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

C H A P T E R 9 P E R F O R M I N G A N A L Y S E S
As before, we first proceed to identify the variables relevant to the analysis in the
corresponding files, and review the documentation on the specific national adaptations to the
questions of interest (Supplements 1, 2, 3). Since we are using teacher-level variables we need
to look into the teacher file and the Student-Teacher Linkage files to find the variables. From
the mathematics teacher file we extract the variable that contains the information on the
mathematics teachers' years of experience (BTBGTAUG), the variable that identifies the
country (IDCNTRY) and the two variables that will allow us to link the teacher information to
the student data (IDTEACH and IDLINK).
In Population 2 there is one teacher file for the mathematics teachers and a second teacher
file for the science teachers. If the user wants to look only at mathematics teachers, then the
user will need to use the mathematics teacher file (BTM1); if the interest is in
the science teachers then the user will need to use the science teacher file
(BTS1); but if the interest is in the mathematics and science teachers combined,
both these files need to be combined by appending or adding one file to the other. In doing
so it is important to keep in mind that although there are variables in common between these
two files, most of them are not. On the other hand, there is only one file for the Population 1
teachers where both mathematics and science teacher variables are found
(ATG1). In each population there is only one student-teacher-link file where
the sampling and achievement information is found.
In our example, our teacher variable of interest (years of teaching experience) is a continuous
variable. However, we want to categorize the teachers into 4 groups (0 to 5 years experience, 6
to 10 years experience, 11 to 20 years experience, and More than 20 years experience). While
reading the Teacher file we use commands in SAS or SPSS to collapse the different values
into four categories and we label them accordingly. We then proceed to read the necessary
information from the Student-Teacher Linkage file. From this file we keep the country
identification (IDCNTRY) and the two variables that will allow us to link the student
information to the teacher data (IDTEACH and IDLINK). We also keep the variable that
indicates the grade for the student (IDGRADER), the mathematics achievement score
(BIMATSCR), and the information necessary to compute the replicate weights for estimating
the JRR standard error. We select the variable that will give us the correct weight for
mathematics teacher variable (MATWGT). If the user is interested in looking at the science
teachers, then the weight variable that should be selected is SCIWGT, and TCHWGT if the user
is interested in analyzing both mathematics and science teachers combined.
The two files are then merged or matched into one file that will then be used with the JACK
macro. These two files will be merged using the variables IDCNTRY, IDTEACH, and
IDLINK. The combination of values for these three variables is unique within the teacher
data, but is repeated in the Student-Teacher Linkage file as many times as the specific teacher
teaches students in a class. After the files are merged, the macro JACK is used and the results
can be printed. Selected results from this analysis are shown in Figure 9.15 and Figure 9.16.
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