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ERP Flashcards

1
Q

Validity

A

The extent to which a measurement correctly represents the concept of study (is it measuring what we want it to measure)

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2
Q

Internal Validity

A

The extent to which the study establishes a trustworthy cause and effect between a treatment and an outcome.

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3
Q

External Validity

A

The extent to which the results of a study can be generalised to other situations

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4
Q

Accuracy

A

How close the measurement in the study is to the actual value.

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5
Q

Reliability

A

How consistent the results are if repeated more than once.

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6
Q

Cross-sectional data

A

Observations at a given point in time.

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7
Q

Panel data

A

Collection of observations of multiple subjects at multiple points in time.

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8
Q

Questions the researcher has to deal with

A
  1. Type of data source (primary vs secondary)
  2. Type of measure (nominal etc)
  3. Level of analysis (firm, strategic business unit, inter-organisational)
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9
Q

Primary data

A
  • By researcher
  • Subjective
  • More customised to study
  • More expensive and time-consuming
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10
Q

Secondary data

A
  • By other agents
  • More objective
  • Less customised cheaper
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11
Q

Types of performance measures

A
  • Financial performance (profitability)
  • Operational performance (marketshare, efficiency)
  • Overall effectiveness
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12
Q

Selection Bias

A

When a sample is fundamentally different from the population.

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13
Q

Resource Bias

A

Choose secondary source as it is cheaper and less time consuming

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14
Q

Popularity Bias

A

The researcher chooses popular variables instead of the right ones to measure what is necessary

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15
Q

Convenience Bias

A

Researchers use easily available measures

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16
Q

Elements of Descriptive/Summary statistics

A
  1. N. of observations
  2. Measure of central tendency
  3. Skewness
  4. Kurtosis
  5. Max/Min/Range
  6. SD/Variance
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17
Q

Best measure of central tendency for nominal data

A

Mode

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18
Q

Values that show significant skewness

A

Outside -1 to 1

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19
Q

Which measure of central tendency to use if distribution is skewed/not skewed

A

Skewed - Median

Not skewed - Mean

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20
Q

What is skewness

A

How much data deviates from normal distribution

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21
Q

What is Kurtosis

A

Degree to which observations cluster at the tail

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22
Q

Kurtosis values

A

Less than 3: platokurtic
3 : Normal/Mesokurtic
More than 3: leptokurtic

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23
Q

Graphical technique for showing kurtosis and skewness

24
Q

Dummy variable

A

Binary variable expressing whether a condition is fulfilled

25
When comparing models, search for the highest...
R squared
26
Assessing your model
Goodness of fit | Coefficient of determination
27
To interpret correlation coefficient
Sign, size, significance
28
Adjusted R squared
Takes into account the number of variables and degrees of freedom
29
How to interpret the regression coefficient
The amount of change in y due to a change in x, ceteris paribus
30
When can we reject H0
When p is smaller than the significance level. It means that x has a significant impact
31
Multicollinearity
High correlation between at least two variables
32
Values of problematic multicollinearity
Usually above 0.7 although some literature says 0.5
33
Consequences of multicollinearity
- Hard to distinguish individual impact - Larger standard error, more insignificance - Non-sensical coefficients
34
Variance Inflation Factor
Quantifies severity of multicollinearity. | Above 10 means there is multicollinearity
35
Potential causes of multicollinearity
- Lagged variables (income last and this year) | - Similar phenomena (unemployment, poverty)
36
Solutions to multicollinearity
- Increase sample size - Drop one of the variables - Transform correlated variable (log transformation or composite variable)
37
Heteroscedasticity
Changing error variance over the range of observations
38
Formula for VIF
VIF= 1/(1-R2)
39
How to check for heteroscedasticity
- Breusch-Pagan test - White test - Scatterplot of residuals
40
Solutions for heteroscedasticity
- Weighted least squares. (Observations with a higher variance get a lower weight in determining the regression coefficent) - Calculate the robust standard error
41
Outlier
A data point that does not follow the general trend of the data.
42
Solutions to outliers
- Remove outlier | - Trim data set
43
There is ommited variable bias if...
- An excluded variable has an effect on the dependant variable - There is endogeneity (correlation between error term and variable)
44
Robustness/Sensitivity analysis
How sensitive changes are to the model. | Test with combinations of variables, data sets and time frames. Do they remain the same?
45
Tips when using moderation
1. Include all interaction terms if hypothesis is conditional 2. Include all constituative variables 3. Do not interpret constituative variables as if they are unconditional 4. Calculate meaningful marginal effect and standard error
46
When to use a moderator
When the hypothesis is conditional (the relationship between two variables is dependant on the value of another variable)
47
Marginal effect on x
∂ y/∂ x=β2x +β4z
48
Marginal effect on y
∂ y/∂ x=β3z+β4x
49
Issues with traditional results tables when there is a moderator
- β2 only captures effect of x when z = 0 - SIgnificance only valid if z = 0 - If β4 is insignificant, it may be significant for higher values of z.
50
What is factor analysis
A technique to reduce large amounts of information into a simple message with minimal information loss.
51
Factor rotation
Visualise factors as axes so that variables load maximally onto 1 factor and minimally on the other.
52
Eigen-values
- Importance of a factor | - Keep only factors with eigen value above 1 (Kaiser's criterion)
53
Scree plot
Keep only the factors that are to the left of the inflection point
54
What rotation procedure should be used?
- Orthogonal - factors independent | - Oblique - factors correlated
55
Reliability analysis
- Degree of consistency | - Cronbach's alpha, above 0,7 or 0,8 to be reliable
56
Explanatory factor analysis
Searching for a structure of variables with the goal of identifying the right factors
57
Confirmatory factor analysis
You have preconceived thoughts on the structure of the data and want to check whether this is right or not

ERP (2 decks)

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