Business Research

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BUSINESS RESEARCH.

Business Research is the process of systematic and indepth study or search for a solution to a problem or an answer to a

question backed by collection, compilation, presentation, analysis and interpretation of relevant details, data and

information. It is also a systematic endeavour to discover valuable facts or relationships.

Definition: Business Research may be defined as the “systematic and objective process of gathering, recording and

analyzing data for aid in making business decisions”.

Systematic-ness and Objectivity are its distinguishing features of Business Research, which is important tool for

managers and decision-makers in corporate and non-corporate organizations

When is Business Research Used?

Business research methods are used in situations of uncertainty, that is, when decision-makers face two or more

courses of action and seek to select the best possible alternative under the circumstances. Business Research is hence

aimed at improving the quality of decision-making which, in turn, benefits the organization and helps ensure its continuity

and efficiency.

Applications

Research applications in marketing

Market & consumer analysis

Product research

Pricing research

Promotional research

Place research

Research applications in finance

Asset pricing, capital markets and corporate finance

Financial derivatives and credit risk modeling research

Market-based accounting research

Auditing and accountability

Other areas: financial forecasting, behavioural finance, volatility analysis

Research applications in human resources

Training & development studies


Selection and staffing studies

Performance appraisal–design and evaluation

Organization planning and development

Incentive and benefits studies

Emerging areas–critical factor analysis, employer branding studies

Research applications in production & operations management

Operation planning and design

Demand forecasting and demand estimation

Process planning

Project management and maintenance effectiveness studies

Logistics and supply chain-design and evaluation

Quality estimations and assurance studies

Unit-1

Differentiate between hypothesis and proposition

The terms proposition and hypothesis both refer to the formulation of a possible answer to a specific scientific question. In

particular, a proposition deals with the connection between two existing concepts. The main difference between the two is

that a hypothesis must be testable, measurable and falsifiable, while a proposition deals with pure concepts for which no

laboratory test is currently available.

Hypotheses and the Scientific Method

Forming a hypothesis is the initial step in developing a theory under the scientific method. It is an educated guess

based on research and working knowledge. For a hypothesis to be considered valid, it must make a prediction that

scientists can test using a repeatable experiment. If a hypothesis cannot be falsified through experimentation, it

cannot be considered part of a valid scientific theory.

Scientific Propositions

A proposition is similar to a hypothesis, but its main purpose is to suggest a link between two concepts in a

situation where the link cannot be verified by experiment. As a result, it relies heavily on prior research, reasonable

assumptions and existing correlative evidence. A scientist can use a proposition to spur further research on a

question or pose one in hopes that further evidence or experimental methods will be discovered that will make it a

testable hypothesis.


Valid Uses for Propositions

Propositions can serve an important role in the scientific process. By suggesting a link between two concepts, a

scientific proposition can suggest promising areas of inquiry for researchers. In areas of study where valid

hypotheses can rarely be made, a proposition may serve as a common assumption that can support further

speculation. This can occur in extremely complex systems, such as those dealt with by sociology and economics,

where an experimental test would be prohibitively expensive or difficult. Propositions are also valuable in areas of

study in which little hard evidence remains, such as archeological and paleontological studies in which only

fragments of evidence have been discovered.

Drawbacks of Propositions

Since a proposition does not rely on testable data, it is more difficult to disprove in a scientific context. It only

needs to be convincing and internally consistent to appear valid. Propositions that satisfy both of these conditions

have nevertheless been found to be wrong or inaccurate when new testable data becomes available. Belief in

propositions that have been commonly accepted for long periods of time may be extremely difficult to overcome,

even if other researchers put more likely propositions forward.

List the six W’s of descriptive research and describe each by giving an example.(pg-83 nkm)

Compare and contrast between quantitative and qualitative methods of data collection

Why is the literature reviewing an important part of the research process? What is the procedure for reviewing the

literature?

Importance of literature review in research

Bring clarity and focus to your research problem

Improve your methodology

Broaden your knowledge base in your research area

Contextualize your findings

Procedure for reviewing the literature

Search existing literature in your area of study

Review the literature selected

Develop a theoretical framework

Develop a conceptual frame work

Hypothesis

A proposition, condition, or principle which is assumed, perhaps without belief, in order to draw out its logical

consequences and by this method to test its accord with facts which are known or may be determined.


A proposition that is stated in a testable form ands that predicts a particular relationship between two (or more)

variables. In other words , if we think that a relationship exits, we first state it as a hypothesis and then test the

hypothesis(Bailey,1978)

A hypotheses is any assumption/presupposition that the researcher makes about the probable direction of the

results that might be obtained on the completion of the research process

Descriptive hypotheses: This is simply a statement about the magnitude, trend, or behaviour of a population under

study.

Relational hypotheses: These are the typical kind of hypotheses which state the expected relationship between

two variables.

Characteristics of Hypothesis

A hypothesis should be simple, specific and conceptually clear.

A hypothesis should be capable of verification.

A hypothesis should be related to the existing body of knowledge.

A hypothesis should be operationalisable


Errors in testing a hypothesis

Type I Error: Rejection of a null hypothesis when it is true.

Type II Error: Acceptance of a null hypothesis when it is false.

UNIT II

What are the reasons that respondents are unwilling to answer certain questions?

The respondent may not be fully informed

The respondent may not remember

He may be unable to express or articulate

The respondent may be unwilling to answer due to-

There may be sensitive information which may cause embarrassment or harm the respondent’s image.

The respondent may not be familiar with the genuine purpose

The question may appear to be irrelevant to the respondent

The respondent will not be willing to reveal traits like aggressiveness (For instance - if he is asked “Do you hit your

wife, sister”, etc.)

Define Validity and reliability. Explain the relationship between the two


Unit-3

Sampling

• Sampling is the process of selecting a small number of elements from a larger defined target group of elements

such that

the information gathered from the small group will allow judgments to be made about the larger groups .

Sampling Terminology(book pg 165 Came In Xam )

1.Population :

• The class ,families from which we select students ,families to questions in order to find answer to research

questions are called population .

2.Sample : small group of students ,families from which you collect the required information .

3.Sample Size :Number of students ,families ,it is represented by n.

4.Sampling Unit : Each student,familly that becomes the basis for selecting your sample is called sampling Unit .

Elementary units or group of such units which besides being clearly defined, identifiable and observable, are convenient for

purpose of sampling are called sampling units. For instance, in a family budget enquiry, usually a family is considered as the


sampling unit since it is found to be convenient for sampling and for ascertaining the required information. In a crop

survey, a farm or a group of farms owned or operated by a household may be considered as the sampling unit.

5. Sampling Frame : A list of population elements (people, companies, houses, cities, etc.) from which units to be sampled

can be selected . In statistics, a sampling frame is the source material or device from which a sample is drawn.[1] It is a list

of all those within a population who can be sampled, and may include individuals, households or institutions. A list of all

the sampling units belonging to the population to be studied with their identification particulars or a map showing the

boundaries of the sampling units is known as sampling frame. Examples of a frame are a list of farms and a list of suitable

area segments like villages in India or counties in the United States. The frame should be up to date and free from errors of

omission and duplication of sampling units.

Types of Sampling

1.Probability Sampling :

a) Simple random Sampling

b) Systematic random Sampling

c) Stratified random Sampling

d) Cluster Sampling

2.Non probability Sampling :

a) Convenience Sampling

b) Judgment Sampling

c) Quota Sampling

d) Snowball Sampling


• Simple random sampling

• Systematic random sampling

• Stratified random sampling

• Cluster sampling

Difference between Research Proposal and Research Report

• While a research proposal is the beginning of a research, research report can be considered its culmination

• Research proposal is a serious document as the approval of research topic and the researcher hinges upon its

presentation and as such any student desirous of pursuing research.

• Research report is also an important document that reflects the effort put in by the student and should be prepared with

sincerity and simplicity in a prescribed format.

• While chosen subject and identified problem are more important in a research proposal, the experimental results and

methodology assume significance in the case of research report

A Research proposal is the out line of proposed research which is goning to be conduct but the Research Report is detailed

informatotion about allraedy conducted research.

Coding

is an interpretive technique that both organizes the data and provides a means to introduce the interpretations of it into

certain quantitative methods. Most coding requires the analyst to read the data and demarcate segments within it, which

may be done at different times throughout the process.[7] Each segment is labeled with a "code" – usually a word or short

http://en.wikipedia.org/wiki/Qualitative_research#cite_note-7


phrase that suggests how the associated data segments inform the research objectives. When coding is complete, the

analyst prepares reports via a mix of: summarizing the prevalence of codes, discussing similarities and differences in related

codes across distinct original sources/contexts, or comparing the relationship between one or more codes.

Some qualitative data that is highly structured (e.g., close-end responses from surveys or tightly defined interview

questions) is typically coded without additional segmenting of the content. In these cases, codes are often applied as a

layer on top of the data. Quantitative analysis of these codes is typically the capstone analytical step for this type of

qualitative data.

Contemporary qualitative data analyses are sometimes supported by computer programs, termed Computer Assisted

Qualitative Data Analysis Software. These programs do not supplant the interpretive nature of coding but rather are aimed

at enhancing the analyst’s efficiency at data storage/retrieval and at applying the codes to the data. Many programs offer

efficiencies in editing and revising coding, which allow for work sharing, peer review, and recursive examination of data.

A frequent criticism of coding method is that it seeks to transform qualitative data into empirically valid data, which

contain: actual value range, structural proportion, contrast ratios, and scientific objective properties; thereby draining the

data of its variety, richness, and individual character. Analysts respond to this criticism by thoroughly expositing their

definitions of codes and linking those codes soundly to the underlying data, therein bringing back some of the richness that

might be absent from a mere list of codes.

RESEARCH REPORT PREPARATION AND PRESENTATION(some from book pg-266)

• A research report is:

– a written document or oral presentation based on a written document that communicates the purpose,

scope, objective(s), hypotheses, methodology, findings, limitations and finally, recommendations of a

research project to others.

– The last stage of a marketing research process.;

– It is more than a summary of findings; rather it is a record of the research process.

• The researcher has to convince the client [and others who may read the report] that the research findings can be

acted on for their own benefit.

Types of research reports

Brief reports

- working papers/basic reports

- survey reports

Detailed reports

Technical reports

Business reports

http://en.wikipedia.org/wiki/Computer_Assisted_Qualitative_Data_Analysis_Software

http://en.wikipedia.org/wiki/Computer_Assisted_Qualitative_Data_Analysis_Software


REPORT PREPARATION AND PRESENTATION PROCESS

Report structure


Oral Presentation

• Should be carefully prepared keeping the audience in mind.

• A good presentation does not mean a lengthy presentation.

• Carefully selected visual aids such as graphs, tables, charts, maps etc. help presentation.

– However, Too many visual aids, particularly statistical tables, could often be boring and may not serve any

purpose.

• During oral presentation, people may seek clarification.

– The speaker must be patient and should not show signs of anger or frustration. He or she should be natural,

establish eye contact with the audience, and interact with them.


– Body language and descriptive gestures are also quite useful.

Hypothesis Testing

What is a Hypothesis?

A hypothesis is an assumption or a statement that may or may not be true.

The hypothesis is tested on the basis of information obtained from a sample.

Hypothesis tests are widely used in business and industry for making decisions.

Instead of asking, for example, what the mean assessed value of an apartment in a multistoried building is, one

may be interested in knowing whether or not the assessed value equals some particular value, say Rs 80 lakh.

Some other examples could be whether a new drug is more effective than the existing drug based on the sample

data, and whether the proportion of smokers in a class is different from 0.30.

Hypothesis testing is the use of statistics to determine the probability that a given hypothesis is true. A process by which an

analyst tests a statistical hypothesis. The methodology employed by the analyst depends on the nature of the data used,

and the goals of the analysis. The goal is to either accept or reject the null hypothesis. Procedure for deciding if a null

hypothesis should be accepted or rejected in favor of an alternate hypothesis. A statistic is computed from a survey or test

result and is analyzed to determine if it falls within a preset acceptance region. If it does, the null hypothesis is accepted

otherwise rejected.

Concepts in Testing of Hypothesis

Null hypothesis: The hypotheses that are proposed with the intent of receiving a rejection for them are called null

hypotheses. This requires that we hypothesize the opposite of what is desired to be proved. For example, if we

want to show that sales and advertisement expenditure are related, we formulate the null hypothesis that they are

not related. Null hypothesis is denoted by H0.

Alternative hypothesis: Rejection of null hypotheses leads to the acceptance of alternative hypotheses. The

rejection of null hypothesis indicates that the relationship between variables (e.g., sales and advertisement

expenditure) or the difference between means (e.g., wages of skilled workers in town 1 and town 2) or the

difference between proportions have statistical significance and the acceptance of the null hypotheses indicates

that these differences are due to chance. Alternative hypothesis is denoted by H1.

One-tailed and two-tailed tests: A test is called one-sided (or one-tailed) only if the null hypothesis gets rejected

when a value of the test statistic falls in one specified tail of the distribution. Further, the test is called two-sided

(or two-tailed) if null hypothesis gets rejected when a value of the test statistic falls in either one or the other of

the two tails of its sampling distribution.

Type I and type II error: if the hypothesis H0 is rejected when it is actually true, the researcher is committing what

is called a type I error. The probability of committing a type I error is denoted by alpha (α). This is termed as the

level of significance. Similarly, if the null hypothesis H0 when false is accepted, the researcher is committing an


error called Type II error. The probability of committing a type II error is denoted by beta (β). The expression 1 – β

is called power of test.

Steps(pg-464 narwsh malhotra)

DISCRIMINANT ANALYSIS

Discriminant analysis is used to predict group membership.

This technique is used to classify individuals/objects into one of the alternative groups on the basis of a set of

predictor variables.

The dependent variable in discriminant analysis is categorical whereas the independent or predictor variables are

either interval or ratio scale in nature.

When there are two groups (categories) of dependent variable, we have two-group discriminant analysis and when

there are more than two groups, it is a case of multiple discriminant analysis.

Objectives of Discriminant Analysis

The objectives of discriminant analysis are the following:

To find a linear combination of variables that discriminate between categories of dependent variable in the best

possible manner.

To find out which independent variables are relatively better in discriminating between groups.

To determine the statistical significance of the discriminant function and whether any statistical difference exists

among groups in terms of predictor variables.

To develop the procedure for assigning new objects, firms or individuals whose profile but not the group identity

are known to one of the two groups.

To evaluate the accuracy of classification, i.e., the percentage of customers that it is able to classify correctly.

Some of the uses of Discriminant Analysis are:

Scale construction: Discriminant analysis is used to identify the variables/statements that are discriminating and on

which people with diverse views will respond differently.

Perceptual mapping: The technique is also used extensively to create attribute-based spatial maps of the

respondent’s mental positioning of brands.

Segment discrimination: To understand what are the key variables on which two or more groups differ from each

other, this technique is extremely useful. Questions to which one may seek answers are as follows:

What are the demographic variables on which potentially successful salesmen and potentially

unsuccessful salesmen differ?

What are the variables on which users/non-users of a product can be differentiated?


What are the economic and psychographic variables on which price-sensitive and non-price

sensitive customers be differentiated?

What are the variables on which the buyers of local/national brand of a product be differentiated?

The mathematical form of the discriminant analysis model is:

Where,

Y = Dependent variable

bs = Coefficients of independent variables

Xs = Predictor or independent variables

• Dependent Variable Y should be a categorized variable whereas the independent variables Xs should be continuous.

(interval or ratio scale)

• Dependent variable should be coded as 0, 1 or 1, 2 in case of two-group discriminant model.

ANALYSIS OF VARIANCE TECHNIQUES(ANOVA)

The test of hypothesis concerning the equality of two population means makes use of both the Z and t tests.

However, if there are more than two populations, the test for the equality of means could be carried out by

considering two populations at a time. This would be a very cumbersome procedure.

One easy way out could be to use the analysis of variance (ANOVA) technique. The technique helps in performing

this test in one go and, therefore, is considered to be important technique of analysis for the researcher.

The basic principle underlying the technique is that the total variation in the dependent variable is broken into two

parts—one which can be attributed to some specific causes and the other that may be attributed to chance.

The one which is attributed to the specific causes is called the variation between samples and the one which is

attributed to chance is termed as the variation within samples.

Therefore, in ANOVA, the total variance may be decomposed into various components corresponding to the

sources of the variation.

For eg. the sales of chairs could differ because of the various styles and sizes of stores selling them. The average

telephone bill of the households could be different because they belong to different income groups and so on.


In ANOVA, the dependent variable in question is metric (interval or ratio scale), whereas the independent variables

are categorical (nominal scale).

If there is one independent variable (one factor) divided into various categories, we have one-way or one-factor

analysis of variance.

In the two-way or two-factor analysis of variance, two factors each divided into the various categories are involved.

FACTOR ANALYSIS

Factor analysis is a multivariate statistical technique in which there is no distinction between dependent and

independent variables.

In factor analysis, all variables under investigation are analysed together to extract the underlined factors.

Factor analysis is a data reduction method.

It is a very useful method to reduce a large number of variables resulting in data complexity to a few manageable

factors.

These factors explain most part of the variations of the original set of data.

A factor is a linear combination of variables.

It is a construct that is not directly observable but that needs to be inferred from the input variables.

The factors are statistically independent.

Factor analysis originated in psychometrics, and is used in behavioral sciences, social sciences, marketing, product

management, operations research, and other applied sciences that deal with large quantities of data.

Uses of Factor Analysis

Scale construction: Factor analysis could be used to develop concise multiple item scales for measuring various

constructs.

Establish antecedents: This method reduces multiple input variables into grouped factors. Thus, the independent

variables can be grouped into broad factors.

Psychographic profiling: Different independent variables are grouped to measure independent factors. These are

then used for identifying personality types.

Segmentation analysis: Factor analysis could also be used for segmentation. For example, there could be different

sets of two-wheelers-customers owning two-wheelers because of different importance they give to factors like

prestige, economy consideration and functional features.

Marketing studies: The technique has extensive use in the field of marketing and can be successfully used for new

product development; product acceptance research, developing of advertising copy, pricing studies and for

branding studies.


For example we can use it to:

• identify the attributes of brands that influence consumers’ choice;

• get an insight into the media habits of various consumers;

• identify the characteristics of price-sensitive customers.

Conditions for a Factor Analysis Exercise

The following conditions must be ensured before executing the technique:

Factor analysis exercise requires metric data. This means the data should be either interval or ratio scale in nature.

The variables for factor analysis are identified through exploratory research which may be conducted by reviewing

the literature on the subject, researches carried out already in this area, by informal interviews of knowledgeable

persons, qualitative analysis like focus group discussions held with a small sample of the respondent population,

analysis of case studies and judgement of the researcher.

As the responses to different statements are obtained through different scales, all the responses need to be

standardized. The standardization helps in comparison of different responses from such scales.

The size of the sample respondents should be at least four to five times more than the number of variables

(number of statements).

The basic principle behind the application of factor analysis is that the initial set of variables should be highly

correlated. If the correlation coefficients between all the variables are small, factor analysis may not be an

appropriate technique.

Steps in a Factor Analysis Exercise

There are basically two steps that are required in a factor analysis exercise.

Extraction of factors:

The first and the foremost step is to decide on how many factors are to be extracted from the given set of data.

The principal component method is discussed very briefly here.

As we know that factors are linear combinations of the variables which are supposed to be highly correlated, the

mathematical form of the same could be written as

The second step in the factor analysis exercise is the rotation of initial factor solutions. This is because the initial

factors are very difficult to interpret. Therefore, the initial solution is rotated so as to yield a solution that can be

interpreted easily.

The varimax rotation method is used.


Applications of Factor Analysis in other Multivariate Techniques

1. Multiple regression – Factor scores can be used in place of independent variables in a multiple regression

estimation. This way we can overcome the problem of multicollinearity.

2. Simplifying the discrimination solution – A number of independent variables in a discriminant model can be

replaced by a set of manageable factors before estimation.

3. Simplifying the cluster analysis solution - To make the data manageable, the variables selected for clustering can

be reduced to a more manageable number using a factor analysis and the obtained factor scores can then be used

to cluster the objects/cases under study.

4. Perceptual mapping in multidimensional scaling - Factor analysis that results in factors can be used as dimensions

with the factor scores as the coordinates to develop attribute-based perceptual maps where one is able to

comprehend the placement of brands or products according to the identified factors under study.

CLUSTER ANALYSIS(diag at pg641,643,644 NKM)

Cluster analysis is a techniques for grouping objects, cases, entities on the basis of multiple variables. The

advantage of the technique is that it is applicable to both metric and non-metric data.

Secondly, the grouping can be done post hoc , i.e. after the primary data survey is over. The technique has wide

applications in all branches of management . However, it is most often used for market segmentation analysis.

Can be used to cluster objects, individuals and entities

Similarity is based on multiple variables

Measures proximity between study variables

Groups that are grouped in one cluster are homogenous as compared to others

Can be conducted on metric, non-metric as well as mixed data

Usage of cluster analysis

Market segmentation – customers/potential customers can be split into smaller more homogenous groups

by using the method.

Segmenting industries – the same grouping principle can be applied for industrial consumers.

Segmenting markets – cities or regions with similar or common traits can be grouped on the basis of

climatic or socio-economic conditions.

Career planning and training analysis – for human resource planning people can be grouped into clusters

on the basis of their educational/experience or aptitude and aspirations.

Segmenting financial sector/instruments – different factors like raw material cost, financial allocations,

seasonality and other factors are being used to group sectors together to understand the growth and

performance of a group of industries.


Statistics associated with cluster analysis


Cluster analysis process

Multidimensional Scaling and Conjoint Analysis

Multidimensional scaling (MDS) is a class of procedures for representing perceptions and preferences of

respondents spatially by means of a visual display.

Perceived or psychological relationships among stimuli are represented as geometric relationships among points in

a multidimensional space.

These geometric representations are often called spatial maps. The axes of the spatial map are assumed to denote

the psychological bases or underlying dimensions respondents use to form perceptions and preferences for stimuli.

MDS is only one of the techniques that can be used for perceptual mapping.

The inputs obtained could be for objects, individuals, brands, corporations or countries.

As a thumb rule, objects are grouped together.

The grouped objects are usually evaluated and compared


with each other so that they can coexist on a spatial map.

The basis of evaluation is that objects exist not in

unidimensional but multidimensional space.

The basis of evaluation might be on defined dimensions.

The basis of evaluation might be on perceived /subjective dimensions.

The basis of evaluation could be on similarity/ dissimilarity or preferences.

Uses of Multidimensional Scaling

Scale construction: Based on similarity or preference data the obtained dimensions can be reproduced as attributes

in a structured- attribute- based questionnaire to validate the existence of the parameters of comparison.

Brand image analysis: To measure the gap or match between brand positioning and brand perception.

New product development: To identify quadrants that are less

crowded and where a launch opportunity exists.

Pricing studies: Spatial maps with and without the price dimension can be made to assess the relevance of

price/benefit trade off.

Communication effectiveness: Before and after spatial maps can be made to measure new advertising impact or

repositioning exercise.


Establishing the strength of the MDS solution

The Kruskal Stress score, i.e. the discrepancy scores obtained between the derived distances on a configured map

and the actual distance as indicated by the respondents’ choice.

The ideal representation would be a stress value of 0%. However, it is acceptable to consider a solution till a 20%

stress between the actual and the derived configuration.

The R-square value: measures the proportion of the variance of the final scaled solution that can be accounted for

by the MDS procedure.

The ideal would be 1. However, an R-square value of 0.6 or above is acceptable.

Split half technique: obtained by splitting the entire sets of obtained responses into two groups and the MDS

obtained by the two groups should more or less match with each other.

Test-retest: the same group could be measured at different intervals of time to see if the spatial maps stay

constant over a time period.

The leave-one-out technique or eliminating one brand to measure the resulting spatial map is another way of

observing the consistency of results.

Assumptions and Limitations of MDS

It is assumed that the similarity of stimulus A to B is the same as the similarity of stimulus B to A.

MDS assumes that the distance (similarity) between two stimuli is some function of their partial similarities on each

of several perceptual dimensions.

When a spatial map is obtained, it is assumed that interpoint distances are ratio scaled and that the axes of the

map are multidimensional interval scaled.


A limitation of MDS is that dimension interpretation relating physical changes in brands or stimuli to changes in the

perceptual map is difficult at best.

Relationship Among MDS, Factor Analysis,and Discriminant Analysis

If the attribute-based approaches are used to obtain input data, spatial maps can also be obtained by using factor

or discriminant analysis.

By factor analyzing the data, one could derive for each respondent, factor scores for each brand. By plotting brand

scores on the factors, a spatial map could be obtained for each respondent. The dimensions would be labeled by

examining the factor loadings, which are estimates of the correlations between attribute ratings and underlying

factors.

To develop spatial maps by means of discriminant analysis, the dependent variable is the brand rated and the

independent or predictor variables are the attribute ratings. A spatial map can be obtained by plotting the

discriminant scores for the brands. The dimensions can be labeled by examining the discriminant weights, or the

weightings of attributes that make up a discriminant function or dimension.

Conjoint Analysis

Conjoint analysis attempts to determine the relative importance consumers attach to salient attributes and the

utilities they attach to the levels of attributes.

The respondents are presented with stimuli that consist of combinations of attribute levels and asked to evaluate

these stimuli in terms of their desirability.

Conjoint procedures attempt to assign values to the levels of each attribute, so that the resulting values or utilities

attached to the stimuli match, as closely as possible, the input evaluations provided by the respondents.

Assumptions and Limitations of Conjoint Analysis


Conjoint analysis assumes that the important attributes of a product can be identified.

It assumes that consumers evaluate the choice alternatives in terms of these attributes and make tradeoffs.

The tradeoff model may not be a good representation of the choice process.

Another limitation is that data collection may be complex, particularly if a large number of attributes are involved

and the model must be estimated at the individual level.

The part-worth functions are not unique.

Education

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