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Psychology year 2 > SSR Exam 3 > Flashcards

SSR Exam 3 Flashcards

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
precision and interpretability
is the theory described in a sufficiently precise fashion that other theorists can interpret it easily and unambiguously
26
coherence and consistency
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
27
prediction and falsifiability
is the theory formulated in such a way that critical tests can be conducted that could reasonably lead to the rejection of the theory
28
post diction and explanation
does the theory provide a genuine explanation of existing results
29
arsimony
Is the theory as simple as possible
30
originality
is the theory new or is it essentially a statement of existing theory
31
breadth
does the theory apply to a broad range of phenomena or is it restricted to a limited domain
32
usability
does the theory have applied implications
33
rationality
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
tautology
a statement guaranteed to be true (a triangle has three sides)
35
hierarchical regression
in which you select predictors based on past work and decide in which order to enter them into the model
36
stepwise regression
bases decisions about the order in which predators enter the model on a purely mathematical criterion
37
multicollinearity
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
perfect collinearity
(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
untrustworthy bs
as collinearity increases, so do the standard errors of the b coefficients.
40
R
measure of correlation between the predicted values of the outcome and the observed values
41
R^2
indicates the variance in the outcome for which the model accounts.
42
measurement
the assignment of numerals to objects or events according to rules
43
scaling
concerns the way numerical values are assigned to psychological constructs
44
collider bias
controlling for common effects will bias the estimation of a causal relationship between variables
45
intra
within individual differences (mechanisms, process models)
46
inter
between individual differences (correlates and causes)
47
manifest variables (x)
ex; scores on cognitive tests, depression symptoms, indicators of attitudes
48
are explained by hidden variables (0)
general intelligence, depression, attitude
49
what is a factor or latent variable statistically
not observed variable derived from observes measures, interpreted as the underlying cause of observed behaviour, summarises many observed measures, local independence
50
local independence
observed measures are uncorrelated conditionally on the scores of the latent variable
51
factor model
observed continuous scores X are explained in smaller number of latent factors F
52
criteria for causality
priority, consistency, exclusivity
53
purification principle
the idea that the more you control variables are included in a model, the more accurate the estimation of the causal effect is.
54
moderation/interaction effect
a statistical model to include the combined effect of two or more predictor variables on an outcome
55
moderator
involves an interaction effect between the moderator and the independent variable
56
grand mean centering
refers to the process of transforming a variable into deviations around a fixed point
57
simple slope analysis
working out the model equations for the predictor and outcome at low, high and average levels of the moderator.
58
mediation
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
2 types of questions in psych
how does X work? why do people differ in X?
60
heritability
how much variation seen in a certain trait within population can be attributed to genetic variation as opposed to environment
61
positive manifold
people who score well on one cognitive test, score good on other cognitive tests as well
62
g-factor
mental power
63
observatins are theory laden(driven)
assumptions about the world/human drive observation
64
atomism
explanations for the functioning of the research object can be found in the object itself
65
berksons paradox
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
mean centring
the scores are centred around zero by subtracting the grand mean from all scores.
67
interpretation bias
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
problematic theories
lack of connectivity, illogical reasoning, lack of falsifiability
69
problematic evidence
post hockey, anecdotal evidence, lack of rigorous hypothesis testing, lack of replication
70
problematic process
misplaced burden of proof, absence of self-corection; stagnation, no peer-review/lack of openness
71
law of similars
cause of the problem is the solution
72
law of infinitesimals
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
theory relevant beliefs
theoretical mechanisms that produce observable behaviour
74
method relevant beliefs
procedures with which we produce and analyse data
75
surveys are inherently subjective
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
survey measurement is context sensitive
social desirability, test-retest reliability is rarely checked in general surveys
77
erroneous purification principle
the idea that the more control variables are included in a model the more accurate the estimation of the causal effect is
78
problem of overcorrection
controlling for mediators on the causal path could lead to an underestimation of the total causal effect
79
homoscedasticity
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
heteroscedasticity
when the variances are very unequal
81
Multicollinearity can be a threat to the estimation of regression coefficients in a regression analysis, because:
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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