User Guide for the TIMSS International Database.pdf - TIMSS and ...

P E R F O R M I N G A N A L Y S E S C H A P T E R 9
In summary, to perform analyses such as those in Figures 9.15 and 9.16 using the Student
and Teacher Background data files the user needs to do the following:
• Identify the variable or variables of interest in the corresponding teacher file and
find out about any specific national adaptations to the variable.
• Retrieve the relevant variable or variables from the teacher data files. There is one
teacher file for the teachers of the Population 1 students, and two for the teachers
of the Population 2 students. If the user is interested in looking at both
mathematics and science teachers combined for the Population 2 students, then
the files for these teachers need to be added or appended to each other.
• Retrieve the relevant variables from the Student-Teacher Linkage file. This
includes the identification information for the country and teacher (IDCNTRY,
IDTEACH, and IDLINK), the achievement score, JRR replication information, and
the sampling weight. If the analysis is to be based on mathematics teachers only,
then the weight variable to use is MATWGT. If the analysis is to be based on the
science teachers only, then the weight variable to be used is SCIWGT. If the
analysis is to be based on the science and mathematics teachers combined, then
the weight variable to be used is TCHWGT.
• Merge the variables from the teacher data files into the Student-Teacher Linkage
files using the variables IDCNTRY, IDTEACH and IDLINK.
• Use the macro JACK with the corresponding arguments and parameters.
• Print out the result file.
9.6 Performing Analyses with School-Level Variables
Although the students in the TIMSS samples were selected from within a sample of schools,
the school-level variables should only be analyzed with respect to the number of students
attending schools of one type or another. In other words, the school-level data should not be
analyzed to make statements about the number of schools with certain characteristics, but
rather to make statements about the number of students attending schools with one
characteristic or another. When school-level variables are analyzed, we recommend that the
user merge the selected school-level variables with the student-level file, and then use the
sampling and weight information contained in the student-level file to make the desired
statements. The examples presented in this section describe how this can be accomplished
using SAS or SPSS.
Lets us say that we want to find out the percent of eighth graders that attend schools located
in a certain geographical area of the country (BCBGCOMM), and their average achievement
in mathematics. As in the previous example, the first step in our analysis is to locate the
variables of interest in the specific codebook and file. We find the variable BCBGCOMM in
the School Background file, and the student weights and scores in the Student Background
file. We then proceed to review the documentation on national adaptations and discover that
Australia has modified this variable slightly to fit their particular context. At this time we
could proceed in one of two ways: we could exclude Australia from our analysis or we could
label the variable accordingly so that we will not be making incorrect inferences about the
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