An Introduction to Monitoring and Evaluation of Healthcare Projects. Monitoring and Evaluation is an integral component for the success of any donor-funded project as it provides accountability and well-informed decisions through the use of data and plan that guides implementation

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QUANTITATIVE AND
QUALITATIVE DATA
UNIT 3
MWALIMU VALOKUMU BABA
MORENA 0708 701 341

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Unit Content
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Introduction
What is Quantitative and qualitative data?
Different types of data
Collection tools and methods for data collection
Qualitative and quantitative data analysis tools and methods
Mixed method approach

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Introduction
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Data is the heart and soul of monitoring and evaluation (M&E). Valid,
reliable and accurate data can reveal and improve the performance and
impact of your intervention and support decision making and learning, while
enhancing your credibility and accountability.
Data is divided into different categories based on how you source them and
the techniques you employ to gather and analyze them
According to source (Primary and secondary data).
Primary data are those that you and your team collect directly from the
main sources, whereas, secondary data are those that were collected by
other organizations, government agencies, or independent research
institutions and individuals and are available for use. Secondary data could
be censuses, surveys, organizational records or other previous research,
extracted from books, journals, reports, newspapers, magazines, data
archives, databases etc

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Introduction Cont….
• Once you are ready to collect your data, you will have to decide upon
the data collection tools and methods.
• Data collection depends on a number of things, including the purpose of
the data, the local context, cost, timeline, availability of skills and
resources, and most importantly, the indicators and key questions you
have identified and how the collected data will be utilized.
• All data are further divided into two broad categories based on the
techniques employed in the field to gather and analyse them –
1) Quantitative data’ and
2) Qualitative data.’

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What is Quantitative data?
This refers to data that can be quantified, verified and manipulated statistically
 Numerical, countable, quantified, and analysed mathematically
 Quantitative data is categorized into:
i) Discrete e.g. number of persons
ii) Continuous e.g. height and time
 Quantitative data are used to:
o Draws correlations between factors
o Best used to generalize to population
o Easy to present in tables and charts
Quantitative data is highly reliable.
o Reliability: extent to which a procedure yields same results on repeated trials

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What is qualitative data?
 Qualitative data are data that describes meaning and is generally non-numerical
 Qualitative data is classified into:
i) Nominal : The gender of a person
ii) Ordinal :
 Qualitative data has greater validity.
o Validity: extent to which an indicator measures what it intends to measure
 Qualitative data:
o Represents the “voice” of the individual or group
o Not generalizable to the population
o Time-consuming to collect and analyse
 Qualitative and Quantitative data can further be classified into:
i) Primary data
ii) Secondary data
 Note that a mixture of both quantitative and qualitative data provides the most
comprehensive set of data for program evaluation.

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Data Collection Methods
1) Questionnaires/Surveys
 Good way of gathering a lot of data and provides a broad perspective.
 Can be administered electronically, mail or face to face.
 Mail/Email surveys have wider reach, relatively cheaper to administer, information is
standardised and privacy can be maintained.
 Prone to low response rate, low in depth, not appropriate among the illiterate and does
not allow for any observation.
 Chances of reporter bias particularly on sensitive issues or where disclosure on trust
is required
 Piloting on sample target group is required for validity purposes and test
appropriateness for the target group

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Data Collection Methods
2) Interviews
 Used when you want to understand impressions and experiences in more detail and be able to expand or clarify responses
 Interviews can be conducted face-to-face or by telephone.
 Range from In-depth, Semi-structured, Unstructured, Structured, Key Informant depending on the information being sought.
Face-to-Face Interview:
Advantages
i)Detailed questions can be asked
ii) Probing can be done to provide rich data
iii) Literacy requirements of participants is not an issue
iv) Non-verbal data can be collected through observation
v) Complex and unknown issues can be explored
vi) Higher response rates
Disadvantages
i)They can be expensive and time consuming
ii) Interviewer training is required to reduce bias
iii) Prone to interviewer and interpreter bias
iv) Sensitive issues maybe challenging
Telephone Interviews or Video conferencing
Advantages
i)Cheaper and faster than face to face interviews to conduct
ii) Use less resources than face to face interviews
iii) Allow to clarify questions
iv) Do not require literacy skills
Disadvantages
i)Making repeated calls if first calls are not answered
ii) Potential bias if call backs (follow up) are not made to those absent

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Data Collection Methods cont…
3) Observations
 Information is gathered about a program as the program’s activities occur
 Data is collected by direct or indirect participation within program activities
 Evaluator better understands the context in which measures are undertaken, and
facing directly the program implementation enables the evaluator to 'feel at home'
with a given issue
 Trained evaluator may also perceive such phenomena that – as they are obvious –
escape others' attention, as well as issues that are not tackled by participants in
interviews (like conflicts, sensitive)
 Observation enables evaluator to exceed participants' selective perception
Possible to present the versatile picture of the program that would not be possible
using only questionnaires and interviews.

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Data Collection Methods cont…
4) Document Analysis (Document Review)
a. Used when program documents or literature are available and can provide insight into the program or evaluation
b. Advantages includes
i. Data already exists
ii. Does not interrupt the program
iii. Little or no burden on others
iv. Can provide historical or comparison data
v. Introduces little bias
c. Disadvantages includes:
i. Time consuming
ii. Data limited to what exists and is available
iii. Data may be incomplete
iv. Requires clearly defining the data you are seeking
d. Examples of documents that can be used as data sources includes:
i. Reports
ii. Brochures, Pamphlets, Posters, Flyers
iii. Logs, Registers, Diaries
iv. Minutes of meetings
v. Memos, Notice boards, Dash boards
vi. Patient records

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Data Collection Methods cont…
5) Focus Group Discussions
 Focus groups or group discussions are useful to further explore a topic, providing a broader understanding of why the target
group may behave or think in a particular way, and assist in determining the reason for attitudes and beliefs.
 Conducted with a small sample of the target group and are used to stimulate discussion and gain greater insights.
Advantages
i. Useful in exploring cultural values and health beliefs
ii. Useful in examining how and why people think in a particular way and how it influences their beliefs and values
iii. Can be used to explore complex issues
iv. Can be used to develop hypothesis for further research
v. Do not require participants to be literate
vi. Allows in-depth discussion
Disadvantages
i. Lack of privacy (confidentiality)
ii. Need to balance group to ensure cultural and gender appropriateness
iii. Potential for risk of ‘group thinking’ rather than individuals
iv. Risk for group domination by few individuals
v. Can be time consuming to conduct and not easy to analyse data
6) Laboratory testing
a. Precise measurement of specific objective phenomenon e.g. weight, water quality
7) Key Informant Interviews

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Data collection tools/technologies
1) Paper tools e.g.
 Checklists
 Questionnaires
 Interview guides/schedules
 Meeting minutes
 Registers
 Reports e.g.
i. Surveillance
ii. Kenya Demographic Health Survey
iii. Census
 Patient records
 Tally sheets

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Data collection tools/technologies cont…
2) Electronic Data Capture
 Smart phones
 Computers
i. Audio Computer Assisted Interview (ACASI)
ii. Computer Assisted Personal Interview (CAPI)
iii. Computer Assisted Self Interview (CASI)
 Electronic Medical Records/ databases
i. C-PAD
ii. IQ-Care
iii. Fan soft
iv. DATIM
v. Kenya EMR
vi. KHIS
 Cameras
 Voice/ Video recorders
 Kenya Open Data Portal
 Open DataKit (ODK)
i. Is a free and open-source set of tools used to manage mobile data collection solutions. ODK can be used to:
• Design data collection forms
• Collect data on a mobile device and send it to a server
• Data aggregation on a server and extraction in any formats
 Personal Digital Assistants (PDAs)
i. Handheld device that combines computing, telephone/fax, Internet and networking features
 Research Electronic Data Capture (REDCap)
i. Free, secure, web-based application designed to support data capture for research studies

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Data Analysis
 Data analysis is the process of evaluating data using analytical and logical reasoning to examine each component
of the data provided
 Data analysis refers to the process of inspecting, cleaning, transforming, and modeling data with the goal of
discovering useful information, suggesting conclusions, and support decision-making
 Data analysis helps in obtaining usable and useful information. Data analysis may:
i. Describe and summarise the data
ii. Identify relationships between variables
iii. Compare variables
iv. Identify the difference between variables
v. Forecast outcomes
 Data analysis must be planned for before data collection
 Plan data analysis as follows:
i . Consider the purpose of your evaluation. How useful is the data in understanding and
improving program?
ii . Decide who will analyse the data. He/she must have training and experience in the
analysis procedures and software to be used
iii. Develop a database management system to collect, organize and store data
iv. Plan for data cleaning procedures
v. Obtain data analysis software
vi. Analysing Quantitative Data

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Quantitative Data Analysis
QUANTITATIVE DATA ANALYSIS
 Quantitative data analysis is the process of using statistical methods to describe, summarize, and compare data. Analysis vary based on the type
of data collected
 Calculating descriptive statistics is the first step in data analysis.
 Descriptive statistics are used to describe the general characteristics of a set of data.
Descriptive statistics include frequencies, counts, averages and percentages.
 Three major steps in conducting quantitative data analysis
1) Conduct statistical tests. Explore the main characteristics of data using basic descriptive statistics such as:
i. Frequencies
ii. Percentages
iii. Ratio
iv. Measures of dispersion
• Standard deviation
• Ranges
v. Measures of central tendency
• Mean
• Median
• Mode
vi. Advanced statistical tests may be used if appropriate
vii. Use descriptive statistics to identify patterns in the data being analysed
viii. Perform universe (univariate), two (bivariate) or more three (multivariate)
variables analysis

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Quantitative Data Analysis
2) Review and interpret your data
i. Conduct inferential statistics depending on interest, expertise and data
collected
ii. Review findings to identify patterns
iii. Look for similarities and differences between responses
iv. Look for any outliers in the data
v. Compare actual results versus program targets
vi. Compare actual progress to projected time frame
vii. Compare results across program sites
viii. Compare program outcomes with that of comparison group
3) Summarize and present your data
i. Develop tables, graphs and charts to report and summarize data findings
ii. Use cross-tabulation to compare two or more variables

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Qualitative Data Analysis
 Qualitative data analysis is a search for general statements about relationships among
categories of data
 Qualitative data analysis relies heavily on interpretation
 During analysis, you will draw on your own experiences and knowledge of your program to
make sense of your data
 Consider the context of your program to determine how the data fit into the bigger picture
 Qualitative data analysis is an interactive process; once you begin to collect data, you will
begin to review it and use your initial findings to shape how you collect and interpret data in the future

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Qualitative Data Analysis
 Develop a systematic approach for analysing your qualitative data following four major steps outlined below:
1) Review your data. Before beginning any analysis, it is important that you understand the data you have collected by reviewing them
several times. Write notes on first impressions you get as you are review data
2) Organize your data. Qualitative data sets tend to be very lengthy and complex. Once you have reviewed your data and are familiar with
what you have, organize your data so that they are more manageable and easy to navigate. Various ways to group your data includes by
date, data collection type or by question asked
3) Code your data
o Coding: process of identifying and labelling themes within data that correspond with the evaluation questions
o Themes: common trends (ideas) appearing repeatedly in the data
o Read through data several times to identify all the themes
4) Interpret your data
• Interpretation involves attaching meaning and significance to your data
• Start by making a list of key themes. Revisit your review notes to factor in
your initial responses to the data.
• Review each theme that arose during the coding process and identify
similarities and differences in responses from participants with differing Characteristics
• Consider the relationships between themes to determine how they may be connected
• Determine what new lessons you have learned about your program and how
• those lessons can be applied to different parts of your program

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Thank You