week 5 exp designs Flashcards

1
Q

noise

A

random variation that disrupts relevant information

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2
Q

what 3 types of noise are there

A

stimulus precision eg auditory stimulus

participant noise eg motivation

precision of data recording eg buffer of exact time

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3
Q

signal

A

core element that a person is trying to focus on or interpret.

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4
Q

what does noise decrease

A

reliability of measurement - more noise = less likely to get same results across measurements

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5
Q

aim of experimental control

A

maximize the signal relative to noise ratio

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6
Q

2 ways to deal with noise

A

minimise

neutralise

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7
Q

how to minimize noise

A

use instruments with high accuracy and precision eg high-end hardware

maximise usability and minimize errors eg make the task easy to understand - minimize ambiguity

maximise ptsp alertness eg take breaks

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8
Q

how to neutralize noise

A

uniform random variation -When random variation is uniformly distributed, over time or across multiple observations, the effects of noise can average out. This means that the random fluctuations (noise) become less disruptive to the overall pattern or signal, as the randomness tends to balance out. This allows the signal to emerge more clearly, as noise doesn’t systematically bias the outcome in a particular direction.

LARGER DATASET/SAMPLE = LESS NOISE

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9
Q

noise is not absent but neutralized by calculating…

A

central tendency

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10
Q

how does higher signal to noise ratio affect type 1 and 2 errors

A

higher signal to noise ratio reduces false negatives (type 2 error, specificity) and false positives (type 1 error, sensitivity) which increases sensitivity and specificity of results

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11
Q

type 2 error or false negative (beta)

A

when a researcher or experimenter incorrectly concludes that there is no significant effect or relationship between variables, despite the fact that one is present.

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12
Q

4 ways to control confounders

A
  1. fix - keep constant
  2. randomise
  3. balancing
  4. measure and model confound
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13
Q

how to fix/keep constant the confounder

A

make the potential confounder a control variable eg lab exp

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14
Q

limitations of fixing/keeping confounder constant

A

fixing is not always possible - order effects

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15
Q

benefit of randomize (neutralize) a confounder

A

converts confounder (systematic variation) into noise (uniform random variation) - becomes unrelated to IV

any random variation can be neutralized by calculating central tendency now

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16
Q

limitations of using balancing to reduce confounder

A

only works if you know confounder beforehand

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17
Q

disadvantage of using randomization to reduce confounder

A

must have sufficient sample size

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18
Q

difference between noise and a confounder

A

Confounder: A variable that causes a distortion in the relationship between the independent and dependent variables, often leading to incorrect conclusions about causality.
Noise: Random, unsystematic variations or errors in the data that obscure the signal or relationship without creating any false causal associations

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19
Q

how to balance confounder variable

A

dividing set into 2 or more subsets with roughly the same characteristics

equalize central tendency across conditions so confounder on DV is neutralized

values of the confounder are distributed in equal frequencies across conditions so central tendency is unbiased

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20
Q

what does measure and model mean in terms of reducing confounder

A

when you can’t fix or neutralize a potential bias, include It when analyzing data

confounder can become an IV - results indicate potential biases

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21
Q

bias

A

unwanted signal producing spurious results

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22
Q

2 types of bias

A

independent and dependent bias

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23
Q

independent bias

A

= systematic errors

systematic tendencies in measurements that are incorrect, but limited to one variable

bias does not vary with the independent variable so can’t be mistaken for causal

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24
Q

dependent bias

A

= confounder

produces bias in DV, that varies with IV. so interferes with results as produces unwanted, spurious results

25
noise and systematic errors reduce...
reliability and accuracy
26
confounders reduce...
internal and external validity
27
how to minimise systematic errors
calibration - comparison and adjustment to reference measure
28
what is the smaller unit of an experiment
trial - one completion of the task providing one datapoint of DV
29
what can you organize trials in
blocks - specific characteristics concerning the control of confounders and manipulation of IV
30
2 major approaches to manipulating IV
repeated measures design aka within-subjects, paired samples between-groups design aka between-subject
31
repeated measures design
all ptsp in all conditions - everyone does everything but IV is manipulated within ptsp 2 ways to organize sequences of repeated measures - blocked vs interleaved
32
strengths of using repeated measures
reduces noise and cofounds by excluding differences between group wanted when practice effects are desirable
33
block design in repeated measures
values of iv in separate blocks participants are exposed to all conditions multiple times, and these conditions are grouped into blocks.
34
cons of using block design
order effects
35
how to deal with order effects
counterbalancing
36
interleaved design in repeated measures
Different tasks, stimuli, or conditions are presented in an alternating or random order.
37
pros of using interleaving method
order effects are controlled through randomization
38
cons of using interleaved design
noise due to unexpected changes interaction effects - bias when effects of sequence differ between conditions
39
pros between-groups design
controls order effects wanted when inexperienced, naive ptsp needed
40
cons of between-groups design
potential noise and confounds because groups vary due to ptsp characteristic large sample needed to allow for neutralizing by randomisation doesn't control for important ptsp characteristics vulnerable to drop out - if one pair drops can't use the other
41
2 types of between groups design
random groups matched pair design
42
what does factorial designs mean
multiple independent variables that can have interaction between factors (iv)
43
2 types of factorial design
manipulation of factors mixed factorial designs
44
pros of factorial designs
internal validity - more than one hypothesis can be tested allows for assessing complex causes relationships eg interaction effects allows for including confounders ecological validity - closer to real world
45
quantitative interaction effects
The effect changes in size but not direction (e.g., caffeine improves reaction times more in the morning than in the afternoon, but it always helps).
46
qualitative interaction effects
The effect changes in direction depending on the level of the other IV (e.g., caffeine improves reaction times in the morning but worsens them in the afternoon).
47
multivariate designs
more than one DV
48
criticisms of little Albert study
no attempts at removing the conditioning after, negative consequences on development
49
what was the monster study
assessment on psychological support on stuttering 22 children from iowa soldiers orphan half received negative treatment eg belittling and half positive
50
ethical issues of the monster study
no consent, debriefing or help after with negative psychological effects eg speech problems for rest of their lives
51
ethical issues of milgram shock exp
deception, no debrief, inflicted insight = pushing ptsp to fave negative characteristics of themselves
52
ethical issues of SPE
no consent, suffered psychological and physical har, hindered right to withdraw and debriefing too late
53
ethical issues of Harlows monkeys
unethical treatment of animal which harmed them beyond exp
54
what does the Belmont report include
informed consent, voluntary participation, no/minimal deception, anonymous data, confidentiality, not harmful, fair selection of ptsp
55
3 factors of ethics
1. informed consent using participation information sheet and gaining consent after all info given 2. debrief 3. review application to ethics committee and approval necessary
56
main problem of independent sample designs
ptsp are different - potential confounders
57
how can u visually examine if a distribution of a variable is biased
with a histogram
58
statistical population depends on the...
hypothesis
59
clever Hans was an example of
primary observer effect - horse was responding to involuntary cues in the body language of human trainer