KNOWLEDGE FOR THE BENEFIT OF HUMANITY KNOWLEDGE FOR THE BENEFIT OF HUMANITY BIOSTATISTICS (HFS3283) INTRODUCTION TO BIOSTATISTICS Dr. Dr. Mohd Mohd Razif Razif Shahril Shahril School of Nutrition & Dietetics School of Nutrition & Dietetics Faculty of Health Sciences Faculty of Health Sciences Universiti Universiti Sultan Sultan Zainal Zainal Abidin Abidin 1
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KNOWLEDGE FOR THE BENEFIT OF HUMANITYKNOWLEDGE FOR THE BENEFIT OF HUMANITY
BIOSTATISTICS (HFS3283)
INTRODUCTION TO BIOSTATISTICS
Dr.Dr. MohdMohd RazifRazif ShahrilShahril
School of Nutrition & Dietetics School of Nutrition & Dietetics
Faculty of Health SciencesFaculty of Health Sciences
UniversitiUniversiti Sultan Sultan ZainalZainal AbidinAbidin
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S C H O O L O F N U T R I T I O N A N D D I E T E T I C S • U N I V E R S I T I S U L T A N Z A I N A L A B I D I N
Synopsis “This course exposes students to different types of data,
measures of central tendency and dispersion, probability,
normal distribution and inferential analysis in biostatistics. It
also covers various basic statistical analyses including
descriptive, parametric and nonparametric tests,
association and prediction models commonly used in
research. This course provides hands-on experience for
students to perform statistical analysis using SPSS and
interpret their outcomes in answering specific research
question”
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S C H O O L O F N U T R I T I O N A N D D I E T E T I C S • U N I V E R S I T I S U L T A N Z A I N A L A B I D I N
Learning Outcomes At the end of this course, students should be able to;
• Describe concepts of descriptive, inferential, parametric
and non-parametric tests in biostatistics.
• Describe concepts of categorical data analysis,
association, prediction, reliability and validity in
biostatistics.
• Choose statistical analysis of data based on types of
variables and objective of analysis using SPSS and
interpret their outcomes.
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S C H O O L O F N U T R I T I O N A N D D I E T E T I C S • U N I V E R S I T I S U L T A N Z A I N A L A B I D I N
Teaching & Learning Strategy Blended Mode Learning (Face to Face + e-Learning)
• Interactive Lecture – offline + online
• Hands-on Practical
• Project
• Self-Directed Learning (Literature, Video)
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S C H O O L O F N U T R I T I O N A N D D I E T E T I C S • U N I V E R S I T I S U L T A N Z A I N A L A B I D I N
Brief Course Outline 1. Introduction to biostatistics
2. Inferential statistics
3. Independent t test
4. ANOVA & Paired t-test
5. Nonparametric analysis
6. Categorical data analysis: Chi square & Fisher exact test
7. Categorical data analysis: OR and RR
8. Correlation
9. Regression
10. Validity analysis
11. Reliability analysis
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S C H O O L O F N U T R I T I O N A N D D I E T E T I C S • U N I V E R S I T I S U L T A N Z A I N A L A B I D I N
Course Schedule
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WEEK / DATE ACTIVITIES
1 MONDAY
(25/01/2016)
Lecture (8.00am - 10.00am) – Dr. Mohd Razif Shahril
INTRODUCTION TO BIOSTATISTICS
Practical (10.00am - 12.00pm) –Dr. Mohd Razif Shahril
INTRODUCTION TO SPSS
2 MONDAY
(01/02/2016)
Lecture (8.00am - 10.00am) –Dr. Mohd Razif Shahril
INFERENTIAL STATISTICS
Practical (10.00am - 12.00pm) –Dr. Mohd Razif Shahril
S C H O O L O F N U T R I T I O N A N D D I E T E T I C S • U N I V E R S I T I S U L T A N Z A I N A L A B I D I N
Course Schedule (cont.)
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WEEK / DATE ACTIVITIES
6 MONDAY
(29/02/2016)
Lecture (8.00am - 10.00am) –Dr. Mohd Razif Shahril
NONPARAMETRIC ANALYSIS
Practical (10.00am - 12.00pm) –Dr. Mohd Razif Shahril
SPSS: NONPARAMETRIC ANALYSIS
7 MONDAY
(07/03/2016)
Lecture (8.00am - 10.00am) –Dr. Mohd Razif Shahril
CATEGORICAL DATA ANALYSIS: CHI SQUARE & FISHER
EXACT TEST
Practical (10.00am - 12.00pm) –Dr. Mohd Razif Shahril
SPSS: CHI SQUARE & FISHER EXACT TEST
THURSDAY
(10/03/2016)
TEST (8.30am - 10.00am) –Dr. Mohd Razif Shahril
8 MONDAY
(14/03/2016)
Lecture (8.00am - 10.00am) –Dr. Mohd Razif Shahril
CATEGORICAL DATA ANALYSIS: OR AND RR
Practical (10.00am - 12.00pm) –Dr. Mohd Razif Shahril
SPSS: OR AND RR
MID SEMESTER BREAK (20/03/2016 – 26/03/2016)
S C H O O L O F N U T R I T I O N A N D D I E T E T I C S • U N I V E R S I T I S U L T A N Z A I N A L A B I D I N
Course Schedule (cont.)
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WEEK / DATE ACTIVITIES
9 MONDAY
(28/03/2016)
Lecture (8.00am - 10.00am) –Dr. Mohd Razif Shahril
CORRELATION
Practical (10.00am - 12.00pm) –Dr. Mohd Razif Shahril
SPSS: CORRELATION
10 MONDAY
(04/04/2016)
Lecture (8.00am - 10.00am) –Dr. Mohd Razif Shahril
REGRESSION
Practical (10.00am - 12.00pm) –Dr. Mohd Razif Shahril
SPSS: REGRESSION
11 MONDAY
(11/04/2016)
Lecture (8.00am - 10.00am) –Dr. Mohd Razif Shahril
RELIABILITY
Practical (10.00am - 12.00pm) –Dr. Mohd Razif Shahril
SPSS: INTERNAL CONSISTENCY & INTRACLASS
CORRELATION
12 MONDAY
(18/04/2016)
Lecture (8.00am - 10.00am) –Dr. Mohd Razif Shahril
VALIDITY
Practical (10.00am - 12.00pm) –Dr. Mohd Razif Shahril
SPSS: FACTOR ANALYSIS
S C H O O L O F N U T R I T I O N A N D D I E T E T I C S • U N I V E R S I T I S U L T A N Z A I N A L A B I D I N
Course Schedule (cont.)
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WEEK / DATE ACTIVITIES
13 MONDAY
(25/04/2016)
Project (8.00am - 11.00am) –Dr. Mohd Razif Shahril
REPORT PREPARATION
14 MONDAY
(02/05/2016)
Project (8.00am - 11.00am) –Dr. Mohd Razif Shahril
REPORT SUBMISSION
STUDY WEEK (06/05/2016 – 09/05/2016)
END OF SEMESTER EXAMINATION (10/05/2016 – 26/05/2016)
Location Makmal Komputer Khadijah
(Khadijah Computer Lab)
S C H O O L O F N U T R I T I O N A N D D I E T E T I C S • U N I V E R S I T I S U L T A N Z A I N A L A B I D I N
Assessment • Continuous Assessment
– Test (30%)
– Project Report (1500 words) (30%)
• Summative Assessment
– End of Semester Examination (40%)
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S C H O O L O F N U T R I T I O N A N D D I E T E T I C S • U N I V E R S I T I S U L T A N Z A I N A L A B I D I N
Main References
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S C H O O L O F N U T R I T I O N A N D D I E T E T I C S • U N I V E R S I T I S U L T A N Z A I N A L A B I D I N
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ATTENDANCE
LESS THAN 80% BARRED FROM EXAMINATION
[NO MC FROM PRIVATE CLINICS!]
S C H O O L O F N U T R I T I O N A N D D I E T E T I C S • U N I V E R S I T I S U L T A N Z A I N A L A B I D I N
Topic Learning Outcomes At the end of this lecture, students should be able to;
• define data and types of data.
• define descriptive statistics, variables and scales.
• explain types of central tendency and dispersion
measurements.
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S C H O O L O F N U T R I T I O N A N D D I E T E T I C S • U N I V E R S I T I S U L T A N Z A I N A L A B I D I N
What is DATA?
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• Definition: A collection of items of information
• Types of data
– Qualitative
– Quantitative
S C H O O L O F N U T R I T I O N A N D D I E T E T I C S • U N I V E R S I T I S U L T A N Z A I N A L A B I D I N
What is DATA? (cont)
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• Qualitative data
– Observation or information characterized by
measurement on a categorical scale (dichotomous,
nominal or ordinal scale).
– Data that describe a quality of the subject studied.
• E.g. gender, ethnic, death or survival, nationality etc.
– Generally describes in terms of percentages or
proportions.
– Mostly displayed by using contingency table, pie
chart, bar charts.
S C H O O L O F N U T R I T I O N A N D D I E T E T I C S • U N I V E R S I T I S U L T A N Z A I N A L A B I D I N
What is DATA? (cont)
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• Quantitative data
– Data in numerical quantities such as continuous
measurements or counts.
– Observation for which the differences between
numbers have meaning on a numerical scale.
– They measure the quantity of something
– Types of numerical scales;
• Continuous scale (e.g. Age, height)
• Discrete scale (e.g. Number of pregnancy)
– Described in terms of means and standard deviation.
– Frequency tables and histograms are most often used
to display this type of information.
S C H O O L O F N U T R I T I O N A N D D I E T E T I C S • U N I V E R S I T I S U L T A N Z A I N A L A B I D I N
How to analyse DATA? (cont)
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• Using STATISTICS!
– A small representative ‘sample’ is used to study a big
‘population’
• Why?
– Expensive to conduct very large study
– Impossible to collect information from everyone in the
population
• Types of statistics
– Descriptive statistics
– Inferential statistics
S C H O O L O F N U T R I T I O N A N D D I E T E T I C S • U N I V E R S I T I S U L T A N Z A I N A L A B I D I N
How to analyse DATA? (cont)
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Population: A complete collection of data on the group under study Sample: A collection of sampling units selected from the population Sampling unit: A member of the population
Sampling unitSampling unit n=1n=1
SampleSample n= 200n= 200
PopulationPopulation N=20,000N=20,000
S C H O O L O F N U T R I T I O N A N D D I E T E T I C S • U N I V E R S I T I S U L T A N Z A I N A L A B I D I N
Descriptive statistics
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• Describe the frequency and distribution to
characterize data collected from a group of
sample to represent the population.
• E.g.
– Percentage of patients attending diabetes clinic
– Gender, age group, education level of the patients
– Patients waiting time for doctors consultation
– Patients fasting glucose and HbA1c level
– Etc.
S C H O O L O F N U T R I T I O N A N D D I E T E T I C S • U N I V E R S I T I S U L T A N Z A I N A L A B I D I N
Variable
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• A variable is a characteristics that can take on
different values for different members of the
group under study
– E.g. a group of university students will be found to
differ in gender, height, attitudes, intelligence and may
ways. These characteristics are called variables.
• Categories of variable
– Continuous vs. Discrete
– Independent vs. Dependent
S C H O O L O F N U T R I T I O N A N D D I E T E T I C S • U N I V E R S I T I S U L T A N Z A I N A L A B I D I N
Variable (cont.)
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• Continuous variable – can take on any values on the measurement scale under
study
– Do not fit into a finite number or categories
– Referred to as measurement data
– E.g. weight, height, age, blood pressure etc.
• Discrete variable – only designated values or integer values i.e. 1, 2, 3…
– Fit into limited categories
– Referred as count data (dichotomous/ multichotomous) • E.g. dichotomous
– Male-Female
– Yes-No
• E.g. multichotomous – Malay-Chinese-Indian
– Man Utd-Arsenal-Chelsea-Man City-Liverpool
S C H O O L O F N U T R I T I O N A N D D I E T E T I C S • U N I V E R S I T I S U L T A N Z A I N A L A B I D I N
Variable (cont.)
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• Independent variable (IV)
– Manipulated in accordance with the purpose of the
investigation
– Set by researcher
• Dependent variable (DV)
– Consequence of the independent variable
– Effected by independent variable
– Outcome
S C H O O L O F N U T R I T I O N A N D D I E T E T I C S • U N I V E R S I T I S U L T A N Z A I N A L A B I D I N
Scales
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• Type of scales
– Nominal – classify observation into categories that
cannot be numerically arranged (no order)
– Ordinal – assign order to categories so that one
category is higher than another
– Interval / ratio –sequential ranking of values (as
ordinal scales)
S C H O O L O F N U T R I T I O N A N D D I E T E T I C S • U N I V E R S I T I S U L T A N Z A I N A L A B I D I N
Measurement of central tendency
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Mean
Mode
Median
S C H O O L O F N U T R I T I O N A N D D I E T E T I C S • U N I V E R S I T I S U L T A N Z A I N A L A B I D I N
Mode
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• Is the most frequent occurring value in a set of