Week 3 - Research and Measurement

Question 1

why do research and analysis?

Answer 1

in order to make the right decision

Question 2

does all data and analysis have value?

Answer 2

NO - only if they help us make a decision

raw data has very little value

Question 3

in hypothesis testing, when do you make a prediction

Answer 3

prior to testing (a priori)

Question 4

what is the purpose of marketing research

Answer 4

inform decision making for business decisions (vs scientific research for instance)

Question 5

what do you call raw data once it has been analysed?

Answer 5

interpreted data, ie, information

Question 6

how should a decision maker be involved?

Answer 6

understand enough to know what’s reliable
tell the research team which questions to answer
potentially make predictions
project manage perhaps
be able to think like a researcher

Question 7

how should a researcher be involved?

Answer 7

convert questions/predictions into testable hypotheses
conduct the applicable research
present results in a way to answer the original question
communicate information clearly - reduce the complex

Question 8

how should administrators be involved?

Answer 8

understand sufficiently to

1) find common ground
2) engage throughout the process

Question 9

what is inferential statistics?

Answer 9

statistical analysis to infer or estimate from a population

based on probability

Question 10

what are the properties of data?

Answer 10

assignment
assignment and order
assignment order, and distance
assignment order, distance, and origin

Question 11

what is the minimal requirement for raw data to be analysed?

Answer 11

must be able to place into categories (at least assignment)
can have:
assignment order, distance, and origin

Question 12

what is assignment for data?

Answer 12

groupings

eg, color, gender, state

Question 13

what is order for data?

Answer 13

data points that can be ordered
eg, birth order, class rank, placement in race

Question 14

what is distance for data?

Answer 14

ability to understand how far apart data points are from each other
eg, one person has 100%, another has 80%, distance is 20ppt

Question 15

what is origin for data?

Answer 15

an unambiguous starting point or point of comparison
eg, zero is the lowest grade, 2018 is the current year

allows measurement of distance between data points AND vs origin

Question 16

what are the four classifications of data?

Answer 16

non-metric

• nominal
• ordinal

metric

• interval
• ratio

Question 17

what is nominal data classification?

Answer 17

nonmetric = nonparametric tests
assignment only
central tendency is only mode (most frequently occurring)

eg, most of these m&ms are blue

Question 18

what is ordinal data classification?

Answer 18

nonmetric
assignment and order
central tendency is only mode or median

eg, shortest to tallest height

Question 19

what is interval data classification?

Answer 19

metric = parametric data analysis available
assignment, order, and distance
(considered continuous because distance between points is measurable)
central tendency: mean, median, and mode (all three)

eg, what is the average length of a canoe

Question 20

what is ratio data classification?

Answer 20

metric
assignment, order, distance, and origin
continuous
all central tendencies (mean, median, and mode)

eg, star ratings between books, consumption over years

Question 21

what are descriptive statistics

Answer 21

a quantitative approach to identifying characteristics about a respondent pool
not a testing method

who answered our questions? what is the make up of our data overall?

Question 22

what tools does descriptive statistics use?

Answer 22

central tendencies (mean, median, mode)
percentages
measures of dispersion
frequency distributions

Question 23

when and how can you use mode?

Answer 23

any data with assignment (nominal)

what’s the most common?

Question 24

how do you use median?

Answer 24

any data with an order property
what’s in the middle if you count from each side?
if two, you average the tie to come up with the answer

Question 25

how do you use the mean?

Answer 25

only if you have distance property

average the group

Question 26

what is a percentage?

Answer 26

a frequency, expressed as a fraction of 100

Question 27

what is a range?

Answer 27

the defined distance between the smallest and largest numbers in the data?

Question 28

how do you measure standard deviation?

Answer 28

what is the average difference between data points and the mean
how similar are numbers on average?

Question 29

how do you measure frequency distribution?

Answer 29

visualise the distribution of the data - say with a bar chart
mode is just picking the tallest bar
can be applied to nominal data

Question 30

what is the difference between census and sample studies?

Answer 30

census = entire population
sample = part of the population

inferential statistics help when you can’t perform a whole census

Question 31

when is a census study better than a sample study?

Answer 31

any time you can do a census study

but often it isn’t reasonably possible

Question 32

what is a population parameter

Answer 32

population parameter = true fact based on 100% observation (census)
statistic = estimate

Question 33

what are the pros and cons of sampling?

Answer 33

pro

• lower cost
• easier and faster data handling

cons

• higher error rate
• errors can drive bad decision making

Question 34

why are sample-based estimates useful?

Answer 34

probability distributions allow for predictable estimates

Question 35

how much does sample drawing matter?

Answer 35

it’s THE most critical part - an error here can lead to skewing or bias

Question 36

how can you draw a sample?

Answer 36

probability - researcher has no role in drawing (eg, random sample)
nonprobability - researcher does have a role (eg, convenience sampling of people nearby)

Question 37

what is probability sampling?

Answer 37

researcher plays no role in buliding the sample
generally near random
similar but not exactly every data point has an equal chance of being selected

Question 38

what is nonprobability sampling?

Answer 38

researcher does play a role in selection

convenience sample is very common - stopping people at the subway for instance

Question 39

why does error occur in statistical inference?

Answer 39

because a sample <> census
thus while it is in theory representative,
often reality can differ

Question 40

what are the two types of errors found in statistical inferences?

Answer 40

sampling error - nonrepresentative sample

nonsampling error - systemic and/or random error not associated with the manner of drawing the sample

Question 41

when should sampling error be suspected

Answer 41

probability sampling (random) - no risk of sampling error, but VERY rarely 100% followed (think - completion bias)
non-probability sampling (selected) - high risk of error, must assume at least a certain level of error (hence statistical significance)

Question 42

when should nonsampling error be suspected?

Answer 42

any time you don’t have a full census

even if the sample is random, if it isn’t complete (eg census) we can never be 100% sure of conclusions

Question 43

what is the null hypothesis?

Answer 43

proof that there is no difference between compared populations
eg, people who take this medicine are definitely no better off than people who don’t
the null hypothesis is generally assumed true until proven false

Question 44

what is a Type 1 error?

Answer 44

telling a man he’s pregnant when he isn’t

rejecting the null hypothesis, when it’s actually True

Question 45

what is a Type 2 error?

Answer 45

telling a man he’s not a man when he really is

accepted the null hypothesis when the null hypothesis is false

normally type 2 is safer

Question 46

can you decrease the likelihood of type 1 or 2 errors?

Answer 46

yes, by selecting significance levels
but decreasing type 1 increases risk of type 2
choose your adventure

Question 47

what are the two categories of data collection?

Answer 47

primary data

secondary data

Question 48

what is secondary data?

Answer 48

collected for a purpose other than this research project

eg, UN data

Question 49

what is primary data?

Answer 49

collected specifically for our hypotheses

Question 50

what are the pros/cons of secondary data?

Answer 50

pros

• available, already there
• price, might be cheap or even free

cons

• relevancy, might not fit needs
• accuracy, why was it collected, what standards were in place?

Question 51

what is big data?

Answer 51

normally secondary data
passively collected
both structured and unstructured
can test hypotheses, but can’t verify cause/effect

Question 52

how is primary data collected?

Answer 52

questioning - survey, interview (might not be answered honestly)
observing - watching, documenting (more honest answers, but harder to understand the why) - on a person or on a company (eg, keyword analysis of company legal policies)

Question 53

how can you establish causality?

Answer 53

only through experimentation

must be very careful to not communicate correlation as causality

Question 54

what three factors are required to prove causality?

Answer 54

evidence of statistical association
temporal ordering
control for competing hypotheses

Question 55

how do you prove causality - evidence of statistical association?

Answer 55

necessary, but insufficient for causality

Question 56

how do you prove causality - temporal ordering?

Answer 56

must prove that A came before B

eg, fire trucks arrived after fire started, not before

Question 57

how do you prove causality - control for competing hypotheses?

Answer 57

look for unmeasured or unobserved hypotheses
alternative hypotheses
randomise away errors through probability sampling and experiment design

churches and liquor stores increase in parallel, but even with temporal ordering, neither causes the other
reality: population growth caused both