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SPSS > Factor Analysis > Flashcards

Factor Analysis Flashcards

1
Q

The amount of variance shared by each VARIABLE with the others is technically called

A

communality

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2
Q

The amount of variance accounted by each FACTOR is technically represented by the mathematical concept of an

A

EIGENVALUE

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3
Q

SPSS will automatically extract those factors with variance greater than ??? if no specific criteria is specified.

A

1

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4
Q

This criteria makes sense if we remeber that all the input variables should be ???? to ensure a mean = 0 and a standard deviation = 1.

A

standardize

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5
Q

Once the factors are extracted, we will interpret their meaning by exploring the ??? matrix.

A

Component

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6
Q

Coefficients in this matrix can be understood as ??? coefficients between Factors and Input Variables.

A

Correlation

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7
Q

If the interpretation is not straightforward, we can use a factor ??? to better interpret Factors.

A

Rotation

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8
Q

Among the different types available, Varimax keeps the property of ??? so the new factors will still being independent.

A

Ortogonality]

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9
Q

Once the interpretation is clear, we can save factor values, technically called factor ??? in our dataset.

A

Scores

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10
Q

The set of input variables for a FACTOR ANALYSIS should exhibit a low degree of redundancy

A

FALSE. Redundancy is, in fact, essential for a good factor analysis.

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11
Q

Orthogonality of factors is not necessarily a realistic assumption

A

TRUE. Sometimes, independency=orthogonality is not realistic and we find more credible some degree of correlation / overlapping between factors.

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12
Q

If input variables were orthogonal, then a factor analysis would not make sense

A

TRUE. If original variables are orthogonal, that means no correlation between them and thus no space for common factors.

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13
Q

Standard/Classic factor analysis is suitable mainly for metric variables (not categorical)

A

YES. Although some technical variants exist for categorical factor analysis, the standard PCA/Factor is only prepared to process metric scale variables.

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14
Q

In a good factor analysis, we expect to get as many factors as possible

A

FALSE. A main goal of factor analysis is dimensionality reduction so, the less the number of factors (without a great loss of information) the better.

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15
Q

When there exists an underlying factor, being unique and common, we will get a high eigenvalue for the first factor

A

TRUE. A high value means a factor accounting for a high proportion of variance.

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16
Q

VARIMAX, QUARTIMAX and EQUAMAX are orthogonal rotations.

A

TRUE. You can find it in the class document. All of them keep orthogonality of initial factors after rotation.

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17
Q

The proportion of variance accounted for by each factor / component equals its eigenvalue divided by the number of variables

A

TRUE. The eigenvalue is the variance=information of each factor. Given that variables are z-scores, the total amount of variance is equal to “N=number of variables”. Dividing the Factor eigenvalue (variance of the factor) by “N” we get the proportion of variance captured by this factor.

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18
Q

Using PCA as factor extraction, we will sometimes get oblique factors solutions

A

FALSE. PCA implies orthogonality between factors.

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19
Q

A low communality for a given input variable may suggest that we can remove that variable from the factor analysis

A

TRUE. A low communality implies that the variable does not share common factors with others.

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20
Q

Factor analysis can de used to get a metric measurement of an unobservable feature

A

TRUE. If we are able to measure some variables that are somehow related to an underlying unobservable feature, FACTOR analysis can extract a metric representation (measurement) of that unobservable feature

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21
Q

Standardized versions of input variables will give all the input variables the same importance / weight in our factor analysis.

A

TRUE. Otherwise, we take a risk of weighting some variables more than others (although this is not the case for PCA, could be a risk when using other extraction methods)

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22
Q

We could use a FACTOR analysis to test if different items in a questionnaire are linked with the same latent/underlying concept

A

TRUE. As mentioned in class, FACTOR analysis could be used to test consistency of a set of items in a list. Those exhibiting low communality with others are supposed to be un-connected with the common underlying factor.

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23
Q

We could use a cluster membership group variable from a CLUSTER analysis as an input field in a FACTOR analysis

A

FALSE. That cluster membership would be a categorical variable and FACTOR is mainly about metric input variables.

24
Q

Factor analysis Is a technique normally used to remove variables that are redundant

A

FALSE. In fact, FACTOR analysis is based on existing redundancy between variables.

25
Q

Is a SUPERVISED analysis technique

A

FALSE. This is not about forecasting / predictive analytics so we don’t have any target.

26
Q

Can be used to create a COMPOSITE INDEX

A

TRUE. This is, in fact, one of the main usages of FACTOR analysis

27
Q

Is a CLASSIFICATION method

A

FALSE. We call classification methods to predictive analysis for categorical variables. Factor is not a predictive technique.

28
Q

Is something mandatory before a segmentation/cluster analysis

A

FALSE. Both techniques may complement to each other for certain exercises but are not formally connected.

29
Q

It is normally used to explore common underlying factors between categorical variables.

A

FALSE. It only works properly for metric variables.

30
Q

Requires a set of variables exhibiting a high degree of multiple correlation

A

TRUE. A high correlation between variables is a good starting point for a factor analysis. Multiple correlation between every variable is preferred to partial correlation between only a couple or a subset of variables.

31
Q

Is commonly used as a dimensionality reduction technique

A

TRUE. This is one of the main goals: to compress the information of several variables into a limited number of factors.

32
Q

Always provides a clear interpretation of every factor extracted.

A

FALSE. The hardest part of a factor analysis is usually the interpretation of factors.

33
Q

Produces some new variables (factors) that can be saved as new variables into the dataset

A

TRUE. The scores of factors can be saved as new variables. This is, in fact, the output of every factor analysis: factors scores.

34
Q

Rotation is used to improve factor interpretation

A

TRUE. This is the goal of rotation.

35
Q

Varimax rotation can be used to increase communality

A

FALSE. Communality will remain the same after VARIMAX rotation

36
Q

There are several techniques to produce rotation

A

TRUE. There are ORTHOGONAL and OBLIQUE rotation methods and also different algorithms for both types.

37
Q

Orthogonal rotation is normally more realistic (it is realistic to assume independence between factors)

A

FALSE. Normally, it is difficult to find uncorrelated FACTORS in the real world.

38
Q

Oblique rotation means a between - factors angle of 90 degrees

A

FALSE. It is the contrary: by using an oblique rotation we allow non orthogonal factors (angle different from 90 degrees)

39
Q

Oblique is more realistic because normally there is always HIGH correlation between VARIABLES

A

FALSE. Orthogonality or obliquity is about relationship between factors NOT between variables. The existence of relationship between variables is a MUST and it is not strictly related with relationship between factors.

40
Q

Rotation does change the factor SCORES

A

FALSE. When we rotate, factors are computed according to a different combination of variables and that means different scores.

41
Q

Related to information not explained by the Factors extracted

A

Specifity

42
Q

Specific Method to extract Factors

A

Principal Components

43
Q

Displays relationship between variables and factors

A

Component plot

44
Q

Shows factor eigenvalues

A

Scree plot

45
Q

Independence / No correlation

A

Orthogonalty

46
Q

Measure that indicates amount of information conveyed by a factor (variance of factor)

A

Eigenvalue

47
Q

Method for Factor Rotation

A

Oblimin

48
Q

If the communality showed by a VARIABLE is very LOW we could normally discard this variable and re - run the analysis

A

TRUE. A low communality means low degree of common factors with the other variables.

49
Q

Each INPUT VARIABLE variable should exhibit high correlation with others (at least, with other input variable and ideally with all the rest)

A

TRUE. This would be the minimum requirement for a variable to be part of a factor analysis. Sharing something in common with, at least, other variable ensures that a common factor (at least for these two variables is feasible).

50
Q

By default, SPSS will retain factors with eigenvalues HIGHER than ONE

A

TRUE. Factors with an amount of variance higher than 1.

51
Q

If wanted, we can extract as many factors as input variables

A

TRUE. Even if it would not make any sense.

52
Q

The factor SCORES will normally present positive and negative values

A

TRUE. This is because a factor is a z-score so a negative value means a value below the mean and a positive one means a factor score above the mean.

53
Q

Using PCA extraction method, FACTORS will be always orthogonal if no rotation is applied

A

TRUE. PCA implies orthogonal factors.

54
Q

The SIGN of the coefficients in the component / structure matrix is NOT very interesting since variables are normally standardized

A

FALSE. The sign of coefficients / loadings illustrate the positive or negative correlation between factors and variables.

55
Q

The higher the coefficients for a variable in a factor ( component / structure matrix), the higher the correlation between the factor and that variable

A

TRUE. The coefficients / loadings represent correlation between factors and variables.

56
Q

Prior to rotation, a variable may exhibit high correlation with more than one factor at the same time

A

TRUE. This is normally what we try to avoid by running rotation. Nevertheless, be aware that, even if we try, rotation does not ensures a completely uneven distribution of component matrix for the rotation solution

57
Q

Factors are always extracted and presented in decreasing order of information/communality

A

TRUE. In the output table, factors are ordered in size of variance.

SPSS (5 decks)

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