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Unit 6
Quantitative Data Analysis
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
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.
Types of quantitative data
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.
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
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.
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.
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.
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.
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.
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.
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.
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.
Tables
• Tables are suitable for use with all types of
data, and are easily produced using word
processing software.
Tables
Tables
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.
Bar chart
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.
Pie chart
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.
Line Graph
• Line graphs are used for showing trends in
data,
References
• https://www.questionpro.com/blog/quantitat
ive-data/#Quantitative_Data_Definition
• https://readingcraze.com/index.php/data-
coding-research-methodology/

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RM UNIT 6.pptx

  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 23.
  • 24. Line Graph • Line graphs are used for showing trends in data,
  • 25.
  • 26.
  • 27.