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research methods > week 5 exp designs > Flashcards

week 5 exp designs Flashcards

1
Q

noise

A

random variation that disrupts relevant information

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2
Q

what 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 likely are you to detect an effect (signal, systematic variation) in a statistical test?

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
Q

noise and systematic errors reduce…

A

reliability and accuracy

26
Q

confounders reduce…

A

internal and external validity

27
Q

how to minimise systematic errors

A

calibration - comparison and adjustment to reference measure

28
Q

what is the smaller unit of an experiment

A

trial - one completion of the task providing one datapoint of DV

29
Q

what can you organize trials in

A

blocks - specific characteristics concerning the control of confounders and manipulation of IV

30
Q

2 major approaches to manipulating IV

A

repeated measures design aka within-subjects, paired samples

between-groups design aka between-subject

31
Q

repeated measures design

A

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
Q

strengths of using repeated measures

A

reduces noise and cofounds by excluding differences between group

wanted when practice effects are desirable

33
Q

block design in repeated measures

A

values of iv in separate blocks

participants are exposed to all conditions multiple times, and these conditions are grouped into blocks.

34
Q

cons of using block design

A

order effects

35
Q

how to deal with order effects

A

counterbalancing

36
Q

interleaved design in repeated measures

A

Different tasks, stimuli, or conditions are presented in an alternating or random order.

37
Q

pros of using interleaving method

A

order effects are controlled through randomization

38
Q

cons of using interleaved design

A

noise due to unexpected changes

interaction effects - bias when effects of sequence differ between conditions

39
Q

pros between-groups design

A

controls order effects

wanted when inexperienced, naive ptsp needed

40
Q

cons of between-groups design

A

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
Q

2 types of between groups design

A

random groups

matched pair design

42
Q

what does factorial designs mean

A

multiple independent variables that can have interaction between factors (iv)

43
Q

2 types of factorial design

A

manipulation of factors

mixed factorial designs

44
Q

pros of factorial designs

A

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
Q

quantitative interaction effects

A

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
Q

qualitative interaction effects

A

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
Q

multivariate designs

A

more than one DV

48
Q

criticisms of little Albert study

A

no attempts at removing the conditioning after, negative consequences on development

49
Q

what was the monster study

A

assessment on psychological support on stuttering

22 children from iowa soldiers orphan

half received negative treatment eg belittling and half positive

50
Q

ethical issues of the monster study

A

no consent, debriefing or help after with negative psychological effects eg speech problems for rest of their lives

51
Q

ethical issues of milgram shock exp

A

deception, no debrief, inflicted insight = pushing ptsp to fave negative characteristics of themselves

52
Q

ethical issues of SPE

A

no consent, suffered psychological and physical har, hindered right to withdraw and debriefing too late

53
Q

ethical issues of Harlows monkeys

A

unethical treatment of animal which harmed them beyond exp

54
Q

what does the Belmont report include

A

informed consent, voluntary participation, no/minimal deception, anonymous data, confidentiality, not harmful, fair selection of ptsp

55
Q

3 factors of ethics

A
  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
Q

main problem of independent sample designs

A

ptsp are different - potential confounders

57
Q

how can u visually examine if a distribution of a variable is biased

A

with a histogram

58
Q

statistical population depends on the…

A

hypothesis

59
Q

clever Hans was an example of

A

primary observer effect - horse was responding to involuntary cues in the body language of human trainer

research methods (11 decks)

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