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MRCPsych Part B > Multivariate Analyses > Flashcards

Multivariate Analyses Flashcards

1
Q

When is multivariable analysis used?

A

For data with one dependent variable but more than one independent variable

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2
Q

What is multivariable analysis used to determine

A

Relative contributions of different causes to a single event or outcome

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3
Q

When is multivariate analysis used?

A

For data with more than one dependent and independent variable

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4
Q

What is used if both dependent and independent variables are continuous?

A

Multiple regression

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5
Q

What is used if the dependent variable consists of dichotomous categoric data (two outcomes)?

A

Logistic regression

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6
Q

What is used if the dependent variable includes a time factor e.g. a survival curve?

A

Cox proportional hazards model

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7
Q

What is used if the dependent variable consists of nominal categorical data i.e. more than 2 outcomes

A

Log-linear analysis

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8
Q

What is used if dependent variable is continuous and independent is categorical?

A

ANOVA

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9
Q

What does ANOVA stand for?

A

Analysis of variance

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10
Q

What is used if there are both categorical and continuous independent variables?

A

ANOCA

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11
Q

What does ANCOVA stand for?

A

Analysis of covariance

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12
Q

What is path analysis?

A

Extension of multiple regression

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13
Q

What can path analysis do?

A

Examine situations where there are several final dependent variables with chains of influence

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14
Q

What is cluster analysis?

A

Multivariate tool used to organise variables into homogenous groups or clusters

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15
Q

What does cluster analysis involve the generation of?

A

A similarity matrix

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16
Q

What does a cluster analysis produce?

A

A dendogram

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17
Q

What is canonical correlation?

A

A multivariate tool used to explore the relationship between two sets of variables

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18
Q

What does canonical correlation involve?

A

Computation of eigenvalues

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19
Q

What is discriminant function analysis?

A

Multivariate technique used to detect which of several variables best discriminates between 2 or more groups

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20
Q

What is discriminant function analysis similar to?

A

Computationally similar to MANOVA

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21
Q

What does MANOVA stand for?

A

Multivariate analysis of variance

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22
Q

Key uses of multivariate analysis

A

Look for interaction between independent variables
Quantify associations
Adjust for potential confounders in controlled study
Develop models to predict values or probabilities of certain outcomes

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23
Q

What test to use if dependent variable is nominal categorical?

A

Log linear analysis

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24
Q

What test to use if dependent variable is categorical dichotomous?

A

Logistic regression

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25
Q

What test to use if dependent variable is categorical dichotomous with a time factor?

A

Cox proportional hazards

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26
Q

What test to use if dependent variable is continuous and independent variable is categorical?

A

ANOVA

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27
Q

What test to use if dependent variable is continuous and independent variable is continuous?

A

Multiple regression

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28
Q

What test to use if dependent variable is continuous and independent variable is categorical and continuous?

A

ANCOVA

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29
Q

What does factor analysis refer to?

A

Set of statistical methods used to detect underlying patterns in relationships among a number of observed variables

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30
Q

Aim of factor analysis

A

To identify whether correlations between a set of multiple observed variables can be summarised in terms of a smaller number of underlying, latent, unobserved variables called factors

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31
Q

Two approaches of factor analysis

A

Exploratory

Confirmatory

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32
Q

When is exploratory factor analysis used?

A

For preliminary investigation of a set of multiple observed variables

33
Q

What does exploratory factor analysis not do?

A

It does not make an a priori assumption about the composition of underlying latent variables or factors

34
Q

Applications of exploratory factor analysis

A

Data reduction when multiple (>25) variables have been measured, to provide a parsimonious description of the data.
Classification of sx into clinically meaningful concepts
Definition of subscales of new measures

35
Q

What is confirmatory factor analysis used for?

A

To test whether a specified factor structure remains valid with new dataset

36
Q

When is confirmatory factor analysis used?

A

For assessing construct validity of questionnaires or tests

37
Q

Stages of factor analysis

A 
Construction of correlation matrix
Extraction
Rotation measuring the eigenvalues
Define factors to be retained
Include as many factors as required
Labelling
38
Q

What types of methods are used in extraction?

A

Common factor analysis

Principal components analysis

39
Q

What is the eigenvalue

A

Eigenvalue is the amount of total variance explained by each factor

40
Q

How can we determine the number of factors to be retained?

A

Kaiser rule
Scree plot
Include as many factors as required until adequate preset proportion of variation is explained by them

41
Q

What is kaiser rule?

A

Only factors with eigenvalues greater than 1 are retained

42
Q

What is scree plot?

A

Plot the component numbers against eigenvalues.

Choose the number that forms the bend before the plot levels off on the right side

43
Q

What happens during labelling and reporting factors?

A

There is general consensus that variables with a factor loading grater than or equal to 0.4 are probably making a significant contribution to that factor in contrast to those with smaller factor loading

44
Q

What is path analysis?

A

Causal modelling and prediction beyond simple regression

45
Q

What happens in path analysis?

A

A set of independent variables is related to a set of dependent variables

46
Q

When is path analysis used?

A

To examine situations where there are various chains of influence amongst variables studied.

47
Q

How is a path relationship displayed?

A

Using arrows that display presumed causal relations.

48
Q

What is the name of the independent variable in path analysis?

A

Exogenous variable

49
Q

What is the name of the dependent variable in path analysis?

A

Endogenous variable

50
Q

What does single headed arrow mean in path analysis?

A

Flows from putative cause to effect

51
Q

What does double headed arrow suggest in path analysis?

A

Mere correlation but no predictive, causal link

52
Q

What is a path coefficent in path analysis?

A

Indicates the direct effect of a variable assumed to be a cause on another variable assumed to be the effect

53
Q

What are the two subscripts with which path coefficients are written?

A

P21 is the path from 1 to 2

i.e. effect is listed first

54
Q

What is recursive in path analysis?

A

Path analysis in which the causal flow is unidirectional

55
Q

When is stratification used?

A

To control for or analyse the effect of confounder variables

56
Q

What is a stratum?

A

Sub-group within a sample often defined by presence or absence of variable of interest

57
Q

Importance of stratifying data according to confounder variables

A

Later one can analyse each strata to find the degree of association between presumed cause and effect

58
Q

What is adjustment in stratification?

A

When we later produce a single overall estimate using various methods for obtaining summary risk values

59
Q

Why is a method of weighting suggested for stratification?

A

Crude summary may not reflect actual risk

60
Q

How can one use weighting for stratification?

A

Mantel-Haenszel procedure

61
Q

What can Mantel-Haenszel procedure be used for in stratification?

A

Weighting

Help to find if variable is just an efect modifier or real confounder

62
Q

What is standardised?

A

Method used in large data sets for public health statistics to produce adjusted rates

63
Q

What is used for standardisation in public health?

A

Hypothetical standard population produced by WHO

64
Q

Types of standardisation

A

Direct

Indirect

65
Q

What happens in direct standardisation?

A

Stratum specific rates from study sample are applied to standard population

66
Q

What does direct standardisation produce?

A

Summary score

67
Q

What happens in indirect standardisation?

A

Stratum specific rates from standard population are applied to study sample

68
Q

What does indirect standardisation give?

A

Expected rates

69
Q

How does one arrive at standardised rates from expected rate?

A

Expected rate is divided by the observed rate

70
Q

What type of confounder is stratification useful for?

A

Known confounders only

71
Q

What can adjustment be applied to?

A

Relative Risk

Odds ratio

72
Q

What happens to sub-strata risk if the third variable is neither an effect modifier nor a confounder?

A

Sub-strata risk does not differ from crude total risk

73
Q

What happens to adjusted vs non-adjusted risk if the third variable is neither an effect modifier nor a confounder

A

Summary risk does not differ much from crude total risk

74
Q

What happens to sub-strata risk if the third variable is both an effect modifier and a confounder?

A

Sub-strata risk differs from crude total risk

75
Q

What happens to summary risk if the third variable is borth an effect modifier and a confounder?

A

Summary risk differs from crude total risk

76
Q

What happens to sub-strata risk if the third variable is an effect modifier but not a confounder?

A

Sub-strata risk differs from crude total risk

77
Q

What happens to summary risk if the third variable is an effect modifier but not a confounder?

A

Summary risk does not differ much from crude total risk

78
Q

What happens to substrata risk if the third variable is a cofounder but not an effect modifier?

A

Substrata risk does not differ from crude total risk

79
Q

What happens to summary risk if the third variable is a confounder but not an effect modifier?

A

Summary risk differs from crude total risk

MRCPsych Part B (64 decks)

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