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 CVAR DVAR This lists the variables that are to be used to classify the students in the data file. This can be a single variable, or a list of variables. The maximum number of variables will depend mostly on the computer resources available to the user at the time. It is recommended to always include the variable that identifies the country. At least one variable has to be specified, usually IDCNTRY. This is the variable for which means are to be computed. Only one variable has to be listed here. If the user wants to find out, for example, the results in mathematics and science, then the macro needs to be invoked separately to generate each table. Although in most cases the continuous variable of interest will be an achievement variable, this can actually be any other continuous variable in the data file. FNAME The name of the data set that contains the variables necessary for the analysis. The name of this file has to be in either the single or multilevel SAS format. The multilevel SAS file format contains the library as well as the data set. It is important to emphasize that this data set must include only those cases that are of interest in the analysis. If the user wants to have specific cases excluded from the analysis, as for example, students with missing variables or select students from a specific grade, this should be done prior to invoking the macro. The simplest and most straightforward way is to invoke the macro using the conventional SAS notation for invoking macros followed by the list of arguments for the analysis enclosed in parenthesis, separated by commas, and in the same order as they are listed above. For example, if the macro is invoked as: %jack(TOTWGT ,JKZONE,JKINDIC,75,IDCNTRY IDGRADER ITSEX, BIMATSCR, BSGALL1); it will compute the mean mathematics achievement, and its corresponding standard error, for boys and girls, by grade, within each country, using the variable TOTWGT as the sampling weight. It will also compute the percent of boys and girls by grade within the country, and its corresponding standard error. The data will be read from the data set BSGALL1, and the standard error of the statistics will be computed based on 75 replicate weights. The file that contains these results is called FINAL and can be found in the WORK library. The variables that are contained in this file are: Classification Variables Each of the classification variables are kept in the resulting file. In the above example there would be three variables in the WORK.FINAL data set. These would be IDCNTRY, IDGRADER, and ITSEX. There is one unique occurrence for each combination of the categories for these variables. T I M S S D A T A B A S E U S E R G U I D E 9 - 1 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 Weight Variable N MNX Contains the estimate in the population that belongs to the group defined by the specific combination of the classification variable categories. In our example this variable is called TOTWGT . Contains the number of cases in the group defined by the specific combination of categories for the classification variables. Contains the weighted mean of the variable DVAR for the group defined by the corresponding combination of classification variable categories. MNX_SE PCT Contains the standard error of the mean for variable specified in DVAR, computed using the JRR method for computing the standard error. Contains the weighted percent of people in the group for the classification variable listed last, within the specific combination of the categories defined by the groups defined initially. In our example, we would obtain the percent of boys and girls, within each combination of country and grade. PCT_SE Contains the standard error of PCT computed using the JRR method for computing the standard error. The file resulting from using this macro can then be printed using a SAS procedure of choice. An example call to this macro, and a subset of the resulting file is presented in Figure 9.6. In this example the macro computes the percent of boys and girls, by grade and by country, and their mean achievement in mathematics. The listing presented in Figure 9.6 is interpreted in the following way. The first line shows that there were 3039 students in the sample for IDCNTRY=36 (Australia), in IDGRADER=1 (Seventh grade), and who had ITSEX=1 (Girls). It is estimated that there are 123649 seventh-grade girls in Australia, their mean mathematics score is 500, with a standard error of 4.3. We can also tell from this line of data that it is estimated that 52 percent of the seventh graders in Australia are girls, and the standard error of this percent is 2.3. The second line shows the same information, but for the seventh-grade boys (ITSEX=2). It is estimated that there are 114646 seventh-grade boys in Australia, their mean mathematics score is 495, with a standard error of 5.2. We can also tell from this line of data that it is estimated that 48 percent of the seventh graders in Australia are boys, and the standard error of this percent is 2.3. 9 - 1 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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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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S A M P L I N G C H A P T E R 3 3.5
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S A M P L I N G C H A P T E R 3 Tab
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S A M P L I N G C H A P T E R 3 SEN
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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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