The document discusses data processing and analysis. It defines data processing as collecting, organizing, and analyzing raw data to extract meaningful information. Key aspects of data processing include data preparation, conversion of raw data into useful information, and outputting results through reports, graphs, and diagrams. Common data processing methods are manual, electronic, and hybrid. The document also outlines steps in data analysis such as compilation, editing, coding, classification, and tabulation. It describes descriptive and inferential statistics used to analyze quantitative and qualitative data. Finally, it discusses various charts, diagrams, tables, and graphs used to visually present analyzed data.

1. DATA PROCESSING / DATA
PREPARATION

2.  The process of producing meaningful information by
collecting all items of data together and systematically
analyzed on them to extract the required information
about them.
 Data preparation (cleaning & organizing data for
analysis) involves logging or checking the data in,
checking the data for correctness, entering data into the
computer, transforming the data and documenting as
well as developing a database structure to integrate
different measures.
 Data processing is to convert raw data into meaningful
information that improves current situation resolutions
and existing problems.
 The data processing is output often takes several forms
such as reports, graphs, and diagrams that make the
data easier to understand and analyze.
 data processing can be done by these three methods
1. Manual data processing (by hand)

3.  Data analysis process includes the following four
steps:
1. Compilation: Compilation process includes
gathering together all the collected data in a
manner that a process of analysis can be initiated.
While compiling the data, care is to be taken to
arrange all the data in an order so that editing and
coding process can be implemented with ease.
2. Editing : Editing implies the checking of the
gathered data for accuracy, utility and
completeness. If the raw data are erroneous or
inconsistent, these deficiencies will be carried
through all subsequent stages of processing and
will greatly distort the result of any enquiry. So the
editor or project director must see that none of the

4. 3. Coding : coding is important for analysis as
numerous replies can be reduced to a small number
of classes through coding. The original data are
transformed into symbols compatible with manual or
computer assisted analysis. Coding can be carried
out before or after the actual data are collected.
Code is an abbreviation, a symbol, a number, or an
alphabet, which is assigned by the investigator to
every schedule item and response category.
4. Classification : The classification of data is
necessary, as many investigators result in large
volumes of raw data which must be reduced to
homogenous groups. In the process of
classification, we divide and arrange the entire data
into different categories, groups, or classes on the
basis of common characteristics. Like geographical
classification, chronological classification, qualitative
( gender, religion), quantitative ( ht eeight, income,

5. 5. Tabulation : It is the recording of the classified
data in accurate mathematical terms, for
example, marking and counting the frequency
tallied. The arrangement of the assembled data
has to be done in concise and logical order. The
tabulation can be done by using simple table or
complex table.

6. DATA ANALYSIS AND
INTERPRETATION

7.  Data analysis means the categorized ordering,
manipulating and summarizing the data to obtain
an answer to the investigation question.”
 Analysis of quantitative data deals with
information collected during investigation, which
can be quantified and statistical calculations can
be computed.
 Analysis of qualitative data is a time consuming,
detail oriented, and seemingly overwhelming task
that provides ways of discerning, examining,
comparing and interpreting meaningful patterns of
themes. It provides the narrative information into
a coherent scheme.

8. Methods of data analysis
1. Descriptive or Summary Statistics ( describing the
data)
2. Inferential statistics
1. Descriptive statistics
- It is used to describe the basic features of a collection
of data in quantitative terms.
- Descriptive statistics is used to organize and
summarize the data to draw meaningful interpretations.
- Descriptive data is used to describe the basic features
of data and to provide simple summaries about the
sample and the measures used in a study.
- Percentages, means of central tendency (mean,
median, mode), and means of dispersion (Standard
deviation, range, and mean deviation) are the examples

9. 2. Inferential Statistics
- It is concerned with populations and used sample
data to make an inference about the population or
to test the hypotheses considered at the
beginning if the investigation.
- it is a conclusion or judgment based on evidence.
Statistical inference are made cautiously and with
great care.
- it helps in drawing inferences from the data, for
example finding the differences, relationship and
association between two or more variables.
- The most commonly used inferential statistical
test are Z- test, t- test, ANOVA, chi- square tests,
etc.

10. S.N TEST NAME SIGNIFICANCE
1. T-TEST
(PAIRED )
T-TEST
(UNPAIRED)
It is used to compare tow quantitative
measurements taken from the same
group
It is used to compare means between
two distinct/ independent groups.
2. Z-TEST - It is used to compare means between
two distinct/ independent groups
3. ANOVA TEST - It is used to compare means between
three or more distinct/ independent
groups but may be used for more than
two repeat measures of same group.
4. Chi- Square
Test
- It is used to find out the association
between two nominal or ordinal sets of
data/ variables.

11. CHARTS AND DIAGRAMS

12.  TABLES
 Frequency distribution table
 Contingency table
 Multiple response table
 Miscellaneous table
 DIAGRAMS AND CHARTS
BAR DIAGRAM
PIA DIAGRAM/SECTOR DIAGRAM
HISTOGRAM FREQUENCY POLYGON
CUMULATIVE FREQUECY CURVE
SCATTERED OR DOTTED DIAGRAMS
PICTOGRAMS
MAP DIAGRAM OR SPOT MAP

13. TABLES
 Table present data in a concise, systematic manner from
masses of statistical data
 Tabulation means a systematic presentation of
information contained in the data in rows and columns in
accordance with some common features and
characteristics.
 Rows are horizontal and columns are vertical
arrangements.
 Parts of tables
 Table number
 Title
 Head notes
 Captions and stubs
 Body of table
 Footnotes
 Source note

14. 1. Frequency distribution table
- These tables present the frequency and
percentage distribution of the information
collected, where an attribute is grouped into
number of classes, which may vary between
three and eight.

15. 2. Contingency table
- Tables that report the frequency distribution of
two nominal variables simultaneously and that
include the totals are known as contingency
tables.
- The categories considered should be mutually
exclusive as well as exhaustive ( observations
cannot be beyond these categories).

16. 3. Multiple response table
- When classification of the cases is done into
categories that are neither exclusive nor
exhaustive, then it is called a multiple-response
table.
- A patient can have two or more complaints, but
only the major ones may be listed. In such cases,
the sum total of frequencies would exceed the
total number of subjects and may lead to
confusion.
- Therefore, the total no. of subjects in case of
multiple responses is given as base and from this
percentages can be calculated.

17. GRAPHICAL PRESENTTAION OF
DATA
1. BAR DIAGRAM
- It is convenient graphical device that is particularly
useful for displaying nominal or ordinal data.
- It is an easy method adopted for visual comparison
of the magnitude of different frequencies.
- Length of the bars drawn vertically or horizontally
indicates the frequency of a character.
- The bar charts are called vertical bar charts (or
column charts), if the bars are placed vertically.
When the bars placed horizontally, it is called
horizontal bar charts.
- There are three types of bar diagrams: simple,
multiple and proportion bar diagrams.

18. - Some of the points to be kept in mind while
making a bar diagram are as follows,
- The width of bars should be uniform throughout the
diagram
- The gap between the bars should be uniform
throughout
- Bars may be vertical or horizontal

21. 2. PIA- DIAGRAM/SECTOR DIAGRAM
- It is another useful pictorial device for presenting
discrete data of qualitative characteristics, such as
age groups, genders, and occupational groups in a
population.
- The total area of the circle represents the entire data
under consideration.
- Size of each angle is calculated by multiple class
percentages with 360 degree or following formula
may be used:
= class frequency/ total observation x 360

23. 3. Histogram
- It is the most commonly graphical representation of
grouped frequency distribution.
- Variables characters of the different groups are
indicated on the horizontal line (x-axis) and
frequencies( number of observation) are indicated
on the horizontal line (y-axis).
- Frequency of each group forms a column or
rectangle.
- Such a diagram is called a histogram.
- The area of rectangle is proportional to the
frequency of the correspondence class interval and
the total area of the histogram is proportional to the
total frequency of all the class intervals.

25. 4. Frequency polygon
- It is a curve obtained by joining the middle top
points of the rectangles in a histogram by straight
lines.
- It gives a polygon, that is figure with many angles.
- In this, the two end points of the line drawn are
joined to the horizontal axis at the midpoint of the
empty class-interval at both ends of the frequency
distribution.
- Frequency polygons are simple and sketch an
outline of data pattern more clearly than
histograms.
- On the same axis, one can plot frequency
polygons of several distributions, thereby making
comparisons possible.

27. 5. LINE GRAPHS
- In this variables in the frequency polygon are
depicted by a line. It is mostly used where data is
collected over a long period of time.
- On x-axis, values of independent variables are
taken and values of dependent variables are taken
on y-axis.
- Vertical may not start from zero, but at some point,
from where frequency starts.
- With reference to x-axis, y-axis the given data may
be plotted and these consecutive points or data are
then joined by straight lines.

29. 6. CUMULATIVE FREQUENCY CURVE / OGIVE
- This graph represents the data of a cumulative
frequency distribution.
- For drawing on this, an ordinary frequency
distribution table is converted into cumulative
frequency table.
- The cumulative frequencies are then plotted
corresponding to the upper limits of the classes.
- The points corresponding to cumulative
frequency at each upper limit of the classes are
joined by a free hand curve.

31. 7. SCATTETED OR DOTTED DIAGRAMS
- It is a graphical presentation that shows the
nature of correlation between two variable
character x and y on the similar features or
characteristics, for example height and weight in
men of 20 years old.
- Therefore, it is also called correlation diagram.
8. PICTOGRAMS OR PICTURE DIAGRAM
- This method is used to impress the frequency of
the occurrence of events to common people,
such as attacks, deaths, numbers of operations,
admissions, accidents, and discharges in a
populations.
-