Week 1

Question 1

Dependent variable

Answer 1

What is being affected (changes as a result of the independent variable)

Question 2

Independent variable

Answer 2

The variables which affect/predict the dependent variable. (Often what is changed)

Question 3

Validity

Answer 3

The extent to which a measure correctly represents the concept of study.

Question 4

Accuracy

Answer 4

How close to the actual value did the measurement achieve? (1 meter compared to 1.4573 meters)

Question 5

Reliability

Answer 5

Extent to which a measure is consistent in what it is intended to measure, replicability.

Question 6

Internal validity

Answer 6

How well the (specific and individual) study has done

Question 7

External validity

Answer 7

Generalizability of results.

Question 8

Cross-sectional data

Answer 8

• Many subjects at a given point in time (people, households, countries)
  - I.E. –> Profits across firms in China in 2020.

Question 9

Time series data

Answer 9

• Same single subject over a given period of time
  - I.E.–> Profits of firm A between 2000-2003

Question 10

Panel (longitudinal) data

Answer 10

• Multiple subjects, different observations for these subjects over a period of time.
  —> Think of it as a mix of
  Cross-sectional + Time Series
  –> I.E. Profits across Chinese firms over the period 2000-2003.

Question 11

Primary data

Answer 11

Data collected by the researcher

Question 12

Secondary data

Answer 12

Data collected by other agencies –> financial statement data, previous surveys, etc…

Question 13

Selection bias

Answer 13

The sample is not random and may not represent the population being studied.
This means it would impact the way you should interpret a paper or data.

Question 14

What are the 4 levels of measurement?

Answer 14

Nominal, Ordinal, Interval (scale), ratio

Question 15

Name the types of categorical variables (2)

Answer 15

• Nominal Variables
• Ordinal Variables

Question 16

Nominal Variables (3)

Answer 16

• These are data measurements where the values represent a category.
• No ranking or order
• No equal or defined distance between each value:
  -> The distance from 1 and 2 are different from the distance from 2 and 3.
  Examples: (Genders, hair color, student nationality, binary variables (yes = 1, no = 0)

Question 17

Dummy Variable Trap (2)

Answer 17

• If a categorical variable can take on ‘k’ different values, then you should only create ‘k-1’ dummy variables to use in the regression model.
• The dummy variable trap occurs when the researcher does not use ‘k-1’, this would affect the outcomes of the results.

Question 18

Ordinal Variables (2)

Answer 18

• These are ordered categories in a logical order.
• There still is no ‘equal’ distance
  Examples: Product quality rating (1 = poor, 2 = average, 3 = good)

Question 19

Name the types of quantitative variables (2)

Answer 19

• Interval (scale) variables
• Ratio variables

Question 20

Interval (scale) variables (3)

Answer 20

• There is information about differences between points on a scale
  (The values or numbers for each data point has a numerical meaning)
• Equal intervals represent equal distances (scaled)
• No absolute 0 –> This is where a value on the scale can achieve negative values
  Example: Temperature in Celcius (You can find negative temperatures)

Question 21

Ratio Variables (5)

Answer 21

• Equal intervals in data represent equal differences.
• There is an absolute zero–> No negative values.
• Ratio variables are either:
  
  • ‘continuous’ (measured, infinite, with decimals) or
  • ‘discrete’ (counted, integers).
    Example: Weight, height, number of people, money earned
    –> In these examples, you do not get negative values

Question 22

Describe the research process: Testing Hypothesis (4)

Answer 22

1. Identify and define variables
   
   • Dependent Variable
   • Independent Variable(s)
2. Collect Data
   
   • Measurement
3. Analyze data
   
   • Graphically & Descriptively
   • Fit a model –> Regression
4. Conclude, discuss

Question 23

When measuring a dependent variable, there are often different ways of measuring that variable (such as performance: financial, operational, etc…).
How can you determine what type(s) of the DV you should include? (3)

Answer 23

1. Type of data source (primary vs secondary)
2. Type of measure (relevant to the study?)
3. Level of analysis (continent? country? city? company?)
   External validity may also play a role–> Generalizability may be attractive to some researchers.

Question 24

How do determine the type of data source?

Answer 24

• What do you want to measure?
• What kind of data to use:
  
  • Primary
  • Secondary

Question 25

How do you identify the type of measure for a study? (i.e. performance)

Answer 25

Is the researcher measuring the relevant variable that correlates to the study?

Question 26

Define Endogeneity and explain its 3 causes:

Answer 26

Endogeneity is present within a regression if the independent variable is correlated to the error term.
This can be due to:
1. Measurement error (in x) - there is a difference between the actual value and the studies measure
2. Omitted variable- A key independent variable is not included in the regression- leading to the influence of that variable effecting the error term.
3. Reverse causality- where the dependent variable may also influence the independent variable.

Question 27

How do you determine the level of analysis for a study?

Answer 27

• This would also be dependent on the research question / focus of the study.
• Researchers can use different levels of analysis to have a more robust analysis.
• It is important to be clear about the level of analysis you used in the interpretations and conclusion.

Question 28

Define selection bias

Answer 28

• This is a bias which occurs in the process of selecting samples or data which have not been adequately randomized.
  
  • If the sample/data that is selected is not appropriately randomized, the sample/data may not be an accurate representation of the population.
    
    • This inaccurate representation of the population may lead to invalid results and conclusions.

Question 29

How can you (statistically) check if your sample accurately represents the population for Quantitative data (1) and Categorical data (1)

Answer 29

• Quantitative Data: t-test
  
  • Using the t-test formula: (Sample mean - Population mean) / (s / root(n))
  • (s / root(n)) = Standard Error.
  • p-value: Measures the probability the sample results occurred by chance.
    –> Therefore: If p-value is low, sample shows good representativeness
• Categorical Data: Chi-Squared test