1
Q
Scientific method
A
- Scientists are empiricists, meaning that they base beliefs on systematic, objective observations of the world. Instead of using experience or intuition
- follows the process of the theory-data cycle
- formulate hypotheses either on the basis of prior observation or theory, Then we systematically collect evidence across many people
2
Q
Theory
A
- an integrated set of related principles that explains and generates predictions about some phenomenon in the world
- is a set of propositions about what people do and why.
- To test a theory, researchers design a study
- broader than hypothesis
3
Q
Hypothesis
A
- a testable prediction about what will happen under specific circumstances if the theory is correct
- a prediction about what will happen based on the theory
- a very specific statement of what we expect to happen
- narrower than a theory
4
Q
Data
A
- a set of observations that are gathered to evaluate the hypothesis
- observations from a study, usually in numerical form, collected from ppl at certain times or in certain situations
- data are the observations we gather to evaluate the hypothesis, to put our hypothesis to the test
5
Q
Replication study
A
- repetition of the study with a new group of participant
- direct and conceptual replication
6
Q
Open science movement
A
- initiative to make scientific research, data, and methods openly accessible and transparent, with the goal of increasing reproducibility of research
- strengthen the verifiability and replicability of studies
- to make all of the methods, data, resources that researchers use in conducting their studies more shareable, more easily accessible to other researchers, in some cases to the public as well
- attempt a replication
7
Q
Meta-analysis
A
- combination of the results of multiple studies
- a study of many studies
- a single study is quite limited in what it can tell us, if we’re able to combine many studies looking at the same type of effect, we can get a better sense of whether the effect exists, of the magnitude of the effect, how big it is
- it’s a statistical method of combining the results of many studies
8
Q
Peer-reviewed
A
- sent out for evaluation to other experts in the field
- critical evaluation of the study’s quality by trained psychological scientists
- taking a critical look at the study, evaluating its strengths, its limitations, whether the conclusions that the researchers drew from their data, from their findings are warranted
9
Q
Variable
A
- anything that can take on different values
- variables could be some characteristic of an individual
10
Q
Manipulated variable
A
- variable intentionally changed by the research
- is one whose levels the researcher controls by assigning different participants to different levels of that variable
11
Q
Measured variable
A
- A variable whose values are simply recorde
12
Q
Operational definition
A
- specific description of how a variable will be measured or manipulated in a study
- Specific ways of measuring or manipulating an abstract variable in a particular study.
- So how do we take something so vague and operationalize it?
- Operationalizing a measured variable usually means turning a variable into a number, so researchers can statistically analyze the data and evaluate the strength of evidence for a hypothesis
- study: certain muscle contractions represent certain expressions, therefore, certain feelings
- study: social media use = time (hours per week) spent passively browsing (but not actively engaging or messaging)
13
Q
Self-report measure
A
- People describe themselves and/or their behavior
- like fixed-response questionnaires (surveys)
- pros: Allows us to “get inside people’s heads, Easy, relatively inexpensive (in the case of surveys)
- cons: social desirability bias, difficulty to verbalize how we feel, not always aware of what we do, memories are inaccurate
14
Q
Social desirability bias
A
- tendency to answer questions in a manner that will be viewed favorably by others
- tendency we have to answer questions that will put us in a good light to others
15
Q
Behavioral observation
A
- Researchers observe and record the occurrence of behavior
- pros: More objective, Real-world behavior, Potential source of nuanced, rich information, Behaviors in natural context
- cons: Resource-intensive, requires extensive training (to be on same page on what to observe), Hard to recruit participants, Reactivity
16
Q
Reactivity
A
- a change in behavior from knowing being observed
17
Q
Indirect measures
A
- Indirect measures still gather information about the behavior of interest using interactions with people, but not through direct observation
- pros: avoid social desirability and reactivity problems, good for sensitive topics
- cons: big gap between construct of interest and operationalization, Can we be sure that we are studying what we think we are studying
18
Q
Population of interest
A
The full set of cases the researcher is interested in
19
Q
Sample
A
The group who participated in research, and who belong to the larger group (the population of interest) that the researcher is interested in understanding
20
Q
Random sample
A
every person in the population of interest has equal chance of inclusion
- not based on WEIRD, White, Educated, Industrialized, Rich, and Democratic
21
Q
Descriptive research
A
- Measuring how people typically think, feel, or behave
- Descriptive research aims to accurately and systematically describe a population, situation or phenomenon
- It can answer what, where, when and how questions, but not why questions
22
Q
Case study
A
- researchers study one or two individuals in depth, often those who have a unique condition
- study: phineas gage
23
Q
Correlational research
A
- A type of study that measures two (or more) variables in the same sample of people, and then observes the relationship between them
- Correlational research tells us, “What kinds of people do this?” or “What’s associated with what?”
24
Q
Scatterplot
A
- A figure used to represent a correlation
- sloping upwards (positive relationship)
- sloping downwards (negative relationship)
- clustered together (strong relationship)
- spread out (weak relationship)
25
Correlation coefficient (the R coefficient)
- indicates a measure of the direction and strength of a relationship between two variables
- ranges from -1.0 to +1.0
- direction of relationship is indicated by - or +
- strength of relationship is indicated by the value:
closer to 0, weaker
closer to -1 or +1, stronger
26
Causation
- 3 things to establish causality:
1. two variables must be correlated
2. one variable must precede the other
3. there must be no reasonable alternative explanations of the pattern of correlation
Experiments:
- In order to make this claim, you need to be able to demonstrate an actual cause and effect relationship, preferably a strong relationship
- A study in which one variable is manipulated, and the other is measured (while all other variables are kept constant)
27
Independent and dependent variable
Independent:
- The manipulated variable in an experiment
Dependent:
- The measured variable in an experiment
Remember:
- The dependent is the one that depends on the independent
28
Random assignment
- participants are as likely to be assigned to one condition as to another
- a procedure used in experimental research, in which a random method is used to decide which participants will be in which group
29
Control group
- a condition comparable to the experimental condition in every way except that it lacks the one “ingredient” hypothesized to produce the expected effect on the dependent variable
- is the condition in which it is absent.
- the one lacking that one key ingredients
30
Mediator vs moderator variable
Mediator: the IV exerts its effect on DV through some other variable
-Mediator explains how one thing affects the other
- A mediator explains why or how two variables are related.
Moderator: the effect of IV on DV is conditional on value of the moderator
-Moderator changes how much one thing affects the other
- A moderator changes the strength or direction of the relationship between two variable
eg. social media use to mental well being is only bad in younger ppl
31
Validity (three types)
- measurement: Are you measuring what you think you are measuring?
- internal: Can we rule out alternative explanations in an experiment? Threatened by the presence of confounds
- have we done enough to rule out alternative explanations?
- external: Can our results be generalized to other samples? Can our results be generalized to other situations?
- the solution is to run more studies, collect more data, use different methods
32
Measurement validity and reliability
reliability: Do you get the same results every time you administer the measure?
- validity is about accuracy
- reliability about consistency
33
Confound
an alternative explanation for a relationship between two variable
34
Placebo effect
May experience improvement after receiving inert substances or inactive treatments
35
Double-blind procedures
neither the experimenters nor the participants know who is in the experimental group or control group
35
internal validity
- Can we rule out alternative explanations in an experiment? Threatened by the presence of confounds
- have we done enough to rule out alternative explanations?
36
Differential attrition
participants drop out from experimental and control groups at different rate
- maybe in the beginning the participants were the same (judged on similar criteria), in the end, as people drop out, we see that they differ
37
External validity
- external: Can our results be generalized to other samples? Can our results be generalized to other situations?
- the solution is to run more studies, collect more data, use different methods
38
Effect size
- values describing the strength of an association or magnitude of the effect
- A numerical estimate of the strength of the relationship between two variables. It can take the form of a correlation coefficient or, for an experiment, the difference between two group means divided by the standard deviations of the two groups.
39
Null hypothesis testing
- The assumption that there is truly no relationship between variables in the population is called the null hypothesis
- When researchers can reject the null hypothesis, they say that the result is “statistically significant.”
40
Statistically significant
- When researchers can reject the null hypothesis, they say that the result is “statistically significant.”
41
Central tendency
- a summary measure that attempts to describe a whole set of data with a single value that represents the middle or centre of its distribution
- the center of the batch of scores.
42
P-value
- a p-value helps researchers determine whether the results of their study are likely due to chance or if there's a meaningful effect.
- For example, if you run an experiment and get a p-value of 0.03, this means there’s a 3% chance that the results happened just by chance if there’s actually no real effect.
- how likely the obtained results are under the null hypothesis
- takes on value between 0 and 1
- p value smaller than .05, reject the null
- p value bigger than .05, did not reject null
- factors that affect size of p-values
- size of the observed effect
- number participants in study
43
average and variability
average:
- This is the central value of a data set.
variability:
- This describes how spread out the scores are in the data set
44
Descriptive statistics
- summarize sets of data (e.g., mean, median, mode, standard deviation)
45
Frequency distribution
- how often each score occurs
- bar graph in which the possible scores on a variable are listed on the x-axis from lowest to highest and the total number of people who got each score is plotted on the y-axis
- A way to show how often each score or value appears in a data set. It can be presented as a table, graph, or chart to show the distribution of scores.
46
Mean, median, mode
- the average. The average score in a data set.
- the middle value. The middle score in a data set when the numbers are arranged in order
- the most common. The most frequently occurring score in a data set.
47
Standard deviation
- how spread out the scores are
- A measure of how spread out the scores are from the mean
- If most students in a class scored close to the mean (e.g., 70, 72, 74), the standard deviation would be low. If the scores are very different (e.g., 50, 90, 100), the standard deviation would be high.
48
Institutional review board (IRB), 3 prinicples
Panel tasked with evaluating whether research study meets ethical standard
- autonomy, beneficence, justice
49
Autonomy
People must give informed consent for participating in research. They cannot be coerced into participating through intimidation or extremely high payouts. There are special consent procedures for vulnerable populations (e.g., children, prisoners) who may not be able to give true informed consent.
50
Beneficence
Proposed research is evaluated on its risks and benefits to the participants, and on the research outcome's potential benefit to society
51
Justice
Research should not be conducted disproportionately on one segment of the population. The participants who bear the burden of the research should be representative of the people who will benefit from the research.
52
Informed consent
researcher must fully explain study procedures, including risks and potential benefits, prior to participation
53
Deception in research
Potential violation of autonomy principle, but may be required to maintain integrity of the study
- Must be scientifically justified
- Must be minimal
- Must be informed of any risks and right to withdraw at any time
- Full debriefing, right to withdraw data
- Should not involve harm to participants
54
Non-human animal research, three prinicples
Replacement
Refinement
Reduction
PSYC 100
flashcards
Decks in class (13)
# Cards
-
U06 - Learning62
-
U01 - Introduction17
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U02 - Research Methods55
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U03 - Genetics & Evolutionary Foundations of Behavior23
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U04 - Biological Bases of Behavior56
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U05 - Sensation & Perception38
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U07 - Consciousness47
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U08 - Memory63
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U09 - Motivation & Emotion73
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U10 - Social Psychology59
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U11 - Personaility28
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Unit 12 - Psychological Disorders and Treatment41
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Unit 13 - Stress & Health21
