Chapter 2 Flashcards
1
Q
Intuitive thinking
A
- quick and reflexive
- output consists mostly of “gut hunches”
- doesn’t require much mental effort
- our brains are largely on autopilot
- ie. first impressions, snap decisions
2
Q
Analytical thinking
A
- slow and reflective
- takes mental effort
- used when trying to reason through a problem, figure out complex concepts
- allows us to override intuitive thinking and reject our gut hunches when they seem to be wrong
3
Q
Heuristic
A
- a mental shortcut or rule of thumb that helps us to streamline our thinking and make sense of our world
4
Q
List 3 main types of research designs
A
- Descriptive methods: observing and describing behaviour
- Correlational designs: examines relationships between variables
- Experimental designs: examines cause and effect
5
Q
List 3 descriptive methods for research designs
A
- Naturalistic observation
- Case study
- Questionnaires/surveys
6
Q
Naturalistic observation
A
- watching behaviour in real-world settings
- observe behaviour without interfering or trying to change it/the environment
- high on external validity
- low on internal validity
- can’t infer causation
- observing may influence people’s behaviour
7
Q
Case study
A
- examine one person or a small number of people, often over a long period of time; in-depth
- useful for studying rare phenomena
- can give researchers ideas to follow up on with larger investigations
- you can learn a lot about one individual
- difficult to generalize to the whole population
- can’t infer causation
- provide existence proofs (demonstrations that a given psychological phenomenon can occur)
8
Q
Questionnaires/survey
A
Two types: Self report and ratings
- Self-report: asking people to report on their own characteristics/knowledge/experience
- Useful for getting a lot of information; easy to use
- Need random selection
- People may not be truthful - Ratings
- May be more truthful than reporting on self
- Subject to halo/horns effect
9
Q
Correlational design
A
- Examines if two variables are associated (related)
- Correlation → can be positive, negative, or zero
- Can help us to predict behaviour
10
Q
Experimental design
A
- Researchers manipulate variables to see whether these manipulations produce differences in participants’ behaviour
- Allows us to infer causation
- High in internal validity
- Sometimes low in external validity
11
Q
Reliability
A
- consistency of measurement
- first concern with any measurement
12
Q
Validity
A
- extent to which a measurement assesses what it claims to measure
- accuracy
13
Q
External validity
A
- the extent to which we can generalize our findings to real-world settings
14
Q
Internal validity
A
- the extent to which we can draw cause-and-effect inferences
15
Q
Random selection
A
- every person in the population has an equal chance of being chosen to participate
16
Q
Random assignment
A
- the experimenter randomly sorts participants into the groups (ie. experimental group, control group)
17
Q
Halo effect
A
- the tendency of ratings of one positive characteristic to “spill over” to influence the rating of other positive characteristics
18
Q
Horns effect
A
- the tendency for the ratings of one negative trait to spill over to influence the ratings of other negative traits
19
Q
Illusory correlation
A
- the perception of a statistical association between two variables where none exists
- form the basis of many superstitions
- tendency to forget non-events; only remember the times when our superstition is correct
20
Q
Experimental group
A
- the group that receives the manipulation
21
Q
Control group
A
- the group that doesn’t receive the manipulation
22
Q
Between-subjects design
A
- a research design in which one group of participants will be randomly assigned to receive some level of the independent variable, while another group will be assigned to the control condition
23
Q
Within-subjects design
A
- a research design in which participants act as their own control group; a researcher will take a measurement before the independent variable manipulation and then measure that same participant again after the independent variable manipulation
24
Q
Independent variable
A
- the variable that the experimenter manipulates
25
Dependent variable
- the variable that the experimenter measures to see whether the manipulation of the independent variable has had an effect
26
Operational definition
- a working definition
- ie. operational definition of the independent/dependent variables would be to specify how we're measuring our variables
- operational definition of chronic worrying = "worrying for more than two hours per day for four consecutive weeks"
27
Confound
- any variable that differs between the experimental and control groups other than the independent variable
28
Placebo effect
- improvement resulting from the mere expectation of improvement
29
Blind
- unaware of whether one is in the experimental or control group
30
Nocebo effect
- harm resulting from the mere expectation of harm
31
Experimenter expectancy effect
- phenomenon in which researchers' hypotheses lead them to unintentionally bias the outcome of a study
32
Double blind
- when neither researchers nor participants are aware of who's in the experimental or control group
33
Demand characteirstics
- cues that participants pick up from a study that allow them to generate guesses regarding the researcher's hypothesis
34
REB
- research ethics board
- reviews all research carefully
- consists of faculty members who are experts in both research and ethics, as well as a member from the community who is not involved in research or the institution who reviews the research
- adhere to a set of national guidelines found in the Tri-Council Policy Statement
35
TCPS
- Tri-Council Policy Statement
- created by three of the major research funding bodies in Canada: CIHR, NSERC, SSHRC
- provide researchers with guidance on conducting research in a culturally sensitive manner
- core principles:
1. Concern for welfare: maximize benefits and minimize harm
2. Respect for person: informed consent
3. Justice: treat people fairly and equitably by distributing benefits and burdens of participating n research
36
CIHR
- Canadian Institutes of Health Research
| - one of three major research funding bodies in Canada
37
NSERC
- Natural Sciences and Engineering Research Council of Canada
- one of three major research funding bodies in Canada
38
SSHRC
- Social Sciences and Humanities Research Council of Canada
| - one of three major research funding bodies in Canada
39
Informed consent
- informing research participants of what is involved in a study before asking them to participate
40
Debriefing
- a process whereby researchers inform participants what the study was about
- sometimes involves explanations of their hypothesis in nontechnical language
- all studies that involve the use of deception require that the participant receive further information upon completion of the project
41
Define descriptive statistics and list the two major types
- statistics that describe data
1. Central tendency
2. Variability
42
List the three measures of central tendency
1. Mean
2. Median
3. Mode
43
Mean
- total score divided by the number of people
44
Median
- obtained by lining up the scores and finding the middle one
45
Mode
- the most frequent score in the data set
46
Define variability and define two types
- gives a sense of how loosely or tightly bunched the scores are
1. Range: difference between the highest and lowest score
2. Standard deviation: average amount that an individual data point differs from the mean
47
Inferential statistics
- statistics that allow us to determine how much we can generalize findings from our sample to the full population
48
Statistical significance
- conduct statistical tests to determine whether we can generalize our findings to the broader population
- generally use a 0.05 level of confidence (5% probability that the finding occurred by chance)
- statistically significant result is believable; probably a real difference in our sample
49
Practical singificance
- real world importance
- with huge sample sizes, virtually all findings (even tiny ones) will become statistically significant
- however, if r is really small (i.e 0.06), then even if the correlation is statistically significant, it is so miniscule in magnitude that it would be essentially useless for predicting anything
50
Base rate
- how common a characteristic or behaviour is in the general population
51
Response sets
- tendency of research participants to distort their responses to questionnaire items
52
Base rate fallacy
- need to take into consideration how common a behaviour of characteristic is in the population
- ie. Just because you find that there are 15x more German than Norwegian alcoholics in Sloshed, Saskatchewan doesn't mean that Germans are more likely to be alcoholic -- there are 25x more Germans than Norwegians in Sloshed.
