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Summary of lectures Empirical Research Project for IB (Pre-MSc/BSc)

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Summary of lectures Empirical Research Project for IB (Pre-MSc/BSc). Got a 7 as a final grade.

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Lecture 1 Measurement Article: Hult et al.

Measurement
Relationship between the numbers and what is being measured. Different types of
measures/data sources leads to different results.

Basic issues in measurement
 Validity
Extent to which a measure correctly represents the concepts of study (link between the
measure that you choose and the concept that you study). For example, you want to study a
person’s net income, but you choose to measure the net income per household.

Internal validity  how well the study was done (do you have good questionnaire?)
External validity  generalize results to other situations (can you transfer your study to
another country/other situations?)

 Accuracy: measure close to actual value / getting the ‘right’ answer on average

 Reliability/consistency
Extent to which a variable is consistent in what it is intended to measure. For example,
conduct the same study twice – will the results be the same?

Organizing your data
Your data can be:
- Cross-sectional: observations at a given point in time (snapshot of what is happening)
- Time series
- Panel (longitudinal): observations over a period of time (days/months/years). Combines cross-
sectional and time series data.

Paper of Hult et al.
 key: the way you measure determines your results (see slides for more info)

The abstract of the paper always captures the main findings of the research – so write an attractive
one!

Questions the researcher has to deal with:
1. Type of data source: primary/secondary
2. Type of measure: e.g. financial performance/operational performance/overall effectiveness
3. Level of analysis: e.g. firm level/business unit/inter-organizational unit level

Selection bias  representativity of your sample
If your sample is not representative. e.g. only taking a sample of large firms or firms that adopt
harmonized accounting standards. These firms are fundamentally different than others so your
interpretation of your results/conclusions can be very different. Or you are looking at only
high-income countries, you cannot generalize your results  you are leaving out the lower
income countries. It is not representative anymore. Only if you have chosen to focus on a
certain group beforehand!

Endogeneity
Correlation between regressor x and error term e. There are three reasons why it takes place:
measurement error (e.g. questionnaire with people mistakes/people can answer wrong
accidently or not honest or when you are coding your data, you can make a mistake), omitted
variable bias (when you left out your analysis an important independent variable) and/or
reverse causality (normally x affects y but now it is reversed).

, Lecture 2 Descriptive statistics Article: Aybar & Ficici

Measurement levels
Nominal: number only tells the category, no ranking. Binary measurement level is when there are two
distinct categories (yes/no)
Ordinal: ordered categories (logical order)
Interval: information about differences between point on a scale, equal intervals represent equal
differences. E.g.: Celsius scale (can go into minus)
Ratio: same as interval but it has an absolute zero (e.g. age/weight, cannot go into minus and the zero
does not really mean anything)

Descriptive statistics/summary statistics
Is step 1 in research, just a summary before the actual analysis. Quantitative description of main
features of data. Gives you information about:
Which are the players of the game?
Which is the nature of the variables? (e.g. discrete, continuous)
Do you see any problems? Things to keep in mind? (e.g. min/max/negative values)
You will get a general view of your data

Which are the summary statistics one can calculate?
1. Number of observations
2. Measures of central tendency (mean/median/mode)
3. Skewness: about the shape of a distribution  specifically about the symmetry of a
distribution compared to a normal distribution

When your distribution is not skewed  mean
When your distribution is skewed  median

If the skewness is not separately shown, you could compare the mean and the median. If the
mean is larger than the median = positively skewed

4. Kurtosis: also says something about the shape of a distribution  specifically about the degree
to which scores cluster at the tails compared to the normal distribution (in points):
Kurtosis = 3 is assumed as a normal distribution (in SPSS this number is 0)
Leptokurtic > 3 heavy tails, maybe also pointy distribution
Platykurtic < 3 light tails, maybe also flatter distribution

5. Minimum and maximum = range (max minus min)
Can also be stated as interquartile range (Q1 lower quartile = 25%, Q3 upper quartile = 75%)

6. Variance and standard deviation
Standard deviation tells you how spread out the data are from the mean + heterogeneity of the
sample


Measure of association
Sometimes part of summary statistics
Correlation/correlation coefficient  strength of the relationship between two variables (positive or
negative, from -1 to +1. Equal to zero = no linear relationship = as one variable changes, the other
stays the same. You always have to interpret correlation on size (degree of correlation), sign (positive
or negative) and significance. Correlation is NOT causation & you do not know if there is a 3 rd
variable that affects variable 1 and 2 for example that leads to the correlation between 1 and 3.


Lecture 3 OLS Regression

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