RESERACH DESIGN methodology Quantitative

OUTLINE
1. Unit of Analysis
2. Types of Data Analysis
3. Sample Selection and size
4. Identification and operational variables
5. Developing and finalizing research framework
6. Descriptive statistics, correlation and regression
7. Write-up research method chapter of an academic
research paper
2

RESEARCH DESIGN
3
The research design constitutes the blueprint for the collection, measurement and
analysis of data.
Research design is the plan and structure of investigation so conceived as to
obtain
answers to research questions.
- the plan is the overall scheme
- structure refers to the theoretical framework
It is basically constitutes the research methodology.
Includes a plan for selecting the sources and types of information used to
answer the
problem statement.
Includes a framework for specifying the relationship among research variables.
Outlines each procedures from the hypothesis to the analysis data.

THE SCIENTIFIC RESEARCH DESIGN
Variables clearly
identified and
labeled
3
PROBLEM
DEFINITION
Research
problem
delineated
7
DATA COLLECTION,
ANALYSIS, AND
INTERPRTATION
8
DEDUCTION
Hypotheses
substantiated?
Research question
answered?
10
Report
Presentation
6
SCIENTIFIC
RESEARCH
DESIGN
9
Report
Writing
4
THEORITICAL
FRAMEWORK
5
GENERATION
OF
HYPOTHESES
1
OBSERVATION
Broad area
of
research
interest
identified
2
PRELIMINARY
DATA GATHERING
Interviewing
Literature survey
11
Managerial
Decision
Making
No
Yes
4

FactorsAffectingTheChoiceOfResearchDesigns
5
1. Time dimension
- Cross sectional or longitudinal
2. The topical scope (breadth and depth)
- Case study or statistical/empirical study
3. The research environment
- Field conditions or laboratory
conditions
- Simulations
4. Subjects perception

4.1UNITOFANALYSIS
6
The unit of analysis is the major entity that you are analysing in your study.
Unit of analysis can varies from:
Individual
Group
Divisions
Industry
Countries
It is important to determine the unit of analysis before embarking on data
collection

4.2TYPES
OF DATA
ANALYSIS
1.Explorator
y
2.Descriptiv
e
3.Causal 7

TYPES OF
DATA
ANALYSIS
8
Exploratory study is undertaken when not much is known about the
situation at hand, or no information is available on how similar
problems or research issues have been solved in the past.
Example:
A service provider wants to know whyhis customers are switching to
other service provider.
Descriptive study is undertaken in order to ascertain and be able
to describe the characteristics of the variables of interest in a
situation.
Example:
A bank manager wants to have a profile of the individuals who have
loan payments outstanding for 6 months and more. It would include
details of their average age, earnings, nature of occupation, full-
time/part-time employment status, and the like. This might help him
to elicit further information or decide right away on the types of
individuals who shouldbe made ineligible for loans in the future

TYPES OF
DATA
ANALYSIS
9
Causal study is delineating one or more factors that
are causing the problem.
Example:
Amarketing manager wants to know if the sales of the company will
increase if he increases the advertising budget.

SAMPLING
Sampling is the process of selecting sufficient number of
elements from the population, so that a study of the sample
and an understanding of its properties or characteristics would
make it possible for us to generalize such properties or
characteristics to the population elements.
The characteristics of the population such as  (the population
mean),  (the population standard deviation), and ² (the
population variance) are referred to as its parameters.
10

SAMPLING
The characteristics of the sample such asX (the sample mean), S
(the standard deviation), and S² (the variation in the sample) are
referred to as sample statistics.
Sample Population
estimate
Statistics
(X,
S,S²)
Parameters
( , ,
²)
1
1

REASONS
FOR
SAMPLING
T
o save cost, time and
other human resources
Study of a sampling, sometimes
produce more reliable
results
Sometimes it is not possibleto
use the entire population-
destructive sampling.
12

IDENTIFICATIO
N AND
OPERATIONAL
OF
VARIABLES
Since theoretical framework is basically
seek to identify the network of relationship
among variable, we need to understand
the different types of research variables.
A variable is anything that can take on
differing or varying values. The values can
differ at various times for the same object
or person or at the same time for
different objects or persons.
A numerical value can be assigned to a
variable based on the variable properties
and should carry a label or code.
13

TYPES OF
VARIABLES
Dependent variable (criterion variable)
Independent variable (predictor variable)
Moderating
variable
Intervening/Mediating variable
Extraneous variable- not
discuss
14

DEPENDENTVARIABLE
Dependent variable (DV) is the variable of object of the study.
 research objectives is to understand and describe the
dependent
variable or explain its variability or predict it.
by analyzing the dependent variable ( i.e. finding what and how
other variables influence it) would shed some insight to the problem
being investigated.
15

INDEPENDENTVARIABLE
An independent variable (IV) is one that influences directly the
dependent variable in either a positive or negative way.
It is also the variable that can be manipulated in order to see the
changes in the dependent variable. In other words, the variance in the
dependent variable is accounted for (or caused) by independent
variable.
16

ASIMPLERELATIONSHIPBETWEEN
INDEPENDENTAND DEPENDENT
VARIABLE
Independent
variable
Dependent
variable
New product
success
Stock market
prices
17

INDEPENDENTANDDEPENDENT
VARIABLES
Variable Type
Production Dependence
Supervision Independence
Training Independence
supervision
Training
Production
18

MODERATING VARIABLE
The moderating variable (MV) is one that has strong contingent effect
on the independent variable-dependent variable relationship but
does not affect the dependent variable directly.
It affects the strength and/or direction of the r/ship
The introduction of a four-day work week (IV) will lead to higher
productivity (DV) especially among younger workers (MV)
19

MODERATING VARIABLE
I
V
D
V
Figure 5.3A Direct relationship of independent and
dependent variable
Availability of
the product Sales
20

M
O
D
E
R
A
T
I
N
G
V
A
R
I
A
B
L
E
IV D
V
MV
Relationship of independent and dependent variable is moderated by other
variables (e.g: age & income)
Availability of
the product
Sales
Age
Income
21

M
E
D
I
A
T
I
N
G
V
A
R
I
A
B
L
E
Mediating variable is one that explains the relationship between
the
two other variables -
- Mediators speak to how or why such effects occur
22

MEDIATING VARIABLE
Mediating variable
Independent variable Dependent variable
Relationship of independent variable and dependent variable as moderated by
othe
variable (supply chain process)
Information
Technolog
y
Organisational
Performance
Supply Chain Process
23

HYPOTHESES
DEVELOPMEN
T
24
What is a
hypotheses?
It is a logically conjectured relationships between two
or more variables expressed in the form of testable
statements.
Several testable statements or hypotheses can be
drawn from the Delta Airline’s case.
Example “ If the pilot are given adequate training to
handle midair crowded situations air-safety
violations will be reduced”

CONTINUED……
25
•The statement can be tested by measuring the extent
of training given to the various pilots (e.g. hours of
training) and the number of air-safety violations
committed by them over a period of time.
•If the test shows that there is a significant negative
correlation between the two, our hypothesis cannot be
substantiated. If the negative relationship cannot be
found, then our hypothesis could be substantiated.
•For a relationship to be considered as statistically
significant, 95% (p=0.05) level of confidence is
required.

VARIOUS FORMATOF
HYPOTHESES STATEMENTS
Directional Hypotheses
 Include the usage of terms such as positive, negative, more than, less than in
the
hypotheses statement.
 Example: to express the relationship
- “The greater the stress experienced in
the job, the lower the job satisfaction of
employees”
 Example: to show the differences
- “ Women are motivated than men”
26

VARIOUS FORMATOF
Non-Directional Hypotheses
Postulate the relationship or difference but offer no indication in the direction of
the
relationships or differences.
Example: to express the relationship
- “There is a relationship between age and
job satisfaction”
Example: to show the differences
- “There is a difference between the work ethic
values American and Asian employees”
Non-Directional hypotheses are used when there has
been unclear or conflicting in
previous studies.
27

VARIOUS FORMATOF
Null and Alternate Hypotheses
The null hypotheses is a preposition that states a definitive, exact relationship
between two variables:
- The population correlation between two variables is zero.
-The difference in the means of two groups in the q population is equal to zero
(or some define number).
Generally, the null hypotheses is expressed as (no) significant relationship between
two variables or no (significant) difference (in term of the mean ) between two
groups.
28

V
A
R
I
O
U
SF
O
R
M
A
T O
F
H
Y
P
O
T
H
E
S
E
S S
T
A
T
E
M
E
N
T
S
The alternate hypotheses is the exact opposite of the null, is a
statement expressing a relationship between two variables
or indicating differences between groups.
The null hypotheses was formulated so that it can be tested
for
possible rejections.
If we are able to reject the null hypotheses, then alternate
hypotheses
could be supported.
29

VARIOUS
FORMATOF
HYPOTHESES
STATEMENTS
The null hypotheses in terms of group differences for a
directional hypotheses, can be written as
follows:
HO: µM = µW or HO: µM - µW = 0
where: HO= null hypotheses
µM = mean motivational level for men
µW= mean motivational level for women
The alternate hypotheses can be written as follows:
HA= µM < µW or HA= µW > µM = 0

VARIOUS
FORMATOF
HYPOTHESES
STATEMENTS
For the non directional hypotheses, the null
hypotheses
can be written as follows:
HO: µAM = µAS OR HO= µAM - µAS =0
where:
HO= null hypotheses
µAM= mean work ethic value of American
µAS= mean work ethic value or Asians
The alternate hypotheses can be written
as follows:
HA: µAM # µAS

VARIOUS FORMATOF
The null hypotheses for the relationship between two variables would be:
Ho: “There is no relationship between stress experienced on the job and
the job satisfaction of employees”
In statistics, it will be expressed as follows:
HO: = 0
where = represents the correlation between stress and job satisfaction,
which in this caseis 0(i.e. no correlation)
The alternate hypotheses can be statistically expressed as :
HA: # 0 (the correlation is negative or positive)

VARIOUS FORMATOF
The non directional null hypotheses in example 5.19 would be
statistically expressed as follows:
H0: = 0
where represents the correlation between age and job
satisfaction,
which in this case is 0 (i.e. no correlation)
The alternate hypotheses can be statistically expressed as:
HA: # 0 (the correlation is negative or positive)

TESTING OF HYPOTHESES
Having formulated the null and alternate hypotheses, the appropriate
statistical test could be applied to the data set.
Two common statistical tests are:
The t-tests (test for significant differences in the means between groups)
The F-tests (test for significant relationship between variables)
3
4

S
T
E
P
S IN H
Y
P
O
T
H
E
S
E
S
T
E
S
T
I
N
G
State the null and alternate hypotheses.
Choose the appropriate statistical test depending on whether
the
data collected are parametric or nonparametric.
- Parametric
 Data collected on an interval or ratio scale
 Sample is assumed to be normally distributed
- Nonparametric
 No explicit assumption on the normally of distribution of sample
 Data collected are normal or ordinal scale

STEPS IN HYPOTHESES
TESTING
Determine the level of significant desired (p= 0.5, or more, or less).
Runthe chosen test on the data set using statistical software.
See whether the output results met the significant level required.
If the result value is larger than the critical value, the null hypotheses
is rejected.
If the result value is less than the critical value, we fail to reject
the null hypotheses.

ERRORSINHYPOTHESES
37
Two types of mistakes are possible while testing the hypotheses: Type I and Type II Error
Your actual health
Sick Well
What
doctor
says
Sick
Y
ouare sick & the doctor
confirms it.
RIGHT
Y
ou are not sick but the
doctor agree that you
are sick
WRONG: Type I Error
Y
ou are sick but the doctor
fail to confirms your real
illness
WRONG: Type II Error
Y
ou are really not sick
RIGHT
Well

Type I Error:
38
A type I error occurs when the null hypothesis (H0) is wrongly rejected.
For example: A type I error would occur if we concluded that the two drugs
produced different effects when in fact there was no difference between
them.
Type II Error:
A type II error occurs when the null hypothesis H0, is not rejected when it is
in fact false.
For example: A type II error would occur if it were concluded that the two
drugs produced the same effect, that is, there is no difference between the
two drugs on average, when in fact they produced different ones.

Reject H0 Don’t Reject H0
Type I Error Right Decision
Right Decision Type II Error
39
Trut
h
Decisio
n
H
0
H
1
• A type I erroris often considered to be more serious, and therefore more important
to avoid, than a type II error.

THE COMPONENTOFTHE
THEORETICALFRAMEWORK
Variable relevant to the study should be clearly identified and
labeled.
Discussions should focus on the interrelationships between two or
more variables and how do they affect the dependent
variable.
If the nature and direction of relationship can be predetermined
based on previous research, some indications should be given
whether the relationship would be positive or otherwise.
40

CONTINUED………….
There are should be clear explanation of whywe would
expect these relationships to exist (based on previous
research findings).
A schematic diagram of the theoretical framework should
be given so that the reader can easily conceptualized the
theorized relationship.
41

CONTINUED…
……
Thecaseof Delta Airlines:
Air-safety violations is the variable of interest, thus it should be
dependent
variable.
The independent variable are:
- Communication among crew members
-Communication between ground control
and crew
- Training received by the cockpit crew
- Decentralization
42

BUILDING-UPTHETHEORETICALFRAMEWORK
Independent variable Dependent variable
Communication among
Cockpit members
Communication between
Ground control and cockpit
Decentralization
Training of cockpit crew
Air-safety
violations
43

Independent variable Intervening variable Dependent variable
Communication among
Cockpit members
Decentralization
Training of cockpit crew
Nervousness
And
diffidence
Air-safety
violations
44

Communication among
Cockpit members
Decentralization
Air-safety
violations
Trainin
g
45

CORRELATION
Correlation is dependence is any statistical relationship between
two
random variables or two sets of data.
Correlation refers to any of a broad class of statistical relationship
involving dependence
46

REGRESSION
Regression analysis is a statistical process
for
estimating the
relationships among variables. It includes many techniques for
modeling and analyzing several variables, when the focus is on the
relationship between a dependent variable and one or more
independent variables (or 'predictors').
Regression analysis is also used to understand which among the
independent variables are related to the dependent variable, and to
explore the forms of these relationships.
47

RESERACH DESIGN methodology Quantitative

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