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 Figure 9.8 SPSS Control Code and Extract of Output File for Using the Macro JACK.SPS get file = "bsgall1.sys" / keep=idcntry idstud idgrader jkindic jkzone totwgt itsex bimatscr . select if not(missing(itsex)). * Now use the macro to get the results. include "jack.sps". jack cvar = idcntry idgrader itsex / dvar = bimatscr / njkr = 75 / jkz = jkzone / jki = jkindic / wgt = totwgt / . sort cases by idcntry idgrader. list var = all. IDCNTRY IDGRADER ITSEX N TOTWGT MNX PCT MNX_SE PCT_SE . . . . . . 36 1 1 3039 123648.61 500.451 51.8891 4.32185 2.32468 36 1 2 2560 114645.55 495.142 48.1109 5.20426 2.32468 36 2 1 3722 114806.50 531.990 49.6409 4.58168 2.13471 36 2 2 3529 116467.28 527.357 50.3591 5.10432 2.13471 40 1 1 1545 45945.00 508.568 53.2220 3.27647 1.63905 40 1 2 1358 40382.01 509.963 46.7780 4.58177 1.63905 40 2 1 1321 42574.48 535.625 50.0342 4.54757 1.79216 40 2 2 1385 42516.32 543.581 49.9658 3.23439 1.79216 56 1 1 1344 31717.08 558.544 49.4211 4.71205 2.76051 56 1 2 1424 32460.16 556.717 50.5789 4.47097 2.76051 56 2 1 1437 37480.16 567.222 49.9276 7.43010 3.70983 56 2 2 1457 37588.84 563.139 50.0724 8.78875 3.70983 840 1 1 1976 1587912.47 473.310 50.3006 5.74234 1.20563 840 1 2 1910 1568934.35 478.069 49.6994 5.74972 1.20563 840 2 1 3561 1574689.22 497.464 49.3897 4.47104 0.77280 840 2 2 3526 1613607.42 501.996 50.6103 5.21162 0.77280 890 1 1 1486 14410.14 495.847 51.3907 3.22189 0.76075 890 1 2 1411 13630.25 500.643 48.6093 3.48741 0.76075 890 2 1 1381 13341.15 536.878 51.3542 3.29050 0.90855 890 2 2 1324 12637.53 544.918 48.6458 3.76508 0.90855 9.4 Performing Analyses with Student-Level Variables Many analyses of the TIMSS data can be undertaken using student-level data. We have already presented one example in the previous section when explaining how to use the two macros provided with the data files. We now proceed to work out another example where all the steps are undertaken, including the invocation of the corresponding SAS and SPSS macro. For example, suppose we want to replicate one of the results presented in the international report. We are interested in looking at the eighth graders' reports on the hours spent each day watching television and videos, and their achievement in mathematics. These are the results that are presented in Figure 9.1 earlier in this chapter. T I M S S D A T A B A S E U S E R G U I D E 9 - 2 1
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 To do this we need to undertake several steps. After reviewing the codebooks and the questionnaire information we find out if the students were asked a question about the number of hours they watch television (see Supplement 2). The data collected from this variable are captured in the variable BSBGDAY1, and this variable is found in the Student Background data file. Our next step is to review the documentation of national adaptations to the questionnaires to ensure that there were no deviations listed for this variable (see Supplement 3). If no changes were made we can continue with our analysis without any modifications. We then proceed to read from the SPSS system file or SAS data set that contains 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), the variable that will be used to select the eighth graders from the data file (IDGRADER), and the variable containing the country identification code (IDCNTRY). In this analysis we will use the data for all countries in the database, although the exact same steps need to be taken if the user wants to examine these variables within a single country or for a select group of countries. The SAS code used to perform this analysis is presented in Figure 9.9, and the SPSS code is presented in Figure 9.10. A selection of the results obtained from these programs are displayed in Figure 9.11 and Figure 9.12. We have included as part of the programs the corresponding value labels and format statements so that the different categories or groups are labeled appropriately. Notice that one of the steps in each of these programs was to select only those students in the eighth grade who chose one of the five options presented in the question, and we recoded the variable so that there would be one category for those who reported watching television or video less than one hour per day. Note also in this analysis that although we have used the data from all the countries, we selected from the system file or SAS data set only those variables that are relevant to our analysis. Although, in general, this analysis is quite feasible with a powerful desktop computer, the user needs to keep in mind that computing and storage requirements for these type of analysis are quite demanding. In general, to perform analyses such as those in figures 9.9 and 9.10 using the Student Background data files, the user needs to do the following: • Identify the variable or variables of interest in the student file and find out about any specific national adaptations to the variable. • Retrieve the relevant variables from the data files, including the achievement score, sampling weights, JRR replication information, and any other variables used in the selection of cases. • Use the macro JACK with the corresponding arguments and parameters. • Print out the result file. 9 - 2 2 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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International Association for the 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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Chapter 3 Sampling and Sampling Wei
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C H A P T E R 4 D A T A C O L L E C
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C H A P T E R 6 A C H I E V E M E N
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C H A P T E R 7 D A T A B A S E F I
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