PPT: Qualitative & Quantitative Data | Data Interpretation for UGC NET PDF Download

 Page 1


Quantitative and 
Qualitative Data
Page 2


Quantitative and 
Qualitative Data
Data Interpretation
Data interpretation assigns meaning to analysed information, determining its significance and implications. It 
involves reviewing data to reach informed conclusions, with proper data mapping being essential as 
information comes from multiple sources.
There are two main types of analysis: quantitative and qualitative. The choice between them depends on 
your measurement scales, which include nominal (non-numeric categories), ordinal (logical order 
categories), interval (equal distances between categories), and ratio scales (features of all three).
When interpreting data, analysts must discern between correlation, causation, and coincidences to draw 
accurate conclusions.
Nominal Scale
Non-numeric categories that cannot be ranked 
or compared quantitatively (e.g., gender, 
nationality)
Ordinal Scale
Categories with logical order (e.g., quality 
ratings: good, very good, fair)
Interval Scale
Equal distances between categories with an 
arbitrary zero point (e.g., temperature in 
Celsius)
Ratio Scale
Contains features of all three scales with a true 
zero point (e.g., height, weight)
Page 3


Quantitative and 
Qualitative Data
Data Interpretation
Data interpretation assigns meaning to analysed information, determining its significance and implications. It 
involves reviewing data to reach informed conclusions, with proper data mapping being essential as 
information comes from multiple sources.
There are two main types of analysis: quantitative and qualitative. The choice between them depends on 
your measurement scales, which include nominal (non-numeric categories), ordinal (logical order 
categories), interval (equal distances between categories), and ratio scales (features of all three).
When interpreting data, analysts must discern between correlation, causation, and coincidences to draw 
accurate conclusions.
Nominal Scale
Non-numeric categories that cannot be ranked 
or compared quantitatively (e.g., gender, 
nationality)
Ordinal Scale
Categories with logical order (e.g., quality 
ratings: good, very good, fair)
Interval Scale
Equal distances between categories with an 
arbitrary zero point (e.g., temperature in 
Celsius)
Ratio Scale
Contains features of all three scales with a true 
zero point (e.g., height, weight)
Qualitative Data 
Interpretation
Qualitative or narrative data is collected through person-to-
person techniques and described as 'categorical'. Rather 
than using numerical values, it relies on descriptive context 
or text to convey meaning.
This type of data must be 'coded' to facilitate grouping and 
labelling into identifiable themes, as it's often open to 
interpretation. The person-to-person collection approach 
follows three basic principles: notice things, collect things, 
and think about things.
Observations
Behaviour 
patterns including 
time spent in 
activities and 
communication 
methods used
Documents
Documentation 
resources coded 
and divided 
based on the type 
of material they 
contain
Interviews
The best 
collection method 
for narrative data, 
with responses 
grouped by 
theme, topic or 
category
Page 4


Quantitative and 
Qualitative Data
Data Interpretation
Data interpretation assigns meaning to analysed information, determining its significance and implications. It 
involves reviewing data to reach informed conclusions, with proper data mapping being essential as 
information comes from multiple sources.
There are two main types of analysis: quantitative and qualitative. The choice between them depends on 
your measurement scales, which include nominal (non-numeric categories), ordinal (logical order 
categories), interval (equal distances between categories), and ratio scales (features of all three).
When interpreting data, analysts must discern between correlation, causation, and coincidences to draw 
accurate conclusions.
Nominal Scale
Non-numeric categories that cannot be ranked 
or compared quantitatively (e.g., gender, 
nationality)
Ordinal Scale
Categories with logical order (e.g., quality 
ratings: good, very good, fair)
Interval Scale
Equal distances between categories with an 
arbitrary zero point (e.g., temperature in 
Celsius)
Ratio Scale
Contains features of all three scales with a true 
zero point (e.g., height, weight)
Qualitative Data 
Interpretation
Qualitative or narrative data is collected through person-to-
person techniques and described as 'categorical'. Rather 
than using numerical values, it relies on descriptive context 
or text to convey meaning.
This type of data must be 'coded' to facilitate grouping and 
labelling into identifiable themes, as it's often open to 
interpretation. The person-to-person collection approach 
follows three basic principles: notice things, collect things, 
and think about things.
Observations
Behaviour 
patterns including 
time spent in 
activities and 
communication 
methods used
Documents
Documentation 
resources coded 
and divided 
based on the type 
of material they 
contain
Interviews
The best 
collection method 
for narrative data, 
with responses 
grouped by 
theme, topic or 
category
Quantitative Data Interpretation
Quantitative data interpretation is fundamentally 'numerical', involving a set of processes to 
analyse numerical data through statistical modelling. It employs various statistical measures to 
derive meaningful insights from raw numbers.
These interpretation processes can be used together or separately, with comparisons made to 
arrive at conclusions. Advanced techniques like regression analysis, cohort analysis, and 
predictive/prescriptive analysis further enhance the depth of quantitative interpretation.
Mean
A numerical average for a 
set of responses, providing a 
central tendency measure
Standard Deviation
Reveals the distribution of 
responses around the mean, 
showing consistency within 
the data set
Frequency Distribution
Measures the rate of 
response appearance within 
a data set, determining the 
degree of consensus
Page 5


Quantitative and 
Qualitative Data
Data Interpretation
Data interpretation assigns meaning to analysed information, determining its significance and implications. It 
involves reviewing data to reach informed conclusions, with proper data mapping being essential as 
information comes from multiple sources.
There are two main types of analysis: quantitative and qualitative. The choice between them depends on 
your measurement scales, which include nominal (non-numeric categories), ordinal (logical order 
categories), interval (equal distances between categories), and ratio scales (features of all three).
When interpreting data, analysts must discern between correlation, causation, and coincidences to draw 
accurate conclusions.
Nominal Scale
Non-numeric categories that cannot be ranked 
or compared quantitatively (e.g., gender, 
nationality)
Ordinal Scale
Categories with logical order (e.g., quality 
ratings: good, very good, fair)
Interval Scale
Equal distances between categories with an 
arbitrary zero point (e.g., temperature in 
Celsius)
Ratio Scale
Contains features of all three scales with a true 
zero point (e.g., height, weight)
Qualitative Data 
Interpretation
Qualitative or narrative data is collected through person-to-
person techniques and described as 'categorical'. Rather 
than using numerical values, it relies on descriptive context 
or text to convey meaning.
This type of data must be 'coded' to facilitate grouping and 
labelling into identifiable themes, as it's often open to 
interpretation. The person-to-person collection approach 
follows three basic principles: notice things, collect things, 
and think about things.
Observations
Behaviour 
patterns including 
time spent in 
activities and 
communication 
methods used
Documents
Documentation 
resources coded 
and divided 
based on the type 
of material they 
contain
Interviews
The best 
collection method 
for narrative data, 
with responses 
grouped by 
theme, topic or 
category
Quantitative Data Interpretation
Quantitative data interpretation is fundamentally 'numerical', involving a set of processes to 
analyse numerical data through statistical modelling. It employs various statistical measures to 
derive meaningful insights from raw numbers.
These interpretation processes can be used together or separately, with comparisons made to 
arrive at conclusions. Advanced techniques like regression analysis, cohort analysis, and 
predictive/prescriptive analysis further enhance the depth of quantitative interpretation.
Mean
A numerical average for a 
set of responses, providing a 
central tendency measure
Standard Deviation
Reveals the distribution of 
responses around the mean, 
showing consistency within 
the data set
Frequency Distribution
Measures the rate of 
response appearance within 
a data set, determining the 
degree of consensus
Importance of Data Interpretation
The purpose of data collection and interpretation is to acquire useful information and make informed 
decisions. Proper data interpretation involves identifying and explaining data, comparing and contrasting 
different data sets, identifying outliers, and making future predictions.
When implemented correctly, data analysis processes provide businesses with significant advantages, 
including better knowledge about their operations and performance, cost efficiencies, and the ability to 
anticipate future needs through trend identification.
1
Informed Decision-making
Comprehensive data analysis should include 
identification, thesis development, data 
collection, and effective data communication
2
Anticipating Needs with Trends
Data insights provide valuable knowledge that 
empowers businesses to prepare for future 
demands
3
Cost Efficiency
Proper implementation of data analysis 
processes can provide businesses with 
profound cost advantages within their 
industries
4
Clear Foresight
Companies that collect and analyse their data 
gain better knowledge about themselves, 
their processes and overall performance