Research Methods for Business, A Skill Building Approach

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Research Methods for

Business, A Skill Building

Approach

Ezzeldin A. Mostafa, MD, PhD, MBA

ESLSCA 27 Research Methodology Ezzeldin A. Mostafa

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Research Methods for Business, A Skill Building Approach

Table of Contents Chapter 1: What is research? .....................................................................................................................................4

Definition ................................................................................................................................................................4

Why do we do research? ........................................................................................................................................4

Areas of business research (Examples) ..................................................................................................................4

Some commonly researched areas in business ......................................................................................................4

Types of business research .....................................................................................................................................5

Chapter 2: Scientific Investigation .............................................................................................................................6

The Hallmarks of scientific investigation ................................................................................................................6

Deduction and induction ........................................................................................................................................7

The building blocks of science ................................................................................................................................7

Chapter 4: The Research Process: ..............................................................................................................................8

Steps 1 to 3: The Broad Problem Area, Preliminary Data Gathering, and Problem Definition ..............................8

(1) The Broad Problem Area ...............................................................................................................................8

(2) Preliminary Data Collection...........................................................................................................................8

Literature Survey ................................................................................................................................................9

(3) Problem Definition ..................................................................................................................................... 10

Chapter 5: The Research Process: ........................................................................................................................... 11

Steps 4 to 5: Theoretical Framework & Hypothesis Development ..................................................................... 11

Variables .......................................................................................................................................................... 11

Types of Variables ............................................................................................................................................ 11

(1) The Dependent Variable ............................................................................................................................ 11

(2) The Independent Variable .......................................................................................................................... 12

(3) The Moderating Variable ........................................................................................................................... 12

(4) The Intervening Variable ............................................................................................................................ 12

Theoretical Framework ....................................................................................................................................... 13

Hypotheses Formats (1) If-then statements ................................................................................................... 13


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(2) Directional & Non-directional .................................................................................................................... 13

(3) Null and alternate Hypotheses................................................................................................................... 14

Chapter 6: The Research Process: ........................................................................................................................... 15

Steps 6: Elements of Research Design ................................................................................................................. 15

(1) Purpose of the Study .................................................................................................................................. 16

(2) The Types of Investigation ......................................................................................................................... 17

(3) Extent of researcher influence. .................................................................................................................. 17

(4) The Study Setting ....................................................................................................................................... 17

(5) Unit of Analysis ........................................................................................................................................... 17

(6) Time horizon .............................................................................................................................................. 17

Exercise (Page 138) ......................................................................................................................................... 18

Chapter 7: Experimental Designs ............................................................................................................................ 19

Lab and Field Experiments................................................................................................................................... 19

Factors Affecting Internal Validity ................................................................................................................... 20

Types of Experimental Designs ............................................................................................................................ 21

Chapter 8: Measurement of Variables: Operational Definition &Scales ................................................................ 23

How Variables are measured ?!! ......................................................................................................................... 23

Operational Definition ......................................................................................................................................... 23

Operationalizing the Concept of Achievement Motivation …….. ...................................................................... 23

Scale Definition : .................................................................................................................................................. 24

Types of Scales:.................................................................................................................................................... 25

Nominal Scale .................................................................................................................................................. 25

Ordinal Scale .................................................................................................................................................... 25

Interval Scale ................................................................................................................................................... 26

Ratio Scale ....................................................................................................................................................... 27

Chapter 9: Measurement: Scaling, Reliability, Validity ........................................................................................... 28

Rating Scale ......................................................................................................................................................... 28

Chapter 10: Data Collection Methods ..................................................................................................................... 31

Chapter 11: Sampling .............................................................................................................................................. 35

Population, Element, Population frame, Sample & Subject : .............................................................................. 35

Sampling .............................................................................................................................................................. 35

Probability & Non probability Sampling ......................................................................................................... 35


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Non probability sampling: ............................................................................................................................... 36

Determining sample size ................................................................................................................................. 36

Sample data and hypothesis testing: .............................................................................................................. 37

Importance of sampling design & sample size ................................................................................................ 37

Efficiency in sampling: ..................................................................................................................................... 37


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Chapter 1: What is research?

Definition The process of finding solutions to a problem after a thorough study and analysis of the situational

factors.

An organized, systematic, data-based, critical, objective, scientific inquiry or investigation into a specific

problems undertaken with the purpose of finding answers or solutions to it.

Why do we do research? UNDRSTANDING: Relationships among variables.

FORECASTING: Future behavior of the variables.

CONTROLLING: Direction of the variables

Areas of business research (Examples) 1. Accounting.

2. Finance.

3. Management research

4. Marketing research.

5. Operations research.

6. Human resources.

7. Organizational behavior.

8. Total Quality Management

9. Consumer Behavior

Some commonly researched areas in business 1. Employee behaviors such as performance, absenteeism, and turnover.

2. Employee attitudes such as job satisfaction, loyalty, and commitment.

3. Employee selection, recruitment, training, and retention.

4. Organizational performance (financial and non-financial)

5. Auditing practices.

6. Consumer behavior.

7. Strategic performance.


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8. Task environment analysis.

9. Brand loyalty.

10. Creating and managing corporate culture.

11. Use of expert systems in daily decision making.

Types of business research 1. Applied research: To solve a current problem faced by the manager in the work setting, demanding a

timely solution. It is the type of research done with the intension of applying the results of the findings to

solve specific problems currently being experienced in the organization.

2. Basic research: To generate a body of knowledge by trying to comprehend how certain problems that

occur in the organization can be solved. It is done chiefly to enhance the understanding of certain

problems that commonly occur in organizational settings, and seek methods to solve them. It is called

basic or Fundamental, or pure research.


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Chapter 2: Scientific Investigation

The Hallmarks of scientific investigation 1. Purposiveness.

2. Rigor.

3. Testability.

4. Replicability.

5. Precision and confidence.

6. Objectivity.

7. Generalizability.

8. Parsimony.

The Hallmarks of scientific investigation (1) Purposiveness

• To define the research objective (s) clearly.

• Problem statement should be stated very clearly at the very beginning of the research.

• 80% of a given problem solution depends on a clear definition of that problem.

(2) Rigor

• A good theoretical base and a sound methodological design would add rigor to a purposive study.

• Rigor in research design makes possible the achievement of the other six hallmarks of science.

• Conclusions drawn from an investigation that lacks a good theoretical foundation would be unscientific.

(3) Testability

• Research should develop testable hypotheses.

• Hypotheses are educated guesses about a specific problem.

• Scientific research lends itself to testing logically developed hypotheses to see whether or

not the data support the educated guesses (Hypotheses) that are developed after a careful

study of the problem situation.

(4) Replicability

• The results of the tests of hypotheses should be supported again and again when the same type of research

is repeated in other similar circumstances.


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• To the extent that the results are repeated, we will have confidence in our research being scientific.

(5) Precision and Confidence

• Precision refers to how close the findings, based on a sample, are to reality. It reflects the degree of

accuracy of the results based on a sample, to what really exists in the universe.

• Confidence: refers to the probability that our estimates are correct (a 95% confidence level).

• The greater the precision and confidence we aim at in our research , the more scientific is the

investigation and the more useful are the results.

(6) Objectivity

• The conclusions drawn through the interpretation of the results of data analysis should be objective.

• Conclusions should be based on facts resulting from the findings from the actual data, and not on our own

subjective or emotional values.

(7) Genralizability

• Refers to the scope of applicability of the research findings in one organizational setting to other settings.

• The wider the range of applicability of the solutions generated by research, the more useful the research is

to the users.

(8) Parsimony

• Simplicity in explaining the phenomena that occur, and in generating solutions for the problems, is

always preferred to complex research frameworks that consider an unmanageable number of factors.

• The wider the range of applicability of the solutions generated by research, the more useful the research is

to the users.

Deduction and induction DEDUCTION (Theory Testing): is the process by which we arrive at a reasoned conclusion by logically

generalizing from a known facts.

Ex: All regular employees can be trusted not to steal. (Premise 1)

Ali is a regular employee (premise 2)

Ali can be trusted not to steal (conclusion)

INDUCTION (Theory Generation): is the process by which where we observe certain phenomena and on this

basis we arrive at conclusions.

The building blocks of science


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Chapter 4: The Research Process:

Steps 1 to 3: The Broad Problem Area, Preliminary Data Gathering, and Problem

Definition

(1) The Broad Problem Area

1. The broad problem area refers to the entire situation where one sees a possible need for research and

problem solving.

2. The broad problem area may contain:

1. Current existing problems

2. A situation requiring improvement.

3. Conceptual issue that needs to be tightened

(2) Preliminary Data Collection

1. There are two types of data:

1. Secondary data: Data that already exist and do not have to be collected by the researcher (

Statistical bulletins, government publications, information published or unpublished and available

from either within or outside the organization)


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2. Primary data: Data gathered for research from the actual site of occurrence of events

(Observation, questionnaires to individuals).

1. Background information of the organization such as:

• The origin and history of the company.

• Size in terms of employees, assets, or both.

• Charter: purpose and ideology.

• Location.

• Interdependent relationships with the external environment.

• Financial positions.

2. Structural factors and management philosophy such as:

• Roles and positions in the organization.

• Extent of specialization.

• Communication channels.

• Control systems.

• Coordination and span of control.

• Reward systems.

• Workflow systems.

3. Perceptions, attitudes, and behavioral responses such as:

• Nature of the work.

• Workflow interdependencies.

• Participation in decision making..

• Clients systems.

• Opportunities for advancement in the organization.

• Organization’s attitudes toward employees’ family responsibilities..

Literature Survey

• Literature survey is the documentation of a comprehensive review of the published and unpublished work

from secondary sources of data in the areas of specific interest to the researcher.

• The purpose of the literature review is to ensure that no important variable is ignored that has in the past

been found to have an impact on the problem.

A good literature review ensures that:

1. Important variables that are likely to influence the problem situation are not left out of the study.

2. A clearer idea emerges as to what variables would be most important to consider (parsimony), why they

would be considered important (Developing theoretical framework).

3. Testability and replicability of the findings of the current research are enhanced.

4. The problem statement can be made with precision and clarity.

5. One does not run the risk of “reinventing the wheel”.

6. The problem investigated is perceived by the scientific community as relevant and significant.


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(3) Problem Definition

1. Symptoms: are indicators of the problem (low productivity, declining customer base).

2. Problem: any situation where a gap exists between the actual and desired ideal state.

3. Problem is a clear precise statement of the question or issue that is to be investigated with the goal of

finding an answer or solution.

Examples of well-defined problems

1. To what extent do structure of the organization and type of information systems installed account for the

variance of the perceived effectiveness of managerial decision making?

2. To what extent has the new advertising campaign been successful in creating the high quality, customer

centered corporate image that it was intended to produce?

3. How has the new packaging affected the sales of the product?

4. How do price and quality rate on consumers’ evaluation of product?

5. What are the effects of downsizing on the long-range growth pattern of organizations?


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Steps 4 to 5: Theoretical Framework & Hypothesis Development

Variables

1. A variable is anything that take on differing or varying value.

2. The values can differ at various times for the same object or person, or values can differ at the same time

for different objects or person.

3. Examples:

1. Exam score.

2. Absenteeism.

3. Motivation.

Types of Variables

(1) The Dependent Variable

1. The dependent variable is the variable of primary interest to the researcher.

2. The researcher goal is to understand and describe the dependent variable, or to explain the variability, or

predict it.

3. Through the analysis of dependent variable, it is possible to find answers or solutions to the problem.


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Examples of Dependent Variable 1. A manager is concerned that the sales of a new product introduced after test marketing is not as high as

he had expected. (Sales)

2. A basic researcher is interested in investigating the debt-to-equity ratio of manufacturing companies in

Egypt. (debt-to-equity)

3. A vice president is concerned that the employees are not loyal to the organization and, in fact, switch their

loyalties to other insinuations. (loyalty)

(2) The Independent Variable

1. An independent variable is one that influences variable in either a positive or a negative way.

2. When the independent variable is present, the dependent variable is also present, and with each unit

increase in the independent variable, there is an increase or decrease in the dependent variable.

3. The variance in the dependent variable is accounted for by the independent variable.

Examples of Independent Variable 1. Research studies indicate that successful new product development has an influence on the stock market

price of the company.

2. Cross-cultural research indicates that managerial values govern the power distance between superiors and

subordinates.

3. A relationship has been found between the number of books that five and six-year-old children have

access to at home and their reading ability.

(3) The Moderating Variable

1. The moderating variable is one that has a strong contingent effect on the independent-dependent variable

relationship.

2. The presence of a third variable (the moderating variable) modifies the original relationship between the

independent and the dependent variables.

Examples of Moderating Variable 1. A relationship has been found between the number of books that five and six-year-old children have

access to at home and their reading ability.

(4) The Intervening Variable

1. An intervening variable is one that surfaces between the time the independent variable operate to

influence the dependent and their impact on the dependent variable.

2. There is a temporal quality or time dimension to the intervening variable.

3. The intervening variable surfaces as a function of the independent variable(s) operating in any situation,

and helps to conceptualize and explain the influence of the independent variable(s) on the dependent

variable.

Examples of the Intervening Variable 1. Where the independent variable workforce diversity influenced the dependent variable organizational

effectiveness, an intervening variable that surfaces as a function of workforce diversity is creative

synergy.


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Theoretical Framework 1. The theoretical framework is the foundation on which the entire research project is based.

2. It is logically developed, described, and elaborated network of the associations among the variables

deemed relevant to the problem situation and which have been identified through such processes as

interviews, observations, and literature survey.

The components of the theoretical Framework

1. The variables considered relevant to the study should e clearly identified and labeled in the discussion.

2. The discussions should state how two or more variables are related to one another. This should be done

for the important relationships that are theorized to exist among variables.

3. If the nature and direction of the relationship can be theorized on the basis of the findings from previous

research, then there should be an indication in the discussions as to whether the relationships would be

positive or negative.

4. There should be a clear explanation of why we would expect these relationships to exist. The arguments

could be drawn from previous research findings.

5. A schematic diagram of the theoretical framework should be given so that the reader can see and easily

comprehend the theorized relationships

Theoretical Framework (Example)

Hypotheses Development

1. A hypothesis is a logically conjectured relationship between two or more variables expressed in the form

of a testable statement.

2. By testing the hypotheses and confirming the conjectured relationships, it is expected that solutions can

be found to correct the problem encountered.

Hypotheses Formats

(1) If-then statements

1. If employees are more healthy, then they will sick leave less frequently.

2. If the company allocated more funds to the promotion budget, then sales volume will increase.

(2) Directional & Non-directional

1. The greater the stress experienced in the job, the lower the job satisfaction of employees. (Directional).

2. Women are more motivated than men. (Directional).

3. There is a relationship between age and job satisfaction. (Non-directional).

4. There is a difference between the work ethic values of American and Asian employees. (Non-directional).


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(3) Null and alternate Hypotheses

1. The null hypothesis is a proposition that states a definitive , exact relationship between two variables.

2. The null hypothesis is expressed as no (significance) relationship between two variables or no

(significance) difference between two groups.

3. The alternate hypothesis is the opposite of the null, it is a statement expressing a relationship between two

variables or indicating differences between groups.

Example: Null and alternate Hypotheses

“Women are more motivated than men”

1. Null hypothesis: ( Ho : U m = U w)

Or (Ho: U w – Um = 0)

2. Alternate Hypothesis: ( Ha : U m > U w)

Or ( Ha : U m < U w)


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Steps 6: Elements of Research Design ASPECTS OF RESEARCH DESIGN

The Research Design

1. Purpose of the study.

2. Types of investigation.

3. Extent of researcher influence.

4. Study setting.

5. Unit of analysis.

6. Time horizon.


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(1) Purpose of the Study

(1) Purpose of the Study

1/1: Exploratory studies

1. Exploratory Study: is undertaken when not much is known about situation at hand, or when no

information is available on how similar problems or research issues have been solved in the past.

2. Exploratory studies are undertaken to better comprehend the nature of the problem, since very few studies

might have been conducted in that area.

1 / 2: Descriptive Studies

1. A descriptive study in undertaken in order to ascertain and be able to describe the characteristics of the

variable of interest in situation.

2. The goal of the descriptive study is to offer a profile or to describe relevant aspects of the phenomena of

interest to the researcher from an individual, organizational, industry-oriented, or other perspective.

1/3: Hypothesis Testing

1. A Hypothesis testing: It explains the nature of certain relationships, or establishes the differences among

groups or the independence of two or more factors in a situation.


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(2) The Types of Investigation

1. A causal study: When the researcher wants to delineate the cause of one or more problems.

2. A correlational study: When the research is interested in delineating the important variables that are

associated with the problem.

3. Examples:

1. A causal study question: Does smoking causes cancer?

2. A correlational study question: “Are smoking and cancer related?”

(3) Extent of researcher influence.

4. The extent to which the researcher interferes with the normal flow of work at the workplace has a direct

bearing on whether the study undertaken is causal or correlational.

5. A correlational study is conducted in the natural environment of the organization.

6. In a causal study, the researcher tries to manipulate certain variables so as to study the effects of such

manipulation on the dependent variables.

(4) The Study Setting

1. Field Studies: are correlational studies done in the organization.

2. Field Experiments: studies conducted to establish cause and effect relationships using the same natural

environment in which employees normally function.

3. Lab experiments: experiments done to establish cause effect relationships beyond the possibility of the

least doubt require the creation of an artificial , contrived environment in which all the extraneous factors

are strictly controlled.

(5) Unit of Analysis

1. It refers to the level of aggregation of the data collected during the subsequent data analysis stage.

2. Examples of the different units of analysis are:

1. Individual.

2. Dyads.

3. Groups.

4. Organizations.

5. Culture.

(6) Time horizon

1. Cross-sectional studies: 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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2. Longitudinal studies: to study people or phenomena at more than one point of time in order to answer the

research question.

Exercise (Page 138)

Below are two scenarios, indicate how the researcher should proceed in each case, that is, determine the

following, giving reasons:

a. The purpose of the study.

b. The type of investigation.

c. The extent of researcher interference.

d. The study setting.

e. The time horizon of the study.

f. The unit of analysis.

Scenario A: Ms. Amal, the owner of a small business (a women’s Dress boutique), has invited a consultant to tell

her how her business is different from similar small businesses within 60-mile radius, with respect to usage of the

most modern computer technology, sales volume, profit margin, and staff training.

Scenario B: Mr. Mamdouh, the owner of several restaurants in Cairo, is concerned about the wide differences in

their profit margins. He would like to try some incentives plans for increasing the efficiency levels of those

restaurants that lag behind. But before he actually does this, he would like to make sure that the idea would work.

He asked a researcher to help him on this issue.


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Chapter 7: Experimental Designs

Lab and Field Experiments To establish that variable X causes variable Y, all three of the following conditions should be met:

1. Both X and Y should co-vary (i.e., when one goes up, the other should also simultaneously go up (or

down).

2. X (the presumed casual factor) should precede Y. In other words, there must be a time sequence in which

the two occur.

3. No other factor should possibly cause the change in the dependent variable.

a. Lab Experiments

b. Lab Experiments


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Factors Affecting Internal Validity

1. History Effects: Certain events or factors that would have an impact on the independent –dependent

variable relationship might unexpectedly occur while the experiment is in progress.

2. Maturation Effects: are a function of the processes –both biological and psychological- operating within

the respondents as a result of the passage of time.

3. Testing Effects: The impact of the pretest –posttest.

4. Instrumentation Effects: effects that might arise because of a change in the measuring instrument

between the pretest and posttest, and not because of the treatment's differential impact at the end.

5. Selection Bias Effect: Improper or unmatched selection of subjects for the experimental and control

groups

6. Statistical Regression: It occurs when the members chosen for the experimental group have extreme

scores on the dependent variable to begin with.

7. Mortality: The mortality or attrition of the members in the experimental or control group or both, as the

experiement progresses.


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Types of Experimental Designs (1) Pretest and posttest experimental group Design

Posttest Treatment PRETEST Group

O2 X O1 Experimental Group

(2) Posttests only with experimental and control groups

Posttest Treatment Group

O1 X Experimental Group

O Control Group

(3) Pretest and posttest experimental and control group Designs


O2 X O1 Experimental Group

O4 O3 Control Group

(4) Solomon four-group design


O2 X O1 1. Experimental

O4 O3 2. Control

O5 X 3. Experimental

O6 4. Control


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Major threats to internal validity

Major threats to internal Validity Types of experimental Design

Testing, History, maturation. 1. Pretest & posttest with one experimental group

only.

Maturation 2. Posttest only with one experimental group and

one control group.

Mortality 3. Pretest & posttest with one experimental and one

control group.

Mortality 4. Solomon four-group design


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Chapter 8: Measurement of Variables: Operational Definition &Scales

Topics that will be covered:

How variables are Measured

Operational Definition

Dimensions and elements of Concepts

What an Operational Definition is Not

The Four Types of Scale

Nominal

Ordinal

Interval

Ratio

How Variables are measured ?!!

Data representing several demographic characteristics are obtained by asking employees simple question:

How long have you been working in the organization?

What is your job title?

Other information could be checked through company records such as absenteeism or employee performance.

Objective Vs. Subjective Nature

There are variables that lend itself to objective and precise measurement and the other is more nebulous

because of its subjective nature .such as motivation, satisfaction, involvement, buyer’s behavior.

Abstract notions are broken to observable characteristics behaviors so that they can be measured in a tangible

way

`“Operationalizing the concepts”

Operational Definition Operationalizing, or operationally defining the concept, to render it measurable, is done by looking at the

behavioral dimensions, facets,or properties denoted by the concept. These are then translated into observable and

measurable elements so as to form an index of measurement of the concept. Operationally defining a concept

involves a series of steps.

Operationalizing the Concept of Achievement Motivation …….. Driven by work and constantly working to drive the satisfaction of having “Achieved and Accomplished”

Elements of Dimension 1

Be constantly working (count the number of working hours)

Reluctant to take time off from work(count of days-off worked)

Preserve even if there are some setbacks(keep track for how frequently people continue to preserve

doing their jobs despite failures


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Find it hard to relax or devote their attention to other than work-related activity


How often do you think of work while you are away from the workplace?

What are your hobbies?

How do you spend your time when you are not at the workplace?

Prefer to work on their own rather than with others (because of wanting to achieve).


Impatient with ineffectiveness and reluctant to work with others.

Engage in challenging jobs rather than easy routine ones but at the same time not excessively challenging

jobs because their probability of their accomplishment is not very high.


A measure of how eager people are to seek challenging jobs can be found by asking employees what

kinds of jobs they prefer.

Employees preferences could vary from fairly routine jobs to jobs with progressive increase in

challenge

They would like to get frequent feedback from their superiors and colleagues as they go along to

know how they are performing


Want to know others’ opinion on how well they are performing both positive and negative.

Constantly seeking feedback from different sources( tracking how often individuals seek feedback)

What an Operational Definition is not

An operational definition is not a description of the correlates of the concept.

Example: success in performance cannot be a dimension of achievement motivation…they are correlated

but the former cannot measure the latter.

Scale Definition : A scale is a tool or mechanism by which individuals are distinguished on how they differ from one

another on the variables of interest to our study .

The scale could be a gross one (Broadly categorize individuals on certain variables) .

Or it could be fine tunes (differentiate individuals on the variable with varying degree of sophistication ).


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Types of Scales:

Nominal Scale

A nominal scale is one that allows the researcher to assign subjects to certain categories or groups (such as

male & female for example).

Nominal scales categorize individuals or objects into mutually exclusive and collectively exhaustive groups.

The information that can be generated from nominal scaling is to calculate the percentage (or frequency).

Example of Nominal scale

Nationality :

American Japanese

Chinese Russian

Australian Polish

German Swiss

Indian Zambian

Others…………….

Ordinal Scale

An ordinal scale rank orders the categories in some meaningful way (not only categorizes the variables to

denote differences among the various categories) .

In an ordinal Scale , the categories are to be ordered according to some preference.

Example of ordinal scale :

Rank the following five characteristics in a job in terms of how important they are for you . You should rank

the most important items as 1 , the next in importance as 2 , and so on , until you have ranked each of them

1,2,3,4, or 5


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Job Characteristic Ranking of importance

The opportunity provided by the job to:

1- Interact with others ____

2- Use a number of different skills ____

3- Complete a whole task from beginning to end ____

4- Serve others ____

5- Work independently ____

Interval Scale

An interval scale allows us to perform certain arithmetical operations on the data collected from the respondents.

Interval scale allows us to measure the distance between any two points on the scale .This helps us to compute

the means & the standard deviations of the responses on the variables .

Example of interval scale :

Indicate the extent to which you agree with the following statements as they relate to your job , by circling the

appropriate number against each , using the scale given below.

Strongly Disagree Neither Agree Agree Strongly

Disagree Nor Disagree Agree_

1 2 3 4 5__

The following opportunities offered by the job are very important to me :

a- Interacting with others 1 2 3 4 5

b- Using a number of different skills 1 2 3 4 5

c- Completing a task 1 2 3 4 5

from beginning to end

d- Serving others 1 2 3 4 5

e- Working independently 1 2 3 4 5


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Ratio Scale

The ratio scale overcomes the deficiency of the arbitrary origin point of the interval scale, in that it has an

absolute (in contrast to an arbitrary) zero point which is a meaningful measurement point.

The ratio scale not only measures the magnitude of the differences between points in the scale but also taps the

proportions in the differences.

It is the most powerful of the four scales because it has a unique zero origin & subsumes all properties of the

other three scales to calculate the ratio of the weights of two individuals.

Example of ratio scale

How many other organizations did you work for before joining this system ?

---------------

How many store do you operate ?

-----------------

Properties of the four scales

Scale Differences Order Distance Unique origin

Nominal Yes No No No

Ordinal Yes Yes No No

Interval Yes Yes Yes No

Ratio Yes Yes Yes Yes


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Chapter 9: Measurement: Scaling, Reliability, Validity

Rating Scale The following rating scales are often used in organizational research:

1. Dichotomous scale.

2. Category scale.

3. Likert scale.

4. Numerical scales.

5. Semantic differential scale.

6. Itemized Rating Scale

7. Fixed or constant sum scale.

8. Graphic rating scale.

9. Consensus scale.

1. Dichotomous Scale

The dichotomous scale is used to elicit a yes or no answer. Note that a nominal scale is used to elicit the response:

a) Do you own a car? ( ) Yes ( ) No

b) Do you smoke? ( ) Yes ( ) No

c) Do you play football? ( ) Yes ( ) No

2. Category Scale

The category scale uses multiple items to elicit a single response as per the following example. This also uses the

nominal scale.

Where in Cairo do you reside?

a) Maadi.

b) Misr El Gadeda.

c) Helwan.

d) Shoubra

e) Others ( specify …..)

3. Likert Scale

The likert scale is designed to examine how strongly subjects agree or disagree with statements on a 5-point scale

with the following anchors:

Strongly

Agree

Agree Neither Agree Nor

Disagree

Disagree Strongly Disagree

5 4 3 2 1


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Likert Scale (Example)

State the extent to which you agree with each of the following statements:

5 4 3 2 1 1. My work is very interesting.

5 4 3 2 1 2. I am not engrossed in my work all the day.

5 4 3 2 1 3. Life without my work will be dull.

4. Semantic Differential Scale

Several bipolar attributes are identified at the extremes of the scale, and respondents are asked to indicate

their attitudes, on what may be called a semantic space, toward a particular individual, object, or event on

each of the attributes. This is treated as an interval scale

Example:

Unresponsive ------------------------------------------------------------ Responsive

Ugly ------------------------------------------------------------ Beautiful

Timid ----------------------------------------------------------- Courageous

5. Numerical Scales

The numerical scale is similar to the semantic differential scale, with the difference that numbers on a 5-point or

7-point scale are provided, with bipolar adjectives at both ends. this is also an interval scale.

Extremely

Displeased

1 2 3 4 5 6 7 Extremely

pleased

6. Itemized Rating scale

a) A 5-point or 7-point scale with anchors, as needed, is provided for each item and the respondent states the

appropriate number on the side of each item, or circles the relevant number against each item. The

responses to the items are summated. This uses an interval scale.

b) Example: Respond to each item using the scale below, and indicate your response number on the line by

each item:

5

Very Likely

4

Likely

3

Neither Unlikely nor Likely

2

Unlikely

1

Very Unlikely


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_____________ 1. I will be changing my job within the next 12 months.

_____________ 2. I will take on new assignments in the near future.

_____________ 3. It is possible that I will be out of this organization with the next 12 months.

7. Fixed or constant sum scale

The respondents are asked to distribute a given number of points across various items. This is more in nature of an

ordinal scale.

Example: In choosing a toilet soap, indicate the importance you attach to each of following five aspects by

distributing a total of 100 points among them.

Fragrance

Color

Shape

Size

Texture of lather

100 Total points

8. Graphic Rating Scale

A graphic representation helps the respondents to indicate their answers to a particular question by placing a mark

at the appropriate point on the line. This is ordinal scale.

Example: On a scale of 1 to 10, how would you rate your supervisor?

10 9 8 7 6 5 4 3 2 1

Excellent Very bad

9. Consensus Scale

Scales are also developed by consensus, where panel of judges select certain item they feel measure the concept

desired to be measured. The items are chosen particularly based on their relevance to the concept.

Such a consensus scale is developed after the selected items are examined and tested for their validity and

reliability.


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Chapter 10: Data Collection Methods

Data Collection defined as :-

They are an integral part of research design as there are several data collection methods each with it’s own

advantages & disadvantages .

Sources of data :-

Primary Data :- Data gathered for research from actual site of occurrence of events .

Secondary Data:- Data already exists as company records or archives ,government publications ,industry

analysis offered by the media ,web sites .

Unstructured Interviews:-

They are so labeled because the interviewer doesn’t enter the interview with a planned sequence of

questions that will be asked to the respondent.

It’s Objective is to bring some Preliminary Issues to surface & understand the situation in it’s totally so

that the researcher can decide what variables need in depth investigations .

The Type & the nature of the questions asked to the individuals vary according to their job level & type of

work done, So Managers at top & middle level might be asked More direct questions about their

Perceptions of problems & situations ,while Employees at lower level may be asked broad ,open end

questions .

Example:-

“Tell me something about your unit & department & perhaps even the organization as a whole ,in terms

of work, employees & what ever else you think it’s important .”

Such request might elicit an Elaborate response from some respondents while others might just give a

one word reply which is not informative .ex (good , boring …,) .

Structured Interviews-:

The interviewer has a list of predetermined questions to be posed to the respondent either personally

or through the telephone or medium of pc.

The questions considered relevant to the problems that has surfaced during the unstructured interview

& the researcher will note them down.

These questions will be asked to everybody in the same manner & the interviewer must comprehend

the purpose & the goal of each question.

Visual Aids :-

Are tools that used in conducting interviews such as (Pictures, line drawings ,cards & other materials) &

these techniques are useful in marketing research, advertising in order to capture likes & dislikes of the

customer to different types of packaging.


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Questioning techniques:-

Funneling:- In the beginning of an unstructured interview ,it’s advisable to ask open-end question to get a general

idea & form some impressions about the situation . Example:-“What are some of your feelings about working for

your organization?”

Unbiased questions:-

Question asked in a way that would ensure the least bias in the response & must not be loaded in order not to

influence on the answers received from the respondent . Example:- “Tell me how you experience your job.”

Clarifying Issues :-

The Researcher must understand issues as respondent means to represent them so it’s advisable to restate or

rephrase important information given by respondent . Example:- “The facilities here are really poor.” we often

have to continue working even when we are dying of thirsty.

Helping the respondent to think through issues:- If the respondent is not able to verbalize her perceptions or

replies “I don’t know” so the researcher should ask the question in a simple way or rephrase it.

Taking notes:- The researcher must take notes as the researcher is taking place or as soon as the interview is

terminated especially if there is more than one interview in the schedule & it will be better to record interviews on

tapes if the respondent has no objection.

Face To Face & Telephone interviews:-

Most Unstructured interviews in organizational research are conducted face to face .while Structured interviews

could be either face to face or through telephone .

Face To Face Advantages :-

1. Researcher can adapt the questions as necessary, clarify doubts,& ensure that the respondent understand

by repeating or rephrasing the questions.

2. Researcher can also pick up how the respondent is nervous or under pressure through his body language

& it’s obviously be impossible to detect this in a telephone interview.

Face To Face disadvantages:-

Geographical limitation & High cost of training interviewers .

Telephone Interview advantages:-

1. Different number of people can be reached (across the country or even internationally ) in a short period

of time .

2. People feel more comfortable while introducing them selves through phone than face to face .

Telephone Interview Disadvantages:-

1. The respondent’s call could be terminated without warning before finishing the interview .

Computer Assisted interviewing :- There are 2 types of CAI:-


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1. Computer Assisted telephone interviewing CATI:- used in research organization as surveys can

be obtained from people all over the world since pc is networked in the telephone system .

2. Computer Assisted Personal Interviewing CAPI:- Respondent can use in there own computers

to run programs by them selves once they receive the software & enter there response .

Questionnaires

A questionnaire is a pre-formulated written set of questions to which respondent record there answers

usually within rather closely defined alternatives.

Questionnaires are an efficient data collection methods when the researcher knows exactly what is

required and how to measure the variables of interest

Questionnaires can be administered personally , mailed to the respondents or electronically distributed.

Personally administered questionnaires

When the survey is confined to a local area and the organization is willing and able to assemble groups of

employees to respond to the questionnaires at the work place personally administering the questionnaires

is a good way to collect data.

Mail questionnaires

The main advantage of mail questionnaires is that a wide geographical area can be covered in the survey

they are mailed to the respondents who can complete them at their own convenience, in their homes, and

at their own pace.

The return rates of mail questionnaire are typically low

Guidelines for questionnaire design

Questionnaire designer should focus on three areas

1. The wording of the questions.

2. Planning of categorizing the issues.

3. The general appearance of the questionnaire.

The wording of the questions

1. Appropriateness of the content of the questions

2. How questions are worded and the level of sophistication of the languages used

3. The type and form of questions asked

4. The sequencing of the questions

5. Personal data sought from the respondents

Important aspects of questionnaires

Good introduction

Organizing questions, giving instructions and guidance and a good alignment

Personal data.

Open ended question at the end.

Concluding the questionnaire.

Electronic questionnaires

On line questionnaires survey are easily designed and administered when microcomputers are hooked up

to computer networks.

Data disks can also be mailed to respondents, how can use their own personal computers for responding

to the questions.


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These can be helpful when the respondents know how to use the computes and feel comfortable

responding in this manner.

Other methods of data collection

Observational surveys

Is made by gathering data without asking questions of respondents. People can be observed in their natural work

environments or in the lab setting, and their activities and behaviors or other items can be recorded

The researcher can play one of two roles:

1. Non participant observer He can collect the needed data without trying to become an integral part

of the organizational system.

2. participant observer : the researcher enters the organization or the research setting actually becoming

a part of the work team.

Structured observational studies: Where the observer has a predetermined set of categories of activates to be

studied

Unstructured observational studies: If the researcher has no definite ideas of the particular aspects that needs

focus the observer will record almost everything that is observed.


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Chapter 11: Sampling Chapter Objectives

Define Sampling, sample, population, elements, subject, and population frame.

Describe & discuss the different sampling designs.

Identify the use of appropriate sampling designs for the different research purposes.

Explain why sample data are used to test hypotheses.

Discuss precision and confidence.

Estimate sample size.

Discuss the factors to be taken into consideration for determining sample size.

Discuss efficiency in sampling.

Discuss generalizability in the context of sampling designs.

Population, Element, Population frame, Sample & Subject :

Population : refers to the entire group of people, events, or things of interest that the

researcher wishes to investigate.

Element : is a single member of the population.

Population frame : is the listing of all of the elements in the population from which the

sample is drawn.

Sample : is a subset of the population ( some but not all elements of the population would form

the sample).

Subject : is a single member of the sample, just as an element is a single member of the

population.

Sampling The process of selecting a sufficient numbers of elements from the population so that by studying the sample and

understanding the properties or the characteristics of the sample subject, it would be possible to generalize the

properties or characteristics to the population elements.

Reasons of sampling:

The reasons of using sampling rather than collecting data from the entire population are fairly obvious. In research

investigations involving so many elements, it would be impossible to collect data from, test and examine every

element.

Representativeness of the sample:

The extent to which the sample that is selected possesses the same characteristics as the population from which it

is drawn.

Probability & Non probability Sampling

Probability Sampling:


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When elements in the population have a known chance of being chosen as subjects in the sample, we resort to a

probability sampling design which can be unrestricted or restricted.

1. Unrestricted Sampling (( Simple random sampling)) : Every element in the population has a known and

equal chance of being selected as a subject.

2. Restricted Sampling (( Complex probability sampling)) : These probability sampling procedures offer a

viable and sometimes more efficient alternative to the unrestricted design.

The 5 most common complex probability sampling designs :

a. Systematic sampling: A probability sampling design that involves choosing every element in the

population.

b. Stratified random sampling: A probability sampling design that first divides the population into

meaningful subsets and then randomly choose the subjects from each subset.

c. Cluster sampling: A probability sampling design in which the sample comprises groups of

elements with intra group heterogeneity and intra group homogeneity.

d. Area sampling: Cluster sampling within a specified area or region.

e. Double sampling: A probability sampling design that involves the process of collecting info from

a set of subject twice.

Non probability sampling:

A sampling design in which the elements in the population do not have a known or predetermined chance of

being selected as sample subjects. This sampling design, which fit into the board categories of convenience &

purposive sampling.

1. Convenience sampling: Which info or data for the research are gathered from members of the

population who are conveniently accessible to the researcher.

2. Purposive sampling: Which the required info is gathered from special or specific targets or groups of

people on some rational bases. There are two major types of purposive sampling are Judgment and Quota.

a. Judgment sampling: Which the sample subject is chosen on the basis of the individual’s ability to

provide the type of special info needed by the researcher.

b. Quota sampling: Which a predetermined proportion of people from different subgroups is sampled.

Determining sample size

Sample size is the actual number of subjects chosen as a sample to represent the population characteristics. The

issues of confidence interval and confidence level are addressed in the following discussion on precision and

confidence.

Precision: The degree of choosiness of the estimated sample characteristics to the population parameters and is

determined by the extent of the variability of the sampling distribution of the sample mean.


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Confidence: The probability estimate of how much reliance can be placed on the findings. The usual accepted

level of confidence in social science research is 95%.

Sample data and hypothesis testing:

So far we have discussed sample data as a means of estimating the population parameters but sample data can be

also used as Hypothesis testing. Procedures of this testing incorporate the same info as in interval estimation, but

the goals behind this two methods are somewhat different.

Importance of sampling design & sample size

If the appropriate sampling design is not used, a large sample size will not- in itself- allow the findings to be

generalized to the population. Similarly, unless the sample size is adequate for the desired level of precision and

confidence.

Efficiency in sampling:

Attained when the sampling design chosen either results in cost reduction to the researcher or offers a greater

degree of accuracy in terms of the sample size

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Research Methods for Business, A Skill Building Approach

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