Multivariate Data Analysis - -Research that involves three or more, or that is concerned with underlying dimensions among multiple
variables, will involve multivariate
statistical analysis.
-Variate - -A mathematica
...
Multivariate Data Analysis - -Research that involves three or more, or that is concerned with underlying dimensions among multiple
variables, will involve multivariate
statistical analysis.
-Variate - -A mathematical way in which a set of
variables can be represented with one
equation.
A linear combination of variables, each
contributing to the overall meaning of the
variate based upon an empirically derived
weight.
-Dependence techniques - -Explain or predict one or more dependent
variables.
Needed when hypotheses involve distinction
between independent and dependent
variables.
Types:
Multiple regression analysis
Multiple discriminant analysis
Multivariate analysis of variance
Structural equations modeling
-Interdependence techniques - -Give meaning to a set of variables or seek to
group things together.
Used when researchers examine questions
that do not distinguish between independent
and dependent variables.
Types:
Factor analysis
Cluster analysis
Multidimensional scaling
-Nominal and ordinal scales are - -nonmetric
-Interval and ratio scales are - -metric
-General linear model - -A way of explaining and predicting a dependent
variable based on fluctuations (variation) from its
mean due to changes in independent variables
-Multiple regression analysis - -An analysis of association in which the effects of two
or more independent variables on a single, intervalscaled
dependent variable are investigated
simultaneously
-Dummy variable - -The way a dichotomous (two group) independent
variable is represented in regression analysis by
assigning a 0 to one group and a 1 to the other.
-R2 in Multiple Regression - -The coefficient of multiple determination in multiple
regression indicates the percentage of variation in Y
explained by all independent variables
-MANOVA - -A multivariate technique that predicts multiple
continuous dependent variables with multiple
categorical independent variables.
-Discriminant analysis - -A statistical technique for predicting the
probability that an object will belong in one of
two or more mutually exclusive categories, based on several independent variables
-Factor analysis - -Statistically identifies a reduced number of
factors from a larger number of measured
variables
Types:
Exploratory (EFA) —performed
when the researcher is uncertain about how many
factors may exist among a set of variables
Confirmatory (CFA)—performed
when the researcher has strong theoretical
expectations about the factor structure before
performing the analysis.
-Factor loading - -Indicates how strongly a measured variable is
correlated with a factor.
-Rule of parsimony - -an explanation
involving fewer components is better than one
involving many more.
-Creating composite scales with factor results - -When a clear pattern of loadings exists, the
researcher may take a simpler approach by
summing the variables with high loadings and
creating a summated scale.
-Communality - -A measure of the percentage of a variable's
variation that is explained by the factors.
A relatively high communality indicates that a
variable has much in common with the other
variables taken as a group.
Communality for any variable is equal to the
sum of the squared loadings for that variable
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