2. Session Objectives
• Understand the different aspects relevant to
designing a research study.
• Identify the scope of any given study and the end use
of the results.
• Describe the type of investigation needed, the study
setting, the extent of researcher interference, the
unit of analysis, and the time horizon of the study.
• Identify which of the two, a causal or a correlational
study, would be more appropriate in a given
situation.
2
3. The Research Design
• In this step we need to design the research in
a way that the requisite data can be gathered
and analyzed to arrive at a solution.
• The research design was originally presented
in a simple in the following Figure
3
5. Research Staging
OBSERVATION
Broad area of
research interest
identified
PROBLEM
DEFINITION
Research Problem
Deliniated
THEORETICAL
FRAMEWORK
Variables clearly
identified and
labeled
GENERATION
OF
HYPOTHESES
PRELIMINARY
DATA GATHERING
Interviewing
Literature survey
NO
DEDUCTION
?
Report
Writing
Hypotheses
substantiated ?
Research Question
answered ?
SCIENTIFIC
RESEARCH
DESIGN
DATA
COLLECTION,
ANALYSIS AND
INTEPRETATION
6. Issues Involved in the Research Design
MEASUREMENT
DETAILS OF STUDY
PROBLEM STATEMENTS
• Exploration
• Description
• Hypotheses
Testing
establishing
• Causal relations
• Corelations
• Group
Differences,
rank, etc
Purpose of
the Study
Type of
Investigation
• Studying event
• Manipulation
• Control
• Simulation
Extent of
Researcher
Interference
• Contrived
• Non-contrived
Study
Setting
• Operational
definition
• Items
• Scaling
• Categorizing
• Coding
Measurements
and Measure
DATA
ANALYSIS
• Feel for
data
• Goodness
of data
Unit of
Analysis
Sampling
Design
• Individuals
• Dyads
• Groups
• Organizations
• etc
• Sampling
Method
• Sampling Size
Time
Horizon
• Crosssectional
• Longitudinal
Data
Collection
Method
• Observation
• Interview
• Questionaire
• Physical
Measurements
• Unobtrusive
• Hypothes
es testing
7. Purpose of The Study
The Nature of Studies:
a. Exploratory Study
b. Descriptive Study
c. Hypothesis Testing (Analytical and Predictive)
d. Case Study Analysis
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8. a. Exploratory Study
• 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.
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9. a. Exploratory Study : Example
• The manager of a multinational corporation is curious
to know if the work ethic values of employees working
in X City would be different from those of Americans.
• That city is a small city, and no information about the
ethic values of its workers.
• Also, the work ethic values mean be different to
people in different cultures.
• The best way to study the above situation is by
conducting an exploratory study, by interviewing the
employees in organizations in Irbid area.
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10. b. Descriptive Study
• Is undertaken in order to ascertain and be able to describe
the characteristics of the variables of interest in a
situation.
• For instance, a study of a the Research Methods 200 class
in terms of the percentage of members who are in their
senior ( will be in the graduation stage), sex composition,
age groupings, number of semesters left until graduation,
can be considered as descriptive in nature.
• In addition, descriptive studies are undertaken in
organizations to learn about and describe the
characteristics of a group of employees, as for example,
the age, education level, job status, and length of service.
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11. b. Descriptive Study : Example
• A bank manager wants to have a profile of the
individuals who have loan payments outstanding
for 6 months and more.
• This profile would include details of their average
age, earnings, nature of occupation, full-time/ parttime employment status, and the like.
• The above information might help the manager to
decide right away on the types of individuals who
should be made ineligible for loans in the future.
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12. b. Descriptive Study : Example
A marketing manager might want to develop a pricing,
sales, distribution, and advertising strategy for his
product.
The manager might ask for information regarding the
competitors, with respect to the following:
1. the percentage of companies who have prices
higher and lower than the industry norm.
2. the percentage of competitors hiring in-house staff
to handle sales and those who use independent
agents.
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13. b. Descriptive Study : Example
3. percentage of sales groups organized by product
line, by accounts, and by region.
4. the types of distribution channels used and the
percentage of customers using each.
5. percentage of competitors spending more dollars on
advertising/promotion than the firm and those
spending less.
6. Percentage of those using the web to sell the
product.
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14. c. Hypotheses Testing
• Studies that engage in hypotheses testing usually
explain the nature of certain relationships, or
establish the differences among groups or the
independence of two or more factors in a situation.
• Hypotheses testing is undertaken to explain the
variance in the dependent variable or to predict
organizational outcomes.
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15. c. Hypotheses Testing : Example
• A marketing manager wants to know if the sales of
the company will increase if he doubles the
advertising dollars.
• Here, the manager would like to know the nature of
the relationship between advertising and sales by
testing the hypothesis:
If advertising is increased, then sales will also go up.
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16. d. Case Study Analysis
• Case studies involve in-depth, contextual analyses of
matters relating to similar situations in other
organizations.
• Case studies, as a problem solving technique, are not
frequently resorted to in organizations because
findings the same type of problem in another
comparable setting is difficult due to the reluctance
of the companies to reveal their problems.
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17. d. Case Study Analysis
• Case studies that are qualitative in nature are,
however, useful in applying solutions to
current problems based on past problemsolving experiences.
• Also, case studies are useful in understanding
certain phenomena, and generating further
theories for empirical testing.
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18. Causal versus Correlational
• A causal study: Is an inquiry to know the
cause of one or more problems.
• A correlational study: Is an inquiry to know
the important variables associated with the
problem.
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19. Causal vs Correlational : Example
• A causal study question:
Does smoking cause cancer?
• A correlational study question:
Are smoking and cancer related?
Or
Are smoking, drinking, and chewing tobacco
associated with cancer?
If so, which of these contributes most to the variance
in the dependent variable?
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20. Causal vs Correlational : Example
• Fears of an earthquake predicted recently in
an area were a causal of a number of crashes
of some houses in the area in order to be
eligible of insurance policy.
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21. Causal vs Correlational : Example
• Increases in interest rates and property taxes,
the recession, and the predicted earthquake
considerably slowed down the business of real
state agents in the country.
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22. Extent of Researcher Interference
With the Study
• The extent of interference by the researcher
with the normal flow of work at the workplace
has a direct bearing on whether the study
undertaken is causal or correlational.
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23. Extent of Researcher Interference
With the Study
• A correlational study is conducted in the
natural environment of the organization with
minimum interference by the researcher with
the normal flow of work.
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24. Extent of Researcher Interference
With the Study
• In studies conducted to establish cause-andeffect relationships, the researcher tries to
manipulate certain variables so as to study
the effects of such manipulation on the
dependent variable of interest.
• In other words, the researcher deliberately
changes certain variables in the setting and
interferes with the events as they normally
occur in the system.
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25. Minimal Interference : Example
• A hospital administrator wants to examine the
relationship between the perceived emotional support in
the system and the stress experienced by the nursing
staff. In other words, she wants to do a correlational
study.
• The researcher will collect data from the nurses ( through
a questionnaire) to indicate how much emotional
support they get in the hospital and to what extent they
experience stress. By correlating the two variables, the
answer is found.
• In this case, beyond administering a questionnaire to the
nurses, the researcher has not interfered with the
normal activities in the hospital.
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26. Moderate Interference
• If the researcher wants to establish a causal
connection between the emotional support in
the hospital and stress, or, wants to
demonstrate that if the nurses had emotional
support, this indeed would cause them to
experience less stress.
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27. Moderate Interference
• To test the cause-and-effect relationship, the
researcher will measure the stress currently
experienced by the nurses in three wards in the
hospital, and then deliberately manipulate the
extent of emotional support given to the three
groups of nurses in the three wards for perhaps a
week, and measure the amount of stress at the end
of that period.
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28. Moderate Interference
• For one group, the researcher will ensure that a
number of lab technicians and doctors help and
comfort the nurses when they face stressful events.
• For a second group of nurses in another ward, the
researcher might arrange for them only a moderate
amount of emotional support and employing only
the lab technicians and excluding doctors.
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29. Moderate Interference
• The third ward might operate without any
emotional support.
• If the experimenter’s theory is correct, then
the reduction in the stress levels before and
after the 1-week period should be greater for
the nurses in the first ward, moderate for
those in the second ward, and nil for the
nurses in the third ward.
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30. Moderate Interference
• We find that not only does the researcher
collect data from nurses on their experienced
stress at two different points in time, but also
manipulated the normal course of events by
deliberately changing the amount of
emotional support received by the nurses in
two wards, while leaving things in the third
ward unchanged.
• Here, the researcher has interfered more than
minimally.
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31. Excessive Interference
• IF the researcher feels, after conducting the previous
experiments, that the results may not be valid since
other external factors might have influenced the stress
levels experience by the nurses.
• For example, during that particular experimental week,
the nurses in one or more wards may not have
experienced high levels of stress because there were no
serious illnesses or deaths in the ward. Hence the
emotional support received might not be related to the
level of stresses experienced.
• The researcher want to make sure that such external
factors that might affect the cause-and-effect
relationship are controlled.
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32. Controlling the External factors
• The researcher might take three groups of medical
students, put them in different rooms, and confront
all of them with the same stressful task.
• For example, he might ask them to describe in detail,
the surgical procedures in performing surgery on a
patient who has not responded to chemotherapy
and keep asking them with more and more
questions.
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33. Controlling the External factors
• Although all are exposed to the same intensive
questioning, one group might get help from a doctor
who voluntarily offers clarifications and help when
students stumble.
• In the second group, a doctor might be nearby, but
might offer clarifications and help only if the group
seeks it.
• In the third group, there is no doctor present and no
help is available.
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34. Controlling the External factors
• In the above example, not only is the support
manipulated, but even the setting in which this
experiment is conducted is artificial inasmuch as the
researcher has taken the subject away from their
normal environment and put them in a totally
different setting.
• The researcher has intervened maximally with the
normal setting, the participants, and their duties.
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35. Excessive Interference
• The extent of researcher interference would
depend on whether the study is correlational
or causal and also the importance of
establishing causal relationship beyond any
doubt.
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36. Study Setting : Contrived and Noncontrived
• Correlational studies are conducted in
noncontrived settings (normal settings),
whereas most causal studies are done in
contrived settings.
• Correlational studies done in organizations
are called field studies.
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37. Study Setting: Contrived and Noncontrived
• Studies conducted to establish cause-and-effect
relationship using the same natural environment in
which employees normally function are called field
experiments.
• Experiments done to establish cause-and- effect
relationship in a contrived environment and strictly
controlled are called lab experiments.
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38. Field Study : Example
• A bank manager wants to analyze the relationship
between interest rates and bank deposit patterns of
clients.
• The researcher tries to correlate the two by looking at
deposits into different kinds of accounts (such as savings,
certificates of deposit, and interest-bearing checking
accounts) as interest rates changed.
• This is a field study where the bank manager has taken
the balances in various types of accounts and correlated
them to the changes in interest rates.
• Research here is done in a noncontrived setting with no
interference with the normal work routine.
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39. Field Experiment : Example
• The bank manager now wants to determine the causeand-effect relationship between interest rate and the
inducements it offers to clients to save and deposit
money in the bank. The researcher selects four branches
within 60/km radius for the experiment.
• For 1 week only, he advertises the annual rate for new
certificates of deposit received during that week. The interest
rate would be 9% in one branch, 8% in another, and 10% in
the third. In the fourth branch, the interest rate remains
unchanged at 5%. Within the week, the researcher would be
able to determine the effects, if any, of interest rates on
deposit mobilization.
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40. Field Experiment : Example
• This example would be a field experiment since
nothing but the interest rate is manipulated, with all
activities occurring in the normal and natural work
environment.
• Hopefully, all four branches chosen would be
compatible in size, number of depositors, deposit
patterns, and the like, so that the interest-savings
relationships are influenced by some third factor.
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41. Lab Experiment : Example
• To be sure about the true relationship between the
interest rate and deposits, the researcher could
create an artificial environment by choosing, for
instance, 40 students who are all business majors in
their final year of study and in the same age. The
researcher splits the students into four groups and
give each one of them $1000, which they are told
they might buy their needs or save for the future, or
both.
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42. Lab Experiment : Example
The researcher offers them interest on what they save
as followings:
• 6% on savings for group 1.
• 8% for group 2.
• 9% for group 3.
• 1% for group 4 ( the old rate of interest).
Here, the researcher has created an artificial
laboratory environment and has manipulated the
interest rates for savings. He also chosen subjects
with similar backgrounds.
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43. Unit of Analysis
• The unit of analysis refers to the level of
aggregation of the data collected during the
subsequent data analysis.
Individual
Dyads
Groups
Organizations
Cultures
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44. Unit of Analysis: Individual
– If the researcher focuses on how to raise
the motivational levels of employees, then
we are interested in individual employees
in the organization. Here the unit of
analysis is the individual (the data will be
gathered from each individual).
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45. Unit of Analysis: Dyads
– If the researcher is interested in studying
two-person interaction, then several twoperson groups also known as dyads, will
become the unit of analysis ( analysis of
husband-wife, and supervisor-subordinate
relationships at the work place.
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46. Unit of Analysis
–Groups as a unit of analysis
–Organizations as a unit of analysis
–Cultures as a unit of analysis
46
47. Example : Individuals as The Unit of Analysis
• The Chief Financial Officer of a manufacturing
company wants to know how many of the staff
would be interested in attending a 3-day seminar on
making appropriate investment decisions.
• Data will have to be collected from each individual
staff member and the unit of analysis is individual.
• The unit of analysis is the individual.
47
48. Example : Dyads as the Unit of Analysis
• A human resources manager wants to first
identify the number of employees in three
departments of the organization who are in
mentoring relationships, and then find out
what the jointly perceived benefits of such a
relationship are.
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49. Example : Dyads as the Unit of Analysis
• Once the mentor and the mentored pairs are
identified, their joint perceptions can be obtained by
treating each pair as one unit.
• If the manager wants data from a sample of 10 pairs,
he will have to deal with 20 individuals, a pair at a
time. The information obtained from each pair will
be a data point for subsequent analysis.
• Thus, the unit of analysis is the dyad.
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50. Example : Groups as Unit of Analysis
• A manager wants to see the patterns of usage of the
newly installed Information System (IS) by the
production, sales, and operations personnel.
• Here three groups of personnel are involved and
information on the number of times the IS is used by
each member in each of the three groups as well as
other relevant issues will be collected and analyzed.
• Here the unit of analysis is the group.
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51. Example : Divisions as the Unit of Analysis
• Johnson & Johnson company wants to see which of
its various divisions (soap, shampoo, body oil, etc.)
have made profits of over 12% during the current
year.
• Here, the profits of each of the divisions will be
examined and the information aggregated across the
various geographical units of the division.
• The unit of analysis will be the division, at which
level the data will be aggregated.
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52. Example : Industry as the Unit of Analysis
• An employment survey specialist wants to see the
proportion of the workforce employed by the health
care, transportation, and manufacturing industries.
• The researcher has to aggregate the data relating to
each of the subunits comprised in each of the
industries and report the proportions of the
workforce employed at the industry level.
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53. Example : Industry as the Unit of Analysis
• The health care industry, for instance, includes
hospitals, nursing homes, small and large clinics, and
other health care providing facilities.
• The data from these subunits will have to be
aggregated to see how many employees are
employed by the heath care industry.
• This will need to be done for each of the other
industries.
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54. Example : Countries as the Unit of Analysis
• The Chief Financial Officer (CFO) of a
multinational corporation wants to know the
profits made during the past 5 years by each
of the subsidiaries in England, Germany, and
France. It is possible that there are many
regional offices of these subsidiaries in each of
these countries.
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55. Example : Countries as the Unit of Analysis
• The profits of the various regional centers for
each country have to be aggregated and the
profits for each country for the past 5 years
provided to the CFO.
• The data will now have to be aggregated at
the country level.
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56. Time Horizon: Cross-Sectional
• Cross-Sectional Studies
A study can be done in which data are
gathered just once, perhaps over a period of
days or weeks or months, in order to answer a
research question.
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57. Cross-sectional : Example
• Data were collected from stock brokers between
April and June of last year to study their concerns in
a turbulent stock market.
• Data has to be collected at one point in time. It is a
cross-sectional design.
• A drug company desirous of investing in research for
a new headache pill conducted a survey among
headachy people to see how many of them would be
interested in trying the new pill.
• This is a one-shot or cross-sectional study to assess
the likely demand for the new product.
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58. Time Horizon: Cross-Sectional Versus
Longitudinal Studies
• Longitudinal Studies
Studying people or phenomena at more than
one point in time in order to answer the
research question.
Because data are gathered at two different
points in time, the study is not cross-sectional
kind, but is carried longitudinally across a
period of time.
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59. Example
• A marketing manager is interested in tracing the
pattern of sales of a particular product in four
different regions of the country on a quarterly basis
for the next 2 years.
• Since the data are collected several times to answer
the same issue, the study falls under the
longitudinal category.
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60. Time Horizon: Cross-Sectional Versus
Longitudinal Studies
• Longitudinal studies take more time and effort and
cost more than cross-sectional studies. However,
will-planned longitudinal studies could help to
identify cause-and-effect relationships.
• For example, one could study the sales volume of a
product before and after an advertisement, and
provided other environmental changes have not
impacted on the results, one could attribute the
increase in the sales volume, if any, to the
advertisement.
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