Research

1. Abebe Tolera (BSc, MPH in Epidemiology )

2. Unit Objectives
At the end of this unit students are
expected to know:
1. What are the different data processing,
analysis and presentation techniques?
2. Understand common statistical packages used
for data analysis

3. What is data
Data/plural of datum is any collection of facts of figures
or raw material to be processed.
▪ Example: Names of students, marks obtained in the
examination, designation of employees, addresses,
quantity, rate, sales figures or anything that is input to
the computer is data.
▪ Even pictures, photographs, drawings, charts and
maps can be treated as data.
▪ Computer processes the data and produces the output
or result

6.  Information:
 A collection of data which conveys some
meaningful idea is information.
 It may provide answers to questions like:
Who
Which
When
Why
What and
How.

7.  Data Processing: any operation or set of
operations performed upon data, whether or not
by automatic means, such as collection,
recording, organization, storage, adaptation or
alteration to convert it into useful information.
 Observations and recordings are done to obtain data,
while analysis is done to obtain information
 Data Processing is the processing of converting data
into useful information.
 Data processing system is the activities, equipment
& personnel involved.

8.  Steps of data processing
 There are 5 steps included in Data
processing:
1. Editing
2. Coding
3. Classification
4. Data Entry
5. Validation
6. Tabulation

9.  Editing: editing of data is a process of
examining the collected raw data to detect
errors and omissions and to correct these
when possible.
 With regards to stages:
 Field Editing
 Central Editing

10.  Coding: refers to process of assigning numerals
or other symbols to answers so that responses can
be put into a limited number of categories or
classes.
 Classification: data having a common
characteristics are placed in one class
 In this way the entire data get divided into a number of
groups or classes.
 Types:
1. Classification according to attributes
2. Classification according to class intervals

11.  Data Entry: after the data has been properly
arranged and coded, it is entered into the
software that performs the eventual cross
tabulation.
 Validation: after the cleaning phase, comes the
validation process.
 It refers to the process of thoroughly checking the
collected data to ensure optimal quality levels.
 All the accumulated data is double checked in order to
ensure that it contains no inconsistencies and is
relevant.

12.  Tabulation: is the process of summarizing raw
data and displaying the same in compact form for
further analysis.
 Benefits:
1. It conserves soace and reduces explanatory
statement to a minimum
2. It facilitates the process of comparison
3. It facilitates the summation of items and
detection of errors
4. It provides a basis for various statistical
computations

13.  Types Of Data Processing:
1. Manual data Processing
2. Electronic Data Processing
3. Real time Processing
4. Batch Processing

15. What Is Data Analysis?
 Data analysis is the process of collecting, modeling,
and analyzing data to extract insights that support
decision-making.
 Research data analysis is a process used
by researchers for reducing large chunk of data to a
story and interpreting it to derive insights or makes
sense.
 Or Data Analysis is the process of systematically
applying statistical and/or logical techniques to
describe and illustrate, condense and recap, and
evaluate data.

16. There are several methods and techniques to
perform analysis depending on the industry
and the aim of the analysis.
All these various methods for data analysis are
largely based on two core areas:
1. Quantitative methods and
2. Qualitative methods in research.

17. Steps in data analysis
1. Data collection and preparation
2. Exploration of data
3. Data analysis techniques/methods

18. Data preparation
1. Collect data
2. Prepare of codebooks
3. Set up structure of data
4. enter data
5. Screen data for errors
Exploration of data
1. Graphs
2. Descriptive stats

19. Why Is Data Analysis Important?
The main purpose of data analysis is to find
meaning in data so that the derived
knowledge can be used to make informed
decisions
By using data analysis you can understand
which channels your customers use to
communicate with you, their demographics,
interests, habits, purchasing behaviors, and
more.

20. 10 Essential Types of Data Analysis
Methods:
1. Cluster analysis
2. Cohort analysis
3. Regression analysis
4. Factor analysis
5. Neural Networks
6. Data Mining
7. Text analysis
8. Monte Carlo simulation.
9. Time series analysis.
10. Sentiment analysis

21. Types Of Data Analysis Methods
1. Descriptive analysis: What happened.
2. Exploratory analysis: How to explore data
relationships.
3. Diagnostic analysis: Why it happened.
4. Predictive analysis: What will happen.
5. Prescriptive analysis: How will it happen.

23.  Steps in Data
Analysis
1. Collaborate your needs
2. Establish your questions
3. Data democratization
4. Clean your data
5. Set your KPIs
6. Omit useless data
7. Build a data management
roadmap
8. Integrate technology
9. Answer your questions
10. Visualize your data
11. Interpretation of data
12. Consider a
autonomous technology
13. Build a narrative
14. Share the load
1. Use Data Analysis tools

25.  Data analysis tools
In order to perform high-quality data analysis, it
is fundamental to use tools and software that
will ensure the best results.
As the analysis industry grows, so does the offer
for services and features that you can exploit.
The four fundamental categories of data
analysis tools for your purposes.

26.  Statistical analysis tools:
These tools are usually designed for data
scientists, statisticians, market researchers, and
mathematicians, as they allow them to perform
complex statistical analyses with methods like
regression analysis, predictive analysis, and
statistical modeling.
 A good tool to perform this type of analysis is:
 R-Studio as it offers a powerful data modeling and
hypothesis testing feature that can cover both academic
and general data analysis.

27.  This tool is one of the favorite ones in the analysis
industry, due to its capability for data cleaning,
data reduction, and performing advanced analysis
with several statistical methods.
 Another relevant tool to mention is SPSS from
IBM.
 The software offers advanced statistical analysis for
users of all skill levels.
 SPSS also works as a cloud service that enables you to
perform analysis anywhere.

28.  Others include:
1. Stata.
2. R.
3. GraphPad Prism.
4. SAS.
5. IBM SPSS.
6. MATLAB.
7. JMP.
8. Minitab.
9. Statistica
10. Excel

30. Qualitative study design
 Case study: single case (shed light on phenomena among
group, individuals, events or institutions).
 Content analysis: systematic collection and objective analysis
of contents
 Historical analysis (narratives): systematic collection and
analysis.
 Participatory action research: individual or group
participation
 Ground theory: beyond existing body of knowledge. theory
development
 Phenomenological: individual life experience of events,
perceptions, perspectives, or understanding.

31.  Qualitative data analysis approaches
1. Grounded theory analysis:
2. Narrative analysis
3. Discourse analysis: language studies
4. Framework analysis: use matrix (rows and
columns
5. Thematic analysis

32.  Coding system (based on study design)
1. Inductive: coding during analysis process
 Qualitative research methods are also described as
inductive, in the sense that a researcher may construct
theories or hypotheses, explanations, and
conceptualizations from details provided by a
participant.
 Embedded in this approach is the perspective that
researchers cannot set aside their experiences,
perceptions, and biases, and thus cannot pretend to be
objective bystanders to the research.

33. 2. Deductive: code listed before analysis start
Replicability and generalizability are not
generally goals of qualitative research

34.  Qualitative data
analysis software
packages
 MAXQDA
 NVivo
 Open Code
 ATLAS.ti
 Quirkos
 HubSpot
 Qualtrics
 Raven's Eye
 Square Feedback
 FreeQDA
 QDA Miner Lite
 ConnectedText
 Visão
 Dedoose
 Qiqqa
 WebQDA