ICT Role in 21st Century Education & its Challenges.pptx
Research and Data Analysi-1.pptx
1. Research and Data Analysis
CH 1: Data Analysis
Karwan H. Saeed
2020-2021 – Spring Semester
2. Content
• The meaning of Research
• Research Process
• Why do we need Data Analysis
• Data and Information
• Types of Data
• Quantitative Vs Qualitative
• Data Collection Methods
4. Research
• A process that people undertake in a systematic way
in order to find out things.
(Saunders, 2018)
• A detailed study of a subject, especially in order to
discover (new) information or reach a (new)
understanding.
(Cambridge Dictionary)
5. Research Cont.
• When listening to the radio, TV, newspapers, political
debates, documentary programs, and advertises,
they refer to research and results of research
6. Research Cont.
• Some of important research discoveries:
• If Alexander Fleming, Scottish scientist, Research and
discovered penicillin.
7. Research Cont.
• In 1543, while on his deathbed, Polish astronomer Nicholas
Copernicus published his theory that the Solar system.
9. Data Analysis
• Why do we analyze data?
To make sense of data we have collected and create
information.
To analyze the primary and secondary data collected
for completion of thesis or articles.
To help organizations with decision-making.
10. Data and Info.
• Data is raw, unorganized facts that need to be processed.
Data can be something simple and seemingly random
and useless until it is organized.
• Example: Each students score.
• Information is When data is processed, organized,
structured or presented in a given context so as to make
it useful.
• Example: The average score or the pass and fail of a
university.
13. Categorical Data
1- Categorical Data
• Categorical data represents characteristics and it is
qualitative type of data.
• The types:
1. Nominal data
2. Ordinal data.
14. Categorical Data
A - Nominal Data
• Defined as a scale used for labeling variables into
distinct classifications and doesn’t involve a
quantitative value or order.
18. Numerical Data
2- Numerical Data:
• Discrete Data: This type of data can’t be measured but it
can be counted, It basically represents information that
can be categorized into a classification. Uses (How Many).
• Ex. Number of students in a class.
• Continuous Data: values that can’t be counted but they
can be measured. Uses (How much).
• Ex. Height, Length, speed.
19. Numerical Data
A - Interval Data:
• defined as a type of data which is measured with a
scale, in which each point is placed at equal distance
from one another.
20. Numerical Data
• One of the features of Interval values data is that
it doesn’t have a “Absolute zero”. That means in
regards to our example, that there is no such thing as
no temperature.
21. Numerical Data
• B – Ratio Data
• defined as a variable measurement scale that makes
the difference between variables known along with
information on the value of true zero and there is no
meaning of negative values.
• Example: Height and Weight.
22. Quantitative Research
• Quantitative: is an approach that examines the
relationship between variables, which are measured
numerically, and analyzed using range of statistical
techniques like (Descriptive, frequency, correlation,
regression, etc.)
• Methods are such as (Questionnaire, numerical data)
• SPSS, STATA, SAS can be used to analyze the data.
23. Qualitative Research
• Qualitative research: It studies the participants
meanings and the relationships between them using a
variety of data collection methods to develop a
conceptual framework to get in-depth understanding of
an individual experience, opinion, or thought.
• Methods are such as (Interview, observation, and
document review).
• NVIVO, ATLAS, etc. can be used to code and analyze the
data.
25. Questionnaire
• a set of questions for obtaining statistically useful or
personal information from individuals it can be a
written or printed questionnaire often with spaces
for answers.
26. Observation
• Observation involves the systematic viewing,
recording, description, analysis and interpretation of
people’s behavior.
• Includes structured and
unstructured observation.
27. Interview
• An Interview is a purposeful conversation between
two or more people, requiring the interviewer to ask
concise and explicit questions, to which the
interviewee is willing to respond, and to listen
attentively
• Includes structured, semi-structured, and
unstructured-in depth interview.
28. Variable
• A variable is defined as anything that has a quantity
or quality that varies and changes.
• Example: student score, happiness, satisfaction,
performance, election, GDP, weather etc.
29. Independent Variable
and Dependent Variable
• an independent variable that causes changes in a
dependent variable.
• a dependent variable that changes in response to
changes in other variables.
• Example:
Independent: Time spend on studying
Dependent: Student Score in exam
31. Types of Variables Analysis
• Univariate Analysis: is the simplest form of data
analysis where the data being analyzed contains only
one variable. Since it's a single variable it doesn’t
deal with causes or relationships.
• Examples: Central tendency analysis, mean, mode,
median, and standard Deviation.
32. Types of Variables Analysis
• Bivariate Analysis: is used to find out if there is a relationship
between two different variables.
• Examples:
1. Differences between an ordinal/nominal with internal/ratio.
Using Chi-square, T-test, ANOVA.
Stress level between male and female.
2. Relationships: A- Correlation (when there is no prediction). For
example, Happiness and exam scores.
B- Linear regression: (Prediction is existed) for example,
Watching TV and eating carrots.
33. Types of Variables Analysis
• Multivariate analysis: is the analysis of three or more
variables.
• Examples: Factor Analysis, MANOVA, Multidimensional
Scaling, Multiple Regression Analysis, Cluster Analysis,
Structural Equation Modeling (SEM).
34. Hypothesis
Testable proposal about the relationship between two or more variables.
Two types of hypothesis:
1- Null hypothesis: predicts that there will not be difference or relationship
between the two variables.
Example: There is no relationship between happiness and exam scores
2. Alternative hypothesis: predicts that there may be a difference or
relationship between the variables.
Example1 : There is a positive relationship between happiness and exam
scores