Weeks 1-2 Flashcards
How Do We Know What We Know?
Three types of knowledge:
- Experience:
Information obtained from our own life
experiences.
Its unfalsifiable.
Its subjective; not everyone has the
same experiences, can have the same
experiences or wants to have the
experience.
Prone to memory biases (i.e.,
reconstruction of events).
Limited sampling. - Intuition:
Unspoken understanding and
assumptions about how the world
works.
Its implicit; so occurs outside of our
unconscious awareness so is not
guided by our existing knowledge or
open to criticism; prone to biases; e.g.
myths.
It’s unfalsifiable.
It’s subjective. - Authority:
Information obtained from an authority
figure/source; teacher, textbook,
parents, school, political leaders,
spiritual leaders etc.
Is often accepted without consideration
for its validity.
The truth can be wielded to the
authority figures will and biases they
may hold.
Lacks falsifiability.
Maybe biased.
*science addresses these flaws.
(3) Core Characteristics of Science:
- Systematic Empiricism:
the rigourous application of the
scientific method to collect valid and
reliable data about our observations of
the world and allows research to be
open for criticism and peer review. . - Addresses Empirical Questions:
Scientific research engages in
hypothesis testing; falisifiable, on topics
we can collect data for, is ethical, not
too broad or narrow and provides novel
insights. - Creates Public Knowledge:
Findings are available to,
communicated and open to critique by
the public.
Review and replicatio of studies
findings by other scientists to rule out
alternative explanations, replicate
findings on other samples and extend
theory.
*biggest difference between science and
authority is its acceptance of critiscims and
challanging perspective.
*it’s not “what” you study but “how” you
study.
*public trust in science has increased since
the introduction of mathematical modelling
and developing of vaccines.
The Scientific Method[s]
*there is more than one cycle
6 steps and
three core elements
- Observation
- Questions
- Hypothesis
- Experiment
- Analysis
- Conclusion
Theory:
o A possible explanation of a broad range
of phenomena.
Hypothesis:
o A more specific explanation of how
something might work.
o About the construct of interest.
Prediction:
o What will happen in my study if my
hypothesis is right.
o A specific prediction on the relationship
between two or more variables.
o Operationionalized variables which
indirectly measure the construct of
interest.
*the cycle of deductive reasoning used in
experimental designs which builds on
previous knowledge.
Two Ways of Knowing:
*both valid approaches
Inductive Reasoning:
o Building a theory/hypothesis from
patterns we see in observational data.
Deductive Reasoning:
o Testing our theory/hypothesis by finding
patterns in observational data.
Three Types of Scientific Claims:
Describe
Predict
Explain
- Describe:
- Frequency claim typically about one
variable at a time.
- Descriptions about how many, how
much, how often and what kinds can be
either quantitiative or qualitative:
o Qualitative:
Coding, thematic anlaysis, enthnography
or diaries are common techniques used
in naturalistic observational work.
o Quantitative:
Mean, median, mode, frequency,
standard deviation, percentiles, z-scores
are quantitiative tools used to describe
the shape of the data. - Predict:
- Association claim about two or more
variables.
- Good way to validate your hypothesis
that there is an association between the
variables.
- Variables are measured, they are not
manipulated.
- Individual group differences with no
manipulation, associations or correlations
can be used.
- Correlation/Association ≠ causaility. - Explain:
- Provides causal explanations of why or
how things occur.
- Manipulation of the independent varaible
(IV) to measure its effects on the
dependent variable (DV).
- This allows us to determin whether the
(IV) causes changes in the DV, by
controlling all other variables.
- Claims about the direction of the
relationship between variables.
Construct Validity refers to…
how do we test it?
refers to whether our variable measures the construct as intended (i.e., our operationalisation of the constuct).
statistics
convergence of studies
Age, not politics, is biggest predictor of who shares fake news on facebook:
Example causal hypothesis
Association study which found that
age was positivley correalted with the
number of shared stories on facebook.
No claims about cause can be made.
They authors build a hypothesis off of
their correlation to explain why this may
be (i.e., they believe that age is
associated with digital literacy which
effects peoples ability to distinguish
between real and fake news stories).
An experimental study is required to
validate their hypothesis.
Example causal hypothesis
If poor digital literacy causes people to
share fake news then we predict that
increases in digital literacy will lead to
increases in the number of fake news
stories shared.
A common error when designing an experiment?
we cannot confirm causality if?
we can not assign people to conditions based on subject factors. we did not manipulate anything therefore we can not claim a causal relationship is present (no control either).
Use The Right Language:
Causal and Association
Frequency:
- how many
Association:
- Correlated with
- Associated with
- Related to
- Relationships between
- X predicts Y (predicts ≠ cause)
- Group differences without a
manipulation
- more or less
Causal:
- Causes/makes/affects
- Increases/decreases
- Heightens/reduces
- Attenuates/potentiates
- Eliminates (bold claim)
*directional
*action verbs
*journalists when discussing scientific
research have a tendency to talk about a
correlational study in causal terms which
contributes to the spread of misinformation.
Reflective Review
(4) Alternative Hypotheses:
Why do people believe in and share fake news?
- Political Motivation Account for Belief in
Fake News
Theory
- People are motivated to believe
information that confirms their worldview
(motivated cognition is a theory; world
view determines what we pay attention
to and understand the world and
behave).
Hypothesis
- People are less sceptical (than they
should be) of information that is
consistent with their view, and more
sceptical (than they should be) of
information that is inconsistent with their
world view.
- Applying the motivated cognition theory
to fake news.
Prediction
- If this is true, news stories that are
consistent with their world view be
shared more than incongruent new
stories.
- Not all fake news or fake news consistent
to their world view is shared
(conservative/liberal political worldviews;
rated higher in
trustworthiness/accuracy)? - Classical Reasoning Account of Belief in
Fake News
Theory
- People don’t engage deliberative
reasoning processes (System 2) unless
necessary.
Hypothesis
- People believe fake news because they
are not expending the mental effort to
evaluate it first.
Prediction
- Headlines/news stories, familiar topics or
non-familiar topics, emotionally salient vs
not emotionally salient topics. - Political Identity Account for Sharing Fake
News
Theory
- People share information to identify and
gain status in a group.
Hypothesis
- People share politically concordant fake
news to signal their identity in a political
group.
Prediction
- People will be more likely to share news
stores consistent with their political
ideologies than incongruent news
stories.
4. Inattention Account for Sharing Fake
News
Theory
- Attention is necessary for deliberate
thought.
Hypothesis
- Social media promotes inattention and
distraction and so prevents deliberation
about news, resulting in more sharing of
fake news.
Prediction
- People who are primed to think about
accuracy of the headline will share more
true news than fake news.
Three Conditions for inferring causation:
- An association between mindfulness
and anxiety (correlation; bidirectional) - Temporal precedence (cause before
effect) IV causes changes in DV. - Control of alternative explanations
(confounds) keep extraneous constant
and manipulate the IV!
Experiments:
All experiments have 2 components (defining features).
- Manipulation
- of one or more independent variables
(cause) to determine its effect on a
dependent variable. - Control
- of alternative explanations (extraneous
variables)
*This allows us to fulfill the three conditions from inferring causation!
(4) Types of Variables:
- Independent variables (IV)
- Manipulated by the experimenter
- The Cause.
* if not manipulated we cannot infer cause! - Dependent variable (DV)
- Measured by the experimenter
- The Effect. - Subject variables
- Pre-existing factors (age, gender,
ethnicity, personality, etc)
- NOT MANIPULATED - Extraneous variables
- All the other variables that contribute to
the DV
- Example for things that contribute to
anxiety and will always be present in a
study: gender, SES, genetics,
environment, social media, physical
health, temperament, trauma/stressful
life events etc.
- Random assignment allows for these
factors to be equal across conditions-
the only difference between groups
should be the level of the independent
variable they experience.
Independent Variables (IV)
4 types of manipulations
Can have 2 or more…
Can be what two levels of measurement?
- Many types of manipulations:
- Treatment Type (psychological,
behavioural, medical/physiological/neural)
- States Manipulated (e.g., emotions,
hunger, sleep-deprivation, meditation;
subject-sate)
- Stimuli Manipulation (colour, size,
familiarity, attractiveness, details;
materials used)
- Context Manipulation (priming, duration,
surrounding items) - Can have 2 or more levels
- Experimental vs control condition
- Two or more experimental conditions
Usually categorical, but theoretical
variable can be categorical or continuous
- Categorical:
- Continuous:
Dependent Variables (DV) Is.... 7 types
- Outcome; measured by the
experimenter
- Questionnaire score
- Behaviour (coded? Automatically
recorded?)
- Response time (attractive/unattractive
faces)
- Accuracy
- Choice (share or not share)
- Decision (believe or not believe)
- Physiological measures (heart rate, skin
conductance)
- Neural measures (EEG, fMRI)Subject Variables:
Doesn’t involve…
4 types
- Might be interested in whether different
types of participants are affected by
your IV differently. - BUT – you can’t manipulate these
variables, so they are not independent
variables. And you cannot make causal
claims about them.
o Demographic variables (gender, age,
culture or ethnicity, income…)
o Traits (personality, abilities,
o Disorders (depression, anxiety, eating
disorders…)
o Life experiences (travel, major, lifestyle
choices…)
Extraneous Variables:
Two types…
We can’t…. so we…
- All other possible variables that could
contribute to variability in the DV.
o Stable (random assignment) Trait anxiety Age Gender Genetic factors Occupation Economic factors Health behaviours
o Unstable (manipulation and random
assignment)
Recent life events
Current mood
Experimenter behaviour
Measurement (e.g., reliability and
validity)
o And, one’s we have not thought of or
identified!
o We cannot control for all of these so we
use random assignment to distribute
extraneous variables across conditions
so they’re equivalent (equivalent groups
not people!).
Confounds:
- Extraneous variables that vary
systematically with the IV.
- Confounds create alternative
explanations for findings.
§ For example, differences between the
mindfulness and waitlist-control
condition:
o Expectations of improvement.
o Time commitment.
o Relationship with group leader.
o Relationship with other group members.
o Relaxation.We get rid of confounds by….
Two techniques used to meet the 3 criteria for inferring causation….
You can not make a …. from one study… we need….
§ We get rid of confounds by designing better conditions. This allows us to infer causation and have stronger internal validity within our study.
§ We can satisfy the three conditions of causality through the use of manipulation and control. Manipulation helps us to determine the direction of relationship (temporal) and control allows us to rule out alternative explanations or confounds.
§ All studies have limitations and are always reported at the end of a scientific article.
§ No study is perfect. There are tradeoffs between all decision researchers make when designing and implementing an experiment that will place limitations on the study.
§ Just because a study is flawed doesn’t mean that its findings are useless.
§ We cannot answer all questions in one study. There are multiple ways to operationalize IV-DV’s and other methodological choices to answer the same question which all provide a useful piece to the puzzle.
§ You cannot make a conclusion from one study; we need different studies that adpot different perspectives, methodologies and flaws which all draw the same conclusion to draw a conclusion and be confident the effect is present (converging operations/consensus). More important to be aware of your studies flaws than to have none. Some studies methodologies can be so flawed that their findings are not valuable in the scientific community- lab example or non-scientific studies run by businesses. Psychology is valuable in any area or industry of life; critical analysis of findings and the implementation of non-scientific studies (i.e., education; ministry of health).
- Types of control groups
Control (do nothing)
wait-list control
alternative treatment (active control)
placebo control
Random Assignment: All participants have.... It MUST be... failures occur when... Differences between the conditions that are not the IV are.... If groups are large enough
§ All participants have an equal likelihood
of being assigned to each experimental
condition.
§ If groups are large enough, controls for
differences in all extraneous variables
(equally distributed across conditions)
§ Assignment must be truly random.
Failures of random assignment occur
when:
o People choose their own conditions
o People are assigned to condition based
on some preference/trait/behaviour
o There are systematic differences
between groups.
§ Any differences between groups other
than the IV are confounds.
Designing a Control Condition:
Determine what….
Create…
If you can’t rule out confounds try…
§ Determine what variable you want to
isolate.
§ Create control conditions that differ
only in that variable (IV; level of IV or
number of conditions).
§ Any remaining differences are
confounds.
§ What if you can’t eliminate a confound?
o Use multiple control conditions that
differ in different ways.
o Recognise that confounds limit your
conclusions
