Statstics in nursing

STATISTICS
IN
NURSING
Prepared By
Monika Devi NR
M.Sc.Nursing
GMCH Jammu

DEFINITION
• Statistics is the science of making effective use of
numerical data which is related to collection, analysis
and interpretation of data.
• Statistics is the study of how to collect, organizes,
analyze, and Interpret data.
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Monika Devi NR M.Sc.Nursing

Importance
• Statistics plays a vitally important role in the research.
• It help to answer important research questions and field in
study.
• Helps you understand how to apply statistical method
• Important to understand what tools are suitable for a
particular research study.
• Statistics enables to understand specified statistical
concepts and procedures.
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TYPES OF STATISTICS
There are two approaches to the statistical analysis of data
1. Descriptive Statistics
• Descriptive statistics are techniques which help the
investigator to organize, summarize and describe
measures of a sample.
2. Inferential statistics
• The inferential approach helps to decide whether the
outcome of the study is a result of factors planned within
design of the study or determined by chance. (Streiner &
Norman, 1996).
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FREQUENCY DISTRIBUTIONS
• Frequency
distribution is a
systematic
arrangement of
values from
lowest to highest
or a method of
organizing
numeric data.
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PRESENTATION OF DATA & SHAPES
1. Tabular presentation
2. Diagrammatic Presentation
3. Graphical Presentation
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.
Tabular Presentation
of Data Arranging
values in columns is
called tabulation. E.g.
The amount of oxygen
content in water
samples
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.
Diagrammatic
Presentation of
data It is a visual
form of presentation
of statistical data in
which data are
presented in the
form of diagrams
such as bars, lines,
circles, maps
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Common Types
• Line Diagram
• Pie diagram
• Bar diagram
• C.Line diagram
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SHAPES OF FREQUENCY DISTRIBUTION
Polygons:
polygons use dots
connected by
straight lines to
show
frequencies.
11

.
Histograms: A
histogram is
constructed by drawing
bars Distribution are
shown in Graphically.
• Graphs denotes the
information of complete
data in different shapes.
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Cont…
• Symmetric
distribution
(Normal ) It consist
of two halves that
are mirror images
of one another.
•
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.
• Asymmetric or Skewed
distribution It is off center and
one tail is longer than the other If
the tail points to the left, the
distribution is negatively skewed, -
When the longer tail points to the
right, the distribution is positively
skewed.
• A distribution with the modal peak
off to one side or the other is
described as skewed. The word
skew literally means "slanted
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Conti..
• Unimodal
distribution It has
only one peak or high
point (i.e., a value with
small / high
frequency),
15

.
• Multimodal
distribution It has
two or more peaks
(i.e., values of high
frequency).
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STATISTICS & DATA ANALYSIS
1. Measures of central
tendency
2. Mean
3. Mode
4. Median
5. Measures of variability
6. Range
7. Standard deviation
8. Correlation
9. Inferential statistics
10. T- test
11. Chi square test
12. ANOVA
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CENTRAL TENDENCY
• It is a statistical measure that identifies a single score as
representative for an entire distribution or group.
1. Mean
2. Mode
3. Median
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Levels of Measure Used
• Interval level variables – Mean
• Nominal variables – Mode
• Ordinal variables - Median Measures of Central
Tendency
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Cont…
• Relationship between mean, median, and mode
is determined by the shape of the distribution
CENTRAL TENDENCY AND THE SHAPE OF THE
DISTRIBUTION
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Range
• It is the difference between the lowest and highest number in the
set.
• Range = Xhighest – Xlowest
• E.g: SAT scores of students at two nursing schools.
• Both distributions have a mean of 500, but the score patterns
are different. School A has a wide range of scores, with some
below 300 and some above 700. This school has many students
who performed among the best also many students who scored
well below average. In school B, there are few students at either
extreme.
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STANDARD DEVIATION
• Standard deviation is the most common measure of variability.
• It is used the mean as a reference point and approximates the
average distance of each score from the mean.
• A deviation (x) is the difference between an individual score
and the mean
• VARIANCE:- The variance is simply the value of the standard
deviation before a square root has been taken
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CORRELATION
• Correlation is a measure of association between two
variables.
• Correlations can be graphed on scatter plot or scatter
diagram.
• Scatter plot: It involves making a rectangular coordinate
graph with the two variables laid out at right angles.
• plot (dots) are shown to help identify subjects.
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CORRELATION COEFFICIENT
(PEARSON’S – R)
Correlation coefficients can be computed with
two variables measured on either the ordinal,
interval, or ratio scale Pearson’s Calculation…..
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INFERENTIAL STATISTICS
• Inferential statistics is a statistical method used to infer result s
of sample (statistic) to population (parameter).
• It is a process of inductive reasoning based on the mathematical
theory of probability - (Fowler, J., Jarvis, P. -2002).
• Component of inferential statistics.
– Hypothesis testing
– Estimation
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Error and Hypothesis testing
• The standard deviation of a sampling distribution of mean is
called the standard error of the mean (SEM). Error
• Various means in the sampling distribution have some error as
estimates of the population mean
• SEM (symbolized as sx) if we use this formula to calculate the
SEM for an SD of 100 with a sample of 25 students we obtain
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ESTIMATION
It is used to estimate a single parameter, like a mean. Estimation
can take in to two forms.
Forms:
• Point estimation : Point estimation involves calculating a
single statistic to estimate the population parameter. Point
estimates convey no information about accuracy
• Interval estimation : it indicates a range of values within
which the parameter has a specified probability of lying
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STATISTICAL TESTS
There are two types of inferential statistics
1. Parametric
2. Non-parametric Tests
Parametric Tests A parametric test is one which specifies
certain conditions about the parameter of the population
from which a sample is taken. E.g t-test, and F-test
(ANOVA).
Non-parametric tests (Distribution-free Statistics) A non-
parametric test is one does not specify any conditions about
the parameter of the population from which the population is
drawn. These tests are called. E.g Chi-squire test.
32

T- TEST
• It is used to testing the differences in group S of
mean.
• T-test can be used when there are two
independent groups (e.G., Experimental versus
control, male versus female), degree of freedom
(df)
• Degree of freedom (df) is describes the number of
events or observations that are free to vary.
• Formula t-test degrees of freedom (df)
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Examples of T-Test
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.
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THE CHI-SQUARE TEST
(Analyzing Frequencies)
• The chi-squire test is used when the data are expressed in terms
of frequencies of proportions or percentages.
• The chi-square statistic is computed by comparing observed
frequencies and expected frequencies
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Analysis of variance (ANOVA)
Is another commonly used parametric procedure for
testing differences between means where there are
three or more groups.
The statistic computed in an anova is the f-ratio ,
variation within groups to get an f-ratio.
Types
• One way anova,
• Two way anova,
• Multifactor anova 38

TYPES of ANOVA
• One-way ANOVA :- It is used with one independent variable and
one dependent variable). •
• Two-way ANOVA or Factorial Analysis of Variance :-
Factorial analysis of variance permits the investigator to analyze the
effects of two or more independent variables on the dependent
variable.
• Analysis of Covariance (ANCOVA) :- It is an inferential statistical
test that enables investigators t adjusts statistically for group
differences that may interfere with obtaining results that relate
specifically to the effects of the independent variable(s) on the
dependent variable(s).
• Multivariate Analysis :- Multivariate analysis refers to a group of
inferential statistical tests that enable the investigator to examine
multiple variables simultaneously.
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Thank You
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Statstics in nursing

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