ANALYSIS OF DATA.pptx

ANALYSIS OF DATA
Miss.Naveena P, M.sc (N),
Professor, HOD,
Department Of Community Health Nursing.

Introduction
Generally Research analysis consists of two main steps :
1. Processing data.
2. Analysis of data
• The collected data may be adequate, valid and reliable to any extent. It
does not serve any worth while purpose unless it is carefully edited,
systematically classified, tabulated, scientifically analyzed, intelligently
interpreted and rationally concluded.

I. PROCESSING OF DATA
Processing of data includes
a. Compilation
b. Editing
c. Coding
d. Classification

a. COMPILATION
• Compilation process includes gathering
together all the collected data in
manner that a process of analysis can
be initiated
• After collecting the data has to be
checked for its accuracy and then
coding is done

b. EDITING
• The process of making changes , deciding what will be removed and what
will be kept in, in order to prepare the accurate data.
• Editing is an important step because any incomplete and inconsistent data
will be carried through all subsequent stages of processing and will distort the
result.
• After editing each form editors initials and date of editing should be placed.

Importance Of Editing
• The collected data is examined to detect
errors, omissions and to correct .
• Careful scrutinizing is done to assure
that data is accurate, complete and well
arranged.

• The editor checks that none of the
question has been skipped.
• All answers have been recorded and
all replies are internally consistent
with each other.

Types Of Editing
1.Field editing
2.Central editing

Field Editing
• It should be done as soon as
possible after the interview.
• Investigator completes whatever
he has written in abbreviated or in
illegible form at the time of
recording the response.

Central Editing
• It is performed after completion of all form.
• The obvious error may be corrected by the
editor.
• Respondents can be contacted for
clarification.
• All the inappropriate answers and wrong
replies are dropped out from final results.

c. CODING
• It refers to the process of transforming collected information or
observations to a set of meaningful, cohesive categories.
• Through coding numerous replies can be reduced to a small
number of classes.
• Code is an abbreviation, a number or an alphabet which is
assigned by the researcher to every schedule item and response
category.

Example Of Abbreviated Coding
Serial NO. Abbreviated
Code
Description
1 QDL Quality of life
2 ADL Activities of Daily Living
3 BMI Body Mass Index
4 BP Blood Pressure
5 HR Heart Rate
6 ECG Electrocardiogram

Example Of Alphabetical Coding
Alphabetical Code Description
A patient age < 40
B patient age 40 - 60
C patient age 60 - 80
D patient age > 80
E patient age not recorded

Example Of Numerical Coding
Participant Age Gender Blood Pressure Heart Rate
1 35 Female 120/80 75
2 42 Male 130/85 80
3 28 Female 110/70 70
4 30 Female 120/90 75
5 33 Male 140/80 80

Important points to be kept in mind during coding
• Codes should be mutually exclusive which means one code
should be specific to only one kind of information
• Categories or classes should be inclusive so that all responses
could be classified in one or the other category
• Separate categories can be created for recording non-
response and no-knowledge responses

• Coding decision may be taken at the designing stage of
questionnaire which is helpful for compute tabulation
• Incase of hand coding some standard methods may be used. Such
as: coding in the margin with a colored pencil or to transcribe the
data questionnaire to a coding sheet.
• Care must be taken to avoid errors in the coding method.

Example
What is your marital status?
a. Married
b. Divorced
c. Separated
d. Widowed
e. Unmarried
f. Prefer not to say

d. CLASSIFICATION
• In this process, we divide and arrange the entire data into different
categories, classifications, groups or classes .
• It is necessary to reduce a large volume of raw data into homogenous
group.
• On the basis of common characteristics groups are placed on one class and
the whole data get divided into a number of groups or classes.
• Data can be classified according to qualities, attributes and class intervals
etc.

i) Classification According To Attributes :
Generally the data are classified on the basis of common
characteristics which can either Descriptive or Numerical
DESCRIPTIVE :
• Descriptive characteristics refers to qualitative phenomenon which
cannot be measured quantitatively.
• Example : Data related to honesty, beauty, literacy etc.
• Only the presence or absence in an individual item is noticed
Such classification can be simple and manifold classification.

Simple classification :
• Only one attribute is considered and the universe is divided
into two classes.
• One class consisting of items having the given attribute and the
other class consisting of items which do not possess the given
attribute
Example :
Are you educated ?
a) Yes b) No

Manifold classification :
• We consider two or more attribute simultaneously and divided the
data into a number of classes.
Example :
Occupation [ ]
a) Labor b) Agriculture c) Private employee
d) Government employee

NUMERICAL :
• The numerical characteristics refers to quantitative
phenomenon which can be measured through some
statistical units
• Example : Data related to height, weight, income etc.

ii. Classification According To Class Intervals:
The data can be divided into class intervals.
Example :
Age [ ]
a) 21 - 25
b)26 - 30
c) 31 - 35
d)36 – 40 and so on…

 By this way the whole data can be divided into number of groups or class
intervals
 Each group of class interval has an upper limit and lower limit known as
class limits.
 The difference between two class limits is called as class magnitude
 The number of items which fall in a given class is known as frequency of that
class.
 All the classes and groups with their frequencies are taken together put in the
form of a table.
 It is described as “group frequency distribution”.

Points to be kept in mind during classification
a) How many classes should be there?
What should be the magnitude?
• The data should be meaningful generally
• We can have 5-15 classes
• The magnitude of each class interval should be same but there can be unequal
magnitudes also
• Incase of items having very higher or very low values, one may use open ended
intervals.
Such as : less than 100 or above 200.

b) How to choose class limits?
The two main types of class intervals used are:
 Inclusive type class interval
 Exclusive type class interval

Inclusive type class interval :
Example :
Age [ ]
a) 5 – 10 - 5 and under 10
b) 10 – 15 - 10 and under 15
c) 15 – 20 - 15 and under 20
d) 20 – 25 - 20 and under 25
• The items whose values are equal to upper limit of class are put in the next
higher class.
Example : An item having value exact 15 would be put in 15 – 20 and not in 10 –
15 class interval

Exclusive type class interval :
Example :
Age [ ]
a) 6 – 10 - Between 6 to 10
b) 11 – 15 - Between 11 to 15
c) 16 – 20 - Between 16 to 20
d) 21 – 25 - Between 21 to 25
• In this type the upper limit of a class interval is also included in the same class
interval.
Example : An item having value 15,will ne put in 11 – 15 class interval

C) How to determine the frequency of each class?
• Frequency can be determined by using tally sheets or other mechanical aids
• In tally sheets class groups are written on a paper and for each item a vertical
line is put in front of class groups in which it falls.
• Generally after every four small vertical lines the fifth line for the item
falling in the same group, an oblique line is drawn through the previous four
lines and the result represents five items. This helps in counting.

Example of Tally Sheet
Group
[Age groups] Tally mark
No of persons
[class frequency]
6 - 10 15
11 - 15 23
16 - 20 42
21 - 25 20
Total 100

II. ANALYSIS OF DATA
Analysis is the process of breaking a complex topic into smaller
parts to gain a better understanding of it.
Analysis may therefore be categorized into two types :
i) Descriptive analysis
ii) Inferential analysis

Descriptive analysis
• Descriptive analysiss helps to describe and organize known
data using charts, bar graphs, etc.
• In this analysis researcher measures the variables and
relationship between two or more variables [cause and
relationship].
• Eenables researchers to present data in a more meaningful way
and easy interpretations can be made.

Descriptive analysis uses two tools to organize and describe data:
These are given as follows:
• Measures of Central Tendency: These help to describe the central
position of the data by using measures such as mean, median , and mode .
• Measures of Dispersion: These measures helps to see how spread out the
data is in a distribution with respect to a central point. Range, standard
deviation, variance, quartiles, and absolute deviation are the measures of
dispersion.

Inferential statistics
• Inferential statistics aims at making inferences,
generalizations and conclusions about the population data.
• It helps researchers to test hypothesis about the entire
population using the information gathered from small subset of
data

Few methodologies used in inferential statistics are as follows:
• Hypothesis Testing: This technique involves the use of hypothesis tests
such as the z test, f test, t test, etc. to make inferences about the
population data. It requires setting up the null hypothesis, alternative
hypothesis, and testing the decision criteria.
• Regression Analysis: Such a technique is used to check the relationship
between dependent and independent variables. The most commonly used
type of regression is linear regression.

Tabulation
Tabulation is a method of organizing data in a structured
table format, making it easier to read and analyze. It
arranges information into rows and columns, it is a simple
grid where data is neatly presented, helping us compare and
summarize information more effectively.

Advantages Of Tabulation :
• Easy understanding
• Quick comparison
• Conserves space
• Helps in easy detection of errors and omission
• Efficient summarization

Guidelines for tabulation
 Appropriate title should be mentioned for each table
 Table should be precise,clear,and easy to understand
 organize data into rows and columns
 clearly define what each row and column represents
 Items should be arranged either in alphabetical,chronological or
geographical order or according to importance which facilitates
comparison

 Add borders and lines to separate rows and columns
 Decimal points should be mentioned in perfect alignment
 Totals can be placed at the bottom of the columns
 Abbreviations and ditto marks should not be used in the table
 If the data is very large ,it should not be crowded in a single table
 The foot notes should be mentioned for understanding
 Sources from where the Data have been obtained must be indicated
below the table

Interpretation of data
• The process of Interpretation is stating that what the findings shows.
• The findings of the study are the results, conclusions, interpretations,
recommendations, generalizations,implications,future research and
nursing practice.
• Errors can be made in the interpretation, so one should be very careful

The common errors of interpretation which must be avoided are:
1. Sometimes an investigators fails to see the problem in proper perspective
2. Investigator may fail to see the problem due to rigid mindset or lack of
imagination
3. Researcher may fail to recognize bias, such as: inadequate and incorrect data
gathering,inacqurate analysis, faulty inferences and generalizations
4. Misinterpretation due to unstudied factors by investigation
5. Inadequate attention

Transcription of Data
The inferences drawn from the
observations can be transferred to a
data sheet by a researcher which is a
summary of all responses from a
research instrument.
Summarization

Manual Transcription :
Long work sheets,sorting
cards or sorting strips could
be used by the researcher to
manually transcript the
responses
Methods of Transcription

Computerized Transcription :
Could be done using a Data base
package by computers,such as :
Spread sheets, text files, or other
data bases.

Presentation Of Data
• Presentation of data refers to the visual representation and
communication of information in a clear and coherent manner.
• It involves using various narrative, tabular and figure formats to
showcase data in a way that is easy for the understanding.
• Effective data presentation enhances communication, aids
decision-making, and facilitates better understanding of
complex information.

Narrative presentation : Narrative presentation of data involves
conveying information in the form of a story or a cohesive written
explanation

Figure : Figure presentation of data involves using visual elements such as
charts, graphs, and diagrams to represent information in a more engaging and
understandable way.

ANALYSIS OF DATA.pptx

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ANALYSIS OF DATA.pptx