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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
survey invitations
should be clear, concise, and engaging to motivate participants
26
branching logic
customizes the survey path based on prior responses, improving relevance and flow
27
self-selection bias
occurs when only certain respondent types participate by volunteering, skewing results
28
reminder frequency
no more than two reminders to increase response without overwhelming participants
29
leading questions
leading questions can create implementation bias by suggesting specific responses
30
identifying most common responses
the mode represents the most frequently selected response in a survey dataset
31
data cleaning
correcting errors and inconsistencies in datasets before analysis
32
variation in data
measures of spread, like standard deviation, show how responses differ from the mean
33
top-box scoring
using the percentage of respondents selecting the highest rating option
34
categorizing open-ended responses
coding organizes open-ended responses into themes for quantitative analysis
35
top 2-box score
adding the percentages for the two highest rating categories, showing positive sentiment
36
median with outliers
the median is less affected by outliers than the mean, making it more representative with skewed data.
37
comparative analysis by demographic
cross tabulation that helps compare survey responses across demographic groups
38
clustering
standard deviation indicates clustering around mean values
39
weighing data
corrects for overrepresented or underrepresented demographics, such as age or gender, to better represent the population
40
summarizing typical values
measures like mean, median, and mode help summarize the central tendency in a dataset
41
segmenting by demographics
cross tabulation (data tables that "cross" results) by demographics, revealing information specific to different groups (subset)
42
describing categorical responses
percentages that summarize categorical data, for reporting on proportions
43
quantifying open-ended responses
coding open-ended responses categorizes them for easier quantitative analysis
44
purpose of inferential statistics
inferences about a population based on sample data
45
null hypothesis
assumes no difference exists between groups in the target population
46
p-value with significance level
if a p-value is lower than the significance level, it suggests a statistically significant effect
47
type I error
occurs when a true null hypothesis is incorrectly rejected (false positive)
48
t-test
used to compare mean scores between two groups on a continuous variable
49
correlation coefficient close to 1
indicates a strong positive relationship between variables
50
p-value in hypothesis testing
The p-value indicates the probability that the null hypothesis is true in the population
51
ANOVA
used when comparing mean scores across three or more groups
52
alternative hypotheses (p-value with null)
If p-value > alpha, fail to reject the null hypothesis, no significance is found
53
paired t-test
to test mean scores between two groups, represented in pairs example: comparing related scores from the same respondents
54
type II error
happens when a false null hypothesis is not rejected (false negative)
55
conjoint analysis
identifies which combinations of features are most valued by customers
56
linear regression
quantifies relationships between dependent and independent variables, used for forecasting
57
best use for chi-square test
chi-square test is best suited for comparing categorical variables, like product preference by region
58
multiple regression
examines multiple factors together to predict an outcome, such as sales
59
margin of error
how uncertain a measurement or estimate is, and how confident we can be in its accuracy

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