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Psychology year 2 > SSR Exam 3 > Flashcards

SSR Exam 3 Flashcards

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1
Q

history

A

refers to events occurring concurrently with treatment that could cause worse performance.

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2
Q

maturation

A

refers to natural occurring changes over time that could be confused for a treatment effect.

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3
Q

selection

A

refers to systematic differences over conditions in respondent characteristics that could also cause the observed effect.

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4
Q

attrition

A

A loss of respondents to treatment or to measurement can produce artifactual effects if that loss is systematically correlated with conditions.

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5
Q

instrumentation

A

the nature of a measure may change over time or conditions in a way that could be confused with a treatment effect.

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6
Q

testing

A

exposure to a test can affect scores on subsequent exposures to that test, which could be confused for a treatment effect.

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7
Q

regression to the mean

A

when units are selected for their extreme scores, they will usually have less extreme scores on other variables, which can be confused with a treatment effect. §

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8
Q

counterfactual

A

knowledge of what would happen to each participant if they had not undergone a certain manipulation

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9
Q

standardization

A

to overcome the problem of independence on the measurement scale we need to convert the covariance into a standard set of units

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10
Q

coefficient of determination R^2(correlation coefficient squared)

A

a measure of the amount of variability in one variable that is shared by the other.

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11
Q

spearmint correlation coefficient

A

non parametric statistic that is useful to minimise the effects of extreme scores or the effects of violations of the assumptions

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12
Q

priority John stuart mill

A

change X precedes change Y

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13
Q

consistency John stuart mill

A

change X varies systematically with change Y

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14
Q

exclusivity John stuart mill

A

there is no alternative explanation for the relationship

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15
Q

INUS condition

A

insufficient but non-redundant part of an unnecessary but sufficient condition

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16
Q

simpsons paradox

A

phenomenon in probability and statistics, in which a trend appears in several different groups of data but disappears or reverses when these groups are combined.

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17
Q

model

A

a formal instantiation of a theory that specifies the theory predictions. a simplified representation of the world that aims to explain observed data.

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18
Q

overfitting

A

a model can end up overfitting the data, that is it can capture not only the variance that results from the cognitive process of interest but also that from random error. at the expense of the generalisability of the model

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19
Q

generalisability of a model

A

the ability of a model to predict new data, that is the degree to which it is capable of predicting all potential samples generated by the same cognitive process, rather than to fit only a particular sample of existing data

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20
Q

complexity of a model

A

the degree to which a model is susceptible to overfitting, that is a models inherent flexibility that enables it to fit diverse patterns of data.

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21
Q

irrelevant specification problem

A

unintended discrepancies between theories and their various formal counterparts de to the modelelrs need to decide on how to bridge the gaps between informal verbal descriptions of theories and formal implementations

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22
Q

bonini paradox

A

when models become more complete and realistic they become less understandable and more opaque.

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23
Q

identification problem

A

for any behaviour there may exist a universe of different models all of which are equally capable of reproducing and explaining the behaviour

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24
Q

descriptive adequacy

A

does the theory accord with the available behavioural physiological neuroscientific and other empirical data?

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25
Q

precision and interpretability

A

is the theory described in a sufficiently precise fashion that other theorists can interpret it easily and unambiguously

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26
Q

coherence and consistency

A

are there logical flaws in the theory? does each component of the theory seem to fit with the others into a coherent whole? is it consistent with theory in other domains

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27
Q

prediction and falsifiability

A

is the theory formulated in such a way that critical tests can be conducted that could reasonably lead to the rejection of the theory

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28
Q

post diction and explanation

A

does the theory provide a genuine explanation of existing results

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29
Q

arsimony

A

Is the theory as simple as possible

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30
Q

originality

A

is the theory new or is it essentially a statement of existing theory

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31
Q

breadth

A

does the theory apply to a broad range of phenomena or is it restricted to a limited domain

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32
Q

usability

A

does the theory have applied implications

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33
Q

rationality

A

does the theory make claims about the architecture of mind that seems reasonable in light of the environmental contingencies that have shaped our evolutionary theory

34
Q

tautology

A

a statement guaranteed to be true (a triangle has three sides)

35
Q

hierarchical regression

A

in which you select predictors based on past work and decide in which order to enter them into the model

36
Q

stepwise regression

A

bases decisions about the order in which predators enter the model on a purely mathematical criterion

37
Q

multicollinearity

A

exists when tehere is a strong correlation between two or more predictos (you don’t want this) can be seen by VIF under 10 and around 1, other thing has to be above 0.20

38
Q

perfect collinearity

A

(you don’t want this) exists when at least one predictor is a perfect linear combination of the others (two predictors that are perfectly correlated)

39
Q

untrustworthy bs

A

as collinearity increases, so do the standard errors of the b coefficients.

40
Q

R

A

measure of correlation between the predicted values of the outcome and the observed values

41
Q

R^2

A

indicates the variance in the outcome for which the model accounts.

42
Q

measurement

A

the assignment of numerals to objects or events according to rules

43
Q

scaling

A

concerns the way numerical values are assigned to psychological constructs

44
Q

collider bias

A

controlling for common effects will bias the estimation of a causal relationship between variables

45
Q

intra

A

within individual differences (mechanisms, process models)

46
Q

inter

A

between individual differences (correlates and causes)

47
Q

manifest variables (x)

A

ex; scores on cognitive tests, depression symptoms, indicators of attitudes

48
Q

are explained by hidden variables (0)

A

general intelligence, depression, attitude

49
Q

what is a factor or latent variable statistically

A

not observed variable derived from observes measures, interpreted as the underlying cause of observed behaviour, summarises many observed measures, local independence

50
Q

local independence

A

observed measures are uncorrelated conditionally on the scores of the latent variable

51
Q

factor model

A

observed continuous scores X are explained in smaller number of latent factors F

52
Q

criteria for causality

A

priority, consistency, exclusivity

53
Q

purification principle

A

the idea that the more you control variables are included in a model, the more accurate the estimation of the causal effect is.

54
Q

moderation/interaction effect

A

a statistical model to include the combined effect of two or more predictor variables on an outcome

55
Q

moderator

A

involves an interaction effect between the moderator and the independent variable

56
Q

grand mean centering

A

refers to the process of transforming a variable into deviations around a fixed point

57
Q

simple slope analysis

A

working out the model equations for the predictor and outcome at low, high and average levels of the moderator.

58
Q

mediation

A

refers to a situation when the relationship between a predictor variable and an outcome variable can be explained by their relationship to a third variable (mediator)

59
Q

2 types of questions in psych

A

how does X work? why do people differ in X?

60
Q

heritability

A

how much variation seen in a certain trait within population can be attributed to genetic variation as opposed to environment

61
Q

positive manifold

A

people who score well on one cognitive test, score good on other cognitive tests as well

62
Q

g-factor

A

mental power

63
Q

observatins are theory laden(driven)

A

assumptions about the world/human drive observation

64
Q

atomism

A

explanations for the functioning of the research object can be found in the object itself

65
Q

berksons paradox

A

occurs when this observation appears true when in reality the two properties are unrelated—or even positively correlated—because members of the population where both are absent are not equally observed. EX: date not hot and nice tegelijk, but we only date people who are either nice or hot so we ignore people who are neither

66
Q

mean centring

A

the scores are centred around zero by subtracting the grand mean from all scores.

67
Q

interpretation bias

A

a bias toward interpretations of data that favor a researchers theory/ the tendency to interpret the failure to confirm predicted outcomes in terms o method relevant beliefs, but confirmed predictions in terms of theory relevant beliefs

68
Q

problematic theories

A

lack of connectivity, illogical reasoning, lack of falsifiability

69
Q

problematic evidence

A

post hockey, anecdotal evidence, lack of rigorous hypothesis testing, lack of replication

70
Q

problematic process

A

misplaced burden of proof, absence of self-corection; stagnation, no peer-review/lack of openness

71
Q

law of similars

A

cause of the problem is the solution

72
Q

law of infinitesimals

A

the smaller the dosage the stronger the effect, doses where it is physically jut possible for a molecule active ingredient to be present would then have the strongest effect

73
Q

theory relevant beliefs

A

theoretical mechanisms that produce observable behaviour

74
Q

method relevant beliefs

A

procedures with which we produce and analyse data

75
Q

surveys are inherently subjective

A

your interpretation and phrasing might be different compared to your respondents, language Is vague and some questions and answers are more vague than others.

76
Q

survey measurement is context sensitive

A

social desirability, test-retest reliability is rarely checked in general surveys

77
Q

erroneous purification principle

A

the idea that the more control variables are included in a model the more accurate the estimation of the causal effect is

78
Q

problem of overcorrection

A

controlling for mediators on the causal path could lead to an underestimation of the total causal effect

79
Q

homoscedasticity

A

at each level of the predictor variables, the variance of the residual terms should eb constant. the residual at each level of the predictor(s) should have the same variance

80
Q

heteroscedasticity

A

when the variances are very unequal

81
Q

Multicollinearity can be a threat to the estimation of regression coefficients in a regression analysis, because:

A
  1. it causes the standard error of the b coefficients to increase, making the estimates of the b coefficients less trustworthy;
  2. it causes the value of the explained variance of the model to decrease;
  3. it makes it difficult to determine the individual importance of the predictors

Psychology year 2 (9 decks)

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