garrett_ranking: Garrett Ranking of Categorical Data

In JobNmadu/Dyn4cast: Dynamic Modeling and Machine Learning Environment

Garrett Ranking of Categorical Data

Description

There are three main types of ranking: Standard competition, Ordinal and Fractional. Garrett's Ranking Technique is the application of fractional ranking in which the data points are ordered and given an ordinal number/rank. The ordering and ranking provide additional information which may not be available from frequency distribution. Again, the ordering is based on the level of seriousness or severity of the data point from the view point of the respondent. Ranking enables ease of comparison and makes grouping more meaningful. It is used in social science, psychology and other survey types of research. This functions performs Garrett Ranking of up to 15 ranks.

Usage

garrett_ranking(data, num_rank, ranking = NULL, m_rank = c(2:15))

Arguments

data	The data for the Garrett Ranking, must be a data.frame.
num_rank	A vector representing the number of ranks applied to the data. If the data is a five-point Likert-type data, then number of ranks is 5.
ranking	A vector of list representing the ranks applied to the data. If not available, positional ranks are applied.
m_rank	The scope of the ranking methods which is between 2 and 15.

Value

A list with the following components:

Data mean table	Table of data ranked using simple average.
Garrett ranked data	Table of data ranked using Garrett mean score.
Garrett value	Table of ranking Garrett values

Examples

garrett_data <- data.frame(garrett_data)
ranking <- c("Serious constraint", "Constraint",
"Not certain it is a constraint", "Not a constraint",
"Not a serious constraint")

## ranking is supplied
garrett_ranking(garrett_data, 5, ranking)

# ranking not supplied
garrett_ranking(garrett_data, 5)

# you can rank subset of the data
garrett_ranking(garrett_data, 8)

garrett_ranking(garrett_data, 4)

JobNmadu/Dyn4cast documentation built on March 5, 2025, 9:56 p.m.