This document discusses quantification and statistical techniques in social research. It begins by defining quantification as the act of counting and measuring human observations and experiences to map them to quantities. Quantitative methods emphasize objective measurements and statistical analysis of data from polls, questionnaires, and surveys. The document then discusses how quantification is used in economics and psychology through gathering empirical data and using statistical analysis techniques like regression analysis. It also outlines several statistical techniques used for data analysis, including descriptive, exploratory, inferential, predictive, and causal techniques. Specific statistical methods discussed include mean, standard deviation, regression, sample size determination, and hypothesis testing.

1. Presented by:
Dr. TRIPTI SHARMA.
QUANTIFICATION IN SOCIAL
RESEARCH

2. INTRODUCTION
 In mathematics and empirical science, quantification is
the act of counting and measuring that maps human
sense observations and experiences into quantities.
 Quantification in this sense is fundamental to the
scientific method.
 Quantitative methods emphasize objective
measurements and the statistical, mathematical, or
numerical analysis of data collected through polls,
questionnaires, and surveys, or by manipulating pre-
existing statistical data using computational
techniques.

3. QUANTIFICATION
 In the social sciences, quantification is an integral part
of economics and psychology.
 Both disciplines gather data (economics by empirical
observation) and (psychology by experimentation)
 Both use statistical techniques such as
regression analysis to draw conclusions from it.

4. STATISTICS

5. INTRODUCTION
 In the modern world of computers and information
technology, the importance of statistics is very well
recognized by all the disciplines. Statistics has
originated as a science of statehood and found
applications slowly and steadily in Agriculture,
Economics, Commerce, Biology, Medicine, Industry,
planning, education and so on. As on date there is no
other human walk of life, where statistics cannot be
applied.

6. MEANING
 Statistics is concerned with scientific methods for
collecting, organizing, summarizing, presenting and
analyzing data as well as deriving valid conclusions and
making reasonable decisions on the basis of this analysis.
Statistics is concerned with the systematic collection of
numerical data and its interpretation.
The word ‘statistic’ is used to refer to
 1. Numerical facts, such as the number of people living in
particular area.
 2. The study of ways of collecting, analyzing and
interpreting the facts.

7. STATISTICS: MEANING
 The practice or science of collecting and analyzing
numerical data in large quantities, especially for the
purpose of inferring proportions in a whole from those
in a representative sample is known as Statistics.

8. SCOPE
 Statistics is not a mere device for collecting numerical
data, but as a means of developing sound techniques
for their handling, analyzing and drawing valid
inferences from them.
 Statistics is applied in every sphere of human activity
social as well as physical like Biology, Commerce,
Education, Planning, Business Management,
Information Technology, etc. It is almost impossible to
find a single department of human activity where
statistics cannot be applied. We now discuss briefly the
applications of statistics in other disciplines.

9. LIMITATION
 Statistics is not suitable to the study of qualitative
phenomenon:
 Statistics does not study individuals:
 Statistical laws are not exact:
 Statistics table may be misused:
 Statistics is only, one of the methods of studying a
problem:

10. CONCLUSION
 Statistics is indispensable in this modern age aptly
termed as "the age of planning".
 Statistical data and techniques of statistical analysis are
immensely useful in solving economical problems
such as wages, price, time series analysis, demand
analysis.

11. STATISTICAL TECHNIQUES OF
DATA ANALYSIS

12.  Data analysis has two prominent methods: qualitative
research and quantitative research. Each method has
their own techniques. Interviews and observations are
forms of qualitative research, while experiments and
surveys are quantitative research.

13. TYPES
 Descriptive
 Exploratory
 Inferential
 Predictive
 Causal.
 Some, however, are more specific, such as qualitative
analysis, which looks for things like patterns
and colors, and quantitative analysis, which focuses on
numbers.

14. PROCESS
 Data analysis is a process of inspecting, cleansing,
transforming, and modeling data with the goal of
discovering useful information, suggesting
conclusions, and supporting decision-making.
 The term data analysis is sometimes used as a
synonym for data modeling.

15. STATISTICAL METHODS
Mean
 The arithmetic mean, more commonly known as “the
average,” is the sum of a list of numbers divided by the
number of items on the list. The mean is useful in
determining the overall trend of a data set or providing
a rapid snapshot of your data. Another advantage of the
mean is that it’s very easy and quick to calculate.

16. Standard Deviation
 The standard deviation, often represented with the
Greek letter sigma, is the measure of a spread of data
around the mean. A high standard deviation signifies
that data is spread more widely from the mean, where
a low standard deviation signals that more data align
with the mean. In a portfolio of data analysis methods,
the standard deviation is useful for quickly
determining dispersion of data points.

17. Regression
 Regression models the relationships between
dependent and explanatory variables, which are
usually charted on a scatter plot. The regression line
also designates whether those relationships are strong
or weak. Regression is commonly taught in high
school or college statistics courses with applications
for science or business in determining trends over
time.

18. Sample Size Determination
 When measuring a large data set or population, like a
workforce, you don’t always need to collect
information from every member of that population – a
sample does the job just as well. The trick is to
determine the right size for a sample to be accurate.
Using proportion and standard deviation methods, you
are able to accurately determine the right sample size
you need to make your data collection statistically
significant.

19. Hypothesis Testing
 Also commonly called t testing, hypothesis testing
assesses if a certain premise is actually true for your
data set or population. In data analysis and statistics,
you consider the result of a hypothesis test statistically
significant if the results couldn’t have happened by
random chance. Hypothesis tests are used in
everything from science and research to business and
economic.