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Psychology year 2 > SSR Exam 4 > Flashcards

SSR Exam 4 Flashcards

1
Q

global network analysis

A

looks at the overall structure of the network

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2
Q

local network analysis

A

looks at the differences between nodes in terms of connectivity, centrality etc

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3
Q

nodes

A

set of entities

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4
Q

edges

A

nodes are connected through

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5
Q

hysterises

A

there is more needed for high connectivity networks (vulnerability network) to get back to normal when experiencing a traumatic life event

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6
Q

degrees

A

number of connections

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7
Q

betweenness

A

how often does a node lie on a path between two other nodes

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8
Q

network theory

A

mental disorders are alternative stable states in a symptom network

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9
Q

small world structure

A

high level of clustering , small average path lengths.

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10
Q

goal of psych assessment

A

to characterise an individual’s standing on individual differences construct of clinical relevance

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11
Q

latent trait models

A

posit the presence of one or more underlying continuous distributions

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12
Q

latent class models

A

based on the supposition of a latent group (class) structure for a personality construct’s dikstribution, and they are typically evaluated via latent class analysis

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13
Q

hybrid models

A

these models combine the continuous aspects of the latent trait models with the discrete aspects of latent class models

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14
Q

zones or rarity

A

locations along the dimension that are unoccupied by some individuals

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15
Q

quasi-continous

A

construct would be bounded at the low end by zero, a complete absence of the quality corresponding with the construct.

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16
Q

discrimination

A

measure of how strongly the item taps into the latent trait

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17
Q

assumption of conditional independence

A

classes are defined by patterns of item endorsement across individuals, assuming that inter-item correlations solely reflect class membership

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18
Q

self organisation

A

process where some form of overall order arises form local interactions between parts of an initially disordered system. the process can be spontaneous when sufficient energy is available, not needing control by any external agent.

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19
Q

bimodality

A

in exactly the same circumstances two stable states are possible

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20
Q

hysteresis

A

is the dependence of the state of a system on its history

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21
Q

finite mixture models

A

the distribution of data (length) is not described by one distribution (normal distribution) but a weighted sum of distributions

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22
Q

latent classes

A

both observed and latent variables are categorical

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23
Q

wilcoxon signed rank test

A

a non parametric aletnartive to the paired samples t-test. it assigns + or - signs to the difference between two repeated measures

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24
Q

reflective latent variable model

A

the attribute is seen as the common cause of observed scores: neuroticism causes worrying about things going wrong

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25
Q

formative latent variable model

A

observed scores define or determine the attribute

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26
Q

subjective probability

A

degree of conviction we have in a hypothesis.

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27
Q

P(H|D) is proportional to P(D|H) x P(H) says that

A

your posterior is proportional to the likelihood times the prior

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28
Q

likelihood

A

if you want to update your personal probability in a hypothesis, the likelihood tells you everything you need to know about the data. it captures all support for a hypothesis provided by the data

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29
Q

likelihood principle

A

the notion that all the information relevant to inference contained in data is provided by the likelihood

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30
Q

probability density distribution

A

if the dependent variable can be assumed to vary continuously, (that is the values do not come in steps)

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31
Q

p-value

A

the probability of rejecting the null, given the null is really true

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32
Q

bayes theorem

A

says that posterior is proportional to likelihood times prior

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33
Q

flat prior/uniform prior

A

if the std dev is infinite , you think all population values are equally likely

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34
Q

bayes factor

A

Bayes equivalent to null hypothesis testing or significance testing.

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35
Q

what is bayes statistics?

A

it is when you use probability to represent uncertainty in all parts of a statistical model/ flexible extension of maximum likelihood

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36
Q

bayesian data analysis is a method for figuring out unknown that requires three things

A

data, a generative model, priors (what ifs the model has before seeing the data)

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37
Q

P(X)

A

degree of belief that X is true.

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38
Q

probability

A

is a measure of the degree of belief or confidence one has in the truth of a proposition

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39
Q

bayesian program steps

A

find a way of assigning a number to a person’s degree of belief, show that a rational betting strategy must satisfy the rules of probability theory, adopt bayes’ rule as a general principle for how to learn from experience

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40
Q

p value

A

prob of the data of encountering a test statistic at least as extreme as the one observed, given that the null nhypothesis is true

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41
Q

confidence interval

A

an x% conf interval for a parameter 0 is an interval (L,U) generated by an algorithm that in repeated use has an X% chance to capture the true value of 0

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42
Q

classical definition of probability

A

proportion of occurrence when a particular experiment is repeated infinitely often (inner different circumstances)

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43
Q

statistical evidence

A

change in conviction (concerning a given hypothesis ) brought about by the data

44
Q

advantages of bayes factor

A

provides a continuous degree of evidence without requiring an all or non decision, allows evidence to be monitored during collection, differentiates between the ‘data support H0’ (evidence for absence) and ‘the data are not informative’ (absence of evidence)

45
Q

difference between dimensions and typologies

A

typology is like categories and dimensions is you can have different points on the scale that you can be

46
Q

what does a combination of a typology and dimension look like?

A

like a hybrid model

47
Q

median split

A

is one method for turning a continuous variable into a categorical one. Essentially, the idea is to find the median of the continuous variable. Any value below the median is put it the category “Low” and every value above it is labeled “High.”

48
Q

complex systems

A

consisting of many sub systems interacting non linearly, like weather, chemical patterns, brains etc

49
Q

CUSP model

A

consists of two stable regions and two thresholds where sudden changes occur. a linear model does not have these features.

50
Q

local independence

A

the underlying assumption of latent variable models. The observed items are conditionally independent of each other given an individual score on the latent variable(s). This means that the latent variable explains why the observed items are related to another.

51
Q

hubs and edges

A

hubs = the symtpoms, edges = the connections between these symptoms

52
Q

degree

A

number of connections

53
Q

highest betweenness

A

how often does a node lie on a path between two other nodes

54
Q

the disease model of mental disorders

A

the problems that people that people encounter in life are ‘symptoms’ of a reasonably small set of underlying disorders that cause these symtpoms (lung tumor = shotness of breath, chest pain and coughing up blood)

55
Q

critical slowing down

A

the theory that in cases where a system is close to a critical tipping point the recovery rate should decrease. It occurs because a system’s internal stabilizing forces become weaker near the point where they break and the system moves into a new regime. Thus critical slowing down is posited to exist at phase transitions, such as ecosystem collapse

56
Q

prior

A

a probability distribution of an uncertain quantity is the prob dis that would express one’s beliefs about this quanitty before some evidence is taken into account.

57
Q

likelihood

A

formed from the joint probability of a sample of data given a set of model parameter values. it is viewed and used as a function of the parameters given the data sample

58
Q

posterior

A

of a random event or an uncertain proposition is the conditional probability that is assigned after the relevant evidence or background is taken into account.

59
Q

marginal likelihood

A

in which some para§meter varibels have been marginalized

60
Q

epistemology

A

refers to a branch of philosophy that is concerned with the theory of knowledge and that tries to answer questions about how we can know and what we can know

61
Q

ontology

A

refers to the assumptions we make about the nature of being, existence or reality

62
Q

positivism

A

holds that the relationship between the world (events objects and other phenomena) and ur sense of perception of the world is straightforward:

63
Q

empiricism

A

holds that our knowledge o the world must arise from the collection and categorisation of our sense perceptions/observatios of the world

64
Q

deductive reasoning

A

in research this means reasoning which begins with theories, which are refined into hypothesis, which are tested through observations of some sort, leads to a confirmation or rejection of the hypotheses

65
Q

realism

A

assumes that a reality exists independent of the observer

66
Q

small q qualitative research

A

research that uses qualitative tools and techniques but within a hypothetic-deducitve framework

67
Q

big Q qualitative researhc

A

refers to the use of qualitative techniques within a qualitative paradigm which rejects notions of objective reality or universal truth and emphasises contextualised understandings

68
Q

nomothetic research

A

which seek generalisable findings that uncover laws to explain objective phenomena

69
Q

idiographic research

A

which seek to examine individual cases in detail to understand an outcome

70
Q

phenomenological

A

methods focus on obtaining detailed description of experience as understood by those who have that experience in order to discern its essence

71
Q

inductive reasoning

A

means reasoning that begins with data, which are examined n light of a study’s research question

72
Q

critical realist outlook

A

assumes that while a reality exists independent of the observer, we cannot know that reality with certainty.

73
Q

social constructionist prspective

A

adopts a critical stance towards the taken-for-granted ways in which we understand the world and ourselves, such as the assumptions that the categories we use to interpret the world correspond to ‘real’ \objectve’ entities

74
Q

relativist

A

stance in which reality is seen as dependnt on the ways we come to know it

75
Q

reflexivity

A

refers to acknowledgement by the researcher of the role played by their interpretative framework or speaking positiion (incl theoretical commitments, personal understandings and personal experiences) in creating their analytic account.

76
Q

sensitivity to context

A

the research should make clear the context of theory and the understandings creatd by previous researchers using similar methods and or analysing similar topics

77
Q

commitment

A

said to involve demonstrating prolonged engagement with the research topic

78
Q

rigour

A

relates to the completeness of the data collection and analysis

79
Q

coherence

A

refers to the quality of the reserch narrative, the ‘fit’ between the reserch question and the philosophical perspective adopted

80
Q

impact and importance

A

relate to the theoretical practical and socio-cultural impact of the study

81
Q

transparancy

A

entails detailing every aspect of the processes of data collection and analysis and disclosing/discussing all aspects of the research process

82
Q

pluralistic analysis

A

exploring the value of applying different qualitative methods with different ontologis and epistemologies to a single data set

83
Q

collective is required for long run relative freqyency

A

the probability of some property(q) occuring is then the proportion of events in the collective with proporty ‘q’

84
Q

alpha level

A

longterm relative frequency of mistakenly rejecting hypothesis H1 if it si true, also known as typ 1 error rate

85
Q

beta level

A

longterm relative frequency of mistakenly rejecting hypothesis H2 if it si true, also known as typ 2 error rate or 1 - power

86
Q

hypothetical-deductive model

A

or method is a proposed description of scientific method. . According to it, scientific inquiry proceeds by formulating a hypothesis in a form that could conceivably be falsified by a test on observable data.

87
Q

qualitative research

A

is a process of naturalistic inquiry that seeks in-depth understanding of social phenomena within their natural setting.

88
Q

quantitative research

A

based on equity theory about relationship satisfaction

89
Q

central assumption qualitative research

A

human behaviour is not a simple response to stimuli, but it depends on how the external world is understood

90
Q

grounded theory

A

how do social processes impact participants? use if : you know little about the subject, if existing theories are not sufficient, when developing a theory, when interested n perception of the world in participants

91
Q

interpretative phenomenological analysis

A

how does a person view this in this context? the goal is to get a detailed picture of how an individual gives meaning to events. crucial here is the role of the researcher: how does the researcher experience the world(and in this case the participant?)

92
Q

discourse analysis

A

how are experiences constructed? social reality is constructed by the way we communicate. How do people construct different versions of their social world, and what benefits do they derive from this?

93
Q

critical realism

A

a reality exists independent of the observer but we cannot know that reality with certainty

94
Q

relativism

A

reality is dependent on the ways we come to know it

95
Q

transparency & coherence

A

coherence between different components

96
Q

commitment & rigour

A

completeness and perseverance of the researcher

97
Q

impact & importance

A

practical and theoretical consequences of the research

98
Q

sensitivity to context

A

beliefs and position of participants & researchers ; place in context of other research and theory

99
Q

ranking the data

A

finding the lowest score on giving it a rank of 1, then finding the next highest score and giving it a rank 2 and so on.

100
Q

five principles of network theory

A

complexity, symtpom component correspodence, direct causal connections, mental disorders follow network structure, hysteresis

101
Q

discrete observed variables (correct/incorrect)& continuous latent variables (intelligence)

A

item respons models

102
Q

discrete OV ((correct/incorrect), discrete LV(disorder)

A

latent class analysis

103
Q

coninuous OV(sub test scores) & continuous LV (intelligence)

A

factor models

104
Q

Continuous OV (test sub scores) & discrete LV (disorder)

A

mixture models

105
Q

ethnography

A

be part of a group for a long time, observe interactions

106
Q

focus group

A

group interview, with a focus on interaction between the group members

Psychology year 2 (9 decks)

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