Lecture 1:

The research question consists of:

• Starts with the research question
• Theory/literature
• Hypothesis: testable prediction
• Then we can define the main variables (dependent and independent variable)
• Then you need to collect data (measurement)
• Analyze data (graphically/descriptively)
• Fit a model
• Conclusion

Measurment: a relationship between the numbers and what is being measured. You can measure variables in different kind of ways. Important to consider:

1. What do you really want to measure
2. What is your research question

Basic issues in measurement:

1. Validity: extent to which a measure correctly represents the concept of a study (refers to the study not a specific variable

• Internal validity:  how well the study was done
• External validity: generalize results to other situations

2. Accuracy: is the measure close to the actual value and did you get the right answer on average?
3. Reliability: extent to which a variable is consistent in what it is intended to measure

It is important to measure the right thing and be clear about what you measure

Organize your data:

• Cross sectional: observations at a given points or time
• Time series
• Panel: both cross sectional and time-series dimensions (over a period of time)

Article Hult et al:

• Focus on why do some firms outperform others
• Performance is important variable (often DV)
• Inconclusive results about determinants of performance
• Conclusions depends on the measurement of performance
• No systematic investigation as to how IB research measures performance (contribution)
• They examine the measurement of performance
• They do that in 96 articles published in the journal between 1995 and 2005
• More specifically: they asses the measurement of performance in 3 dimenstions:

1. Type of data source
2. Type of measure
3. Level of analysis

• What did they find: Most studies do not measure performance in a manner that captures the multifacted nature of the construct
• We describe the implications of these results and offer suggestions for improving future practice (present non binding guidelines)-what they do with their findings

Questions the researcher has to deal with:

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

1. Primary data: collected by researcher à time consuming, but original
2. Secondary dataL collected by other agencies; cheap, but lacks originality and may not be fitting to the research question

• How should we measure performance, because you can measure the same thing in different ways

1. Financial performance: reflects economic goals
2. Operational performance: non financial, like innovation, productivity and satisfaction
3. Overall effectiveness: e.g. reputation (related to both)

• Which level of analysis to focus on (remember external validity):

1. Firm
2. Strategic business unit (SBU)
3. Inter-organizational unit (cannot be found in Hult et al)
4. In general at very different levels, country, region, industry, firm etc.

Why do all they matter: potentially different results and conclusions. The importance of the measurement

But: Is one right and the other wrong. Should we all agree on a single measure to use? So which one to choose? It depends on the research question

Tip:

• Be clear about what you want to do
• Choose the appropriate measure for your analysis
• Justify your decision
• Be clear about you limitations

Selection bias:

• Be careful about the interpretation of your result and be careful about what conclusion you draw.
• Can compare with another sample

Endogeneity:

• Correlation between regressor and error term
• Reasons: measurement error, omitted variable (any variable that is not included as dependent variable but could influence the dependent variable) and reverse causality (causality that is not really causality)
• Standard OLS estimate biased
• There are solutions