Exam 4 Flashcards
What is the difference between the control and treatment groups?
The control does not feel the manipulated effect while the treatment group does.
How do we determine causation?
- Covariance: there is a difference between the two groups
- Temporal precedence: can prove that one variable causes the other/occurs after it (rain storm causes gloomy mood)
- Third variable: ensuring the variables do influence one another (internal validity) and controlling for possible third variables
What is the difference between systematic and unsystematic variability.
- Systematic variability: purposefully created variability through the IV
- Example: Half of the participants were tested on their mood when it was rainy (purposefully testing the affect of rain on mood). - Unsystematic variability: unexplained, random variability in the IV groups
- Example: Some participants were tested when it was raining (random rainstorm that is now a confound within an experiment)
What is a design confound?
A unwanted variable that still may influence the study.
- Example: Students who take the SAT on a rainy day may do worse than other students
Define selection effects.
Effects found in a study that were due to a faulty group assignment procedure.
- Example: Completing a political poll outside of a college campus and no where else.
How can we avoid selection effects?
- Random assignment
- Matched-groups design: randomly sort people based on an attribute (IQ, age, gender)
Define the difference between between-groups and within-groups design.
- Between-groups: different groups of participants experience different levels of the IV
- Within-groups: the same group of participants experience all levels of the IV.
What are the two basic types of independent groups design (between-groups)?
- Post-test only design: participants are only tested once
- Example: testing people’s fondness for massages and how likely they would be to go get one - Pretest-posttest design: participants experience an IV once, but are tested on the DV twice
- Example: testing people’s stress level before and after a massage
What is the pro and con of doing a pretest-posttest instead of simply a post-test?
Pro: Able to see if the groups are equal from the beginning
Con: Participants could potentially figure out what the researchers are attempting to find
What are the two basic designs for within-groups design?
- Repeated-measures design: one group are measured after each level of the IV
- Example: Participants intelligence score on a rainy day vs a sunny day. - Concurrent measures design: participants are exposed to all levels of the IV at once
- Example: the study on attachment, babies were exposed to the mom leaving and their reaction were the levels
What are the advantages and disadvantages of a within-groups design?
Advantages:
1. ensures that participants are equal across the board
2. study has more power (easier to know if the significance is legit)
3. fewer participants needed
Disadvantages:
1. Carryover effects, impact of one treatment influencing the reaction to the next
2. Practice effects: getting better with repetition
3. Fatigue effects: loss of interest over time
Define counterbalancing.
Ensuring that the order of the IV does not impact the significance.
- Example: shuffling the questions for an intelligence study for each participant
What are some possible negative influences on a study?
- Confounds
- Weak manipulation
- ‘Noisy’ Measurements (too much unsystematic variance, unexplained variability
How can graphs be misleading?
- Biased scale
- Sneak sampling
- Interpolation (assuming information)
- Extrapolation (using assumptions to make external generalizations)
- Inaccurate values (distorting data)
What are the main types of graphs for displaying data?
- Scatterplots
- Line graphs
- Bar graphs
- Pictorial graphs
- Pie charts
- Word clouds
- Multivariable graphs
What are some threats to internal validity in a study?
**M - Maturation threat **
**R - Regression threat **
S - Selection Bias
M - Mortality/attribution threat
I - Instrumentation threats
T - Testing threat
H - History threat
E - extra ones: observer bias, researcher bias, demand characteristics (participants who figured out the hypothesis and change their behavior due to this), placebo effects, situation noise
Define the Maturation threat.
Definition: Can’t prove the differences didn’t occur spontaneously
Prevention: A comparison group
Define the regression effect.
Definition: When individuals scores begin extreme but end up leveling out to average
**Prevention: **Through sorting out extreme scores/bad participants and proper random assignment
Define mortality/attribution effect.
Definition: as a study goes on, people drop out of the study
**Prevention: **Through removing their scores from previous data
Define instrumentation threats.
**Defintion: **The measurement instrument changes over time due to repeated use
**Prevention: **Through post-test design only, retrain/recode measurement tools
Define testing threat.
Definition: participants behavior/scores change due to the study being repeated measures
Prevention: through using between-subjects, different forms of the test (Form A vs Form B of an exam), or a comparison group
Define history threat.
Definition: external event that happens to everyone in the study
Prevention: through a comparison group
Define the observer/researcher bias.
Observer: observers recording what they want to see instead of what actually is occurring
**Researcher: ** researcher finds/searches for what they want to find
Define demand characteristics.
Definition:Participants who were able to guess the hypothesis
Prevention: double-blind study, blind design/masked design
