1 WELCOME TO MARKETING/BUSINESS RESEARCH. 2 MARKETING RESEARCH Definition: Used to implement the...

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WELCOME TO MARKETING/BUSINESS

RESEARCH

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MARKETING RESEARCH

Definition:

Used to implement the ____________

What is that?? (think intro)

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Research is used to:

Identify problems/opportunities

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generate, and refine marketing actions

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Plan and Implement the Marketing Mix

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Research used to:

Monitor marketing performance

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When is Market Research Warranted:

Time Constraints

Availability of Data

Nature of Decisions

Costs vs. Benefits

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Sources of Marketing Data

Internal sales records customer

complaints inventory ...

External Syndicated Standardized Customized Advertising

Agencies Field Services Tabulation Houses Commercial

Databases

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Research is NOT a Cure-All!

Classic Blunders

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Why do I have to be here?

You will use research for decisions

Can easily bias research

Numbers lie

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RESEARCH ETHICS

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In The Beginning 1950 Fear and Authority Studies

Animal Protection

Internal Review Boards http://www.wvu.edu/~rc/irb/

irb_guid/exempt.rtf

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Business Ethics

Definition:

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Teleological Ethics Definition:Teleological moral

systems are characterized primarily by a focus on the consequences which any action might have (for that reason, they are often referred to as consequentalist moral systems, and both terms are used here). Thus, in order to make correct moral choices, we have to have some understanding of what will result from our choices. When we make choices which result in the correct consequences, then we are acting morally; when we make choices which result in the incorrect consequences, then we are acting immorally.

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Deontological Ethics Definition:Deontological moral systems

are characterized primarily by a focus upon adherence to independent moral rules or duties. Thus, in order to make the correct moral choices, we simply have to understand what our moral duties are and what correct rules exist which regulate those duties. When we follow our duty, we are behaving morally. When we fail to follow our duty, we are behaving immorally.

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Kohlberg – Value Maturity Model Three levels of maturity with six

stages of development Self-centered level – (1) obedience and

punishment, (2) naively egoistic orientations

Conformity level – (3) good person, (4) “doing duty” orientations

Principled level – (5) contractual legalistic, (6) conscience of principle orientations

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Which is the “right” perspective

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Respondent’s Right to Choose Can’t force compliance

Captive subject pools Status of the researcher

Insure that incentivesdo not create pressure

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Respondent’s Right To Safety

Preserve anonymity Preserve privacy No mental stress

respect subjects debrief subjects

Protect when questions are detrimental to subject

Inform when special equipment used

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Respondent’s Right to be Informed

Informed consent/assent Parental consent Observation??

consider risks consider alternative methods

Deception

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Solutions Actively think about ethics when

designing the study http://cme.cancer.gov/c01/

Government Institutional Review Board

Ethics Codes AMA

Ethics Checklist

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THE RESEARCH PROCESS

Stages in the Research Process

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Define the Problem (Stage 1)

Research objectives

Research questions

Properly formulate the problem

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Conduct a Situation Analysis -- Part of Problem Definition

General environment

Competitive products or services

Consumers

Marketing Programs

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Determine Research Design (Stage 2)

How much should you spend?

What type of design should you use? exploratory descriptive causal

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Exploratory Designs

Used when you do not have a good understanding of the problem and need to gain insight Used to:

Methods:

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Descriptive Designs

Used to describe the characteristics of consumers, competitors, etc.…

Methods

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Causal Designs

Used to determine cause and effect relationships. MUST use experiments which

include:

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Preparation of the Design

Determine source of the data primary secondary

Determine data collection method qualitative quantitative

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Sampling(stage 3)

Sampling defined:

Who is to be sampled (the target population)?

How big should the sample be? Which sampling technique should be

used?

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Data Gathering(stage 4)

Method Used

Stages

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Data Processing and Analysis (Stage 5)

Editing

Coding

Analysis

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Conclusion and Report Preparation (Stage 6) Written--the only tangible from the

study interesting easy to read managerial implications

Oral interesting convincing

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Secondary Research

The Place to Begin

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Secondary Data

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Secondary Data Advantages

Time Money Improve over other studies Point of comparison for trends Increase understanding of problem

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Secondary Data Disadvantages:Problems of Fit

Measurement units differ

Class definitions differ

Out of date

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Secondary Data Disadvantages:Problems of Accuracy

Primary vs. secondary source

Purpose of publication

General evidence of quality

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Internal Secondary Data Sales invoices Warranty cards Departmental

records Sales records

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Locating External Secondary Data

Identify what you need to know Develop a list of key terms and

people Examine directories and guides Write letters to key contacts Talk to reference librarian Do a computer search Pull the information together

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THE LAWAlways conduct secondary

data search before you do primary data collection.

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Qualitative Interviewing Techniques

1. Focus Groups2. Projective Techniques

3. Depth Interviews4. Observation

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Definitions (Yuck!):

Inquiry -- Person responds to a set of Questions

Disguised:

Undisguised:

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Final Definitions (I promise):

Structured: Questions: Answers:

Unstructured: Questions: Answers:

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Qualitative Methods

“Touchy-feely” – no numbers Examine thoughts, feelings,

motivations… Can be results be projected to the

population? Yes No Can spot trends 3 to 4 years before

they show up in surveys

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Focus Groups

______ homogeneous people carefully recruited

Lasts _____

Types: Round-table (Comfortable room with one-way

mirror) Telephone Internet

Moderator Guide

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Focus Group Moderator Keeps discussion focused Truly believe that participants have

wisdom Encourages shy to talk and dominant

participants to be quiet Should say little, but keep eye contact Accepts all answers Must be a quick study

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Uses of Focus Groups

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Advantages/Disadvantages of Focus Groups

Advantages:1.2.3.4.5.6.7.

Disadvantages:1.2.3.4.

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Conducting a Focus Group Register

participants (demographic information)

Small talk Introductions

welcome why they are here guidelines or ground

rules opening question

Ask questions Anticipate flow Control your

reactions Probe as needed Summarize the

discussion

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Conducting a Focus Group:Guidelines

Always Include:taping discussiondo not talk over othersno names attachedsponsor of studyrole of moderator to guide onlyfeel free to talk to each otherdone byfirst name basisno wrong answers only differing opinions

May Include:don’t need to agree but listen to their viewsno cell phones or pagerswho will listen to tapeswho will see the reporthow the report will be usedstrictly research and no saleslocation of the bathroomshelp yourself to refreshments

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Developing Questions for Focus Groups

Where to Begin:

General Rules:

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Question Categories

Opening questions Introductory questions Transition questions Key questions Ending Questions

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Projective Questions: Used when subjects

cannot or will not directly communicate feelings

“A man is least himself when he talks in his own person; when he is given a mask he will tell the truth.”

E.g., TATs, inkblot

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Word Association

Examine brand/service image Measure frequency of responses

and no responses Response Latency

Example

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Sentence Completion Gives more

direction than word association

Examples: When visiting the

President be sure to_____________.

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Unfinished Story Finish the story or

tell why the person acted the way he or she did.

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Third Person Role Play

What would the typical person do in this situation?

We tend to think others are like ourselves, yet we are more willing to tell the truth about “others”

Example: Why would your neighbor buy a

Mercedes

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Cartoon Completion Subjects fill in the

bubble – suggests a dialogue between the characters

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Draw a Picture

Subject given a topic to draw Examples:

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DEPTH INTERVIEWS

One-on-one interviews

Try to uncover underlying motivations, prejudices and attitudes toward sensitive information

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Depth Interviewing Analysis

Laddering

Attributes

Consequences

Values

Coca

Cola

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When to use depth interviews:

Sensitive subject matter Need intensive probing Respondent interaction unlikely to

be helpful Have lots of $$$$ and time Need detailed responses (> 15

minutes)

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Some Boring Definitions:

Ethnographic/Observational Research

Direct Observation:

Indirect Observation

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Observation can be disguised or undisguised

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Observation of Physical Objects

Naturalist Inquiry

Physical Trace evidence wear on floor tiles

Garbology

Pantry Audit

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Mechanical Observation Television/Internet

Scanners

Eye Tracking

Psychogalvanometer

Response Latency

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Experimental Research Methods

Looking at Cause and Effect Relationships

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Experiment

Definition:

variable manipulate independent variable dependent variable

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Requirements for an Experiment

Must have two or more groups of subjects experimental group(s) control group(s)

Must use random assignments to groups controls for extraneous factors

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Research Environments

Laboratory experiment

Field experiment

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Can NEVER prove causation ( X Y)

Can only INFER such a relationship

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Reasons for Association between X & Y :

Common causes drowning and ice cream consumption

Confounded factors AIDS test of Rivavion

Coincidence Causation

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Evidence to Support Causation

Concomitant Variation Temporal Ordering (time order of

occurrence)

Elimination of Other Causes

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Concomitant Variation Required for Causation

1. Concomitant variation

positively negatively

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Temporal Ordering Required for Causation

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Elimination of Other Possible Causes Required for Causation

You must think this through, no one will give you a list to check

Most difficult of the criteria to determine

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Internal Validity Definition:

Threatened by: history maturation instrumentation selection bias (non-random assignment

) testing

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External Validity

Definition:

Threats to external validity reactive/interactive testing effects

surrogate situations

demand artifacts

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Experimental Designs --Notation:

RR = random assignment of respondents

X = exposure to one of the possibly many treatments

0 = observation of measurement of the respondent

T = treatment effects

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One-Shot (After Only)

X O

Problems?

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One-group Pretest-Posttest

O1 X O2

Problems?

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Static Group

X O1

O2

PROBLEMS?

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Before/After With Control

RR O1 X O2

RR O3 O4

Problems?

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After Only With Control

RR X 01

RR 02

Problems?

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SURVEY INTERVIEWINGTECHNIQUES

Methods that Use Large Sample Sizes and Create

Results that Can Be Projected to the Populations

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Mail Surveys/Self-Administered Questionnaires

Def:

-cold-panels-fax- e-mail

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Internet/Computer Assisted Surveys

Allow for lots of branching/interactive

Allows for personalization

Great anonymity

Representative Samples

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Other Survey Methods Telephone

Personal in-home (Door-to-Door) -

Mall intercepts

-can interact with product replacing ___________

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Each Method Has Advantages and Disadvantages

See page 172 for a summary

TREND – USE A COMBINATION OF METHODS

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Things to consider when choosing method

Versatility - Visual cues

- Degree of structure

- Complexity of questions

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Consider Quantity of Data Function of

questionnaire length - shortest

________

- moderate length ________

-longest _________

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Consider Sample Control Contact the right people

mailing list quality

interviewer qualifying

phone unlisted

Random Sampling error

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Consider the Quality of Data

Response bias (see next slide) Interviewer bias Interviewer cheating Poor questionnaire design Sample bias

Systematic Errors

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Response Biases

Acquiescence Extremity Interviewer Auspices Social Desirability

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Consider Non-Response Error

Problems occur because the people responding to the questionnaire differ significantly from those not responding

Possible Self-selection bias Example

-survey 500 students to see if they need transportation to and from school - 50 answer and say yes

-conclude that all 450 that did not answer do not need it Did you make the correct conclusion?

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Your Turn

Make up your own example of nonresponse bias:

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How to Increase Response Rate

Prior notification Motivate with

rewards

Good looking questionnaires

Good cover letter Follow-up

Make it fun!!

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Consider Speed Phone is ____

Computer-assisted phone/internet is _____

Mail is ____

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Consider Cost Internet:

relatively inexpensive

Mail: depends on pre-contacts and follow-ups

Telephone: next most expensive

Mall/In-home $30 up to $100

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Specific Uses for Methods Cold mail

respondents very interested in topic Mail panels

general information, in-home use Phone

nationwide samples Mall intercept

copy tests, product tests, branding/package testing

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Measurement

Assigning Numbers To Reflect the Degree or Amount of a

Characteristic

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MEASURES OF CENTRAL TENDENCY

MODE

MEDIAN

MEAN

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Measurement Scales Series of items that

are arranged progressively according to value or magnitude

A series into which an item can be place according to its quantification

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Nominal Scale

Identification only

No order to the numbers

Examples:

Measure of Central Tendency:

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Ordinal Scale

Ranked data

Distance between two numbers is unknown and uneven

Examples:


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Interval Scale

Rank to the data

Equal distance between numbers

No “natural zero”

We assume a lot of scales are interval


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Ratio Scales

Rank to the data

Equal distance between numbers

“natural zero” where zero means “none”


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YOUR TURN --Write a question for each type of scale

Nominal

Ordinal

Interval

Ratio

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Criteria For Good Measurement

Reliability

Validity

Sensitivity

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1) Reliability of Scales

Coefficient Alpha Are the results on

questions measuring the same thing consistent?

Single item scales more suspect to random error

Test/retest Are consistent results

found on repeated measures

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2) Validity

Are we measuring what we think we are measuring?

Content validity (Face validity)

Pragmatic validity

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3) Sensitivity Refers to an instruments ability to

accurately measure variability in stimuli or responses

Example: I love to eat chocolate Agree vs Disagree

Strongly mildly neither mildly strongly agree agree agree or disagree disagree

disagree

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Noncomparative Continuous Graphic Rating Scales Place a mark on the line indicating

how important it is to have each of the following at your vacation resort:

Alpine slides _____________________unimportant important

5 inch line 127 mm 1/20 inch

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Graphic Rating Scales

Happy faces

Thermometer

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Noncomparative Itemized Rating Scale Several categories from which the

respondent can choose

Top-box method: How likely are you to buy a Sony DVD player in the

next 3 mos. definitely will buy Probably will buy Might buy Probably will not buy Definitely will not buy

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Examples of Itemized Rating Scales

Likert

Semantic Differential

Staple

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Likert-type Scales

Sentences with which the respondent agrees or disagrees

It would be cool to have a candy-red 1965 convertible Mustang

SD D Neither A SA

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Likert-type Scales

Code such that higher numbers mean better things

Can create summated scales to form an index

Assume __________ scale

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Series of attitude scales where repeated judgments about a concept are made

Opposite adjective words or phrases Use several of these and sum them

Fast __:__:__:__:__:__:__ SlowBad __:__:__:__:__:__:__ GoodService ServiceTasty __:__:__:__:__:__:__ Not TastyFood Food

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Code such that higher numbers mean better things or more of something

Make an overall score--sum the items Develop a snake diagram (image

profile) to compare competitors

Assume _____ scaling

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Staple Scale

Use +5 (describes completely) to -5 (does not describe at all)

Assume _______ scaling Good for phone Easy to construct May look difficult for respondent-5 -4 -3 -2 -1 FUN +1 +2 +3 +4 +5

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Questions for Itemized Response Scales

How many categories?

Balanced or Unbalanced?

Should you have a neutral point?

Forced or unforced?

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Comparative Scales

Compare one set of objects directly with another sensitive easy can create artificial differences

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Paired Comparison

Which do you prefer?____ Barry Manilow____ Counting Crows

____ Barry Manilow____ Rolling Stones

____ Rolling Stones____ Counting Crows

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Paired Comparison Table

Manilow Crows Stones

Manilow ----- 0.90 0.85

Crows0.10 ---- 0.60

Stones 0.15 0.40 ----

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Calculation of Rank-Order Values

Manilow Crows StonesManilow

Crows

Stones

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Rank-order Scales

Respondents are simultaneously presented with several objects that they rank order

Please rate the following from 1=most preferred to 4= least preferred

Pizza Hut -Domino’s Mario’s -Little Ceaser’s

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Comparative Continuous Graphic Rating Scale

Similarity ratings used for perceptual maps

Pitt and WVU _________________________Exactly Completelythe same different

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Constant Sum Scales

Assign chips or points to attributes

Very careful with instructions

Difficult for the respondent

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Developing Questionnaires

The Art and Science of Questionnaire Design

132

Preliminary Considerations

What information is required?

Who are the target respondents?

What data collection method will be used?

133

Managerial Orientation

Make sure that all information in the questionnaire is useful to the manager (demographics

and first question are possible exceptions)

134

Make Sure Questions Are Understandable

Do you need more than one question?

Do respondents have the information needed to answer the question?

135

Understandable Questions, cont.

Can respondents remember the information?

Is it too much work to get the information?

136

Ways of Dealing with Sensitive or Embarrassing Questions State behavior is not unusual. Early or late in the questionnaire?

early late Give categories for responses. Phrase how others might act.

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Need Mutually Exclusive and Exhaustive Responses

Responses should not overlap

Must cover the entire range

Example:

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Use Natural and Familiar Language

Simple language

Language that the target market uses

Avoid ambiguous words: DO NOT USE:

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Avoid Bias

No loaded questions

Watch for sequence bias

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No Double-Barreled Questions

A question that calls for two responses

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Response Formats

Open ended--respondent answers in his or her own words

Uses:

Bad points:

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Itemized Questions (close-ended)

Fixed alternatives Advantages:

MUST PRETEST

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Types of Close-ended Questions

Multichotomous (More than 2 responses)

Dichotomous (Only two responses)

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Questionnaire Flow

Cover letter

First question very important, must be _____________ _____________

Demographics late in the questionnaire

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Sequencing

Funnel

Inverted funnel

Keep questions on related topic together

Be very careful with branching

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Layout

Booklets for multi-page questionnaires

Attractive Title, date, return

address on first page

Color code branching

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Layout

Number the questions Put the answers in all UPPER CASE

letters What is better?

white space save a page

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Pretest the Questionnaire

First with a personal interview

Make corrections Next using the

real method If you do not

pretest, you are being __________________

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Sampling

The Statistical Adventure Begins

150

Populations

Def:

Census

Sample

Which is better? census? sample?

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Step 1: Define the Target Population

Must be very specific: What is a user? What demographics matter? Are there geographic boundaries? What is the relevant time period?

What is an element?

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Step 2: Specify a Sampling Frame

Def:

153

Sample Frame Problems

List may not match the target population

over-registration

under-registration

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Step 3: Selecting a Sampling Method

Probability samples

example: Non-probability samples

example:

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What’s the Big Deal?

Probability samples let us estimate _________

We can calculate a confidence interval

So, probability samples are more representative than non-probability samples. true false

156

Simple Random Sampling

Probability sample Number each unit in the sampling

frame Pick ___ units using a random

numbers table NOT haphazard

157

Take a Simple Random Sample (SRS) of n=3

Element Attitude toward Motel 6Natasha 6Scotty 7Kalie 4Lynn 2Gregory 8Paul 4John 7

158

Stratified Sample

Decide on stratification variable

homogeneous groups related to dept. variable

Divide population into a few mutually exclusive and exhaustive strata

Take a SRS from each strata

159

Proportionate Stratified Sample

Choose sample from strata in same proportion as they are in the population

Population SampleStrataproportion proportion

160

Disproportionate Stratified Sample

Take a larger sample from the strata with ________ variance

What is variance?

Exercise: Develop two populations with 8 elements each. Population 1: high variance, low mean Population 2: low variance, high mean

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Disproportionate Stratified Sample

Population Sample

StrataVarianceproportion proportion

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Why use Stratified Samples?

Make sure that you include certain subgroups

More precise, IF we use the right stratification variable margin of error is ___________ sampling distribution is __________ confidence intervals are __________

What is the right variable?

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Cluster Sampling

Divide population into lots of heterogeneous clusters

Take a SRS of clusters Either:

sample all elements in the selected clusters

OR take a SRS of elements in the selected clusters

164

Why use Cluster Samples

Cheap Easy Likely to be the

way the sampling frame is set up

Problem not precise, lacks

statistical efficiency

165

Non-probability Sample: Cannot estimate margin of error

Convenience or accidental sample

If the sample size is really large, we know we have a representative sample true false

166

Judgment or Purposive Sample

Elements selected because they can serve the research purpose--they are believed to be representative

Snowball sample

167

Quota Sample

Attempts to be representative by sampling characteristics in the same proportion as the population

Interviewer chooses sample

Are these representative? _____

168

Step 4: Determine the Sample Size

Must take into consideration: cost time industry standards statistical precision

Discuss this in detail in the next chapter

169

Step 5: Select Elements

Actually collect the data Clean-up the data Put the data into the computer

170

Characteristics of Interest

Population

N

U (mu)

o2 (sigma squared)

O (sigma)

Samplen

X (x bar)

Sx2

Sx

# of elements

Mean

Variance

Standard Deviation

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Step 6: Estimate the Characteristics of Interest

Sample mean:sum of the sample elements

X= number of elements in sample

Sample variance = Sx 2

sum of deviations around the mean squared

sample size minus 1

172

Sample Standard Deviation

The square root of the sample variance = sx

Has a specific meaning

173

Sampling Error

The difference between the : population parameter

and the sample statistic

We look at confidence intervals to estimate this but not until the next chapter

174

Non-sampling Error

(i.e., all other kinds of errors except for sampling error!)

175

Types of Non-Sampling Error

Sampling frame

Poor questions

Poor branching

Item non-response

176

More Non-Sampling Errors

Non-response

Interviewer bias

Interviewer cheating

Coding and editing problems

177

Which is the Larger Problem?

Sampling error

Non-sampling error

178

Sample Size Determination

Everything You Ever Wanted to Know About Sampling Distributions--And More!

179

Sampling Distribution

A frequency distribution of all the means obtained from all the samples of a given size

Example: $$ spent on CD’s at Tracks Daffy 34.00 Donald 72.00 Sylvester 36.00

All samples of n=2

180

Your Turn

Develop a sampling distribution using n=2

Calculate the population mean CAR

A B C D E

Expected 3 4 5 0 1Life

181

Sampling Distributions

The distribution of sample means is skinnier than the distribution of elements Why?

The distribution is normal The sampling distribution mean

equals the population mean

182

Standard Error

The variability in the sampling distribution

Tells you how reliable your estimate of the population mean is

If this is big (good or bad) If this is small (good or bad) WHY?

183

Standard Error

Sx standard deviation

square root of the sample size

As the samples size gets bigger, the standard error gets __________

184

Confidence Intervals

CI= Xbar +/- z (standard error) Where:

z= _____ for 68% confidence z= _____ for 95% confidence z= _____ for 99.7% confidence

What confidence level should you use?

185

Develop a Confidence Interval

Estimate the average number of trips to the beach taken by WVU students during their 4-6 year career xbar = 5 SD = 1.5 95% Confidence

Level n=100

186

So,

There is a 95% chance that if all WVU students were sampled regarding the number of beach trips that the findings would differ from our results by no more than ____ in either direction.

187

or, maybe better, If I were to conduct this study 100

times, then I would get _____ different confidence intervals. If I have a 95% confidence interval the ____ of the 100 CI’s will contain the true population mean (mu) and ____ will not.

I sure hope that the confidence interval I got is one of the 95 that contains mu!

188

Confidence Interval Issues

Reliability how often we are correct

Precision how wide the confidence interval is

The smaller the n, the _____ the CI Given a particular n, the CI will be

_______ when we increase the reliability

189

Factors that Influence n Precision (H)

how skinny must can your CI be in order to be able to take action on the results?

I will go to a water park. DW PW Maybe PWN DWN

I will pay _____ for a musical card.

I will pay _____ for a motorcycle.

190

More Factors That Influence n

Confidence level (z)

Population SD

Time, money and personnel

191

Sample Size for Interval or Ratio Data

Z2

n= H2 * s2

Where:z= 1, 1.96, or 3

H= precision (+/-) Hs2= variance (or standard deviation

squared)

192

Example: Average Number of Books Bought Per Semester

H=0.25 s=1.5 Confidence =

95%

193

Sample Size for Nominal Data

Z2

n = H2 * (P) (Q)Where:

Z= 1, 1.96, or 3H= a percentage (e.g., 0.03--NOT 3)P = initial estimate of the population proportionQ= (1-P)

194

n for Proportion of WVU Students Who Read the DA

Do you read the DA?

1. YES 2. NO

Estimate that 60% read the DA

Want a 99 % CI Want a +/- 3%

precision

195

The Final Sample Size

Compute n for all nominal, interval and ratio questions most conservative

limited resources

196

Non-statistical Approaches to n

All you can afford method: subtract costs from budget

figure out cost per interview

divide leftover budget by cost per interview

Rules of thumb

197

Coding and Editing

Getting the data ready for analysis

198

Coding Each response must have its own

variable name

Variable names can have up to 8 characters

Assigning numbers to responses to enter data into computer

199

Creating a Coding Sheet Must have a filename at top of

questionnaire Name_data.txt

First variable is ALWAYS the ________Why?

Write the variable names on the questionnaire next to the matching response

200

Coding Coding Open-Ended Questions:

Code open-ended nominal __________ EX: What State is your current state of residence?

Code open-ended numerical – enter _______ Ex: How much would you pay for this product?

201

Coding Coding fixed-alternative responses:

Assigned numbers should be logical

One variable needed for each answer the respondent will give rank order

semantic differential

“Check all that apply”

202

Editing

Cleaning up the data

Field edit check for legibility check for completeness

203

Office Editing

Outliers

Missing data

Blunders

Inconsistencies

204

Hypothesis Testing

Using the SAS System to Analyze Questionnaires

205

Statistically Significant

Are these results for real, or did they just occur by chance?

Remember, in sampling, all numbers have ranges

206

Alpha and p-values

Alpha value: the error rate you

are willing to accept

P-value the error

associated with rejecting the null hypothesis

207

Chi-square & T tables

For BOTH distributions Area under the curve

= Alpha & p-value are

areas under the curve critical value--

associated with an alpha level

calculated value--associated with a p-value

t-distribution

chi-square distribution

208

Chi-Square Goodness of Fit

When to use: number of variables ________ scaling of variable _________

Basic idea: could the numbers you get (the

observed value) come from a population which has the pattern I expect? (the expected value)

209

Chi-square Goodness of Fit

Ho: This sample could have come from a population which has this pattern: __________________________________ __________________________________

Ha: There is a different pattern in the population than I expect (or hope).

210


Chi-square calculated= sum of (Observedi -Expected i ) 2

Expected i

degrees of freedom = number of cells - 1

Alpha Value Table Value

211

Now Graph

chi-square calculated

chi-square table value

212


What type of dairy treat do you like best?1. hard scoop ice cream2. soft serve ice cream3. chocolate covered

ice cream bars

Ho:

Ha:

Chi-square Calculated:

Degrees of Freedom:Chi-square Table:Graph

213

Chi-square Goodness of Fit Rules

If the chi-square calculated is in the tail, then _______ Ho; conclude the pattern in the data. is NOT what you expected or wanted.

If chi-square calculated is in the hump, then _______ Ho; conclude, the pattern IS what you expected or wanted.

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Do It Yourself Using SAS --

What is the pattern for the favorite brand of soda?

Ho:

Ha:

Chi-square Calculated:

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Do It Yourself Using SAS --(cont.)

Degrees of Freedom: Chi-square Calculated: Chi-square Table: Graph

Conclusion:

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Chi-square for Two Variables

When to use: number of variables ________ scaling of variables ________

Basic Idea: Compare the values you actually get

from you study to the values you would expect if there was ____________between the two variables

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Chi-square for Two Variables

Ho: There is no relationship between ____ and _____

Ha: There is a relationship between ____ and _____; SPECIFICALLY _________________

NO CALCULATIONS!! SAS DOES THIS ONE

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Chi-Square for Two Variables

Alpha level

Probability level

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Chi-Square for Two Variables

If the probability level is > _____, do not reject Ho, conclude________________

If the probability level is < _____ then reject Ho, conclude ______________ AND specify the nature of the relationship.

CAREFUL--Do not just assume that the relationship you predicted is correct

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If you Reject HO -- How Strong is The Relationship?

Look at Phi Phi < 0.10 is

______ Between 0.11 and

0.40 is __________ Phi > 0.40 is

_______

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Do it yourself using SAS

Ho:

Ha:

Chi-square calculated: Probability level: Alpha level Phi: Conclusion:

222

Rank-Order Tests

It’s 12:00. Do you know what

your Ha is?

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Rank-order Data & Chi-square

When to use: number of variables ________ scaling of variable ________

Basic idea: compare the observed value (________________) with the values you would expect if NO PREFERENCE was shown in the data

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Hypotheses & Calculations

Ho: There is no ranking in the data--there is no preference.

Ha: _____________________________, specifically, ______________________.

Chi-square calculated:Sum of (Observedi -

Expectedi)2 Expectedi

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Rank-order Chi-square First, multiply the rank-order data for each

variable Variable 1 score = 1 (___) + 2 (___) +3 (___) ... Variable 2 score = 1 (___) + 2 (___) +3 (___) ... Variable 3 score = 1 (___) + 2 (___) +3 (___) ...

Compute expected value Add up the total scores and divide by the

number of variables

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Example

Ranking of movies: Mulan, Private Ryan, Titanic (n=20)

Mulan PR Titanic1 10 7 32 4 8 8

3 6 5 9

Mulan ranking = 1 (___) + 2 (___) +3 (___) PR ranking = 1 (___) + 2 (___) +3 (___) Titanic ranking = 1 (___) + 2 (___) +3 (___)

Expected value:

227

Rank-order Chi-square

Degrees of freedom Alpha level Chi-square table Graph chi-square and

chi-square calculated Conclude Managerial

implications

228

Rules If the chi-square calculated is in the

tail, then _______ Ho, conclude that there is a preference shown in the data. EXAMINE THE DATA TO DETERMINE PREFERENCE. (It may not be what you hypothesized!)

If the chi-square calculated is in the hump, then ___________ Ho. Conclude there is no preference shown.

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Do This With the Soda Rankings

Rank calculations

Expected value Ho:

Ha:

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Do This With the Soda Rankings (continued)

Chi-square calculated:

Chi-square table: Graph:

Conclusion: reject or do not reject Ho Managerial implication

231

T-test for One Mean

When to use: number of variables __________ scaling of variables __________

Basic idea Look at the confidence intervals. Any numbers

in the same confidence intervals are considered the same.

Key question--If my sample mean (xbar) is ___, can my population mean (mu) be ___?

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Hypotheses for T-test for One Mean

Interested in the average number of sodas drunk per day.

Ho: The opposite of Ha: The population mean is equal or (less/greater than or equal to) the the number hypothesized.

Ha: What you need to be actionable. The population mean is (less than/ greater than) _____.

Note: it may be easier to write Ha first.

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Example Ho and Ha

Ho: × = µ Ha: × ≠ µ (two-tailed test)

Ho: X ≥ µ Ha: X < µ (one-tailed test – lower tail)

Ho: X ≤ µ Ha: X > µ (one-tailed test – upper tail)

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Calculations

T calculated = xbar - mustandard error

Where:xbar = sample meanmu = hypothesized population

mean

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More T Calculations

Degrees of freedom=n-1

Alpha level= T-table value =

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Now Graph

t-calculated and t-table value on a normal curve

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Rules for T-test for One Mean

If the calculated t-value is in the hump, ________ Ho. Conclude that your Ha is not correct.

If the calculated t-value is in the tail then _____ Ho. Examine your data to see if Ha or the opposite of Ha is correct.

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Practice Once

Ho: Ha: The populations purchase intention for a

gumball machine is >4.

X-Bar: 4.5 SE= 0.15, n=60 T-calculated Degrees of freedom T-table Graph Conclusion: Reject or Do not Reject Ho Managerial implication:

239

Practice Again!

Ho: Ha: The populations purchase intention for a

gumball machine is >4.

X-Bar: 2.3 SE= 0.18, n=60 T-calculated Degrees of freedom T-table Graph Conclusion: Reject or Do not Reject Ho Managerial implication:

240

Now Use SAS

Ho: Ha: The average population rating for Coke

when consumers know it is Coke is >6.

T-calculated Degrees of freedom T-table Graph Conclusion: Reject or Do not Reject Ho Managerial implication:

241

T-test for Two Means

When to use: Number of variables = _______ One variable (the groups) is _______ scaled One variable (the dependent variable) is

________ scaled Basic idea:

See if the confidence intervals for the two different groups overlap. If they do, then _________________________________ .

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Hypotheses for T-test for Two Means

Is there a difference between the number of sodas males drink per day and the number of sodas females drink per day?

Ho: The two groups are the same with respect to __________ .

Ha: The two groups are different with respect to _______. Specifically, ______________.

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More on T-tests for Two Means No calculations

Check to see if variances are equal or unequal Look at “Equality of Variances” –

Ho: variances are equal Ha: variances are not equal

If p>.05 accept Ho and use equal variances If p<.05 reject Ho and use unequal

variances

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More on T-tests for two means

Check the T-test table to see if you should accept or reject your Ho:

• T-value =(either for equal or unequal variance)

• P-value =

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Rules

If the probability level is ________ 0.05, then ________ Ho. Conclude that the two groups are different. LOOK AT THE DATA TO DETERMINE WHAT THE DIFFERENCE IS.

If the probability level is ______ 0.05, then __________ Ho. Conclude that the two groups are the same.

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Your Turn

Is there a difference in the number of sodas drunk per day between people who drink soda with breakfast, and people who do not?

Nominal variable= ___________ Interval variable = ___________ Ho: Ha: Probability level Conclude--reject or do not reject ho Managerial Implication

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Your Turn again Is there a difference in the number of sodas

drunk per day between people who drink soda with breakfast, and people who do not?

Nominal variable= ___________ Interval variable = ___________ Ho: Ha: Probability level Conclude--reject or do not reject ho Managerial Implication

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ANOVA When to use:

Testing mean differences between groups Have more than 2 groups Want to test interactions between 2 variables

Same as a t-test except that you have more than two groups Number of variables = _______ Some variables (the groups) are _______ scaled One variable (the dependent variable) is ________

scaled

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Ho: all the means are equal

Ha: one of the means differs (specify how the mean differs)

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No Calculations: SAS does this

Use Proc GLMClass: the nominally scaled variable(s)Model: specifies the dependent variable,

the dependent variable and interactions

e.g., class= age; model liking= age;mean = age;

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Interpretation:Dependent variable: liking

Sum ofSource DF Squares Mean

Square

Model 6 93.52 15.59

Error 75 433.02 5.77

Corrected Total 81 526.55

Source F Value Pr>FModel 2.70 0.02

NOTE:to determine significance – check the p value (if p less than .05 reject Ho)

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Do it yourself using SAS

You want to test whether age has an impact on the number of sodas consumed per day

HO:

HA:

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F-calculated Alpha P-value

Conclusion:

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THE END!!!

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