Creating an Index of Economic Social and Cultural Status (ESCS)

Once factor and reliability analyses confirmed the consistency of the created scales, factor scores were saved and retained for further analyses.

8.3.6.3 Correlation Analyses

As a final step of the data reduction process, bivariate correlation analysis were performed in order to determine the association between background scales and mathematics and science achievement. First, correlations between the obtained scales, as well as single variables that were retained for further analyses, were verified to ensure that no multicollinearity occur in the data. Multicollinearity may occur when predictors are highly correlated with other predictors in the model. In multiple regressions (as will be applied for the multilevel analysis), this can interfere with determining the precise effect of each predictor in the final model. In cases of high correlations between variables, it therefore is suggested to either remove one of the con-cerned predictors or to combine the highly correlating variables into a new one (Cohen et al., 2007). For the current analyses, inter-item correlations between indicator variables higher than 0.8 (Field, 2004, p. 132) were further investigated.

For this study, the Pearson product moment coefficient was calculated with help of the IEA IDB Analyzer (see 8.3.1). The coefficients range from -1 to 1, indicating direction and strength of the relationship. Usually, correlations below 0.35 are classified as “low”, between 0.35 and 0.65 as “medium”, and above 0.65 as “high”. However, this study will apply a minimum value of 0.2 for the correlation coefficient. This low cut-off point was chosen as in exploratory studies, when considering the high sample size, it might be worthy to also explore low correlations in exploratory relationship research (Cohen et al., 2007, p. 536). A correlation could also be de-scribed as the common variance that is obtained by squaring the correlation results (Cohen et al., 2007, p. 536). This means that a correlation of 0.2 then would explain 4% of the shared variance. Those percentages of explained variances that are relatively low, nevertheless, might still be important from a policy perspective (Teddlie & Reynolds, 2000, p. 98). All level 1 cor-relations were calculated based on the country-specific student sample sizes listed in Table 7-1, while level 2 correlations were calculated between the course averages of the predictor variables and the corresponding course averages of the outcome scores.

8.4 Creating an Index of Economic Social and Cultural Status (ESCS)

environment variables on student’s academic achievement. Controlling for the student back-ground should allow for disentangling the home backback-ground from the school environment ef-fects, hence allowing to better capture the school effects “net of” the influences from the social background, as suggested for example by Buchmann (2002, p. 151).

In modern national and international assessments, a variety of variables are used to capture the social family background and, depending on the use of the indicator, different techniques are used for its construction. Sirin (2005, p. 418), conducting a meta-analysis in this area, found that in social studies the social background was often defined as relating to the concept of the socio-economic status (SES). SES, according to Mueller and Parcel (1981, p. 14), describes an individual’s (or a family’s) position in a hierarchically-organized society to access or exert con-trol over wealth and power – often with parental income, parental education, and parental oc-cupation as core indicators. Recent research is oriented by the theoretical foundations of the underlying processes, often drawing from the theory of capitals by Bourdieu which also will be the main focus for the current study (see also section 3.3.4.3 for more details on theories to explain disparities in the social background of students). Bourdieu not only focused on the im-portance of economic capital, but also elaborated that the cultural and social capital of a family are important to finally explain (and maintain) social disparities, which in turn influences the school learning experiences of the children. More recent studies, therefore, extend the econom-ical component by a cultural component to more comprehensively and accurately measure so-cial background conditions. However, variables related to soso-cial capital are scarce in interna-tional assessments, probably because the measurement of social capital is far more complex than for other forms of capital, as it would need comprehensive information about the network of the family or person. Accordingly, the TIMSS 2015 data does not contain relevant variables well suited to measure the social capital. As concluded from the discussions detailed in section 3.3.4.3, it could be assumed that especially in the GCC countries, among the national popula-tions, social capital might be of high importance – because it is the social network and the proximity to the ruling elite that defines access to material goods, power, and also education.

To capture the economic and cultural capital of a family, a variety of variables are available in the TIMSS questionnaires, as detailed below.

Economical capital can be measured by questions related to the absolute or relative economic wealth of a family, or certain home possessions, which express economic wealth. Suitable coun-try-specific items of GCC countries asked in TIMSS 2015 might comprise for example: swim-ming pool (United Arab Emirates, Bahrain), luxury car (United Arab Emirates), private house

maid (Qatar), or private garden (Saudi Arabia). However, as exhibited above, items selected by the GCC countries vary across the region, indicating diminishing suitability for the current project, which is focused on regional comparability.

As questions directly related to income and economic wealth usually generate high missing rates, the occupational status of the parents is often used as a proxy for the economic situation of a family. A certain occupation also usually requires a particular education level, which makes occupation therefore also partly an indicator for the institutionalized cultural capital. However, as the current study does not intend to separate the effects between both forms of capital, using the occupational level of the parents here is deemed as suitable.

As a starting point for the comparison of different occupations of parents, in terms of wealth characteristics or prestige, a standardized manner of collecting occupational information is needed. For this purpose, the International Labour Office (2012) developed the International Classification of Occupations (ISCO), allowing for the hierarchical categorization of jobs into clearly defined groups according to its tasks and duties. Based on this standardized job classi-fication, different models are available from which to derive information about the prestige of a certain occupation. For international comparisons, such as in PISA, the International Socio-Economic Index of Occupational Status (ISEI) is mainly used (Ehmke & Siegle, 2005; Marks, Cresswell, & Ainley, 2006). Based on the occupational data of 16 countries, the ISEI was de-veloped by Ganzeboom, Graaf, and Treiman (1992) and can be interpreted as measuring “the attributes of occupations that convert a person’s main resource (education) into a person’s main reward (income)” (Ganzeboom et al., 1992, pp. 8–9). Occupations coded with the ISCO classi-fication can be transferred to a corresponding value between 16 and 90 on the ISEI scale. While the occupational data in TIMSS were not collected according to the ISCO classification system, they can still be represented on the ISEI scale, following a matching procedure developed by Caro and Cortés (2012) for PIRLS 2006. As the parental occupation classification in the TIMSS 2015 questionnaires still matches the classification used in PIRLS 2006 exactly, the TIMSS 2015 parental occupations can also be translated to the ISEI scores using the same matching procedure. The obtained ISEI scores for each of the original TIMSS occupational categories are shown in Table 8-2.

.

Table 8-2: Match between TIMSS occupation categories (Variables ASBH23A/B) and ISEI scores following the procedure of Caro and Cortés (2012)

Notes. ASBH23A = Father’s occupation level/ ASBH23B = Mother’s occupation level ISEI = Economic Index of Occupational Status

The main indicator for cultural capital in international comparative assessments is usually the educational level of the parents, which more precisely is an indicator of the institutionalized cultural capital. To ensure a standardized and comparable collection of the parental level of education among different countries, in TIMSS, as well as in other comparative assessments, the International Standard Classification of Education (ISCED; UNESCO, 2012b) is used.

TIMSS data is collected according to the most recent ISCED 2011 standard. Similar to Ehmke and Siegle (2005), the ISCED levels were converted to an approximation of the number of school years in order to obtain the respective education level, allowing for a better comparison among the countries and especially between the different levels of education. The data for the conversion was obtained from the UNESCO Institute for Statistics [UIS] (2017). Table 8-3 shows the final matching values used after evaluating the UNESCO data.

ASBH23A/B Label

1 Has never worked outside the home for pay 22 2 Small business owner (< 25 employees) 57

3 Clerk 49

4 Service or sales worker 45

5 Skilled agricultural or fishery worker 31

6 Craft or trade worker 37

7 Plant or machine operator 33

8 General laborers 24

9 Corporate manager or senior official 67

10 Professional 73

11 Technician or associate professional 52 TIMSS 2015 original Occupational Categories ISEI

Score

Table 8-3: Match between ISCED, TIMSS educational categories, and years of schooling

Note. Years of schooling for the different ISCED levels approximated by statistics from UIS (2017).

ISCED = International Standard Classification of Education (UNESCO, 2012b)

As an additional measure for cultural capital, indicators related to the objectified form of cul-tural capital are also often used. While the objects themselves are just material goods that can easily be exchanged to a form of economic capital, cultural goods such as books or musical instruments can only be adequately used by those who also possess the corresponding necessary incorporated cultural capital (Bourdieu, 1983, p. 190). Additionally, participation in cultural events may complete the indicators of cultural capital, but related questions are not available in the TIMSS 2015 questionnaires, and comparability between countries is doubtful.

TIMSS 2015, however, contains information about the number of books at home. It should be noted here that especially in Arab countries, the number of books might be of limited value as an indicator for cultural capital. This assumption can be drawn due to a long tradition in the region of orally transmitting information, and a late introduction of printing technology (Rob-inson, 1993). Correspondingly, the associations between number of books at home and student achievement can be expected to be somewhat lower when compared to Western countries, es-pecially for the Arab national population. Nevertheless, the number of books may work better for the large non-national populations in the GCC countries, and also may have increased value for the new group of national businessmen. Therefore, the variable still should be included here.

Hence, for the current study, the following variables will be included for the index creation: the highest occupation level of the parents transferred into HISEI scores, the highest education level of the parents converted into years of schooling, and the number of books at home.

Modeling of the main components related to economic and cultural capital follow the concep-tion of Ehmke and Siegle (2005), who used different variables to combine the economic and cultural aspects into a common index, namely the Economic, Social, and Cultural Status (ESCS)

Bahrain Kuwait Oman Qatar Saudi Arabia

United Arab Emirates

Did not go to school 0.0 0.0 0.0 0.0 0.0 0.0

1 Some primary or lower secondary 6.0 5.0 6.0 6.0 6.0 5.0

2 Lower secondary 9.0 9.0 9.0 9.0 9.0 9.0

3 Upper secondary 12.0 12.0 12.0 12.0 12.0 12.0

4 Post-secondary, non-tertiary 14.0 14.0 13.0 13.0 13.0 13.0

5 Short cycle tertiary 14.0 14.5 14.5 14.0 14.5 14.0

6 Bachelor or equivalent 16.0 16.0 16.0 16.0 16.0 16.0

7 18.0 18.0 18.0 18.0 18.0 18.0

8 Masters/Doctor 21.0 21.0 21.0 21.0 21.0 21.0

Years of schooling ISCED

2011

levels TIMSS education levels

index. Analyzing PISA data, they could show that the ESCS index covers the student back-ground more comprehensively than single concepts of economic or cultural capital used in ear-lier studies, and they could also explain more variance in the student achievement when com-pared to using single variables. Moreover, the current study applies complex multilevel models with a number of different variables, which also calls for a parsimonious conception of the student background portion. Ehmke and Siegle (2005) used as a basis for their index the highest occupational status of either parents measured on the ISEI scale (the so-called HISEI), the high-est parental education level converted into years of schooling (HISCED), and a measure of different home possessions. Here, ISEI and years of schooling are calculated separately for both parents in order to yield a greater reliability, with more variables included, and to achieve a better balance between concepts and variables, as argued by Caro and Cortés (2012, p. 25). As the country-specific home possessions defined by the GCC countries turned out to be quite different across the region, and combinations of common home possession items included in the TIMSS questionnaires only showed very low correlations with student achievement, the number of books at home was the only item included in the creation of the ESCS index, instead of a larger set of home possessions (similar to the approach of Schulz-Heidorf, 2016, p. 140).

Table 8-4 and Table 8-5 show the correlation between the variables used for the background model and mathematics and science achievement, respectively. The tables are based on the student level sample sizes presented in Table 7-1. Results show that of all variables used to create the ESCS Index, the Z-standardized educational levels of the parents (ZSJSBH20A = years of schooling of the father, ZSJSBH20B = years of schooling of the mother) have the highest correlation with student achievement in all countries. They are followed, with the ex-ception of the maternal occupational status in Bahrain, by both of the Z-standardized paternal ISEI scores (variables ZJSBH23A and ZJSBH23B). The number of books at home seems to be less important, especially in Kuwait and Saudi Arabia. The created ESCS index (variable F_ESCS) exhibits higher correlation with achievement, in comparison to each of its compo-nents, in all countries.

As discussed in section 3.3.4.3, in the Gulf region, nationality and gender are also important determinants for an individual’s position in society; therefore, both variables will also be used in the subsequent multilevel models to better capture the student’s social background. Nation-ality is based on the question “Was your father born in country” – as in all GCC countries the birthplace of the father is the main determinant for the nationality of his children (see APPEN-DIX B for more details).

Table 8-4: Correlation between SES variables and mathematics achievement

Notes. Significant correlations (0.05 level [2-tailed]) are marked in bold

BHR = Bahrain, KWT = Kuwait, OMN = Oman, QAT = Qatar, SAU = Saudi Arabia, ARE = United Arab Emirates

Table 8-5: Correlation between SES variables and science achievement

Notes. Significant correlations (0.05 level [2-tailed]) are marked in bold

BHR = Bahrain, KWT = Kuwait, OMN = Oman, QAT = Qatar, SAU = Saudi Arabia, ARE = United Arab Emirates