Karten 43 Karten
Lernende 5 Lernende
Sprache English
Stufe Universität
Erstellt / Aktualisiert 03.01.2018 / 17.05.2021
Lizenzierung Keine Angabe
0 Exakte Antworten 33 Text Antworten 10 Multiple Choice Antworten
Fenster schliessen

Why do firms conduct empirical research?

  • Others do it too

  • Seek consensus

  • Justify decisions and opinions

  • Have knowledge and evaluation capabilities to understand the consequences of decisions

  • => decisions based on empirical research are better (measured by results)

Fenster schliessen

Three main research designs?

  1. Exploratory: helps learn more about the problem, terms and definitions, or identify research opportunities. Qualitative research

  2. Descriptive: describes the phenomena of interest. Secondary data analysis, survey research

  3. Experimental: uncovers underlying causes of a problem. Experiments
Fenster schliessen

Deductive approach?

start with a broad literature review and theory, narrow it down to specific hypotheses and test these through the collection of data.

"Classical research approach"

Fenster schliessen

Inductive approach?

move from very specific (“interesting but unexplained”) observations in our data to a detection of patterns, up to a formulation of tentative hypotheses and a theoretical framework.

"Big data approach" 

Fenster schliessen

Ethicals principles in empirical research?

  • Voluntary participation: Make sure that study participants are taking part in the study voluntarily and are not coerced.

  • Informed consent: Inform participants about the procedures and risks involved in your research and get participants consent to participate.

  • Anonymity: Assure participants that no one, including yourself, will be able to link the data to a specific individual (Not always possible. Then assure at least confidentiality)

  • Confidentiality: Assure participants that identifying information about them acquired through your study will not be released to anyone outside the study.

  • No data fabrication or manipulation

Fenster schliessen

Internal validity

  • Extent to which changes in the dependent variables(s) can be explained by the experimental manipulation and not by external factors
    => Degree to which a causal conclusion can be drawn

Fenster schliessen

Exernal validity

  • Extent to which the results of the experiment can be generalized – from sample to population
    => Degree to which findings are representative

  • => no external validity without internal validity

Fenster schliessen

Threats to internal validity (extraneous variables)?


  • History (what happens during the experiment)

  • Maturation (changes in the test units themselves)

  • Mortality (survivorship bias)

  • Instrumentation (changes to the measuring instrument)

  • Experimenter effect (reaction to the experimenter due to age, sex, race, …)

  • Socially desirable behavior and/or demand effects

  • Selection bias (due to the improper selection of test units)

  • Testing effects / reactivity (caused by the process of experimentation)

  • Regression to the mean (test units move to the mean in the process of the experiment)

Fenster schliessen

Difference between true and quasi-experiment?

  • True: randomization

  • Quasi: no randomization

Fenster schliessen

How to control extranouos variables?

  • Randomization

  • Statistical control: measuring extraneous variables and adjusting for their effects through statistical methods (e.g. demographics).

  • Matching: matching participants on a set of key variables before assigning them to conditions.

  • Design control: using specific experimental designs to control for confounding effects (treating extraneous variables as additional IV’s)

Fenster schliessen

Two general factorial experiment design and their (dis-)advantages?

  • => between-group designs: one group gets only one treatment, study different groups

    • +: Simplicity, Lower fatigue and practice effects, Useful if it’s impossible to switch to other experimental conditions (e.g. male vs. female)

    • -: High number of participants necessary, particularly for complex designs (many IV’s with many levels), Weaker effects (manipulation needs to be strong)


  • => within-subject designs: every participant gets several or all treatments, repeated testing

    • +: Economy (fewer participants necessary), Higher sensitivity (higher chance that manipulation has an effect)

    • -: Carry-over effects from one to condition to the next, Fatigue and practice effects, Remedy: Counterbalancing (random sequence)

Fenster schliessen

The 4 primary scales and their characteristics?

  1. Nominal: Numbers identify and classify objects (e.g., gender, social security number).

  2. Ordinal: Numbers indicate relative positions of objects but not the magnitude of differences between them (e.g., preferences).

    Special case rating scale: actually an ordinal scale, but can in most cases be interpreted as interval scale

  3. Interval: Intervals between data points on the scale are equal (e.g., temperature).

  4. Ratio: All powers of the prior scales as well as a meaningful absolute zero (e.g., age, length).

Fenster schliessen

Scaling techniques?

Lizenzierung: Keine Angabe

Comparative (nonmetric)

  • Paired comparison: What do you like more, Pepsi or Coke?

  • Rank order: order your favourite softdrinks brands.

  • Constant sum: distribute the 5 points to your favourite brands.

Noncomparative (metric)

  • Continuous rating scale: place a mark on a continuous line

  • Itemized rating scale

    • Likert: scale from 1 (strongly agree) to 5 (strongly disagree)

    • Semantic differential: Seven-point scale with bipolar labels (powerful - - - - - - - weak)

    • Stapel: Unipolar ten-point scale from -5 to +5 without 0.

Fenster schliessen

A scale from 1 (strongly agree) to 5 (strongly disagree) is a?

Likert Scale

Rank order

Constant sum

Semantic differential


Fenster schliessen

A seven-point scale with bipolar labels (eg. powerful - - - - - - - weak) is a?

Likert Scale

Paired comparison

Semantic differential


Fenster schliessen

A unipolar ten-point scale from -5 to +5 without 0 is a?


Likert scale

Semantic differential

Fenster schliessen

What is objectivity?

Results are independent of examiner

Fenster schliessen

What is reliability?

- consistent results, if measurements are repeated

- free from random error (but not free from systematic error)

Fenster schliessen

What is validity?

Whether what was tryed to measure was really measured.

Fenster schliessen

A measurement that is objective, cannot be reliable.



Fenster schliessen

A measurement that is not objective, cannot be reliable.



Fenster schliessen

A non reliable measurement lacks validity



Fenster schliessen

A non reliable measurement does not lack validity



Fenster schliessen

What is sampling? Name techniques.

Process of selecting test units

  • Non Probability sampling

    • Convenience samples: samples drawn at the convenience of the interviewer

    • Judgmental samples: requires a judgment or an “educated guess” as to who should represent the population

    • Quota samples: specified percentages of the total sample for various types of individuals to be interviewed

    • Snowball samples: require respondents to provide the names of prospective respondents

  • Probability Sampling (

    • Simple random sampling: the probability of being selected into the sample is “known” and equal for all members of the population.

    • Systematic sampling: way to select a random sample from a directory or list that is much more efficient than simple random sampling

    • Stratified sampling: separates the population into different subgroups and then samples all of these subgroups

    • Cluster sampling: method in which the population is divided into subgroups, called “clusters,” each of which could represent the entire population

Fenster schliessen

Measures of Central Tendency

Lizenzierung: Keine Angabe
Fenster schliessen

4 measures of variation?

Lizenzierung: Keine Angabe
Fenster schliessen

when data distribution is symmetrical then

mean ≠ median

mean = median

mode = midrange = midhinge

mode ≠ midrange ≠ midhinge

Fenster schliessen

What is the empirical rule?

68% within \(1\sigma\)

95% within \(2\sigma\)


Fenster schliessen

What is a Type I error?

error of falsely rejecting a null hypothesis

Fenster schliessen

What is a Type II error?

incorrectly retaining a false null hypothesis