quantitative data analysis presentation
TRANSCRIPT
-
7/28/2019 Quantitative Data Analysis Presentation
1/23
Assessment Committee 2009Division of Campus Life,Emory University
-
7/28/2019 Quantitative Data Analysis Presentation
2/23
What is quantitative data analysis? Types of quantitative data used in
assessment
Descriptive statistics Utilizing Microsoft Excel
Introduction to inferential statistics
Presenting quantitative data
-
7/28/2019 Quantitative Data Analysis Presentation
3/23
-
7/28/2019 Quantitative Data Analysis Presentation
4/23
Making sense of numbers.
Using numbers to inform decision-making.
-
7/28/2019 Quantitative Data Analysis Presentation
5/23
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!
-
7/28/2019 Quantitative Data Analysis Presentation
6/23
Not everything can be quantified!
http://myhome.iolfree.ie/~lightbulb/Tone.html -
7/28/2019 Quantitative Data Analysis Presentation
7/23
Just like it sounds these describe aspectsthings about a group of numbers.
-
7/28/2019 Quantitative Data Analysis Presentation
8/23
Sum Mean
Median
Range Variance
Standard deviation
-
7/28/2019 Quantitative Data Analysis Presentation
9/23
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.
-
7/28/2019 Quantitative Data Analysis Presentation
10/23
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
-
7/28/2019 Quantitative Data Analysis Presentation
11/23
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
-
7/28/2019 Quantitative Data Analysis Presentation
12/23
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
-
7/28/2019 Quantitative Data Analysis Presentation
13/23
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
-
7/28/2019 Quantitative Data Analysis Presentation
14/23
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
-
7/28/2019 Quantitative Data Analysis Presentation
15/23
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.
-
7/28/2019 Quantitative Data Analysis Presentation
16/23
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.
-
7/28/2019 Quantitative Data Analysis Presentation
17/23
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.
-
7/28/2019 Quantitative Data Analysis Presentation
18/23
Beware the correlation-causation fallacy.
-
7/28/2019 Quantitative Data Analysis Presentation
19/23
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!
-
7/28/2019 Quantitative Data Analysis Presentation
20/23
Consider practical vs. statistical significance.Dont be beholden to statistics. Inferential
statistics are a tool, not the answer!
-
7/28/2019 Quantitative Data Analysis Presentation
21/23
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
-
7/28/2019 Quantitative Data Analysis Presentation
22/23
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
1 2 3 4 5 6 7 8
Achieved GPA
Hours mentored
Relationship between GPA and hoursmentored
Series1
-
7/28/2019 Quantitative Data Analysis Presentation
23/23
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).