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0 Exakte Antworten 33 Text Antworten 10 Multiple Choice Antworten
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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)

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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
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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"

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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" 

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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

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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

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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

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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)

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Difference between true and quasi-experiment?

  • True: randomization

  • Quasi: no randomization

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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)

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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)

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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).

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Scaling techniques?

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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.

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

Likert Scale

Rank order

Constant sum

Semantic differential

Stapel

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

Likert Scale

Paired comparison

Semantic differential

Stapel

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

Stapel

Likert scale

Semantic differential

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What is objectivity?

Results are independent of examiner

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What is reliability?

- consistent results, if measurements are repeated

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

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What is validity?

Whether what was tryed to measure was really measured.

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A measurement that is objective, cannot be reliable.

true

false

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A measurement that is not objective, cannot be reliable.

true

false

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A non reliable measurement lacks validity

true

false

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A non reliable measurement does not lack validity

true

false

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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

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Measures of Central Tendency

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4 measures of variation?

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when data distribution is symmetrical then

mean ≠ median

mean = median

mode = midrange = midhinge

mode ≠ midrange ≠ midhinge

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What is the empirical rule?

68% within \(1\sigma\)

95% within \(2\sigma\)

 

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What is a Type I error?

error of falsely rejecting a null hypothesis

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What is a Type II error?

incorrectly retaining a false null hypothesis