Quantitative data analysis

1. Unit 6
Quantitative Data Analysis

2. Content
• 6.1 Types of quantitative data
• 6.2 Data coding
• 6.3 Visual aids for quantitative data analysis
• 6.4 Using statistics for quantitative data
analysis
• 6.5 Interpretation of data analysis results
• 6.6 Evaluating quantitative data analysis

3. Quantitative Data
• Quantitative data is the value of data in the
form of counts or numbers where each data
set has a unique numerical value.
• This data is any quantifiable information that
researchers can use for mathematical
calculations and statistical analysis to make
real-life decisions based on these
mathematical derivations.

4. Types of quantitative data

5. Types of quantitative data
• Counter: Count equated with entities. For
example, the number of people downloading
a particular application from the App Store.
• Measurement of physical objects: Calculating
measurement of any physical thing. For
example, the HR executive carefully measures
the size of each cubicle assigned to the newly
joined employees.

6. Types of quantitative data
• Sensory calculation: Mechanism to naturally
“sense” the measured parameters to create a
constant source of information. For example, a
digital camera converts electromagnetic
information to a string of numerical data.
• Projection of data: Future data projection can be
made using algorithms and other mathematical
analysis tools. For example, a marketer will
predict an increase in sales after launching a new
product with a thorough analysis

7. Types of quantitative data
• Quantification of qualitative entities: Identify
numbers to qualitative information. For
example, asking respondents of an online
survey to share the likelihood of
recommendation on a scale of 0-10.

8. Types of Quantitative data
• Nominal data : Nominal data is that which
describes categories and has no actual
numeric value. For example : Gender
• Ordinal data : With ordinal data, numbers are
allocated to a quantitative scale.
• A common use of ordinal data is in
categorizing responses to Like scale-based
questions, where numbers are assigned to the
range of responses.

9. Types of Quantitative data
• Interval data : Interval data is like ordinal data,
but now measurements are made against a
quantitative scale where the differences, or
intervals, between points of the scale are
consistently the same size, that is, the ranking
of the categories is proportionate.
• You can therefore state the difference
between any two data values precisely.

10. Types of Quantitative data
• Ratio data : Ratio data is like interval data, but
there is a true zero to the measurement scale
being used. For example, people’s age, weight,
or height, or companies’ number of
subsidiaries, head count of employees or
annual turnover.

11. Data Coding
• Data coding in research methodology is a
preliminary step to analyzing data.
• The data that is obtained from surveys,
experiments or secondary sources are in raw
form.
• This data needs to be refined and organized to
evaluate and draw conclusions.
• Data coding is not an easy job and the person or
persons involved in data coding must have
knowledge and experience of it.

12. What is a code?
• A code in research methodology is a short word
or phrase describing the meaning and context of
the whole sentence, phrase or paragraph.
• The code makes the process of data analysis
easier.
• Numerical quantities can be assigned to codes
and thus these quantities can be interpreted.
• Codes help quantify qualitative data and give
meaning to raw data.

13. What is data coding?
• Data coding is the process of driving codes from the observed
data.
• In qualitative research the data is either obtained from
observations, interviews or from questionnaires.
• The purpose of data coding is to bring out the essence and
meaning of the data that respondents have provided.
• The data coder extract preliminary codes from the observed
data, the preliminary codes are further filtered and refined to
obtain more accurate precise and concise codes.
• Later, in the evaluation of data the researcher assigns values,
percentages or other numerical quantities to these codes to
draw inferences.
• It should be kept in mind that the purpose of data coding is
not to just to eliminate excessive data but to summarize it
meaningfully.

14. Visual aids for quantitative data
Analysis.
• The simplest form of analysis uses tables and
charts to present the data in a visual way that
allows you to explore it and ‘see’ values and
patterns in it.
• These tables and charts can also be included
in the write-up of your research, so the reader
sees what you see.

15. Tables
• Tables are suitable for use with all types of
data, and are easily produced using word
processing software.

16. Tables

17. Tables

18. Bar Charts
• Bar charts are often used for displaying
frequencies.
• The classic bar chart uses horizontal or vertical
bars (column charts) to show discrete, numerical
comparisons across categories.
• One axis of the chart shows the specific
categories being compared, and the other axis
represents a discrete value scale.
• They are best used to show change over time,
compare different categories, or compare parts of
a whole.

19. Bar chart

20. Pie Charts
• A pie chart is a graphical representation
technique that displays data in a circular-
shaped graph
• A pie chart is a pictorial representation of data
in the form of a circular chart.

21. Pie chart

22. Scatter graph
• A scatter graph can be used to show a relationship
between two variables.
• You plot your data as points on a graph, where the x-
axis represents the values of one variable, and the y-
axis represents the values of the other variable.
• If no line can be seen around which the data points
tend to cluster, then there is no relationship between
the variables.
• The more closely the points tend to cluster around a
line, the closer a relationship there is between the
variables.

24. Line Graph
• Line graphs are used for showing trends in
data,

28. References
• https://www.questionpro.com/blog/quantitat
ive-data/#Quantitative_Data_Definition
• https://readingcraze.com/index.php/data-
coding-research-methodology/