Quantitative research in management: methodology. Introduction to IBM SPSS презентация

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

ASSESSMENT REQUIREMENTS
Class attendance/assignment
Hometasks
Final exam (written form)
Test (multiple choice questions)
Task (problem solution)

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

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

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

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.))

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

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

HYPOTHESIS: DEPENDENT AND INDEPENDENT VARIABLES

HYPOTHESIS: DEPENDENT AND INDEPENDENT VARIABLES

HOW TO MEASURE VARIABLES
3 main types of scales
Nominal scale
Ordinal scale
Quantitative 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)

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

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)

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

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

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

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.

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, …

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

HYPOTHESIS: MEASURING VARIABLES

HYPOTHESIS: MEASURING VARIABLES

WHY IS IT SO IMPORTANT (VARIABLES, SCALES, …)

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

HYPOTHESIS: MAIN AND ALTERNATIVE

HYPOTHESIS: MAIN AND ALTERNATIVE

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
INTRODUCTION TO IBM SPSS

HOW TO CHANGE LANGUAGE IN IBM SPSS

HOW TO CHANGE LANGUAGE IN IBM SPSS

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

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)

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

BASIC COMMANDS: DATA SORTING
Data – Sort Cases
Example №1:
sort respondents by age
sort respondents by family status and education

BASIC COMMANDS: DATA SELECTION
Data – Select Cases
Example №1:
select only men for future analysis
select only respondents with income more than 64000 rub.

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

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.)