Dit document bevat volledige en duidelijke notities van alle lessen van het vak Business Research Methods gegeven door Ma Frank en Kathleen Cleeren in KULeuven Antwerpen. Dit is in januari een openboek examen. Dit is een samenvatting voor alle studenten die een Engelse Master doen.
BUSINESS RESEARCH METHODS – PROF CLEEREN
Inhoudsopgave
1 linear regression analysis................................................................................................................. 2
1.1 When to use a linear regression? ............................................................................................ 2
1.2 Creating dummy variables ....................................................................................................... 3
1.3 Example linear regression ....................................................................................................... 4
1.4 Linear regression in Stata ........................................................................................................ 5
1.4.1 Model diagnostics – Steps ............................................................................................... 6
1.5 Model comparison approach ................................................................................................ 13
2 Research methodology: Moderation and mediation ................................................................... 15
2.1 What is moderation? ............................................................................................................. 15
2.2 How to test for moderation? ................................................................................................. 16
2.3 What is mediation? ............................................................................................................... 17
2.4 How to test for mediation according to Baron and Kenny .................................................... 18
2.5 Sobel test and bootstrapping ................................................................................................ 20
2.5.1 sobel test ....................................................................................................................... 20
2.5.2 bootstrapping ................................................................................................................ 21
2.6 examples ................................................................................................................................ 22
3 Logistic Regression......................................................................................................................... 23
3.1 example logistic regression ................................................................................................... 24
4 Factor Analysis ............................................................................................................................... 28
4.1 Introduction to factor analysis............................................................................................... 28
4.2 Factor analysis in Stata .......................................................................................................... 28
4.2.1 running fa in 5 steps ...................................................................................................... 28
4.3 example ................................................................................................................................. 29
4.3.1 exercise .......................................................................................................................... 34
5 panel data ...................................................................................................................................... 37
5.1 Different types of data........................................................................................................... 37
5.2 panel data .............................................................................................................................. 37
1
,1 LINEAR REGRESSION ANALYSIS
1.1 WHEN TO USE A LINEAR REGRESSION?
Linear regression versus logistic regression?
* Categorical variables need to be converted to dummy variables (binary: 1/0)!
First: think which technique is valuable before starting analysing
Dependent: what you want to explain
Independent: variables that explains
Metric: variable has not categories and any value can be possible --> numbers don’t mean
anything
Categorical: you have different categories --> every category get a number so the numbers
have a meaning
Categorical independent variable = create dummy variables
More than 2 groups: multinomial logistic regression (we will not discuss this)
Less than 2 or 2 groups: binary logistic regression
ANOVA: typical technique to analyse experimental data
Exercises
1) a person´s decision to buy a private (store) label
Survey:
‘I tend to buy private labels very often (8 to 10 of my grocery purchases is a private label)’
O Yes
O No
Private label: brand that is offered by the store (e.g. Carrefour brand)
Which technique are we using to analyse this?
Binary logistic regression because our dependent variable has 2 groups and some of the customer
characteristics are not metric
2
,2) Someone´s attitude towards buying private label (or store) products
Survey:
‘I tend to buy a lot of private labels’
1 2 3 4 5 6 7 (1= totally disagree.. 7 = totally agree)
Which technique are we using to analyse this?
Likert scale: these numbers have a meaning (because 7 agrees much more)
Linear regression analysis
Metric
3) someone´s attitude towards buying private labels
Survey:
‘I am a person who:
O buys private labels 8 to 10 times in 10 grocery purchases
O buys private labels 4 to 7 times in 10 grocery purchases
O buys private labels 1 to 3 times in 10 grocery purchases
O buys private labels 0 times in 10 grocery purchases
Which technique are we using to analyse this?
Categoric dependent variable
Independent variable: there is at least 1 categoric variable (gender)
Multinomial logistic regression: dependent is categorical and has more than 2 groups
→ You can use different questions to measure it
1.2 CREATING DUMMY VARIABLES
- Transform categorical independent variables into dummy (1/0) variables (aka indicator
variables) in a linear (and logistic) regression
- Dummy variable trap!
▪ # dummies = # response categories – 1
- Gender: Male:
O Male (1) O Yes (1)
O Female (2) O No (0)
Dummy variable = 0/1 variable
If we have 3 categories we only include 2 (male, female, others --> male and female) → WHY?
Because of perfect multicollinearity if we include 3 variables. If we have 3 categories, we only
need info from 2 categories because we can predict information for the 3the one based on the
other 2.
Use ‘tabulate’ and ‘generate’ command
- The command ‘tabulate+generate’:
▪ Returns frequencies of ‘gender’
▪ Automatically recodes into
dummy variables
3
, - Two new dummy variables appear in the ‘variables’ window
Stata creates a dummy variable for each category
- Age:
O < 20 (1)
O 20-35 (2)
O 36-50 (3)
O > 50 (4)
- How many dummy variables?
Use of i.prefix in stata
- i. prefix before the name of the variable can be used in many commands in STATA
▪ F.e. i.gender or i.age
- i.prefix makes sure that the specified variable is treated as a categorical variable
- STATA will include the right numner of dummy variables in the analyses
1.3 EXAMPLE LINEAR REGRESSION
- The manager of a pizza restaurant wants to research the impact of different factors on consumer
satisfaction [Satisfaction].
- On the basis of discussions with employees, 5 factors
- were identified that could play a role:
▪ Reception [reception]
▪ Service [service]
▪ Waiting time [waiting time]
▪ Quality of the food [food quality]
▪ Price [price]
4
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