Lecture notes Research Methodology HFS4343 UniSZA

1. KNOWLEDGE FOR THE BENEFIT OF HUMANITYKNOWLEDGE FOR THE BENEFIT OF HUMANITY
RESEARCH METHODOLOGY (HFS4343)
IDENTIFYING VARIABLES &
CONSTRUCTING HYPOTHESIS
Dr.Dr. MohdMohd RazifRazif ShahrilShahril
School of Nutrition & DieteticsSchool of Nutrition & Dietetics
Faculty of Health SciencesFaculty of Health Sciences
UniversitiUniversiti SultanSultan ZainalZainal AbidinAbidin
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2. 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;
• differentiate between a concept and a variable.
• convert concepts into variables.
• identify types of variables.
• identify types of measurement scales.
• define hypothesis and its function in research.
• demonstrate how hypotheses are tested and formulated
• list different types of hypotheses and their applications
• explain how errors in the testing of a hypothesis can
occur
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3. 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 variable?
• A rational units of analysis that can assume any one of a
number of designated sets of values.
• A concept that can be measured on any one of the four
types of measurement scales, which have varying
degrees of precision in measurement.
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4. 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
Converting concepts into variables
• Operationalization – how the concept will be measured?
– Process of identifying indicators
• Indicators – a set of criteria reflective of the concept,
which can then converted into variables
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ConceptsConcepts IndicatorsIndicators VariablesVariables

5. 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
Converting concepts into variables (cont.)
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• Example 1
CONCEPTSCONCEPTS INDICATORSINDICATORS
VARIABLESVARIABLES
VARIABLESVARIABLES DECISIONDECISION LEVELLEVEL
Wealth 1. Income
2. Assets
1. Total income per
year
2. Total values of home,
cars, boats,
investments
1 if > RM 150,000
2 if > RM 300,000

6. 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
Converting concepts into variables (cont.)
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• Example 2
CONCEPTSCONCEPTS INDICATORSINDICATORS
VARIABLESVARIABLES
VARIABLESVARIABLES DECISIONDECISION LEVELLEVEL
High
academic
achieve-
ment
1. Average
marks
obtained in
exam
2. Average
marks
obtained in
practical
3. Aggregate
marks
1. Percentage of marks
2. Percentage of marks
3. Percentage of marks
1 if > 70%
2 if > 80%
3 if > 90%

7. 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
Converting concepts into variables (cont.)
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• Example 3
CONCEPTSCONCEPTS INDICATORSINDICATORS
VARIABLESVARIABLES
VARIABLESVARIABLES DECISIONDECISION LEVELLEVEL
Effective-
ness of
health
program
1. Changes in
nutritional
status
(weight)
2. Changes in
nutritional
status
(morbidity)
1. Changes in weight in
the past 3 months
2. Changes in morbidity
type
Whether the
difference in before-
and-after levels is
statistically
significant
Point prevalence
increase or decrease
in each variable

8. 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
Types of variable
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VariablesVariables
CauseCause
ModelModel
StudyStudy
DesignDesign
Unit ofUnit of
MeasureMeasure--
mentment

9. 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
Types of variable – Cause Model
• Change variable (Independent Variable)
– the cause supposed to be responsible for bringing about
change(s) in a phenomenon or situation
• Outcome variable (Dependent Variable)
– the outcome or change(s) brought about by introduction of an
independent variable
• Variables which affect / influence (Extraneous Variable)
– several factors operating in a real-life situation may affect
changes in the dependent variable
• Connecting or linking variables (Intervening Variable)
– confounding variables which links independent and dependent
variable
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10. 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
Types of variable – Cause Model (cont.)
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11. 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
Types of variable – Cause Model (cont.)
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3333

12. 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
Types of variable – Cause Model (cont.)
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3333
4444

13. 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
Types of variable – Study Design
• Active Variable
– those variable that can be manipulated, changed or
controlled.
• Attribute Variable
– those variables that cannot be manipulated, changed
or controlled and that reflect the characteristics of the
study population e.g. age, gender, education and
income
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14. 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
Types of variable – Study Design (cont)
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15. 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
Types of variable – Unit of Measurement
• Categorical Variable – measured in nominal or ordinal
measurement scales
– Constant variable – has only one category or value
– Dichotomous – has only two categories
– Polychotomous – can be divided into more than two
categories
• Continuous Variable – measured in interval or ratio
scale
– have continuity in their measurement
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2222

16. 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
Types of measurement scales
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Nominal orNominal or
classificatoryclassificatory
scalescale
Ordinal orOrdinal or
ranking scaleranking scale
Interval scaleInterval scale Ratio scaleRatio scale

17. 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
Types of measurement scales - nominal
• Each subgroup has a characteristics/ property which is
common to all classified within that subgroup.
• Example;
– Gender – Male, Female
– Marital status – Single, Married, Widowed, Divorced
– Medical history – Hypertension, Diabetes, IHD, Cancer, Obesity
– Colour – Red, Blue, Green, Yellow, Black
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18. 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
Types of measurement scales – ordinal
• It has the characteristics of a nominal scale e.g.
Individuals, groups, characteristics classified under a
subgroup have a common characteristics.
• Subgroups have a relationship to one another. Arranged
in ascending or descending order.
• Example;
– Income – above average, average, below average
– Obesity – underweight, normal, overweight, obese
– Likert scale – strongly disagree, disagree, neutral, agree,
strongly agree
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19. 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
Types of measurement scales – interval
• It has all the characteristics of an ordinal scale.
• It has a unit of measurement with an arbitrary starting
and terminating point.
• Divided into equally spaced units/ intervals.
• Example;
– Income – RM501 – RM1000, RM1001 – RM1500, RM1501 –
RM2000
– Body fat – 11% - 15%, 16% - 20%, 21% - 25%
– Age group – 31 – 40 y/o, 41 – 50 y/o, 51 – 60 y/o
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20. 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
Types of measurement scales – ratio
• It has all the properties of an interval scale
• It has a fixed starting point e.g. a zero point
• Example;
– Income – RM
– Age – years/ months
– Weight – kg
– Height – cm
– Physical activity – METmin/week
– Energy intake - kcal
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21. 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 hypothesis?
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hunch
assumption
suspicion
idea
statements

22. 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 hypothesis? (cont.)
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• A hypothesis is a conjectural statement of the relationship
between two or more variable.
• A proposition, condition, or principle which is assumed,
perhaps without belief, in order to draw out its logical
consequences, and by this method to test its accord with facts
which are known or may be determined.
• A proposition that is stated in a testable form and that predicts
a particular relationship between two (or more) variables
• Characteristics of a hypothesis;
• It is a tentative proposition
• Its validity is unknown
• In most cases, it specifies a relationship between two or
more variables.

23. 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
Functions of a hypothesis
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The formulation of a hypothesis provides a study with
focus and tells specific aspects of a research problem
to investigate
The formulation of a hypothesis provides a study with
focus and tells specific aspects of a research problem
to investigate
Tells us what data to collect and what not to collect,
thereby providing focus to the study
Tells us what data to collect and what not to collect,
thereby providing focus to the study
As it provided a focus, the construction of a
hypothesis enhances objectivity in a study
As it provided a focus, the construction of a
hypothesis enhances objectivity in a study
Enable us to add to the formulation of theory and
conclude specifically what is true or what is false
Enable us to add to the formulation of theory and
conclude specifically what is true or what is false

24. 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
The process of testing a hypothesis
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PhaseI
Formulate
your hunch
or
assumption
PhaseII
Collect the
required
data
PhaseIII
Analyse
data to
draw
concluison

25. 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
Characteristics of a hypothesis
• A hypothesis should be simple, specific and conceptually
clear.
• A hypothesis should be capable of verification.
• A hypothesis should be related to the existing body of
knowledge.
• A hypothesis should be operationalisiable.
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26. 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
Types of hypothesis
• Research Hypothesis
– The basis of our investigation
– Four types;
• Hypothesis of difference
• Hypothesis of point prevalence
• Hypothesis of association
• Hypothesis of no difference (null hypothesis)
• Alternate Hypothesis
– Explicitly specify the relationship that will be considered as true
in case the research hypothesis proves to be wrong
– Opposite of research hypothesis
– Null hypothesis (H0) or hypothesis of no difference
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27. 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
Types of hypothesis
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28. 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
Errors in testing a hypothesis
• Incorrect conclusion about the validity f a hypothesis
may be drawn if
– The study design selected is faulty
– The sampling procedure adopted is faulty
– The method of data collection is inaccurate
– The analysis is wrong
– The statistical procedures applied are inappropriate
– The conclusion drawn are incorrect
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29. 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
Errors in testing a hypothesis (cont.)
• Rejection of a null hypothesis when it is true
– Type I error
• Acceptance of a null hypothesis when it is false
– Type II error
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TRUE FALSE
ACCEPT Correct decision Type II error
REJECT Type I error Correct decision
When all null hypothesis is actually;
When your
decision is to;

30. 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
Errors in testing a hypothesis (cont.)
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TRUE FALSE
ACCEPT Correct decision Type II error
REJECT Type I error Correct decision
When all null hypothesis is actually;
When your
decision is to;

31. Thank YouThank You
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