4 research design + sampling methods dr. hueihsia holloman

Wk 4 Data Collection

Dr. Huei Holloman

Week 4 Objectives

The goal of this research is to discover the real nature of the problem & to suggest new

possible solutions or new ideas.

A food manufacturer wants to know the demographics of people who purchase organic foods.

A firm is considering hiring American celebrity Paris Hilton to endorse its products.

British Airways would like to test in-flight Internet services on one of its regular flights from New

York to Tokyo. The company charges $30 one week and $15 the next week.

This type of study attempts to discover answers to the following questions: who, what, when,

where, or how much.

A manufacturer investigates whether consumers will buy a new pill that replaces eating a meal.

Cosmopolitan magazine sends out a cover in selected markets featuring a female model to

half of its readers and a cover with a female and male model to the other half of its readers to

test differences in purchase response between the two groups.

A hair-care manufacturer interviews wholesalers, retailers, and customers to determine the

potential for a new shampoo package.

This type of research attempts to capture a population’s characteristics by making inference

from a sample’s characteristics and testing hypotheses.

Descriptive

On the CBS television show Undercover Boss, top executives disguised as middle level or

lower

Chapter 11

Measurement

McGraw-Hill/Irwin Copyright © 2011 by The McGraw-Hill Companies, Inc. All Rights Reserved.

11-7

Learning Objectives

Understand . . .

• The distinction between measuring objects, properties, and

indicants of properties.

• The similarities and differences between the four scale types used in

measurement and when each is used.

• The four major sources of measurement error.

• The criteria for evaluating good measurement.

11-8

Measurements Will Vary Over Time

“The only man who behaved sensibly was my tailor; he took my

measurement anew every time he saw me, while all the rest

went on with their old measurements and expected them to fit me.”

George Bernard Shaw

playwright and essayist

11-9

PulsePoint:

Research Revelation

32.5 The percent of corporations

using or planning to use cloud

computing—using software

and server space via Internet

sources.

Measurement in research consists of:

• assigning numbers to empirical events, objects or properties, or

activities in compliance w/ a set of rules.

• Textbook uses an example of auto show attendance.

• A mapping rule is a scheme for assigning numbers to aspects of an

empirical event.

11-11

Characteristics of Measurement

11-12

Levels of Measurement

Ordinal

interval

Ratio

Nominal Classification


Ordinal

interval

Ratio


Order ( > or < )

Classification

• Order means that the numbers are ordered. One number is

greater than, less than, or equal to another number.

E.g., Pizza Hut is better than Papa Johns, ranking

11-14

From

Investigative to

Measurement

Questions

11-15

Ordinal Scales

• Ordinal data require conformity to a

logical postulate, which states:

If a is greater than b, and

b is greater than c, then

a is greater than c.

• The appropriate measure of central

tendency is the median. The median is

the midpoint of a distribution. A

percentile or quartile reveals the

dispersion.

11-16


Ordinal

interval

Ratio


Order

Classification

Order

Classification Distance

11-17


Ordinal

interval

Ratio


Order

Classification

Order


Natural Origin

Order


Ratio Scales

11-18

Examples

Weight

Height

Number of children

• Ratio data : actual amounts of a variable.

• E.g., monetary values, population counts, distances, return rates, and

amounts of time.

• Central tendency and coefficients of variation may also be calculated.

• Higher levels of measurement generally yield more information and are

appropriate for more powerful statistical procedures.

11-19

Sources of Error

1. Respondents may also suffer from temporary factors like fatigue and

boredom.

2. Any condition that places a strain on the interview

3. The interviewer can distort responses by rewording, paraphrasing, or

reordering questions.

• Stereotypes in appearance and action also introduce bias.

• Careless mechanical processing will distort findings and can also

introduce problems in the data analysis stage through incorrect

coding, careless tabulation, and faulty statistical calculation.

4. A defective instrument

• confusing and ambiguous.

• not explore all the potentially important issues.

11-20

Evaluating Measurement Tools

Criteria

Validity

Practicality Reliability

• Validity is the extent to which a test measures what we actually wish to

measure.

• Reliability refers to the accuracy and precision of a measurement

procedure.

• Practicality is concerned with a wide range of factors of economy,

convenience, and interpretability.

11-21

Understanding Validity and Reliability

Reliability & Validity

11-23

Validity Determinants

Content

Construct Criterion

11-24

Increasing Content Validity

Content Literature

Search

Expert

Interviews

Group

Interviews

Question

Database

Etc.

11-25


Content

Construct

11-26

Increasing Construct Validity

New measure of trust

Known measure of trust

Empathy

Credibility

11-27


Content

Construct Criterion

11-28

Judging Criterion Validity

Relevance

Freedom from bias

Reliability

Availability

Criterion

11-29

Reliability Estimates

Stability

Internal

Consistency Equivalence

Practicality

Economy Interpretability Convenience

11-31

Key Terms

• Internal validity

• Interval scale

• Mapping rules

• Measurement

• Nominal scale

• Objects

• Ordinal scale

• Practicality

• Properties

• Ratio scale

• Reliability

– Equivalence

– Internal consistency

– Stability

• Validity

– Construct

– Contents

– Criterion-related

Chapter 12

Measurement

Scales


12-33

Learning Objectives

Understand…

• The nature of attitudes and their relationship to behavior.

• The critical decisions involved in selecting an appropriate

measurement scale.

• The characteristics and use of rating, ranking, sorting,

and other preference scales.

12-34

Measurements are Relative

“Any measurement must take into account the

position of the observer. There is no such thing

as measurement absolute, there is only

measurement relative.”

Jeanette Winterson

journalist and author

12-35

PulsePoint:

Research Revelation

34 The percent of workers who

are considered truly loyal.

12-36

The Scaling

Process

12-37

Nature of Attitudes

Cognitive I think oatmeal is healthier

than corn flakes for breakfast.

Affective

Behavioral

I hate corn flakes.

I intend to eat more oatmeal

for breakfast.

“All survey questions must be actionable if you want

results.”

Frank Schmidt, senior scientist

The Gallup Organization

Response biases & sampling

Improving Predictability of Attitudes

Reference

groups

Multiple

measures Factors

Strong

Specific

Basis

Direct

Factors the applicability of attitudinal research for business.

1. Specific attitudes are better predictors of behavior

2. Strong attitudes are better predictors of behavior composed of little intensity or

topic interest.

3. Direct experiences with the attitude object produce behavior more reliably.

4. Cognitive-based attitudes influence behaviors better than affective-based

attitudes.

12-40

Measurement Scales

“All survey questions must be actionable if you want

results.”

Frank Schmidt, senior scientist


Selecting a Measurement Scale

Research objectives Response types

Data properties Number of

dimensions

Forced or unforced

choices

Balanced or

unbalanced

Rater errors Number of

scale points

Attitude scaling: process of assessing an attitudinal disposition using a number

that represents a person’s score on an attitudinal continuum ranging from an

extremely favorable disposition to an extremely unfavorable one.

12-42

Response Types

Rating scale

Ranking scale

Categorization

Sorting

12-43

Dimensions

-Religion, depression symptoms, democracy

Unidimensional

Multi-dimensional

12-44

Balanced or Unbalanced

Very bad

Bad

Neither good nor bad

Good

Very good

Poor

Fair

Good

Very good

Excellent

How good an actress is Angelina Jolie?

12-45

Forced or Unforced Choices

Very bad

Bad


Good

Very good

Very bad

Bad


Good

Very good

No opinion

Don’t know


Number of Scale Points

Very bad

Bad


Good

Very good

Very bad

Somewhat bad

A little bad


A little good

Somewhat good

Very good


Rater Errors

Error of

central tendency

Error of leniency

•Adjust strength of

descriptive adjectives

•Space intermediate

descriptive phrases

farther apart

•Provide smaller

differences

in meaning between

terms near the

ends of the scale

•Use more scale points

12-48

Rater Errors

Primacy Effect

Recency Effect

Reverse order of

alternatives periodically

or randomly

Rater Errors

Halo Effect

• Rate one trait

at a time

• Reveal one trait

per page

• Reverse anchors

periodically

• The halo effect is the systematic bias that the rater introduces

by carrying over a generalized impression of the subject from

one rating to another.

e.g., a teacher may expect that a student who did well on the

first exam to do well on the second.

12-50

Simple Category Scale

I plan to purchase a MindWriter laptop in the

12 months.

Yes

No

12-51

Multiple-Choice,

Single-Response Scale

What newspaper do you read most often for financial news?

East City Gazette

West City Tribune

Regional newspaper

National newspaper

Other (specify:_____________)

12-52

Multiple-Choice, Multiple-Response

Scale What sources did you use when designing your new

home? Please check all that apply.

Online planning services

Magazines

Independent contractor/builder

Designer

Architect

Other (specify:_____________)

12-53

Likert Scale

The Internet is superior to traditional libraries for

comprehensive searches.

Strongly disagree

Disagree

Neither agree nor disagree

Agree

Strongly agree

Semantic Differential

• studies of brand and institutional image, employee morale, safety,

financial soundness, trust, etc.

• usually with 7 points, by which one or more participants rate one or

more concepts on each scale item.

• Proposition: an object can have several dimensional meaning located

in multidimensional property space, called semantic space.

Adapting SD Scales

Convenience of Reaching the Store from Your Location

Nearby ___: ___: ___: ___: ___: ___: ___: Distant

Short time required to reach store ___: ___: ___: ___: ___: ___: ___: Long time required to reach store

Difficult drive ___: ___: ___: ___: ___: ___: ___: Easy Drive

Difficult to find parking place ___: ___: ___: ___: ___: ___: ___: Easy to find parking place

Convenient to other stores I shop ___: ___: ___: ___: ___: ___: ___: Inconvenient to other stores I shop

Products offered

Wide selection of different

kinds of products

___: ___: ___: ___: ___: ___: ___:

Limited selection of different

kinds of products

Fully stocked ___: ___: ___: ___: ___: ___: ___: Understocked

Undependable products ___: ___: ___: ___: ___: ___: ___: Dependable products

High quality ___: ___: ___: ___: ___: ___: ___: Low quality

Numerous brands ___: ___: ___: ___: ___: ___: ___: Few brands

Unknown brands ___: ___: ___: ___: ___: ___: ___: Well-known brands

12-56

SD Scale for Analyzing Actor

Candidates

12-57

Graphic of SD Analysis

Numerical Scale

• Numerical scales have equal intervals that separate their numeric

scale points. The verbal anchors serve as the labels for the extreme

points.

• Numerical scales are often 5-point scales but may have 7 or 10

points.

• The participants write a number from the scale next to each item.

• It produces either ordinal or interval data.

Multiple Rating List

Scales “Please indicate how important or unimportant each service characteristic is:”

IMPORTANT UNIMPORTANT

Fast, reliable repair 7 6 5 4 3 2 1

Service at my location 7 6 5 4 3 2 1

Maintenance by manufacturer 7 6 5 4 3 2 1

Knowledgeable technicians 7 6 5 4 3 2 1

Notification of upgrades 7 6 5 4 3 2 1

Service contract after warranty 7 6 5 4 3 2 1

Exhibit 12-3: A multiple rating scale is similar to the numerical

scale but differs in 2 ways:

1) it accepts a circled response from the rater, and

2) the layout facilitates visualization of the results.

• This scale produces interval data.

• Used as an alternative to the semantic differential, especially when it

is difficult to find bipolar adjectives that match the investigative

question.

• interval data.

Stapel Scales: 3 attributes of corporate image.

Constant-Sum

Scales

• The participant allocates points to more than one attribute or property indicant,

such that they total a constant sum, usually 100 or 10.

• Participant precision and patience suffer when too many stimuli are

proportioned and summed.

• A participant’s ability to add may also be taxed.

• Its advantage is its compatibility with percent and the fact that alternatives that

are perceived to be equal can be so scored.

• This scale produces interval data.

12-62

Graphic Rating Scales

12-63

Ranking Scales

(see next slides…)

Paired-comparison scale

Forced ranking scale

Comparative scale

Paired-Comparison Scale

Forced Ranking

Scale

• This method is faster than paired comparisons and is usually easier and

more motivating to the participant.

• A drawback of this scale is the limited number of stimuli (usually no

more than 7) that can be handed by the participant.

• This scale produces ordinal data.

12-66

Comparative Scale

Sorting

12-68

MindWriter Scaling Likert Scale

The problem that prompted service/repair was resolved

Strongly

Disagree

Disagree

Neither Agree

Nor Disagree

Agree

Strongly

Agree

1 2 3 4 5

Numerical Scale (MindWriter’s Favorite)

To what extent are you satisfied that the problem that prompted service/repair was

resolved?

Very

Dissatisfied

Very

Satisfied

1 2 3 4 5

Hybrid Expectation Scale

Resolution of the problem that prompted service/repair.

Met Few

Expectations

Met Some

Expectations

Met Most

Expectations

Met All

Expectations

Exceeded

Expectations

1 2 3 4 5

12-69

Ideal Scalogram Pattern (social distance, organizational

hierarchies, and evolutionary product stages)

Item

Participant

Score

2

4

1

3

X X X X 4

__ X X X 3

__ __ X X 2

__ __ __ X 1

__ __ __ __

0

* X = agree; __ = disagree.

Key Terms

• Attitude

• Balanced rating scale

• Categorization

• Comparative scale

• Constant-sum scale

• Cumulative scale

• Error of central tendency

• Error of leniency

• Forced-choice rating scale

• Forced ranking scale

• Graphic rating scale

• Halo effect

• Item analysis

• Likert scale

• Multidimensional scale

• Multiple-choice, multiple-response

scale

• Multiple-choice,

single-response scale

• Multiple rating list

• Numerical scale

• Paired-comparison scale

• Q-sort

• Ranking scale

• Rating scale

• Scaling

• Scalogram analysis

• Semantic differential

• Simple category scale

12-71

Key Terms

• Sorting

• Stapel scale

• Summated rating scale

• Unbalanced rating scale

• Unforced-choice rating scale

• Unidimensional scale

Chapter 13

Questionnaires

and

Instruments


13-73

Learning Objectives

Understand...

• The link forged between the management dilemma and the

communication instrument by the management-research question

hierarchy.

• The influence of the communication method on instrument design.

• The three general classes of information and what each contributes

to the instrument.

13-74

Learning Objectives

Understand . . .

• The influence of question content, question wording,

response strategy, and preliminary analysis planning on

question construction.

• Each of the numerous question design issues influencing

instrument quality, reliability, and validity.

• The sources for measurement questions

• The importance of pretesting questions and instruments.

13-75

Measurement Skepticism

“Research that asks consumers what they did and why is

incredibly helpful. Research that asks consumers what

they are going to do can often be taken with a grain of

salt.”

Al Ries

author, co-founder, and chairman

Ries & Ries.

13-76

PulsePoint:

Research Revelation

60 The percent of businesses hit

annually by cybercrime.

13-77

Overall Flowchart for Instrument Design

13-78

Flowchart for Instrument Design Phase 1

Strategic Concerns in Instrument Design

What type of scale is needed?

What communication approach will be used?

Should the questions be structured?

Should the questioning be disguised?

13-80

Technology Affects

Questionnaire Development

WebSurveyor used to write an instrument.

Write questionnaires more quickly

Create visually driven instruments

Eliminate manual data entry

Save time in data analysis

13-81

Disguising Study

Objectives

Situations

where

disguise is

unnecessary

Willingly shared,

Conscious-level

information

Reluctantly shared,

Conscious-level

information

Knowable,

Limited-conscious-

level information

Subconscious-level

information

13-82

Dummy Table for American Eating Habits

Age

Use of Convenience Foods

Always

Use

Use

Frequently

Use

Sometimes Rarely Use Never Use

18-24

25-34

35-44

55-64

65+

13-83

Flowchart for Instrument Design Phase 2

Question Categories and Structure

Administrative Target Classification

3 categories of measurement questions.

1. Administrative questions identify the participant, interviewer, interviewer

location, and conditions. These questions are rarely asked of the participant

but are necessary for studying patterns within the data and identify possible

error sources.

2. Classification questions usually cover sociological-demographic variables that

allow participants’ answers to be grouped so that patterns are revealed and

can be studied. These questions usually appear at the end of a survey.

3. Target questions address the investigative questions of a specific study.

These are grouped by topic in the survey. Target questions may be structured

or unstructured.

13-85

Engagement = Convenience

“Participants are becoming more and more

aware of the value of their time. The key to

maintaining a quality dialog with them is to

make it really convenient for them to

engage, whenever and wherever they want.”

Tom Anderson

managing partner

Anderson Analytics

13-86

Question Content

Should this question be asked?

Is the question of proper scope and coverage?

Can the participant adequately

answer this question as asked?

Will the participant willingly

answer this question as asked?

Criteria of Question Wording

Criteria

Shared

vocabulary Single

meaning

Misleading

assumptions

Adequate

alternatives

Personalized

Biased

1. Is the question stated in terms of a shared vocabulary?

2. Does the question contain vocabulary with a single meaning?

3. Does the question contain unsupported or misleading

assumptions?

4. Does the question contain biased wording?

5. Is the question correctly personalized?

6. Are adequate alternatives presented within the question?

Response Strategy

Factors

Objectives

of the study

Participant’s level

of information

Degree to which participants

have thought through topic

Ease and clarity with which

participant communicates

Participant’s

motivation to

share

In choosing response options in questions, researchers must consider

these factors.

13-89

Free-Response Strategy - open-ended questions

What factors influenced your enrollment in Metro U?

____________________________________________

____________________________________________

Dichotomous Response Strategy

Did you attend the “A Day at College”

program at Metro U?

Yes

No

Which one of the following factors was

most influential

in your decision to attend Metro U?

Good academic standing

Specific program of study desired

Enjoyable campus life

Many friends from home

High quality of faculty

Multiple Choice Response Strategy

Checklist Response Strategy

Which of the following factors influenced your decision to enroll in Metro U?

(Check all that apply.)

Tuition cost

Specific program of study desired

Parents’ preferences

Opinion of brother or sister

Many friends from home attend

High quality of faculty

Strongly influential Somewhat Not at all

Good academic reputation

Enjoyable campus life

Many friends

High quality faculty

Semester calendar

Ranking

Please rank-order your top three factors from the following list based on their

influence in encouraging you to apply to Metro U. Use 1 to indicate the most

encouraging factor, 2 the next most encouraging factor, etc.

_____ Opportunity to play collegiate sports

_____ Closeness to home

_____ Enjoyable campus life

_____ Good academic reputation

_____ High quality of faculty

13-93

Summary of Scale Types

Type Restrictions Scale

Items

Data Type

Rating Scales

Simple Category

Scale

• Needs mutually exclusive choices One or

more

Nominal

Multiple Choice

Single-Response

Scale

• Needs mutually exclusive choices

• May use exhaustive list or ‘other’

Many Nominal

Multiple Choice

Multiple-Response

Scale

(checklist)

• Needs mutually exclusive choices

• Needs exhaustive list or ‘other’

Many Nominal

Likert Scale • Needs definitive positive or

negative statements with which to

agree/disagree

One or

more

Ordinal

Likert-type Scale •Needs definitive positive or

negative statements with which to

agree/disagree

One or

more

Ordinal

13-94


Type Restrictions Scale Items Data Type

Rating Scales

Numerical

Scale

Needs concepts with standardized

meanings;

Needs number anchors of the scale or end-

points

Score is a measurement of graphical space

One or many Ordinal or

Interval

Multiple

Rating List

Scale

Needs words that are opposites to anchor

the end-points on the verbal scale

Up to 10 Ordinal

Fixed Sum

Scale

Participant needs ability to calculate total

to some fixed number, often 100.

Two or more Interval or

Ratio


Type Restrictions Scale Items Data Type

Rating Scales

Stapel Scale Needs verbal labels that are

operationally defined or standard.

One or more Ordinal or

Interval

Graphic

Rating Scale

Needs visual images that can be

interpreted as positive or negative

anchors

Score is a measurement of graphical

space from one anchor.

One or more Ordinal

(Interval, or

Ratio)

Ranking Scales

Paired

Comparison

Scale

• Number is controlled by

participant’s stamina and interest.

Up to 10 Ordinal

Forced

Ranking Scale

• Needs mutually exclusive choices. Up to 10 Ordinal or

Interval

Comparative

Scale

• Can use verbal or graphical scale. Up to 10 Ordinal

Internet Survey Scale Options

13-97

Internet Survey Scale Options

13-98

Internet Survey

Scale Options

Sources of Questions

• Handbook of Marketing Scales

• The Gallup Poll Cumulative

Index

• Measures of Personality and

Social-Psychological Attitudes

• Measures of Political Attitudes

• Index to International Public

Opinion

• Sourcebook of Harris National

Surveys

• Marketing Scales Handbook

• American Social Attitudes Data

Sourcebook

13-100

Flowchart for

Instrument Design

Phase 3

13-101

Guidelines for Question Sequencing

Interesting topics early

Simple topics early

Sensitive questions later

Classification questions later

Transition between topics

Reference changes limited

13-102

Illustrating the Funnel Approach

1. How do you think this country is getting along in its relations with

other countries?

2. How do you think we are doing in our relations with Iran?

3. Do you think we ought to be dealing with Iran differently than we

are now? (If yes) What should we be doing differently?

4. Some people say we should get tougher with Iran and others think

we are too tough as it is; how do you feel about it?

13-103

Branching Question

13-104

Components of Questionnaires

13-105

MindWriter Survey

13-106

Overcoming Instrument Problems

Build rapport

Redesign question process

Explore alternatives

Use other methods

Pretest

13-107

Key Terms

• Administrative question

• Branched question

• Buffer question

• Checklist

• Classification question

• Dichotomous question

• Disguised question

• Double-barreled question

• Free-response question

• Interview schedule

• Leading question

• Multiple-choice question

• Pretesting

• Primacy effect

• Ranking question

• Rating question

• Recency effort

• Screen question

• Structured response

• Target question

– Structured

– Unstructured

• Unstructured response

Chapter 14

Sampling


14-109

Small Samples Can Enlighten

“The proof of the pudding is in the eating.

By a small sample we may judge of the

whole piece.”

Miguel de Cervantes Saavedra

author

14-110

PulsePoint:

Research Revelation

80 The average number of text

messages sent per day by

American teens.

Sampling Methods

14-112

The Nature of Sampling

•Population

•Population Element

•Census

•Sample

•Sampling frame

14-113

Why Sample?

Greater

accuracy

Availability of

elements

Greater

speed

Sampling

provides

Lower cost

14-114

What Is a Sufficiently Large Sample?

“In recent Gallup ‘Poll on polls,’ . . . When asked about the

scientific sampling foundation on which polls are based . .

. most said that a survey of 1,500 – 2,000 respondents—a

larger than average sample size for national polls—cannot

represent the views of all Americans.”

Frank Newport

The Gallup Poll editor in chief


14-115

When Is a Census Appropriate?

Necessary Feasible

14-116

What Is a Valid Sample?

Accurate Precise

14-117

Sampling Design

within the Research Process

14-118

Types of Sampling Designs

Element Selection Probability Nonprobability

Unrestricted Simple random Convenience

Restricted Complex random Purposive

Systematic Judgment

Cluster Quota

Stratified Snowball

Double

14-119

Steps in Sampling Design

What is the target population?

What are the parameters of interest?

What is the sampling frame?

What is the appropriate sampling

method?

What size sample is needed?

14-120

When to Use Larger Sample?

Desired

precision

Number of

subgroups

Confidence

level

Population

variance

Small error

range

Simple Random

Advantages

• Easy to implement with

random dialing

Disadvantages

• Requires list of population

elements

• Time consuming

• Larger sample needed

• Produces larger errors

• High cost

Systematic

Advantages

• Simple to design

• Easier than simple random

• Easy to determine sampling

distribution of mean or proportion

Disadvantages

• Periodicity within population

may skew sample and results

• Trends in list may bias results

• Moderate cost

14-122

Stratified

Advantages

• Control of sample size in strata

• Increased statistical efficiency

• Provides data to represent and

analyze subgroups

• Enables use of different

methods in strata

Disadvantages

• Increased error if subgroups

are selected at different rates

• Especially expensive if strata

on population must be created

• High cost

14-123

Cluster

Advantages

• Provides an unbiased estimate

of population parameters if

properly done

• Economically more efficient

than simple random

• Lowest cost per sample

• Easy to do without list

Disadvantages

• Often lower statistical efficiency

due to subgroups being

homogeneous rather than

heterogeneous

• Moderate cost

14-124

Stratified and Cluster Sampling

Stratified

• Population divided into few

subgroups

• Homogeneity within subgroups

• Heterogeneity between

subgroups

• Choice of elements from within

each subgroup

Cluster

• Population divided into many

subgroups

• Heterogeneity within subgroups

• Homogeneity between

subgroups

• Random choice of subgroups

14-125

Area Sampling

14-126

Double Sampling

Advantages

• May reduce costs if first stage

results in enough data to

stratify or cluster the population

Disadvantages

• Increased costs if

discriminately used

14-127

Nonprobability Samples

Cost

Feasibility

Time

No need to

generalize

Limited objectives

14-128

Nonprobability Sampling Methods

Convenience

Judgment

Quota

Snowball

14-129

Key Terms

• Area sampling

• Census

• Cluster sampling

• Convenience sampling

• Disproportionate stratified sampling

• Double sampling

• Judgment sampling

• Multiphase sampling

• Nonprobability sampling

• Population

• Population element

• Population parameters

• Population proportion of incidence

• Probability sampling

• Proportionate stratified

sampling

• Quota sampling

• Sample statistics

• Sampling

• Sampling error

• Sampling frame

• Sequential sampling

• Simple random sample

• Skip interval

• Snowball sampling

• Stratified random sampling

• Systematic sampling

• Systematic variance

Appendix 14a

Determining Sample

Size


14-131

Random Samples

14-132

Increasing Precision

14-133

Confidence Levels & the Normal

Curve

14-134

Standard Errors

Standard Error

(Z score)

% of Area Approximate

Degree of

Confidence

1.00 68.27 68%

1.65 90.10 90%

1.96 95.00 95%

3.00 99.73 99%

14-135

Central Limit Theorem

14-136

Estimates of Dining Visits

Confidence Z score % of Area Interval Range

(visits per month)

68% 1.00 68.27 9.48-10.52

90% 1.65 90.10 9.14-10.86

95% 1.96 95.00 8.98-11.02

99% 3.00 99.73 8.44-11.56

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Calculating Sample Size for Questions involving

Means

Precision

Confidence level

Size of interval estimate

Population Dispersion

Need for FPA

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Metro U Sample Size for Means

Steps Information

Desired confidence level 95% (z = 1.96)

Size of the interval estimate .5 meals per month

Expected range in population 0 to 30 meals

Sample mean 10

Standard deviation 4.1

Need for finite population

adjustment

No

Standard error of the mean .5/1.96 = .255

Sample size (4.1)2/ (.255)2 = 259

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Proxies of the Population Dispersion

• Previous research on the topic

• Pilot test or pretest

• Rule-of-thumb calculation

– 1/6 of the range

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Metro U Sample Size for Proportions

Steps Information

Desired confidence level 95% (z = 1.96)

Size of the interval estimate .10 (10%)

Expected range in population 0 to 100%

Sample proportion with given attribute 30%

Sample dispersion Pq = .30(1-.30) = .21

Finite population adjustment No

Standard error of the proportion .10/1.96 = .051

Sample size .21/ (.051)2 = 81

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Appendix 14a: Key Terms

• Central limit theorem

• Confidence interval

• Confidence level

• Interval estimate

• Point estimate

• Proportion

Addendum: Keynote

CloseUp


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Keynote Experiment

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Keynote Experiment (cont.)

Determining

Sample Size

Appendix 14a


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Random Samples

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Confidence Levels

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Metro U. Dining Club Study

Documents

4 research design + sampling methods dr. hueihsia holloman