Lecture notes of all lectures of the course ARM (SOW-PSB3RS45E). The notes include the pictures used in the slides. I completed this course with a 7.5 :)
Applied Research Methods – Colleges
Week 1
General topics
Scientific research and theory
Types of research
- Observations: finding phenomena
- Correlations and quasi-experiments: finding relationships
between variables. By finding the correlation, we still don’t know
why there is a relation
- Experiments: finding causal explanations
- All of them: developing and testing theories of experience and
behaviour
How do you tell a good theory from a bad one?
- Precision.
- Parsimony: we try to explain a phenomenon with as few
assumptions as possible. The fewer assumptions you need to
predict something, the better
- Testability and falsifiability: if it can be tested, and it can be
proven wrong
The validity of scientific research
Types of validity
- Internal validity: did the intervention rather than a confounded
variable cause the results?
- External validity: how far can these results be generalized?
- Construct validity: Which aspects of the intervention caused the
results?
- Statistical validity: are the statistical conclusions correct?
Correlational research
Correlational research questions
- How closely are two variables related? correlation
- How can I predict one variable if I know the other regression
How can correlations be used and interpreted
- Correlation: can be used for direction and size
- Regression: can be used to predict, this can be more or less
precise depending on the correlation
Beware of causal interpretations
Correlation and causality
1
, An example of the causality problem
Depressed patients think more negatively about themselves than
others negative correlation of depression and thinking, but how
do they influence each other?
- Negative thinking causes depression?
- Depression causes negative thinking?
- Depression and negative thinking cause each other?
- A third variable (genetic, neurological) causes both
depression and negative thinking?
More examples of dubious causalities
- The number of crimes and the number of churches in a cite
are correlated does religion cause crime?
- Sales of ice cream and drowning rates are correlated
does ice cream cause drowning?
- Shoe size is positively correlated with alcoholism, and
negatively correlated with anxiety do big feet cause
alcoholism, but protect from anxiety?
The relation is not symmetric: if causality, then correlation. But not:
if correlation, then causality
And temporal order does not prove causality, either: If A is the
cause of B, A must happen before. But not: if A happens before B, A
is the cause of B
- Even if two variables are both correlated and temporally
ordered, the earlier one does not have to be the cause of the
later one
- Correlation is a necessary, but not a sufficient precondition for
causation
The one and only way to establish a causal relationship underlying a
correlation is to conduct an experiment
Variables in experiments
Independent variables (manipulated by experimenter)
- What is a good independent variable?
- How many levels of the variable? The more levels you have, the
better you can judge whether there is for example a linear
regression. But also for every level you add, it might force you to
add more participants
Dependent variables (measured by experimenter)
- What is a good dependent variable?
2
, - Beware of floor effects and ceiling effects. Try to avoid situations
where everybody scores low or high independent from what you
are measuring
Control variables (controlled by experimenter)
- Holding them constant. You don’t want to mix up your dependent
and independent variables
- Turning them into independent variables. If you don’t want to
control them or can’t control them, manipulate them
Between-subjects vs. within-subjects designs
Between-subjects designs (independent groups)
- Every subject experiences only one level of the independent
variable: random assignment to the treatment we want to
compare. Random assignment is very powerful, but it requires a
large number of participants
Within-subjects designs (repeated measures)
- Every subject experiences every of the independent variable:
order effects?. It is easier to find the difference between groups
with a within-subjects design than a between-subject design
because it is more powerful.
- You have to counterbalance the order because the results
may differ if you use easy – intermediate – hard vs. hard –
intermediate – easy conditions.
Problems of experimental designs
Particularly critical in clinical psychology
- Quasi-experiments instead of random assignment
- External validity
- Laboratory vs. everyday life. Is a laboratory setting
generalizable to everyday life?
- Patients vs. analogue populations. E.g., do highly
anxious student generalize to phobic patients?
- Low sample size low statistical power
Effect size and statistical power
Effect size and statistical power: why bother?
- How many participants will I probably need in my study?
- Why do so many experiments in psychology yield non-significant
results?
- Why should I better not believe many of the significant results I
read about?
3
, Two types of errors
Problems in generalizing from the small
experimental sample to the population
ß: false negative rate
α: false positive rate
Why are the effect size and statistical power there?
Effect size: how large is a difference/correlation/relationship?
Statistical power: what is the probability that this effect will be
statistically significant in an experiment?
Situations where this is important
- Experiment in preparation: determine necessary sample size
- Experiment completed: determine power of the experiment
- Evaluation of published studies: are the effects for real?
Effect sizes: Cohen’s d as a simple example
Situation: comparing two group means by a t-test
In a t-test you assume that you have two groups with
both a mean that differs from each other
Effect size d
How large is d typically in Psychology?
0.5 is considered a medium size effect. This
means that these two groups overlap
considerately. The study is average.
0.2 is considered small
0.8 is considered large
What affects power?
Effects size: larger effects are easier to find than small effects. You
need a huge sample to detect a small effect
Sample size: effects are easier to find with many participants.
Alpha error: increasing the alpha error reduces the
beta error. If you are more willing to accept false
positive, you will have fewer false negative and the
other way around.
4
Les avantages d'acheter des résumés chez Stuvia:
Qualité garantie par les avis des clients
Les clients de Stuvia ont évalués plus de 700 000 résumés. C'est comme ça que vous savez que vous achetez les meilleurs documents.
L’achat facile et rapide
Vous pouvez payer rapidement avec iDeal, carte de crédit ou Stuvia-crédit pour les résumés. Il n'y a pas d'adhésion nécessaire.
Focus sur l’essentiel
Vos camarades écrivent eux-mêmes les notes d’étude, c’est pourquoi les documents sont toujours fiables et à jour. Cela garantit que vous arrivez rapidement au coeur du matériel.
Foire aux questions
Qu'est-ce que j'obtiens en achetant ce document ?
Vous obtenez un PDF, disponible immédiatement après votre achat. Le document acheté est accessible à tout moment, n'importe où et indéfiniment via votre profil.
Garantie de remboursement : comment ça marche ?
Notre garantie de satisfaction garantit que vous trouverez toujours un document d'étude qui vous convient. Vous remplissez un formulaire et notre équipe du service client s'occupe du reste.
Auprès de qui est-ce que j'achète ce résumé ?
Stuvia est une place de marché. Alors, vous n'achetez donc pas ce document chez nous, mais auprès du vendeur smaasbach. Stuvia facilite les paiements au vendeur.
Est-ce que j'aurai un abonnement?
Non, vous n'achetez ce résumé que pour 2,99 €. Vous n'êtes lié à rien après votre achat.
