In cases where researchers have both interval and ordinal data, Tuckman (1988) recommended transforming the interval data into ordinal data, calculating Spearman's rho
in order to determine if the relationships are significant.
A Spearman's rho
of -.434 was calculated, showing a fair but not statistically significant relationship.
The statistic on which my approach is based is known as Spearman's rho
. It is one of many statistics that researchers use to analyze quantitative data.
A Spearman's Rho
test was conducted to determine whether the percentage of artefacts with step terminated retouch correlates with the AIUR for each class.
However, the statement is inaccurate since the text includes the use of Spearman's rho
in correlational studies and chi-square tests of association on naturally occurring observations.
Serum ferritin levels were inversely correlated with the severity of RLS symptoms (Spearman's rho
-0.53, p < 0.05).
correlations were conducted between self and other scores for the student and professor samples separately.
No significant correlation was observed between the levels of serum sIL-2R and serum IgG4 before (Spearman's rho
: -0.08, P = 0.776) and after (Spearman's rho
: 0.225, P = 0.450) treatment.
The two measures show a tight correlation (Spearman's Rho
= -0.952, p < [10.sup.-10], 95% Confidence Intervals, CI = from -0.805 to -0.988); the correlation remains high and significant using the alternative algorithm for preprocessing pupil (blink removal only: Spearman's Rho
= -0.806, p = 0.008, 95% CI = 0.359-0.952).
In addition Spearman's rho
, which measures the strength of correlation, depicted a negative relationship between the variables.
The Spearman's rho
correlation co-efficient was 0.886 for two observers.
Median (interquartile range [IQR]), Mann-Whitney U test, Kruskal Wallis test and Spearman's rho
correlation (rho) were used for non- normally distributed quantitative variables.