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'sT2, and Tukey's test of additivity.
Models.The following models of reliability are available:
v Alpha (Cronbach).This model is a model of internal consistency, based on the average inter-item correlation.
v Split-half.This model splits the scale into two parts and examines the correlation between the parts. v Guttman.This model computes Guttman's lower bounds for true reliability.
v Parallel.This model assumes that all items have equal variances and equal error variances across replications.
v Strict parallel.This model makes the assumptions of the Parallel model and also assumes equal means across items.
Reliability Analysis Data Considerations
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.
To Obtain a Reliability Analysis 1. From the menus choose:
Analyze>Scale>Reliability Analysis...
2. Select two or more variables as potential components of an additive scale. 3. Choose a model from the Model drop-down list.
Reliability Analysis Statistics
You can select various statistics that describe your scale and items. Statistics that are reported by default include the number of cases, the number of items, and reliability estimates as follows:
v Alpha models.Coefficient alpha; for dichotomous data, this is equivalent to the Kuder-Richardson 20 (KR20) coefficient.
v Split-half models.Correlation between forms, Guttman split-half reliability, Spearman-Brown reliability (equal and unequal length), and coefficient alpha for each half.
v Guttman models.Reliability coefficients lambda 1 through lambda 6.
v Parallel and Strict parallel models.Test for goodness of fit of model; estimates of error variance, common variance, and true variance; estimated common inter-item correlation; estimated reliability; and unbiased estimate of reliability.
Descriptives for.Produces descriptive statistics for scales or items across cases. v Item.Produces descriptive statistics for items across cases.
v Scale.Produces descriptive statistics for scales.
v Scale if item deleted.Displays summary statistics comparing each item to the scale that is composed of the other items. Statistics include scale mean and variance if the item were to be deleted from the scale, correlation between the item and the scale that is composed of other items, and Cronbach's alpha if the item were to be deleted from the scale.
Summaries.Provides descriptive statistics of item distributions across all items in the scale.
v Means. Summary statistics for item means. The smallest, largest, and average item means, the range and variance of item means, and the ratio of the largest to the smallest item means are displayed. v Variances. Summary statistics for item variances. The smallest, largest, and average item variances, the
range and variance of item variances, and the ratio of the largest to the smallest item variances are displayed.
v Covariances. Summary statistics for inter-item covariances. The smallest, largest, and average inter-item covariances, the range and variance of inter-item covariances, and the ratio of the largest to the
smallest inter-item covariances are displayed.
v Correlations. Summary statistics for inter-item correlations. The smallest, largest, and average inter-item correlations, the range and variance of inter-item correlations, and the ratio of the largest to the
smallest inter-item correlations are displayed.
Inter-Item.Produces matrices of correlations or covariances between items. ANOVA Table.Produces tests of equal means.
v F test. Displays a repeated measures analysis-of-variance table.
v Friedman chi-square. Displays Friedman's chi-square and Kendall's coefficient of concordance. This option is appropriate for data that are in the form of ranks. The chi-square test replaces the usual F test in the ANOVA table.
v Cochran chi-square. Displays Cochran's Q. This option is appropriate for data that are dichotomous. The Q statistic replaces the usual F statistic in the ANOVA table.
Hotelling's T-square.Produces a multivariate test of the null hypothesis that all items on the scale have the same mean.
Tukey's test of additivity.Produces a test of the assumption that there is no multiplicative interaction among the items.
Intraclass correlation coefficient.Produces measures of consistency or agreement of values within cases. v Model.Select the model for calculating the intraclass correlation coefficient. Available models are
Two-Way Mixed, Two-Way Random, and One-Way Random. SelectTwo-Way Mixedwhen people effects are random and the item effects are fixed, selectTwo-Way Random when people effects and the item effects are random, or selectOne-Way Randomwhen people effects are random.
v Confidence interval.Specify the level for the confidence interval. The default is 95%.
v Test value.Specify the hypothesized value of the coefficient for the hypothesis test. This value is the value to which the observed value is compared. The default value is 0.
RELIABILITY Command Additional Features
The command syntax language also allows you to:v Read and analyze a correlation matrix. v Write a correlation matrix for later analysis.
v Specify splits other than equal halves for the split-half method. See theCommand Syntax Referencefor complete syntax information.