Quantitative research in management: methodology. Introduction to IBM SPSS

1. Research methodology

RESEARCH METHODOLOGY
Olga Konnikova
Ass.Prof. of Marketing Department, Saint-Petersburg State University of Economics
PhD in Economics
E-mail: [email protected]

2. Schedule

SCHEDULE
September, 6, 2019 (Friday) 18.30-21.40 class № 2024
September, 13, 2019 (Friday) 18.30-21.40 class № 2024
October, 11, 2019 (Friday) 18.30-21.40 class № 2024
October, 29, 2019 (Friday) 18.30-21.40 class № 2024
November, 1, 2019 (Friday) 18.30-21.40 class № 2024
November, 8, 2019 (Friday) 18.30-21.40 class № 2024
Exam
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3. Assessment requirements

ASSESSMENT REQUIREMENTS
Class attendance/assignment
Hometasks
Final exam (written form)
Test (multiple choice questions)
Task (problem solution)
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4. We will learn how to:

WE WILL LEARN HOW TO:
1. Formulate research hypotheses
2. Select and conduct suitable types of statistical analysis to test hypotheses
3. Present the research results in the most understandable text and graphic form
4. Make predictions using multiple linear regression models and interpret their results
5. Conduct market segmentation and allocate clusters using the combination of characteristics
6. Predict the choice of the consumer according to the data we know about him
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5. Agenda

AGENDA
1.
Quantitative research in Management: methodology. Introduction to IBM SPSS.
2.
Data visualization. Descriptive statistics. Cross-tabulating (Contingency tables).
3.
Analysis of variance (dispersion analysis)
4.
Correlation and regression analysis
5.
Cluster analysis
6.
Summary
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6. Topic 1.

TOPIC 1.
QUANTITATIVE RESEARCH IN MANAGEMENT: METHODOLOGY. INTRODUCTION TO IBM SPSS.

7. Types of research

TYPES OF RESEARCH
Desk-based (secondary) research (based on statistics, financial reports, …)
Empirical (primary) research (based on surveys, observation, experiments, …)
Qualitative (interviews, focus groups, expert surveys)
Quantitative (using different types of questionnaires (written, panel, telephone, PC, Internet, etc.))
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8. Analyzing quantitative data

ANALYZING QUANTITATIVE DATA
Desk-based information:
- Statistics
- Financial statements
- CRM
- …
Empirical information:
- Questionnaires
- …
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9. Hypothesis

HYPOTHESIS
Hypothesis is the assumption of the connection of variables
In any hypothesis, a dependent and independent variable (-s) can be singled out
For each variable, you need to clearly understand in which scale it is measured (=how it cab be
measured?)
The number of hypotheses is unlimited
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10. Hypothesis: dependent and independent variables

HYPOTHESIS: DEPENDENT AND INDEPENDENT VARIABLES
Independent variable (-s)
Hypothesis
Dependent variable (-s)
The number of training equipment and
training specialists in a gym has a positive
effect on the frequency of visits
When buying perfume, brand has a
stronger impact on customers than price
Women more often
unplanned shopping
than
men
do
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11. Hypothesis: dependent and independent variables

HYPOTHESIS: DEPENDENT AND INDEPENDENT VARIABLES
Hypothesis
Independent variable (-s)
The number of training equipment
and training specialists in a gym Number of training equipment
has a positive effect on the Number of training specialists
frequency of visits
Dependent variable (-s)
Frequency of visits
Desire to buy
When buying perfume, the brand
Impact of brand
has a stronger impact on
Impact of price
customers than price
Women more often than men do
Gender
unplanned shopping
OR
Willingness to buy
OR
Purchase fact
Frequency of unplanned shopping
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12. How to measure variables

HOW TO MEASURE VARIABLES
3 main types of scales
1. Nominal scale
2. Ordinal scale
3. Quantitative scale
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13. Nominal scale

NOMINAL SCALE
Objects are classified by the presence and absence of a certain attribute
Categories of the attribute are not compared or measured in any way
Nominal scale is called binary if the number of categories is only two
E.g.: gender (male/female), native city (Moscow/London/New York), fact of purchase (yes or no)
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14. Example of nominal / binary scale

EXAMPLE OF NOMINAL / BINARY SCALE
1. What kind of soft drink do you prefer?
Dr.Pepper
Pepsi
Sprite
2. Would you continue to buy your favorite cosmetics brand if its price rose by 10%?
yes
no
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15. Ordinal scale

ORDINAL SCALE
Categories have a logical order
We can compare the intensity of studied feature in the object, so we can dispose the categories on the
basis of "more - less", but without indicating how much more or less
There are different types of ordinal scales: scale of importance, Likert-type scale, interval scale, …
E.g. level of education (bachelor/master/PhD)
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16. Example of ordinal scale

EXAMPLE OF ORDINAL SCALE
1. Rank these beverages according to the degree of your preference
Dr.Pepper
Pepsi
Sprite
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17. OrdinaL interval scale

ORDINAL INTERVAL SCALE
The categories in this type of scale are not only logically ordered, but also separated by certain intervals
Example:
2. Evaluate each drink on a 5-point scale, where 1 - do not like it, 5 - extremely like
Dr.Pepper
12345
Pepsi
12345
Sprite
12345
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18. OrdinaL scale of importance

ORDINAL SCALE OF IMPORTANCE
Intervals determine the degree of importance of a characteristic (-s)
Example:
3. Indicate how important each of these factors is for you when purchasing soft drinks
Factor
Almost
unimportant
Partly
unimportant
Sometimes
important,
sometimes not
A bit
important
Very
important
Critically
important
Price
Bottle design
Taste
Amount of sugar
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19. OrdinaL Likert-type scale

ORDINAL LIKERT-TYPE SCALE
Respondents are given some statements and they are asked to what extent they agree or disagree with them
Example:
4. Below there are several statements regarding soft drinks. Please indicate the extent of your agreement or disagreement
with each of them.
Statement
Definitely
disagree
Generally
disagree
Partly
agree
Generally
agree
Definitely
agree
Strongly
agree
I believe that the taste of Pepsi is more
intense than that of Cola
A bottle of Sprite is more attracted to the
touch than a bottle of Pepsi
I believe that the consumption of soft
drinks is harmful to the stomach
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20. Quantitative scale

QUANTITATIVE SCALE
A measurement that uses absolute zero and, therefore, allows to make comparison of absolute values
of categories
Quantitative scale can be easily transformed into interval one
E.g. age, income, …
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21. Example of quantitative scale

EXAMPLE OF QUANTITATIVE SCALE
Indicate how many times a month you buy each of the drinks listed:
Dr.Pepper -
Pepsi -
Sprite -
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22. Hypothesis: measuring variables

Scale for
Independent
measuring
Hypothesis
HYPOTHESIS: MEASURING
variableVARIABLES
(-s)
independent
variable (-s)
The number of training
equipment and training
specialists in a gym has a
positive effect on the
frequency of visits
Number of training
equipment
Number of training
specialists
Dependent
variable (-s)
Scale for
measuring
dependent
variable (-s)
Frequency of visits
When buying perfume, the
brand has a stronger Impact of brand
impact on customers than Impact of price
price
Desire to buy
OR
Willingness to buy
OR
Purchase fact
Women more often than
men
do
unplanned Gender
shopping
Frequency
of
unplanned shopping
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23. Hypothesis: measuring variables

Scale for
measuring
Independent
Hypothesis MEASURING VARIABLES
independent
HYPOTHESIS:
variable (-s)
variable (-s)
The number of training
equipment and training
specialists in a gym has
a positive effect on the
frequency of visits
Number of
equipment
Number of
specialists
training Quantitative
Frequency of visits
Quantitative
Desire to buy
OR
Willingness to buy
OR
Purchase fact
Nominal (binary)
OR
Ordinal interval
training Quantitative
When buying perfume,
the
brand
has
a Impact of brand
stronger impact on Impact of price
customers than price
Women more often
than men do unplanned Gender
shopping
Scale for
measuring
Dependent variable
dependent variable
(-s)
(-s)
Ordinal
importance
Ordinal
importance
Nominal (binary)
Frequency
of
unplanned shopping
*Only binary
Quantitative
OR
Ordinal interval
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24. Why is it so important (variables, scales, …)

WHY IS IT SO IMPORTANT (VARIABLES, SCALES, …)
INDEPENDENT VARIABLE
TYPE OF VARIABLE
Quantitative scale
DEPENDENT
VARIABLE
Nominal / Ordinary
scale
Quantitative scale
Nominal / Ordinary
scale
Correlation and
regression analysis
Analysis of variance
(dispersion analysis)
Discriminant analysis
Cross-tabulating
(Contingency tables)
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25. Hypothesis: main and alternative

HYPOTHESIS: MAIN AND ALTERNATIVE
Main hypothesis (H0) states that there is no connection between variables
Alternative hypothesis (H1) states that there is any type of connection between variables
According to the methodology of quantitative research, we must first test the main hypothesis, and only if it
is refuted, check the alternative hypothesis
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26. Hypothesis: main and alternative

HYPOTHESIS: MAIN AND ALTERNATIVE
Main hypothesis
Alternative hypothesis
*gym
*perfume
*unplanned shopping
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27. Hypothesis: main and alternative

HYPOTHESIS: MAIN AND ALTERNATIVE
Main hypothesis
Alternative hypothesis
Gyms of the same network with different number of In the gyms of the network, where there are more
specialists and equipment are visited by the same specialists and equipment, the average number of
frequency
visits is more
When buying perfume, the price and the brand have Consumers more often buy perfume under the
the same influence on consumers
influence of the brand and not the price
Men and women equally often do unplanned shopping Women more often than men do unplanned shopping
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28. Introduction to ibm spss

INTRODUCTION TO IBM SPSS
SPSS (Statistical Package for Social Sciences OR Superior Performing
Software Systems) — a system (software package) of statistical information
processing that provides the user with a wide range of data transformation
and analysis capabilities, as well as visual representation of obtained results
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29. How to change language in IBM SPSS

HOW TO CHANGE LANGUAGE IN IBM SPSS
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30. How to change language in IBM SPSS

HOW TO CHANGE LANGUAGE IN IBM SPSS
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31. The way of data organizing

THE WAY OF DATA ORGANIZING
Two main windows (views) – window with the data (“Data View”) and window with the information about
variables (“Variables View”)
“Data view” window – rows contain observations, columns contain variables
Observation can be a respondent, product, brand, enterprise, …
Variable can be a question in the survey or some data we know about the observation
“Variables View” window – rows contain variables from the “Data view” and columns contain its description as
name, type, width and so on
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32. Variables view

VARIABLES VIEW
Name – the working title of the variable (e.g. child) - no spaces, maximum 10 symbols
Type – type of the data in variable (numeric, string, data etc., e.g. numeric)
Label – full name of the variable (e.g. Number of children) – any number of spaces and symbols
Values – possible codes of the variable (e.g. 1 – high, 2 – medium, 3 – small).
All variables in nominal and ordinal scale should be coded.
Missing – missing values code in order not to take into account (optional, e.g. “98” – code for missing values)
Measure – type of the variable (scale, ordinal, nominal)
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33. SPSS Statistics menu tools

SPSS STATISTICS MENU TOOLS
File – import, export functions, to save, to open, to create a new project etc.
Data – all types of work with data presentation, such as sort, split, select etc.
Transform – all kinds of data transformations, such as computing variables, recoding variables, missing
data replacing
Analyze – everything related to data analysis, such as descriptives, methods and models
Graph – graphical visualization of the data
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34. Basic commands: data sorting

BASIC COMMANDS: DATA SORTING
Data – Sort Cases
Example №1:
sort respondents by age
sort respondents by family status and education
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35. Basic commands: data selection

BASIC COMMANDS: DATA SELECTION
Data – Select Cases
Example №1:
select only men for future analysis
select only respondents with income more than 64000 rub.
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36. Basic commands: Creating a new variable

BASIC COMMANDS: CREATING A NEW VARIABLE
Transform – Compute variable
Example №1:
Create a new variable ”Income in euro” by transforming Income variable according to the exchange rate
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37. Basic commands: Recoding one variable into another

BASIC COMMANDS: RECODING ONE VARIABLE INTO ANOTHER
Transform – Recode into Different Variables
Example №1:
Divide respondents into ”High level income” (>50000 rub.) and ”Low level income” (<50000 rub.)
Divide respondents into ”Younger” (< 35 y.o.) and ”Older” (>35 y.o.)
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38. Task №1

TASK №1
For 6 hypotheses in the file Task №1, determine dependent and independent variables, in which scale
each variable is measured, formulate main and alternative hypothesis
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