Research In Business

Research in Business

Why Study Research? – Research provides you with the knowledge and

skills needed for the fast-paced decisionmaking environment – Search for knowledge through objective and systematic method of finding solution to the problem is research – Systematic approach concerns generalization and formulation

Why managers need Better Information  Global

and domestic competitions is more vigorous – Organizations are increasingly practicing data mining

and data warehousing- data warehousing market consists of tools, technologies, and methodologies that allow for the construction, usage, management, and maintenance of the hardware and software used for a data warehouse, as well as the actual data itself. To discover patterns and trends in the data

The Value of Acquiring Skills  To

gather more information before selecting a course of action  To do a high-level research study  To understand research design  To evaluate and resolve a current management dilemma  To establish a career as a research specialist

Different Styles of Research  2. 3. 4. 5.    

Applied Research( action) Improved agricultural production Treat or cure a specific disease Improve energy efficiency of homes ,offices or modes of transporation Global overpopulation Pure Research(generations and formulations) Quantitative and qualitative research Descriptive and analytical research Conceptual and emperical reseach

What is Good Research?  Following

the standards of the scientific method:

– Purpose clearly defined-problem involved and decision – – – –

made Research process detailed-permit others Research design thoroughly planned-yeild accurate results High ethical standards applied –standards and norms Limitations frankly revealed

What is Good Research?  Following

the standards of the scientific method (cont.) – Adequate analysis for decision-maker’s needs – Findings presented unambiguously –clear

logical,executive summary – Conclusions justified-conclusions matched with detailed finding – Researcher’s experience reflected

Role of buisness research in decision making  Problem

identification

 Problem

prioritization

 Problem

resolution

 Implementing

course of action

Factors affecting business research  Time

constraint

 Availability

of resources

 Nature

of information sought

 Benefit

versus cost

Scientific Thinking

The Essential Tenets of Science  Direct

observation of phenomena  Clearly defined variables, methods, and procedures  Empirically testable hypotheses  Ability to rule out rival hypotheses-more facts or greater variety or scope  Statistical justification of conclusions  Self-correcting process

Ways to Communicate  Exposition – Descriptive statements that merely state and so

not give reason  Argument – Allows us to explain, interpret, defend,

challenge, and explore meaning

Important Arguments in Research  Deduction

is a form of inference that purports to conclusive  Induction draws conclusions form one or more particular facts

The Building Blocks of Theory  Concepts  Constructs  Definitions  Variables  Propositions  Theories  Models

and Hypotheses

Understanding Concepts A

concept is a bundle of meanings or characteristics associated with certain events  Concepts have been developed over time through shared usage  The success of research hinges on: – How clearly we conceptualize and – How well others understand the concepts we

use

What is a Construct?  An

image or idea specifically invented for a given research purpose  Composed of concepts  Therotical level of concepts

Types of Variables  Independent  Dependent  Moderating  Extraneous-infinite

number

 Intervening  Dichotomous  Symbol

to assign numerals and value

The Role of the Hypotheses  Guides

the direction of study  Provides a framework for organizing the conclusions that result  Identifies the fact that are relevant and those that are not  Suggest which form of research design is likely to be most appropriate

What is a Good Hypotheses? A

good hypotheses should fulfill 3 conditions: – Must be adequate for its purpose – Must be testable – Must be better than its rivals

The Value of a Theory  Narrows

the range of facts we need to study  Summarizes what is know about an object of study  Used to predict further facts that should be found  Systematically interrelated concepts,defination that are advanced to explain and predict phenomenon

The Research Process

Steps in Research Process  Identifing

and defining problem  Planning the research design  Selecting the research method  Selecting a sampling procedure  Data collection  Evaluating the data  Presenting the research project

The Management- Research Question Hierarchy  Measurement

Questions  Investigative Questions  Research Questions  Management Questions  Management Dilemma

 Level

5

 Level

4

 Level

3  Level 2  Level

1

Working with Hierarchy  Management

Dilemma

– The symptoms of an actual problem – Not difficult to identify a dilemma, however

choosing one to focus on may be difficult

Working with the Hierarchy  Management

Question Categories

– Choice of purpose or objective – Generation and Evaluation of solutions – Troubleshooting or control situation

Working with the Hierarchy  Fine

tune the research question

– Examine concepts and constructs – Break research question into specific second

and third level questions – Determine what evidence answers the various questions and hypothesis – Set the scope of your study

Working with the Hierarchy  Investigative

Questions

– Questions the researcher must answer to

satisfactorily arrive at a conclusion about the research question

Working with the Hierarchy  Measurement

Questions

– The questions we actually ask or extract from

respondents

Other Process in the Hierarchy  Exploration – Recent developments – Predictions by informed figures about the

prospects of technology – Identification of those involved in the area – Accounts of successful ventures and failures by others in the field

Research Process Problems  The

Favored Technique Syndrome  Company Database Strip-Mining  Unresearchable Question  Ill-Defined Management Problems  Politically Motivated Research

Designing the Study – Select a research design from the large variety

of methods, techniques, procedures, protocols and sampling plans

Resource Allocation and Budget  Guides

to plan a budget

– Project planning – Data gathering – Analysis, interpretation, and reporting

 Types

of budgeting

– Rule of thumb – Departmental or functional area – Task

Evaluation Methods  Ex

Post Facto Evaluation  Prior Evaluation  Option Analysis  Decision Theory

Contents of a Research Proposal A

statement of the research question  A brief description of research methodology  Data collection  Data preparation  Data analysis and interpretation  Research reporting

Data Collection  Characterized

by

– Abstractness – Verifiability – Elusiveness – Closeness to the phenomenon

 Secondary

Data  Primary Data

Final Steps in Research Data

analysis

– Reporting the results Executive Summary  Overview of the research  Implementation strategies for the recommendations  Technical appendix 

The Research Proposal

Purpose of the Research Proposal  To

present the question to be researched and its importance  To discuss the research efforts of others who have worked on related questions  To suggest the data necessary for solving the question

The Research Sponsor All research has a sponsor in one form or another: In a corporate setting, management sponsors research In an academic environment, the student is responsible to the class instructor

What are the Benefits of the Proposal to a Researcher?  Allows

the researcher to plan and review the project’s steps  Serves as a guide throughout the investigation  Forces time and budget estimates

Types of Research Proposals  Internal  External

Proposal Complexity 3

Levels of Complexity

– The Exploratory study is used for the most

simple proposals – The Small-scale study is more complex and common in business – The Large-scale professional study is the most complex, costing millions of dollars

How to Structure the Research Proposal?  Create  Put

proposal modules

together various modules to tailor your proposal to the intended audience

Modules in a Research Proposal  Executive

Summary  Problem statement  Research objectives  Literature Reviews  Importance of the Study  Research Design  Data Analysis  Nature and Form of Results

 Qualifications

of

Researcher  Budget  Schedule  Facilities and Special Resources  Project Management  Bibliography  Appendixes

What to include in the Appendixes? A

glossary of concepts, constructs, and definitions  Samples of the measurement instrument  Other materials that reinforce the body of the proposal

Evaluating the Research Proposal  Proposal

must be neatly written  Major topic should be easily found and logically organized  Proposal must meet specific guidelines set by the sponsor  Technical writing style must be clearly understood and explained

Ethics in Business Research

What are Research Ethics?  Ethics

are norms or standards of behavior that guide moral choices about our behavior and our relationships with others

 The

goal is to ensure that no one is harmed or suffers adverse consequences from research activities

Ethical Treatment of Respondents and Subjects  Begin

data collection by explaining to the respondent the benefits expected from the research  Explain to the respondent that their rights and well-being will be adequately protected, and say how this will be done  Be certain that interviews obtain the informed consent of the respondent

Deception  The

respondent is told only part of the truth when the truth is fully compromised – To prevent biasing the respondents before the

survey or experiment – To protect the confidentiality of a third party

Issues Related to Protecting Respondents  Informed

consent  Debriefing  Confidentiality  Right to Privacy

Ethical Issues Related to the Client  Sponsor

nondisclosure  Purpose nondisclosure  Findings nondisclosure – Right to quality research

Ethical Issues Related to Researchers and Team Members  Safety  Ethical

behavior of assistants

 Protection

of anonymity

Design Strategies

What is Research Design? A

plan for selecting the sources and types of information used to answer research questions  A frame work for specifying the relationships among the study variables  A blueprint that outlines each procedure from the hypothesis to the analysis

Classifications of Designs  Exploratory

study is usually to develop hypotheses or questions for further research

 Formal

study is to test the hypotheses or answer the research question posed

Methods of Data Collection  Monitoring,

which includes observational

studies  Interrogation/ Communication mode

The Power of a Researcher  In

an experiment, the researcher attempts to control and/or manipulate the variables in the study  In an ex post facto design, the researcher has no control over the variables, they can only report what has happened

What type of Study to use?  Descriptive

is how one variable produces changes in another  Causal tries to explain relationships among variables

The Time Dimension  Cross-sectional

studies are carried out once and the represent a snapshot of one point and time  Longitudinal studies are repeated over an extended period

The Topical Scope  Statistical

studies attempt to capture a population’s characteristic’s by making inferences form a sample’s characteristics  Case studies place more emphasis on a full contextual analysis of fewer events or conditions and their interrelations

The Research Environment  Field

Conditions

 Laboratory

Conditions

 Simulations

A Subjects’ Perceptions  Usefulness

of a design may be reduced when people in the study perceive that research is being conducted

 Subject’s

perceptions influence the outcomes of the research

Why do Exploratory Studies?  Exploration

is particularly useful when researchers lack a clear idea of the problems

Data Collection Techniques  Qualitative

Techniques  Secondary Data  Focus Groups  Two-stage Design

The Concept of a Causal Study  The

essential element of causation is that A “produces” B or A “forces” B to occur

Relationships that Occur with a Causal Study  Symmetrical  Reciprocal  Asymmetrical

Types of Asymmetrical Relationships  Stimulus-Response  Property-Disposition  Disposition-Behavior  Property-Behavior

Achieving the Ideal Experimental Design  Random

Assignment

 Matching – Manipulation and control of variables

Measurement

Measurement  Selecting

observable empirical events

 Using

numbers or symbols to represent aspects of the events

 Applying

a mapping rule to connect the observation to the symbol

What is Measured?  Objects-things

of ordinary experience and that are not that concrete

 Properties-characteristics

of objects

Characteristics of Data  Order  Interval  Origin

between numbers

of number series

Data Types   Nominal

Order Interval Origin none - none - none

 Ordinal

yes

-

unequal -

 Interval

yes - equal or unequal -none

 Ratio

yes

-

equal

none

- zero

Sources of Measurement Differences  Respondent  Situational

factors  Measurer or researcher  Instrument

Validity  Content

Validity

 Criterion-Related

Validity

– Concurrent – Predictive

 Construct

Validity

Reliability  Stability – Test-retest

 Equivalence – Parallel forms

 Internal

Consistency

– Split-half – KR20 – Cronbach’s alpha

Practicality  Economy  Convenience  Interpretability

Chapter 8 Scaling Design

What is Scaling?  Assigning

numbers to indicants of the properties of objects

Types of Response Scales  Rating

Scales

 Ranking

Scales

Types of Rating Scales  Simple

category  Multiple choice, multiple response  Likert scale  Semantic differential

 Numerical  Multiple

fixed

rating  Fixed sum  Stapel  Graphic rating

Rating Scales Problems to Avoid  Leniency  Negative

Leniency  Central Tendency  Halo Effect

Types of Ranking Scales  Paired-comparison  Forced

Ranking

 Comparative

Dimensions of a Scale Unidimensional Multidimensional

Scale Design Techniques  Arbitrary  Consensus  Item

Analysis

– Cumulative

 Factor

Sampling Design

Selection of Elements  Sampling  Population  Population  Census

Element

What is a Good Sample?  Accurate  Precision

of estimate

Types of Sampling Designs  Probability  Nonprobability

Steps in Sampling Design  What

is the relevant population?  What are the parameters of interest?  What is the sampling frame?  What is the type of sample?  What size sample is needed?  How much will it cost?

Concepts to help understand Probability Sampling  Standard

error of the mean

 Confidence  Central

interval

limit theorem

Probability Sampling Designs  Simple

Random  Systemic  Stratified – Proportionate

 Cluster  Double

Designing Cluster Samples  How

homogeneous are the clusters?  Shall we seek equal or unequal clusters?  How large a cluster shall we take?  Shall we use a single-stage or multistage cluster?  How large a sample is needed?

Nonprobability Sampling  Reasons

to use Nonprobability Sampling instead of Probability Sampling – The nonprobability procedure satisfactorily – – – –

meets the sampling objectives Lower cost Limited Time Not as much human error as selecting a completely random sample Total list population not available

Nonprobability Sampling Designs  Convenience

Sampling  Purposive Sampling – Judgement Sampling – Quota Sampling

 Snowball

Sampling

Secondary Data Sources

Information is Classifies by Two Sources:  Primary

Data

 Secondary

Data

Uses of Secondary Data  Provides

specific reference or citation on some point  Helps decide what further research needs to be done  Justifies bypassing the costs and benefits of doing primary research  May be used as the sole basis for a research study

Classifying Secondary Data  By

Source  By Category  By Medium  By Database format

Classifying Secondary Data by Source Internal External

Classifying Secondary Data by Category  Database  Periodicals  Government

Documents  Special Collections

Classifying Secondary Data by Medium  Hard

copy

– Local-area on-line – Internet

The Library’s Role in Research  Resources

may be acquired through interlibrary loans (ILL)  Certain Databases are available on a localarea network (LAN)  Access to the internet an commercial CD/ DVD-ROM

Strategy for Searching for Secondary Data  Select

and analyze a topic  Explore the topic and state a hypothesis  Get an overview and retrospective information  Get more current and specific information  Get more in-depth information  Evaluate and close the library research

Using Search Engines and Indexes  The

search engine consists of two elements:

– Robot/Crawler – Indexer

How to Keep Track of Research?  Be

selective in what you record

 Decide

how to record what you will extract from the published material

 Develop

an orderly recording system

Survey Methods: Communicating with Respondents

Communication Approach Impacts the Research Process

 Creation

and selection of measurement questions  Sampling issues, drive contact and callback procedures  Instrument design, which incorporates attempts to reduce error and create respondent-screening procedures  Data collection procedures and possible interviewer training

Personal Interview  Requirements

for success

– Availability of the needed information from the

respondent – An understanding by the respondent of his or her role – Adequate motivation by the respondent to cooperate

Personal Interview  To

Increase Respondent’s Receptiveness they must – believe the experience will be pleasant and

satisfying – think answering the survey is an important and worthwhile use of their time – have any mental reservations satisfied

The Interview  Introduction – Establish a good relationship

 Gather

the data

– Probing

 Record

the Interview

Probing Styles A

brief assertion of understanding and interest  An expectant pause  Repeating the question  Repeating the respondent’s reply  A neutral question or comment  Question clarification

Interview Problems  Non-response  Response

error

 Interviewer – Cost

error

error

Telephone Interview  Types – Computer-assisted telephone interviewing – Computer-administered telephone survey

 Problems – Non-contact rate – Refusal rate

Self-Administered  Types – Intercept study – Mail survey

 Disadvantages – Large non-response error – Cannot obtain detailed or large amounts of

information

Concurrent Techniques to Improve Mail Response  Reduce

Length  Survey Sponsorship  Return Envelopes  Postage  Personalization

 Anonymity  Size,

color, and reproduction  Money Incentives  Deadline Dates  Cover Letters

Outsourcing Survey Services  Research

Firms Provide

– Centralized-location interviewing – Focus group facilities – Trained staff with experience – Data-processing and statistical analysis

capabilities – Access to point of scale data  Panels

Instruments For Respondent Communication

3 Phases of the Instrument Design Process  Developing

the instrument design process

 Constructing

and refining the measurement

questions  Drafting

and refining the instrument

Developing the Instrument Design Strategy  You

must go through four question levels:

– The management question – Research question – Investigative questions – Measurement questions

Strategic Concerns of Instrument Design  What

type of data is needed to answer the management question  What communication approach will be used – Should the question be structured, unstructured,

or some combination – Should the question be disguised or undisguised

Ways to Interact with the Respondent  Personal

Interview  Telephone  Mail  Computer

What are the Three Types of Measurement Questions?  Target  Classification  Administrative

4 Questions for Selecting Appropriate Question Content  Should

this question be asked?  Is the question of proper scope and coverage?  Can the respondent adequately answer this question, as asked?  Will the respondent willingly answer this question, as asked?

How to test a Respondent’s Knowledge  Filter

Questions

 Screen

Questions

Question Wording Criteria  Is

the question stated in terms of a shared vocabulary?  Does the question contain vocabulary with a single meaning?  Does the question contain unsupported assumptions?  Is the question correctly personalized?  Are adequate alternatives presented within the question?

What Dictates Your Response Strategy?  Characteristics

of respondents  Nature of the topic being studied  Type of data needed  Your analysis plan

Types of Response Questions  Free-response  Dichotomous  Multiple  Rating  Ranking

choice

Guidelines to Refining the Instrument  Awaken

the respondent’s interests

 Use

buffer questions as a guide to request sensitive information

 Use

the funnel approach to move to more specific questions

Final Step Toward Improving Survey Results  Pre-testing

is an established practice for discovering errors and useful for training the research team

Observational Studies

Observation  Non-behavioral

observation

– Record analysis – Physical condition analysis – Physical process analysis

 Behavioral

observation

– nonverbal analysis – Linguistic analysis – Extra-linguistic analysis – Spatial analysis

Advantages of the Observational Method  Only

method available to collect certain types of data  Collect the original data at the time it occurs  Secure information that participants would ignore because it’s so common it is not seen as relevant

Advantages of the Observational Method (cont..)  Capture

the whole event as it occurs in its natural environment  Subjects seem to accept an observational intrusion better than they respond to questioning

Limitations of the Observational Method  Observer

or recording equipment must be at the scene of the event when it takes place  Slow process  Expensive process  Most reliable results are restricted to information that can be learned by overt action or surface indicators

Limitations of the Observational Method (cont..)  Research

environment is more likely suited to subjective assessment and recording of data than to quantification of events  Limited as a way to learn about the past  Cannot observe rationale for actions, only actions themselves

Relationship between observer and subject  Direct

or indirect observation  Observer’s presence known or unknown to the subject  Observer’s involvement level with the respondent

Observation  Direct  Indirect  Participant  Simple  Systematic

Guidelines for selecting observers  Ability

to concentrate in a setting full of distractions  Ability to remember details of an experience  Ability to be unobtrusive in the observational situation

Data collection  Who?  What? – Event Sampling – Time Sampling

 When?  How?

Experimentation

Types of variables in Experiments  Independent  Dependent

Variables

Variables

What are the Advantages of an Experiment?  Researcher’s

ability to manipulate the independent variable  Contamination from extraneous variables can be controlled more efficiently  Convenience and cost  Replication

What are the Disadvantages?  Artificiality

of the laboratory  Generalization from non-probability samples  Larger budgets needed  Restricted to problems of the present or immediate future  Ethical limits to manipulation of people

How to Conduct an Experiment?  Select

relevant variables  Specify the treatment levels  Control the experimental environment  Choose the experimental design  Select and assign the subjects  Pilot-test, revise, and test  Analyze the data

Ways to Assign Subjects?  Random

Assignment

 Matching  Quota

Assignment

Matrix

Does a Measure Accomplish What it Claims?  Internal

validity

 External

validity

Variations in Experimental Designs  Pre-experimental

designs

 True

experimental designs

 Field

experiments

Types of Pre-experimental Designs?  One-shot

case study

 One-group  Static

pretest-posttest design

group comparison

Types of True Experimental Designs  Pretest-posttest  Posttest

control group design

only control group design

Operational Extensions of True Designs  Completely

randomized designs  Randomized block design  Latin square  Factorial design  Covariance analysis

What are Field Experiments: Quasi or Semi?  Non

equivalent control group design

 Separate  Group

sample pretest-posttest design

time series design

Data preparation and Preliminary Analysis

Editing  Detects

errors and omissions, corrects them when possible, and certifies that minimum data quality standards are achieved

Editing (cont..)  Guarantees

that data are

– accurate – consistent with other information – uniformly entered – complete – arranged to simplify coding and tabulation

Coding  Rules

that guide the establishment of category sets – Appropriate to the research problem and

purpose – Exhaustive – Mutually exclusive – Derived from one classification principal

Content Analysis  Follows

a systematic process with the selection of a unitization scheme – Syntactical unit – Referential unit – Propositional unit – Thematic unit

Data Entry Options  Optical

Scanning  Spreadsheets  Data warehouse – Transformation and cleaning – End-user access tools

 Data

marts

Descriptive Statistics  Distribution

 Variance

 Standard

 Standard

normal distribution  Central tendency – Mean – Median – Mode

 Variability

deviation

 Range  Interquartile  Skewness  Kurtosis

range

Techniques to Display and Examine Distributions  Frequency

table  Histograms – Display all intervals in a distribution, even

without observed values – Examine the shape of the distribution for Skewness, kurtosis, and the modal pattern  Stem

and leaf display

Techniques (cont.)  Box

and whisker-plot

– Rectangular plot tat encompasses 50% of the

data values – A center line marking the median and going go through the width of the box – The edges of the box (hinges) – Whiskers that extend from the right and left hinges to the largest and smallest values

Techniques (cont.)  Transformation – To improve interpretation and compatibility

with other data sets – To enhance symmetry and stabilize spread – To improve linear relationships between and among variables

Data Mining Techniques  Data

visualization

– Dimensions – Measurements – Hierarchies

 Clustering  Neural

networks  Tree Models  Classification

Data Mining Techniques (cont.)  Market-Basket

Analysis  Sequence Based Analysis  Fuzzy Logic  Genetic Algorithms  Fractal-base Transformation

Data Mining Process  Sample  Explore  Modify  Model  Assess

Hypothesis Testing

Two Approaches to Hypothesis Testing  Classical

Statistics

 Bayesian

Statistics

Types of Hypotheses  Null  Alternative

The Logic of Hypothesis Testing  Two

tailed test

 One

tailed test

Decision Errors in Testing  Type

I error

 Type

II error

Testing for Statistical Significance  State

the null hypothesis  Choose the statistical test  Select the desired level of significance  Compute the calculated difference value  Obtain the critical value  Make the decision

What are Significant Tests?  Parametric

tests

 Non-parametric

tests

How to Test the Null Hypothesis  Analysis

of variance (ANOVA)

How to select a test  Does

the test involve one sample, two samples, or k samples?  If two samples or k samples are involved, are the individual cases independent or related?  Is the measurement scale nominal, ordinal, interval, or ratio?

When to use the K Related Sample Tests  The

grouping factor has more than two levels  Observations or subjects are matched or the same subject is measured more than once  The data are at least interval

Measures of Association

Bivariate Correlation vs.. Nonparametric Measures of Association  Parametric

correlation requires two continuous variables measured on an interval or ratio scale  The coefficient does not distinguish between independent and dependent variables

Bivariate Correlation Analysis  Pearson

correlation coefficient

– r symbolized the coefficient’s estimate of linear

association based on sampling data – Correlation Coefficients reveal the magnitude and direction of relationships – Coefficient’s sign (+ or -) signifies the direction of the relationship  Assumptions – Linearity –

of r

Bivariate Correlation Analysis  Scatterplots – Provide a means for visual inspection of data – Both direction and shape of a relationship are

conveyed

Interpretation of Coefficients  Coefficient

of determination  Correlation matrix – used to display coefficients for more than two

variables  Correlation

causation

coefficient does not imply

Interpretation of Coefficients  Suggests

alternate explanations for correlation results – X causes Y, or Y causes X, or X&Y are

activated by one or more other variables, or X&Y influence each other reciprocally  Practical

Significance  Statistical Significance  Artifact correlations

Bivariate Linear Regression  Used

to make simple and multiple predictions  Regression coefficients – Slope – Intercept

 Error

term  Method of least squares

Interpreting Linear Regression  Residuals  Prediction

and confidence bands

Interpreting Linear Regression  Goodness

of fit

– Zero slopes come from  Y completely unrelated to X and no systematic pattern is evident  Constant values of Y for every value of X  data are related, but represented by a nonlinear function – Tests t test  F test 



Non-parametric Measures of Association  Measures

for nominal data

– When there is no relationship at all, coefficient

should be 0 – When there is a complete dependency, the coefficient should display unity or 1

Non-parametric Measures of Association  Chi-square

based measure

– Phi – Cramer’s V – Contingency coefficient of C

 Proportional – Lambda – Tau

reduction in error (PRE)

Characteristics of Ordinal Data  Concordant-

subject ranks higher on one variable also ranks higher on the other variable  Discordant- subject ranks higher on one variable is ranked lower on the other variable

Measures for Ordinal Data  Gamma  Somer’s

d  Spearman’s rho  Kendall’s tau b  Kendall’s tau c – No assumption of bivariate normal distribution – Values range from +1.0 to -1.0

Multivariate Analysis: An Overview

Selecting a Multivariate Technique  Dependency  Interdependency

What are Dependency Techniques?  Multiple

regression  Discriminant analysis  Multivariate analysis if variance, or MANOVA  Linear structural relationships, or LISREL  Conjoint analysis

What are Interdependency Techniques?  Factor

analysis

 Cluster

analysis

 Multidimensional

scaling (MDS)

Use Multiple Regression as a Descriptive Tool  Predict

values for a criterion variable by developing a self-weighting estimating equation  Control for confounding variables to better evaluate the contribution other variables  Test and explain causal theories

Uses for Discriminant Analysis  Classify

persons or objects into various

groups  Analyze known groups to determine the relative influence of specific factors

Why Use MANOVA?  In

business research, MANOVA can be used to test differences among samples of employees, customers, manufactured items, and production parts.

The Two Models of LISREL  Measurement  Structural

equation

Applications for Conjoint Analysis  Market

Research

 Product

development

What is Factor Analysis?  Computational

techniques that reduce variables to a manageable number

 Measurement

statistics

Five Basic Steps to the Application of Cluster Studies  Selection

of the sample to be clustered  Definition of the variables on which to measure the objects, events, or people  computation of similarities among the entities through correlation, Euclidean distances, and other techniques  Selection of mutually exclusive clusters  Cluster comparison and validation

What does Multidimensional Scaling Do?  Creates

a special description of a respondent’s perception about a product, service, or other object of interest

Written and Oral Reports

Written Research Report  Short

report

– Tell the reader why you are writing – If in response, remind reader the exact point,

answer it, and follow with details – Write in expository style with brevity and directness – Write report today and leave it for tomorrow to review before sending it – Attach detailed material as appendices when needed

Written Research Report  Long

report

– Technical report – Management report

Research Report Components  Prefatory

Items

– Letter of transmittal – Title page – Authorization letter – Executive summary – Table of contents

 Introduction – Problem Statement – Research objectives – Background

 Methodology – Sampling design – Research design – Data collection – Data analysis – Limitations

 Conclusions – Summary and

conclusions – Recommendations – Appendices – Bibliography

Written Report Considerations  Order – Sentence outline – Topic outline

 Readability  Pace  Tone

indices

Presentation of Statistics  Text

paragraph  Semi-tabular form  Tables  Graphics

Graphics  Line

graphs  Area charts  Pie charts  Bar charts  Pictograph  3-D graphics

 Control

charts

– Outliners- observations

that fall outside the control lines – Runs- data points in a series above or below the central line  Pareto

diagram

Oral Presentations

 Preparation – Length – Content

– Opening – Findings and conclusions – Recommendations

 Outline  Delivery – Vocal Characteristics – Physical Characteristics

Audiovisuals  Chalkboard

and whiteboards  Handout material  Flip charts  Overhead transparencies  Slides  Computer drawn visuals  Computer animation