The document provides an overview of data analysis for nursing students. It discusses the importance of statistical training for establishing cause-and-effect relationships and measuring health outcomes. The key steps of data analysis are described, including computing, editing, coding, selecting software, entering, cleaning and classifying data. Both descriptive and inferential statistical methods are covered. Descriptive statistics summarize and describe data through measures like frequency, percentage, mean, median and mode. Inferential statistics allow drawing conclusions about populations from samples using parametric or nonparametric tests. Qualitative data analysis involves coding, identifying themes in the data, and interpreting patterns.

1. Prof. Rupa Varma
Principal,
MKSSS, Sitabai Nargundkar
College of Nursing For Women,
Nagpur
Introduction to Data Analysis

2. • We nurses during period of our study, learn best method of
nursing process
• After graduation, we go through, research papers presented
at conferences and in current journals to know new methods
for nursing practices
• Training in statistics has been recognized as indispensible for
students
• If we want to establish cause and effect relationship, we need
statistics
• If we want to measure state of health and also burden of
disease in community, we need statistics.

3. • Data can be defined as a
systematic record.
• It is the different values of
that quantity represented
together in a set.
• It is a collection of facts and
figures to be used for a
specific purpose such as a
survey or analysis.
• When arranged in an
organized form, can be called
information.

4. Analysis-
• Analysis is the process of
organizing and synthesizing
the data so as to answer
reserch questions and
hypothesis.
• Analysis is the process of
breaking a complex topic
into smaller parts to gain
better understanding of it.

5. • Almost end of the ladder of research process.
• After collection of data we enter into exciting phase
of research process- Analysis, Presentation and
interpretation.
• Data analysis is the reduction and organization
of a body of data to produce results that can be
interpreted by the researcher;
• A variety of quantitative and qualitative metods may
be used, depending upon the nature of the data to
be analyzed and the design of the study.

6. • Data analysis is the most complex and mysterious of
all of the phases of a qualitative project, and the one
that receives the least thoughtful discussion in the
literature
• Data after collection has to be processed and
analyzed in accordance with the outline down for
the purpose at the time of developing research plan.
• In process of analysis, relationships or differences
supporting or conflicting with hypotheses are
subjected to statistical tests of significance, to
conclude the study.

7. • Research consists of two larger steps
1) Gathering the data
2) Analysis of gathered data
• But, no amount of analysis can validly extract
from the data factors which are not present.

8. Types of data:
Qualitative & Quantitative
Data
Qualitative Quantitative
It was great fun
Discrete-5….. Continuous
3.345

9. 1) Qualitative Data:
• They represent some characteristics or attributes.
• They depict descriptions that may be observed but
cannot be computed or calculated.
• For example, data on attributes such as intelligence,
honesty, wisdom, cleanliness, and creativity collected
using the students of your class a sample would be
classified as qualitative.
• They are more exploratory than conclusive in nature.

10. 2) Quantitative Data:
• These can be measured and not simply observed.
• They can be numerically represented and calculations
can be performed on them.
• For example, data on the number of students playing
different sports from your class gives an estimate of
how many of the total students play which sport.
• This information is numerical and can be classified as
quantitative.

12. • It is intermediary stage between data collection and
data interpretation.
• Steps
 Identifying or compiling data structures
 Editing the data
Coding
Organizing the Data
Classifying the data
Analyzing the data
Tabulation of data

13. • The collected data may be
adequate, valid and reliable
to any extent, it does not
serve any worthwhile
purpose unless it is carefully
edited, systematically
analyzed, intelligently
interpreted and rationally
concluded.

14. • It means putting
together or composing
the collected data.
• It includes arranging all
the collected data in a
sequence.
• In orderly and organized
manner.

15. • Monitor the data for quality and
comprehensiveness.
• Each piece of data should be
checked for accuracy and
completeness.

16. • Coding is translating answers into numerical value.
• Assigning numbers to various categories of a variable
that can be entered into database in a form which can be
analyzed easily.
• Coding of all the forms should be done on daily basis in
order to manage the work.
• In case of open ended question, it is to be placed in
various categories and each category is assigned a code.
• Coding of open ended question require lot of efforts
from researcher.

17. a. Selecting software
package
b. Entering the data
c. Cleaning the data/
managing missing value

18. a) Selecting a software package:
• Except in small studies, the researchers always make
use of computer for analysis of data.
• SPSS (Statistical Package for social Sciences) is
prefferred by majority of the researchers.
b) Entering the Data:
• The Data can be entered directly from the
questionnaires.
• It is always good to have two persons entering the data
to prevent errors and mistakes.

19. c) Data Cleaning and managing the missing value:
• After completing whole data entry, the reseaecher
must take out the print of that file.
• Checking the data for error is called cleaning.
• If any respondant may not have given an answer for
a question.
• Some data analysis programs have a mechanism for
handling missing values, giving them a code or not
using that case in the data analysis.

20. • The calssification of data implies that the collected
raw data is categorized into common groups having
common features.
• It can be according to the numerical characteristics
or according to attributes.
• The Numeical characteristics are classified on the
basis of class intervals.
• As per attributes, the data is classified on the basis of
common characteristics like literacy, sex, marital
status, etc.

21. • First the discriptive statistics is
employed to obtain the
frequencies of discriptive
variables.
• This is followed by application
of inferential statestics to test
the hypothesis, research
question or objectives.

22. • Tabulation is the orderly
arrangement of data in rows and
columns.
• It conserves space.
• It facilitates rocess of comparison
and summerization.
• It also facilitates detection of
errors and ommissions and
establishes the basis of various
statestical computation.

23. Computing the data
Editing the data
Coding the data
Selecting the software
Entering the data
Data cleaning and managing the missing
valve
Classification of data
Analysis of data-descriptive &
inferential statistical tests
Tabulation of data

24. • Quantitative data analysis is the process of using
statistical meyhods to describe, summerize and
campare the data.
• It provides quantifiable and easy to understand
results.
• First it is importantto identify level of measurement
associated with quantitative data.

25. • It is very important to understand the scales or levels
of measurements before learning about the analysis
of data.
• There are four levels of measurements:
1. Nominal Measurement
2. Ordilnal Measurement
3. Interval Measurement
4. Ratio Measurement

27. Nominal Measurement
Ordilnal Measurement
Interval Measurement
Ratio Measurement
Qualitative measures-
Calssify into nonnumeric
Catagogies
Quantitative measures-
Measurement is numerical

28. The Nominal scale, sometimes
called qualitative type, that
differentiate between Items or
subjects only on the basis of their
names or categories or qualities
Examples Include Gender,
nationality, language, style, Marital
status, student ID, state of
residence.

29. An ordinal scale not only classifies subject but also
ranks them in terms of the degree to which they
possess a characteristics of interest. An ordinal scale
indicates relative position
The order of ranking is iposed on categories
Example:
Health Staus
A) Poor B) Fair
C) Good D) Excellent

30. There is specification of ranking of objects
on an attribute of the distance between
those objects.
There is, more or less, equal numerical
distance between intervals
Example: Temperature
A) 100- 800 B) 40o-500

31. This is the highest level of
measurement and has the
properties of other three
levels.
Has absolute zero point.
Examples: Biophysical
Parameters
a) Weight b) Height
c) Volume d) Bood Presure

32. • There are two specific types of quantitative data
analysis methods- descriptive and inferential
method.
1) Descriptive Statistics
• Descriptive Statistics are used to summerize and
describe data.
• It constitute the frequency distribution of the data,
measures of central tendancy and measures of
dispersion.

33. Classification of descriptive analysis
1) Frequency and percentage distribution
2) Measures of central tendacy
• Mean
• Median
• Mode
3) Measures of dispers or variability
• Range
• Standard Deviation

34. 1) Frequency and percentage
distribution
• It is the frequency of
occurance of score or value
in given set of data.
• The scores or values may be
systematically arrenged from
highest to lowest or lowest
to highest.
• It is better to show
percentage also along with
each frequency score.
Age
(Yrs)
Frequency
(f)
Percentage
(%)
21 5 10
22 15 30
23 20 40
24 5 10
25 5 10
N = 50= ∑f ∑%= 100
Frequency distribution of patient's age

35. 2) Measures of central tendacy
MEAN
• Add all the numbers then divided
by the amount of numbers
• 9,3,1,8,3,6(30 ÷ 6=5),mean is 5
MEDIAN
• Order the set of numbers, the
median is the middle number
• 9,3,1,8,3,6(1,3,3,6,8,9) median is
4.5
MODE
• The most common number
• 9,3,1,8,3,6 mode is 3

36. 3) Measures of dispers or
variability
a) Range
The distance between the highest score and the lowest
score in a distribution.
When to use
 Sample description is desired.
 Variance or standard deviation
Cannot be computed.
 Have ordinal data.
 Presenting your results to
people with little .

37. Cont...
2) Standard Deviation
The most commonly used measure of variability
that indicates the average to which the scores deviate
from the mean.
Uses
• Biological studies
• Fitting a normal curve to a
frequency distribution
• Measure of dispersion

38. Cont...
2) Inferential Statestics
• Inferential Statistics are numerical values that enable
the researcher to draw conclusion about a
population based on the characteristics of a
population sample.
• The two main types of inferential statestics are
Nonparametric and Paramrtric.

40. cont..
• Examples of nonparametric tests includethe Chi
square test, the Cochran Q test, the Fisher exact test
• Examples of parametric test include the test, 1-way
analysis of variance (ANOVA), repeated-measures
ANOVA, Person correlation, simple linear and
nonlinear regression, and multivariate linear and
nonlinear regressions.

41. • The analysis of data in qualitative research is the
most challenging exercise.
• The basis of analysis of qualitative data lies in
coading and thematic analysis.
• Thematic analysis is most commonly used method.
• In thematic method data is carefully looked at and
common issued that recur are identified.
• This leads to identification of main thems that
summerize all the views that have been
colcted.

42. Step 1: Read The interview verbatim carefully.
Step 2: Decide the codes and themes thhat are covered in
data
Step 3: Mark the quotations (Paragraphs, lines or words)
Step 4: Assign the codes.
Step 5: Make a list of items uner one theme from across the
interviews
Step 6: Check the relationships between rhems
Step 7: Interprit the patterns
Step 8: Mention the quotes in reports.