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Business Research Techniques for Pre-Master > L6 Lab Experimental research > Flashcards

L6 Lab Experimental research Flashcards

1
Q

Why an experiment?

A

To test causality: cause and effect relationship among variables

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2
Q

Characteristics of causality

A

‒ X and Y co-occur (correlation)
‒ A logical explanation for the effect of X on Y is needed
‒ X proceeds Y in time
‒ No other cause (Z) explains the co-occurrence of X and Y

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3
Q

What is an experiment?

A

Data collection method where one or more IVs are manipulated to measure the effect on the DV, and where you control for other causes

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4
Q

Independent variable

A

Variable that is manipulated (aka the “treatment” variable)
‒ E.g., price, packaging, degree of advertising, employee bonus, …
‒ Ways to manipulate:
» Presence vs. absence (e.g., bonus vs. no bonus)
» Frequency (e.g., high bonus vs. low bonus vs. no bonus)
» Type (e.g., punishment vs. reward)

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5
Q

Dependent variable “y” or “o”

A

Variable that is measured
‒ E.g., sales, click-through rate, purchase intention, attitude, motivation, performance,…
‒ Can be nominal, ordinal, interval, or ratio

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6
Q

Extraneous variable

A

Every possible variable that can influence the DV, other than the IV
‒ E.g., store location, age, gender, culture, …

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7
Q

Internal validity

A

To what extent does the research design permit us to say that the independent variable causes a change in the dependent variable

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8
Q

External validity

A

To what extern are the results found in the lab setting transferable or generalizable to actual organizational or field setting.

  • Without internal, no external validity
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9
Q

Confound

A 
A variable (Z) that threatens internal validity
- roadblock
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10
Q

Lab experiment

A

Artificial setting to have as much control as possible over the manipulations (incl online experiment)

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11
Q

Field experiment

A

Natural environment where manipulation is possible
− Problems with randomization
− Problems to exclude external influences

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12
Q

Threats to internal validity

A
  • history effect
  • maturation effect
  • testing effect
  • instrumentation effect
  • selection bias effect
  • mortality effect
  • statistical regression effect
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13
Q

History effect

A

Events/factors outside the experiment have an

impact on the DV during the experiment

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14
Q

Maturation effect

A

Biological/psychological of participant changes over time

  • growing older
  • getting hungry
  • getting tires
  • getting bored
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15
Q

Testing effect

A

Prior testing affects the DV

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16
Q

Instrumentation effect

A

The observed effect is due to a
change in measurement

  • different person that gives interview. (same as different teacher)
17
Q

Selection bias effect

A

Incorrect selection of respondents (experimental and/or control group)

  • not only bonus leads to better work, the other group also is higher educated which you did not know
18
Q

Mortality effect

A

Drop out of respondents during experiment

19
Q

statistical regression effect

A

extreme scores in the beginning and less extreme in the end

20
Q

How to increase internal validity

A
  • Randomization
  • Design control (Control group and extra group)
  • Statistical control
21
Q

Randomization

A

Random allocation of participants to different conditions (selection bias, but also instrumentation, history, mortality)

22
Q

Control group (design control)

A

Include group that does NOT receive the treatment (history and maturation, but also instrumentation, and statistical regression)

23
Q

Extra groups (design control)

A

E.g., groups without pre-test, but with an experimental manipulation (to exclude the effects of pre-testing) (testing)

24
Q

Statistical control

A

Measure extraneous variables, and include these in the statistical analysis (covariance analysis) (history and selection bias)

25
Q

Pre-experimental designs

A

NO RANDOMIZATION!

  • One-Shot Case Study
  • One-Group Pretest-Posttest
  • Static Group
26
Q

One-Shot Case Study

A
  • Cause-effect
  • X O
  • No comparison possible
27
Q

One-Group Pretest-Posttest

A
  • Investigate influence of a variable
  • O X O
  • No control for extraneous variables
28
Q

Static Group

A
  • Comparing a treatment with a control group
  • EG: X O1
  • CG: O2
  • EG and CG can differ
29
Q

True experimental designs

A

DO HAVE RANDOMIZATION OF PARTICIPANTS

  • Pretest Posttest Control Group
  • Posttest-Only Control Group
  • Solomon Four- Group
30
Q

Pretest Posttest Control Group

A
  • Control for confounds
  • EG: R O1 X O2
  • CG: R O3 O4
  • The old workhorse of traditional experimentation
31
Q

Posttest-Only Control Group

A
  • When pre-tests are not possible
  • EG: R X O1
  • CG: R O2
  • More simple than PPCG – mortality effects possible
32
Q

Solomon Four- Group

A

Minimize effects of pre-testing

  • EG1: R O1 XO2
  • CG1: R O3 O4
  • EG2: R X O5
  • CG2: R O6
  • Strongest form of experimental control – takes into account re- test effects
33
Q

Quasi experimental designs

A
  • Time series

- Multiple time series

34
Q

time series

A
  • When one group is available, and little control over manipulation and participants
  • O1 O2 O3 X O4 O5
  • Sensitive for history effects and re-test effects
35
Q

Multiple Time Series

A
  • Time series with control group
  • EG: O1 O2 O3 X O4 O5
  • CG: O6 O7 O8 O9 O10
  • Different tests possible (pre vs post / EG vs CG)
36
Q

Factorial Design

A
  • Design with more than one IV

* Test for interaction / moderation effects

37
Q

Full Factorial Experiment

A

If IV’s are categorical and DV is continuous, as is often the case in experimental designs

38
Q

One IV

A
  • Two experimental conditions –> T-test, Univariate Analysis of (co-) Variance (One-Way ANOVA)
  • Two or more experimental conditions –> Univariate Analysis of (co-) Variance (One- Way ANOVA)
39
Q

More than one IV

A

Multivariate Analysis of (co-) Variance (e.g., Two-Way ANOVA)

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