Reliability analysis allows you to study the
properties of measurement scales and the items that
compose the scales. The Reliability Analysis procedure
calculates a number of commonly used
measures of scale reliability and also provides
information about the relationships between
individual items in the scale. Intraclass correlation
coefficients can be used to compute inter-rater
reliability estimates.
Example. Does my questionnaire measure customer satisfaction in
a useful way? Using reliability
analysis, you can determine the extent to which the
items in your questionnaire are related to each
other, you can get an overall index of the
repeatability or internal consistency of the scale as a
whole, and you can identify problem items that should
be excluded from the scale.
Statistics. Descriptives for each variable and for the scale,
summary statistics across items,
inter-item correlations and covariances, reliability
estimates, ANOVA table, intraclass correlation
coefficients, Hotelling’s T2, and Tukey’s test of additivity.
Models. The following models of reliability are available:
·
Alpha (Cronbach). This model is a model of internal consistency, based
on the average
·
inter-item correlation.
·
Split-half. This
model splits the scale into two parts and examines the correlation between
·
the parts.
·
Guttman. This
model computes Guttman’s lower bounds for true reliability.
·
Parallel. This
model assumes that all items have equal variances and equal error variances
·
across replications.
·
Strict parallel. This model makes the assumptions of the Parallel model
and also assumes
·
equal means across items.
Data. Data can be dichotomous, ordinal, or interval, but the
data should be coded numerically.
Assumptions. Observations should be independent, and errors should
be uncorrelated between
items. Each pair of items should have a bivariate
normal distribution. Scales should be additive,
so that each item is linearly related to the total
score.
Related procedures. If you want to explore the dimensionality of your
scale items (to see whether
more than one construct is needed to account for the
pattern of item scores), use factor analysis
or multidimensional scaling. To identify homogeneous
groups of variables, use hierarchical
cluster
analysis to cluster variables.
multibody dynamics
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