Research# Data collection

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

3. Content
• Types of variable analysis
• Hypothesis

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.

8. Research Process

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.

11. Types of Data

12. Types of Data

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.

15. Categorical Data

16. Categorical Data
B - Ordinal Data
• defined as a variable measurement scale used to
simply depict the order of variables.

17. Categorical Data

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.

24. Data Collection methods

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

30. Example

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