Research Methodology

Business Problems & Decision Making Business today is problem resolution

management. There are several major problems that have

to be resolved both at the global and national levels, the industry and corporation level and the internal departmental and divisional/functional levels.

Decision Making

Some times in the midst of last century Chester Barnard, a retired telephone executive and author of The Functions of the Executive, imported the term “decision making” from the lexicon of public administration into the business world.

Replace narrower descriptors such as “resource allocation” and “policy making.”

Decision Making

Decisions are the essence of management. They’re what managers do–sit around all day making (or avoiding) decisions.

Decision making is a kind of fortune-telling, a bet on the future.

Making Decision is the most important job of any executive. It’s also the toughest and the riskiest. Bad decisions can damage a business and a career, sometimes irreparably.

Decision making is an art and science.

Every problem or decision-making situation has some aspects of it certain, some uncertain and some ambiguous.

Complete certainty implies that all the information that the decision maker needs is available, reliable and accurate, and hence, the exact nature of the problem can be easily defined, classified, formulated and specified.

You do not need research here.

But most business problems are vexed with uncertainty: that is, information about solution-alternatives is incomplete, and hence, research is needed to gather additional information to clarify the nature of the problem.

Many business problems are even vexed with ambiguity: that is, the nature of the problem to be solved is unclear.

Hence, research objectives are vague and decision alternatives are difficult to define. Ambiguous problems need more serious and prolonged research.

What is Research?

“All progress is born of inquiry. Doubt is often better than overconfidence, for it leads to inquiry, and inquiry leads to invention”

What is research?

Research simply means a search for facts. Answers to questions and solutions to

problems. It is purposive investigation. It is an organized

inquiry.

When we do research, we seek to know what is in order to understand, explain, and predict phenomena.

E.g -We might want to answer the question “What will be the department’s reaction to the new flexible work schedule?”

Business Research

Managers have to make sense of what is going on both within their organization and in its environment in order to take effective decisions..

Business Research is about the process of collecting, analyzing and interpreting the information needed for this.

Systematic inquiry that provides information to guide managerial decisions.

It is a process of planning, acquiring, analysing, and disseminating relevant data, information and insights to decision makers in a ways that mobilize the organisation to take appropriate actions that in turn, maximise business performance

Minute Maid’s Research Based Decision Making

Leverage consumer, customer and marketplace knowledge to identify ,develop and influence business strategies and tactics that will generate growth in operating income year after year.

Acquisition Analytics Insights Knowledge

Activation

Research Methodology & Methods

In general, methodology is the study of process in a research inquiry.

Methodology seeks to determine the most appropriate methods of inquiry for a given disciplinary research (e.g., business research, marketing research, HR research).

In general, methods are approaches to scientific inquiry in any discipline

There are several complementary analytic approaches to scientific inquiry such as quantitative methods, qualitative methods, historical methods, case studies

Methodology refers to the “entire scientific quest” of research”.

Methodology constitutes a whole range of strategies and procedures.

Hence, methods per se are only one small part of the methodological endeavor.

Types of Research

Identify the difference between these two…..

i) A Researcher wants understand consumer perception about the New iPhone 5 of Apple.

ii) Apple wants to understand consumer perception about its New iPhone5 as it is planning to relaunch the product.

Types of Research

Basic (pure) Research and Applied Research

Basic Research

Attempts to expand the limits of knowledge. Conducted to verify the acceptability of a

given theory or to discover more about a certain concept.

Theory building and verifying the theory is the major objective of basic research

Mostly academic in nature. Ex-expansion in Marketing P’s

Typical basic Research Mr X wanted to verify the law of diminishing

marginal utility. Mr.Y wants study the relationship between

non-monetary benifts and employee motivation

Mr.Z wants to verify Herzberg’s motivation theory.

Applied Research

AR is conducted when decision must be made about a specific real life problem.

Encompasses those studies undertaken to answer questions about a specific problems.

Business research is always an applied research

Is primarily driven by a need to address a particular organizational issue.

Typical Applied research A company is looking at whether to invest in one

substantial development project or several smaller projects.

A company is experiencing labor turnover because recent recruits seem to be leaving not long after completing their initial training.

A company is loosing its market share in a particular market segment.

Basic & Applied Research Compared….Basic Research

Theoretical Academic Generalisable Seeking understand

Applied Research

Problem –Solving Practitioner Specific Seeking to

change

Types of Business Research

Business Research is further classified into 3 types;

Exploratory Research Descriptive Research Causal Research

Exploratory Research

If the problem is ambiguous, then we need exploratory research to clarify ambiguous problems.

Example-Entering into a new unknown market segment (Somalian Market)

Ambiguous problems need more serious and prolonged research by multi-disciplinary researchers.

It rarely involves structured questionnaire, large samples and other methods.

Exploratory research is used to understand and define the problem

Company could explore the somalian market using below tools;

A review of academic & trade literature to identify the relevant demographic & psychographic of somalians

Interview with experts to determine the business environment.

Comparatively analysis of the best and worst companies operating in that country

Focus group to understand the consumer behavior.

Exploratory research does not provide concluding evidence for a particular action-solution; it helps to crystallize a problem and identify information needed for further research.

Helps to generate hypothesis for further research.

Hypothesis – Somalia market has potential for soft drinks

Expert surveys, Pilot surveys, secondary data analysis are the methods used in exploratory research.

Descriptive Research

When the problem is defined but the variables that constitute it are uncertain, then we need descriptive research that describes the characteristics of a population or phenomenon that circumscribe the problem.

DR seeks to determine the answers to who, what, when, where and how questions

Descriptive research assumes that researcher has much prior knowledge about the problem.

Thus information needed is clearly defined & as a result it is preplanned & well structured.

Descriptive research helps in decision making.

DR research leads to conclusive evidence for making decisions. Hence accuracy is of paramount importance in DR.

It also tells us the correlation between variables.(if the objective of the research is to find out the relationship)

Requires planned & structured design

Descriptive Research questions

Impact of training on employee performance. Impact of advertisement on demand or sales An analysis of market potential of Somalian

market for soft drinks.

Causal Research

Given exploratory research and descriptive research, we may have by now several sets of conclusive evidence regarding the nature of the research problem in terms of the variables that compose it, and the relationships between variables.

The stage is now set for causal research that

identifies cause-and-effect relationships among variables.

In business mangers continually make decisions based on the assumed causal relationship between variables.

Validity of the causal research should be via formal research.

Under causal research independent variables are manipulated in a relatively controlled environment.

Example - Causal relationship between advertisement & sales.

Training and employee performance.

What might happen to product sales if changes are made to product’s package?

What might happen to the performance of employees if flexi time is introduced?

Causal Research helps us to predict the phenomenon.

Deduction and Induction MethodsMeanings are conveyed through 2

types of discourse:1. Exposition: consists of statements

that describe without attempting to explain

2. Argument: allows to explain, defend and challenge meaning.

Two types of arguments:3. deduction & 4. induction methods

Deduction and Induction Methods

In research, we often refer to the two broad scientific methods as the deductive and inductive approaches.

In research, argument is used in an attempt to convince the reader of the truth or falsity of some thesis. Two of the methods used are induction and deduction.

Deduction Method

Deduction is the identification of an unknown particular, drawn from its resemblance to a set of known facts.

Deductive reasoning works from the more general to the more specific..

A process of reasoning that starts with a general truth, applies that truth to a specific case (resulting in a second piece of evidence), and from those two pieces of evidence (premises), draws a specific conclusion about the specific case.

Arguments based on laws, rules and accepted principles are generally used for Deductive Reasoning.

Testing the theory

Example

All employees at BankOne can be trusted to observe the ethical code (Major Premise )

Sara is an employee of BankOne (Specific Case)

Sara Can be trusted to observe the ethical code.

Example: Free access to public education is a key factor in the success of industrialized nations like the United States. (major premise)

India is working to become a successful, industrialized nation. (specific case)

Therefore, India should provide free access to public education for its citizens.

(conclusion)

Deductive method of research is basically a pure research.

But business decision making is (should be) evidence based.

Hence deducing from general theory without testing may lead to poor decision making.

Induction Method

Process of reasoning (arguing) which infers a general conclusion based on individual cases, examples, specific bits of evidence, and other specific types of premises.

Process of establishing a general proposition on the basis of observation of particular facts.

Induction occurs when we observe a fact and ask, “Why is this?”

In answer to this question, we advance a tentative explanation (hypothesis).

The hypothesis is plausible if it explains the event or condition (fact) that prompted the question.

Inductive Reasoning

Observation

Pattern

Hypothesis

Theory

suppose a firm spends $1 million on a regional promotional campaign and sales did not increase.

Under such circumstances, we ask, “Why didn’t sales increase?”

Promotional campaign was poorly executed. Sales will not increase if the promotion

campaign is poorly executed

The conclusion explains the facts and facts support the conclusion.

Combining induction & deduction

Induction and deduction are used together in research reasoning.

Over the course of time,theory construction is often the result of a combination of deductive and inductive reasoning.

Our experiences lead us to draw conclusions that we then try to verify empirically using the scientific method

Research Language

Concept Construct Definitions Operational Definitions Variables Propositions & Hypothesis

Concept

Concept provides common ground to understand and communicate information about objects and events.

A concept is a generally accepted collection of meanings or characteristics associated with certain events, objects, conditions, situations, and behaviors.

Classifying and categorizing objects or events that have common characteristics beyond any single observation creates concepts.

Constructs

Concepts have progressive levels of abstraction—that is, the degree to which the concept does or does not have something objective to refer to.

Table is an objective concept. An abstraction like personality is much more

difficult to visualize. Such abstract concepts are often called

constructs.

A construct is an image or abstract idea specifically invented for a given research.

We build constructs by combining the simpler, more concrete concepts, especially when the idea or image we intend to convey is not subject to direct observation.

For example - Heather is a human resource analyst at CadSoft, an architectural software company that employs technical writers to write product manuals, and she is analyzing task attributes of a job in need of redesign.

She knows the job description for technical writer consists of three components: presentation quality, language skill, and job interest.

Her job analysis reveals even more characteristics.

Heather has not yet measured the last construct, “job interest.”

It is the least observable and the most difficult to measure.

It will likely be composed of numerous concepts—many of which will be quite abstract.

Researchers sometimes refer to such entities as hypothetical constructs because they can be inferred only from the data.

A Researcher would like to measure Environmental Friendliness of Consumers

Definitions

Confusion about the meaning of concepts

can destroy a research study’s value

without the researcher or client even

knowing it. If words have different

meanings to the parties involved, then the

parties are not communicating well.

Definitions are one way to reduce this

danger.

Researchers struggle with two types of definitions:

Dictionary Definitions Salary - A fixed regular payment, typically paid

on a monthly or biweekly basis but often expressed as an annual sum, made by an employer

Operational Definitions. In research, we measure concepts and

constructs, and this requires more rigorous definitions.

An operational definition is a definition stated in terms of specific criteria for testing or measurement.

These terms must refer to empirical standards (i.e., we must be able to count, measure, or in some other way gather the information through our senses).

The operational definitions must be so clear that any competent person using them would classify the object in the same way.

Example During her research project with the military,

Myra observed numerous shells that, when fired, did not explode on impact.

She knew the Army called this as dud shell. But if asked, Myra applied the operational

term dud shell only to “a shell that, once fired from a cannon, could not be made to explode by any amount of manipulation, human or mechanical.”

Based on her operational definition, the town’s residents rarely encountered “duds” during their excursions onto the firing range.

Variables What things should be studied to address a

problem? A variable is anything that varies or

changes from one instance to another.

A characteristic, number, or  quantity that increases or decreases over time, or takes different values in different situations.

If a variable can take on any value between

two specified values, it is called

a continuous variable Income, temperature, age, and a test score are

examples of continuous variables. These variables may take on values within a

given range or, in some cases, an infinite set.

Discrete & Continuous Variable

Discrete Variable Discrete variables are also called categorical

variables. A discrete variable, X, can take on a finite number of numerical values, categories or codes.

Discrete variables do not have decimals. Color, Gender, preferences etc..

Independent and dependent variables Researchers are most interested in relationships

among variables. For example, does a newspaper coupon

influence product purchase.

The dependent variable -- also called the response variable -- is the output of a process or statistical analysis.

Its name comes from the fact that it depends on or responds to other variables.

Typically, the dependent variable is the result we want to achieve.

An independent variable is an input to a process or analysis that influences the dependent variable.

While there can only be one dependent variable in a study, there may be multiple independent variables.

Example -The introduction of a four-day working week (IV) will lead to higher productivity (DV).

Moderating or Interaction Variables A moderating or interaction variable is a second

independent variable that is included because it is believed to have a significant contributory on the original IV–DV relationship.

The introduction of a four-day working week (IV) will lead to higher productivity (DV), especially among younger workers (MV).

Extraneous Variables A large number of extraneous variables (EVs)

exists that might conceivably affect a given relationship.

Some can be treated as IVs or MVs, but most must either be assumed or excluded from the study.

Fortunately, an infinite number of variables has little or no effect on a given situation.

Still, we want to check whether our results are influenced by them.

Therefore, we include them as control variables (CVs) in our investigation to ensure that our results are not biased by not including them.

Intervening Variables An intervening variable facilitates a better

understanding of the relationship between the independent and dependent variables when the variables appear to not have a definite connection. 

Thus, while we may recognize that a four-day working week results in higher productivity, we might think that this is not the whole story.

Working week length affects some intervening variable (IVV) that, in turn, results in higher productivity.

One might view the intervening variable (IVV) to be job satisfaction.

Hence the new hypothesis is;

The introduction of a four-day working week (IV) will lead to higher productivity (DV) by increasing job satisfaction (IVV).

An intervening variable is an internal state that is used to explain relationships between observed variables.

Propositions and Hypotheses We define a proposition as a statement about

observable phenomena (concept) that may be judged as true or false

When a proposition is formulated for empirical testing, we call it a hypothesis.

As a statement about the variable or the relationship between two or more variables, a hypothesis is of a tentative and conjectural nature.

Descriptive Hypotheses Descriptive Hypotheses state the existence,

size, form, or distribution of some variable. For Example; In Detroit , our potato chip market share stands

at 13.7 percent. American cities are experiencing budget

difficulties. Majority of Company Z stockholders favor

increasing the company’s cash dividend.

Relational Hypotheses

These are statements that describe a relationship between two variables with respect to some case.

For example, “Foreign (variable) cars are perceived by American consumers to be of better quality (variable) than domestic cars.”

Relational hypotheses are of two types Correlational hypotheses state that the

variables occur together in some specified manner without implying that one causes the other.

By labeling these as Correlational hypotheses, we make no claim that one variable causes the other to change or take on different values.

Ex- There is high correlation between advertisement and demand

With causal (explanatory) hypotheses, there is an implication that the existence of or a change in one variable causes or leads to a change in the other variable.

An increase in family income (IV) leads to an increase in the percentage of income saved (DV).

Research Process

Business research is in fact the process of business problem resolution.

This process follows scientific method which includes following major steps;

Steps in Research Process

1.Problem Identification & Definition

2.Problem Formulation

3.Problem Specification

4.Planning Research Methods-Survey, questionnaire, sampling, data collection & analysis & interpretation.

5. Problem-resolution Alternatives Investigation

6.Best Resolution Selection - implementation

Step 1 Problem Identification & Definition

“If I were given one hour to save the planet, I would spend 59 minutes defining the problem and one minute resolving it,” Albert Einstein.

A problem definition indicates a specific managerial decision area to be clarified or problem to be solved.

It specifies the research questions to be answered & the objectives of the research.

A problem is a “system at unrest”. A system is anything (subject, object,

property or event) that is made of two or more parts.

Business is a system- any of these systems could be at unrest at a given time .(marketing,HR,finance,legal,envionmental etc.)

“A problem is a deviation from some standard or norm of desired performance”.

Hence, problems should be distinguished from decisions.

Decisions always involve a choice among various ways of getting a particular problem resolved or a task accomplished.

Problems & Opportunities

A problem exists when a gap exists between what was supposed to happen and what did happen, i.e., failure to meet an objective.

An opportunity occurs when there is a gap between what did happen and what could have happened… called an opportunity.

Problem Identification

Mechanisms for identifying the problem in an organization Balanced Score Card Techniques SWOT Analysis Dash Boards Performance Appraisals

Problem identification through BSC Balanced Score Card conceptual framework which translates an

organization's strategic planning into a set of performance indicators distributed among four perspectives. (Financial,Customer,Internal Business Processes & Learning & Growth)

Kaplan and Norton have rightly said, "The balanced scorecard is like the dials in an airplane cockpit; it gives managers complex information at a glance”.

In each perspective the organisation must define;

-Strategic Objectives – Strategy for achieving that perspective

-Measures –How progress is achieved

-Targets - Target values for each measure

- Initiatives – What will be done to reach the goal

SWOT Analysis Strategy is a match which org. makes

between internal resources & External opportunities & Risks

Assesses the organization in terms of internal & External factors

Directly aids the strategic formulation of any organization

Dash Boards

Helps to understand the problems at departmental level.

Dashboards often provide at-a-glance views of KPIs relevant to a particular department(e.g. sales, marketing, human resource, or production etc..)

Dash Board

Performance appraisal

Helps identify problems at individual level Identifying the gaps in desired and actual

performance.

Problem definition

Problem definition refers to the process of defining and developing a decision statement and the steps involved in translating it into more precise research terminology, including a set of research objectives.

A decision statement is a written expression of the key question(s) that a research user wishes to answer.

It is the reason that research is being considered. It must be well stated and relevant.

Problem definition involves stating the general problem & identifying the specific components of the problem.

The researcher then further expresses these in precise and research terminology by creating research hypotheses from the research objectives.

These are expressed as deliverables in the research proposal.

Importance of problem definitions

“Organizational teams speed toward a solution, fearing that if they spend too much time defining the problem, their superiors will punish them for taking so long to get to the starting line”- Dwayne Spradlin

Today’s solution becomes tomorrows problem.

Problem well identified & defined is problem half solved.

If problem is not identified and defined properly – lead to Type III error.

Research can be designed and conducted properly only when the research problem is properly defined.

When we describe the problem precisely, then we do not need to gather all the facts and figures about the problem, but just the relevant ones

Likely to waste time and money and reduce the odds of success than one that strives at the outset to achieve an in-depth understanding of the problem.

We must describe or define a problem precisely; even properly frame the problem and draw a boundary around it – e.g., this is the domain (the “what”) of the problem, and this is not its domain; this is where the problem occurred and this s where it did not; this is when the problem occurred; this is the extent to which the problem occurred, and so on

Situation Analysis

Situation Analysis

. A situation analysis involves the gathering of

background information to familiarize researchers and managers with the decision-making environment.

Interviews with key decision makers can be one of the best ways to identify key problem symptoms.

Once symptoms are identified, then the researcher must probe to identify possible causes of these changes.

Probing is an interview technique that tries to draw deeper and more elaborate explanations from the discussion.

This discussion may involve potential problem causes.

This probing process will likely be very helpful in identifying key variables that are prime candidates for study.

One of the most important questions the researcher can ask during these interviews is, “what has changed just before the symptom?”

Then, the researcher should probe to identify potential causes of the change

Consider the Case………. James: David, it is clear that your recruitment

costs have been increasing since the start of the year. What other changes have occurred inside of your business within the past year?

David: We are facing high labor turnover. We are struggling to retain our drivers. Hence we have to recruit drivers regularly just to replace left drivers.

James (probing): Tell me, what led to this high labor turnovers?

David: I wish I knew. James: Have you noticed changes in your

customers? David: We do see that they are a little irritated

due to some of the problems of getting their freight delivered successfully.

James: Has there been a change in personnel?

David: Yes, morale of employees is very low. They seemed to be dissatisfied with their job.

Researchers should make sure that they have uncovered all possible relevant symptoms and considered their potential causes.

Pilot study (interview with few employees,customers,distributors etc) is also helpful at this stage to identify some potential causes of the problem.

The situation analysis ends once researcher has a clear idea of the problem.

Step 2 Problem Formulation

Any problem can be expressed in the following simple structure: P = f (X, Y),

where X is a set of controllable variables, Y is a set of uncontrollable variables.

Need to identify the major sets of variables that compose or constitute the problem.

Identify almost all the relevant variables that cause the problem.

Categorize variables into “controllable” and “uncontrollable” from the company’s viewpoint.

Controllable variables are those the company can handle and control given its current resources.

Uncontrollable variables are related to the competitors, markets, legal environments and global factors.

Potential causes for Driver Turnover .. Controllable

variable Lack of

incentives Poor working

environment Poor Welfare

facilities Low salary

Uncontrollable Variables

Attractive employment in competitors

Attractive alternative employment

Personal problems like health etc

Step 2 Problem Specification

Explain inter-variable relationship.

Relationship between controllable variable and uncontrollable variables.

Turnover at Deland Trucking…

Controllable variable

Lack of incentives

Poor working environment

Poor Welfare facilities

Low salary

Uncontrollable Variables

Attractive employment in competitors

Attractive alternative employment

Personal problems like health etc

Develop Research Questions, Research Objectives & Hypotheses Decision statements must be translated into

research objectives. At this point, the researcher is starting to

visualize what will need to be measured and what type of study will be needed.

Decision Statement – To reduce driver turnover

Research objectives- T o analyse the reasons behind high turnover at Deland?

Step -4 Data Collection and Analysis

Determine the unit of analysis based on the problem at hand.

Survey the unit of analysis Analyze the data Interpret the data

Step -5 Alternate Problem Resolution Techniques Based on the step 4,generate problem

resolution techniques. Specify the solutions that would eliminate the

symptoms.

Step 6 -Selection of Best Problem Resolution Technique for Implementation Based on the analysis of the alternate

resolution techniques ,select the best one which maximizes company’s objective.

Choose the alternative which is Viable & feasible.

Which would increase consumer satisfaction & consumer loyalty

Which would increase employee satisfaction & employee loyalty.

Primary Data Collection -Overview of Survey Research

Source of data Data -Facts collected for reference or

analysis. Constitutes the foundation of statistical

analysis and interpretation. Required test the validity of hypothesis.

Data can be obtained from 3 imp.sources

a) Secondary source

b)Internal Source

c) Primary source

Secondary Data

When the investigator uses the data which has already been collected by others, such data are called secondary data.

Journals,Reports,Govt publications, publications of research organizations, trade and professional bodies

Internal Data

Internal data are the by-product of routine business record keeping like accounting,finance,production,personnel, quality control etc.

If a researcher is able to detect the actual problem from the internal data, then research is not required.

Primary data

Measurement observed and recorded as part of an original study.

When the data required for a particular study can be found neither in internal records nor in secondary sources – need to collect primary data.

Survey Research The purpose of survey research is to collect

primary data—data gathered and assembled specifically for the project at hand.

Often research entails asking people—called respondents—to provide answers to written or spoken questions.

Thus, a survey is defined as a method of collecting primary data based on communication with a representative sample of individuals.

Sampling & Sampling Procedures

Survey Research

Census Sample Survey

Sampling & Sampling Procedures

A population (universe) is any complete group—for example, of people, sales territories, stores, or college students—that shares some common set of characteristics.

Population possess the information needed by the researcher

The term population element refers to an individual member of the population.

A census is an investigation of all the individual elements that make up the population—a total enumeration rather than a sample.

Census is required when organizations need information every individual units.

Government census

This method is very expensive and cumbersome.

Therefore census is not a practical method of gathering information.

Sample & Sampling

A sample is a subset, or some part,of a larger population.

The purpose of sampling is to estimate an unknown characteristic of a population.

The process of selecting a sample from population is called sampling

Why sampling?

In a scientific study in which the objective is to determine an unknown population value, why should a sample rather than a complete census be taken?

Pragmatic Reasons

Applied business research projects usually have budget and time constraints.

If Ford Motor Corporation wished to take a census of past purchasers’ reactions to the company’s recalls of defective models, the researchers would have to contact millions of automobile buyers.

Sampling cuts costs, reduces labor requirements, and gathers vital information quickly.

Accurate and Reliable Results

Another major reason for sampling is that most properly selected samples give results that are reasonably accurate.

A sample may on occasion be more accurate than a census.

Interviewer mistakes, tabulation errors, and other non sampling errors may increase during a census because of the increased volume of work.

In a sample, increased accuracy may sometimes be possible because the fieldwork and tabulation of data can be more closely supervised.

In a field survey, a small, well-trained, closely supervised group may do a more careful and accurate job of collecting information than a large group of nonprofessional interviewers who try to contact everyone.

Destruction of Test Units

Many research projects, especially those in quality-control testing, require the destruction of the items being tested.

If a manufacturer of firecrackers wished to find out whether each unit met a specific production standard, no product would be left after the testing.

So census is impractical in such cases.

The sample Designing process

Step 1-Define the target population

Step 2 –Select a sampling frame

Step 3 –Select sampling method

Step 4 –Determine sample size

Step 5 –Select actual units & conduct fieldwork

Step 1-Define the target population

Target population possess the information required by the researcher.

The target population should be defined in the light of research objective.

Target population is often implied in research objective itself.

The question to answer is, “Whom do we

want to talk to?” For example – Drivers who have completed

atleast one years of service at Deland Trucking

Step 2–Select a sampling frame

In practice, the sample will be drawn from a list of population elements that often differs somewhat from the defined target population.

A list of elements from which the sample may be drawn is called a sampling frame.

Sampling is carried out on the sampling frame & not on target population

Theoretically ,the target population and sampling frame are the same

Sampling frame error

The sampling unit is a single element or group of elements subject to selection in the sample.

Step 3 –Select sampling method

- Several alternative ways to take a sample are available.

-These alternatives are classified as;

- probability techniques and nonprobability techniques.

Probability Sampling Techniques

In probability sampling, every element in the population has a known, nonzero probability of selection.

In nonprobability sampling, the probability of any particular member of the population being chosen is unknown.

Because the probability sampling process is random, the bias inherent in nonprobability sampling procedures is eliminated.

Probability sampling has 4 major methods;

Simple Random Sampling Systematic Random Sampling Stratified Sampling & Cluster Sampling

Simple Random Sampling

The sampling procedure that ensures each element in the population will have an equal chance of being included in the sample is called simple random sampling

-Two ways of selecting random samples from population;

-Lottery Method

-Random Tables

Lottery Method

- When the sample size is small we use lottery method of random sampling.

Suppose a researcher is interested in selecting a simple random sample of all the Maruthi Suzuki dealers in Karnataka.(select 35 of 100 dealers).

Each dealer’s name is assigned a number from 1 to 100.

The numbers can be written on paper slips, and all the slips can be placed in a bowl.

After the slips of paper have been thoroughly mixed, one is selected for each sampling unit.

Mixing the slips after each selection will ensure that those at the bottom of the bowl will continue to have an equal chance of being selected in the sample.

Random Tables

-When the sample size is very large, lottery method may be very difficult & time consuming.

- We use random table to select the samples

Systematic (Quasi-Random) Sampling

Sample elements are selected from the population at uniform intervals.

This interval is known as sample fraction Example- Researcher wants to take a sample

size of 30 from a population of 900; Sample fraction = N/n (N-total no.of units in

the population; n-sample size). Sample fraction = 900/30 = 30

For obtaining the sample the first member can be selected randomly & after that every 30th member of the population is included in the sample.

Stratified Random Sampling

- Under stratified sampling the population is divided into various strata and sampling is drawn from each strata using simple random sampling

Suppose a firm wishes to investigate consumers who currently subscribe to an HDTV (high definition television) service.

The researchers may wish to ensure that each brand of HDTV televisions is included proportionately in the sample

Strict probability sampling procedures would likely under represent certain brands and overrepresented other brands.

Population (owners of HDTV)

Sony Samsung Panasonic Videocon

(25) (25) (25) (25)

- Simple random is used to select 25 samples from each strata

Cluster (area) Sampling

An economically efficient sampling technique in which the primary sampling unit is not the individual element in the population but a large cluster of elements.

Useful when the population element is scattered.

Consider a researcher who would like study the labor problems faced the employees of IT industry.

- So the researcher first obtains the sampling frame of IT firms in India.( say 500 companies).

-Further among 500 companies he selects 50 companies randomly.

- Then he selects sample employees from each companies through random sampling.

Ideally a cluster should be as heterogeneous as the population itself—a mirror image of the population.

A problem may arise with cluster sampling if the characteristics and attitudes of the elements within the cluster are too similar

This problem may be mitigated by selecting a large number of sampled clusters.

Multistage Area Sampling

Multistage area sampling involves two or

more steps that combine some of the probability techniques already described.

Typically, geographic areas are randomly selected in progressively smaller (lower-population) units.

Example – Researcher wants to study the impact of organized retail on unorganized retail in India.

He would first randomly select 10 states.

Then he would randomly select 5 major cities from each state.

Finally he would randomly select 50 unorganized retailers from each city.

Non Probability sampling

Nonprobability sampling - A sampling technique in which units of the sample are selected on the basis of personal judgment or convenience; the probability of any particular member of the population being chosen is unknown.

Probability sampling has 4 techniques;

1.Convenience Sampling

2.Judgement Sampling

3.Quota Sampling

4.Snowball Sampling

Convenience Sampling

Convenience sampling refers to sampling by obtaining people or units that are conveniently available.

A research team may determine that the most convenient and economical method is to set up an interviewing booth from which to intercept consumers at a shopping center.

Researchers generally use convenience samples to obtain a large number of completed questionnaires quickly and economically.

Convenience sampling is also used when sampling frame is not available

The user of research based on a convenience sample should remember that projecting the results beyond the specific sample is inappropriate.

Convenience samples are best used for exploratory research when additional research will subsequently be conducted with a probability sample.

Judgment(Purposive) Sampling

Judgment (purposive) sampling is a nonprobability sampling technique in which an experienced individual selects the sample based on his or her judgment about some appropriate characteristics required of the sample member.

Researchers select samples that satisfy their specific purposes, even if they are not fully representative

The Wholesale price index (WPI) is based on a judgment sample of market-basket items, expected to reflect a representative sample of items consumed by most Indians.

Test-market cities often are selected because they are viewed as typical cities whose demographic profiles closely match the national profile

In a study of labor problems ,you want to talk only with those who have experienced on-the- job discrimination & other problems.

Quota Sampling Quota sampling is very similar to stratified

random sampling. In quota sampling, the population is first

segmented into mutually exclusive sub-groups.

Then judgment or convenience sampling is used to select the subjects or units from each segment based on a specified proportion.

Population (owners of HDTV)

Sony Samsung Panasonic Videocon

(25) (25) (25) (25)

- Convenient or judgment sampling is used to select the quota for each strata

In quota sampling, the interviewer has a quota to achieve.

For example, an interviewer in a particular city may be assigned 100 interviews, 35 with owners of Sony TVs, 30 with owners of Samsung TVs, 18 with owners of Panasonic TVs, and the rest with owners of other brands

The interviewer is responsible for finding enough people to meet the quota.

Snowball Sampling

Snowball sampling is useful when respondents are difficult to identify and are best located through referral network.

In this method individuals are discovered through network referals.

The group is then used to refer the researcher to others who possess similar characterstics,who in turn identify others

Snowball sampling is used study very unique problems like drug culture, teenage gang activities, insider trading,HIV patients etc.

Step 4 –Determine sample size

Researcher should select reasonable size of sample to get accurate results.

Too small sample size would not represent the population – sample errors

Too large sample size would result in administrative errors affecting the results.

The rule thumb is to take 10% of the population as sample.

Sampling size based on judgement;- The importance of the decision.- The nature of research- Nature of analysis – multivariate analysis- Sample size used in similar studies- Resource constraints - The greater the number of sub groups of

interest within sample, the greater the sample size must be, as each subgroup must meet minimum sample size requirements

Factors in Determining Sample Size for Questions Involving Means

Three factors are required to specify sample size: (1) the heterogeneity (i.e., variance) of the

population;) (2) the magnitude of acceptable error (i.e., some

amount) (3) the confidence level (i.e., 90 percent, 95

percent, 99 percent).

1. the heterogeneity

The determination of sample size heavily depends on the variability within the sample

Only a small sample is required if the population is homogeneous.

For example, predicting the average age of college students requires a smaller sample than predicting the average age of people who visit the zoo on a given Sunday afternoon.

2.The magnitude of error the magnitude of error indicates how precise

the estimate must be. A range values within which the value of a

parameter lies. From a managerial perspective, the

importance of the decision in terms of profitability will influence the researcher’s specifications of the range of error.

If, for example, favorable results from a test-market sample will result in the construction of a new plant, the acceptable range of error

probably will be small.

3.Confidence level The third factor of concern is the confidence

level. confidence level Statistical measure of the

number of times out of 100 that test results can be expected to be within a specified range.

Higher the confidence level, larger the sample required.

In most business research, we will typically use the 95 percent confidence level.

Estimating Sample Size for Questions Involving Means

1. Estimate the standard deviation of the population.

2. Make a judgment about the allowable magnitude of error(E).

3. Determine a confidence level.

The judgment about the allowable error and the confidence level are the manager’s decision to make.

Calculating E

Confidence interval E can be calculated using formula;

where

__ X sample mean

E range of error Zcl = Z value associated with confidence

interval Standard error of the mean.

The standard error is the standard deviation of the sampling distribution of statistic

Standard error can be calculated using the formula;

Where , S= Standard deviation of the sample n = sample size

Suppose you are a financial planner and are interested in knowing how long investors tend to own individual stocks. In a survey of 100 investors, you find that the mean length of time a stock is held is is 37.5 months, with a standard deviation (S) of 12.0 months. Even though 37.5 months is the “expected value” and the best guess for the true mean in the population , the likelihood is that the mean is not exactly 37.5. Compute the confidence interval at 95% confidence level.

E can be obtained using the formula

Find Standard Error;

Thus;

E = 1.96 x 1.2

E = 3.252

We can thus expect that u is in the range from 35.148 to 39.852 months.(95 % of the time)

The other problem is estimating the standard deviation of the population.

Ideally, similar studies conducted in the past will give a basis for judging the standard deviation.

In practice, researchers who lack prior information may conduct a pilot study to estimate the population parameters

The formula for sample size is;

n= (ZS/E)2

Z standardized value that corresponds to the confidence level

S sample standard deviation or estimate of the population standard deviation

E acceptable magnitude of error

Suppose a survey researcher studying annual expenditures on lipstick wishes to have a 95 percent confidence level (Z 1.96) and a range of error (E) of less than $2. If the estimate of the standard deviation is $29, calculate the sample size.

If a range of error (E) of $4 is acceptable, the necessary sample size will be reduced:

Factors in Determining Sample Size for Proportions Researchers frequently are concerned with

determining sample size for problems that involve

estimating population proportions or percentages. To determine sample size for a proportion, the

researcher must make a judgment about confidence level and confidence interval.

An estimate of the expected proportion of successes must be made, based on intuition or prior information.

Sample size is calculated using the formula;

n number of items in sample -Square of the confidence level in standard

error units. p estimated proportion of successes q 1 – p, or estimated proportion of failures E 2 - square of the allowance for error between the

true proportion and the sample proportion,

Confidence interval (E) for population Proportion

Example – The human resource director of a large organisation wanted to know what proportion of all population who had ever been interviewed for a job with his organisation had been hired.He was willing to settle for 95% confidence interval . A random sample of 50 interviews revealed that 15.2% of the persons in the sample had been hired. Calculate E.

Suppose a researcher believes that a simple random sample will show that 60 percent of the population (p) recognizes the name of an automobile dealership. The researcher wishes to estimate with 95 percent confidence (Zc.l. 1.96) that the allowance for sampling error is not greater than 3.5 percentage points (E).

Step 5 –Select actual units & conduct fieldwork

After sample technique and sample is decided, researchers actually collects the data from the unit of analysis through fieldwork.

Researcher uses various tools like questionnaire,interview,observation, experience survey & focus group to collect the data from target population.

Errors in Survey Research

Statistical error is the difference between the value of a sample statistic of interest and the value of the corresponding population parameter.

Total error = Sampling error + systematic error

Sampling error An estimation made from a sample is not the

same as a census count. Sampling error is the difference between the

sample result and the result of a census conducted using identical procedures.

The sampling units, even if properly selected according to sampling theory, may not perfectly represent the population.

Random sampling error occurs because of chance variation in the scientific selection of sampling units.

Random sampling error is a function of sample size.

As sample size increases, random sampling error decreases.

Systematic(non-sampling) Error

Systematic error, results from some imperfect aspect of the research design or from a mistake in the execution of the research.

Systematic error is classified into: respondent error and administrative error.

Respondent error Surveys ask people for answers. If people cooperate and give truthful answers,

a survey will likely accomplish its goal.

Nonresponse Error The statistical differences between a survey

that includes only those who responded and a survey that also included those who failed to respond are referred to as nonresponse error.

People who are not contacted or who refuse to cooperate are called nonrespondents.

Non response error is very high in email survey and telephonic interview.

Response Bias A response bias occurs when respondents

tend to answer questions with a certain slant. People may consciously or unconsciously

misrepresent the truth. If a distortion of measurement occurs

because respondents’ answers are falsified or misrepresented, either intentionally or inadvertently, the resulting sample bias will be a response bias.

Deliberate Falsification Occasionally people deliberately give false

answers. A response bias may occur when people

misrepresent answers to appear intelligent, conceal personal information, avoid embarrassment, and so on.

Unconscious Misrepresentation Even when a respondent is consciously trying

to be truthful and cooperative, response bias can arise from the question content, or some other stimulus.

In many cases consumers cannot adequately express their feelings in words.

The cause may be questions that are vague or ambiguous.

Researchers may ask someone to describe his or her frustration when using a computer. The problem is, the researcher may be interested in software problems while the respondent is thinking of hardware issues.

A survey in the Philippines found that, despite seemingly high toothpaste usage, only a tiny percentage of people responded positively when asked, “Do you use toothpaste?”.

As it turned out, people in the Philippines tend to refer to toothpaste by using the brand name Colgate.

When researchers returned and asked, “Do you use Colgate?” the positive response rate soared.

As the time following a purchase or a shopping event increases, people become more likely to underreport information about that event.

Time lapse influences people’s ability to precisely remember and communicate specific factors.

Types of Response Bias Response bias falls into four specific

categories: Acquiescence bias Extremity bias Interviewer bias, and Social desirability bias.

Acquiescence bias Some respondents are very agreeable. They seem to agree to practically every

statement they are asked about. A tendency to agree with all or most

questions is known as acquiescence bias.

Extremity Bias. Some individuals tend to use extremes when

responding to questions. For example, they may choose only “1” or

“10” on a ten-point scale. Others consistently refuse to use extreme

positions and tend to respond more neutrally—“I never give a 10 because nothing is really perfect.”

Interviewer Bias. Response bias may arise from the interplay

between interviewer and respondent. If the interviewer’s presence influences

respondents to give untrue or modified answers – interviewer bias

The interviewer’s age, gender, style of dress, tone of voice, facial expressions, or other nonverbal characteristics may have some influence on a respondent’s answers.

Social Desirability Bias. Social desirability bias may occur either

consciously or unconsciously because the respondent wishes to create a favorable impression or save face in the presence of an interviewer.

Administrative error DATA PROCESSING ERROR Processing data by computer, like any

arithmetic or procedural process, is subject to error because data must be edited, coded, and entered into the computer by people.

The accuracy of data processed by computer depends on correct data entry and programming.

can be minimized by establishing careful procedures for verifying each step in the data processing stage.

INTERVIEWER ERROR Interviewer error is introduced when

interviewers record answers wrongly or are unable to write fast enough to record answers verbatim.

INTERVIEWER CHEATING Interviewer cheating occurs when an

interviewer falsifies entire questionnaires or fills inanswers to questions that have not been answered.

Tools of data collection

Questionnaires, Interviews, Observation, Case study; Experience Survey; Focus Groups;

Questionnaire In the J.D. Power survey, consumers were

asked whether they were familiar with twenty-two different emerging technologies.

Then they were asked about their interest in each technology, rating their interest using a scale (“definitely interested,” “probably interested,” and so on).

Next, the study indicated the likely price of each technology, and consumers were again asked their interest, given the price.

Learning price information often changed consumers’ interest levels.

Night vision systems appealed to 72 percent of consumers, placing it in second place in the rankings.

But when consumers learned the systems would likely add $1,500 to the price of a car, this technology dropped to a rank of 17, near the bottom.

In contrast, HD radio ranked in sixteenth place until consumers saw a price tag of just $150. That price pushed the feature up to third place.

Shows how extremely useful information can be gathered with a questionnaire.

It also shows how results can differ by exactly what question is asked and the amount of information provided.

Questionnaire

Questionnaire is a formalized set of questions for obtaining information from respondents.

The research questionnaire development stage is critically important as the information provided is only as good as the questions asked.

Objectives of a questionnaire

Any questionnaire has three specific objectives

First, it must translate the information needed into a set of specific question that respondents can and will answer.

Second, a questionnaire must motivate and encourage the respondents to become involved in the process of data collection –reduce non response error.

Thirdly, the questionnaire should minimize the response error.

A questionnaire can be a major source of response error.

Minimizing this error is an important objective of questionnaire designing.

Guidelines for Constructing Questions Few hard and-fast rules exist in guiding the

development of a questionnaire.

Fortunately, research experience has yielded some guidelines that help prevent the most common mistakes.

Specify the information needed

As the research progresses, the information needed becomes more and more clearly defined.

Questionnaire depends the information needed for the research

Individual Question content

Once the information needed is clear, researcher has to decide upon the individual questions.

Is the question necessary? Every question should contribute to the

information needed or serve specific purpose.

In certain situations it is useful to ask some neutral questions to build the rapport.

Avoid asking unnecessary and embarrassing questions.

Are several questions needed instead of one?

Once ascertain that the question is necessary, make sure that it is sufficient to get the required information.

Sometimes, several questions are needed to obtain required information in an unambiguous manner

Consider the question; “Do you think Coca –Cola is tasty and

refreshing?” Known as double-barreled questions. To obtain the required information two distinct

questions should be asked.

“Do you think Coca –Cola refreshing?” “Do you think Coca –Cola is tasty?”

Another example of multiple question is the “Why” question.

“Why do you shop at Nike Town?” The possible answers may include: “to buy

athletic shoes,” its more conveniently located,” “it was recommended by my best friend”

The first answer tells why the respondent shop.

Second answer reveals what respondents like about Nike Town

Third tells how the respondents learned about Nike Town.

Why question about the use of the product involves two aspects: 1) Attribute of the product, and 2)influences leading to knowledge of it.

What do you like about Nike Town as compared other stores?

How did you first happen to shop in Nike Town?

Choosing Question Structure A question may be open ended or closed

ended.

Open ended questions are free answer question that respondents answer in their own words.

What names of local banks can you think of? What comes to mind when you look at this

advertisement? In what way, if any, could this product be

changed or improved?

Open-ended response questions are most beneficial when the researcher is conducting exploratory research, especially when the range of responses is not yet known.

Also, open-ended response questions are valuable at the beginning of an interview. They are good first questions because they allow respondents to warm up to the questioning process.

They are also good last questions for a fixed-alternative questionnaire.

For example, an employee satisfaction survey may collect data with a series of fixed-alternative questions, then conclude with “Can you provide one suggestion on how our organization can enhance employee satisfaction?”

The cost of administering open-ended response questions is substantially higher than that of administering fixed-alternative questions because the job of editing, coding, and analyzing the data is quite extensive.

As each respondent’s answer is somewhat unique, there is some difficulty in categorizing and summarizing the answers

Another potential disadvantage of the open-ended response question is the possibility that interviewer bias will influence the answer.

While most interviewer instructions state that answers are to be recorded verbatim, rarely does even the best interviewer get every word spoken by the respondent.

Interviewers have a tendency to take shortcuts.

When this occurs, the interviewer may well introduce error because the final answer may reflect a combination of the respondent’s and interviewer’s ideas.

In addition, articulate individuals tend to give longer answers to open-ended response questions.

Such respondents often are better educated therefore may not be good representatives of the entire population.

Increase in non-response error.

Jet Airways wants to ascertain the image it has in the minds of its patrons. Develop some open ended questions to understand the image of patrons.

Using Fixed Alternative Questions In contrast, fixed-alternative questions require

less interviewer skill, take less time, and are easier for the respondent to answer.

Closed questions are classified into standardized groupings prior to data collection.

However, when a researcher is unaware of the potential responses to a question, fixed alternative questions obviously cannot be used.

Unanticipated alternatives emerge when respondents believe that closed answers do not adequately reflect their feelings.

Therefore, a researcher may find exploratory research with open-ended responses valuable before writing a fixed answer questionnaire.

Types of Fixed-Alternative Questions The simple-dichotomy (dichotomous)

question requires the respondent to choose one of two alternatives. The answer can be a simple “yes” or “no” or a choice between “this” and “that.”

Did you have any overnight travel for work-related activities last month?

Yes No

Several types of questions provide the respondent with multiple-choice alternatives

Determinant-choice question requires the respondent to choose one—and only one—response from among several possible alternatives.

Please give us some information about your flight. In which section of the aircraft did you sit?

First class Business class Coach class

The frequency-determination question is a determinant-choice question that asks for an answer about the general frequency of occurrence.

How frequently do you watch MTV? Every day 5–6 times a week 2–4 times a week Once a week Less than once a week Never

The checklist question allows the respondent to provide multiple answers to a single question.

Please check which, if any, of the following sources of information about investments you regularly use.

Personal advice of your broker(s) Brokerage newsletters Brokerage research reports Investment advisory service(s) Conversations with other investors Web page(s) Other (please specify) __________

Attitude rating scales, such as the Likert scale, semantic differential, Stapel scale, and so on, are also fixed-alternative questions.

A major problem in developing dichotomous or multiple-choice alternatives is establishing the response alternatives.

Alternatives should be totally exhaustive, meaning that all the response options are covered and that every respondent has an alternative to check.

The alternatives should also be mutually exclusive, meaning there should be no overlap among categories and

Example (incorrect classification)

$10,000–$30,000 $30,000–$50,000 $50,000–$70,000 $70,000–$90,000 $90,000–$110,000 Over $110,000

Correct classification

Less than $10,000 $10,000–$29,999 $30,000–$49,999 $50,000–$69,999 $70,000–$89,999 $90,000–$109,999 Over $110,000

Choosing question wording Avoid Complexity: Use Simple,

Conversational Language Use ordinary words –should match the

vocabulary of the respondents. Technical jargons should also be avoided. For Example, Do you think the distribution of soft drinks is

adequate? Do you think soft drinks are readily available

when you want to buy them?

Use unambiguous words

Words used should have single meaning that is known to the respondents.

Consider the question; In a typical month how often do you shop in

dept. stores? Never Occasionally Sometimes Often Regularly

The answer to this question are fraught with response bias.

Three respondents who shop once a month may check three different categories: Occasionally, sometimes, and often.

Less than once 1 or 2 times 3 or 4 times More than 4 times

Provides consistent frame of reference for all the respondents.

Categories are objectively defined and, respondents are no longer free to interpret them in their own way.

Avoid leading & biased question

A leading question is one that clues the respondent to what answer is desired or leads the respondent to answer in a certain way.

Ex, Do you think that patriotic Indians should buy

imported automobiles when that put Indians labor out of work?

Yes No Don’t Know

This question would not help determine the preferences of Indians for imported Versus domestic car.

A better question would be; Do you think Indians should buy imported

automobiles?

Bias may also arise when the respondents are given clues about the sponsor of the project.

Is Colgate your favorite toothpaste? More unbiased way of asking this question; What is your favorite toothpaste brand?

Likewise, the mention of prestigious or nonprestigous name can bias the response

Example, Do you agree with American Dental

Association that Colgate is effective in preventing cavities?

An unbiased question would be; Is Colgate effective in preventing cavities?

Avoid loaded questions. They also contain researcher bias. Loaded questions suggest a socially

desirable answer or are emotionally charged. Examples: Intelligent people can see

meaning and opportunity in today economic chaos: Do you?

Most of our MBA students are rank holders. What rank did you get and when and in which subjects?

If you have to ask them, ask them at the end of the Questionnaire.

By then, initial mistrust has been overcome, rapport has been created, and respondents may be more willing to share delicate information.

Avoid Implicit Assumptions Questions should not be worded so that answer is

dependent upon implicit assumption about what will as a consequence.

1.Are you in favor of a balanced budget?

2.Are you in favor of a balanced budget if it would result in an increase in the personal income tax?

Are you interested in Night Vision System? Are you interested in Night Vision System if it

would cost $1500?

Avoid Complex calculation Suppose we were interested in household’s

annual per capita expenditure on groceries. If we asked the question

What is the annual per capita expenditure on groceries in your household?

The better way would be ask two question What is the monthly(weekly) expenditures on

groceries in your households How many members are there in your

household?

Determining the order of the question Opening Questions Opening question opens the door to

cooperation. Opening question should be simple and

interesting. Not to include too many open ended

questions in the beginning. Questions that ask respondents for their open

ions can be good opening questions.

Type of information required

The type of information obtained may be classified as:

1) Basic Information – information directly related to research problem.

2) Classification information- Consists of socio-economic and demographic characteristics

3) Identification Information: name, address, email, telephone etc..

As general guideline, basic information should be obtained first, followed by classification, finally identification information.

Logical Order

All the questions that deal with particular topic should be asked before beginning a new topic.

Pretesting the questionnairePretesting refers to the testing of the

questionnaire on a small sample of respondents to identify and eliminate potential problems.

Questionnaire should not be used without pretesing.

All aspects of the questionnaire to be tested. Respondents for the pretest and for the

actual survey to be drawn from the same population.

Pretest are best done by personal interviews, researcher can observe respondents reactions and attitude.

After the necessary changes have been made another pretest could be conducted by mail, telephone etc.. If those methods are to be used.

Ordinarily pretest sample size is small varying from 15 to 30 respondents.

Pretesting should be continued until no further changes are needed.

Attitude measurement Scales Attitudes are Hypothetical Constructs cannot directly be observed.

For example - attitude toward working environment, towards a brand etc..

We can measure an attitude by making an inference based on the way an individual responds to multiple scale indicators.

Attitudes are subjective and personal. Attitude influences the behaviour. Purchase decisions are based upon the attitudes.

Attitude has three components, namely cognitive, affective and the behavioural.

Cognitive This refers to the respondents’ beliefs,

knowledge or awareness about an event or an object.

This is usually acquired from friends, periodicals etc. Sometimes, it is also known as the belief component. Statements like – (a) I am aware of the product ‘X’ (b) I have no idea about the product ‘B’ (c) That institute is excellent.

Affective This refers to the respondent’s liking or

preferences for an object. This is also known as the feeling component.

(a) I like the product ‘A’ (b) Advertisement ‘X’ is poor. This component reveals the buyers’ positive

or negative attitude towards the product.

Behavioral This refers to the respondent’s intention to

buy. This is a situation prior to the purchase. In

marketing, the usage and buying pattern depends on this component.

This is also known as action component.

For each of the following statements, identify the appropriate component of attitude;

I do not like carrot juice: The compensation package for MBA

graduates has gone down because of the recession

I like the recent airtel advertisement on TV I understand that Santro gives a better

mileage than Wagon R I prefer plastic bottles than glass bottles Mohan says that he loves britannia biscuits

because they are tastier and will always eat them.

Importance of Measuring Attitudes

Most managers hold the intuitive belief that changing consumers’ or employees’ attitudes toward their company or their company’s products or services is a major goal.

Because modifying attitudes plays a pervasive role in developing strategies to address these goals, the measurement of attitudes is an important task.

Attitude rating scale Rating asks the respondent to estimate the

magnitude or the extent to which some characteristic exists.

The rating task involves marking a response indicating one’s position using one or more attitudinal scales

Simple Attitude Scales

In its most basic form, attitude scaling requires that an individual agree or disagree with a statement or respond to a single question.

For example, respondents in a political poll may be asked whether they agree or disagree with the statement “The president should run for re-election.”

This type of rating scale merely classifies respondents into one of two categories, thus having only the properties of a nominal scale, and the types of mathematical analysis that may be used with this basic scale are limited.

Category Scales A rating scale that consists of several

response categories, often providing respondents with alternatives to indicate positions on a continuum.

By having more choices for a respondent, the potential exists to provide more information.

However, if the researcher tries to represent something that is truly bipolar (yes/no, female/male, member/nonmember, and so on) with more than two categories, error may be introduced.

Method of Summated Ratings: The Likert Scale Summated ratings was developed by Rensis

Likert. With the Likert scale, respondents indicate

their attitudes by checking how strongly they agree or disagree with carefully constructed statements, ranging from very positive to very negative attitudes toward some object.

Individuals generally choose from approximately five response alternatives strongly agree, agree, uncertain, disagree, and strongly disagree

Researchers assign scores, or weights, to each possible response.

These scores are summated to get one score to analyze the result.

Evaluation of Globus—the Super Market by respondent

# Likert scale items Strongly disagree

Disagree

Neither agree nor disagree

Agree Strongly agree

1 Salesmen at the shopping mall are nice

2 Shopping mall does not have enough parking space

3 Prices of items are reasonable.

4 Mall has wide range of products to choose

5 Mall operating hours are inconvenient

6 The arrangement of items in the mall is confusing

EXAMPLE

Each degree of agreement is given a numerical score and the respondents total score is computed by summing these scores. This total score of respondent reveals the particular opinion of a person.

Likert Scale are of ordinal type, they enable one to rank attitudes, but not to measure the difference between attitudes.

The higher the respondent’s score, the more favourable is the attitude. For example, if there are two shopping malls, ABC and XYZ and if the scores using the Likert Scale are 30 and 60 respectively, we can conclude that the customers’ attitude towards XYZ is more favourable than ABC.

REVERSE RECODING If a statement is framed negatively the

numerical scores would need to be reversed. This organization ignores employee

welfare Strongly Agree 1 Agree 2 Neutral 3 Disagree 4 Strongly disagree 5 This is done by reverse recoding the

negative item so that a strong agreement really indicates an unfavorable response rather than a favorable attitude.

Semantic Differential Scale Semantic differential scale is 7 point rating

scale with endpoints associated with bipolar labels that have semantic meanings.

Respondents rate the objects on a number of itemized, 7 point scale bounded at each stage by one of the two bipolar adjectives

Bipolar adjectives—such as “good” and “bad,” “modern” and “old fashioned,” or “clean” and “dirty”—anchor the beginning and the end (or poles) of the scale.

Example –rate the President of India Strong --- ---- ---- ---- --- ---- --- Weak Decisive --- ---- ---- ---- --- ---- --- Indecisive Good --- ---- ---- ---- --- ---- --- Bad Cheap -- ---- ---- ---- --- ---- --- Expensive Active --- ---- ---- ---- --- ---- --- Passive Lazy --- ---- ---- ---- --- ---- ---

Industrious

The blanks are numbered from 1 to 7 and then the responses are averaged for each dimension.

The average is plotted on the form and provides a profile of the connotation of the target concept.

Suppose Jet Airways wants to ascertain the image it has in the minds of its patrons. Construct a seven-item Likert scales and Semantic Deferential Scale to measure the perceived image of the airlines. Make sure that the seven items under each format correspond to the same seven dimensions. [For the sake of simplicity, we will use all favorable statements in case of Likert scale]

Likert Scale – Please state your degree of agreement/disagreement on a 5-point scale, where 1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, and 5 = strongly agree for the following statements.

S.No. StatementStrongly disagree

Disagree Neutral AgreeStrongly

agree

i)

ii)

iii)

iv)

v)

vi)

vii)

Semantic Differential Scale – In semantic differential scale, the above-mentioned seven items/statements are put in the form of bipolar adjectives/phrases. It is shown below like this.

1 □ □ □ □ □ □ □

2 □ □ □ □ □ □ □

3 □ □ □ □ □ □ □

4 □ □ □ □ □ □ □

5 □ □ □ □ □ □ □

6 □ □ □ □ □ □ □

7 □ □ □ □ □ □ □

Stapel Scale Stapel scale is used as an alternative to the

semantic, especially when it is difficult to find bipolar adjectives, that match the investigative question.

The scale is composed of word(phrase) identifying the different dimensions of the construct studied and set of 10 response categories.

This scale has some distinctive features:- Each item has only one word/phrase

indicating the dimension it represents. Each item has ten response categories.

The response categories have numerical labels but no verbal labels.

Criteria for good measurement The selected scale should result in

valid,reliable and generalizable result. Three criteria for good measurement are,

Reliability Validity & Generalizability

Reliability

Extent to which scale produces consistent results if repeated measurements are done.

A measure is reliable when different attempts at measuring something converge on the same result.

the concept of reliability revolves around consistency.

Think of a scale to measure weight. You would expect this scale to be consistent from one time to the next.

If you stepped on the scale and it read 140 pounds, then got off and back on, you would expect it to again read 140.

If it read 110 the second time, the scale would not be reliable.

Reliability has three approaches Test-Retest Reliability Alternative-Forms Reliability Internal Consistency Reliability

Test-Retest Reliability

The test-retest method of determining reliability involves administering the same scale or measure to the same respondents at two separate times to test for stability.

If the measure is stable over time, the test, administered under the same conditions each time, should obtain similar results.

Test -retest reliability represents a measure’s repeatability.

Suppose a researcher at one time attempts to measure buying intentions and finds that 12 percent of the population is willing to purchase a product. If the study is repeated a few weeks later under similar conditions, and the researcher again finds that 12 percent of the population is willing to purchase the product, the measure appears to be reliable.

The degree of similarity between the two measurements is determined by computing correlation coefficient.

The higher the correlation coefficient, the greater the reliability.

Test-Retest has two major problems; First it is sensitive to the time interval. Longer

the time interval, lower the reliability. Attitude may change between measurements.

Second, it may be impossible to make repeated measurements .

Alternative –Forms Reliability In alternative –Forms reliability ,two

equivalent forms of the scale are constructed. The same respondents are measured at two different times with different scale form being administered each time.

The scores from the administration of the alternative scale-forms are correlated to assess reliability.

Two major problems; It is very difficult and time consuming to

construct an equivalent form of the scale. It may be impossible to make repeated

measurements.

Internal Consistency Reliability Used to assess the reliability of a summated

scale In multiple item measure each respondent’s

answer to each question are aggregated to form overall score.

It is possible that the indicators do not relate the same thing – they lack coherence.

We need to be sure that all the items are related to each other.

If they are not some of the items may be unrelated and therefore indicative of something else

Opinion leadership in technology adoption 1.My opinion on hardware seem not to count

with other people. 2.When other people choose to adopt a

hardware/software product, they turn to me for advice.

3.Other people select hardware/software rarely based on what I have suggested to them.

4.I often persuade other people to adopt software/hardware that I like

5.Other people rarely come to me for advice about choosing HW/SW products.

One popular way of testing internal reliability is split half method.

If the research has 12 items ,split half method randomly splits these 12 items into two groups of 6 items each.

Then the degree of correlation between the scores of the halves are calculated.

If there is strong positive correlation then the scale is reliable.

Cronbach’s alpha is a commonly used test of internal reliability.

The computed alpha varies between 0 to 1. In business research if Cronbach’s alpha is

more than 0.70,scale is considered to be reliable.

Validity Validity has to do with whether or not a

measure of a concept really measures that concept

Perfect validity requires that there be no measurement error.

Researchers distinguish between number of types of validity.

Face Validity At the very minimum, a researcher who

develops a new measure should establish that it has face validity.

The measure apparently reflects the content of the concept in question.

Thus a scale designed to measure employee satisfaction would be considered inadequate if it omitted any of the major dimension.

The content validity might be established by asking other people whether or not the measure seems to be getting at the concept.

Possibly people with expertise in a field might be asked to act as judges.

Face validity is an essentially intuitive process.

Content validity Content validity refers to the degree that a

measure covers the domain of interest. Do the items capture the entire scope, but not

go beyond, the concept we are measuring? If an exam is supposed to cover chapters 1–

5, it is fair for students to expect that questions should come from all five chapters, rather than just one or two.(FV)

It is also fair to assume that the questions will not come from chapter 6.

Thus, when students complain about the material on an exam, they are often claiming it lacks content validity.

Similarly, an evaluation of an employee’s job performance should cover all important aspects of the job, but not something outside of the employee’s specified duties.

It has been argued that shoppers receive value from two primary elements.

Hedonic shopping value refers to the pleasure and enjoyment one gets from the shopping experience.

While utilitarian shopping value refers to value received from the actual acquisition of the product desired.

If a researcher assessing shopping value only asked questions regarding the utilitarian aspects of shopping, we could argue the measure lacks content validity

Construct validity exists when a measure reliably measures and truthfully represents a unique concept.

we must be sure our measures look like they are measuring what they are intended to measure (face validity) and cover only the domain of interest (content validity).

If so, we can assess convergent validity and discriminant validity.

These forms of validity represent how unique or distinct a measure is.

Convergent validity requires that concepts that should be related are indeed related.

For example, in business we believe customer satisfaction and customer loyalty are related. If we have measures of both, we would expect them to be positively correlated.

If we found no significant correlation between our measures of satisfaction and our measures of loyalty, it would bring into question the convergent validity of these measures.

On the other hand, our customer satisfaction measure should not correlate too highly with the loyalty measure if the two concepts are truly different.

As a rough rule of thumb, when two scales are correlated above 0.75,discriminant validity may be questioned.

Observation in Business Research In business research, observation is a

systematic process of recording behavioral patterns of people, objects, and occurrences as they happen to obtain information about the phenomenon of interest.

No questioning or communicating with people is needed.

Researchers who use observation as a method of data collection either witness and record information while watching events take place or take advantage of some tracking system such as CCTV.

In survey research the finding is based on what people say and not what people do.

Eg- In the field of leadership research researchers have relied heavily on the responses of subordinates and what they say leaders do rather than what leaders actually do.

The major advantage of observation studies over surveys, which obtain self-reported data from respondents, is that the data are free from distortions, inaccuracies, or other response biases due to memory error, social desirability bias, and so on.

The data are recorded when the actual behavior takes place.

Observation is the only method to study animal behavior.

To study child behavior..

Study consumer behavior…

What can be observed?

The Nature of Observation Studies Business researchers can observe people,

objects, events, or other phenomena using either human observers or machines designed for specific observation tasks.

Human or mechanical observation is generally unobtrusive, meaning no communication with a respondent takes place.

For example, rather than asking an employee how long it takes to handle an insurance claim, a researcher might observe and record the time it takes for different steps in this process.

Or, rather than ask a consumer how long they spend shopping for produce, a researcher can watch shoppers in a supermarket and note the time each spends in the produce area

The unobtrusive or nonreactive nature of the observation method often generates data without a subject’s knowledge.

A situation in which an observer’s presence is known to the subject involves visible observation.

A situation in which a subject is unaware that observation is taking place is hidden observation.

Hidden, unobtrusive observation minimizes respondent error.

Asking subjects to participate in the research is not required when they are unaware that they are being observed.

Covert observational research - The researchers do not identify themselves. Either they mix in with the subjects undetected, or they observe from a distance.

The advantages of this approach are: (1) It is not necessary to get the subjects’ cooperation, and

(2) The subjects’ behaviour will not be contaminated by the presence of the researcher.

Overt observational research - The researchers identify themselves as researchers and explain the purpose of their observations.

The problem with this approach is subjects may modify their behaviour when they know they are being watched.

They portray their “ideal self” rather than their true self.

Natural and Contrived Observation Natural observation involves observing

behavior as it takes place in the environment. Ex-One could observe the behavior of

respondents eating fast food at KFC. Observe behavior of consumers at a Mall.

Contrived observation - respondents behavior is observed in an artificial environment such as test kitchen.

Natural environment but artificial participant.-Mystery Shopping

Corporate dinner-observe the table etiquettes of participants.

Observation of Human Behavior Whereas surveys emphasize verbal

responses, observation studies emphasize and allow for the systematic recording of nonverbal behavior.

Behavioral scientists have recognized that nonverbal behavior can be a communication process by which meanings are exchanged among individuals.

Head nods, smiles, raised eyebrows, and other facial expressions or body movements have been recognized as communication symbols.

For example, a hypothesis about customer-salesperson interactions is that the salesperson would signal status based on the importance of each transaction.

In low-importance transactions, in which potential customers are plentiful and easily replaced ,the salesperson may show definite nonverbal signs of higher status than the customer.

When customers are scarce, as in big-ticket purchase situations, the opposite should be true.

One way to test this hypothesis would be with an observation study using the nonverbal communication

Complementary Evidence The results of observation studies may

extend the results of other forms of research by providing complementary evidence concerning individuals’ “true” feelings.

Focus group interviews often are conducted For the interpretation of nonverbal behavior such as facial expressions or head nods to supplement information from interviews.

Observation of Physical Objects

Physical phenomena may be the subject of observation study.

Researcher observes physical evidence of past behavior - trace analysis

A classic example of physical-trace evidence in a nonprofit setting was erosion on the floor tiles around the hatching-chick exhibit at Chicago’s Museum of Science and Industry.

These tiles had to be replaced every six weeks; tiles in other parts of the museum did not need to be replaced for years.

The selective erosion of tiles, was a measure of the relative popularity of exhibits.

Parlin designed an observation study to persuade Campbell’s Soup Company to advertise in the Saturday Evening Post.

Campbell’s was reluctant to advertise because it believed that the Post was read primarily by working people who would prefer to make soup.

To demonstrate that rich people weren’t the target market, Parlin selected a sample of Philadelphia garbage routes

Garbage from each specific area of the city that was selected was dumped on the floor of a local National Guard Armory.

Parlin had the number of Campbell’s soup cans in each pile counted.

The results indicated that the garbage from the rich people’s homes didn’t contain many cans of Campbell’s soup.

The garbage piles from the blue-collar area showed a larger number of Campbell’s soup cans.

What is most interesting about the garbage project is that observations can be compared with the results of surveys about food consumption—and garbage does not lie.

Content Analysis Besides observing people and physical

objects, researchers may use content analysis, which obtains data by observing and analyzing the contents or messages of advertisements, newspaper articles, television programs, letters, and the like.

Content analysis studies the message itself and involves the design of a systematic observation and recording procedure for qualitative description of the manifest content of communication.

For example, content analysis of advertisements might evaluate their use of words, themes, characters etc..

Content analysis might be used to investigate questions such as whether some advertisers use certain themes, appeals, claims, or deceptive practices more than others.

Content analysis also can explore the information content of television commercials directed at children, the company images portrayed in ads, and numerous other aspects of advertising.

Focus Group Method A focus group interview is an

unstructured, free-flowing interview with a small group of people, usually between six and ten.

An interview conducted by a trained moderator in a non-structured manner with the small group of respondents.

The value of findings lies in the unexpected findings often obtained from a free flowing group discussion.

Characteristics of the group

Group Size Group Composition Physical Setting Time Duration Recoding Moderator

6 -10 Homogenous; Prescreened Relaxed, Informal 1 to 3 hours Use of audio/ Vedio device Interpersonal and

communication skills of the moderator

Broad Framework Topic Agenda

1.Introduction(15 minutes)

Serve Tea/coffee

Introduce the research team

Aim of the focus group

Conventions( Confidentiality, speak one at time, open debate, everybody’s views etc)

2.Discussion Topics

i) Current Trading Climate (15 Minutes)

ii) Main Challenges in the business environment (20 Minutes)

iii) Government policies and small firms( 20 minutes)

iv) Globalization and small firms (20 minutes

V) Succession problem of small firms (20 minutes)

3.Summing Up

Thanks for participation

Invite back to next event

Reimburse expenses

4.Lunch/Dinner

Application of focus group Can be used in exploratory research

requiring some preliminary understanding & insights.

Can be conducted to analyse & interpret the observation data.

Is very useful to get in-depth understanding.

Some of the areas… Understanding consumers’ perceptions,

preferences concerning a product. Generating new product concepts Generating new ideas about older products. Developing creative concepts for

advertisements Obtaining preliminary consumer reaction to

specific marketing program.

Experimental Research Ideally, managers want to know how a

change in one event will change another event of interest.

Causal research attempts to establish that when we do one thing, another thing will follow.

While we use the term “cause” frequently in our everyday language, scientifically establishing something as a cause is not so easy.

A causal inference can only be supported when very specific evidence exists.

Three critical pieces of causal evidence are:

1. Temporal Sequence

2. Concomitant Variance

3. Nonspurious Association

Temporal Sequence Temporal sequence deals with the time order

of events.

In other words, having an appropriate causal order of events, or temporal sequence, is one criterion for causality. Simply put, the cause must occur before the effect.

It would be difficult for a restaurant manager to blame a decrease in sales on a new chef if the drop in sales occurred before the new chef arrived.

If a change in the CEO causes a change in stock prices, the CEO change must occur before the change in stock values.

Concomitant variation occurs when two events “covary” or

“correlate,” meaning they vary systematically.

In causal terms, concomitant variation means that when a change in the cause occurs, a change in the outcome also is observed.

A correlation coefficient is used to represent concomitant variation.

Causality cannot possibly exist when there is no systematic variation between the variables.

For example, if a retail store never changes its employees’ vacation policy, then the vacation policy cannot possibly be responsible for a change in employee satisfaction.

Nonspurious Association Nonspurious association means any co

variation between a cause and an effect is true, rather than due to some other variable.

A spurious association is one that is not true. Often, a causal inference cannot be made

even though the other two conditions exist because both the cause and effect have some common cause.

For instance, there is a strong, positive correlation between ice cream purchases and murder rates.

Do people become murderers after eating ice cream?

Should we outlaw the sale of ice cream? A third variable is actually important here.

People purchase more ice cream when the weather is hot. People are also more active and likely to commit a violent crime when it is hot.

In summary, causal research should do all of the following:

1. Establish the appropriate causal order or sequence of events

2. Measure the concomitant variation between the presumed cause and the presumed effect

3. Examine the possibility of spuriousness by considering the presence of alternative plausible causal factors

Degrees of Causality Absolute causality means the cause is

necessary and sufficient to bring about the effect.

For example, a warning label used on cigarette packages claims “smoking causes cancer.”

Is this true in an absolute sense? Thus, if we find only one smoker who does

not eventually get cancer, the claim is false.

Conditional causality means that a cause is necessary but not sufficient to bring about an effect.

One way to think about conditional causality is that the cause can bring about the effect, but it cannot do so alone.

If other conditions are right, the cause can bring about the effect.

This is a weaker causal inference.

Experimental Research Experiments are widely used in causal

research designs. Experimental research allows a researcher to

control the research situation so that causal relationships among variables may be evaluated.

The experimenter manipulates one or more independent variables and holds constant all other possible independent variables while observing effects on dependent variable(s).

Independent variables are expected to determine the outcomes of interest.

In an experiment, they are controlled by the researcher through manipulations.

Dependent variables are the outcomes of interest to the researcher and the decision makers.

A famous experiment in the marketing field investigated the influence of brand name on consumers’ taste perceptions.

An experimenter manipulated whether consumers preferred the taste of beer in labeled or unlabeled bottles.

One week respondents were given a six-pack containing bottles labeled only with letters (A, B, C).

The following week, respondents received another six-pack with brand labels (like Budweiser, Coors, Miller, and so forth).

The experimenter measured reactions to the beers after each tasting and observed difference taste. In every case, the beer itself was the same.

Therefore, the differences observed in taste, the key dependent variable, could only be attributable to the difference in labeling.

There is atleast one independent variable (IV) and one dependent variable(DV).

Researcher hypothesizes that in some way the “IV” causes the DV to occur.

The researcher manipulates the independent variable and then observes whether the hypothesized dependent variable is affected by the intervention.

Designing an Experiment toMinimize Experimental Error

Experimental designs involve no less than four important design elements. These issues include

(1) manipulation of the independent variable(s);

(2) selection and measurement of the dependent variable(s);

(3) selection and assignment of experimental subjects; and

(4) control over extraneous variables

Manipulation of the Independent Variable The thing that makes independent variables

special in experimentation is that the researcher actually creates his or her values.

This is how the researcher manipulates, and therefore controls, Independent variables.

Experimental independent variables are hypothesized to be causal influences.

Experimental variables can be categorical as well as continuous variables.

In branding and taste perception experiment variables are categorical.

In sales and advertisement causation, variables are continuous.

The task for the researcher is to select appropriate levels of that variable as experimental treatments.

For example, consumers might not perceive a difference between $1.24 and $1.29, but likely will notice the difference between $1.29 and $2.5

The levels should be noticeably different and realistic.

EXPERIMENTAL AND CONTROL GROUPS

An experimental group is one in which an experimental treatment is administered.

A control group is one in which no experimental treatment is administered.

Experimental treatment -The term referring to the way an experimental variable is manipulated.

The experimental groups, or treatment group, receives the treatment, and it is compared against the control group, which does not.

Selection and Measurement of the Dependent Variable

Unless the dependent variables are relevant and truly represent an outcome of interest, the experiment will not be useful.

In some situations, however, clearly defining the dependent variable is not so easy.

If researchers are experimenting with different forms of advertising appeals, defining the dependent variable may be more difficult.

For example, measures of advertising awareness, recall, changes in brand preference, or sales might be possible dependent variables.

Choosing the right dependent variable is part of the problem definition process.

The experimenter’s choice of a dependent variable determines what type of answer is given to assist managers in decision making.

The introduction of the original Crystal Pepsi illustrates the need to think beyond consumers’ initial reactions.

When Crystal Pepsi was introduced, the initial trial rate was high, but only a small percentage of customers made repeat purchases.

The brand never achieved high repeat sales within a sufficiently large market segment.

Brand awareness, trial purchase, and repeat purchase are all possible dependent variables in an experiment.

The dependent variable therefore should be considered carefully.

Selection and Assignment of Test Units Test units are the subjects or entities

whose responses to the experimental treatment are measured or observed.

Individual consumers, employees, organizational units, sales territories, market segments,or other entities may be the test units.

People, whether as customers or employees, are the most common test units in most organizational behavior, human resources, and marketing experiments.

Randomization Randomization—the random assignment

of subject and treatments to groups—is one device for equally distributing the effects of extraneous variables to all conditions.

These nuisance variables, items that may affect the dependent measure but are not of primary interest, often cannot be eliminated.

However, they will be controlled because they are likely to exist to the same degree in every experimental cell if subjects are randomly assigned.

Matching Matching the respondents on the basis of

pertinent background information is another technique for controlling the extraneous variables by assigning subjects in a way that their characteristics are the same in each group.

This is best thought of in terms of demographic characteristics.

If a subject’s sex is expected to influence dependent variable responses then the researcher may make sure that there are equal numbers of men and women in each experimental cell.

In general, if a researcher believes that certain extraneous variables may affect the dependent variable, he or she can make sure that the subjects in each group are the same on these characteristics.

Control Over Extraneous Variable Systematic error can occur when the

extraneous variables are allowed to influence the dependent variables.

When the extraneous variables have not been controlled or eliminated, the results will be confounded.

A confound means that there is an alternative explanation beyond the experimental variables for any observed differences in the dependent variable.

Once a potential confound is identified, the validity of the experiment is severely questioned.

In a simple experimental group–control group experiment, if subjects in the experimental group are always administered treatment in the morning and subjects in the control group always receive the treatment in the afternoon, a systematic error occurs.

In such a situation, time of day represents a confound.

Since extraneous variables can produce confounded results, they must be identified before the experiment if at all possible.

One issue with significant business and public policy implications is cigarette smoking.

Does cigarette advertising cause young people to smoke?

One of the primary reasons for the inconclusiveness of this debate is the failure for most of the research to control for extraneous variables

For instance, consider a study in which two groups of U.S. high school students are studied over the course of a year.

One is exposed to foreign television media in which American cigarettes are more often shown in a flattering and glamorous light.

The other group is a control group in which their exposure to media is not controlled.

At the end of the year, the experimental group reports a greater frequency and incidence of cigarette smoking.

Did the increased media exposure involving cigarettes cause smoking behavior?

Was the demographic makeup of the two groups the same?

it is well known that different ethnic groups have different smoking rates. Approximately 28 percent of all high school students report smoking, but the rate is higher among Hispanic teens.

Similarly, smoking varies with social class. Were the two groups comprised of individuals from comparable social classes?

Because an experimenter does not want extraneous variables to affect the results, he or she must eliminate or control such variables.

It is always better to spend time thinking about how to control for possible extraneous variables before the experiment

Editing, Coding and Data Analysis

Types of Data Mining Frequency Distribution Descriptive statistics Associative Statistics Difference Statistics Inferential Statistics

Frequency Distribution Simply report the number of responses

received into any category by counting responses in each response category.

Compare numbers in each category by frequencies or percentage frequencies.

Can display by charts. If the data is only nominal, only frequency

distribution can be used. And the analysis just stops here.

Descriptive Statistics If the data under observation are interval-

scaled, then the frequency distribution can be summarized by the following measures;

a) measures of central tendency

b) Measures of dispersion

c)Measures of shape

Associative statistics Procedures investigate systematic

relationship among two or more variables. In general one may be interested in

magnitude and direction of association. Correlation and cross tabulation(chi square,

phi, eta, Cramer V etc) are the usual procedures of associative analysis

Difference Statistics These procedures analyze statistically

significant differences between statistics such as means of a two or more groups in a sample.

A common test is t test, or the analysis of variance.

Inferential Statistics These statistical procedures allow

researchers to draw general conclusion from the sample.

Inferential analysis includes hypotheses testing and estimating the true population values based on sample information.

Multivariate Data Analysis Research that involves three or more

variables, or that is concerned with underlying dimensions among multiple variables, will involve multivariate statistical analysis.

Multivariate statistical methods analyze multiple variables simultaneously.

Classifying Multivariate Techniques

Dependence Techniques When hypotheses involve distinction between independent and dependent variables, dependence techniques are needed.

Predicting the dependent variable “sales” on the basis of numerous independent variables is a problem frequently investigated with dependence techniques.

Multiple regression analysis, multiple discriminant analysis, multivariate analysis of variance,and structural equations modeling are all dependence methods.

Interdependence Techniques When researchers examine questions that do

not distinguish between independent and dependent variables, interdependence techniques are used.

No one variable or variable subset is to be predicted from or explained by the others.

The most common interdependence methods are factor analysis,cluster analysis, and multidimensional scaling.

A manager might utilize these techniques to determine which employee motivation items tend to group together (factor analysis), to identify profitable customer market segments (cluster analysis), or to provide a perceptual map of cities being considered for a new plant (multidimensional scaling).

Influence of Measurement Scales

Which interdependence techniques should I use?

Multiple Regression Analysis Multiple regression analysis is an

extension of simple regression analysis allowing a metric dependent variable to be predicted by multiple independent variables.

In simple regression model one dependent variable (sales) is explained by one independent variable(price).

Yet reality is more complicated and several additional factors probably affect sales.

The simple regression equation can be expanded to represent multiple regression analysis:

Y = b0 + b1X1 + b2X2 + b3X3 + . . . + bnXn.

In general regression analysis requires metric data(interval or ratio).

Less-than interval (nonmetric) independent variables can be used in multiple regression.

This can be done by implementing dummy variable coding.

A dummy variable is a variable that uses a 0 and a 1 to code the different levels of dichotomous variable.

Multiple dummy variables can be included in a regression model

Assume that a toy manufacturer wishes to explain store sales (dependent variable) using a sample of stores from Canada and Europe. Several hypotheses are offered:

H1: Competitor’s sales are related negatively to our firm’s sales.

H2: Sales are higher in communities that have a sales office than when no sales office is present.

H3: competitors price is related positively to sales.

Regression equation: Y = 102.18 + 0.387X1 + 115.2X2 + 6.73X3

All the signs in the equation are positive. Thus, the regression equation indicates that sales are positively related to X1, X2, and X3.

The coefficients show the effect on the dependent variable of a 1-unit increase in any of the independent variables.

REGRESSION COEFFICIENTS IN MULTIPLE REGRESSION in simple regression, the coefficient b1

represents the slope of X on Y. Multiple regression involves multiple slope

estimates, or regression weights. One challenge in regression models is to

understand how one independent variable affects the dependent variable, considering the effect of other independent variables

When the independent variables are related to each other, the regression weight associated with one independent variable is affected by the regression weight of another.

Regression coefficients are unaffected by each other only when independent variables are totally independent.

When the independent variables are related to each other, the regression weight associated with one independent variable is affected by the regression weight of another.

This is called multicollinearity. Most regression programs can compute

variance inflation factors (VIF) for each variable.

As a rule of thumb, VIF above 5.0 suggests problems with multicollinearity

R 2 MULTIPLE REGRESSION The coefficient of multiple determination in

multiple regression indicates the percentage of variation in Y explained by the combination of all independent variables.

For example, a value of R2 = 0.845 means that 84.5 percent of the variance in the dependent variable is explained by the independent variables.

If two independent variables are truly independent (uncorrelated with each other), the R2 for a multiple regression model is equal to the separate R2 values that would result from two separate simple regression models.

Cluster Analysis Cluster analysis is a multivariate approach

for identifying objects or individuals that are similar to one another in some respect.

Cluster analysis classifies individuals or objects into a small number of mutually exclusive and exhaustive groups.

Objects or individuals are assigned to groups so that there is great similarity within groups and much less similarity between groups.

What is Cluster? A group of relatively homogeneous cases or

observations. Similar to one another within the same cluster Dissimilar to the objects in other clusters Cluster analysis Finding similarities between data according to

the characteristics found in the data and grouping similar data objects into clusters

Groupingof data objects such that the objects within a group are similar(or related) to one another and different from (or unrelated to) the objects in other groups

The cluster should have high internal (within-cluster) homogeneity and high external (between-cluster) heterogeneity.

An important tool for the business researcher. For example, an organization may want to

group its employees based on their insurance or retirement needs, or on job performance dimensions.

Similarly, a business may wish to identify market segments by identifying subjects or individuals who have similar needs, lifestyles, or responses to marketing promotions.

For example, imagine four clusters or market segments in the vacation travel industry.

They are: (1) The elite – they want top level service and expect to be pampered;

(2) The escapists – they want to get away and just relax; (3) The educationalist – they want to see

new things, go to museums, have a safari, or experience new cultures;

(4) the sports person – they want the golf course, tennis court, surfing, deep-sea fishing, climbing, etc. Different brochures and advertising is required for each of these.

For example- Vacation behavior is represented on two

dimensions: number of vacation days and dollar expenditures on vacations during a given year.

The scatter diagram represents the distance between each individual in two-dimensional space.

The diagram portrays three clear-cut clusters. The first subgroup—consisting of individuals

L, H, and B—suggests a group of individuals who have many vacation days but do not spend much money on their vacations.

The second cluster—represented by individuals A, I, K, G, and F—represents intermediate values on both variables: average amounts of vacation days and average dollar expenditures on vacations.

The third group—individuals C, J, E, and D—consists of individuals who have relatively few vacation days but spend large amounts on vacations.

The logic of cluster analysis is to group individuals or objects by their similarity to or distance from each other

Managers frequently are interested in finding test-market cities that are very similar so that no extraneous variation will cause differences between the experimental and control markets.

In this study the objects to be clustered were cities.

The characteristics of the cities, such as population, retail sales, number of retail outlets, and percentage of nonwhites, were used to identify the groups.

Cities such as Omaha, Oklahoma City, Dayton, Columbus, and Fort Worth were similar

And cities such as Newark, Cleveland, Pittsburgh, Buffalo, and Baltimore were similar, but individual cities within each group were dissimilar to those within other groups or clusters.

The purpose of cluster analysis is to determine how many groups really exist and to define their composition.

Factor Analysis Factor analysis is a prototypical

multivariate, interdependence technique. Factor analysis is a technique of

statistically identifying a reduced number of factors from a larger number of measured variables

Factors are usually latent constructs like attitude satisfaction, personality etc.

A researcher need not distinguish between independent and dependent variables to conduct factor analysis.

Factor Analysis is a data reduction technique

Multivariate statistical approach that summarizes the information from many variables into a reduced set of variables.

Rule of parsimony The rule of parsimony suggests that an

explanation involving fewer components is better than one involving more.

The Big five personality traits were extracted using Factor Analysis.(OCEAN).

Beneath each factor, a cluster of correlated specific traits is found.

 For example, extraversion includes such related qualities as gregariousness, assertiveness, excitement seeking, warmth, activity etc

A common usage of FA is in developing objective instruments for measuring constructs which are not directly observable.

Factor analysis can be divided into two types: 1. Exploratory factor analysis (EFA)—

performed when the researcher is uncertain about how many factors may exist among a set of variables.

2. Confirmatory factor analysis (CFA)—performed when the researcher has strong theoretical expectations about the factor structure (number of factors and which variables relate to each factor) before performing the analysis.

Suppose a researcher is asked to examine how feelings of nostalgia in a restaurant influence customer loyalty.

Three hundred fifty customers at restaurants around the country are interviewed and asked to respond to the following Likert scales (1 = Strongly Disagree to 7 = Strongly Agree):

X1—I feel a strong connection to the past when I am in this place.

X2—This place evokes memories of the past. X3—I feel a yearning to relive past

experiences when I dine here. X4—This place looks like a page out of the

past. X5—I am willing to pay more to dine in this

restaurant. X6—I feel very loyal to this establishment. X7—I would recommend this place to others. X8—I will go out of my way to dine here.

EFA provides two important pieces of information:

1. How many factors exist among a set of variables?

2. What variables are related to or “load on” which factors?

Factor Loadings Correlation between a variable and a

factor. A factor loading indicates how strongly

correlated a measured variable is with that factor. In other words, to what extent does a variable “load” on a factor?

EFA depends on the loadings for proper interpretation.

A latent construct can be interpreted based on the pattern of loadings and the content of the variables.

In this way, the latent construct is measured indirectly by the variables.

The thick arrows indicate high loading estimates and the thin dotted lines correspond to weak loading estimates.

Factors are interpreted by examining any patterns that emerge from the factor results. Here, a clear pattern emerges.

The first four variables produce high loadings on factor 1 and the last four variables produce high loadings on factor 2.

The first four variables all have content consistent with nostalgia and the second four variables all have content consistent with customer loyalty, the two factors can easily be labeled.

Factor one represents the latent construct nostalgia and factor 2 represents the latent construct customer loyalty.

Factor Analysis in SPSS In SPSS, the click-through sequence is as

follows: ● ANALYZE ● DATA REDUCTION ● FACTOR ANALYSIS This produces a dialog box. Now follow the

steps below to get results : ● Highlight variables X1 to X8 (either

individually or in multiples).

Click the to move them into the “Variables” ▶

window. ● Click “ROTATION.” ● Select VARIMAX. ● Optional: Click “OPTIONS.” ● Select “SORTED BY SIZE.” ● Select “SUPPRESS ABSOLUTE VALUES

LESS THAN.” Click “CONTINUE.” ● Click “OK.”

Quoting Reference/Bibliography. Generally, a reference list contains only those

sources researcher actually referred to in his research.

So, for each resource on researcher’s list, there will be some citation in his research.

For Example …………expected to increase to USD 750-

850 billion by 2015 (Deloitt,2013). Deloitte (2013),Indian Retail Market Opening

more doors, Deloitte, January 2013.

A bibliography contains those sources researcher consulted but didn't actually cite in his research + reference.

APA Style: Handling Quotations, Citations, and References In-Text Quotations When using APA format, follow the author-

date method of citation.

This means that the author's last name and the year of publication for the source should appear in the text, and a complete reference should appear in the reference list.

Examples:

Smith (1970) compared reaction times . . . In a recent study of reaction times (Smith,

1970), . . . In 1970, Smith compared reaction times . . .

Short Quotations

To indicate short quotations (fewer than 40 words) in the text, enclose the quotation within double quotation marks.

Provide the author, year, and specific page citation in the text, and include a complete reference in the reference list.

Examples: She stated, "The placebo effect disappeared

when behaviors were studied in this manner" (Miele, 1993, p. 276),but she did not clarify which behaviors were studied.

According to Miele (1993), "The placebo effect disappeared when behaviors were studied in this manner" (p.276).

Miele (1993) found that "the placebo effect disappeared" in this case (p. 276), but what will the next step in researching this issue be?

A long quotation A quotation of 40 or more words should be

formatted as a freestanding, indented block of text without quotation marks.

Weston (1948) argues that:

One of the most important phases of our special guests was to get information that would throw light on degeneration of the facial pattern that occurs so often in our modern civilization. This has its expression in the narrowing and lengthening of the face and the development of crooked teeth. (p. 174)

A quotation with no page numbers If you quote from online material and there

are no page numbers (e.g. HTML based document), use the paragraph number (para.) instead.

"Prevalence rates of antenatal major and minor depression have been estimated in community-based studies to range from 7% to 15% of all pregnancies" (Grote, Swartz, Geibel & Zuckoff, 2009, para. 2).

Citing from a secondary source

When you find a quote (e.g. Arnett) within in a work that you have read (e.g. Claiborne & Drewery) and you wish to refer to the original quote (Arnett), this is called citing from a secondary source.

Arnett (2000, as cited in Claiborne & Drewery, 2010) suggests there is an emerging adult stage in the lifespan of humans, covering young people between the ages of 18 and 25 years.

APA Reference Style Your reference list should appear at the end

of your essay. It provides the information necessary for a reader to locate and retrieve any source you cite in the essay.

Each source you cite in the essay must appear in your reference list; likewise, each entry in the reference list must be cited in your text.

Some Basic rules

All references should be double-spaced. Capitalize only the first word of a title or

subtitle of a work. Italicize titles of books and journals. Each entry is separated from the next by a

double space Authors' names are inverted (last name first)

Your reference list should be alphabetized by authors' last names.

If you have more than one work by a particular author, order them by publication date, oldest to newest (thus a 1991 article would appear before a 1996 article).

Basic Forms for Sources in Print An article in a periodical (such as a

journal, newspaper, or magazine). Author, A. A., Author, B. B., & Author, C. C.

(Year of Publication, add month and day of publication for daily, weekly, or monthly publications). Title of article. Title of periodical, Volume Number, pages.

Ex- Harlow, H. F. (1983). Fundamentals for

preparing psychology journal articles. Journal of Comparative and Physiological Psychology, 55, 893-896.

Magazine/Newspaper Henry, W. A., III. (1990, April 9). Making the

grade in today's schools. Time, 135, 28-31.

A non periodical (such as a book, report, et c)

Author, A. A. (Year of Publication). Title of work. Location: Publisher.

Ex- Calfee, R. C., & Valencia, R. R. (1991). APA

guide to preparing manuscripts for journal publication. Washington,DC: American Psychological Association.

A government publication National Institute of Mental Health. (1990).

Clinical training in serious mental illness Washington, DC: U.S. Government Printing Office.

Part of a non-periodical (such as a book chapter or an article in a collection)

Author, A. A., & Author, B. B. (Year of Publication). Title of chapter. In A. Editor & B. Editor (Eds.), Title of book (pages of chapter). Location: Publisher.

Ex-O'Neil, J. M., & Egan, J. (1992). Men's and women's gender role journeys: Metaphor for healing, transition, and transformation. In B. R. Wainrib (Ed.), Gender issues across the life cycle (pp. 107-123). New York: Springer.

Basic Forms for Electronic Sources A web page Author, A. A., & Author, B. B. (Date of

Publication or Revision). Title of full work [online]. Retrieved month, day, year, from source Web site: URL.

Chou, L., McClintock, R., Moretti, F. & Nix, D. H. (1993.) Technology and education: New wine in new bottles:Choosing pasts and imagining educational futures. Retrieved August 24, 2000, from Columbia University, Institute for Learning Technologies Web site:

http://www.ilt.columbia.edu/publications/papers/newwine1.html.

An online journal or magazine

Author, A. A., & Author, B. B. (Date of Publication). Title of article. Title of periodical, Retrieved month, day, year, from URL..

Frederickson, B. L. (2000, March 7). Cultivating positive emotions to optimize health and well-being. Prevention & Treatment, 3 Article. Retrieved November 20, 2000, from http://journals.apa.org/prevention/volume3/pre0030001a.html