INTRODUCTION
In order to make the analysis on the collected data, it is classified and
tabulated.
• The collected data is classified into classes and sub-classes according to some
characteristics. This process is known as ‘Classification’.
• The process of tabulation is to present the classified data in a systematic tabular form.
KEYWORDS
RAW DATA
The collected data is complex and unorganised mass of figures, which is very difficult
to analyse and interpret. This form of unorganised data is called Raw Data.
MEANING OF CLASSIFICATION
Classification is the process of arranging data into sequence and groups according to
their common characteristics or separating them into different but related parts.
• Under classification, on the basis of chosen characteristics, the similarity and dissimilarity in
the various items are noted and items exhibiting similarity are grouped together in one class.
Through classification, we try to strike a note of homogeneity in the heterogeneous
elements of the collected information.
Classification of data serves the following purposes:
• It condenses the raw data into a form, which is suitable for statistical analysis.
• It removes complexities and highlights the features of the data.
• It facilitates comparisons and in drawing inferences from the data.
• It provides information about the mutual relationships among the elements of a data set.
• It helps in statistical analysis by separating elements of the data set into homogeneous
groups.
,Objectives of classification
• To simplify and condense the mass of data:
In classification ,the aim is to eliminate unnecessary details and convert the
huge mass of complex data into simple ,condensed, logical and comprehensible form.
Example: The huge and fragmented data collected during a population census has to be classified
according to gender, marital status, education, occupation, etc to ascertain the structure and
nature of the population.
• To explain similarity and dissimilarity of Data:
Classification facilities the grouping of data according to certain similarities(affinities) and
dissimilarities(diversities).This enables the investigators to grasp them easily. Facts like educated and
uneducated, married and unmarried, employed and unemployed etc are kept in separate classes.
• To facilitate comparisons:
Classification enables us to make meaningful comparisons ,draw inferences and locate fact.
• To study the relationships:
Classification helps in finding out cause and effect relationship based on some criteria
between the data.
• Example: The characteristics of income and education can be related after classifying the
mass of data.
• To prepare the data for tabulation:
Only classified data can be presented in tabular form. Classification, thus provides a basis
for tabulation and further statistical processing.
• To present a mental picture:
The process of classification enables one to form a mental picture of objects and
summarised data can easily be remembered.
Requisites of a good classification
• Suitability:
The classification should conform to the object of the enquiry.
Example: If investigation is conducted to inquire into the economic conditions
of workers, then it will be of no use to classify them on the basis of religion.
• Unambiguous:
The classification should not lead to any ambiguity or confusion.
• Exhaustiveness:
, Classification should be so exhaustive that every unit of the series should find the place in
one group or another.
• Flexibility:
A good classification should be capable of being adjusted according to the changed
situations and conditions.
• Mutually Exclusive:
The classes must not overlap so that an observed value belongs to one and only one of
the classes.
• Stability:
The principal of classification should remain the same throughout the analysis, otherwise
it will not be possible to get meaningful results.
• Homogeneity:
It is said to be homogeneous if similar items are placed in a class. All units belonging to
a group should exhibit similar characteristics.
Methods of Classification
Statistical data is classified after taking into account the nature, scope and purpose
of an investigation. Data is classified on four factors.
• Geographical Classification(Spatial Classification)
When the data is classified according to geographical location or region (such as
countries, states, districts etc) it is known as geographical classification. When population of different
states is presented, it is according to geographical location.
Example
, • Chronological Classification(Temporal Classification)
When the data is classified with respect to time (such as decade, years, months etc),
such type of classification is known as chronological classification.
Example
• Qualitative Classification
In qualitative classification, data is classified on the basis of descriptive
characteristics or on the basis of attributes like sex, literacy, religion, caste, education etc, which
cannot be quantified.
• Simple Classification:
When facts are classified into two classes according to one attribute only, then the
classification is said to be simple.
Example: If we divide the population of a city into two groups (males and females), then
classification is simple.
In order to make the analysis on the collected data, it is classified and
tabulated.
• The collected data is classified into classes and sub-classes according to some
characteristics. This process is known as ‘Classification’.
• The process of tabulation is to present the classified data in a systematic tabular form.
KEYWORDS
RAW DATA
The collected data is complex and unorganised mass of figures, which is very difficult
to analyse and interpret. This form of unorganised data is called Raw Data.
MEANING OF CLASSIFICATION
Classification is the process of arranging data into sequence and groups according to
their common characteristics or separating them into different but related parts.
• Under classification, on the basis of chosen characteristics, the similarity and dissimilarity in
the various items are noted and items exhibiting similarity are grouped together in one class.
Through classification, we try to strike a note of homogeneity in the heterogeneous
elements of the collected information.
Classification of data serves the following purposes:
• It condenses the raw data into a form, which is suitable for statistical analysis.
• It removes complexities and highlights the features of the data.
• It facilitates comparisons and in drawing inferences from the data.
• It provides information about the mutual relationships among the elements of a data set.
• It helps in statistical analysis by separating elements of the data set into homogeneous
groups.
,Objectives of classification
• To simplify and condense the mass of data:
In classification ,the aim is to eliminate unnecessary details and convert the
huge mass of complex data into simple ,condensed, logical and comprehensible form.
Example: The huge and fragmented data collected during a population census has to be classified
according to gender, marital status, education, occupation, etc to ascertain the structure and
nature of the population.
• To explain similarity and dissimilarity of Data:
Classification facilities the grouping of data according to certain similarities(affinities) and
dissimilarities(diversities).This enables the investigators to grasp them easily. Facts like educated and
uneducated, married and unmarried, employed and unemployed etc are kept in separate classes.
• To facilitate comparisons:
Classification enables us to make meaningful comparisons ,draw inferences and locate fact.
• To study the relationships:
Classification helps in finding out cause and effect relationship based on some criteria
between the data.
• Example: The characteristics of income and education can be related after classifying the
mass of data.
• To prepare the data for tabulation:
Only classified data can be presented in tabular form. Classification, thus provides a basis
for tabulation and further statistical processing.
• To present a mental picture:
The process of classification enables one to form a mental picture of objects and
summarised data can easily be remembered.
Requisites of a good classification
• Suitability:
The classification should conform to the object of the enquiry.
Example: If investigation is conducted to inquire into the economic conditions
of workers, then it will be of no use to classify them on the basis of religion.
• Unambiguous:
The classification should not lead to any ambiguity or confusion.
• Exhaustiveness:
, Classification should be so exhaustive that every unit of the series should find the place in
one group or another.
• Flexibility:
A good classification should be capable of being adjusted according to the changed
situations and conditions.
• Mutually Exclusive:
The classes must not overlap so that an observed value belongs to one and only one of
the classes.
• Stability:
The principal of classification should remain the same throughout the analysis, otherwise
it will not be possible to get meaningful results.
• Homogeneity:
It is said to be homogeneous if similar items are placed in a class. All units belonging to
a group should exhibit similar characteristics.
Methods of Classification
Statistical data is classified after taking into account the nature, scope and purpose
of an investigation. Data is classified on four factors.
• Geographical Classification(Spatial Classification)
When the data is classified according to geographical location or region (such as
countries, states, districts etc) it is known as geographical classification. When population of different
states is presented, it is according to geographical location.
Example
, • Chronological Classification(Temporal Classification)
When the data is classified with respect to time (such as decade, years, months etc),
such type of classification is known as chronological classification.
Example
• Qualitative Classification
In qualitative classification, data is classified on the basis of descriptive
characteristics or on the basis of attributes like sex, literacy, religion, caste, education etc, which
cannot be quantified.
• Simple Classification:
When facts are classified into two classes according to one attribute only, then the
classification is said to be simple.
Example: If we divide the population of a city into two groups (males and females), then
classification is simple.
