quantitative data analysis presentation

7/28/2019 Quantitative Data Analysis Presentation

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Assessment Committee 2009Division of Campus Life,Emory University


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What is quantitative data analysis? Types of quantitative data used in

assessment

Descriptive statistics Utilizing Microsoft Excel

Introduction to inferential statistics

Presenting quantitative data


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Making sense of numbers.

Using numbers to inform decision-making.


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Categorical Nominal: names

Ordinal: 1st, 2nd, 3rd.

Continuous Ratio: consistent distance between each point

Interval: there is a zero starting point

There is an important difference in how youwork with categorical and continuousvariables!


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Not everything can be quantified!
http://myhome.iolfree.ie/~lightbulb/Tone.html


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Just like it sounds these describe aspectsthings about a group of numbers.


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Sum Mean

Median

Range Variance

Standard deviation


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What is it? The total

How to get it: Add up all of the numbers.

There are a total of 13 participants.

Sum is used to calculate other statistics.


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What is it? The average of all of the numbers

How to get it:

Add up all of the numbers and divide by totalsample size. In math-speak: (x1+x2++xn)/n.Often notated as (xn)/n

For our example: Mean age: 19.3 Mean GPA: 2.84 Mean hours mentored: 4.53


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What is it? The middle number, when all of the numbers are

arranged in increasing order

How to get it: Put numbers in order from least to greatest, and find themiddle number. If you have an even-sized sample themedian is the mean of the two middle numbers.

For our example: Median age: 19 Median GPA: 2.85 Median hours mentored: 5


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What is it? The spread between the smallest and largest

number in the sample.

How to get it: Find the smallest and largest numbers. Subtract

the smallest from the largest.

For our example: Age: 23-17 = 6

GPA: 4.0 1.50 = 2.5 Hours mentored: 8-1 = 7


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What is it? A measure of the variation in the sample, or how spread

out it is. How far does each number vary from the mean?

How to get it: In math-speak: (x M)2/(n-1).

Hit the easy button and use Excel to calculate this foryou.

In our example: Age: 2.39744 GPA: .05437 Hours mentored: 5.6026


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What is it? A commonly used measure of how spread out

individual numbers are from the median

How to get it: Take the square root of the variance. Or use the

easy button and have Excel calculate it for you.

In our example: Age: 1.54837 GPA: 0.7374 Hours mentored: 2.367


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Used to show relationships between variables.Can be used to explain or predict theserelationships.

Dont be intimidated! Inferential statistics are

a tool that you can learn to utilize withpatience and practice.


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Variety of statistical tests: Chi-squared, T-tests, analysis of variance, regression, etcetera.

Conveniently many of these tests can be doneusing software that can be downloaded forFREE if you are an Emory staff member.


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Statistical tests look for significance, aconcept that measures the degree to whichyour results can be obtained due to chance.

In social science/educational research theterm = .05 is often used. This means thereis a 5% or less chance that the results are due

to chance.


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Beware the correlation-causation fallacy.


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Consider the use of inferential statistics whenyou are designing your assessment project.

Consult with someone who has statisticalexperience as you develop your ownstatistical confidence.

Inferential statistical are not always necessaryor desirable!


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Consider practical vs. statistical significance.Dont be beholden to statistics. Inferential

statistics are a tool, not the answer!


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Age GPA Gender Hours

Dick 20 1.9 M 1

Edward 19 1.5 M 1

Emmett 20 2.1 M 2

Lauren 20 2.4 F 3

Mike 19 2.75 M 4

Benjie 18 3 M 4

Joe 19 2.85 M 5

Larry 17 2.75 M 5

Rose 18 3.3 F 5

Bob 18 3.1 M 6

Kate 19 3.4 F 7

Sally 21 4 F 8

Sylvia 23 3.9 F 8

Sum 251 36.95 59

Avg 19.308 2.8423 4.5385

Variance 2.3974 0.5437 5.6026

Std Dev 1.5484 0.7374 2.367

Median 19 2.85 5


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0

0.5

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1.5

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2.5

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3.5

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4.5

1 2 3 4 5 6 7 8

Achieved GPA

Hours mentored

Relationship between GPA and hoursmentored

Series1


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Thirteen students participated in the minoritymentoring program. A strong positivecorrelation was found between the number ofhours mentored and achieved GPA (.965),

between hours mentored and gender (.578),and between gender and achieved GPA (.622).

quantitative data analysis presentation

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