Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

ERP > ERP > Flashcards

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)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

Internal Validity

A

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

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

External Validity

A

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

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

Accuracy

A

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

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

Reliability

A

How consistent the results are if repeated more than once.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

Cross-sectional data

A

Observations at a given point in time.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

Panel data

A

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

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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)
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

Primary data

A
  • By researcher
  • Subjective
  • More customised to study
  • More expensive and time-consuming
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

Secondary data

A
  • By other agents
  • More objective
  • Less customised cheaper
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

Types of performance measures

A
  • Financial performance (profitability)
  • Operational performance (marketshare, efficiency)
  • Overall effectiveness
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

Selection Bias

A

When a sample is fundamentally different from the population.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

Resource Bias

A

Choose secondary source as it is cheaper and less time consuming

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

Popularity Bias

A

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

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

Convenience Bias

A

Researchers use easily available measures

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

Best measure of central tendency for nominal data

A

Mode

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

Values that show significant skewness

A

Outside -1 to 1

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

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

A

Skewed - Median

Not skewed - Mean

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
20
Q

What is skewness

A

How much data deviates from normal distribution

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
21
Q

What is Kurtosis

A

Degree to which observations cluster at the tail

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
22
Q

Kurtosis values

A

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

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
23
Q

Graphical technique for showing kurtosis and skewness

A

Histogram

24
Q

Dummy variable

A

Binary variable expressing whether a condition is fulfilled

25
Q

When comparing models, search for the highest…

A

R squared

26
Q

Assessing your model

A

Goodness of fit

Coefficient of determination

27
Q

To interpret correlation coefficient

A

Sign, size, significance

28
Q

Adjusted R squared

A

Takes into account the number of variables and degrees of freedom

29
Q

How to interpret the regression coefficient

A

The amount of change in y due to a change in x, ceteris paribus

30
Q

When can we reject H0

A

When p is smaller than the significance level. It means that x has a significant impact

31
Q

Multicollinearity

A

High correlation between at least two variables

32
Q

Values of problematic multicollinearity

A

Usually above 0.7 although some literature says 0.5

33
Q

Consequences of multicollinearity

A
  • Hard to distinguish individual impact
  • Larger standard error, more insignificance
  • Non-sensical coefficients
34
Q

Variance Inflation Factor

A

Quantifies severity of multicollinearity.

Above 10 means there is multicollinearity

35
Q

Potential causes of multicollinearity

A
  • Lagged variables (income last and this year)

- Similar phenomena (unemployment, poverty)

36
Q

Solutions to multicollinearity

A
  • Increase sample size
  • Drop one of the variables
  • Transform correlated variable (log transformation or composite variable)
37
Q

Heteroscedasticity

A

Changing error variance over the range of observations

38
Q

Formula for VIF

A

VIF= 1/(1-R2)

39
Q

How to check for heteroscedasticity

A
  • Breusch-Pagan test
  • White test
  • Scatterplot of residuals
40
Q

Solutions for heteroscedasticity

A
  • Weighted least squares. (Observations with a higher variance get a lower weight in determining the regression coefficent)
  • Calculate the robust standard error
41
Q

Outlier

A

A data point that does not follow the general trend of the data.

42
Q

Solutions to outliers

A
  • Remove outlier

- Trim data set

43
Q

There is ommited variable bias if…

A
  • An excluded variable has an effect on the dependant variable
  • There is endogeneity (correlation between error term and variable)
44
Q

Robustness/Sensitivity analysis

A

How sensitive changes are to the model.

Test with combinations of variables, data sets and time frames. Do they remain the same?

45
Q

Tips when using moderation

A
  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
Q

When to use a moderator

A

When the hypothesis is conditional (the relationship between two variables is dependant on the value of another variable)

47
Q

Marginal effect on x

A

∂ y/∂ x=β2x +β4z

48
Q

Marginal effect on y

A

∂ y/∂ x=β3z+β4x

49
Q

Issues with traditional results tables when there is a moderator

A
  • 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
Q

What is factor analysis

A

A technique to reduce large amounts of information into a simple message with minimal information loss.

51
Q

Factor rotation

A

Visualise factors as axes so that variables load maximally onto 1 factor and minimally on the other.

52
Q

Eigen-values

A
  • Importance of a factor

- Keep only factors with eigen value above 1 (Kaiser’s criterion)

53
Q

Scree plot

A

Keep only the factors that are to the left of the inflection point

54
Q

What rotation procedure should be used?

A
  • Orthogonal - factors independent

- Oblique - factors correlated

55
Q

Reliability analysis

A
  • Degree of consistency

- Cronbach’s alpha, above 0,7 or 0,8 to be reliable

56
Q

Explanatory factor analysis

A

Searching for a structure of variables with the goal of identifying the right factors

57
Q

Confirmatory factor analysis

A

You have preconceived thoughts on the structure of the data and want to check whether this is right or not

ERP (2 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2024 Bold Learning Solutions. Terms and Conditions