This document provides an overview of quantitative data analysis methods for nursing research. It discusses inferential statistical tests that can be used for different data types and group structures, including chi-square tests, Mann-Whitney U tests, Kruskal-Wallis tests, t-tests, ANOVA, and correlation analyses. Examples are given of how to interpret the output of these tests and assess their assumptions. Resources for further learning about medical statistics and research methods are also listed.
1. NURSING STUDIES MSc/Dip NURSING
NURSING STUDIES
RESEARCH METHODS IN NURSING AND
HEALTHCARE B
QUANTITATIVE DATA ANALYSIS 2
Dr. Sheila Rodgers
Nursing Studies
University of Edinburgh
2. NURSING STUDIES MSc/Dip NURSING Research B
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This lecture aims to enable students:
•to know which inferential tests can be
applied to what kind of data
•to be able to interpret inferential
statistical analysis output
3. MSc/Dip NURSING Research and Evaluation
INFERENTIAL TESTS
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Nominal Ordinal lnterval / No grps Paired
ratio
X2 / Fisher’s Mann Whitney U test T - test 2 No
exact test
X2 / Fisher’s Kruskall Wallis test ANOVA 3+ No
exact test
McNemar test Wilcoxon signed ranks Paired T - 2 Yes
test test
Cochran’s Q Friedman test RANOVA 3+ Yes
test
Spearman’s rank Pearson’s r 2 variables Yes
correlation co-effiecient
5. MSc/Dip NURSING Research and Evaluation
INFERENTIAL TESTS
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NURSING STUDIES
Nominal Ordinal lnterval / No grps Paired
ratio
X2 / Fisher’s Mann Whitney U test T - test 2 No
exact test
X2 / Fisher’s Kruskall Wallis test ANOVA 3+ No
exact test
McNemar test Wilcoxon signed ranks Paired T - 2 Yes
test test
Cochran’s Q Friedman test RANOVA 3+ Yes
test
Spearman’s rank Pearson’s r 2 variables Yes
correlation co-effiecient
6. NURSING STUDIES MSc/Dip NURSING Research B
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Chi Square X2
Assumes:
Random sampling
Independent groups
Expected frequency of each cell greater
than 0
Expected frequency of at least 5 for at
least 80% of cells
9. NURSING STUDIES MSc/Dip NURSING Research B
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Fisher’s Exact test – when the expected
frequency of a cell is <5
McNemar test for paired or matched data with
two groups
Cochran’s Q test for paired or matched data
with 3+ groups
10. NURSING STUDIES MSc/Dip NURSING Research B
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MANN WHITNEY U-TEST
Assumes:
Random sampling
Independent groups
Ordinal level data
Compares the ranks of the scores from two
groups to test if the distributions are the same
or not.
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KRUSKAL – WALLIS TEST
Makes the same assumptions as the MW test
plus a minimum of 5 cases per group.
Compares the ranks of the scores from 3+
groups to test of the distributions are the same
or not.
df= no of groups - 1
14. NURSING STUDIES MSc/Dip NURSING Research B
T-TEST
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Assumes:
•Random sampling
•Normal distribution
•Equal variance
•At least interval measurement
Compares the mean differences of the two
groups
16. NURSING STUDIES MSc/Dip NURSING Research B
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ANALYSIS OF VARIANCE
Assumes:
• Random sampling
• Normal distribution
• Populations have equal variance
• At least interval measurement
• Groups are not matches or pairs
• 3+ groups or independent variables
17. MSc/Dip NURSING Research B
A Water filled B Gel heel C sheepskin
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gloves pad heel cover
24 12 20
38 10 28
26 15 23
17 19 10
21 14 15
X=31.2 X=14.0 X=19.2
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CORRELATION
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CORRELATION
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20. MSc/Dip NURSING Research B
CORRELATION
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22. MSc/Dip NURSING Research B
CORRELATION
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• Enables the prediction of Y on the basis of X.
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• The coefficient gives the strength and
magnitude of the relationship.
• Coefficients range from 0 to + 1 and 0 to -1.
• Scatter plots help determine the nature of
relationships.
• R 2 = proportion of the variance in one
variable that can be explained by variability in
the second variable (coefficient of
23. MSc/Dip NURSING Research B
CORRELATION
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Pearson's r or Pearson product moment assumes;
• both dependent and independent variables are
at least interval or ratio scale
• random sampling
• variables are normally distributed
• linear relationships
df= no of cases - 2
24. NURSING STUDIES MSc/Dip NURSING Research B
SPEARMAN’S RANK ORDER CORRELATION
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CO-EFFICIENT
• both dependent and independent variables are
at least ordinal scale
• random sampling
• linear relationships
df= no of cases - 2
25. MSc/Dip NURSING Research and Evaluation
INFERENTIAL TESTS
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Nominal Ordinal lnterval / No grps Paired
ratio
X2 / Fisher’s Mann Whitney U test T - test 2 No
exact test
X2 / Fisher’s Kruskall Wallis test ANOVA 3+ No
exact test
McNemar test Wilcoxon signed ranks Paired T - 2 Yes
test test
Cochran’s Q Friedman test RANOVA 3+ Yes
test
Spearman’s rank Pearson’s r 2 variables Yes
correlation co-effiecient
26. MSc/Dip NURSING Research and Evaluation
University of Leicester Hospital NHS site has a short online module on statistics which
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can be useful to look at. It also includes some information on medical statistics such
as odds ratios.
Follow the link below and look at the modules and look at the one on ‘Introduction to
Statistics’.
http://www.uhl-library.nhs.uk/training.html
'Principles of searching' e-learning (10 mins) - an introduction to database
searching
Research methods - types of research methods and hierarchy of evidence
Critical Reading Made Easy - an introduction to critical appraisal principles
and tools
Introduction to statistics - displaying, summarising and testing data
Editor's Notes
This table is also available as a separate document so you can down load it and print it off. Hopefully it is a simple way for you to ‘look up’ what type of data you are working with, the number of groups involved and whether the data is paired or not. Paired data is when the two sets of data relate closely to each other and this kind of data needs different kinds of tests. An example of parried data is when it comes from the same person in an experiment. There may be of before and after treatment on the same person and the measure taken before the intervention is then compared to the measure take afterwards. It might not even be the same person but if you have matched individuals in experimental groups closely on things like age, gender, health conditions etc then you should also treat this as paired data. So firstly decide what type of data it is and look it up across the top row. Then decide how many groups are involved and whether it is paired data or not.
For example, I want to know if there is any difference between different methods of preventing pressure ulcers on patients heels so I set up a trial to measure the pressure underneath different pressure relieving devices. One is a gel pad, and the other is a ward made device by filling a latex glove with water to make a water based pad. I decide not to compare this to doing nothing as that might be quite negligent to do on real patients who are at risk. So I compare these two other ways of relieving pressure to the usual ward practice of using a a sheepskin pad. This then becomes the third type of intervention. The pressure measurements are made by a scientific device which measures quite accurately. The data then is ratio and I have 3 or more groups. The groups are not paired or matched in any way.
Which test do you think I should use to see if there is any statistical difference between the heel pressures ?
Compares between group variance to within group variance to give the F ratio
To conclude Use the table to help you make judgements when reviewing published papers and deciding what tests you should use in any research proposals. Be aware though that data should be normally distributed to use the interval or ratio level tests.
To conclude Use the table to help you make judgements when reviewing published papers and deciding what tests you should use in any research proposals. Be aware though that data should be normally distributed to use the interval or ratio level tests.