Summary IRM (introduction to research in marketing) spring

INTRO TO MARKETING RESEARCH
1. INTRODUCTION (LECTURE)
Course objectives: develop..

 Knowledge:
o Theoretical: be able to describe objectives and principles, test assumptions, and interpret
outcomes of multivariate methods dealt with in the course
o Marketing: be able to identify methods useful to solve a given marketing problem, and assess
managerial implications
 Skills: be able to apply multivariate methods/solve marketing problems using SPSS

HBBA: CHAPTER 1

1.1 Defining Multivariate Analysis

HBBA: ‘Broadly speaking, it refers to all statistical methods that simultaneously analyze multiple measurements
on each individual or object under investigation’

Multiple measurements  measure different types of variables.

1.2 Some Basic Concepts

Measurement scales:

 Nonmetric scales:
o Nominal  Characteristics: unique definition/identification classification. Phenomena: e.g.
brand name, gender, student ANR. Appropriate Methods of Analysis/Statistics: e.g. %, Chi
square test. Example: Shampoo Brand Identification: Pantene 1, Elvive 2, Etos 3
o Ordinal  Characteristics: indicate ‘order’, sequence. Phenomena: e.g. preference ranking,
level of education (ranking  1 is more than the other). Appropriate methods of
analysis/statistics: percentiles, median (in the middle), rank correlation + all previous statistics.
Example: Shampoo Brand Preference  Etos 3 (least preferred) < Elvive 2 < Pantene 1
 Example: Shopping frequency


= ordinal scale gives you one unit difference. 3th column: actual frequency.
Ordinal scale tells you which one is more or less, but not how much more or less.



Metric scales:

 Interval  Characteristics: arbitrary origin. Phenomena: e.g. attribute scores, price index. Appropriate
Methods of analysis: arithmetic average, range, standard deviation, product-moment correlation, +
previous methods. Example: Shampoo Brand Quality score  Pantene 95, Elvive 90, Etos 49. Lowest =
0, highest = 100.  Gives an ordering. But we can see how much the differences are. With ordinal data,
you didn’t know that.
 Ratio  Characteristics: unique origin. Phenomena: e.g. age, cost, number of customers. Appropriate
methods of analysis: geometric average, coefficient of variation, + all previous methods. Example:
Shampoo Brand Price  Pantene 3.75, Elvive 4.66, Etos 2.89 (Euros/300ml)
o Difference between interval and ratio? Zero = zero, clear what zero means (ratio)  unique
origin.

Errors: Reliability and Validity

 Reliability: is the measure ‘consistent’ correctly registered

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,  Validity: does the measure capture the concept it is supposed to measure?

Statistical Significance and Power

 Hypothesis testing  to examine differences. We use samples and never examine the complete
population, which can result in:

o Type I error () = probability of test showing
statistical significance when it is not present
(‘false positive’). In reality no different, test tells
you that there is a difference. (We focus on alfa!!
Alpha not higher than 5%)
o Power (1-) = probability of test showing
statistical significance when it is present. There
was a difference in reality, but your test told you
it wasn’t.
 Suppose that the truth is ‘no difference’: what
would error-free population measure, lead to?



= population difference 0  no difference




 Suppose that the truth is: ‘no difference’: what would sample measures, with error, lead to?


= if I move the cut-off value to the right, the alpha will decrease, and
thus the type I error risk is getting lower. You want to prevent type I
error (but change type 2 error increases).




 Power  probability that if there is an effect in reality and you also find an affect.
o Power depends on:
  (+)  larger alpha = larger power
 Effect size (+)  larger effect size = larger power (effect size = what you want to
measure  size of correlation (for example between advertising and sales)).
 Sample Size n (+)  larger sample = larger power
o Implications:
 Anticipate consequences of , effect and n
 Assess/incorporate power when interpreting results

1.3 Types of Multivariate Methods: Dependence or Interdependence techniques

Dependence techniques:

 One or more variables can be identified as dependent variables and the remaining as independent
variables.
 Choice of dependence techniques depends on the number of DV’s involved in analysis.

Interdependence techniques:

 Whole set of interdependent relationships is examined


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,  Further classified as having focus on variable or objects




HBBA CHAPTER 2: PRELIMINARY DATA ANALYSIS AND DATA PREPARATION

EXAM: Whatever is mention in the slides, you have to learn it in HS2, here it is very brief, but no new things.
Example  “what does missing at random mean”….

2.1 Conduct preliminary analysis: graphical inspection and simple analysis

Why?

 Get a feel for data
 Suggest possible problems (and remedies) in next steps

How?

 Univariate profiling
 Bivariate analysis

2.2 Detect outliers

What are outliers? “Observations with a unique combination of characteristics
identifiable as distinctly different from the other observations”

Outliers:

 There are two basic types of outliers:
o ‘Good’: true value (probably) – not errors/mistakes, real values that gives variation.
o ‘Bad’: something is wrong? ( in many cases)
 To distinguish these types, one should investigate the causes
o Procedural error
o Exceptional circumstances (Cause known or unknown)


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