This document discusses non-parametric statistical tests including the Wilcoxon rank sum test, Kruskal-Wallis test, and Spearman/Kendall correlation. It provides an overview of when to use these tests, their assumptions, procedures, advantages and disadvantages. Examples are given to illustrate how to perform the Wilcoxon rank sum test, Kruskal-Wallis test, and Wilcoxon signed rank test step-by-step. SPSS output is also shown for these tests.
OBJECTIVES:
Run the test of hypothesis for mean difference using paired samples. Construct a confidence interval for the difference in population means using paired samples.
Observation of interest will be the difference in the readings
before and after intervention called paired difference observation.
Paired t test:
A paired t-test is used to compare two means where you have two samples in which observations in one sample can be paired with observations in the other sample.
Examples of where this might occur are:
Before-and-after observations on the same subjects (e.g. students’ test
results before and after a particular module or course).
A comparison of two different methods of measurement or two different treatments where the measurements/treatments are applied to the same subjects (e.g. blood pressure measurements using a sphygmomanometer and a dynamap).
When there is a relationship between the groups, such as identical twins.
This test is concerned with the pair-wise differences
between sets of data.
This means that each data point in one group has a related data point in the other group (groups always have equal numbers).
ASSUMPTIONS:
The sample or samples are randomly selected
The sample data are dependent
The distribution of differences is approximately normally
distributed.
Note: The under root is onto the entire numerator and denominator, so you should take the root after solving it entirely
where “t” has (n-1) degrees of freedom and “n” is
the total number of pairs.
Statistical inference: Statistical Power, ANOVA, and Post Hoc testsEugene Yan Ziyou
This deck was used in the IDA facilitation of the John Hopkins' Data Science Specialization course for Statistical Inference. It covers the topics in week 4 (statistical power, ANOVA, and post hoc tests).
The data and R script for the lab session can be found here: https://github.com/eugeneyan/Statistical-Inference
When you run Analysis of Variance (ANOVA), the results will tell you if there is a difference in means. However, it won’t pinpoint the pairs of means that are different.
Through this ppt you could learn what is Wilcoxon Signed Ranked Test. This will teach you the condition and criteria where it can be run and the way to use the test.
v When to Choose a Statistical Tests OR When NOT to Choose? v Parametric vs. Non-Parametric Tests (Comparison)
v Parameters to check when Choosing a Statistical Test:
- Distribution of Data
- Type of data/Variable
- Types of Analysis (What’s the hypothesis)
- No of groups or data-sets
- Data Group Design
v Snapshot of all statistical test and “How” to Choose using above parameters v Explanation using Examples:
- Mann Whitney U Test
- Wilcoxon Sign Rank Test
- Spearman’s co-relation
- Chi-Square Test
v Conclusion
OBJECTIVES:
Run the test of hypothesis for mean difference using paired samples. Construct a confidence interval for the difference in population means using paired samples.
Observation of interest will be the difference in the readings
before and after intervention called paired difference observation.
Paired t test:
A paired t-test is used to compare two means where you have two samples in which observations in one sample can be paired with observations in the other sample.
Examples of where this might occur are:
Before-and-after observations on the same subjects (e.g. students’ test
results before and after a particular module or course).
A comparison of two different methods of measurement or two different treatments where the measurements/treatments are applied to the same subjects (e.g. blood pressure measurements using a sphygmomanometer and a dynamap).
When there is a relationship between the groups, such as identical twins.
This test is concerned with the pair-wise differences
between sets of data.
This means that each data point in one group has a related data point in the other group (groups always have equal numbers).
ASSUMPTIONS:
The sample or samples are randomly selected
The sample data are dependent
The distribution of differences is approximately normally
distributed.
Note: The under root is onto the entire numerator and denominator, so you should take the root after solving it entirely
where “t” has (n-1) degrees of freedom and “n” is
the total number of pairs.
Statistical inference: Statistical Power, ANOVA, and Post Hoc testsEugene Yan Ziyou
This deck was used in the IDA facilitation of the John Hopkins' Data Science Specialization course for Statistical Inference. It covers the topics in week 4 (statistical power, ANOVA, and post hoc tests).
The data and R script for the lab session can be found here: https://github.com/eugeneyan/Statistical-Inference
When you run Analysis of Variance (ANOVA), the results will tell you if there is a difference in means. However, it won’t pinpoint the pairs of means that are different.
Through this ppt you could learn what is Wilcoxon Signed Ranked Test. This will teach you the condition and criteria where it can be run and the way to use the test.
v When to Choose a Statistical Tests OR When NOT to Choose? v Parametric vs. Non-Parametric Tests (Comparison)
v Parameters to check when Choosing a Statistical Test:
- Distribution of Data
- Type of data/Variable
- Types of Analysis (What’s the hypothesis)
- No of groups or data-sets
- Data Group Design
v Snapshot of all statistical test and “How” to Choose using above parameters v Explanation using Examples:
- Mann Whitney U Test
- Wilcoxon Sign Rank Test
- Spearman’s co-relation
- Chi-Square Test
v Conclusion
• Non parametric tests are distribution free methods, which do not rely on assumptions that the data are drawn from a given probability distribution. As such it is the opposite of parametric statistics
• In non- parametric tests we do not assume that a particular distribution is applicable or that a certain value is attached to a parameter of the population.
When to use non parametric test???
1) Sample distribution is unknown.
2) When the population distribution is abnormal
Non-parametric tests focus on order or ranking
1) Data is changed from scores to ranks or signs
2) A parametric test focuses on the mean difference, and equivalent non-parametric test focuses on the difference between medians.
1) Chi – square test
• First formulated by Helmert and then it was developed by Karl Pearson
• It is both parametric and non-parametric test but more of non - parametric test.
• The test involves calculation of a quantity called Chi square.
• Follows specific distribution known as Chi square distribution
• It is used to test the significance of difference between 2 proportions and can be used when there are more than 2 groups to be compared.
Applications
1) Test of proportion
2) Test of association
3) Test of goodness of fit
Criteria for applying Chi- square test
• Groups: More than 2 independent
• Data: Qualitative
• Sample size: Small or Large, random sample
• Distribution: Non-Normal (Distribution free)
• Lowest expected frequency in any cell should be greater than 5
• No group should contain less than 10 items
Example: If there are two groups, one of which has received oral hygiene instructions and the other has not received any instructions and if it is desired to test if the occurrence of new cavities is associated with the instructions.
2) Fischer Exact Test
• Used when one or more of the expected counts in a 2×2 table is small.
• Used to calculate the exact probability of finding the observed numbers by using the fischer exact probability test.
3) Mc Nemar Test
• Used to compare before and after findings in the same individual or to compare findings in a matched analysis (for dichotomous variables).
Example: comparing the attitudes of medical students toward confidence in statistics analysis before and after the intensive statistics course.
4) Sign Test
• Sign test is used to find out the statistical significance of differences in matched pair comparisons.
• Its based on + or – signs of observations in a sample and not on their numerical magnitudes.
• For each subject, subtract the 2nd score from the 1st, and write down the sign of the difference.
It can be used
a. in place of a one-sample t-test
b. in place of a paired t-test or
c. for ordered categorial data where a numerical scale is inappropriate but where it is possible to rank the observations.
5) Wilcoxon signed rank test
• Analogous to paired ‘t’ test
6) Mann Whitney Test
• similar to the student’s t test
7) Spearman’s rank correlation - similar to pearson's correlation.
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The "New Drug Discovery and Development" process involves the identification, design, testing, and manufacturing of novel pharmaceutical compounds with the aim of introducing new and improved treatments for various medical conditions. This comprehensive endeavor encompasses various stages, including target identification, preclinical studies, clinical trials, regulatory approval, and post-market surveillance. It involves multidisciplinary collaboration among scientists, researchers, clinicians, regulatory experts, and pharmaceutical companies to bring innovative therapies to market and address unmet medical needs.
Prix Galien International 2024 Forum ProgramLevi Shapiro
June 20, 2024, Prix Galien International and Jerusalem Ethics Forum in ROME. Detailed agenda including panels:
- ADVANCES IN CARDIOLOGY: A NEW PARADIGM IS COMING
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ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
Recomendações da OMS sobre cuidados maternos e neonatais para uma experiência pós-natal positiva.
Em consonância com os ODS – Objetivos do Desenvolvimento Sustentável e a Estratégia Global para a Saúde das Mulheres, Crianças e Adolescentes, e aplicando uma abordagem baseada nos direitos humanos, os esforços de cuidados pós-natais devem expandir-se para além da cobertura e da simples sobrevivência, de modo a incluir cuidados de qualidade.
Estas diretrizes visam melhorar a qualidade dos cuidados pós-natais essenciais e de rotina prestados às mulheres e aos recém-nascidos, com o objetivo final de melhorar a saúde e o bem-estar materno e neonatal.
Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
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- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
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There are a number of conditions that present acutely, predominantly with pain and/or swelling
A careful and detailed history and examination, and in some cases, investigations allow differentiation between these diagnoses. A prompt diagnosis is essential as the patient may require urgent surgical intervention
Testicular torsion refers to twisting of the spermatic cord, causing ischaemia of the testicle.
Testicular torsion results from inadequate fixation of the testis to the tunica vaginalis producing ischemia from reduced arterial inflow and venous outflow obstruction.
The prevalence of testicular torsion in adult patients hospitalized with acute scrotal pain is approximately 25 to 50 percent
2. Explore
• It is the first step in the analytic process
• to explore the characteristics of the data
• to screen for errors and correct them
• to look for distribution patterns - normal
distribution or not
• May require transformation before further
analysis using parametric methods
• Or may need analysis using non-parametric
techniques
3. Choosing an appropriate method
• Number of groups of observations
• Independent or dependent groups of
observations
• Type of data
• Distribution of data
• Objective of analysis
4. Nonparametric Test
Procedures
• Statistic does not depend on population
distribution
• Data may be nominally or ordinally scaled
– Example: Male-female
• May involve population parameters such
as median
• Based on analysis of ranks
• Example: Wilcoxon rank sum test
5. non-parametric tests
Variable 1 Variable 2 Criteria Type of Test
Qualitative Qualitative Sample size < 20 or (< 40 but Fisher Test
Dichotomus Dichotomus with at least one expected
value < 5)
Qualitative Quantitative Data not normally distributed Wilcoxon Rank Sum
Dichotomus Test or U Mann-
Whitney Test
Qualitative Quantitative Data not normally distributed Kruskal-Wallis One
Polinomial Way ANOVA Test
Quantitative Quantitative Repeated measurement of the Wilcoxon Rank Sign
same individual & item Test
Continous or Quantitative - Data not normally distributed Spearman/Kendall
ordinal continous Rank Correlation
8. Mann-Whitney U Test/
Wilcoxon Rank Sum
• Non-parametric comparison of 2 groups
• Requires all the observations to be ranked
as if they were from a single sample
9. Mann-Whitney U Test/
Wilcoxon Rank Sum
• Tests two independent population medians
• Non-parametric test procedure
• Assumptions
– Ordinal, interval, or ratio scaled data
– Population is nearly symmetrical
• Bell-shaped, rectangular etc.
• Can use normal approximation if ni > 10
10. Mann-Whitney U Test/
Wilcoxon Rank Sum Procedure
• Assign ranks, Ri , to the n1 + n2 sample
observations
– If unequal sample sizes, let n1 refer to smaller-
sized sample
– Smallest value = rank of 1
– Same value -> Average ties
• Sum the ranks, Ti , for each group
• Test statistic is T1 (smallest group)
11. Example
• Comparing the blood nores kerja glu
glucose level 234 1 124
between taxi drivers
243 1 141
(code 3) and bus
drivers (code 1) 244 1 93.6
410 3 139
508 3 104
821 3 105
829 3 96.2
832 3 95
12. Example step 2
nores kerja glu rank
• Arrange the blood
244 1 93.6 1
glucose level in
832 3 95 2
ascending order.
Give rank from 829 3 96.2 3
lowest to highest. 508 3 104 4
• If the same values, 821 3 105 5
take the mean 234 1 124 6
rank.
410 3 139 7
243 1 141 8
13. Example step 3
• Total up the rank in the smaller group
– Bus drivers; T = 6+8+1=15
– Taxi drivers; T = 7+4+5+3+2=21
• Compare the result with the respective table
at n1 and n2 ; 3, 5.
• T is between the two critical range (6 – 21).
Only significant if T= or <6,
or T= or > 21.
• Conclusion: p > 0.05; Null Hypothesis
NOT REJECTED
14. Refer to Table A8.
Look at n1, n2 ; 3, 5.
For p=0.05, the critical
range is between 6 to 21.
Only significant if; T < 6,
or T > 21.
Therefore p > 0.05
15.
16. SPSS Output
Ranks
KERJA N Mean Rank Sum of Ranks
GLU 1.00 3 5.00 15.00
3.00 5 4.20 21.00
Total 8
Test Statisticsb
GLU
Mann-Whitney U 6.000
Wilcoxon W 21.000
Z -.447
Asymp. Sig. (2-tailed) .655
Exact Sig. [2*(1-tailed a
.786
Sig.)]
a. Not corrected for ties.
b. Grouping Variable: KERJA
17. The only way for the result
to be significant
Is for all the data of the smallest
group to be at one end or the
other.
18. Assume the results were like this
nores kerja glu rank
• The bus drivers all
244 1 93.6 1
had lower blood
832 1 95 2
glucose level
compared to the 829 1 96.2 3
taxi drivers. 508 3 104 4
821 3 105 5
234 3 124 6
410 3 139 7
243 3 141 8
19. Now the result is significant
• Total up the rank in the smaller group
– Bus drivers; T = 1+2+3=6
– Taxi drivers; T = 4+5+6+7+8=30
• Compare the result with the respective table
at n1 and n2 ; 3, 5.
• T is exactly the value of the lower critical
range (6 – 21). Now significant since T= or <
6
• Conclusion: p < 0.05; Null Hypothesis is
REJECTED
20. Refer to Table A8.
Look at n1, n2 ; 3, 5.
For p=0.05, the critical
range is between 6 to 21.
Only significant if; T < 6,
or T > 21.
Therefore p < 0.05
21. Let’s try it the other way!
nores kerja glu rank
• The bus drivers all
244 3 93.6 1
had higher blood
832 3 95 2
glucose level
compared to the 829 3 96.2 3
taxi drivers. 508 3 104 4
821 3 105 5
234 1 124 6
410 1 139 7
243 1 141 8
22. Now the result is also significant
•Total up the rank in the smaller group
–Bus drivers; T = 6+7+8=21
–Taxi drivers; T = 1+2+3+4+5=15
•Compare the result with the respective table at n1
•T is exactly the value of the upper critical range (6
•Conclusion: p < 0.05; Null Hypothesis is
23. Refer to Table A8.
Look at n1, n2 ; 3, 5.
For p=0.05, the critical
range is between 6 to 21.
Only significant if; T < 6,
or T > 21.
Therefore p < 0.05
25. Kruskal-Wallis Rank Test
for c Medians
• Tests the equality of more than 2 (c)
population medians
• Non-parametric test procedure
• Used to analyze completely randomized
experimental designs
• Can use χ2 distribution to approximate if
each sample group size nj > 5
– Degrees of freedom = c - 1
26. Kruskal-Wallis Rank Test
Assumptions
• Independent random samples are drawn
• Continuous dependent variable
• Ordinal, interval, or ratio scaled data
• Populations have same variability
• Populations have same shape
27. Kruskal-Wallis Rank Test
Procedure
• Assign ranks, Ri , to
the n combined
observations
– Smallest value = 1
– Largest value = n
– Average ties
• Test statistic Squared total of
each group
2
12 Ti
H= ∑ − 3(n + 1)
n(n + 1) ni
28. Kruskal-Wallis Rank Test
Example
As production manager, you Mach1 Mach2 Mach3
want to see if 3 filling 25.40 23.40 20.00
machines have different 26.31 21.80 22.20
median filling times. You 24.10 23.50 19.75
assign 15 similarly trained & 23.74 22.75 20.60
experienced workers, 25.10 21.60 20.40
5 per machine, to the
machines. At the .05 level,
is there a difference in
median filling times?
29. Kruskal-Wallis Rank Test
Solution
H0: M1 = M2 = M3 Test Statistic:
H1: Not all equal
α = .05
df = c - 1 = 3 - 1 = 2
Critical Value(s):
Decision:
α = .05
Conclusion:
0 5.991 χ2
32. Kruskal-Wallis Rank Test
Solution
H0: M1 = M2 = M3 Test Statistic:
H1: Not all equal H = 11.58
α = .05 Refer to Chi-Square table
df = c - 1 = 3 - 1 = 2
Critical Value(s):
Decision:
Reject at α = .05
α = .05
Conclusion:
There is evidence pop.
0 5.991 χ2 medians are different
33. Refer to Table 3.
Look at df = 2.
H = 11.58, larger than
10.60 (p=0.005) but
smaller than 13.82
(p=0.001).
13.82>11.58>10.60
Therefore if H=11.58,
0.001<p<0.005.
34.
35. SPSS Output
Ranks
MESIN N Mean Rank
MASA 1.00 5 13.00
2.00 5 7.60
3.00 5 3.40
Total 15
Test Statisticsa,b
masa
Chi-Square 11.580
df 2
Asymp. Sig. .003
a. Kruskal Wallis Test
b. Grouping Variable: mesin
37. Example
nores bps1 bps2
237 147 131
835 166 150
• Whether there 251 159 147
is any 809 150 139
difference of 241
233
170
164
160
155
the systolic 272 154 145
blood pressure 239
261
186
155
178
147
taken at 2 246 176 170
different time 247 186 181
254 155 150
for 36 patients. 258 151 147
288 152 148
829 115 111
257 162 159
38. Step 2
• Calculate the
difference
between the two
values.
• Rank them
accordingly,
ignoring + or -.
• Total up the + &
- separately
39. Step 3
• Total up the ranks of the positives and the
negatives. These are T+ dan T-.
• T+ = 152 and T- = 409
• Take the smallest value i.e. 152 and refer to
the appropriate table. Critical value for n = 33
(3 zero values so 36 - 3) for significance at
0.05 is 171. Therefore < critical range.
• Therefore : Null hypothesis rejected.
• Conclusion: There is a difference of blood
pressure measured at two different times.
40. Refer to Table A7.
Look at n=33.
Take the smallest value
T+=152. Critical value for
n = 33 (3 zero values) for
significance at 0.05 is 171.
Therefore 152 < critical
range; 0.02 < p < 0.05
41.
42. SPSS Output
Ranks
N Mean Rank Sum of Ranks
BPS2 - BPS1 Negative Ranks 21a 19.48 409.00
Positive Ranks 12b 12.67 152.00
Ties 3c
Total 36
a. BPS2 < BPS1
b. BPS2 > BPS1
c. BPS2 = BPS1
Test Statisticsb
BPS2 - BPS1
Z -2.300a
Asymp. Sig. (2-tailed) .021
a. Based on positive ranks.
b. Wilcoxon Signed Ranks Test
43. Spearman/Kendall Correlation
• To find correlation between a related pair
of continuous data; or
• Between 1 Continuous, 1 Categorical
Variable (Ordinal)
• e.g., association between Likert Scale on work
satisfaction and work output.
44. Spearman's rank correlation
coefficient
• In statistics, Spearman's rank correlation coefficient,
named for Charles Spearman and often denoted by the
Greek letter ρ (rho), is a non-parametric measure of
correlation – that is, it assesses how well an arbitrary
monotonic function could describe the relationship
between two variables, without making any assumptions
about the frequency distribution of the variables. Unlike
the Pearson product-moment correlation coefficient, it
does not require the assumption that the relationship
between the variables is linear, nor does it require the
variables to be measured on interval scales; it can be
used for variables measured at the ordinal level.
45. Example
•Correlation between
sphericity and visual
acuity.
•Sphericity of the eyeball
is continuous data while
visual acuity is ordinal
data (6/6, 6/9, 6/12,
6/18, 6/24), therefore
Spearman correlation is
the most suitable.
•The Spearman rho
correlation coefficient is
-0.108 and p is 0.117. P
is larger than 0.05,
therefore there is no
significant association
between sphericity and
visual acuity.
46. Example 2
•- Correlation between glucose level and systolic
blood pressure.
•Based on the data given, prepare the following
table;
•For every variable, sort the data by rank. For ties,
take the average.
•Calculate the difference of rank, d for every pair
and square it. Take the total.
•Include the value into the following formula;
•∑ d2 = 4921.5 n = 32
•Therefore rs = 1-((6*4921.5)/(32*(322-1)))
= 0.097966.
This is the value of Spearman correlation
coefficient (or ϒ).
•Compare the value against the Spearman table;
•p is larger than 0.05.
•Therefore there is no association between systolic
47. Spearman’s
table
•0.097966 is the value
of Spearman correlation
coefficient (or ϒ).
•Compare the value
against the Spearman
table;
•p is larger than 0.05.
•Therefore there is no
association between
systolic BP and blood
glucose level.
48. SPSS Output
Correlations
GLU BPS1
Spearman's rho GLU Correlation Coefficient 1.000 .097
Sig. (2-tailed) . .599
N 32 32
BPS1 Correlation Coefficient .097 1.000
Sig. (2-tailed) .599 .
N 32 32
49. Presentation
• Never sufficient to present the results
solely as a p value
• Median and quartiles should be given
• For small samples, the median and range
can be given
50. Take Home Message
• Both parametric & non-parametric
methods can be used for continuous data
• Parametric methods are preferred if all
assumptions are met
• Reasons
– Amenable to estimation and confidence
interval
– Readily extended to the more complicated
data structures
51. Take Home Message
• We should carry out either parametric or non-
parametric test on a set of data, not both.
• When there are doubts of the validity of the
assumptions for the parametric method, a non
parametric analysis is carried out
• If the assumptions are met, the two methods
should give very similar answers
• If the answers differ, then the non-parametric
method is more likely to be reliable