further
research
Data gap conduct further research
Methodology depends on questions being asked
and resources available
Relevance
of finding
Reasons
for finding
Consider
other data
Conduct
further
research
25. Key messages
Use the right graph for the right data
Tables – can display a large amount of data
Graphs/charts – visual, easier to detect patterns
Label the components of your graphic
Interpreting data adds meaning by making
connections and comparisons to program
Service data are good at tracking progress &
identifying concerns – do not show causality
27. Learning Objectives
Use basic statistics to measure coverage and
retention
Develop graphs that display performance
measures (utilization, trends)
Interpret performance measures for
programmatic decision making
28. Small group activity
Form groups of 4–6
Each group reviews 2 worksheets from Excel file
and answers the questions (1 hr 45 min)
Each group presents 2 findings from each
worksheet, focusing on the programmatic
relevance of the findings (10 min per group)
Audience provides feedback on analysis and
interpretation (notes errors, additional
interpretation) (10 min per group)
Editor's Notes
We have already discussed how best to analyze data to reflect your program’s coverage and retention. The results of your data analysis tell you WHAT is happening in your program.
In this module, we will cover formats that effectively summarize data so that you can interpret the findings and begin to discover WHY your program is functioning as the results indicate.
In this session, the learner will:
Note to facilitator: Read slide.
The two main ways of summarizing data are by using tables and charts or graphs.
A table is the simplest way of summarizing a set of observations. A table has rows and columns containing data, which can be in the form of absolute numbers or percentages, or both.
Charts and graphs are visual representations of numerical data and, if well designed, will convey the general patterns of the data.
To make your graphics as self-explanatory as possible, there are several things to always include:
Every table or graph should have a title or heading
The x- and y-axes of a graph should be labeled – include value labels, such as a percentage sign; include a legend
Always cite the source of your data and put the date of data were collection or publication
Provide the sample size or the number of people to which the graph is referring (N)
Include a footnote if the graphic isn’t self-explanatory
These points will pre-empt questions and explain the data. In the next several slides, we’ll see examples of these points.
Let’s start with tables. Most tables show a frequency distribution, which is a set of categories with numerical counts. Here, you see the year as the category and the number of births as the numerical count.
What should be added to this table to provide the reader with more information?
Note to facilitator: Wait for a participant response before answering.
Answer – Title
Answer – Data source
Another common way to summarize data is with relative frequency – which is the percentage of the total number of observations that appear in that interval.
It is computed by dividing the number of values within an interval by the total number of values in the table, then multiplying by 100 to get the percentage.
In this table, you see the proportion of the total number of births between 1990 and 1929 (132) by 10-year intervals.
The calculation for the first relative frequency is: 35/132 = 0.265 x 100 = 26.5 (approx 27%).
To interpret this table, we should look at the relative frequencies. What do they tell us?
We can see data across the three decades and what percentage of births occurred in each one. The largest percentage of children were born between 1920 and 1929, compared to the other two decades.
We can interpret the data further by calculating the average or the mean number of births across 30 years. This will give us a summary of the data.
Although they are easier to read than tables, charts provide less detail. The loss of detail may be replaced by a better understanding of the data.
We’re going to review the most commonly used charts and graphs in Excel/PowerPoint. Later, we’ll have you use data to create your own graphics, which may go beyond those presented here.
Bar charts are used to compare data across categories.
Line graphs are used to display trends over time.
Pie charts show percentages or the contribution of each value to a total.
In this bar chart, we’re comparing the categories of data, which are the different sites. You see a comparison between sites by quarters and between quarters over time.
What should be added to this chart to provide the reader with more information?
NOTE to facilitator: Wait for a participant response before answering (and then show next slide).
On the next slide, we see how the graph has been improved and is now self-explanatory.
You see we’ve added a title. By adding a title, you know the population to which the graph is referring.
We’ve added labels for each axis. Labeling the y-axis (vertical) was critical because now we know that the values are percentages rather than absolute numbers.
We’ve added the source of the data – this let’s us know from where the data are derived and where to find additional information about this topic.
And we’ve clarified the quarters with months.
Now let’s interpret this chart.
You will note that we have added the target for the number of new enrollees tested for HIV.
The target is to test 50% of new enrollees at each site in each quarter.
We see that sites 1 & 3 have met their targets, but that site 2 has not; it is at 30% new enrollees tested. What percentage of the target has this site met?
NOTE to facilitator: Wait for a participant response before answering.
30/50 = 0.6 or 60%
A stacked bar chart is often used to represent components of a whole and compare the wholes (or multiple values).
Here, you see the number of months female and male patients have been enrolled in HIV care, by age group. By looking within each bar, you see the age breakdown by gender, and by looking at both bars together, you can compare the number of months enrolled for both males and females.
A line graph should be used to display trends over time. While bar charts also are useful for showing time trends, line graphs are particularly useful when there are many data points. In this case, we have four data points for each clinic.
Here, you see the number of clinicians working in each clinic during years 1–4. You will note the asterisk in the title. This asterisk clarifies the definition of clinical to include both doctors and nurses.
What can be added to this graph to make it more clear?NOTE to facilitator: Wait for a participant response before answering. After someone participates, go to next slide.
Data source is added and the actual years are defined.
A pie chart displays the contribution of each value to the total. In pie charts, the values always add up to 100.
In this case, we used the chart to show the contribution of patients enrolled each quarter to the total enrollment for the year. For example, the first quarter contributed the largest percentage (59%) of enrolled patients.
Once we have transformed data into information by summarizing them with tables, graphs, or narrative, we need to interpret the data. That is, we need to consider the relevance of the findings to our program – the potential reasons for the findings – and possible next steps.
In this process, we move from the ‘what’ is happening in our programs to the ‘why’ it is happening.
Data interpretation is the process of making sense of the information. It allows us to ask: What does this information tell me about the program?
Here, you see a flow chart of the steps involved in interpreting data …
NOTE to facilitator: Read the steps outlined in the diagram.
We start by wanting to know the relevance of our findings. Seeking the relevance of a finding is to:
NOTE to facilitator: Read slide.
When interpreting data and seeking the relevance of our findings, we may ask these questions:
NOTE to facilitator: Read slide.
Asking these questions will help you to put the data in the context of your program.
When seeking potential reasons for the finding, we often will need additional information that will put our findings into the context of the program.
Supplementing the findings with expert opinion is a good way to do this. For example, talk to others with knowledge of the program or target population, who have in-depth knowledge about the subject matter, and get their opinions about possible causes.
For example, if your data show that you have not met your targets, you may want to know if:
the community is aware of the service? To answer this, you could talk to community leaders or other providers to get their opinions.
Sometimes ad hoc conversations with experts are insufficient. To get a more accurate explanation of your findings, you often will have to consider other data resources.
Let’s go back to the finding of ‘the program has not met its annual target’. Can we understand why this is happening by looking at other program indicators?
You may want to calculate the nurse-to-client ratio to determine if the facility is sufficiently staffed to meet the client load.
You also may want to review commodity data with client load to determine if there are shortages of commodities.
While it is important to consider other indicators in your analysis, remember – descriptive statistics do not show causality. In these cases, look at other data sources.
Other data sources include:
NOTE to facilitator: Read slide.
Once you review additional data, it may become apparent that these data are not sufficient to explain the reasons for your findings – that a data gap exists. In these instances, it may be necessary to conduct further research.
The types of research designs that are applied will depend on the questions that need to be answered, and of course will be tempered by the feasibility and expense involved with obtaining the new data.
We have come to the end of the module on Data Presentation and Interpretation. The key messages of this module include:
NOTE to facilitator: Read slide.
In this small group work session, you will have the opportunity to practice analysis, presentation, and interpretation.
By the end of this session, the learner will:
NOTE to facilitator: Read slide.
NOTE to facilitator: Assign two worksheets per group. Participants should spend 1 hour and 45 minutes answering the questions on the worksheets. Remind participants after 50 minutes that they should begin working on their second worksheet to ensure that they have adequate time to address both worksheets.
After 1 hour 45 minutes, ask participants to present their results. Each group will be given about 10 minutes for its presentation. Then spend 10 minutes discussing the presentation with the larger group. The plenary (or facilitator) should point out errors or inaccuracies and provide feedback on how to better analyze, interpret, or present the information.