Download User`s Guide

User’s Guide
Table of Contents
Poster Copy
Download Information
Installation Information
Usage Survey
Menus
Definitions
Z-test
Single Sample t-test
Dependent t-test
Average standard deviation denominator example
Standard deviation of differences denominator example
Independent t-test
Hedge’s g example
Glass’s delta example
Correlation
1
2
3
4
5
6
8
9
11
13
15
16
17
20
21
22
2
Download MOTE
Go to http://www.aggieerin.com/mote.
Click download on the left hand side of the screen.
Copy the program to somewhere you can find the file on
your computer.
3
Install MOTE
Make sure you have at least JAVA 6.0 by verifying your version here.
Link: http://java.com/en/download/installed.jsp
That’s it! Double click MOTE.jar to get started.
Double click to go!
4
Survey
Please note you can fill this survey out online here or email the lab directly here.
Link: http://aggieerin.com/mote/survey.php
Email: [email protected]
Not at all
1
1
1
1
1
1
1
How helpful is the user’s guide?
How readable is the user’s guide?
How complete is the user’s guide?
How easy is MOTE to download/install?
How easy is MOTE to use?
How applicable is MOTE to what you do?
How useful would MOTE be for teaching?
Overall, I think MOTE is:
I would use MOTE to:
I would suggest for MOTE:
I experienced errors (please explain):
I found incorrect calculations (please explain):
5
2
2
2
2
2
2
2
Somewhat
3
3
3
3
3
3
3
4
4
4
4
4
4
4
Very
5
5
5
5
5
5
5
Menus
The edit menu contains the
reset button to clear out
your data.
The measures menu
contains the different
options for effect sizes
listed by effect size and
type of test.
6
The help menu contains
links to the user’s guide
and an about window
(coming soon!).
7
User’s Guide
Cohen’s d
Cohen’s d is one of the most well-known calculations for effect size. The general
formula gives the standardized distance between the two population means, or how
much the two populations do not overlap. The formula for d is very adaptive and can be
used for many different between samples and within samples tests. As such, each test
is listed, followed by the formula for d for that test, as well as how to find the information
for the calculation in both SPSS and SAS.
Related effect sizes:
Hedge’s g
As Cohen’s d tends to be positively biased, Hedge’s g corrects for this to create an
unbiased calculation.
Glass’s delta
Glass’ delta is a form of effect size found by dividing the difference of the two groups by
the standard deviation of only the control group.
8
Z-test
Cohen’s d can be found in two ways for a Z-test:
1. By using the means of the populations:
𝑀−𝑢
(1)
𝑜
2. By using the z-statistic:
𝑍
(2)
√𝑁
The values for the mean of the population (µ) and the standard deviation of the
population (σ) should already be known. The mean for the sample and number of
people in the sample can be found in your SPSS output as follows:
Descriptive Statistics
N
Mean
KnowlOfES
30
Valid N (listwise)
30
12.8000
Number of individuals
in the sample
Examples for SAS output as follows:
Number of
individuals in the
sample
Sample mean
9
Sample mean
Example:
So, let us assume that the population has a mean of 10 on our “knowledge of effect
size” scale, with a standard deviation of 2. Given the information above, we know that
we have a sample mean of 12.8 with 30 people in our sample.
Through the program:
By putting this in MOTE we come up with an effect size of 1.4 with a lower 95%
confidence limit of -2.52, and an upper 95% confidence limit of 5.32.
10
Single Sample t-test
Cohen’s d can be found in two ways for a single sample t-test:
1. By using the means of the populations:
𝑀− 𝜇
(3)
𝑆𝐷
2.
By using the t-statistic:
𝑡
(4)
√𝑁
The value for the mean of the population (µ) should already be known. The mean
and standard deviation for the sample as well as the number of people in the
sample can be found in your SPSS output as follows:
Number of
individuals in the
sample
Sample mean
Sample
standard
deviation
t-statistic
The same information can be found in your SAS output as follows:
Number of
individuals in
the sample
Sample
standard
deviation
Sample mean
t-statistic
11
Example:
If you have a known population mean of 7 and want to know the difference between the
population and your sample, you would input your information into MOTE and get an
effect size of -0.43, with a lower 95% confidence limit of -0.77, and an upper 95%
confidence limit of -0.08.
12
Dependent Samples t-test
Cohen’s d can be found in three ways for a dependent samples t-test:
1. By using the average standard deviation:
𝑀𝑑𝑖𝑓𝑓
(5)
(𝑆𝐷1+𝑆𝐷2)⁄2
2. By using the difference of the standard deviations:
𝑀𝑑𝑖𝑓𝑓
(6)
𝑆𝐷𝑑𝑖𝑓𝑓
3. By using the t-statistic:
𝑡
√𝑁
(7)
The necessary information can be found in your SPSS output as follows:
Sample standard deviation time 1
Sample standard deviation time 2
Number of individuals in the sample
Mean difference
Standard deviation of the
difference scores
t-statistic
13
The same information can be found in your SAS output as follows:
Sample standard deviation time 1
Number of individuals
in the sample
Mean difference
Standard deviation of the difference scores
Sample standard deviation time 2
t-statistic
14
Example:
If we are looking for a difference on a measure between time one and time two, we can
either use our Dependent t (Averages) page with the following information to produce
an effect size of -0.06, with a lower 95% confidence interval of -0.35, and an upper 95%
confidence limit of 0.23.
15
Or, we can use our Dependent t (SD Difference) page with the following information to
produce an effect size of -0.07, with a lower 95% confidence interval of -0.36, and an
upper 95% confidence limit of 0.22.
16
Independent Samples t-test
Cohen’s d can be found in three ways for an independent samples t-test:
1. By using the pooled standard deviation:
𝑀1−𝑀2
(8)
𝑆𝑝𝑜𝑜𝑙𝑒𝑑
Where Spooled is found by:
(𝑛−1)𝑆𝐷2 +(𝑛−1)𝑆𝐷2
√
(9)
𝑛+𝑛−2
2. By using the t-statistic and the degrees of freedom:
2𝑡
(10)
√𝑑𝑓
3. By using the t-statistic and number of individuals in the sample:
𝑛+𝑛
𝑡 √(
𝑛∗𝑛
)(
𝑛+𝑛
)
(11)
𝑛∗𝑛−2
Additionally, Hedge’s g can be calculated by multiplying your d found above by a
correction factor for independent samples t-tests:
1−(
3
4(𝑛+𝑛)−9
)
(12)
Also, you can calculate Glass’s delta for independent samples t-tests, by using
the standard deviation of the control group:
𝑀−𝑀
(13)
𝑆𝐷𝑐𝑜𝑛𝑡𝑟𝑜𝑙
17
The necessary information can be found in your SPSS output as follows:
Mean of group 1
Number of individuals in group 1
Mean of group 2
Standard
deviation of
group 1
Standard
deviation of
group 2
Number of
individuals
in group 2
t-statistic
Degrees of freedom
The same information can be found in your SAS output as follows:
Mean of group 1
Number of individuals in group 1
Number of
individuals
in group 2
Mean of group 2
Standard
deviation of
group 1
Standard
deviation of
group 2
Pooled standard
deviation
t-statistic
Degrees of freedom
18
Example:
If you are looking for differences between two independent groups, for instance a
control group (group 1) and a treatment group (group 2), you can input the information
into the Independent t Test page to produce an effect size of -0.71, with a lower 95%
confidence interval of -1.21, and an upper 95% confidence limit of -0.20.
19
Similarly, you can input the information into the Hedge’s g - Independent t Test page to
produce an effect size of -0.70, with a lower 95% confidence interval of -1.21, and an
upper 95% confidence limit of -0.20.
20
You could also input the information into the Glass’s delta - Independent t Test page to
produce an effect size of -0.87, with a lower 95% confidence interval of -1.21, and an
upper 95% confidence limit of -0.20.
21
Correlation
Cohen’s d can be found with the information for r in one way:
1. By using the obtained value of r:
2
4𝑟
√ ⁄(1 − 𝑟 2 )
(14)
The necessary information can be found in your SPSS output as follows:
Correlations
DataA
DataB
Pearson Correlation
1
.451**
Sig. (2-tailed)
.001
N
50
50
**
DataB Pearson Correlation
.451
1
Sig. (2-tailed)
.001
N
50
50
**. Correlation is significant at the 0.01 level (2-tailed).
DataA
Correlation
coefficient (r)
Number of
individuals in
the sample
The same information can be found in your SAS output as follows:
Number of
individuals in
the sample
Correlation
coefficient (r)
22
Example:
Suppose a researcher is trying to identify if a relationship exists between two variables
that we will designate DataA and DataB. To determine if these two variables are
correlated we will run a bivariate correlation and use the output in MOTE page to
produce an effect size of 1.01, with a lower 95% confidence interval of 0.20, and an
upper 95% confidence limit of 0.79.
23