|
Description of the project:
A pre/post-test
was conducted to assess how much students understand basic concepts of a mathematics
course. A survey of 20 items was conducted at the beginning and the end of a semester. The
survey consists of 5 items of background information, 5 items of attitude questions and 10
items of knowledge questions. For demonstration purpose, 2 background, 2 attitudes
and 3 knowledge items will be used here.
Variables in the
data set:
 | College
|
| Grade: 1:
Freshman, 2: Sophomore, 3: Junior, 4: Senior, 5: Graduate
|
| Attitude 1 :
values are 1 to 5, with 1: strongly agree, 2: Agree, 3: Disagree and 4: Strongly
disagree, 5: Don't Know
|
| Attitude 2:
values are 1 to 5, with 1: strongly agree, 2: Agree, 3: Disagree and 4: Strongly
disagree, 5: Don't Know
|
| Q1: choose from
1 to 5 with 5 being 'Not familiar'. Only one answer is correct.
|
| Q2: choose from
1 to 5 with 5 being 'Not familiar'. Only one answer is correct.
|
| Q3: choose from
1 to 5 with 5 being 'Not familiar'. Only one answer is correct.
|
Sample size: This
assessment is conducted every year. The data is a portion of the pre and post tests of a
math course for this year. Ninety students' data are used for this demonstration. |
In this on-line
workshop, you will find many movie clips. Each movie clip will demonstrate some specific
usage of SPSS.
Analysis
For
analyzing the math assessment data, we need to do the following data manipulation:
click
here to watch Reading Files
click
here to watch Defining Variables
click
here to watch Merging Files
click
here to watch Transforming Variables
After
the data manipulation, we may conduct the following analysis:
click
here to watch Frequencies and Descriptives
Perform frequency and descriptive summary
click
here to watch Crosstabs Procedures
Perform crosstabs analysis to compare.
click
here to watch Explore Procedures
Check for normality for pre and post score
variables.
click
here to watch T-test
Perform paired t-test to compare pre and post
scores.
click
here to watch One-way ANOVA
Check for constant variance of the post scores
for each gender group, and for each grade level.
click
here to watch Univariate GLM
Perform Analysis of Covariance to compare the
post scores for gender difference and grade level difference with pre-test scores as a
covariate.