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Marketing Research > Midterm 1 > Flashcards

Midterm 1 Flashcards

1
Q

What is Marketing Research?

A

A collection of techniques for collecting, recording, analyzing and interpreting date for business decision making.

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2
Q

Backward marketing research

A

Research done from the end, product-forward. Produces action-oriented results.

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3
Q

How to qualify marketing research

A

Reliability, cost, potential profits with/without info, management’s ability to use info

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4
Q

Expected profit

A 
E(profit) = probability*profit1 + probability*profit2
E(info) = E(Profit + Info) - E(Profit)
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5
Q

Exploratory Research

A

Used when there is an ambiguous problem. Requires secondary data, qualitative data.

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6
Q

Descriptive Research

A

Used when the problem is somewhat defined, need to know how to solve it. Requires a survey.

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7
Q

Causal Research

A

Used when problem is clearly defined to find solutions. Requires an experiment.

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8
Q

Primary data sources

A

Benefits: Up to date, perfect fit for questions, control over how it is collected.
Qualitative: Focus group, interview
Quantitative: Survey, Experiment

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9
Q

Secondary data sources

A

Benefits: Cheaper than primary data, readily available. Good starting point.

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10
Q

Sources of Data

A

Scanner Data: literally use scanners, see who’s buying what. Can’t see why
Single-source Data: Nielsen ex. Use stream of data from single consumer (panel)
Geo-segmentation: Segmentation by demographics, lifestyle

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11
Q

Attitudes, Intentions, Behavior

A

Attitude: a positive or negative evaluation of a product
Intention: an indication of an individual’s readiness to perform a given behavior
Behavior: an individual’s observable response

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12
Q

When to use qualitative research?

A

Exploratory studies: to establish basis for quantitative research
New product development: after quantitative research to identify gaps

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13
Q

Focus groups: Pros and cons

A

Pros: Easy, good for in-depth info, complex issues can be discussed, one person’s experiences stimulate others
Cons: Superficial reactions, not quantifiable, group process may stunt frank exchange, minority viewpoints may not be heard

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14
Q

Zaltman Metaphor Elicitation Technique

A

Uses metaphor to reveal unconscious thoughts and thoughts based on sensory info

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15
Q

Primary Data: Qual vs. Quant

A

Qual: Small # of cases, unstructured data, nonstatistical inference, give richer understanding
Quant: large # of cases, structured, statistical inference, recommends final course of action

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16
Q

Types of scales

A

Nominal: Categorical. Measures frequency of recorded variable. Ex: Male or female
Ordinal: Categorical. Can be ranked. Ex: 1-5 min, 6-10 min, etc.
Interval: Quantitative. Data can be treated as numbers. Ex: strongly disagree - strongly agree (1-5)
Ratio: Quantitative. Fill-in the blank, numbers are numbers. Ex: what is your annual salary? ___

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17
Q

Central tendency

A

Mean.

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18
Q

Dispersion

A

Variance/standard deviation

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19
Q

Ratio scales

A

Likert: agree - disagree
Rank-order: rank items by importance
Paired comparison: which of these two do you prefer?

20
Q

Requirements of causal inference

A
  1. Correlation
  2. Temporal Antecedence (x must come before y)
  3. No third factor driving both
21
Q

What kind of data can be used to determine causation?

A

Only experimental data. Correlational data (scanner, surveys) cannot be used.

22
Q

Reliability

A

Will I get the same result if I measure again? Affected by random error.

23
Q

Validity

A

Am I measuring what I am supposed to measure? Affected by systematic error.

24
Q

Observed v. True Score

A

O = T + e(systematic) + e(random)

25
Q

Internal validity

A

Accuracy, measuring what you meant to. Extent to which results reflect the truth.

26
Q

External validity

A

Can you generalize the results? Extent to which results will hold beyond experimental setting

27
Q

Threats to internal validity

A 
History effect
Maturation effect
Pre-test effect
Instrument Variation
Statistical regression
Selection effect
Mortality
28
Q

Threats to external validity

A 
History effect
Maturation effect
Pre-test effect
Instrument Variation
Statistical regression
Selection effect
Mortality
Reactive bias
Non-representative sample, environment, materials used
29
Q

Between-subjects design

A

Experimental design in which each subject only receives one treatment. Comparisons are made between different groups of subjects

30
Q

Within-subjects design

A

Experimental design in which subjects receive more than one treatment. Statistically superior, but not always possible.

31
Q

Experimental design notation

A 
O: Any observation or measurement
X: Exposure of experimental units to the treatment
EG: Experimental group
CG: Control group
R: Random assignment
32
Q

Interaction Effect

A

Effect of one independent variable on the dependent variable changes depending on the level of another independent variable

33
Q

Main Effect

A

The effect of one of the independent variables averaging over all levels of other variables

34
Q

Types of Sampling

A

Non-probability sampling: Population elements are sampled in a non-random manner. Exploratory stage, pre-test, cost effective

Probability sampling: Every element has a known, non-zero probability of inclusion in the sample

35
Q

Non-probability sampling

A

Judgmental sampling: use experts’ judgment to identify samples
Snowball sampling: One respondent identifies other respondents (small, specialized community)
Quota sampling: sample a minimum number from each specified subgroup in the population

36
Q

Probability Sampling

A

Simple random: Everyone has the same known nonzero probability of inclusion
Stratified: Population is divided into strata. Sample a proportion of each strata (either equal or based on size or variability)
Cluster: Population is divided into clusters. Each cluster is selected randomly. Everyone within cluster is a respondent.

37
Q

Target Population

A

Define the relevant population

38
Q

Sampling Frame

A

List of elements from which the sample may be drawn

39
Q

Sampling Unit

A

Group that is selected for the sample

40
Q

Mean (Mu, x bar)

A

Population mean = (1/N)(X1 + X2+…+Xi)

Sample mean = (1/n)(X1 + X2+…+Xi)

41
Q

Variance (omega^2, s^2)

A

Pop = (1/N)[(X1-popmean)^2 +…+(Xi-popmean)^2]
= SD^2
Sample = (1/n-1)[(X1-sampmean)^2 +…+(Xi-sampmean)^2]

42
Q

Standard deviation (omega, SD)

A

SD = square root of variance

43
Q

Normal distribution

A

1 SD from mean: 68%
2 SD from mean: 95%
3 SD from mean: 99.7%

44
Q

Standard deviation of the sample mean

A

SD/(square root of sample n)

45
Q

Confidence interval

A

68% confidence interval: (Xbar -SD, Xbar +SD)
95% confidence interval: (Xbar -2SD, Xbar +2SD)
99.7% confidence interval: (Xbar -3SD, Xbar +3SD)
OR
(Xbar -ZSD, Xbar +ZSD)
Z = 1, 1.96, 2.58

Marketing Research (1 decks)

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