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Quantitative Research
RONIN
undertakes quantitative research on a global scale, using all
the available forms of data collection – telephone, web, mail,
fax and person-to-person. In many cases, this is provided as a
full service contract. As well, RONIN provides a high quality,
cost effective "field and tab" service. Indeed, a large
number of other market research firms subcontract their international
field work to RONIN, utilizing their expertise in such industries
as pharmaceuticals, travel and hospitality, medical, and utilities
with RONIN's expertise in data collection.
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Our
quantitative research is managed from both of our offices, but the data
collection is centralized in London, England. In this office we have
150 stations for CATI which operate 24 hours each day conducting interviews,
in the appropriate language, in some 44 countries around the world.
Both B2B and B2C interviews are conducted.
Coupled
with this is our web-based interviewing capability, where a load-balanced
server farm enables rapid response in a fault tolerant environment.
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Advanced Analysis
Advanced
analytical techniques can assist the understanding of what the
data collected means. The techniques which are used will depend
on the result being looked for; e.g., if targeting is the need,
clustering, factor analysis and CHAID are useful; if optimization
of a product bundle is the need, choice modeling and conjoint
are useful. The particular technique will depend on characteristics
of the data set and the use to which the result will be put. RONIN
has solid experience in the following techniques and will use
either one or, in some cases, a battery of these to match the
needs of the project.
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Nonparametric
Tests
If distributions
are not normal; i.e., non-parametric, such as those that are flat, peaked,
or strongly skewed, non-parametric statistics are recommended. These
statistics are particularly relevant in the IT realm where data frequently
does not fit into a normal distribution.
T
and Z Tests
T-tests are typically used for determining whether or not one group
significantly differs from another on some type of metric. For instance,
we may discover that females, on average, spend significantly more hours
than males at health-related Internet sites. When conducting numerous
t-tests the probability of reporting that a result is significant when
it actually is not (i.e., Type I error), dramatically increases. In
such cases, one should use Anova (explained below) to help control for
chance findings.
One of the most
useful z-test applications in market research is determining whether
or not one proportion significantly differs from another. For example,
we may discover that the proportion of Internet users in one geographic
region exceeds that of another.
Correlation
Analysis (r)
Correlation measures the degree of relationship between one variable
and another. There may be, for instance, a high correlation between
those who have two or more phone lines in their household and time spent
on the Internet. One must note, however, that correlation is a measure
of linear (i.e., straight line) relationships. If the two variables
of interest have a non-linear relationship such as an inverted U, the
correlation coefficient (r) will fail to detect a relationship when
one is actually present.
Analysis
of Variance (ANOVA)
This procedure is useful for detecting mean differences among three
or more groups. ANOVA is a viable alternative to conducting numerous
t-tests because the analysis controls for chance findings (Type I error).
To assess differences in the average number of hours spent on the Internet
among PC owners in four countries, ANOVA would be an appropriate tool.
Similar to other statistical techniques, ANOVA is not immune from Type
I error when used repeatedly with a data set. To address this problem,
MANOVA (explained below) should be employed.
Multivariate
Analysis of Variance (MANOVA)
This analysis can detect mean differences among a number of different
groups on several different measures while protecting for chance findings.
The method is an efficient and powerful analysis for large research
studies in which there are a variety of segments being assessed on a
number of different measures.
Analysis
of Covariance (ANCOVA)
This procedure is useful for detecting mean differences among three
or more groups while holding one variable constant. To assess differences
in the average number of hours spent on the Internet among PC owners
in four countries, while controlling for access speed, ANCOVA would
be an appropriate analytic tool. ANCOVA is particularly useful in research
situations where a variable, such as income, gender, education, or age,
can potentially obscure or bias the results.
Multivariate
Analysis of Covariance (MANCOVA)
This analysis can detect mean differences among a number of different
groups on several different measures, while holding one or more variables
constant. The method is useful for research studies where there are
a variety of segments being assessed on a number of different measures,
where one or more variables needs to be controlled for that may potentially
bias the results.
Repeated
Measures ANOVA, ANCOVA, MANOVA, MANCOVA
In ANOVA, ANCOVA, MANOVA, MANCOVA , the respondent is assessed once
for each measure. In repeated measures ANOVA-based designs, the respondent
is measured several times. For instance, data collected through measuring
the number of online purchase transactions made by different buying
segments per quarter would be appropriate for this analysis. Accuracy
is increased when measuring a respondent on several occasions as opposed
to one, thus making a repeated measures approach one of the more powerful
analytic techniques.
Conjoint
and Discrete Choice
These techniques identify buyer preferences for product features, the
most desired set of features for a product, and what tradeoffs buyers
are willing to make for their desired product. The techniques are thus
effective tools for developing a successful product design and bundling
of product or service offerings.
CHAID,
Exhaustive CHAID, C & RT, QUEST
These are all tree-based tools that segment groups of respondents that
share similar characteristics. CHAID (Chi-squared Automatic Interaction
Detector) and Exhaustive CHAID are ideal for visualizing large data
sets for consumer profiles and segments. C&RT (Classification and
Regression Tree) and QUEST (Quick Unbiased Efficient Statistical Tree)
provide similar results but, unlike CHAID techniques, produce trees
with binary splits which are more appropriate for some types of research.
All four techniques are effective variable reduction tools and precursors
to other types of analyses, such as regression and higher-order predictive
models.
Discriminant
Analysis
Discriminant analysis is useful for finding a group of variables (i.e.,
a discriminant function) that distinguishes one group from another.
Although it works well for group membership situations, it is not as
robust to statistical violations as, for example, logistic regression
that will provide similar information.
Factor
Analysis, Principal Components, and Cluster Analysis
In the realm of market research, these variable reduction schemes identify
underlying dimensions of what respondents may be thinking when, for
example, evaluating a product or service. Please note that these analyses
do not test whether the dimensions that surface relate to a specified
outcome (e.g., an online purchase). Regression or higher-order predictive
models, such as RPM, is required to assess whether the dimensions have
any predictive value.
Perceptual
Mapping
This technique is particularly effective for exploring branding issues.
Several brands can be compared and contrasted, on a number of different
attributes, in one comprehensive picture. A perceptual map may indicate
that several brands of laptops are perceived similarly in terms of price,
performance, and wireless capabilities, but not in terms of reliability
and warranty coverage. Another advantage of perceptual maps is that
the data required to construct them is straightforward and typically
not difficult to collect – consumers usually rate the product/service
attributes on simple Likert-type scales (e.g., ranging from strongly
agree to strongly disagree).
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