Research data management concerns the collection of the data the to the analyzing , finally to the presenting the results.

1. Research Data Management
(Collecting, Presenting and Analyzing)
FACULTY OF ELECTRICAL-ELECTRONICS AND COMPUTER
ENGINEERING
UNIVERSITY OF AKSARAY 2017
By: Mahmoud Al-Rawy
ma91tx@gmail.com
https://sites.google.com/site/mahmoud91tx/home

2. Motivation
 Can we research without data?
 How can we resolve the problem without supporting data?
 How do we convince other, that your data are sufficient to
support the solution?
 Where do we go to find data?
 Can we have imaginary data in research?
 Can we have data simulation for research?

3. Data
 Data is a set of values of qualitative or quantitative variables

4. The Major Two Groups of Data
 Quantitative data : its numerical data for example :
 The number of Apples, Age , Temperature , ... etc.
 Qualitative data : its descriptive or observations data for example :
 Colors , Smell , Major , ... etc.

5. Quantitative data & Qualitative data
 Quantitative data :
 Four wheels
 Tow doors
 Qualitative data :
 Red
 Comfortable seats
Considering the bellow car

6. Quantitative Data
 Discrete: Discrete data is based on counts. Only a finite number of values
is possible, and the values cannot be subdivided meaningfully like :
 Number of children in a household
 Number of languages a person speaks
 Number of people sleeping in stats class
 Continuous: Continuous data are not restricted to defined separate values, but
can occupy any value over a continuous range like :
 Height of children
 Weight of cars
 Time to wake up in the morning
 Speed of the train

7. Qualitative data
 Open: To open question when you leave a comment and these
comments doesn’t collate neatly like
 How do you feel now
 How was your day
 Attribute : this when we have specific thing from a set of passible answers
and what we find that people often lump attribute and discrete data together.
Like when we have attribute data like color it sometimes discrete an attribute
data used interchangeably as terms.
 Nominal data
 Ordinal data

8. Sources of Data
 Primary Source : These data is directly collected from the source of
origin
 Take the answers of question we’ve asked for our researches just right from
the source of origin
 Secondary Source : collecting data already compiled by some other
individual or an organization
 If we want to the number of the bank branches in Turkey. We can take the data from the
internet and it will be considered as a secondary sources of data

9. Sources of Data

10. Types Of Data
 Primary Data : it is considered to be the firs hand information
 The data which are originally collected in the process of investigation .
 Secondary Data : which is already by some third person or organization
 If we want to the number of the bank branches in Turkey. We can take the data from the
internet and it will be considered as a secondary sources of data

11. Types Of Data

12. Differences Between Primary and
Secondary Data
 Primary Data :
 Real-time data.
 Sure about source or data.
 Help to give results/finding.
 Costly and time consuming
process.
 More fixable
 Secondary Data :
 Past data.
 No sure about source or data.
 Refining the problem.
 Cheep and no time consuming
process.
 Less fixable.

13. Scales of Measurement
 Measurement: the process of applying numbers to objects
according to a set of rules.
 Nominal.
 Ordinal.
 Interval.
 Ratio.

14. Scales of Measurement
 Jogging competitions

15. Data collection

16. Collecting Quantitative Data
 There are a variety of techniques that can be used to collect data in
a quantitative research study. However, all of them are geared
towards numerical collection.
 This numerical data can be collected by means of:
 Observation.
 Interview.
 Questionnaires.
 Scales.
 physiological measurement.

17. Collection Qualitative Data
 Also there are a variety of techniques that can be used to collect
data in a qualitative research study.
 Including :
 Individual interviews.
 Observation.
 Diaries.
 Focus groups.
 Drawings.

18. Presenting Data

19. Methods of Presenting Data
 The main methods of presenting numerical data are through
graphs, tables and text incorporation.

20. Presenting Quantitative Data
 Individual variable
Compare two or more variables

21. Individual Variable (i)
 Frequency Distribution Table.

22. Individual Variable (ii)
 Bar Graph/Chart

23. Individual Variable (iii)
 Histogram Graph/Chart

24. Individual Variable (v)
 Pictogram

25. Individual Variable (vi)
 Line Graph

26. Individual Variable (vi)
 Pie Charts

27. Compare two or more variables

28. Analysis Data

29. Quantitative Data Analysis
 Analyze the data from quantitative research study in order to make
sense of it and to make accessible to the researcher.
 Data analysis consists of:
 Hypothesis.
 Variables.
 statistical analysis.

30. Hypothesis
 Hypothesis/Null hypothesis
 A hypothesis is a logical supposition, a reasonable guess, or
a suggested answer to a problem.
 A null hypothesis is a hypothesis that says there is no
statistical significance between the two variables.
 Tomato plants exhibit a higher rate of growth when planted in compost
rather than in soil.
 Tomato plants do not exhibit a higher rate of growth when planted in
compost rather than soil.

31. Variables
A manipulated independent variable.
Control of other variables (dependent variables).
The observed effect of the independent variable on
the dependent variables.

32. Statistical Analysis
 Statistics may be used to describe data that have been collected, and
explains
 how the data looks.
 what is the center point of the data.
 how the data is spread.
 how parts of the data may be related to one another.

33. Statistical Analysis
 Descriptive Statistical
 Count: 4000 Olives
 Green: 3000 Olives
 Black: 1000 Olives
 Green: 75%
 Black: 25%
 Inferential Statistical
 Count: 21,000 Olives
 Green: 14,070 Olives
 Black: 6,930 Olives
 Green: 67%
 Black: 33%

34. Qualitative Data Analysis
 Qualitative data analysis involves such processes as coding
(open, axial, and selective), categorizing and making sense of
the essential meanings of the phenomenon.

35. Stages in open, axial and selective
coding.

36. Open Coding
 During open coding, the data that have been collected
are divided into segments and then they are
scrutinized for commonalities that could reflect
categories or themes.

37. Axial Coding
 Axial coding involves putting data
back together in new ways by
making connections between
categories.

38. Selective Coding
 This is the process of selecting the core (or main) category, and
then systematically relating it to the other
categories.