Marketing Research Test 3

Front 1

represents how favorable a customer is toward a business.

Back 1

Net Promoter Scale

Front 2

≤ 6

Back 2

detractor

Front 3

7-8

Back 3

passively satisfied

Front 4

≥ 9

Back 4

signifies a promoter

Front 5

assigning numbers in a reliable and valid way

Back 5

measurement

Front 6

A generalized idea about a class of objects,
attributes, occurrences, or processes.

Back 6

concept

Front 7

identifying scales involved in a research process.

Back 7

operationalization

Front 8

range of values exhibited in observing a concept.

Back 8

scales

Front 9

certain value on a scale corresponds to some true value of a concept.

Back 9

correspondence

Front 10

different values of a concept.

Back 10

variables

Front 11

Concepts measured with multiple variables.

Back 11

constructs

Front 12

Assigns a value to an object for identification
or classification purposes.
Most elementary level of measurement

Back 12

nominal scale

Front 13

Ranking scales allowing things to be arranged
based on how much of some concept they
possible.
Have nominal properties.

Back 13

ordinal scale

Front 14

Capture information about differences in quantities of
a concept from one observation to the next.
•Have both nominal and ordinal properties.

Back 14

interval scale

Front 15

Highest form of measurement.
•Have all the properties of interval scales with the
additional attribute of representing absolute
quantities.
•Absolute zero.

Back 15

ratio scale

Front 16

•Measures that can take on only one of a finite
number of values.

Back 16

discrete measures

Front 17

Measures that reflect the intensity of a
concept by assigning values that can take on
any value along some scale range

Back 17

continuous measures

Front 18

Single characteristics or fundamental features that
pertain to an object, person, or issue

Back 18

attributes

Front 19

Assign a value based on how much of the concept
being measured is associated with an observation.
•Indexes often are formed by putting several variables
together.

Back 19

index measures

Front 20

Assign a value to an observation based on a
mathematical derivation of multiple variables

Back 20

composite measures

Front 21

A scale created by simply summing (adding
together) the response to each item making up
the composite measure.

Back 21

summated scale

Front 22

Means that the value assigned for a response is
treated oppositely from the other items.

Back 22

reverse coding

Front 23

Represents a measure’s homogeneity or the extent to
which each indicator of a concept converges on some
common meaning.

Back 23

internal consistency

Front 24

Checks the results of one-half of a set of scaled items against the results from the other half.

Back 24

split half method

Front 25

The most commonly applied estimate of a multiple item scale’s reliability.

•Represents the average of all possible split-half reliabilities for
a construct.

Back 25

coefficient alpha

Front 26

Administering the same scale or measure to
the same respondents at two separate points
in time to test for stability.
•Represents a measure’s repeatability.

Back 26

test retest method

Front 27

Extent to which individual measures’ content match
the intended concept’s definition

Back 27

face content validity

Front 28

The ability of a measure to correlate with other
standard measures of similar constructs or established
criteria.

Back 28

criterion validity

Front 29

Exists when a measure reliably measures and truthfully
represents a unique concept.
•Consists of several components, including face validity,
convergent validity, criterion validity, and discriminant validity

Back 29

construct validity

Front 30

Depends on internal consistency so that
multiple measures converge on a consistent
meaning.

Back 30

convergent validity

Front 31

Represents how unique or distinct is a
measure; a scale should not correlate too
highly with a measure of a different construct.

Back 31

discriminant validity

Front 32

The feelings or emotions toward an object

Back 32

affective component

Front 33

Knowledge and beliefs about an object

Back 33

cognitive component

Front 34

Represents action undertaken as a result of the
affective and cognitive components

Back 34

behavioral component

Front 35

Consists of several response categories, often
providing respondents with alternatives to indicate
positions on a continuum.
•Question wording is extremely important.

Back 35

category scale

Front 36

A popular means for measuring attitudes.
•Respondents indicate their own attitudes by checking
how strongly they agree or disagree with statements.

Back 36

likert scale

Front 37

A series of a series of bipolar rating scales with
opposite terms on either end, such as “good”
and “bad,” “modern” and “old-fashioned.”

Back 37

semantic differential

Front 38

•Respondents are asked to divide a constant sum to
indicate the relative importance of attributes.
•Respondents often sort cards, but the task may also be a
rating task (e.g., indicating brand preference).

Back 38

constant sum scale

Front 39

A measure of attitude that allows respondents to rate an object by choosing any point along a graphic
continuum.

Advantage:
Allows the researcher to choose any interval desired for scoring purposes.

Back 39

graphite rating scale

Front 40

Respondents simply order alternatives on some characteristic.
•An ordinal scale may be developed by asking respondents to rank
order (from most preferred to least preferred) a set of objects or
attributes.

Back 40

Ranking

Front 41

A measurement technique that involves presenting the
respondent with two objects and asking the respondent to pick
the preferred object; more than two objects may be presented,
but comparisons are made in pairs.
•Number of comparisons = [(n)(n-1)/2]

Back 41

paired comparisons

Front 42

How Many Scale Categories or
Response Positions?

Back 42

Five to eight points are optimal for
sensitivity.
•The researcher must determine the
number of positions that is best for the
specific project.

Front 43

•A fixed-alternative rating scale with an equal
number of positive and negative categories; a
neutral point or point of indifference is at the
center of the scale.

Back 43

balanced rating scale

Front 44

A fixed-alternative rating scale that has more
response categories at one end than the other
resulting in an unequal number of positive and
negative categories.

Back 44

unbalanced rating scale

Front 45

A fixed-alternative rating scale that requires
respondents to choose one of the fixed
alternatives.

Back 45

forced choice rating scale

Front 46

A fixed-alternative rating scale that provides a
“don’t know” or “no opinion” category or
allowing respondents to indicate that they
cannot say which alternative is their choice.

Back 46

non forced rating scale

Front 47

Factors affecting the choice of using a
single item or a measure made up from
responses to several items depends on:

Back 47

•The complexity of the phenomenon measured.
•The number of dimensions of the
phenomenon.
•The level of abstraction of the phenomenon

Front 48

A model that constructs an attitude score
based on the multiplicative sum of beliefs
about an option times the evaluation of those
belief characteristics.
•Key advantage: Results are diagnostic

Back 48

multi attribute model

Front 49

The behavioral expectations of an individual
toward an attitudinal object.

Back 49

behavioral intention

Front 50

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

Back 50

sample

Front 51

Any complete group of entities that share
some common set of characteristics.

Back 51

population universe

Front 52

•An individual member of a population.

Back 52

population element

Front 53

An investigation of all the individual elements
that make up a population.

Back 53

census

Front 54

Why sample?
pragmatic reasons

Back 54

Budget and time constraints.
•Limited access to total population.

Front 55

why sample?
accurate and reliable results

Back 55

•Samples can yield reasonably accurate information.
•Strong similarities in population elements makes
sampling possible.
•Sampling may be more accurate than a census.

Front 56

why sample?
destruction of test units

Back 56

Sampling reduces the costs of research in finite
populations

Front 57

defining the target population

Back 57

•What is the relevant population?
•Whom do we want to talk to?
•Population is operationally defined by specific and explicit
tangible characteristics.

Front 58

A list of elements from which a sample may be drawn;
also called working population.

Back 58

the sampling frame

Front 59

Occurs when certain sample elements are not listed or are not accurately represented in a sampling frame.

Back 59

sampling frame error

Front 60

Pioneered sampling
theories.
•Most based on the idea
of random sampling,
which is increasingly
difficult to do.
•Major companies have
turned to online
samples.

Back 60

George Gallup

Front 61

Sampling services (list brokers)
•Provide lists or databases of the names, addresses, phone
numbers, and e-mail addresses of specific populations.
•Reverse directory
–A directory similar to a telephone directory except that listings
are by city and street address or by phone number rather than
alphabetical by last name.

Back 61

sampling frame

Front 62

Lists of respondents who have agreed to participate in
marketing research via e-mail.

Back 62

online panels

Front 63

Availability of sampling frames varies dramatically around the
world.

Back 63

international research

Front 64

A single element or group of elements subject
to selection in the sample.

Back 64

sampling unit

Front 65

A unit selected in the first stage of sampling

Back 65

primary sampling unit

Front 66

A unit selected in the second stage of
sampling.

Back 66

secondary sampling unit

Front 67

A unit selected in the third stage of sampling

Back 67

tertiary sampling unit

Front 68

The difference between the sample result and the
result of a census conducted using identical
procedures.
•A statistical fluctuation that occurs because of chance
variations in the elements selected for a sample.

Back 68

random sampling error

Front 69

Systematic (nonsampling) error results from
nonsampling factors, primarily the nature of a study’s
design and the correctness of execution.
•It is not due to chance fluctuation

Back 69

systematic sampling error

Front 70

A sampling technique in which every member
of the population has a known, nonzero
probability of selection.

Back 70

probabily sampling

Front 71

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

Back 71

non probability sampling

Front 72

Obtaining those people or units that are most
conveniently available.

Back 72

convenience sampling

Front 73

An experienced individual selects the sample based on
personal judgment about some appropriate
characteristic of the sample member.

Back 73

judgement sampling

Front 74

Ensures that various subgroups of a population will be
represented on pertinent characteristics to the exact
extent that the investigator desires

Back 74

quota sampling

Front 75

Possible Sources Of Bias with Quota Sampling
Respondents chosen because they were:

Back 75

•Similar to interviewer
•Easily found
•Willing to be interviewed
• Middle class

Front 76

advantages of quota sampling

Back 76

Speed of data collection
•Lower costs
•Convenience

Front 77

A sampling procedure in which initial
respondents are selected by probability
methods and additional respondents are
obtained from information provided by the
initial respondents.

Back 77

snowball sampling

Front 78

Assures each element in the population of an
equal chance of being included in the sample

Back 78

simple random sampling

Front 79

A starting point is selected by a random
process and then every nth number on the list
is selected.

Back 79

systematic sampling

Front 80

Simple random subsamples that are more or
less equal on some characteristic are drawn
from within each stratum of the population.

Back 80

stratified sampling

Front 81

The number of sampling units drawn from
each stratum is in proportion to the population
size of that stratum

Back 81

proportional stratified sampling

Front 82

The sample size for each stratum is allocated
according to analytical considerations.

Back 82

disproportional stratified sampling

Front 83

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

Back 83

cluster sampling

Front 84

Involves using a combination of two or more
probability sampling techniques.
•Typically, geographic areas are randomly selected in
progressively smaller (lower-population) units.
•Researchers may take as many steps as necessary to
achieve a representative sample.

Back 84

multistage area sampling

Front 85

What is the Appropriate Sample Design?
Criteria considered:

Back 85

•Degree of accuracy
•Resources
•Time
•Advance knowledge of the population
•National versus loc

Front 86

Describe characteristics of populations or
samples.

Back 86

descriptive statistics

Front 87

Make inferences about whole populations
from a sample.

Back 87

inferential statistics

Front 88

Variables in a sample or measures computed
from sample data.

Back 88

sample statistics

Front 89

A set of data organized by summarizing the
number of times a particular value of a variable
occurs.

Back 89

frequency distribution

Front 90

A frequency distribution organized into a table
(or graph) that summarizes percentage values
associated with particular values of a variable

Back 90

percentage distribution

Front 91

The long-run relative frequency with which an
event will occur

Back 91

probability

Front 92

The percentage of elements that meet some
criterion.

Back 92

proportion

Front 93

Proportion of respondents who chose the most
positive choice in a multiple choice question.
•The portion that would most likely recommend a
business to a friend or most likely make a purchase

Back 93

Top Box scores

Front 94

the value that occurs the most often

Back 94

mode

Front 95

The distance between the smallest and the
largest values of a frequency distribution.

Back 95

range

Front 96

Indicate how far any observation is from the
mean

Back 96

deviation scores

Front 97

•A measure of variability or dispersion.
•Its square root is the standard deviation.

Back 97

variance

Front 98

A quantitative index of a distribution’s spread, or
variability; the square root of the variance for a
distribution.
•The average of the amount of variance for a
distribution

Back 98

standard deviation

Front 99

know how to get standard deviation

Back 99

***slide 16

Front 100

A symmetrical, bell-shaped distribution (normal curve)
that describes the expected probability distribution of
many chance occurrences.
•99% of its values are within ± 3 standard deviations
from its mean.
•Example: IQ scores

Back 100

normal distribution

Front 101

•A purely theoretical probability distribution that
reflects a specific normal curve for the standardized
value, z.

Back 101

standardized normal distribution

Front 102

1. It is symmetrical about its mean.

2.The mean identifies the normal curve’s highest point
(the mode) and the vertical line about which this
normal curve is symmetrical.

3.The normal curve has an infinite number of cases (it is
a continuous distribution), and the area under the
curve has a probability density equal to 1.0.

4.The standardized normal distribution has a mean of 0
and a standard deviation of 1.

Back 102

Characteristics of a Standardized Normal
Distribution

Front 103

•Used to compare an individual value to the
population mean in units of the standard
deviation

Back 103

standardized values

Front 104

A frequency distribution of the elements of a population.

Back 104

population distribution

Front 105

A frequency distribution of a sample.

Back 105

sample distribution

Front 106

A theoretical probability distribution of sample means for all possible samples of a certain size drawn from a particular population.

Back 106

sampling distribution

Front 107

The standard deviation of the sampling distribution.

Back 107

standard error of the mean

Front 108

The theory that, as sample size increases, the
distribution of sample means of size n,
randomly selected, approaches a normal
distribution.

Back 108

central limit theorem

Front 109

An estimate of the population mean in the form of a
single value, usually the sample mean.

Back 109

point estimates

Front 110

A specified range of numbers within which a
population mean is expected to lie.
•An estimate of the population mean based on the
knowledge that it will be equal to the sample mean
plus or minus a small sampling error

Back 110

confidence interval estimates

Front 111

•A percentage or decimal value that tells how confident
a researcher can be about being correct.

•It states the long-run percentage of confidence
intervals that will include the true population mean.

•The crux of the problem for a researcher is to
determine how much random sampling error to
tolerate.

•Traditionally, researchers have used the 95%
confidence level (a 5% tolerance for error).

Back 111

confidence level

Front 112

•Random sampling error varies with samples of
different sizes.
•Increases in sample size reduce sampling error
at a decreasing rate.
•Diminishing returns - random sampling error is
inversely proportional to the square root of n

Back 112

random error and sample size

Front 113

•A heterogeneous population has more variance (a
larger standard deviation) which will require a larger
sample.

•A homogeneous population has less variance (a
smaller standard deviation) which permits a smaller
sample.

Back 113

variance

Front 114

How precise must the estimate be?

Back 114

magnitude of error

Front 115

How much error will be tolerated?

Back 115

confidence level

Front 116

Conducting a pilot study to estimate the population
parameters so that another, larger sample of the
appropriate sample size may be drawn.

Back 116

sequential sampling