cov.lambda4: Compute Covariance Maximized Lambda4

Description Usage Arguments Value Author(s) Examples

Description

This code estimates maximized lambda4, a split-half reliability estimate. The function splits the halves by specifying a two column list of paired inter-item covariances in descending order. It then calculates Guttman's lambda4 on every possible split-half while preserving the inter-item pairings. The function then returns a list of the Lambda4s and then takes the minimum, maximum, median, and mean of the list. This calculation is most appropiately applied to tests with multiple factors.

Usage

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  cov.lambda4(x, method = "Hunt", missing = "complete",
    show.lambda4s = FALSE, show.splits = FALSE,
    standardize = FALSE)

Arguments

x

Can be either a data matrix or a covariance matrix.

method

Can specify either "Hunt" or "Osburn".

missing

How to handle missing values.

show.lambda4s

If TRUE then the estimates for each split are included in the output.

show.splits

If TRUE then a binary matrix is exported that describes the ways the items were split.

standardize

When TRUE results are standardized by using the correlation matrix instead of the covariance matrix for computation.

Value

estimates

The mean, median, max, and min of the split-half reliabilities.

lambda4s

A vector of maximized split-half reliabilities.

method

The method chosen. Either "Hunt" or "Osburn".

Analysis.Details

Returns the number of variables and the number of split-half reliabilities.

Splits

The binary indicators of the splits for the min, max, and median split-half reliability.

show.splits

Logical argument selected to show the splits.

show.lambdas4s

Logical argument selected to show the split-half reliabilities.

Author(s)

Tyler Hunt [email protected]

Examples

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cov.lambda4(Rosenberg, method="Hunt")
cov.lambda4(Rosenberg, method="Osburn")

JackStat/Lambda4 documentation built on May 7, 2019, 10:16 a.m.