U2: Foundations: Methods and Approaches Flashcards

1
Q

experiment

A

investigation trying to understand relations of cause and effect

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2
Q

independent variable

A

manipulated variable

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3
Q

dependent variable

A

measured variable

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4
Q

control variable

A

variable that is constant

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5
Q

representativeness

A

degree which sample accurately reflects the population

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6
Q

sampling bias

A

bias that occurs when choosing sample

types: bias of selection, self-selection bias, prescreening/advertising bias, healthy user bias

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7
Q

bias of selection

A

unrepresentative selection, occurs when people are selected in a physical space

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8
Q

self-selection bias

A

when subjects have some control over whether they participate

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9
Q

pre-screening/advertising bias

A

how volunteers are screen/when advertising is placed skewing the sample (i.e. “stop smoking treatment” add, leads to you getting people who are motivated even without the treatment)

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10
Q

healthy user bias

A

when study populations is in better shape than the general population

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11
Q

single-blind design

A

subjects don’t know if they’re in experimental or control group

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12
Q

double-blind design

A

neither subjects nor researchers know if the latter is in the experimental or control group

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13
Q

placebo

A

sugar pill, used to trick control group to thinking they’re getting the treatment

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14
Q

correlation research

A

assessing the degree of association between two or more variables

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15
Q

confounding variable/third variable/extraneous variable

A

unknown factor that plays a role

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16
Q

longitudinal study

A

correlation study, study over a long period of time with the same subjects

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17
Q

cross-sectional study

A

correlational study, testing a wide variety of subjects from different backgrounds to increase generalizability

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18
Q

case studies

A

clinical research, study of single individual to allow for general conclusions about other cases

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19
Q

survey

A

used for correlation research, way to accumulate data through questionares/interviews

20
Q

conceptual definition

A

theory or issue being studied

21
Q

operational definition

A

the way a theory/issue will be directly observed/measured in the study, has to be internally and externally valid

22
Q

internal validity

A

certainty with which the result of an experiment can be attributed to manipulation of the independent variable and not other extraneous variables

23
Q

external validity

A

extent to which findings can be generalized to other contexts

24
Q

naturalistic observation

A

observation of subjects in natural setting, outside of a lab

25
descriptive statistics
summarize data
26
inferential statistics
testing hypotheses about data and determining how confident to be about inferences about the data
27
central tendency
descriptive statistics, characterizes the typical value in a set of data
28
positive skew
most values are on lower end, some are larger values
29
negative skew
most values on higher end, some are lower values
30
standard deviation
average dispersion of numbers around the mean | in typical distribution, 68% of all scores are within one standard deviation and 95% are within two standard deviations
31
pearson correlation coefficient
``` describes the linear relationship between two attributed scale of -1, 0, 1 1 = perfect positive correlation -1 = perfect negative correlation 0 = attributes are not related ```
32
positive correlation
as X increases, Y increases
33
negative correlation
as X increases, Y decreases
34
sample size
N or n number of observations or individuals measured the larger the sample size the more accurate it will be to the general population
35
null hypothesis
a treatment had no effect in an experiment | inferential statistics will allow possibility of rejecting null hypothesis with known level of confidence
36
alternative hypothesis
a treatment had an effect
37
alpha
the accepted probability that a result of an experiment can be attributed to chance rather than manipulation of the independent variable this is always possible so alpha is usually set to 0.05 -- which means that experiment's results are statistically significant if probability of the results happening by chance in less than 5%
38
type I error
false positive, conclusion that a difference exists when it actually doesn't
39
type II error
false negative, conclusion that there is no difference when there actually is a difference
40
p-value
probability of making a type I error, indicates that results are statistically significant (not only due to chance)
41
deception
used only if informing participants of nature of experiment might bias results
42
stanley milgram
obedience experiment, convinced people they were administering painful electric shock to other participants (who knew the true nature of the experiment)
43
confederates
people who pretend to be participants but actually know the nature of the study
44
institutional review boards (IRBs)
assess research plans before approval to make sure it meets ethical standards
45
ethical standards
- informed consent: participants must give consent and can leave the study at any time - debriefing: after study is done. participants must be told the exact purpose of their participation - confidentiality