RESEARCH METHODOLOGY FOR MARKETING
Prof: Deniz Lefkeli dlefkeli@luiss.it
TA: Davide Gabriele Muzi dmuzi@luiss.it
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MARKETING RESEARCH PROCESS
Problem De ntion:
1) Should a new product be introduced? Determine consumer
preferences and purchase intentions for the proposed new
product
2) Should the advertising campaign be changed? Determine
the e ectiveness of the current advertising campaign
3) Should the price of the brand be increased? Determine the
price elasticity of demand and the impact on sales and pro ts
of various levels of price changes.
Research approach developed:
You formulate a theoretical framework, you formulate
research questions, formulate hypothesis and what you
expect in the end.
Research design:
Primary research requires a research design. The research
design is a detailed blueprint (a design plan) used to guide
the conduct of marketing research so that the research
questions are answered, and the research objectives are
realized. Research may be either qualitative or quantitative
DATA: Secondary and primary
SECONDARY DATA
Secondary data are pieces of info that have already been collected for a di erent purpose but
may be relevant to the research problems at hand. Secondary data are useful for addressing a
number of research questions (es. Estimating market potential, analyzing competitors, sales
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Always look for secondary data rst!!!
There are some advantages and disadvantages.
Advantages are: low cost, less e ort, more timely, some info is available only from secondary data
sources like market shares and industry data from trade associations.
Then there are some disadvantages. The rst one is lack of availability. For some research
questions there are simply no available data. For example, if Kraft General Foods wanted to
evaluate the test, texture, and color of three new gourmet brownie mixes, there are no secondary
data that would answer these questions.
The second disadvantage is Lack of relevance. May be measured in units that cannot be used by
the researcher. May relate to a sample other than the intended target. May be outdate.
The last disadvantage is Inaccuracy. Always assess the accuracy of the data. There are a number
of potential sources of error when a researcher gathers, codes, analyzes, and presents data.
PRIMARY DATA
Primary data, in contrast, are survey, observation, or experimental data collected to address the
problem currently under investigation.
TYPES OF MARKETING RESEARCH
• Qualitative vs. quantitative
• Ad-hoc vs. continuous vs. panel
• B-to-B vs. Consumer
• Applied vs. Scienti c
Types of marketing research based on objectives:
• Exploratory research: gather preliminary information that will help de ne the problem and
suggest hypotheses (qualitative techniques).
• Descriptive Research: describe customer’s attitudes and demographics. Determine product’s
market potential (surveys, observational or other data).
• Casual Research: test hypotheses about cause and e ect relationships (experiments)
Qualitative research:
Qualitative research is a loosely de ned term. It implies that the research ndings are not
determined by quanti cation or quantitative analysis.
The methods include:
1) depth interviews
2) Projective techniques: indirect interviewing methods which enable sampled respondents to
project their view, beliefs and feelings onto a third party or into some task situation. The
researcher sets up a situation for the respondents asking them to express their own views, or
to complete/interpret some ambiguous stimulus presented to them.
(free word association, sentence completion, un nished scenario/story completion,
cartoon completion test —> es. People that buy at H&M are….., Radiohead is a band
for….., Juventus is the team of……)
3) Focus groups: a group of people who discuss a project under the direction of a moderator.
The goal of focus group research is to learn and understand what people have to say and why.
The emphasis is on getting people to talk at length and in detail about the subject at hand.
The intent is to nd out how they feel about a product, concept, idea, or organization, how it
ts into their lives, and their emotional involvement with it.
Synergy - together, the group can provide more insights that insights obtained individually
Stimulation - group interaction excites people
Spontaneity/serendipity - participants may get idea on the spot and discuss them.
4) Observation (ethnography)
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forecasting, assessing industry trends, alerting the manager to potential problems, providing
preliminary info to guide subsequent primary data collection).
Are info previously gathered for a di erent purpose that may be relevant to the problem at hand.
Can come from sources internal to the organization or external.
The internet has, in many ways, enables the gathering of secondary data.
Secondary data are generally useful, low cost, rapidly available sources of informations.
Qualitative Research vs Quantitative Research
If questions includes more
exploration you should do
qualitative research.
In quantitive research it’s more
about testing of predictions, you
already have idea. Limiting
probing
For sample sized in qualitative
research is small
In quantitive research we have a
large sample.
qualitative research you can do earn a lot of info.
In quantitative research it varies.
The information per respondent in
Qualitative research requires interviewers to have experience, to be moderators.
Quantitive research requires fewer specialized skills. You should have marketing research skills
like data analysis, problem identi cation. Requires skills but fewer than qualitative research
In qualitative research we have a subjective, interpretive analysis. It depends on issue, interviewer,
respondent…
In quantitative research we have statical analysis, summarization.
The tools used in qualitative research
are for example: tape recorders,
projection devices, video, pictures…
In quantitative research they use
questionnaire, computers,
printouts…
In qualitative research there is a low
ability to replicate while in a
quantitative research it’s high (data,
more objective, statistical analysis.
You expect to nd same results
under same conditions, you replicate
ndings).
In qualitative research the researchers need a psychology, sociology, social psychology,
consumer behavior training. While for quantitative research they need statistics, decision models,
computer programming marketing training.
In qualitative research there is an exploratory type of research while for quantitative research it’s
descriptive or causal (you describe what happens).
CONCLUSIVE RESEARCH
Provides speci c information that aids the decision maker in evaluating alternative courses of
action. Statistical methods & research methodologies are used to increase the reliability of the
information. Data tends to be speci c & decisive and this type of research is more structured and
formal than exploratory data.
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5) …. and other methods
There are two types of conclusive research
1) Descriptive research: describes attitudes, perceptions, characteristics, activities and situation.
Examines who, what, when, why, and how questions. Determine the degree of association
between variables. They are build on previous information. They show relationships between
variables. Representative samples are required. You need structured research plans and
substantial resource.
Observation: involves recording behavior patterns. Cognitive phenomena cannot be
observed. Interpretation of data may be a problem. Not all activity can be recorded only
short periods can be observed. Observes bias possible. Possible invasion of privacy.
So it’s not always possible and not always the ideal method to use.
There are di erent types of observation:
Mechanical Observation: mechanical devices record the phenomena. (Ex. Tra c
counters, web tra c, scanners, physiological measures like eye tracking/
pupilometer/voice pitch).
Survey: use of a questionnaire to gather facts, opinions, and attitudes. You design a
questionnaire to obtain information. Surveys are the most common method for collection
of primary data. There are several types of surveys, including door-to-door interviewing, all
intercept, telephone interviewing (became very popular in the last two decades), mail, and
internet survey (popularity is growing).
Survey methods: telephone (traditional telephone, computer-assisted telephone
interviewing), personal (in homes, mail intercept, computer-assisted personal interviewing),
mail (mail/fax interview, mail panel), electronic (email, internet)
Structure:
1) you start with opening sentence (respondent is asked to be available to
answer, brie y explaining the purpose of the serve and the ways in which
the answer will be analyzed)
2) introduction: general questions aiming at introducing the research topic and
capture the respondent attention.
3) technical section: most important part, therefore it must be compiled very
attentively. Speci c questions aiming at obtaining the information requested
by the research itself
4) demographical section: questions of a personal nature concerning the socio
demographic characteristics of the respondent, necessary for the
description of the research sample.
2) Causal research: provides evidence that a cause and e ect relationship exists or does not
exist. Control all variables. When you change something, you observe a change also in other
variables. kkkkkkkkPremise is that something (independent variable) directly in uences the
behavior of something else (dependent variable)
To establish whether two variable are causally related, that is, whether a change in the
independent variable X results in a change in the dependent variable Y, you must establish:
1) time order: the cause must have occurred before the e ect
2) Concomitant variation (statistical association): changes in the value of the
independent variable must be accompanied by changes in the value of the
dependent variable
3) Non spuriousness: it must be established that the independent variable X and only
X was the cause of changes in the dependent variable Y; rival explanation must be
ruled out.
Correlation is di erent from causation.
Correlation related to closeness, implying a relationship between objects, people events, etc…
Correlation is a measure of association that tests whether a relationship exists between two
variables. It indicates both the strength of the association of its direction. The Pearson productmoment correlation coe cient, written as r, can be describe a linear relationship between two
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variables. The value of r can range from 0.0, indicating no relationship between the two variables,
to positive or negative 1.0, indicating a strong linear relationship between two variables.
It does not imply causation.
You identify cause and relationships through experiments.
An experiment is a research method in which conditions are controller so that 1 or more
independent variables can be manipulated to test a hypothesis about a dependent variable. It
allows evaluation of causal relationships among variables while all other variables are eliminated
or controlled.
It helps you evaluate cause and relationship.
Variables are
• Independent: the one thing you change. Limit to only one in an experiment (liquid used to water
each plant)
• Dependent: the change that happens because of the independent variable (height or health of
plant)
• Controlled: everything you want to remain constant and unchanging. (Type of plant, pot size,
amount of liquid, soil type, etc…)
Basic issues in experimental design is that you need to manipulate the independent variable,
select and measure of the depend variable, select and assign of subject, control over extraneous
variables.
Independent variable that can be manipulated or altered, independently of any other variable.
Hypothesized to be the causal in uence, experimental treatments.
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There are di erent types of experimental designs:
• between subject design: each participant takes part in 1 experimental condition. Analysis of
di erences between groups of participants from di erent condition
• Within subject design (repeated measure design): subjects take part in all conditions. Analysis of
di erences results from same participants over di erent conditions. It is used in experimental
situations comparing di erent treatment conditions and also to investigate changes occurring
over time.
• Factorial design: used to examine the e ects that the manipulation of at least 2 independent
variables (simultaneously at di erent levels) has upon the dependent variable. The impact that
each independent variable has on the dependent variable is referred to as the main e ect.
Dependent variable may also be impacted by the interaction of the indipendent variables. This is
the interaction e ect. (The e ect of X1 on Y changes when second variable X2 added (X1*X2).
ex. Gucci Brand Management want to know if consumers’ willingness to buy the luxury
brand changes if the brand uses 2 di erent types of advertising languages (abstract vs
concrete) and if the brand logo is high vs low prominent.
Dependent variable is consumers’ willingness to buy the Gucci brand
independent variables are language (abstract vs concrete) and logo prominence (high vs
low)
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SURVEY/QUESTIONNAIRE DESIGN
QUESTIONNAIR DESING:
Use revalidated multi item scales to measure
perceptions, attitude, motivations, intentions. Using
pre validated scales does not mean that the scales
will be valid and reliable on you sample! You should
still test reliability and validity of those scales!
Avoid to pick 1-2 items from the scale! Use the whole
scale (at least three items is recommended)
Try to avoid combining items from di erent scales of
the same construct.
You can nd pre validated multi item scales: from the
methodology section in published academic papers
that you used in you literature review or from
marketing scales handbooks or from google scholar.
What is a multi item scales (summated scale)? It is
used to measure a construct using several (3 or more)
items.
You need to use previously developed scales for your
research. Inspect reliability and validity rst. If the
scale is reliable and valid, take average or sum of
items.
Ex. Perceives quality
1) X is of high quality
2) The likely quality of x is extremely high
3) The likelihood that X would be functional is very high
4) The likelihood that X is reliable is very high
5) X must be of very good quality
6) X appears to be of very poor quality
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TYPE OF QUESTIONS
Questions can be unstructured
(open) or structured (set of response
alternatives and response format.
They can be multiple choice,
dichotomous which are the ones that
o er two possible answers like yes
or no or true or false, and scales)
QUESTIONNAIR DESING CHECH LIST
1) STEP 1: INFORMATION NEEDED
• Ensure info obtained fully addresses all components of the problem
• Must have clear idea of the target population
2) STEP 2: INTERVIEWING METHOD
• Ensure that the proper interviewing method is being used given the study objectives
3) STEP 3: INDIVIDUAL QUESTIONS
• Is each question necessary?
• Is each question unambiguous?
• Are the proper number of questions needed to measure a particular construct being
asked?
4) STEP 4: INABILITY/UNWILLINGNESS TO ANSWER
• Is the respondent informed?
• Can the respondent remember?
• Can the respondent articulate?
• Is the information sensitive?
• Make the information request legitimate
• Minimize e ort required of respondent
5) STEP 5: QUESTION STRUCTURE
• Use structured questions whenever possible
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• Response alternatives should include all possible answers
• Response alternatives should be mutually exclusive
• If, on a particular question, a large % of respondents may be expected to be neutral,
include a neutral alternative
STEP 6: QUESTIONS WORDING: DO NOT USE
• Double barreled question (do you think iPhone is reliable and cheap?)
• Leading questions (do you think that a good Italian citizen…)
• Biasing questions (agree- neutral - disagree - strongly disagree)
• Implicit assumptions/alternative (do you like to buy imported brands?)
• Generalizations
DO USE
• De ne the issue
• Simple language
• Ordinary words/known vocabulary
• Short questions
• Unambiguous words (avoid usually, probably, normally… use instead daily/weakly..)
• Speci c questions (in the past year, did you purchase X…)
STEP 7: QUESTION ORDER
• Identi cation/classi cation info
• Di cult/dull questions
• Funnel approach
• Logical order (branching)
• Ex. Introduction and explanation (brie ng), qualifying questions (easy), speci c questions
(more di cult), demographics (or beginning or end of questionnaire), thanks (debrie ng)
STEP 8: FORM& LAYOUT
• Divide questionnaire into multiple parts (blocks in Qualtrics)
• Number each question (use progress tool)
• Pretest each questions
• Keep scale questions in same general vicinity
• Avoid temptation to “ ll the blank spaces”
• Professional appearance/professional brie ng
• Place directions as close to the questions as possible
PRETESTING
• Always try to pretest rst
• Test everything. Especially in experimental survey, download pretest data and inspect
whether conditions and DV are ne
• Pretest sample should be extremely similar to test sample
• Pretest using same methodology as the main test
• If make changes to questionnaire, or methodology, pretest again before main test
MEASUREMENT AND SCALING
Measurement is the process of assigning numbers or labels to the attributes of objects, persons,
states, or events in accordance with speci c rules
VARIABLES
When we operationalize a concept, we are creating variables: any characteristics that varies
(meaning it must have at least two values). Any event, situation, behavior, or individual
characteristic that varies.
Research questions and hypotheses consist of x and y variable.
• independent “IV”: X dependent “DV”: Y
• Is X related to Y? What is the e ect of X on Y?
• What is the e ect of package shape (X) on consumers’ taste perception (Y)?
• Package shape (rounded vs angular) is a variable because it has two values
MEASUREMENT SCALE
A scale is a quantifying measure - a combination of items that is progressively arranged according
to value or magnitude. Purpose is to quantitatively represent an item’s, person’s, or event’s place
in the scaling continuum.
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There are two type of scales
1) non metric scale
• Nominal: like numbers assigned to runners. Uses numerals to identify objects, individuals,
events, or groups. Numbers are assigned to categories as “names”. Which number is
assigned to which category is completely arbitrary. Typical descriptive statistics is
frequency counts, percentages/modes. Inferential is chi-square test, binomial test.
Characterized by identity.
• Ordinal: like rank order of winners (3rd, 2nd, 1st). In additional to identi cation, the
numerals provide info about the relative amount of some characteristics, determines
greater or less then (order). Ex. Raking soft drinks from 1 to 5 with 1 being the most
preferred and 5 the least preferred. Typical statistics is median, percentile. Inferential is
rank order correlation (spearman), Friedman ANOVA. Characterized by identity, magnitude,
unequal distance, not true zero point
2) Metric scale
• Interval: like performance rating on a 0 to 10 scale. Has all the properties of nominal and
ordinal scales + equal intervals between consecutive points; preferred measure for
complex concept or constructs. Ex. The yogurt is tasty (1 = strongly disagree, 5 = strongly
agree). Typical descriptive statistics is mean, variance, range. Inferential is Pearson
correlation, t tests, ANOVA, regression, factor analyses. Interval is characterized by
identity, magnitude, equal distance, and does not have a true zero point, the number 0 is
arbitrary (for example 0º can indicate no temperature)
• Ratio: time to nish, in seconds. Incorporates all the properties of nominal, ordinal, and
interval scales + it includes an absolute zero point. Ex are age, weight, population of Italy,
cost, sales, market shares, income… inferential is Pearson correlation, t tests, ANOVA,
regression, factor analyses. Typical descriptive statistics is means/variance + a few higher
order statistics. Is characterized by identity, magnitude, equal distance, absolute/true zero.
All mathematical operations are possible. Scales with an absolute zero and equal interval
are considered ratio scales.
How to classify a scale?
• Description (identity): each number has a particular meaning
• Order (magnitude): numbers have an inherent order from smaller to large
• Distance (Equal intervals): the di erences between numbers (units) anywhere on the scale are
the same (di erence between 8 an 9 is the same as the di erence between 66 and 67)
• Origin (absolute/true zero): the zero point represents the absence of the property being
measured (no money, no behavior, non correct)
SCALING TECHNIQUES
Comparative scales involve
the direct comparison of two
or more objects.
• Paired comparison: “for
each pari of the two seat
sports car listed, place a
check beside the one you
would most prefer if you had
to choose between the two”.
• Rank order: respondents
are presented with several
objects simultaneously. Then
asked to order or rank them
according to some criterion.
Data obtained are ordinal in
nature (arranged or ranked in
order of magnitude).
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Commonly used to measure preferences among brands and brand attitude.
An example is “rank the radar detection features in order of your preference. Place the number 1
next to the most preferred, 2 by the second choice, and so forth.”
• Constant sum: respondents are asked to allocate a constant sum of units among a set of
stimulus objects with respect to some criterion. Units allocated represent the importance attached
to the objects. Data obtained are interval in nature. Allows for ne discrimination among
alternatives.
Ex. “Taking all the supplier characteristics we’ve just discussed and now considering cost, what is
their relative importance to you (dividing 100 units between).
Non comparative scales are scales where objects or stimuli are scaled independently of each
other.
• Continuous rating:
• Likert scale: extremely popular means for measuring attitudes. Respondents indicate their own
attitudes by checking how strongly they agree/disagree with statement.
Response alternatives: strongly agree, agree, uncertain, disagree, and strongly disagree.
Generally use either a 5- or 7-point scale.
• Semantic Di erential Scales: a series of numbered (usually seven-point) bipolar rating scales.
Bipolar adjectives (for example “good” or “bad”), anchor Bothe ends (or poles) of the scale.
A weight is assigned to each position on the rating scales.
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Itemized rating scale decisions
• number of scale categories
GOOD: anything between 5-9
• Odd vs even number of categories-depends…
odd —> + neutral point; even —>+ force a response
• Balances vs. unbalanced scale
• favorable: balanced scale
• “No opinion” vs “forcing scale”
use situations if expected to have “no opinion”
• Nature of verbal description
• Physical form
MULTI ITEM SCALES (SUMMATED SCALE)
Used to measure a construct using several (3 or more) items.
Use previously developed scaled for your research. Inspect reliability and validity rst. If the scale
is reliable and valid, take average or sum of items.
SAMPLING
A sample is a group of people, objects, or items that are taken from a larger population for
measurement. The sample should be representative of the population to ensure that we can
generalize the ndings from the research sample to the population as a whole.
In statistics sampling is the process of choosing a representative sample from a target population
and collecting data from that sample in order to understand something about the population as a
whole.
There are some advantages like time and cost
And there are some disadvantages because you analyze only a sample, there is associated
uncertainty (error).
Most properly selected samples give su ciently accurate results —> representative sample
Probability sampling: every member
of the population has a known, non
zero probability of being selected.
Ensure representativeness. Ensure
precision.
Non probability sampling: the
probability of any particular member
being chosen for the sample is
unknown. Cheaper but unable to
generalize and potential for bias.
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NON-PROBABILITY SAMPLING METHODS
• Convenience samples (ease of
access): sample is selected from
elements of a population that are
easily accessible (convenient). Also
called haphazard or accidental
sampling
• Judgment samples: the selection
criteria are based on personal
judgment that the element is
representative of the population
under study (you chose who you
think should be in the study). Also
called purposive sampling.
• Quota samples: non probability
samples in which population subgroups are classi ed on the basis of researcher judgments. It
should not be confused with strati ed sampling
• Snowball samples: non probability samples in which selection of additional respondents is
based on referrals from the initial respondents (friend of friend…). Initial respondents are selected
by probability method.
PROBABILITY SAMPLING METHODS
• Simple random sampling:
the probability of selection is
sample size dividend by
population size. A probability
sample in which every
element of the population
has a known and equal
probability of being selected
into the sample
• Strati ed random sampling:
involves the following two
procedures.
1)the parent population is
divided into mutually
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exclusive and collectively exhaustive subsets (strata). Each stratum is more or less equal on
some characteristic.
2) A simple random sample is chose from each subset.
You use strati ed sampling to investigate characteristics of interest by subgroups; strati cation
allows for adequate representation of di erent subgroups. It increases precision (reduce sampling
error).
- Proportionate strati ed sampling: take sample size in (same) proportion to size of the
population in each subgroup or stratum. For example you take 1% from each subgroup.
- Disproportionate strati ed sampling: sample size not necessarily in proportion to population
subgroup size. From example you take 2% from one subgroup and 1% from the other
subgroup.
• Cluster sampling: is a two step procedure. Population is divided into mutually exclusive and
collective exhaustive subsets. A random sample of the subsets is selected. In ones stage closer
sampling, all elements in the randomly selected subsets are included. In two stage cluster
sampling, a sample is selected probabilistically from each randomly selected subset.
You generally use cluster sampling because it has lower costs but it’s less accurate.
There is a di erence between strati cation and clustering: the variable used for strati cation must
be related to research focus, while the variable used for clustering must not be related to research
focus.
• Systematic sampling: probability sampling in which the entire population is numbered. The rst
number is drawn randomly. Subsequent elements are drawn using a skip interval (systematic).
Skip interval = population size / sample size
SAMPLE SIZE DETERMINATION
1) Convenience: say… about 100
2) Rule of thumb: at least 30 per each subgroup that will be analyzed/at least 5 observations per
variable in the model
3) Budget constraint: have a $300 budget for sampling. On average it costs $2 per returned
questionnaire. Then go for sample size of 150.
4) Comparable studies or industry average
5) Statistically calculating using priors
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SYMBOLS FOR POPULATION AND SAMPLE VARIABLES
The standard deviation of the sample is the degree to which individuals within the sample di er
from the sample mean.
The standard error of the sample mean is an estimate of how far the sample mean is likely to be
from population mean. Standard errors helps us identify a range of estimated that we can be
con dent. It includes the population parameter
CONFIDENCE INTERVAL
Con dence interval (CI) is a type of interval computed from the statistics of the observed data,
that might contain the true value of an unknown population parameter.
The interval has an associated con dence level that quanti es the level of con dence that the
parameter lies in the interval. Since the observed data are random samples from the true
population, the con dence interval obtained from the data is also random.
The con dence level is designated prior to examining the data. Most commonly, the 95%
con dence level is used. However, other con dence levels can be used, for example 90% and
99%.
A 95% level of con dence would mean that if 100 con dence intervals were constructed, each
based on a di erent sample from the same population, we would expect 95 of the intervals to
contain the population mean.
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EXPLORATORY DATA ANALYSIS
DATA CLEANING AND CONSISTENCY CHECKS
Consistency check identify data that are out of range, logically inconsistent, or have extreme
values. You can use “frequencies” or “minimum/maximum values from descriptive statistics”
option on SPSS.
Extreme values should be closely examined.
Missing values are indicated “.” In SPSS data set.
DESCPRIPTIVE AND INFERENTIAL STATISTICS
Descriptive statistics describe the data set that’s being analyzed but does’t allow us to draw any
conclusions or make any inferences about the data, other than visual “it look like…” type
statements. Hence we need another branch of statistics: inferential statistics.
Inferential statistics is also a set of methods, but it is used to draw conclusions or inference about
characteristics of populations based on data from a sample. Inferential statistics includes making
inference, hypothesis testing, and determining relationships.
DESCRIPTIVE STATISTICS
Are methods of organizing, summarizing, and presenting data in a convenient and informative
way.
• graphical techniques and numerical techniques. The actual methods used depends on what info
we would like to extracts —> measures of central location
• Mean, mod, median —> measures of variability (dispersion)
• Standard deviation/variance, range, quartile —> measures of shape
• Skewness, kurtois
FREQUENCY DISTRIBUTIONS —> FREQUENCY TABLE
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Analyze —> Descriptive statistics —> Frequencies, Charts
You can use HISTOGRAMS to represent frequency tables.
The distribution of a statistical data set (or a
population) is a listing or function showing all
the possible values (or intervals) of the data
and how often they occur.
When a distribution of categorical data is
organized, you see the number or
percentage of individuals in each group.
When a distribution of metric data is
organized, they’re often ordered from
smallest to largest, broken into reasonably
sized groups (if appropriate), and then put
into graphs and charts to examine the shape,
center, and amount of variability in the data
You can use PIE/BAR CHARTS for Nonmetric variables, like for example gender
• Central tendency: numbers that describe what is typical or average (central) in a distribution. Tell
you about typical (or central) scores.
• Measures of Variability: numbers that describe diversity or variability in the distribution. Reveal
how far from the typical or central score that the distribution tends to vary.
These two types of measures together help us to sum up a distribution of scores without looking
at each and every score.
MEASURES OF CENTRAL LOCATION
MEAN
The mean, or average value, is the most commonly used measure of central tendency. The
means, μ, is given by observed value of the variable X divided by number of observations (sample
size)
In SPSS analyze —> descriptive —> statistics —> descriptive (then select variable(s))
MEDIAN
The median of a sample is the middle value when the data are arranged in ascending or
descending order. If the number of data points is even, the median is usually estimate as the
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midpoint between the two middle values - by adding the two middle values and dividing their sum
by 2. The median is the 50th percentile.
It is used when data is not normally distirbuted.
Ex. 2 4 6 7 8 —> median is 6
2 4 6 7 8 9 —> median is (6+7)/2 = 6.5
MODE
The mode is the value that occurs most frequently. It represents the higher peak of the
distribution.
The mode is a good measure of location when the variable is inherently categorical (non
metric) or has otherwise been grouped into categories.
Mode is a suitable summary statistics for nominal varibles.
If you have a symmetric distribution (like normal
distribution), mode/median and mean values will
be the same.
MEASURES OF VARIABILITY
VARIANCE
Variance: a measure of variation for interval- ratio variables; it is the average of the
squared deviations from the means.
it’s always positive values.
When data is clustered around the means, variance is small. When data is scattered,
variance is big
STANDARD DEVIATION
Is a measure of variation for interval- ratio variables; it is equal to the square root of the
variance. It is a measure that is used to quantify the amount of variation or dispersion of a
set of data values. A low standard deviation indicates that the data points tend to be
close to the mean of the set, while a high standard deviation indicates that the data points
are spread out over a wider range of values.
Wherever you report a mean, you should report the standard deviation as well.
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THE RANGE
Range = highest score - lowest scor4e
Is a measure of variation in interval-ratio variables. It is the di erence between the highest
(max) and the lowest (min) scores in the distribution
MEASURES OF SHAPE
Analyze —> Frequencies —> statistics —> distributions…
Or
Analzye —> descrittive statistics —> descriptives —> options
SKEWNESS
The tendency of the deviations from the mean to be larger in one direction that in the
other. It can be thought of as the tendency for one tail of the distribution to be heavier
than the other
KURTOSIS
Is a measure of the relative peakedness of a atness of the curve de ned by the
frequency distribution. The kurtosis of a normal distribution is zero. If the kurtosis is
positive, then the distribution is more peaked than a normal distribution. A negative value
means that the distribution is atter than a normal distribution.
BIVARIATE VARIABLES
Measuring association between two variables:
• metric variables —> Pairwise Correlations [analyze —> bivariate —>
• Nonmetric variables —> cross tabulation (frequency distribution table for two non metric
variables) [analyze —> descriptive statistics —> crosstabs]
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INFERENTIAL STATISTICS
Statistical inference is the process of making an estimate, prediction, or decision about a
population based on a sample. Is the use of probability theory to make inferences about a
population from sample data
Hypothesis testing —> competing theories that we want to test about a population are
called hypothesis in statistics.
Speci cally, we label these competing theories as Null Hypothesis (H0) and Alternative
Hypothesis (H1)
H0: the null hypothesis is the status quo or the prevailing viewpoint (no di erence)
H1: the alternative hypothesis is the competing belief (statement that indicates the
opposite of the null hypothesis). It is the statement that the researches is hoping to prove.
Large populations make investigating each member impractical and expensive, plus it’s
been shown that observing 100% of a population is not perfect. It’s easier and cheaper to
take a sample and make inferences about the population from the sample.
However such conclusions and estimates are not always going to be correct. For this
reason, we build into the statical inference “measures of reliability”, namely con dence
level and signi cance level.
Signi cance level ( alpha): critical probability in choosing between the null hypothesis
and the alternative hypothesis. The alpha level is set before we collect data. It de ned
how much of an error we are willing to make to say we made a di erence. If we’re wrong,
it’s an alpha error or Type 1 error.
Strong support: = 0.01 (99% con dence)
Support: = 0.05 (95% con dence)
Marginally support: = 0.10 (90% con dence
The p value (sig. in SPSS) is calculated after we gather the data. It’s the calculated
probability of a mistake by saying it works. Describes the percent of the population/area
under the curve (in the tail) that is beyond our statistic
2 TAILED TEST
The critical value is the number that
separates the “blue zone” from the
middle.
In a t test, in order to be statistically
signi cant the t score needs to be in
the “blue zone”
If = 0.05, then 2.5% of the area is
in each tail
If the t score calculated is in the blue
area (t score > table value or p value
for t score < 0.025), reject H0 i.e.
signi cant (con rm H1)
If the t score calculated is not in blue
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area (-table value < t score < table value or p value for t score >0.025), do not reject H0. It
means there is no signi cant proof to reject H0 (i.e. not signi cant)
1 TAILED TEST
The critical value is either + or
-, but not both.
In this case, you would have
statistical signi cance (p <
0.05) if t ≥ 1.645
If the t score calculated is in
blue ares (t score > table value
or p value for t score < 0.05),
reject H0 i.e. signi cant
(con rm H1)
If the t score calculated is not
in blue area (t score< table
value or p value for t score >
0.05), do not reject H0. It
means there is no signi cant
proof to reject H0 (i.e. not signi cant)
Rejecting the null hypothesis H0 when in fact it is true is called a Type 1 error.
Accepting the null hypothesis H0 when in fact it is not true is called a Type 2 error.
Rejecting the null hypothesis is usually considered the more serious error than accepting
it.
STATISTICAL TESTS: BIVARIATE TESTS
• Frequency Distribution (2 nonmetric variables) —> x2 (chi square statistic)
• Means (one sample) —> z (if is known), t (if is unknown)
• Means (two samples) —> independent t test, paired t-test
• Means (more than two) —> ANOVA
CHI SQUARE STATISTIC (x2)
Is used to test the statistical signi cance of the observed association in a cross tabulation
(reports frequencies for two nonmetric variables). Only nominal variables are involved!
Chi-square always positive: one tail!
T DISTRIBUTION
• Simple sample t: we have only 1 group, want to test against a hypothetical mean.
The aim is to test if population mean is equal to a hypothesized value. We test mean to
a numeric value, k.
H0: population mean is equal to k
H1: population means is not equal to k
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—>for 2 sided t test —> if p-value of data is smaller than 0.025, we reject H0 (H1 is
con rmed/signi cant)
If p-value of data is not smaller then 0.025, we do not reject H0 (H1 is not con rmed)
—> for 1 sided t test—> if p-value is smaller than 0.5, we reject H0.
If p-value of data is not smaller than 0.05, we do not reject H0
• Independent-samples t: we have 2 means: 2 groups; no relation between groups. For
example people randomly assigned to a single group
For example we want to test if male consumers’ repurchase intention of iPhone
exceeds female consumers’ repurchase intention with a sample of 128 subjects
H0: males repurchase intention is not larger than females repurchase intention
H1: males repurchase intention is larger than females repurchase intention
The aim is to test if the means of two independent groups are equal
What about variances? Equal or unequal? —> LEVENE’S TEST
If p value of data is smaller than 0.05, we reject H0
If p value of data is not smaller than 0.05, we do not reject H0
—> Levene test informs us if the variances of the two groups are equal or not
H0: equal variances
H1: unequal variances —> use t test with equal variances to make conclusions
• Paired-samples (dependent) t: we have two means. Either same people in both groups,
or people are related. For example husband-wife, left hand-right hand, doctor-patient,
hospital patient-visitor, befog-after, pre-post test…
The aim is to test if the means of two paired groups are equal
We use it when the observations are not independent of one another
Besides, when population variance is unknowns (the usual case)
NON PARAMETRIC TEST: CHI-SQUARE INDEPENDENCE TEST
While a frequency distribution describes one variable at a time, a cross-tabulation
describes two or more variables simultaneously.
We use only nonmetric (categorical) variables
Cross tabulation results in tables that re ect the joint distribution of two or more variables
with a limited number of categories or distinct valeus.
Expected value: the average value in a cell if the sampling procedure is repeated many
times
Observed value: the value in the cell in one sampling procedure
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SUMMARY
SCALE VALIDITY: FACTOR ANALYSIS
Factor analysis is a technique that serves to identify groups of variables that are related
(to combine related questions/items or variables) to create new factors.
Groups of variables that are related will be combined
The purpose is to discover underlying patters in data and to nd smaller number of
variables (factors) which could largely explain observed variables (variance and
covariance)
Ex.
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Factor: underlying dimension that explains the correlation among a set of variables.
So the interdependence among variables is examined. All observed variables correlate
with each other and depend on unobserved factors
Variables are not classi ed as dependent or independent.
In contrast to regression/ANOVA, there is no dependent variable. We just look at the
correlations between variables to summarize
If two items are highly correlated, they must represent the same phenomenon.
But suppose a whole group of variables provide information that represents this underling
phenomena.
Factor analysis looks for the phenomena underlying the observed variance and
covariance in a set of variables.
These phenomena are called “factors” or “principal components”
The main purpose in marketing research is to
• identify underlying constructs in the data (for example scale validation)
• Reduce the number of variables to a more manageable set (for example data reduction/
scale construction rst step)
Factor analysis works with correlation/covariance matrix
Factor analysis can work with variables as well as observations
There are two types
1) exploratory FA (principle components FA)
It is exploratory when you do not have a prede ned idea of the structure or how many
dimensions are in a set of variables
2) Con rmatory FA (structural equation models (SEM))
It is con rmatory when you want to test speci c hypothesis about the structure or the
number of dimensions underlying a set of variables (in your data you may think there
are two constructs and you want to verify that)
How many factors?
• rule of thumb: all included factors (prior to rotation) must explain at least as much
variance as an “average variable”
• Eigenvalues criteria: eigenvalue represents the amount of variance in the original
variables associated with a factor. Sum of the sequa of the factor loading of each
variable on a factor represents the Eigen value. Only factors with Eigenvalues created
than 1.0 are retained (explaining more variance than the average component)
• Percentage of variance criteria: number of factors extracted is determined when the
cumulative percentage of variance extracted by the factors reaches a satisfactory level
(at least 60% recommended)
• Screen plot criteria: plot of the eigenvalues against the number of factors in order of
extraction. The shape of the plot determines the number of factors (elbow point gives
the number of factors retained). Keep all factors before the breaking point or elbow.
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Factor rotation:
Factor analysis can generate several solutions (loadings & factor scores) for any data set.
Each solution is called a “factor rotation”. Each time the factors are rotated the pattern of
loadings changes. Geometrically, rotation simply means that the axes are rotated.
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There are two types of rotation methods
• orthogonal rotation (for example VARIMAX): factors are independent (no correlation).
Makes factor interpretation easier. Each factor tend to load high on some variables and
low on others. VARIMAX setting is recommended when you want to identify variables to
create indexes or new variables without inter-correlated components. If you want the
factors to be correlated (oblique rotation) you need to use the option PROMAX
• Oblique rotation (for example OBLIMIN): factors are dependent (correlations)
Commonalities represent variance shared between observed variables and factor. May
delete variables that have low communalities (< .5). Low communality indicates a problem
on the item.
So try to nd a clear factor structure (subjective component)
There are some variables for possible removal like variables loading on several factors
(double loaded items/cross loadings), variables with low communalities (<.5), and
variables with low loading (<.30).
There are also size for practical signi cance:
> .30 minimal
> .40 more important
> .50 signi cant for practical purposes
RE-SPECIFICATION
Remove variables from analysis
—> individual variables
Drop variables/items with value < .3
Delete the lowest rst and then continue one at a time until all remaining variables have
values >.3 (some suggest 0.5)
FACTOR SCORES
Scores for each respondent on the derived factors
- per factor on factor score is available
- Naïve method (summated scale means/average of items on the factor (equal weight)
Factor scores can be used for further multivariate analyses like regression analysis and
cluster analysis (for segmentation).
SCALE RELIABILITY: RELIABILITY ANALYSIS
Reliability analysis is the overall consistency of a measure. A measure is said to have a
high reliability if it produces similar results under consistent conditions.
Scores that are highly reliable are accurate, reproducible, and consistent from one testing
occasion to another. That is, if the testing process were repeated with a group of test
takers, essentially the same results would be obtained.
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Reliability is detected through Cronbach’s alpha index
Reliability does not imply validity. That is, a reliable measure that is measuring something
consistently is not necessarily measure what you want to be measured.
ANALYSIS OF VARIANCE (ANOVA)
The analysis of variance is a procedure that tests whether di erences exists between two
or more population means. The null hypothesis, typically, is that all means are equal.
To do this, the techniques analyzes the sample variances (total variance in the DV).
Analysis of variance must have a dependent variable that is metric (measured using an
interval or ratio scale). There must also be one or more independent variables that are all
categorical (non metric). Categorical independent variables are also called factors.
ONE-WAY ANALYSIS OF VARIANCE
Marketing researchers are often interested in examining the di erences in the means
values of the dependent variable for several categories of a single independent variable or
factor.
For example:
• do the various segments di er in terms of their volume of product consumption?
• Do the brand evaluations of groups exposed to di erent commercials vary?
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• What is the e ect of consumers’ familiarity with the store (measured as high, medium,
and low) on preference for the store?
ASSUMPTIONS OF ANALYSIS OF VARIANCE
The observations are independent (error terms are uncorrelated).
Each group is approximately normal
• check this by looking at histograms and/or normal quantile plots, or use normality
test of dependent variable (DV(Y)) —> kolmogorov-smirnov normality test
• Can handle some nonnormality, but not sever outliers
• If there is severe non normality, use nonparametric ANOVA
Variance of each group are approximately equal
• Levene’s test for equal variances: if the test if not rejected H0, we verify the
assumption
H0: variances of each group are equal (homoscedasticity)
H1: variances of each group are not equal (heteroskedasticity)
1-WAY ANOVA: F-TEST AND TESTING DIFFERENCES
The F-test is the ratio of the two variance estimates
F = (variances between groups) / (variance within groups)
F = 0 if the group means are identical
F > 0 if not
F could be >0 by chance.
If p-value of F test statistic obtained from data is smaller than signi cance level ( ), we
reject H0.
Reject H0 means that at least one of the mean Y is signi cantly di erent than the others.
If the null hypothesis of equal category means is not rejected, then the independent
variable does not have a signi cant e ect on the dependent variable.
On the other hand, if the null hypothesis is rejected, then the e ect of the independent
variable is signi cant
If the F test is signi cant in ANOVA table, then we intend to nd the pairs of groups are
signi cantly di erent
A comparison of the category mean values will indicate the nature of the e ect of the
independent variable
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N-WAY ANALYSIS OF VARIANCE
In marketing research, one is often concerned with the e ect of more than one factor
simultaneously.
For example:
• how do advertising levels (high, medium, and low) interact with price levels (high,
medium, and low) to in uence a brand’s sale?
• Do educational levels (less than high school, high school graduate, some college, and
college graduate) and age (less than 35, 35-55, more than 55) a ect consumption of a
brand?
• What is the e ect of consumers’ familiarity with a department store (high, medium, and
low) and store image (positive, neutral, and negative) on preference for the store?
N-WAY ANOVA-HYPOTHESIS TESTING
Consider two factors X1 and X2
The signi cance of the overall e ect (model t) is tested by an F test.
• H0: all means are equal
• H1: at least one mean is statistically di erent
The signi cance of the main e ect of each factor may be tested using an F test as well
• Main e ect of X1 on the DV
• H0: means equal H1: means not equal
Main
e ect of X2 on the DV
•
• H0: means equal H1: means not equal
If the overall e ect is signi cant, the next step is to examine the signi cance of the
interaction e ect. This is also tested using and F test
• interaction (moderating e ect) is the joint factor e ects in which the e ect of one factor
depends on the levels of the other factors (X1*X2 is the interaction term)
• H0: mean (ABij) = 0 (two factors A and B are independent)
• Groups of the factor A are equal
• H0: means of all groups of the factor B are equal
N-WAY ANOVA: CLASSIFICATION OF INTERACTION EFFECTS
Use the interaction plot to
identify the interaction type
• If lines are parallel (the
di erences between levels of
X2 is constant in levels of X1),
no interaction e ect
• An ordinal interaction occurs
when one group’s predicted
means is always greater than
another group’s predicted
means
• When two or more group
means switch or cross, a
disordinal interaction occurs
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TWO-WAY ANOVA (TWO FACTORS)
Consider two factors X1 and X2: X1 is food package (sustainable vs not sustainable) and
X2 is food (healthy vs unhealthy).
We wan tot test the fact of these two factors on WTB
We need 4 groups
• e ect of food package on WTB (main e ect)
• E ect of food on WTB (main e ect)
• Conjoint e ect of food package and food on WTB (interaction/moderation e ect)
H1: the usage of a sustainable package (vs. not sustainable) increases WTB, consumers
are more willing to buy sustainable (vs. not sustainable) packages
H2: unhealthy (vs healthy) food increases WTB. Consumers are more willing to buy
unhealthy (vs healthy) foods
H3: food healthiness is a moderating e ect package type and WTB. Unhealthy food
packed in a sustainable package show a higher WTB than healthy foods packed in a
sustainable package. No di erence are expected for WTB unhealthy packages.
ANALYSIS OF COVARIANCE (ANCOVA)
Whee examining the di erences in the mean values of the dependent variable, it is often
necessary to take into account the in uence of uncontrolled independent variables.
For example: in determining how di erent groups exposed to di erent commercials
evaluate a brand, it may be necessary to control for prior knowledge. In determining how
di erent price levels will a ect a household’s cereal consumption, it may be essential to
take household size into account.
It’s an extension of ANOVA in which main e ects and interactions are assessed on DV
scores after the DV has been adjusted for by the DV’s relationship with one or more
Covariates (CVs). Combines linear regression and ANOVA
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ANCOVA: COVARIATE
A covariate is a variable that is related to the DV, which you can’t manipulate, but you
want to account for its relationship with the DV.
A covariate is a (continuous) variable that is not part of the main experimental
manipulation but has an e ect on the dependent variable.
Including covariates enables us to:m
• explain more within group variance, thereby increasing the power of our test
• Remove the bias of a confounding (extraneous) variable
ONE-WAY ANCOVA: BASIC REQUIREMENTS
1 DV (I, R) - continuous (metric)
1 IV (N, O) - discrete (non metric)
1 CV (I, R) - continuous (metric)
TWO-WAY ANCOVA (two factors and one covariate)
We examined two factors and their interaction. We are suspicious that maybe subject’s
environmental concern might interfere the ndings. Thus, results are not due to out
manipulation but in uence by environmental concern.
We have a variable indicating environmental concern of a subject measured with a nine
point likert scale (covariate).
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REGRESSION
CORRELATION
Correlation measures the strength and direction of a relationship between two variables.
For exmaple: the realtionship between consumption and price; advertising and sales;
company size and advertising budget; customer satisfaction and loyalty.
The existence of a positive correlation of x and y does not mean that it is the increase in x
which leads to an increase in y, but only that the two variables move together (to some
extent). Correlation does not imply causation.
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r = 1 —> perfect positive association
r = 0 —> no association
r = -1 —> perfect negative association
0.9 —> strong (+) association
0.5 —> moderate (+) association
0.25 —> weak (+) association
Od ANALYSIS
Focus: the realationship between a metric dependent variable (Y) and one or more
independent variables (X1, X2,… Xj)
Application
• Causal analysis:
Example: can we explain brand loyalty through perceived quality, perceive price and
speci c consumer characteristics?
Which factors in uence brand loyalty?
What is the e ect of antecedents on brand loyalty?
• E ect prediction
Example: how do price, advertising, promotion, and distribution a ect sales?
• Time series analysis
Example: can we predict future sales on the basis of previous sales estimates?
Error term is everything which is not accounted for by the linear relationship (population).
Intercept and regression coe ssion are unknown parameters. They need to be estimated.
b0 + b1 are sample parameter estimates obtained on sample data
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Model t: can we trust the ndings from sample to make conclusions about the
population (do our regression results have an explanatory power?)
1. Is the estimate regression model overall signi cant?
—> F test (from ANOVA table)
H0: all regression coe cients are equal to 0
H1: not all regressions coe cients are zero
if you reject H0: not all regression coe cients are 0. There is model t.
if you don’t reject H0: all regression coe cients are 0. There is no model t.
2. How much of the variance in Y is explained by X?
—> R-Square or Adjusted R-square
it has to be bigger or equal to zero and smaller or equal to 1
larger R2 indicates a good model t
it always increases with the inclusion of other predictors.
REGRESSION ASSUMPTIONS
• No multicollinearity (high correlation among independent variables)
• Error term is normally distributed
• Linearity
• No heteroskedasticity (variance of the error term is a constant) —> homoscedasticity:
residuals should vary randomly around zero and the spread of the residual should be
about the same through the plot (no systematic patterns)
• No autocorrelation (error term are not correlated): relevant if you have time ordered data
• Mean of the error term = 0
NORMALITY OF RESIDUALS (e)
Use one-sample Kolmogorov-Smirnov test to test normality of standardized residuals.
A normal probability plot is found by plotting the residuals of the observed sample against
the corresponding residuals of a standard normal distribution.
- if the plot shows a straight line, it is reasonable to assume that the observed sample
comes from a normal distribution
- If the points deviate a lot from a straight line, there is evidence against the assumption
that the random errors are an independent sample from a normal distribution.
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MODERATING AND MEDIATING EFFECTS
How can we incorporate nominal variables into regression?
1) analyze each subgroup separately: generates di erent slope and intercept for each
group
2) Dummy variabels. Dummy = a dichotomous variables coded to indicate the presence
or absence of something (yes/no, male/female, churn/not churn)
D=1 if yes, D=0 if no
First you create a separate dummy variable for all nominal categories.
For example: gender —> DFEMALE: coded as 1 for all women, zero for men - DMALE:
coded as 1 for all men, zero for women.
Then you include all but one dummy variables into a multiple regression model.
Why can’t you include DFEMALE and DMALE in the same regression model? Because
they are perfectly correlated (negatively) r = -1, so the regression model “blows up”.
For any set of nominal categories, a full set of dummies contains redundant information:
DMALE and DFEMALE contain same information, dropping one removes redundant
information.
MODERATION AND MEDIATION
Moderating variable: is one that in uences the form and strength of a relationship
between two other variables.
Mediating variable: is one that explains the relationship between the two other variables.
Mediator is expiation of why the two variables are related
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