A presentation on data Analysis for MBA.

1. DATA ANALYSIS

2. INTRODUCTION
1. The data, after collection, has to be prepared for
analysis.
2. Collected data is raw and it must be converted the
form that is suitable for the required analysis
3. The result of the analysis are affected a lot by the form
of the data.
4. So, proper data preparation is must to get reliable
result

3. IMPORTANT STEPS
QUESTIONNAIRE
CHECKING EDITING CODING CLASSIFICATION
TABULATION
GRAPHICAL
REPRESENTATION
DATA CLEANING
DA
TA ADJUSTING

4. QUESTIONNAIRE CHECKING
When the data is collected through questionnaires, the first steps of data
preparation process is to check the questionnaires if they are accepted or not.
NOT ACCEPTED IF:
 Incomplete partially or fully.
 Answered by a person who has inadequate knowledge
which gives the impression that the impression that the
respondent could not understand the questions.

5. EDITING
Editing of data is a process of examining the collected raw data (specially in
surveys to detect errors and omissions and to correct these when possible.
FIELD EDITING
CENTRAL EDITING
Translating
or
rewriting
Wrong
and
replacement

6. 1) EDITING
 Editing of data is a process of examining th collected raw data (especially in surveys) t
detect errors and omissions and to correct thes when possible.
 Editing is done to assure that the data ar accurate, consistent withother
facts gathere uniformly entered, as completed as possible and have been well
arranged to facilitate coding and tabulation.
 EDITING FIELD EDITING
CENTRAL EDITING

7. FIELD EDITING
Field editing consists in the review of the reporting forms by the
investigator for completing (translating or rewriting) what the latter
has written in abbreviated and/or in illegible form at the time of
recording the respondents’ responses.
This type of editing is necessary in view of the fact that
individual writing styles often can be difficult for others to decipher.
CENTRAL EDITING
Central editing should take place when all forms or schedules
have been completed and returned to the office.
This type of editing implies that all forms should get a thorough
editing by a single editor in a small study and by a team of editors
in case of a large inquiry.

8. 3) TABULATION
Tabulation is the process of summarizing raw data and displaying the same in
compact form (i.e., In the form of statistical tables) for further analysis.
 In A broader sense, tabulation is an orderly
arrangement of data in columns and rows.
Tabulation is essential because of the following reasons
It conserves space and reduces explanatory and
descriptive statement to a minimum.
It facilitates the process of comparison.
It facilitates the summation of items and the detection of errors and omissions.
It provides a basis for various statistical
computations.

9. CODING
• Coding refers to the process of assigning numerals or other symbols to answers so
that responses can be put into limited number of categories or classes.

10. 2) CODING
Coding refers to the process of assigning
numerals or other symbols to answers so
that responses can be put into a limited
number of categories or classes.
Coding is necessary for efficient analysis
and through it the several replies may be
reduced to a small number of classes which
contain the critical information required for
analysis.
Coding decisions should usually be taken
at the designing stage of the questionnaire.
It makes it possible to precode the
quesistionnaire choices and which in turn is
helpful for computer tabulation as one can
straight forward key punch from the original
questionnaires.

11. CLASSIFICATION
group or
Classification of data which happens to be the process of arranging data
classes on the basis of common characteristics.

12. 3) CLASSIFICATION
Classification of data which happens to be the
process of arranging data in groups or classes on the
basis of common characteristics.
Data having a common characteristic are placed in
one class and in this way the entire data get divided
into a number of groups or classes.
 TYPES OF CLASSIFICATION ACC. TO ATTRIBUTES
ACC. TO CLASS INTERVAL
ACC. TO ATTRIBUTES
data are classified on the basis of common characteristics which can either be
descriptive (such as literacy, sex, honesty, etc.) or numerical (such as weight,
height, income, etc.)
ACC. TO CLASS INTERVAL EXCLUSIVE TYPE
12/62014
INCLUSIVE TYPE

13. • Attributes
only their presence and absence in
an individual items can be noticed.
• Class-intervals
size of each class into which a range of
a variable is divided.

14. TABULATION
• Tabulation is the process of summarizing raw data and displaying the same in
compact form( i.e., in the form of statistical tables ) for further analysis.
• Tabulation is an orderly arrangement of data in columns and rows.

15. GRAPHICAL REPRESENTATION
• Graphs help to understand the data easily.
• Most common graphs are bar charts and pie charts.

16. DATA CLEANING
• Checking the data for consistency and treatment for missing value.

17. DATA ADJUSTING
analysis
• Data adjusting is not always necessary but it may improve the quality
sometimes.

18.  SPSS (Statistical Package for the Social Sciences) is a software package widely used for statistical analysis
in social sciences, such as psychology, sociology, economics, and other fields of research. It provides a
comprehensive set of tools and capabilities for data management, data manipulation, and statistical
analysis.
 SPSS allows researchers to perform various tasks, including data cleaning, data transformation, descriptive
statistics, hypothesis testing, regression analysis, factor analysis, cluster analysis, and more. It offers a user-
friendly interface that facilitates the analysis process, making it accessible to users with different levels of
statistical expertise.
 The software allows users to import data from various sources, including spreadsheets and databases, and
provides a range of options for data visualization through charts, graphs, and tables. SPSS also supports
the creation of custom reports and the integration of statistical output with other applications.
 Although SPSS was initially designed for social science research, it has gained popularity in other fields as
well due to its versatility and extensive statistical capabilities. However, it's worth noting that SPSS has
faced competition from other statistical software packages such as R and Python, which provide open-
source alternatives with more flexibility and customization options.
SPSS

19. https://www.youtube.com/watch?v=Wp2eUfLlusk
https://www.youtube.com/watch?v=Wp2eUfLlusk