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

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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 T I M S S D A T A B A S E U S E R G U I D E 9 - 3 3
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 specific groups. In the latter case, since we want to also explore the results for Australia we take the precaution of labeling the values for variable BCBGCOMM in a generic way before we proceed with the analysis. After these considerations, we then proceed to read the School Background file and keep only the variables that are relevant to our analysis. In this case we keep the country identification (IDCNTRY) and school identification (IDSCHOOL). We keep these variables because these are the variables that will allow us to merge the school data to the student data. We also keep from the School Background file the variable of interest, in this case BCBGCOMM. We then read the variables of interest from the student data file. First we read the identification of the country and the school (IDCNTRY and IDSCHOOL) which will allow us to merge the student data to the school data. We also select from this variable the international mathematics achievement score (BIMATSCR), the sampling weight for the student (TOTWGT ), the variables that contain the jackknife replication information (JKZONE and JKINDIC), and the variable that will be used to select the eighth graders from the data file (IDGRADER). We then proceed to merge the school information with the student information using the variables IDCNTRY and IDSCHOOL as merge variables, and then use the macro JACK to obtain the corresponding percents of students within each group, and their mean achievement in mathematics. The computer code used to run this analysis in SAS and SPSS can be found in Figure 9.17 and Figure 9.18, and the results are shown in Figure 9.19 and Figure 9.20. 9 - 3 4 T I M S S D A T A B A S E U S E R G U I D E
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Contents Chapter 1: Overview of TIM
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C O N T E N T S 7.3 Codebook Files
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C O N T E N T S Table 7.7 Classific
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Chapter 1 Overview of TIMSS and the
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I N T R O D U C T I O N C H A P T E
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Chapter 2 TIMSS Instruments and Boo
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I N S T R U M E N T S C H A P T E R
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