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
Voordelen van het kopen van samenvattingen bij Stuvia op een rij:
√ Verzekerd van kwaliteit door reviews
Stuvia-klanten hebben meer dan 700.000 samenvattingen beoordeeld. Zo weet je zeker dat je de beste documenten koopt!
Snel en makkelijk kopen
Je betaalt supersnel en eenmalig met iDeal, Bancontact of creditcard voor de samenvatting. Zonder lidmaatschap.
Focus op de essentie
Samenvattingen worden geschreven voor en door anderen. Daarom zijn de samenvattingen altijd betrouwbaar en actueel. Zo kom je snel tot de kern!
Veelgestelde vragen
Wat krijg ik als ik dit document koop?
Je krijgt een PDF, die direct beschikbaar is na je aankoop. Het gekochte document is altijd, overal en oneindig toegankelijk via je profiel.
Tevredenheidsgarantie: hoe werkt dat?
Onze tevredenheidsgarantie zorgt ervoor dat je altijd een studiedocument vindt dat goed bij je past. Je vult een formulier in en onze klantenservice regelt de rest.
Van wie koop ik deze samenvatting?
Stuvia is een marktplaats, je koop dit document dus niet van ons, maar van verkoper smaasbach. Stuvia faciliteert de betaling aan de verkoper.
Zit ik meteen vast aan een abonnement?
Nee, je koopt alleen deze samenvatting voor €2,99. Je zit daarna nergens aan vast.
