Which of the following statistical techniques may be successfully used to analyse research data available on ordinal scale only?
Answer:
To analyze research data available on an ordinal scale, it is essential to select statistical techniques that appropriately handle the type of data where the exact differences between values are not known, but the order of values is meaningful. Let’s break down the options provided:
Quartile Deviation:
Quartile deviation, also known as semi-interquartile range, is a measure of dispersion. This can be used for ordinal data since it focuses on the ranks rather than the actual values or distances between them.
Student’s t‐test:
Student’s t-test is typically used for interval or ratio data to compare the means of two groups. This test is not suitable for ordinal data as it assumes equal intervals between data points, which ordinal data does not have.
Percentile Ranks:
Percentile ranks can be used to describe the position of a value within an ordered list of values, making it appropriate for ordinal data.
Chi‐square test:
The Chi-square test can be used for categorical data, which includes ordinal data, to test for associations between two variables. This test does not assume any specific distribution and works well with ordinal data.
Spearman’s correlation method:
Spearman’s correlation is a non-parametric measure of rank correlation which assesses how well the relationship between two variables can be described using a monotonic function. It is suitable for ordinal data as it relies on rank-ordering.
Final Answer:
The correct options for analyzing ordinal scale data are:
A. Quartile Deviation
C. Percentile Ranks
D. Chi‐square test
E. Spearman’s correlation method
However, if the question asks for the single best statistical technique, it would be E. Spearman’s correlation method, as it explicitly deals with rank-order data and is widely used for ordinal data analysis.