kuliah 6-research designprocess

RESEARCH DESIGN PROCESS

FAKULTAS TEKNIK SIPIL DAN LINGKUNGAN

INSTITUT TEKNOLOGI BANDUNG

Kuliah

SI-5098 Metoda Penelitian

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.

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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

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The Research Design

FIGURE 6.1Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E

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Research Staging

THEORETICAL FRAMEWORK

Variables clearly identified and

labeled

GENERATION OF

HYPOTHESES

SCIENTIFIC RESEARCH

DESIGN

DATA COLLECTION,

ANALYSIS AND INTEPRETATION DEDUCTION

Hypotheses substantiated ?

Research Question answered ?

PROBLEM DEFINITION

Research Problem Deliniated

OBSERVATION

Broad area of research interest

identified

PRELIMINARY DATA GATHERING

Interviewing Literature survey

Report Writing

?

NO

Issues Involved in the Research Design

Purpose of

the Study

•Exploration

•Description

•Hypotheses

Testing

Type of

Investigation

establishing

•Causal relations

•Corelations

•Group

Differences,

rank, etc Extent of

Researcher

Interference

•Studying event

•Manipulation

•Control

•Simulation

Study

Setting

•Contrived

•Non-contrived

Measurements

and Measure

•Operational

definition

• Items

•Scaling

•Categorizing

•Coding

Unit of

Analysis

• Individuals

•Dyads

•Groups

•Organizations

• etc

Sampling

Design

•Sampling

Method

•Sampling Size

Time

Horizon

•Cross-

sectional

• Longitudinal

Data

Collection

Method

•Observation

• Interview

•Questionaire

•Physical

Measurements

•Unobtrusive

PR

OB

LE

M S

TAT

EM

EN

TS

• Feel for

data

•Goodness

of data

•Hypothes

es testing

DETAILS OF STUDY MEASUREMENT

DATA

ANALYSIS

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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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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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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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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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/ part-time 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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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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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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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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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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• 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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• Case studies that are qualitative in nature are, however, useful in applying solutions to current problems based on past problem-solving experiences.

• Also, case studies are useful in understanding certain phenomena, and generating further theories for empirical testing.

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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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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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• 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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• 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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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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• 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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• In studies conducted to establish cause-and-effect 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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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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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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• 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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• 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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• 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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• 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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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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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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• 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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• 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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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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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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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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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. 38

Field Experiment : Example

• The bank manager now wants to determine the cause-and-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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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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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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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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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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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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Unit of Analysis: Dyads

– If the researcher is interested in studying two-person interaction, then several two-person 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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Unit of Analysis

–Groups as a unit of analysis

–Organizations as a unit of analysis

–Cultures as a unit of analysis

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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.

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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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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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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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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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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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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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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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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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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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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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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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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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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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kuliah 6-research designprocess

Technology

research study

descriptive study

given study

correlational study

research issues

research question

research methods

research design4