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Marketing Research Test 3
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3rd marketing research exam chaps 13, 15, 16, 17
Terms in this set (90)
what is the once occasion where you must pay attention to sample size
the sample is small
what is the percent requirement a sample size must be of a population
must be 5%
are sample size and sample representativness related?
no
what determines REPRESENTATIVNESS?
the selection method
what determines ACCURACY?
sample size
accuracy of a sample
a measure of how closely it reports the true values of the population it represents
sample error
amount of error that exists within your sample
what does a large sample ensure?
that it has good accuracy; low sample error
what is the only perfectly accurate sample
a census
the higher the accuracy is the lower the ___ ____ is
sample error
can a random sample size be a very tiny part of the population but still be accurate?
yes
what does the size of a random sample depend on?
1. clients desired accuracy (acceptable sample error) 2. cost of data collection for that sample size
confidence interval approach
the most correct method by which to determine sample size
higher accuracy = lower___
lower error
higher error = lower____
lower accuracy
what does sample error depend on
the size of the population
what happens when a sample size reaches 1000?
very little gain in accuracy occurs as it gets even bigger... even if you increase it to 2000 or 3000.... law of diminishing returns
sample error formula
1.96 x (sq. rt( (p*q)/n)))
variability
amount of similarity or dissimilarity in respondents answers to a particular question
what does p mean
the percent of people saying "yes"
what does q mean
100-p
what is the maximum variability in a population
50/50
what is the minimum variability in a population
90/10
what does increased variability mean for sample size
the more variability in a population, the larger will be the required sample size
what does n mean
the sample size
confidence interval
defines endpoints based on knowledge of the area under a bell-shaped curve
95% confidence interval=
1.96
99% confidence interval=
2.58
confidence interval verbalized
I am 95% sure that 88% of the students enjoyed spring break +/- 20%
are sample size and size of the population related
usually no
3 items needed to compute sample size
1. variability 2. acceptable sample error 3. confidence level
what is e
acceptable sample error/ expressed as a percent
what is z
the confidence interval
acceptable sample error
the amount of sample the researcher will permit to be associated with the survey
how do you estimate variability?
use prior experience, research, and intuition
arbitrary approach to sample size
determines sample size by relying on erroneous rule of thumb
good/bad of arbitrary sample sizes
good- simple/easy to apply
bad- not efficient/not economical
conventional approach to sample size
looks at previous/similar sample sizes from studies and uses those sizes
good/bad of conventional sample size
bad- ignores special circumstances/may perpetuate other surveys mistakes/may be too small or too large-- doesn't seem to be anything good
statistical analysis approach to sample size
use particular analysis- such as subgroups- in order to get a good ample size
if you calculate n=(z^2(p*q))/e^2) and n=5% what do you need to do
run the answer through a new equation n(sq.root N-n/n-1))
if you're n=5% and you run it through the new N equation, what will likely happen to your new n
the n will likely decrease
data entry
the creation of a computer file that holds the raw data taken from all of the questionnaires deemed suitable for analysis
data coding
identification of codes that pertain to the possible responses for each question in the questionnaire
summarization def + example
use of measures and statistical values to describe the data matrix i.e. average, mean, median, mode
ex: the average respondent's age is 44
conceptualization def + example
use of words or graphics that managers can relate to
ex: the pie graph shows that few respondents are younger than 30 years of age
communication def + example
describes the underlying patterns or relationships.. basically describe patterns that you see
ex: More women are in college than men
generalization def + example
indicates how sample findings relate to the population
ex: this means that from 32% to 40% of the target market purchases our brand on a regular basis
data matrix
coded, raw data from the survey
5 types of statistical analysis to reduce the data matrix
1. descriptive analysis 2. inferential analysis 3. differences analysis 4. associative analysis 5. predictive analysis
4 benefits of data summarization
1. It condenses the data 2. It applies understandable conceptualizations 3. it communicates underlying patterns 4. it generalizes sample findings to the population
descriptive analysis def + example
summarizes the basic findings of a sample (mean median mode frequency etc)
ex: describe the typical respondent/ describe how similar respondents are to the typical respondent
inferential analysis def + example
generalize results from target variable and sample population and paste them over the entire population (confidence interval)
ex: estimate the population values
differences analysis def + example
determine if differences exist; evaluate statistical significance of difference in the means of two or more groups in a sample (t test of differences analysis of variance)
ex: evaluate the difference in means between two groups in the sample
associative analysis def + example
determine how two variables are related (correlation/cross-tabs)
ex: determine if two variables are related in a systematic way
predictive analysis def + example
find complex relationships within the variables in the data set to Forecast the future (multiple regression)
ex: determine the dispositions of several variables' influences on a key variable
3 measures of central tendency
mean, median, mode
measures of variability
reveal the typical difference between the values in a set of values
3 examples of measures of variability
1. frequency distribution 2. range 3. standard deviation
what equals the standard deviation squared
variance
(what scale¢ral tendency used with this ques)
what is your gender?
measurement level: nominal scale
central tendency: mode
(what scale¢ral tendency used with this ques)
rank these 5 brands 1-5
measurement level: ordinal scale
central tendency: median
(what scale¢ral tendency used with this ques)
on a scale of 1-5 how does Starbucks rate on customer service
measurement level: interval scale
central tendency: mean
(what scale¢ral tendency used with this ques)
about how many times did you go shopping last week
measurement level: ratio scale
central tendency: mean
statistics vs. parameters
statistics are sample values vs. parameters are corresponding population values
parameters
values that are computed from a complete census and are precise and valid measures of the population
statistics
values that are computed from a complete census
inference
a form of logic where you make a generalization about an entire class based on what you have observed from a small set of members from that class
2 things an inference is based on
1. sample size
2. variability
what format are population parameters in?
greek letters
what format are sample statistics in?
roman letters
what does statistical inference take into account?
that large, random samples are more accurate than small ones
as "n" increases, what happens to error?
error decreases
as variability increase what happens to error?
error increases
2 types of statistical inference
1. parameter estimation
2. hypothesis testing
standard error is a measure of what
variability
what do you never solve for with SPSS
PERCENTAGE
s_xbar
standard error of the mean
s_p
standard error of the percentage
what does the range of your estimate of the population depend on? (2 things)
sample size
variability
5 steps to computing a confidence interval
1. determine the sample statistic 2. identify the sample size 3. determine the variability 4. decide on the level of confidence 5. perform computations to determine the upper and lower boundaries of the confidence interval range
5 steps to intuitive hypothesis testing
1. state what you believe (90% of students hate marketing research)
2. draw a random sample and determine the sample statistic (from a class of 100, 90 hate marketing research)
3. compare the statistic to the parameter (90 is the same as 90!)
4. decide whether or not the sample supports the hypothesis (everyone hates marketing research as much as I thought!)
5. If the sample doesn't match the hypothesis, revise it to match (nope, its clear that nearly everyone hate marketing research)
sampling distribution concept
our sample is one of many theoretical samples that comprise a bell-shaped curve with the hypothesized value as the mean
directional hypothesis
one where you say the hypothesized mean is greater than or less than some amount (i.e. not equal)
market segmentation is based on
differences between groups and consumers
statistical signfigance of differences
the differences found in the sample can be generalized to the population
4 requirements of differences between groups in order for them to be useful
must be:
1. statistically significant
2. meaningful
3. stable
4. actionable
when should a t-test be used
when the sample size is 30 or less
what test do you use if the sample size is greater than 30?
z tests
what is x
the mean
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