Spearman Rank Order

Good afternoon class. My name is Victoria Shirley.

My presentation for our research course is on the spearman rank order correlation.

So a little bit of history, um

Charles Spearman who was formerly an army officer, then turned psychologist

um introduced his spearman rank order correlation in 1904.

After publishing his first paper,

this paper outlined how to analyze observations from different variables.

He performed a series of studies on school Children and adults and his first

paper published in 1904 was entitled The Proof and Association Between Two Things.

Um

Spearman was born in London on September 10th, 1863 and he died September 17th,

1945 at age 82 in London.

So some background on the spearman correlation.

The goal is to measure the strength and

direction of association between two ranked variables.

Uh We use the spearman correlation when the assumptions

of the Pearson correlation are markedly violated.

Uh The spearman correlation determines the strength

and direction of a monotonic relationship between two

variables rather than the strength and direction of

the linear linear relationship between two variables,

which is what Pearson's correlation determines.

And hopefully another student will have outlined pearson's correlation. So we can

see the difference in the

jus.

So next a little background on monotonic relationships because I

was not super familiar with these

um a monotonic relationship can be described

as the value of one variable increases.

So does the value of another variable

which I outlined as number one and number one,

um number two

as the value of one variable increases,

the other valuable variable value decreases, which you can see as number two,

this would be a negative correlation and a positive correlation

and monotonic relationships will show a linear pattern.

So

for the spearman rank correlation,

there is some calculations that need to be made.

Um Here you can see the rank correlation formula.

Um And

for this formula, a coefficient will need to be calculated.

And the goal again is to show the strength of a relationship between two variables.

A

goal is to have a minimum of 10 variables to account for an even number of ranking.

So to do this formula, we would convert the observed values into ranks,

accounting for any ties which you will see in an example, later

determine the difference between the ranks,

we square the difference and then sum the squared differences

determine hypotheses, perform tests and plot data based on findings.

So why we use the spearman rank correlation in research

could be used in either qualitative or quantitative data I believe.

Um because it is used to show the correlation between two different variables.

And the best way to visualize is on a scatter plot.

Um The picture here I found it shows

a positive correlation between reaction time and age,

which not all of us might agree with.

But in this example, that is what they determined

data level. I took this image from Dr

Weaver slides that we used on a previous module.

Um And from this,

I believe that the spearman rank correlation would

be classified as a level one or meta

analysis.

Um because we can take the data um

analyze rank and then graph to show correlation

and we can use either qualitative or quantitative variables.

Why do we use the spearman rank and research?

Um It is beneficial in research because it allows the researchers to take any data,

quantitative or qualitative

rank and turn it into a visual that can show positive or negative effects.

Um

This is the first of two examples that I've placed in this slide show.

Um This first example depicts the price of water versus the distance from a gallery.

Um And this example was taken from Doctor

Chaudry.

So here she mapped out 10 convenience stores and the distance from the gallery.

She then ranked the distance.

And as you can see convenience store number 10

was 980 m.

And she ranked that as number one,

then she took the price of the water

and ranked those.

And then as you can see, there was ties.

So as we mentioned before, we take the mean of the ties and rank them accordingly

same for the distance.

Then in the center here, this explains how she did all of her calculations.

And at the bottom, you can see her rank coefficient

was negative 0.73

which will then depict a

negative correlation on her scatter plot which we can see here

um price of a convenience item

and to the left price in Euros of the water

on the bottom shops along the distance

and you can see again a negative correlation.

So another example that I found uh was entitled a

measure of the impact of employee motivation on multicultural team performance

using the spearman rank correlation coefficient.

The goal of the research was to determine the

impact of employee motivation on multicultural team performance.

This example was taking qualitative data and transforming

it into a visual using quantitative figures.

So an outline of the study um

this researcher distributed 90 questionnaires

to an English company. Only 60 employees responded.

Um

The questionnaires outline things such as skills cul

cultural values and other things that we will see in a moment.

Um And on the questionnaires ask the employees to

rank

um from 1 to 5, strongly agree, strongly disagree and so on.

He created a problem statement which was

businesses presently face challenges with human capital,

especially in the areas of teamwork, talent retention and employee motivation.

His purpose statement,

the main idea behind the study is

to determine how motivated workforce can influence am

multicultural team setting

and develop two hypotheses,

stating that there would be a

significant relationship between an employee's motivation

and multicultural team performance or there

would not be a significant relationship.

And here we can see where he

um created a

spreadsheet of his findings.

So the variables that were placed on the questionnaire were skills,

cultural values, satisfaction level, teamwork, relationship,

level of motivation and communication breakdown

and the employees percentages of their responses are seen,

then he took the responses and applied them to his first hypothesis.

And as you can see, he ranked all of them 1 to 5,

strongly agree, agree, neutral, disagree, strongly disagree.

And then on the next one because I couldn't fit it all in one image,

you can see that his calculation was 0.9 which is positive.

The next image shows hypothesis two which again

all the variables strongly agree agree

number of responses

and R

and then this spearman rank correlation

was found to be 0.7 which is also positive but less than the first.

So here we will see results and his results show that there

is a significant relationship between employees

motivation and multicultural team performance.

And his correlation calculation coefficients are seen here.

Um In this example, the researcher did not use a scatter plot to show his results.

However, with these calculations, you can see which

was the better hypothesis.

This study is a prime example of how qualitative and quantitative

data can be used together to show relationships of variables.

And here are my references for this presentation.

Thank you for listening and I look

forward to listening to everyone else's presentations.

Spearman Rank Order

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