Introduction to nursing Statistics.pptx

Mrs. D. Melba Sahaya Sweety RN,RM
PhD Nursing , MSc Nursing (Pediatric Nursing), BSc Nursing
Associate Professor
Department of Pediatric Nursing
Enam Nursing College, Savar,
Bangladesh.
1

COURSE OBJECTIVES
 This Nursing core course provides students an opportunity to
develop statistical literacy and reasoning to critically read and
evaluate nursing literature.
 The core content focus on descriptive statistic, parametric and
non – parametric bivariate statistic, and multivariate statistic
methods.
 Through this course, students will be able to analyze data
critically understanding the relevance and use of various
statistics in nursing research.
 They will also be able to select appropriate research idea,
develop questionnaires, and learn manage data using the
statistical package for Social Sciences.
2

NURSING STATISTICS
• The word statistics comes from the Italian words Statista means Statement and a
German word Statistik means Political state..
• It is a science of learning from numbers/data.
• Francis Galton (1822-1911) has been called the father of Biostatistics.
• Statistics is a branch of mathematics dealing with the collection, analysis,
interpretation, and presentation of masses of numerical data. (Merriam-Webster).
• Data analysis is an important steps of a research process to make the information
more meaningful and understandable to other.
• Statistics is a vital part of human knowledge. Nursing practice is mostly based on
empirical evidence. Evidence-based-practice requires nurses to read literature that
consists of quantitative research reports.
• Although nursing often includes prevention of illnesses, promotion of health and
caring for the sick, it also involves statistical skills such as measurements,
drawing, and interpreting charts and diagrams.
INTRODUCTION
3

NURSING STATISTICS
DEFINITION OF STATISTICS
• Statistics is defined as the
collection, organization,
presentation, analysis and
interpretation of numerical
data.
- Croxton and Cowden
Statistics is the science of methods
and procedures for collecting,
classifying, summarizing and
analyzing data and for making
scientific inference from such data.
- PV Sukhatme
4

NURSING STATISTICS
IMPORTANCE OF STATISTICS IN NURSING
NURSING PRACTICE
 Nurses can use statistics to identify patterns in vital signs and symptoms so they
can make informed decisions to better respond to a patient 's changing medical
status.
 Even the use data sheets or frequency charts to document the timing of
medications given to patients is a way nurses can use statistics.
 Knowledge of statistics helps medical professionals evaluate studies that assess
the efficacy of treatments and interventions.
 Statistics in health care convey valuable information about the health of a society.
5

NURSING STATISTICS
NURSING PRACTICE
 Nursing knowledge based on empirical research plays a fundamental role in the
development of evidence-based nursing practice.
 The ability to interpret and use quantitative findings from nursing research is an
essential skill for advanced practice nurses to ensure provision of the best care
possible .
 Statistics is integral part of the nursing profession.
 It has a direct affect on patient care in a variety of settings as well as the potential
to change policies and procedure on a wider scale.
6

NURSING STATISTICS
NURSING RESEARCH
• Statistics guide the nurse researcher to link the statistical analyses they
chosen with the research question, design and level of data collected.
• It allows nurse researcher to critically analyze the result.
• It provide organization and meaning to a data.
• It help the nurse researcher to understand how to apply statistical methods.
7

• The word Data is plural so data is a set of scores,
measurements or observations that are typically numeric .
• A datum (singular) is a single measurement or observation,
usually referred to as a score or raw score.
• Data is defined as factors known or assumed as facts, making
the basis of reasoning or calculation
What is Data?
NURSING STATISTICS
8

TYPES OF DATA
Qualitative Data
Binary Data
Nominal Data
Ordinal Data
Quantitative Data
Discrete Data Continuous
Data
Interval
Ratio
NURSING STATISTICS
9

• Qualitative data deals with characteristics and descriptors that can't be
easily measured, but can be observed subjectively. Eg. smells, tastes, textures,
attractiveness, and color. It is also referred as attributable data.
1. Binary data place things in one of two mutually exclusive categories:
right/wrong, true/false, or accept/reject.
2. Nominal ( Unordered) Data : The assigned individual items number or
category that do not have an implicit or natural value or rank.(Gender: 1= male
and 2= female)
3. Ordinal (Ordered) Data : The items are assigned to categories that have some
kind of implicit or natural order. E.g,"Short, Medium, or Tall." Rating from 1 to
5 on scale where 5 is most appropriate.
TYPES OF DATA
NURSING STATISTICS
10

• Quantitative data deals with numbers and things you can measure objectively:
E.g; height, weight, length, temperature, volume, area etc. It is number value
1. Discrete data : The data in a whole number is called discrete data. For instance, the
number of children in a family , pulse rate, ESR, blood sugar, blood pressure etc.
2. Continuous data : The data that can be measured in fractional values such as Height,
Weight, body temperature, chest circumference etc.. Are called continuous data. It is
further classified in to Interval and ratio
i. Interval : The data with Known difference between the variables such as time.
ii. Ratio : The data that have measurable variable where difference can be determined
such as Height, Weight, body temperature, chest circumference etc..
NURSING STATISTICS
11
TYPES OF DATA

Statistical data are often classified according to the no. of variables being studied. Univariate
Data : The data consisting of measurement of only one variable is called univariate data For
Example:- A survey to estimate the average weight of MBBS 2nd Year students in Enam
medical college. Since this study have one variable weight, So it is called Univariate data.
Bivariate Data : The data consisting of measurement of two variables are called bivariate
data. For Example:- A study to assess the relationship between the height and weight of
MBBS 2nd year students in Enam medical college. In this study the researcher is assessing the
relationship of two variables i.e, height and weight.
TYPES OF STATISTICAL DATA
NURSING STATISTICS
12

Multi - variate Data : The data consisting of measurement of two
or more variable are called Multi-variate data For Example:- A
study to assess the effectiveness of Aerobic exercise on Blood
pressure, stress, BMI, and quality of life among Hypertensive
clients .
TYPES OF STATISTICAL DATA
NURSING STATISTICS
13

• Descriptive Statistics:
Descriptive statistics are: “methods for organizing, displaying,
and describing data using tables, graphs and summary
measures” (Mann, 1991, 2010, )
• Inferential Statistics: an inference: “a conclusion about a
population based on logical reasoning from data gathered about
a smaller sample” (Zedeck, 2014,). Inferential statistics could
therefore be defined as the field of statistics that tries to say
something about a population, based on a sample from that
population.
CLASIFICATION OF STATISTICS
NURSING STATISTICS
14

CLASSIFICATION OF
STATISTICS
Descriptive Statistics
Measures of
Condensatio
n
Frequency
Distributio
n ,
Graphical
Presentati
on &
Percentag
e
Measur
es of
Centra
l
tenden
cy
Mean,
Median
&
Mode
Measures
of
Dispersio
n
Standard
Deviatio
n, Mean
deviation
,
Quartile
deviation
,
Variance
& Range
Measure
s of
Relation
ship
Coefficien
t of
correlatio
n,
regressio
n etc.
Measures of
skewness
Inferential Statistics
Paramet
ric Test
One way
ANOVA,
Repeated
Measure
ANOVA, t test,
Simple and
non linear
regression,
Karl Pearson
correlation
etc..
Non –
parametric
Test
Mann –
Whitney U
test, Median
test,
Wilcoxon
rank sum
test, chi-
square test,
Fisher exact
test, etc..
NURSING STATISTICS
15

Frequency distribution is a systematic arrangement
of values from lowest to highest or a method of organizing
numeric data
22 23 25
23 16 20
15 24 23
24 23 16
23 18 22
20 25 25
4
FREQUENCY DISTRIBUTION
No.s (x) Frequency (f)
15 I
16 II
18 I
20 II
22 II
23 IIII
24 II
25 III
Σ f =18
DESCRIPTIVE STATISTICS
16

.
▫ Tabular presentation
▫ Diagrammatic Presentation
▫ Graphical Presentation
A.Tabular Presentation of Data
▫ Arranging values in columns is called
tabulation
▫ E.g. Frequency and Percentage
Distribution of Demographic Variables
in Experimental and Control Group
5
S.
No
.
Demographic
Variables
Experimental
Group
Control
Group
Total
f % f % N %
1 Age of the
child
1) 4yrs1month
- 4yrs5month
2) 4yrs6month
- 5yrs
11
19
37
63
15
15
50
50
26
34
43
57
2 Sex of the child
1) Male
2) Female
19
11
63
37
14
16
47
53
33
27
55
45
PRESENTATION OF DATAAND SHAPES
17

.
B. 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
▫ Common Types
▫ Line Diagram
▫ Pie diagram
▫ Bar diagram
c. Line graph
5
0
10
20
30
40
50
60
70
Male Female
Male Female
Figure : 1 Simple bar diagram showing
the percentage of male and female in
sawar city
42 %
58 %
Sales
1st Qtr 2nd Qtr
3rd Qtr 4th Qtr
PRESENTATION OF DATAAND
SHAPES
18

2. Polygons: polygons
⚫use dots connected by straight lines
to show frequencies.
Distribution are shown in Graphically. Graphs denotes the information of complete data
in different shapes
6
SHAPES OF FREQUENCY DISTRIBUTION
1. Histograms: A histogram is
constructed by drawing bar
19

3.Symmetric distribution (Normal )
It consist of two halves that are mirror images of one another.
4.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."
7
20

5. Unimodal distribution
It has only one peak or high point
• (i.e., a value with small / high
frequency),
6. Multimodal distribution
It has two or more peaks
(i.e., values of high frequency).
8
21

⚫Measures of central tendency
⚫Mean
⚫Mode
⚫Median
⚫Measures of variability
⚫Range
⚫Standard deviation
⚫Correlation
⚫Inferential statistics
⚫T- test
⚫Chi square test
⚫ANOVA
9
STATISTICS AND DATAANALYSIS
22

⚫It is a statistical measure that identifies a single score as representative for
an entire distribution or group.
- Mean
- Mode
- Median
Measures of Central Tendency
1. Mean
2. Mode
3. Median
⚫Levels of measure used:
⚫Interval level variables
⚫Nominal variables
⚫Ordinal variables
10
CENTRAL TENDENCY
23

Example: 3,4,5,6,7
▫ 3+4+5+6+7= 25, 25 n =5 The mean = 5
Exercise 1.
What is the average of these numbers?
567, 432, 902, 693, 356, 996
11
CENTRAL TENDENCY – 1. MEAN
Where
X = Mean
Σx = sum of all observations
And n = total number of
observations
24

The mode in a set of data is the number that occurs the most
Example 25, 10, 10, 25, 5, 10, 25, 10, 5
Mode = 10
Find the mode of these numbers. 100, 95, 100, 90,75,100, 85, 95
3. Median
The median is a set of data , which is the middle number.Also arrange all the
data from lowest to highest and then take the middle number.
E.g :
Exercise 3:
odd : 3, 5, 8, 10, 11  median=8
even: 3, 3, 4, 5, 7, 8  median=(4+4)/2= 4
Find the median
1. 67 34 85 33 84 & 2. 12 14 16 18 19 20
12
CENTRAL TENDENCY – 2. MODE, 3.MEDIAN
25

Relationship between mean, median, and mode is determined by the shape
of the distribution
13
CENTRAL TENDENCY AND THE SHAPE OF THE DISTRIBUTION
If a frequency distribution graph
has a symmetrical frequency
curve, then mean, median and
mode will be equal
In case of a positively skewed
frequency distribution, the mean
is always greater than median
and the median is always greater
than the mode.
In case of a negatively skewed
frequency distribution, the
mean is always lesser than
median and the median is
always lesser than the mode.
26

⚫Variability provides a quantitative measure of the degree t o which scores in
a distribution are spread out or clustered
together.
⚫If data has two distributions (Bivariante) with the same mean known as
variability and have different shapes.
Measure of variability or Disperson
Range
Standard deviation
Correlation & co-efficient
14
VARIABILITY ( DISPERSION)
27

Ran
ge
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 Ahas 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.
15
VARIABILITY ( DISPERSION) – 1. RANGE
28

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.
• VARIANCE
▫ The variance is simply the value of the standard deviation before a
square root has been taken
16
VARIABILITY ( DISPERSION) – 2. STANDARD DEVIATION, 3.
VARIANCE
29

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.
18
CORRELATION
30

Scattered or dotted diagram
High degree of Negative
Correlation
Low degree of Positive
Correlation
Low degree of Negative
Correlation
NO Correlation
CORRELATION
31

Correlation coefficients can be computed with two variables measured on either
the ordinal, interval, or ratio scale
Pearson’s
20
CORRELATION COEFFICIENT
32

• Inferential statistics is a statistical method used to infer results
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
21
INFERENTIAL STATISTICS
33

⚫The standard deviation of a sampling distribution of mean is called the Standard Error of
the Mean (SEM). Standard error of the mean (SEM) measures how far the sample
mean (average) of the data is likely to be from the true population mean.
⚫SEM (symbolized as S
If we use this formula to calculate the SEM for an SD of 100
with a sample of 25 students we obtain
22
ERROR AND HYPOTHESIS TESTING
34

Errors
RejectH0 Don'treject H0
Truth
H0 Type I Error Rightdecision
H1 Right decision Type II Error
Type I error ()
Accepting the experimental hypothesis when the null hypothesis is true
Type II error ()
Accepting the null hypothesis when the experimental hypothesis is true
23
ERROR AND HYPOTHESIS TESTING
35

• Astudy was conducted to determine the difference of knowledge score of hypertension
between male and female adults in savar the result revealed t statistic 2.678, df 99, P value
was 0.009(level of significance set at 0.05) and mean difference 1.14
• Hypothesis
▫ HO: there is no difference of knowledge score of hypertension
between male and female adults in Savar
▫ HA: there is the differences of knowledge score of hypertension between
male and female adults in savar
• Interpret the result
▫ P=0.009, α=0.05, p<α. Reject HO
• conclusion
▫ There is difference of knowledge score of hypertension between male and
female adults in savar
24
HYPOTHESIS TESTING
36

⚫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
25
ESTIMATION
37

There are two types of inferential statistics
1. Parametric
2. Non-parametric Tests
1. 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)
2. 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
26
STATISTICAL TESTS
38

• 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)
27
T - TEST (STUDENT t TEST)
39

 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
⚫FORMULAS
Chi-square
Degrees of freedom (df) = [(R -1)(C - 1)].
30
THE CHI- SQUARE TEST
40

It is another commonly used parametric procedure for testing differences between means
where there are three or more groups.
The statistic computed in anANOVAis the F-ratio , variation within groups to get an F-
ratio.
Types
One wayAnova, two wayANOVA, multifactorANOVA
Formulas MEAN SQUARE (MS) F- Ratio
31
ANALYSIS OF VARIANCE
41
41

• 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.
32
TYPES OF ANOVA
42

• In statistics as well as in quantitative methodology, the set of
data are collected and selected from a statistical population
with the help of some defined procedures. There are two
different types of data sets namely, population and sample. So
basically when we calculate the mean deviation, variance and
standard deviation, it is necessary for us to know if we are
referring to the entire population or to only sample data. If the
population size is denoted by n then the sample size of that
population is given by n-1.
INTRODUCTION
44

“A population is a entire set of individual or objects having some
common characteristics in which a researcher is interested.” - Polit
and Beck (2017)
In statistics, a population is the pool of individuals from which a
statistical sample is drawn for a study. Thus, any selection of
individuals grouped by a common feature can be said to be a
population.
The quantity that describes the outcome of measuring the whole
population is called a parameter. A parameter is a number that
refers to the entire population.
POPULATION
45

POPULATION
The population in which
whose unit is not available in
solid form is known as the
hypothetical population. A
population consists of sets of
observations, objects etc that
are all something in
common. In some situations,
the populations are only
hypothetical. Examples are
an outcome of rolling the
dice, the outcome of tossing
a coin.
Hypothetical Population
The population
whose unit is
available in
solid form is
known as
existent
population.
Examples are
books, students
etc.
Existent
Population
The infinite
population is also
known as an
uncountable
population.
Example of an
infinite population
is the number of
germs in the
patient’s body is
uncountable.
Infinite
Population
The finite population
is also known as a
countable population
Examples of finite
populations are
employees of a
company, potential
consumer in a
market.
Finite Population
46

Study
population: is
the members of
the sample
population who
actually
participate in the
study
Sample
Population: is
the individuals
from the source
population who
are asked to
participate
Accessible
Population or
source
population: is
the portion of the
target population
that is accessible
to the researcher.
Target
population: The
entire population
in which a
researcher is
interested to
generalize the
study results.
POPULATION
47

“Sample is subset of accessible population selected to participate in a
study.” - Polit and Beck (2017)
Sample refers to the final subset of population drawn from the sampling
frame, either by random or non- random method, from which data are
collected by defined method of observation. Or
The sample is an unbiased subset of the population that best represents the
whole data.
The quantity that describe the outcome of measuring the sample is called
Statistic.
The process of selecting samples from the population is known as sampling.
SAMPLE
48

POPULATION SAMPLE
The measurable Quality is called Parameters The measurable quality is called Statistic
The population is a complete set The sample is a subset of population
It contain all member of a specified group It is a subset that represent the entire
population
Report are a true representation of opinion Report have a margin of error and confidence
interval.
It focus on identifying the characteristics It focus on making inference about population
Symbol for denoting the mean is µ, Standard
deviation is σ and the Variance is σ2
Symbol for denoting the mean is x , Standard
Deviation is s and the variance is s2
DIFFERENCE BETWEEN POPULATION AND
SAMPLE
49

• Learning to analyze and think critically is a valuable skill. Article analysis
scrutinizes the claims in the article and the evidence that supports them.
The article analysis includes a summary of the article and it also explains
briefly what the article is all about and it discusses the significance of the
article.
• The important thing to understand before writing a article analysis is the
word analysis or how to analyze because the core of this article analysis
paper is the analysis itself. When analyzing literature , news articles, or
research articles, the main objective is to cover all major points of the piece
that is being analyzed. In writing an analysis, one’s critical thinking
skills are put to the test.
51

1.Inform . The article analysis must have a clear
summary along with details that will provide the
reader some clarity.
2.Persuade . A article analysis not only aims to
educate its readers but also to persuade them into
looking further into a particular stance. The writer of
the article analysis must see if the author of the article
has presented enough arguments with logical
reasoning in order to persuade the reader if the author
did a thorough job of presenting his arguments.
Purpose
Inform
Persuade
Motivate
Entertain
52

3.Entertain . A article analysis does not have to be written in such a formal
way that it prevents the reader from feeling entertained and hungry for more
information. Adding a little creativity in writing a article analysis will not hurt
and will certainly help keep the readers engaged all throughout the material.
4.Motivate . Lastly, a article analysis must be able to
motivate the reader to find out more about what they have
just read. After getting them engaged and interested in the
analysis, the next step is making sure that they are moved
to reflect on themselves or the society or in search of
further information supporting or countering the article.
53

54
1.Summarizing an Article
Annotating an Article
Analyzing an Article

1. Summarizing an Article
 Read the article once without writing anything down.
The first reading should be used to learn concepts and gain a general grasp of
the content.
 Look up any terms or words that are unclear .
If the article is technical, then understand all the concepts before begin to
analyze.
 Write a short three to four sentences as a summary of the article.
If not to do it ,reread it again for content.
 Consider explaining the article aloud if that is easier than writing.
If you can explain the outline and content of the article in non-technical 55

2. Annotating an Article
 Make a photocopy of the article. For notes taking and Citation purpose.
 Read the article a second time to underscore thematic concepts.
Read the article slowly and mark in the margins as much go on.
 Highlight the thesis of the article.
This should be the main argument that the writer is making or trying to prove.
 Underline concepts that recur frequently throughout the article.
Underline supporting points and make notes about them in the margins as you go
along. If you are reading a scientific paper, look for methods, evidence, and results.
This is the accepted structure of most scientific papers.
 Make notes of any concepts that are not fully proven or explained.
These annotations will save time during the writing process.
56

3. Analyzing an Article
 Write the summary or abstract of the article. This can serve as your
introduction.
 Provide some cursory research about the writer of the article.
Their qualifications will prove whether their opinions are part of an area of
expertise. In historical articles, this will also establish whether the author is a
primary or secondary source. State whether you believe the author could be
guilty of a bias.
 Establish the audience of the article.
For example, if the audience is the general public, but the author uses very
technical terms, it may not be a convincing article.
 Decide the purpose of the article.
This may also be the thesis, or what the author is trying to prove. The author
may propose questions and answer them later. 57

3. Analyzing an Article
 Answer how successfully the author proves the thesis.
State examples, such as in-text citations, to outline particularly successful or failed
arguments. Move through the article establishing how meaningful and cohesive their
arguments were. Refer back to annotations to find quotations or questions about the
validity of an argument.
 Compare the article to other articles on the same subject.
Analyze one article in light of another. State which argument was more convincing and
why.
 Write any questions that were left unanswered.
Decide if the author could have improved their article by providing more evidence or in-
depth research on a topic.
 Explain why the article matters to the reader and to the world in general.
At this point, the analyzer should state their opinion about the topic. Some classes ask for
the reader's opinions, while others demand a very scientific critique.
 Create a Works Cited page for citations if any. 58

Introduction
Summary
Analysis
Conclusion
59

1. Introduction
 Introduce the analyzing article. Specify the title of the
article, the author(s)’s name, and the year it was published
– if available.
 Explain briefly what the article is all about by discussing
the main ideas to make the paper easier to read.
 Then, identify the significance of the article before
discussing the own statement, vision, and ideas that are
related to the paper you are analyzing.
60

2.Summary
 After briefly introducing the article to be analyzed, start
to summarize the article and make sure to cover all the
key information that needs to be discussed.
 Do not leave anything out that is crucial in the analysis.
 Then identify if the research methods used by the
author(s) are suitable for the study or if there are better
methods to be used.
61

3. Analysis
•The analysis should be a balanced discussion and evaluation of the
strengths, weakness and notable features of the text. Express your opinion
about whether the article was a clear, thorough, and useful explanation of
the subject
•Remember to base your discussion on specific criteria.
•Good analysis also include other sources to support your evaluation
(remember to reference).
•If your analysis is more positive than negative, then present the negative
points first and the positive last. If your critique is more negative than
positive, then present the positive points first and the negative last.
62

4. Conclusion
•Conclude the research article analysis by presenting your
recommendations on how to make the research article better and
lessen its weaknesses.
•Commend the research article where it should rightfully be
supported and do not hesitate to point out where it is lacking.
•Remember to remain unbiased when writing the research article
analysis.
•Finally, do not forget to properly cite your sources because doing
so is important.
63

Research Paper
Section
Questions to Ask
Abstract •Does the abstract clearly describe the paper’s
objectives?
•Does the abstract correspond to the info presented
in the research paper?
•Does the abstract contain any information that is
not investigated in the paper?
Introduction •Does the author present the reasons for
conducting the study?
•Does the into include background information?
•Is there a clear thesis statement in the
introduction? 64

Research Paper Section Questions to Ask
Methods •Are the methods presented clearly enough?
•Were the standard or modified methods used?
•If modified, were the changes explained effectively?
•Did the author indicate the limitations and the problems
that arose while using the chosen methods?
•Are the selected methods appropriate for the given
research paper?
Results •Are the findings adequate and logical?
•Is the data presented precisely?
•If there are any tables or diagrams, are they easily-
understandable?
•Are the results helpful for the understanding of the
topic? 65

Research Paper
Section
Questions to Ask
Discussion •Did the author meet the objectives?
•If the author did not meet the objectives, do they
provide any explanation for that?
•Do the findings interpreted adequately?
•Is the author biased?
•Does the author discuss the percent of errors that might
occur while conducting the research?
References •Are all of the outside sources cited?
•Does the author cite their own work in the research
paper?
•Do the reference list and in-text citations correspond to
the chosen formatting style? 66

Introduction to nursing Statistics.pptx

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Introduction to nursing Statistics.pptx

Similar to Introduction to nursing Statistics.pptx (20)

More from Melba Shaya Sweety

More from Melba Shaya Sweety (20)

Recently uploaded

Recently uploaded (20)

Introduction to nursing Statistics.pptx