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DATA
INDEX
•Data
•Collection of data
•Organization of data
•Presentation of data
•Analysis of data
DATA
Data is any information in
raw or organized form
using alphabets , numbers
or symbols that refers to or
represents preferences,
ideas, objects, traits,
categories etc.
FOR EXAMPLE :
1. There is a lot Food that goes waste in India every
year.
• The amount of Food that goes waste in
India every year was more than 40%
valued at 58,000 crore.
2. The Population of India is Growing Rapidly.
• In India, 96.5% kids go to School : Survey
DATA
Qualitative
data
Quantitative
data
QUALITATIVE DATA
Qualitative data is a categorical
measurement expressed not in terms
of numbers, but rather by means of a
natural language description.
QUANTITATIVE DATA
Quantitative data is a
numerical
measurement
expressed not by
means of a natural
language description,
but rather in terms of
numbers.
DISTINCTION
Qualitative Data Quantitative Data
•Deals with descriptions.
•Data can be observed but
not measured.
•Colors, textures, smells,
tastes, appearance, beauty,
etc.
•Qualitative → Quality
Deals with numbers.
• Data which can be
measured.
• Length, height, area,
volume, weight, speed,
time, temperature, humidity,
sound levels, cost,
members, ages, etc.
• Quantitative → Quantity
COLLECTION
OF
DATA
DATA COLLECTION
Data collection is the process of
gathering and measuring information
on variables of interest, in an
established systematic fashion that
enables one to answer stated research
questions, test hypotheses, and
evaluate outcomes
SOURCES OF DATA
Primary Sources
INTERVIEW
QUESTIONNAIRE
INVESTIGATION
Secondary Sources
PUBLISHED
UNPUBLISHED
PRIMARY DATA
Primary data is a type of information that is
obtained directly from first-hand sources by means
of surveys, observation or experimentation. It is data
that has not been previously published and is
derived from a new or original research study and
collected at the source such as in marketing.
SOURCES OF COLLECTION OF PRIMARY
DATA
Direct Personal Interview – Data is personally
collected by the interviewer
Telephonic interviews – Data is collected
through an interview over the telephone with the
interviewer.
SOURCES OF COLLECTION OF PRIMARY
DATA
Indirect Oral Investigation – Data is
collected from third parties who have
information about subject of enquiry.
Information from correspondents – Data is
collected from agents appointed in the area of
investigation.
SOURCES OF COLLECTION OF PRIMARY
DATA
Mailed questionnaire – Data
is collected through
questionnaire mailed to the
informant.
Questionnaire filled by
enumerators – Data is
collected by trained
enumerators who fill
questionnaires.
ESSENTIALS OF A GOOD
QUESTIONNAIRE
• A covering letter with
objectives and scope of
survey.
• Minimum number of
questions.
• Avoid personal
questions.
• Questions should be
clear and simple.
• Questions should be
logically arranged
HOW TO COLLECT PRIMARY DATA?
These are the ways to collect primary data:
1. Sampling : It is a process through which we
choose a smaller group to collect data that can be
the best representative of the population.
2. Survey : It can be done in face to face
mode(interviews) or indirect mode (Telephone,
Internet etc.).
3. Census : It is method in which data is collected
from every unit of population.
TYPES OF SAMPLING :
Random Sampling :
It is a sampling method in which all the
items have equal chance of being selected
and the individuals who are selected are
just like the ones who are not selected.
Stratified Random Sampling :
It is a process to gather data by
separating the actual population into the
distinct subset or strata, and then
choosing simple random samples from
each stratum.
Census Method Sampling Method
1) Every unit of population studied
2) Reliable and accurate results
3) Expensive method
4) Suitable when population is of
homogenous nature.
1) Few units of population are
studied
2) Less Reliable and accurate
results
3) Less expensive method
4) Suitable when population is
of heterogeneous nature.
SECONDARY DATA
Secondary data is data that is not collected by
the person who is doing research. An
example of secondary data is a community
assessment done by another organization
but used to substantiate another
organization's research.
SOURCES OF SECONDARY DATA
• Published Source
• Government publications, Semi-government
publications etc.
• Unpublished Source
• Census of India, National Sample Survey
Organization [They are collected by the
organizations for their own record]
PRIMARY DATA VS SECONDARY DATA
Primary Data Secondary Data
Original and New Re-used and Old
Customized as per the
need of the research
May not be directly
linked with research
Primary Sources Secondary Sources
Less economical More economical
High on reliability Low on reliability
POINTS TAKEN INTO CONSIDERATION
BEFORE COLLECTION OF DATA
• Identify the problem or research on whatever you
need to explore.
• How will you differentiate b/w figures or record.
• How to present data in more meticulous way.
• What are the things required in collection of DATA
• What is the purpose of collection of DATA
• Source of DATA collection
PROCESS OF DATA COLLECTION
Data
Collection
Data
Analysis
Drawing
Inferences
Population
Sample
ORGANIZATION
OF
DATA
ORGANIZATION OF
DATA
Organization of Data: The process of grouping and
organizing data according to their characteristics is
known as Organization of data.
Steps in Organization:
• Classify the data for further statistical analysis;
• Prepare a frequency distribution table;
• Form classes;
• The method of tally marking.
BASIC TERMS
• Class : Each given internal is called a class e.g., 0-5, 5-10.
• Class limit: There are two limits upper limit and lower limit.
• Class interval: Difference between upper limit and lower limit.
• Range: Difference between upper limit and lower limit.
• Mid-point or Mid Value: ½(Upper limit - Lower limit)
• Frequency: Number of items [observations] falling within a
particular class.
• Cumulative Frequency Series: It is obtained by successively
adding the frequencies of the values of the classes according
to a certain law.
Statistical
Series
Individual
Series
Frequency
Series
Discrete Continuous
Inclusive
Exclusive
INDIVIDUAL SERIES
The arrangement of raw data individually. It can be
expressed in two ways.
• Alphabetical arrangement : Alphabetical order
• Array: Ascending or descending order.
EXAMPLE:
Marks in Maths in a class of 20 students:
68 89 78 92 74 85 76 83 89 59
78 95 64 89 51 47 37 76 87 91
FREQUENCY DISTRIBUTION
Frequency distribution refers to a table in which
observed values of a variable are classified
according to their numerical magnitude. It has two
types:
• Discrete Series: A variable is called discrete if the
variable can take only some particular values.
• Continuous Series: A variable is called continuous if
it can take any value in a given range.
DISCRETE SERIES
AGE OF STUDENTS NO. OF STUDENTS
16 4
17 12
18 21
19 8
20 5
CONTINUOUS SERIES
CLASS INTERVAL FREQUENCY CUMULATIVE
FREQUENCY
0-10 4 4
10-20 12 16
20-30 21 37
30-40 8 45
40-50 5 50
TYPES OF CONTINUOUS SERIES
• Exclusive Series: Excluding the upper limit of these
classes, all the items of the class are included in
the class itself. E.g., :
• Inclusive Series: Upper class limits of classes are
included in the respective classes . E.g.,
Marks 0-10 10-20 20-30 30-40
Number of
Students
2 5 2 1
Marks 0-9 10-19 20-29 30-39
Number of
Students
2 5 2 1
DATA
PRESENTATION
OF
PRESENTATION OF DATA
The mass data collected should be
presented in a suitable, concise form for
further analysis. The collected data may
be presented in the form of tabular or
diagrammatic or graphic form.
Modes of
Presentation
Tabular form
Diagrmatic
form
Line
diagrams
Bar
diagrams
Pie charts
TABULAR FORM
Data is presented in a form of table with the help of
inserting rows and column.
PIE CHART
Pie chart is a specialized graph used in statistics. The
independent variable is plotted around a circle in either a
clockwise direction or a counterclockwise direction.The
dependent variable (usually a percentage) is rendered as an
arc whose measure is proportional to the magnitude of the
quantity.
HISTOGRAM
A histogram is a representation of tabulated frequencies,
shown as adjacent rectangles, erected over discrete
intervals (bins), with an area equal to the frequency of the
observations in the interval.
BAR DIAGRAMS
A bar chart or bar graph is a chart with rectangular bars
with lengths proportional to the values that they represent.
The bars can be plotted vertically or horizontally. A vertical
bar chart is sometimes called a column bar chart.
OGIVES
A distribution curve in which the frequencies are
cumulative is called Ogive. It can be More than
Ogive or Less than Ogive.
METHOD TO DRAW OGIVES
ANALYSIS OF DATA
The data presented should be carefully analysed for
making inference from the presented data such as
measures of central tendencies, dispersion,
correlation, regression etc
• MEAN
• The mean is the sum the total observations divided by
the number of observations. It refers to an average.
• MODE
• Mode gives the most frequently used common value.
• MEDIAN
• In statistics, the median is the numerical value
separating the higher half of a data from the lower half.
Data

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Data

  • 2. INDEX •Data •Collection of data •Organization of data •Presentation of data •Analysis of data
  • 3. DATA Data is any information in raw or organized form using alphabets , numbers or symbols that refers to or represents preferences, ideas, objects, traits, categories etc.
  • 4. FOR EXAMPLE : 1. There is a lot Food that goes waste in India every year. • The amount of Food that goes waste in India every year was more than 40% valued at 58,000 crore. 2. The Population of India is Growing Rapidly. • In India, 96.5% kids go to School : Survey
  • 6. QUALITATIVE DATA Qualitative data is a categorical measurement expressed not in terms of numbers, but rather by means of a natural language description.
  • 7. QUANTITATIVE DATA Quantitative data is a numerical measurement expressed not by means of a natural language description, but rather in terms of numbers.
  • 8. DISTINCTION Qualitative Data Quantitative Data •Deals with descriptions. •Data can be observed but not measured. •Colors, textures, smells, tastes, appearance, beauty, etc. •Qualitative → Quality Deals with numbers. • Data which can be measured. • Length, height, area, volume, weight, speed, time, temperature, humidity, sound levels, cost, members, ages, etc. • Quantitative → Quantity
  • 9.
  • 11. DATA COLLECTION Data collection is the process of gathering and measuring information on variables of interest, in an established systematic fashion that enables one to answer stated research questions, test hypotheses, and evaluate outcomes
  • 12. SOURCES OF DATA Primary Sources INTERVIEW QUESTIONNAIRE INVESTIGATION Secondary Sources PUBLISHED UNPUBLISHED
  • 13. PRIMARY DATA Primary data is a type of information that is obtained directly from first-hand sources by means of surveys, observation or experimentation. It is data that has not been previously published and is derived from a new or original research study and collected at the source such as in marketing.
  • 14. SOURCES OF COLLECTION OF PRIMARY DATA Direct Personal Interview – Data is personally collected by the interviewer Telephonic interviews – Data is collected through an interview over the telephone with the interviewer.
  • 15. SOURCES OF COLLECTION OF PRIMARY DATA Indirect Oral Investigation – Data is collected from third parties who have information about subject of enquiry. Information from correspondents – Data is collected from agents appointed in the area of investigation.
  • 16. SOURCES OF COLLECTION OF PRIMARY DATA Mailed questionnaire – Data is collected through questionnaire mailed to the informant. Questionnaire filled by enumerators – Data is collected by trained enumerators who fill questionnaires.
  • 17. ESSENTIALS OF A GOOD QUESTIONNAIRE • A covering letter with objectives and scope of survey. • Minimum number of questions. • Avoid personal questions. • Questions should be clear and simple. • Questions should be logically arranged
  • 18. HOW TO COLLECT PRIMARY DATA? These are the ways to collect primary data: 1. Sampling : It is a process through which we choose a smaller group to collect data that can be the best representative of the population. 2. Survey : It can be done in face to face mode(interviews) or indirect mode (Telephone, Internet etc.). 3. Census : It is method in which data is collected from every unit of population.
  • 19. TYPES OF SAMPLING : Random Sampling : It is a sampling method in which all the items have equal chance of being selected and the individuals who are selected are just like the ones who are not selected. Stratified Random Sampling : It is a process to gather data by separating the actual population into the distinct subset or strata, and then choosing simple random samples from each stratum.
  • 20. Census Method Sampling Method 1) Every unit of population studied 2) Reliable and accurate results 3) Expensive method 4) Suitable when population is of homogenous nature. 1) Few units of population are studied 2) Less Reliable and accurate results 3) Less expensive method 4) Suitable when population is of heterogeneous nature.
  • 21. SECONDARY DATA Secondary data is data that is not collected by the person who is doing research. An example of secondary data is a community assessment done by another organization but used to substantiate another organization's research.
  • 22. SOURCES OF SECONDARY DATA • Published Source • Government publications, Semi-government publications etc. • Unpublished Source • Census of India, National Sample Survey Organization [They are collected by the organizations for their own record]
  • 23. PRIMARY DATA VS SECONDARY DATA Primary Data Secondary Data Original and New Re-used and Old Customized as per the need of the research May not be directly linked with research Primary Sources Secondary Sources Less economical More economical High on reliability Low on reliability
  • 24. POINTS TAKEN INTO CONSIDERATION BEFORE COLLECTION OF DATA • Identify the problem or research on whatever you need to explore. • How will you differentiate b/w figures or record. • How to present data in more meticulous way. • What are the things required in collection of DATA • What is the purpose of collection of DATA • Source of DATA collection
  • 25. PROCESS OF DATA COLLECTION Data Collection Data Analysis Drawing Inferences Population Sample
  • 27. ORGANIZATION OF DATA Organization of Data: The process of grouping and organizing data according to their characteristics is known as Organization of data. Steps in Organization: • Classify the data for further statistical analysis; • Prepare a frequency distribution table; • Form classes; • The method of tally marking.
  • 28. BASIC TERMS • Class : Each given internal is called a class e.g., 0-5, 5-10. • Class limit: There are two limits upper limit and lower limit. • Class interval: Difference between upper limit and lower limit. • Range: Difference between upper limit and lower limit. • Mid-point or Mid Value: ½(Upper limit - Lower limit) • Frequency: Number of items [observations] falling within a particular class. • Cumulative Frequency Series: It is obtained by successively adding the frequencies of the values of the classes according to a certain law.
  • 30. INDIVIDUAL SERIES The arrangement of raw data individually. It can be expressed in two ways. • Alphabetical arrangement : Alphabetical order • Array: Ascending or descending order. EXAMPLE: Marks in Maths in a class of 20 students: 68 89 78 92 74 85 76 83 89 59 78 95 64 89 51 47 37 76 87 91
  • 31. FREQUENCY DISTRIBUTION Frequency distribution refers to a table in which observed values of a variable are classified according to their numerical magnitude. It has two types: • Discrete Series: A variable is called discrete if the variable can take only some particular values. • Continuous Series: A variable is called continuous if it can take any value in a given range.
  • 32. DISCRETE SERIES AGE OF STUDENTS NO. OF STUDENTS 16 4 17 12 18 21 19 8 20 5 CONTINUOUS SERIES CLASS INTERVAL FREQUENCY CUMULATIVE FREQUENCY 0-10 4 4 10-20 12 16 20-30 21 37 30-40 8 45 40-50 5 50
  • 33. TYPES OF CONTINUOUS SERIES • Exclusive Series: Excluding the upper limit of these classes, all the items of the class are included in the class itself. E.g., : • Inclusive Series: Upper class limits of classes are included in the respective classes . E.g., Marks 0-10 10-20 20-30 30-40 Number of Students 2 5 2 1 Marks 0-9 10-19 20-29 30-39 Number of Students 2 5 2 1
  • 35. PRESENTATION OF DATA The mass data collected should be presented in a suitable, concise form for further analysis. The collected data may be presented in the form of tabular or diagrammatic or graphic form.
  • 37. TABULAR FORM Data is presented in a form of table with the help of inserting rows and column.
  • 38. PIE CHART Pie chart is a specialized graph used in statistics. The independent variable is plotted around a circle in either a clockwise direction or a counterclockwise direction.The dependent variable (usually a percentage) is rendered as an arc whose measure is proportional to the magnitude of the quantity.
  • 39. HISTOGRAM A histogram is a representation of tabulated frequencies, shown as adjacent rectangles, erected over discrete intervals (bins), with an area equal to the frequency of the observations in the interval.
  • 40. BAR DIAGRAMS A bar chart or bar graph is a chart with rectangular bars with lengths proportional to the values that they represent. The bars can be plotted vertically or horizontally. A vertical bar chart is sometimes called a column bar chart.
  • 41. OGIVES A distribution curve in which the frequencies are cumulative is called Ogive. It can be More than Ogive or Less than Ogive.
  • 42. METHOD TO DRAW OGIVES
  • 43. ANALYSIS OF DATA The data presented should be carefully analysed for making inference from the presented data such as measures of central tendencies, dispersion, correlation, regression etc
  • 44. • MEAN • The mean is the sum the total observations divided by the number of observations. It refers to an average. • MODE • Mode gives the most frequently used common value. • MEDIAN • In statistics, the median is the numerical value separating the higher half of a data from the lower half.