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DAPR3 HA > WK 11 > Flashcards

WK 11 Flashcards

1
Q

How do you interpret the meaning of the factors?

A

Based on the size and the sign of the loadings that you deem to be salient

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2
Q

In most research, what are salient loadings?

A

salient loadings are greater than or equal to |0.3| or |0.4|

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3
Q

What is a salient relationship?

A

Salient relationship = big enough relationship for us to be interested in
(It is of a signifcant size that we would note and retain in our analysis)

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4
Q

What do we do if we see an item that does not load on any factor above this salient value of 0.3?

A

We would deem it as one that isn’t being explained well or accounted for by our factor solution, and ear mark it as one we may remove from our analysis

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5
Q

What is a heywood case?

A

factor loadings > 1

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6
Q

What does a Heywood case tell you?

A

tells you there is a problem in the analysis

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7
Q

If a factor does not have a primary loading from at least 3 items that you are analysing, what do you do?

A

We query basically whether we have done this over-extraction

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8
Q

What loading criteria is in place for EFA solutions?

A

All factors load on 3+ items at salient level

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9
Q

What is the EFA criteria for the items?

A

All items have at least one loading above salinet cut off

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10
Q

What is the EFA criteria for heywood cases?

A

No heywood cases

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11
Q

What is the EFA criteria for complex items?

A

Complex items removed in accordance with goals

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12
Q

What is the EFA criteria for item content?

A

Item content of factors is coherent and substantively meaningful

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13
Q

What is one way to test replicability?

A

One way to test replicability is to see whether similar factors appear when similar data are collected

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14
Q

Aside from similarity checks, what is another way of testing replicability?

A

Test this formally by collecting data on another sample or split one large sample into two (exploratory vs confirmatory)

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15
Q

What is factor congruence?

A

It is a way of saying if I look across samples, how consistent is the factor solution I identify

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16
Q

What does factor congruence enable?

A

It enables us to look at similarity of solutions

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17
Q

What is congruence coeffiecents?

A

Essentially correlations between factor loadings stacked on top of each other

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18
Q

What is confirmatory factor analysis?

A

This tests the model where we explicitly say that this item has to relate with this factor -> We specify the grouping
- it is not driven by data

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19
Q

What is target rotation?

A

Target rotation is where you give the rotation algorithm, what items should relate to what factors (give it a structure)

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20
Q

What does it mean when the congruence coefficient is 1?

A

It suggests that there’s a perfect match between the factor solutions.

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21
Q

If the loading is comparatively high in one sample and comparatively high in the other, what do this mean?

A

that’s going to lead to a higher correlation because basically the rank is roughly the same.

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22
Q

In confirmatory factor analysis what do we do?

A

We specify a model and test how well it fits the data

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23
Q

How do we specify a model in CFA?

A

We specify a model by indicating what loadings we believe will be zero -> we then try to reject this model

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24
Q

What does it mean if the correlations derived from the CFA do not match the correlations from your data?

A

It probably means that parameters set to zero should not have been set to zero

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25
Q

What do you do in unit-weighting?

A

You sum raw scores on the observed variables which have primary loadings on each factor

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26
Q

What items do you sum in unit-weighting?

A

Which items to sum is a matter of defining what loadings are salient

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27
Q

What do Thurstone or Thompson method do?

A

They compute scores from observed item correlations and loadings (weighted method of producing scores)

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28
Q

What does Bartlett method focus on?

A

Focuses on minimizing the sums of squares for unique factors

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29
Q

What are three ways to factor scores?

A

unit weighting, thurstone method, bartlett method

30
Q

What is the assumption in unit-weighting?

A

Assume things are equally weighted

31
Q

What is a negative consequence of unit-weighting or regression method?

A

We assume that our factors will correlate with each other -> has a tendency to inflate correlations if we have an orthogonal solution

32
Q

If we have forced an orthogonal correlation, what will we see?

A

We will see a pattern of correlations that are not the same as our factor analysis

33
Q

What does the Anderson-Rubin method do?

A

It preserves orthogonality of factors

34
Q

What does the Ten Berge method generalise and do?

A

It generalises the Anderson-Rubin method to preserve correlations (or lack thereof) between factors

35
Q

When is Ten Berge score preferable?

A

If the goal is to try find higher-order factors, the correlations between the scores are important and so Ten Berge scores are preferable

36
Q

What are the crucial determinants of minimal sample size?

A

Communalities and items to factor ratio

37
Q

When can we get away with a smaller sample size?

A

When there are clear groupings

38
Q

When planning a study what should you do to determine your minimum sample size?

A
  • think of how many factors you expect and get many items measuring each
  • use pilot data and previous studies to make an educated guess about what communalities you’re going to expect
39
Q

In sample size, when can you have fewer subjects?

A
  • if communalities were wide or high
  • if the items to factor ratio is high
40
Q

When are communalities most important?

A

When item to factor ratio = 20:7
(interaction effect)

41
Q

What is an observed score

A

observed score = true score + error

42
Q

What is the assumption in parallelism?

A

Assumes that both tests have approximately the same relationship to the true score with the same error variance

43
Q

What is the tau equivalence assumption?

A

Assumes that both tests have the same relation to the true score, but error varies

44
Q

What is the congeneric assumption?

A

When we have more than 4 tests, we can relax the relation assumption in regard to tau equivalence and assume each is an imperfect measure of the true score

45
Q

What are the three assumptions of parallel tests?

A

Parallelism, tau equivalence and congeneric

46
Q

What is split half reliability?

A

This measure indicates how internally consistent the test it

47
Q

What is the issue with split-half reliabiltiy?

A

When we increase the number of items, the number of possible splits that we have gets very large

48
Q

What can we decide in split-half reliability?

A

We have a choice of (n), this can mean that we end up with a range of possible values for our split half reliability

49
Q

What is Cronbach’s alpha?

A

Cronbach’s alpha takes idea of correlating subsets of items to its logical end -> basically boiling down to split half on pairs

50
Q

What does Cronbach’s alpha provide information on?

A

Gives sense of average correlations of all pairs of items existing in the measure

51
Q

What is Cronbach’s alpha often used to denote?

A

It is often used to denote what’s called a unidimensional construct

52
Q

What does Spearman-Brown propehcy formula calculate?

A

It calculates how much can we improve Cronbach’s alpha through the addition of more items based on the original alpha and then adding more items

53
Q

What can any item measure?

A

They can measure:
- a “general” factor that load on all items
- a “group” or “specific” factor that loads on a subset of items

54
Q

What are the two internal consistency measures of McDonald’s omega?

A

McDonald’s omega hierarchical and omega total

55
Q

What is omega hierarchical?

A

It is essentially the proportion of variance that is general to an overarching factor in your analysis (amount of shared variance)

56
Q

What is McDonald’s omega total?

A

It is the total proportion of reliable item variation

57
Q

What is test-retest reliability?

A

Correlation between tests taken at 2+ time points

58
Q

What is the risk of test-retest reliability?

A

People remember answers

59
Q

What are we concerned about when picking time periods for test-retest reliability?

A

We are concerned whether people are going to show differential changes across the time period

60
Q

What is interrater reliability?

A

Multiple people rating targets - we look at consistency across ratings

61
Q

What is intraclass correlations?

A

Splits variance of a set of ratings into multiple components

62
Q

What is content validity?

A

Refers to the fact that tests should only contain content to the intended construct

63
Q

What is face validity?

A

Those taking the test, does the test appear to measure what it was designed to measure

64
Q

What is construct validity?

A

Looks at the way in which the measure relates to other things

65
Q

What is convergent validity?

A

Measures should have high correlations with other measures of the same construct

66
Q

What is discriminant validity?

A

Measure should have low correlations with measures of different constructs

67
Q

What is nomological net validity?

A

Measure should have expected patterns correlations with different sets of constructs

68
Q

What is reliability?

A

Relation of true score with observed score

69
Q

What is validity?

A

Correlations with other measures play a key role

70
Q

What is the relationship beteen reliability and validity?

A

Logically, a score or measure cannot correlate with anything more than it correlates with itself, so reliabilty is the limit on validity

DAPR3 HA (5 decks)

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