Welcome to Daves Data Demonstration

1
Welcome to Daves Data Demonstration

• This presentation is designed for users of SPSS
  with some familiarity with the program and a
  willingness to experiment with the syntax editor.
  If youre new to the software, take a deep
  breath and relax, there are screenshots to guide
  you and items that you can simply cut and paste
  if you need to.

2
A few things to start

• A few basic commands to know
• The paste button on the interface opens up the
  syntax editor and pastes what youve done onto
  that. This is your friend when youre creating
  data.
• In order for SPSS to know how big a set you want,
  create your variables in variable view first,
  then using data view, input a dummy value in the
  row representing your last participant. This
  sets the sample size for you very easily.
• Keep variable names simple and short, it will
  save you quite a bit of time.

3
Choose the statistical test for which you need to
generate data.
Independent Samples t-test
Two factor ANOVA
Paired Samples t-test
Repeated Measures ANOVA
One way ANOVA
Correlation/ Regression
4
Independent samples t-test

• What you need
• Column one Grouping variable
• Column two Dependent variable

The next slides show you what the SPSS window
will look like using the Transform ? Compute
command followed by selecting the If button.
First is the sequence of commands to generate the
grouping variable, second is the commands for the
dependent variable.
5
(No Transcript)
6
(No Transcript)
7
Below is the appropriate syntax for generating
the categorical variable (gender) and the
dependent variable (reaction time).
Categorical variable
IF (CASENUM lt 20) gender 0 . EXECUTE . IF
(CASENUM gt 20) gender 1 . EXECUTE .
This value represents the mean. Making the
difference between the means larger or smaller
will change the outcome of your test. Left click
to see how to adjust this for a non-significant
result.
Dependent variable
IF (gender 0) rt RV.NORMAL(600,50) . EXECUTE
. IF (gender 1) rt RV.NORMAL(675,50)
. EXECUTE .
IF (gender 0) rt RV.NORMAL(600,50) . EXECUTE
. IF (gender 1) rt RV.NORMAL(605,50)
. EXECUTE .
Return to test Selection page
View Again
8
Paired samples t-test

• What you need
• One dependent variable (DV) measured twice for
  the same participants
• Column one DV time one
• Column two DV time two

The next slide shows you what the SPSS window
will look like using the Transform ? Compute
command. Repeat the exact same process for both
variables.
9
(No Transcript)
10
Below is the appropriate syntax for generating
the two measurements of the dependent variable
(in this case self-esteem).
Notice the difference between the means. Making
this difference smaller will make it more likely
to find non-significant results. Click to see
the syntax for nonsignificant data.
COMPUTE se1 RV.NORMAL(20,5) . EXECUTE
. COMPUTE se2 RV.NORMAL(30,6) . EXECUTE .
COMPUTE se1 RV.NORMAL(20,5) . EXECUTE
. COMPUTE se2 RV.NORMAL(21,6) . EXECUTE .
Return to test Selection page
View Again
11
One way ANOVA

• Data creation for a one way ANOVA is an identical
  process to the Independent Samples t-test. Click
  on the button below to view the screenshots for
  the independent samples case if youd like to
  review it.
• The next slide contains the syntax information
  for a significant one way ANOVA.

Independent Samples T-test
Syntax for One-way ANOVA
12

• Below is the appropriate syntax for generating
  the GPA terms for each different grade level.

IF (grade 1) GPA RV.NORMAL(2,.4) . EXECUTE
. IF (grade 2) GPA RV.NORMAL(2.5,.4). EXECUTE
. IF (grade 3) GPA RV.NORMAL(3,.4). EXECUTE
.
Return to test Selection page
View Again
13
Two factor ANOVA

• Generating good Two factor ANOVA data can be
  tricky. However, the main thing to remember is
  that you want to create your data in a cell by
  cell fashion. With a little good old fashion
  pencil and paper planning, you can lay out main
  effects and interactions and then use If
  commands to fill things in cell by cell.

14
Creating your groups
IF (CASENUM lt 10) grade 1 . EXECUTE . IF
(CASENUM gt 10) grade 2 . EXECUTE . IF
(CASENUM gt 20) grade 3. EXECUTE . IF
(CASENUM gt 30) grade 4. EXECUTE .
So the next step is to put together the next
group, but you want to keep things balanced.
Lets consider gender as our second grouping
variable.
15
The second grouping variable
16
The second grouping variable

• By selecting approximately 50 of the cases,
  this creates a new filter variable. Then, it is
  simple cutting and pasting to move the numbers
  from the filter column into the gender column.
  After you cut and paste, then turn select cases
  off and youve got gender distributed across the
  whole set.

17

• Finally, its just a matter of writing the
  syntax for your random normally distributed
  dependent variable

IF (grade 1 and gender 0) GPA RV.NORMAL
(2,.4) . EXECUTE. IF (grade 1 and gender 1)
GPA RV.NORMAL (2.4,.4) . EXECUTE. IF (grade
2 and gender 0) GPA RV.NORMAL (2.4,.4)
. EXECUTE. IF (grade 2 and gender 1) GPA
RV.NORMAL (2.6,.4) . EXECUTE.
IF (grade 3 and gender 0) GPA RV.NORMAL
(3,.4) . EXECUTE. IF (grade 3 and gender 1)
GPA RV.NORMAL (2.9,.4) . EXECUTE. IF (grade
4 and gender 0) GPA RV.NORMAL (3.1,.4)
. EXECUTE. IF (grade 4 and gender 1) GPA
RV.NORMAL (3.2,.4) . EXECUTE.
Return to test Selection page
View Again
18
Repeated Measures ANOVA

• Data creation for a repeated measures ANOVA is an
  identical process to the Paired Samples t-test.
  Click on the button below to view the screenshots
  for the paired samples case if youd like to
  review it.
• The next slide contains the syntax information
  for a significant one way ANOVA.

Paired Samples T-test
Syntax for repeated measures ANOVA
19

• Below is the appropriate syntax for generating
  the multiple measurements of the dependent
  variable (in this case self-esteem).

COMPUTE se1 RV.NORMAL(20,5) . EXECUTE
. COMPUTE se2 RV.NORMAL(30,6) . EXECUTE
. COMPUTE se3 RV.NORMAL(37,4). EXECUTE.
Return to test Selection page
View Again
20
Correlation/Regression

• Correlational data is slightly different to
  create. In most other situations, were
  comparing means with each other. In the case of
  correlation, we have to construct the dataset in
  such a way as to reflect the relationship in the
  data. No longer can we just change mean values
  of normal distributions.
• The next slides contain the screenshots for the
  creation of the correlational data as well as the
  syntax for a pairwise comparison.

21
Step one
The first step in this process is relatively
easy. Simply create a normally distributed
continuous variable (in this case its anxiety
level).
22
Step two

• In this step you have to establish the
  relationship between the variables. The key is
  to create the next variable (depression) in such
  a way that anxiety plays a role in its creation.

23

• Below is the appropriate syntax for generating
  the multiple measurements of the dependent
  variable (in this case self-esteem).

COMPUTE anx RV.NORMAL(30,5) . EXECUTE . COMPUTE
dep anx RV.NORMAL(10,.3) . EXECUTE .
In this case, the mean of the specified normal
distribution does not play a major role in
establishing the significance of the correlation.
The two key elements are the plus sign, which
indicates that youre creating a positive
relationship, and the standard deviation on the
second variable. Because were using a very
small standard deviation, well have a high
magnitude correlation. Make the standard
deviation larger, and youll reduce the strength
of the correlation.
COMPUTE anx RV.NORMAL(30,5) . EXECUTE . COMPUTE
dep anx RV.NORMAL(10,9) . EXECUTE .
Return to test Selection page
View Again