Research 2 Factor analysis

• Created by: CaliFish
• Created on: 20-04-17 12:40
Define Factor Analysis
Factor analysis is a data reduction tool, that finds common underlying dimensions in the data, by reducing a large number of variables to a smaller set, that are representative and meaningful, whilst keeping as much info as possible
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give 3 over names for these variables
items, constructs, questions on the questionnaire
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what is a factor
the meaningful cluster of these variables
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what is another name for a factor
a latent variable
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are these factors independent of each other
yes, but the extent of this depends on the analysis you run
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how does it reduce the data
by removing redundancy and duplication
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define redundancy
a variable unrelated to any of the concepts
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define duplication
a variable measuring the same ting as another variable
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what is the minimum number of participants for a factor analysis
100
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what number of participants is wanted
300
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if under 300, what is the ratio for the number of participants and the number of variables
10:1
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if under 300, what is the ratio for the number of variables and the number of factors
4:1
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if under 300, what is the ratio for the number of participants and the number of factors
6:1
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in small samples correlation coefficients do what
fluctuate more than normal
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how many steps are involved in exploratory factor analysis
5
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name them
1. preliminary analysis: data screening 2. suitability checks 3. factor extraction 4. factor rotation 5. reliability analysis
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define data screening
The process of inspecting raw data for errors and correcting them prior to doing data analysis.
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what are the three substages within preliminary analysis: data screening
SD checks, Normality checks, Correlation checks
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the SD's should not be above what
1.5
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why
because otherwise all the data would be loaded at the two ends
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the SD's should not be below what
0.5
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why
because otherwise all the data is clustered around the centre
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what level should data be measured at
interval level
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how can you check for normality
K-S, Skew and kurtosis scores and histograms
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draw positive skew
***
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draw negative skew
***
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draw positive kurtosis (what is this called)
***
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draw negative kurtosis (what is this called)
***
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is the normality assumption important
yes, if you want to generalise the results of your analysis beyond the sample you're using
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where do you look to check your correlations
The R matrix
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should your variables be correlated
yes, to an extent
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why should your variables be correlated
if items are measuring the same construct, they will be correlated
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item's below what value, should be removed
0.3
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define multicollinearity
variables are highly correlated
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item's above what value, should be removed
0.8
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define singularity
variables are perfectly correlated
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what correlation coefficient value is this represented by
1.0
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how else, other than the correlation coefficient can you test for multicollinearity and singularity
by checking the DETERMINANT value
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what value is acceptable
anything above 0.00001
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the determinant value ranges between what
0-1
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how do you check if the final data set (it is finalised after dealing with SD's, normality and correlations) is suitable for factor analysis
measure sampling adequacy and test for the assumption of sphericity
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what SPSS statistic measures sampling adequacy
KMO
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explain exactly what it is measuring
measuring the proportion of variance among variables that might be common variance / the relationships between variables
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what is the KMO value range
0 - 1
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the higher the value the better but Kaiser recommends accepting values greater than what?
0.5
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how do you report KMO?
a sentence verbally explaining how strong/weak the realtionship between the variables are (KMO = x), indicating it is acceptable/unacceptable to proceed with analysis
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what statistic is used to test for sphericity
Bartletts test
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what is it testing for
Tests the null hypothesis that the original correlation matrix is an identity matrix
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what is an identity matrix
a matrix where all coefficients are 0
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we want our variables to have a what
relationship
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this is shown through what
a significant value
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how do you report for bartletts test of sphericity
Bartlett's test of sphericity was/was not significant, X squared (df) = value , p < .value, indicating it is acceptable/unacceptable to proceed with analysis
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what are the two factor extraction methods
extraction based on Eiegenvalues greater than 1 using kaisers criteria and the sree plot
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Kaisers criterian is only accurate if what
There are less than 30 variables and communalities after extraction are greater than 0.7 or When the sample size exceeds 250 and the average communality is greater than 0.6
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IF still no, what can you do
check the scree plot
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what do eigenavlues represent
the amount of variance explained by a factor
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what does a communality represent
how much percent of variance in a variable is explained by all factor
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do you want eigenvalues and communalities to be high or low
the higher the better, this means the more variance being explained
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what does the scree plot, plot
plots the eigenvalues of the factors extracted
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you can measure how many factors you have by doing what
how many points are to the left of the point of inflection
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why do we use factor rotation
to aid interpretation
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the relative contribution of a variable to a factor
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what does it do to help us
it maximises the loading of a variable on one factor while minimsing its loadings on all other factors, this distributes the variance explained (Relative importance) more evenly between factors
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explain this in short
it optimises the data
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what are the two types of factor rotation
orthogonal and oblique
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when should you use orthogonal
when factors are uncorrelated
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give an example of one
varimax
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when should you use oblique
when factors are inter-correlated
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give an example one
direct oblimin
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what do you have to do for orthogonal that you DONT have to do for oblique
keep 90 degree
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Other cards in this set

Card 2

Front

give 3 over names for these variables

Back

items, constructs, questions on the questionnaire

Card 3

what is a factor

Back Card 4

Front

what is another name for a factor

Back Card 5

Front

are these factors independent of each other

Back 