Summary Research Methods and Techniques II: schema hoorcolleges
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Course
OMT II
Institution
Vrije Universiteit Brussel (VUB)
A clear/structured diagram of the essentials from the Research Methods and Techniques II (OMT II) lectures. Very handy for learning or repeating by heart! :)
Fundamental VS applied research
(Focus in psychology → fundamental)
Continuously evolving
Experiment ‘how does color affect motivation’ → was falsified by replication study
Publishing
- Peer-review process
→ Role of editor
→ Role of reviewers
→ Reject, revise and resubmit, accept
- Acceptance rate = 10%
→ Number of submissions keeps growing, but acceptance rate is still the same
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,Researchers…
use a comparison group
control for third variables
try to evaluate information without bias
Research is better than…
experience
intuition
authority arguments
Research VS experience
Experience is confounded
Catharsis hypothesis = release negative emotions by venting them (or doing something to
get them out)
- Tested in experiment;
→ Punching bag with steve’s face → more aggressive
→ Normal punching bag → somewhere in between
→ Quiet group → least aggressive
- Show the opposite of the catharsis hypothesis is true
Research is probabilistic (what you conclude in 1 experiment is not accurate for everyone)
Cognitive biases
A good story Tendency to believe a good story about a
theory/phenomenon.
Availability heuristic Tendency to focus on information that is readily available.
Present/Present bias Tendency to rather settle for a smaller present reward then
wait for a larger future reward, in a trade-off situation.
Confirmation bias Tendency to believe/give more attention to information and
theories that confirm our prior beliefs.
Confirmation hypothesis testing (Worse than confirmation bias) Setting up hypotheses in
such a way that in advance, they already know it will be
confirmed (an UNCONSCIOUS process).
Bias blind spot Tendency to recognize the impact of biases on the judgment
of others, while failing to see the impact of biases on one's
own judgment.
Dunning-Kruger effect People who have expertise about something are more quiet.
People who know less, will shout the loudest about their
beliefs.
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,2. Foundations of research
Variables VS constants
- Variable = something that varies, meaning that it has at least two levels or values
- Constant (= opposite of variable)
Independent VS dependent variables
Measured VS manipulated variables
Conceptual VS operational variables
Way of describing a variable Definition
Construct, conceptual variable The name of the concept being studied.
Conceptual definition A careful theoretical definition of the construct
(abstract concepts).
Operational definition How the construct is measured or manipulated in an actual
study (operationalization)
Three claims
Type of claim Type of research
Frequency claims Descriptive - Each claim is about 1 variable that is measured
research - One study can make multiple frequency claims
about multiple variables
Association claims Correlational - Not always correlations to explain associations
research - Types of associations:
→ Positive VS negative
→ Linear VS curvilinear
→ Absent (nul effect/no association)
- Often visualized with scatterplots
+ = increasing line
- = decreasing line
- “Data source dozen”; what you see in data
might be very different from what correlations
tell you
Causal claims Experimental - Stronger words for causal effects than for
research association effects
The four big validities
Construct validity How well the variables in a study are measured or manipulated.
Statistical validity How well the numbers support the claim (how strong the effect is and the
precision of the estimate.
External validity The extent to which the results of a study generalize to some larger
population as well as to other times or situations.
Internal validity In a relationship between one variable (A) and another (B), the extent to
which A, rather than some other variable (C) is responsible for changes in B.
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, Four types of validity & three claims
Claim Validity Questions
Frequency Construct - How well was the variable in question measured?
Statistical - What is the confidence interval of the estimate?
- Are there other estimates of the same percentage?
Internal - Not about causality → so not important
External - To what population can we generalize?
- How representative is the sample?
- Was it a random sample?
Association Construct - How well were variables measured?
- Did they measure what they were supposed to?
Statistical - What is the estimated effect size?
- How precise is the estimate?
- What do estimates from other studies say?
Internal - Not about causal claims → so not important
(avoid making causal claims about simple associations)
External - To what populations, setting, time can we generalize?
- How representative is the sample?
Causal Construct - How well was the independent variable manipulated?
- How well was the dependent variable measured?
Statistical - What is the estimated effect size?
- How precise is the estimate?
- What does the estimate from other studies say?
Internal - Was the study an experiment?
- Does the study achieve temporal precedence?
- Does the study control for alternative explanations?
- Does the study avoid internal threats?
External - To what populations, setting, time can we generalize?
- How representative is the sample?
- How representative are the manipulations and measures?
Conditions for causality
Covariance Being able to show that there is a relationship between the
variables.
Temporal precedence The cause needs to come first in time and the effect needs to come
afterwards.
No alternative explanations (Controlling for confounders) Any differences we observed between
(most difficult) groups are only due to our differences in our independent variable
(can be ruled out by random assignment = DIFFERENT from
random selection).
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