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marketing research > exam 2 > Flashcards

exam 2 Flashcards

1
Q

diminishing returns in sampling

A

after a certain sample size, increases provide minimal additional accuracy

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2
Q

stratified sampling

A

dividing a population into subgroups and sampling from each to ensure representation across all groups

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3
Q

confidence level

A

a 95% confidence level means 95% of survey repetitions would fall within the
margin of error, if a study were replicated the same results would be returned

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4
Q

convenience sampling

A

who the researcher has easiest access to, convenience samples may not represent the population accurately

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5
Q

probability sampling

A

gives each population member an equal chance of being selected

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6
Q

efficiency in sampling

A

sampling helps researchers make inferences about a population without surveying everyone

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7
Q

quota sampling

A

ensures the sample reflects certain population characteristics without random selection

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8
Q

incidence rate

A

represents the proportion of the general population meeting the survey criteria

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9
Q

stratified sampling

A

ensures each region or subgroup is proportionally represented

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10
Q

snowball sampling

A

effective for hard-to-reach populations by using current participants to recruit others

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11
Q

sample size determinants

A

survey length does not directly affect sample size; confidence level, margin of
error, and population size do

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12
Q

referrals

A

snowball sampling involves using referrals from current participants to reach more respondents

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13
Q

known selection

A

in probability sampling, every population member has a known selection chance,
ensuring representativeness

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14
Q

non-probability sampling

A

less representative, limiting generalizability of results

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15
Q

survey length

A

long, complex surveys can reduce response rates, participants are less likely to complete them

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16
Q

mail surveys

A

mail surveys generally have lower response rates compared to online surveys

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17
Q

incentives

A

offering incentives can encourage participants to complete the survey, boosting response rates

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18
Q

cost-effective survey

A

online surveys are more cost-effective due to lower distribution and collection costs

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19
Q

confidentiality

A

restricting access to personal data to
authorized personnel only

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20
Q

integrated survey tools

A

online survey software offers tools to create, distribute, and analyze surveys

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21
Q

anonymity

A

online surveys are preferred for sensitive topics as they allow anonymity and reduce bias

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22
Q

high response rates

A

improves data reliability and better reflect the population’s views

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23
Q

reducing survey bias

A

randomizing questions and using neutral language helps reduce survey response
bias

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24
Q

ethical data collection

A

ensures participant confidentiality and informed consent

25
Q

survey invitations

A

should be clear, concise, and engaging to motivate participants

26
Q

branching logic

A

customizes the survey path based on prior responses, improving relevance and flow

27
Q

self-selection bias

A

occurs when only certain respondent types participate by volunteering, skewing results

28
Q

reminder frequency

A

no more than two reminders to increase response without overwhelming participants

29
Q

leading questions

A

leading questions can create implementation bias by suggesting specific responses

30
Q

identifying most common responses

A

the mode represents the most frequently selected response in a survey dataset

31
Q

data cleaning

A

correcting errors and inconsistencies in datasets before analysis

32
Q

variation in data

A

measures of spread, like standard deviation, show how responses differ from the mean

33
Q

top-box scoring

A

using the percentage of respondents selecting the highest rating option

34
Q

categorizing open-ended responses

A

coding organizes open-ended responses into themes for quantitative analysis

35
Q

top 2-box score

A

adding the percentages for the two highest rating categories, showing positive sentiment

36
Q

median with outliers

A

the median is less affected by outliers than the mean, making it more
representative with skewed data.

37
Q

comparative analysis by demographic

A

cross tabulation that helps compare survey responses across demographic groups

38
Q

clustering

A

standard deviation indicates clustering around mean values

39
Q

weighing data

A

corrects for overrepresented or underrepresented demographics, such as age or gender, to better represent the population

40
Q

summarizing typical values

A

measures like mean, median, and mode help summarize the central tendency in
a dataset

41
Q

segmenting by demographics

A

cross tabulation (data tables that “cross” results) by demographics, revealing information specific to different groups (subset)

42
Q

describing categorical responses

A

percentages that summarize categorical data, for reporting on proportions

43
Q

quantifying open-ended responses

A

coding open-ended responses categorizes them for easier quantitative analysis

44
Q

purpose of inferential statistics

A

inferences about a population based on sample data

45
Q

null hypothesis

A

assumes no difference exists between groups in the target population

46
Q

p-value with significance level

A

if a p-value is lower than the significance level, it suggests a statistically
significant effect

47
Q

type I error

A

occurs when a true null hypothesis is incorrectly rejected (false positive)

48
Q

t-test

A

used to compare mean scores between two groups on a continuous
variable

49
Q

correlation coefficient close to 1

A

indicates a strong positive relationship
between variables

50
Q

p-value in hypothesis testing

A

The p-value indicates the probability that the null hypothesis is true in the
population

51
Q

ANOVA

A

used when comparing mean scores across three or more groups

52
Q

alternative hypotheses (p-value with null)

A

If p-value > alpha, fail to reject the null hypothesis, no significance is found

53
Q

paired t-test

A

to test mean scores between two groups, represented in pairs

example: comparing related scores from the same respondents

54
Q

type II error

A

happens when a false null hypothesis is not rejected (false negative)

55
Q

conjoint analysis

A

identifies which combinations of features are most valued by customers

56
Q

linear regression

A

quantifies relationships between dependent and independent
variables, used for forecasting

57
Q

best use for chi-square test

A

chi-square test is best suited for comparing categorical variables, like product preference by region

58
Q

multiple regression

A

examines multiple factors together to predict an outcome, such as sales

59
Q

margin of error

A

how uncertain a measurement or estimate is, and how confident we can be in its accuracy

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