HOC8:EXPERIMENTS Flashcards
What is an experiment
Used in deductive research, for cause-and-effect studies
what is an independent variable
variable that researcher directly manipulates
what is an dependent variable
observable outcome
what is a treatment
manipulation of IV to by creating different levels of IV to assess impact on Dv
what is a pre-test and post-test
measurement of DV b4 and after
what is a subject
it is a unit of observation
what does control mean
any means to keep other factors of a situation constant
explain field experiments
studying a subject in its natural environment
- Definition: Conducted to establish cause-and-effect relationships using a natural, not contrived, environment.
- Setting: Takes place in the subjects’ natural environment where they typically function.
- Subjects: Can include employees, consumers, managers, children, etc.
- Interference: Limited interference by the researcher to maintain the authenticity of the setting.
- Purpose: Investigates real-world scenarios to draw conclusions about causal relationships.
explain what a lab experiment is study in artificial environment
is study in artificial environment
- Definition: Controlled experiments conducted in a controlled and artificial environment created by the researcher.
- Purpose: To establish cause-and-effect relationships by manipulating variables.
- Characteristics: High control over variables, potential for high internal validity.
- Interference: Researcher actively creates the experimental environment.
- Examples: Psychological studies, medical trials, laboratory research.
what are the 7 steps of conducting an experiment
- select relevant variables
- specify the treatment levels
- control experiment environment
- choose the experimental design
- select and assign the participants
- pilot test, revise and test again
- do the experiment and analyze data
explain the meaning of casual relationship
- Definition: A relationship where changes in one variable cause changes in another, establishing cause-and-effect connections.
- Controlled Environment: In organizational settings, controlling covariates is challenging, especially in natural settings where events occur spontaneously.
- Field Experiment Challenge: Even in field experiments, maintaining a completely natural environment is difficult once participants become aware of the experiment.
- Examples: Studying leadership effects on productivity, where controlling all variables is not always feasible in real-world scenarios.
what is treatment ( or manipulation)
creating different levels of the IV to assess impact on DV -> is required to examine casual effect of an IV on a DV
what does control mean
Control
- if we want to examine cause-and-effect relationship between X ( IV) and Y ( DV) → then other factor , A , might influence Y
- example : studying effect on training on learning effectiveness
- X : training (IV)
- Y : learning effectiveness (DV)
- A : previous web experience
- A if a confounding ( or contaminating ) factor ⇒ may distort effect of X on Y → A must be eliminated
- How to asses true effect of training on learning effectiveness? ⇒ by controlling previous web experience
- how practically? by not including in the experiment those employees who had some web experience
what are matching groups in controlling contamination
matching various groups by picking the confounding/contaminating characteristics and deliberately spreading them across groups
- Less effective. Factors not always known
what are randomization groups in controlling contamination
- Definition: Randomization involves randomly assigning members to different groups, ensuring each member has an equal chance of being in any group.
- Analogy: Comparable to throwing names in a hat and drawing them randomly.
- Objective: Achieves an equal distribution of confounding variables among groups, making each group comparable.
- Importance: Minimizes the impact of potential confounding factors, enhancing the validity of experimental results.
what is the comparison between matching groups and randomization
matching groups = less effective since :
-possibly confounding factors are not always known ,
-even if cofounding factors are known , then matching is not always possible
randomization =
- through normal distribution , confounding factors will be spread , and sample bias reduced
-most reliable with larger samples
what is internal validity
confidence we place in cause-and-effect relationship
->Evaluates the study’s internal validity, considering the choice of independent variable (IV) and the removal of confounding factors.
->Significance: The higher the internal validity of the research, the more confidently we can assert a cause-and-effect relationship between variables A and B.
what is external validity ( generalizability)
= how does It generalize across people, settings (Larger in field)
“does the observed relationship generalize across people , settings and times?” → “ to what extent are the results found in lab setting transferable or generalizable to actual organizational or field settings ( i.e. the real world)?”
what are the different validity threats
- History effects – unexpected events may disturb and cofound the cause-and-effect relationship
- Maturation effect – passage of time effects variables (gets tired) -> ex.when respondents get tired = internal validity threatened
- Testing effects – effects of exposure to pre/post tests
- Selection bias effect – from imperfect selection of participants -> types of participants selected for a lab experiment may be very different from the types of participants in natural setting -> generalizing is problematic = external validity threatened ( randomization or matching groups is highly recommended )
- Mortality effects – composition of group changes
- Statistical regression effects (regression to the mean) = extreme values tend to score closer to the mean after the treatment
- Instrumentation effects – changes in measuring tools = internal validity threatened
explain Quasi-experimental designs
Pre-test and post-test experimental group design
-No control group
-Lack of randomness (self-selected groups)
-Easy to conduct
Post-tests only with experimental and control groups
-Pre-test not easy
-Not certain that two groups are equivalent
-Lack of randomness
Time series design
-Data on same variable collected at regular intervals
-Repeated measurements essential
-Good If no risk of reactivity
-Control group may be added
what is the true-experimental designs
Pre-test and post-test experimental and control group design
- Both groups -» pre and post test
- Random groups
Solomon four group design
- Strong validity ( internal and external)
-useful if there are concerns that the post-test might be influenced by the pre-test -> yet difficult to set up
-2 groups treatment, 2 non
-2 groups pre-test, 2 non
Factorial design
-2 or more manipulations at the same time
-enables to test effect of two or more manipulations at the same time on the dependent variable -> enables to look for interaction
(double) blind studies
-Do not know if receiving treatment ( the participants)
what is the application of true experimental design
for each type of true experimental design , think of two effects that might pose a threat to validity
what are the advantages of true experimental design
- Appropriate for establishing causal linkage of one variable to another
- Manipulation of IV is easy
- Control group serves as a comparison
- Pre-test and post-test allow for checking if manipulation occurred before outcome
- Contamination can be controlled for effectively
- Replication helps to find average effect of IV across people, time, situations
what are the disadvantages of true experimental design
- Artificiality of lab setting
- Generalization from non-probability samples
- Number of variables researcher can include is more limited than in survey research
- Costly if repetition is required, compared against surveysIs study of present or future, not about past
- Factors should be easy to manipulate (↔ respondent’s education, social competence,…)
- Ethical limits
what is true experimental design
involves randomly assigning participants to different conditions, manipulating an independent variable, and carefully controlling for potential confounding factors to establish a cause-and-effect relationship.
