University of Oslo Workshops June 29-30 Conference July 1-3 ...

in Latvia’s primary education (fourth grade). Data from two IEA studies, PIRLS 2006 and
TIMSS 2007, were used. Even though Latvia’s overall results in the international arena at
the primary school level have always looked rather good (Latvia scored well above the
international average in reading literacy, mathematics, and science), how these scores are
distributed across the population is very important. Intraclass correlations and variance
components alone provide little more than an indication of equity, or in this instance
inequity, in student achievement, and further analyses of contextual information is
necessary to explain these results. Socio-economic status was the most important
determinant of student achievement, but socio-economic backgrounds of individual
students could not fully explain the urbanization effect. Low achievement equity in
education proves to be a problem of segregation by socio-economic status, and the
urbanization effect is significant mostly because the segregation was more obvious in the
rural areas of Latvia. Almost half of the originally stated urbanization effect was explained
by controlling for the proportions of disadvantaged students in different schools.
Keywords: equity in education; achievement gap by urbanization; socioeconomic
background; community composition effects
Exploring the Measurement Profiles of Socioeconomic Background and their
differences in Reading Achievement: A Two-level Latent Class Analysis
Kajsa Yang Hansen, University of Gothenburg, Sweden
Ingrid Munck, University of Gothenburg, Sweden
Appling two-level latent class analysis technique, the proposed study is to explore the
psychometric profiles of SES and to examine the reading achievement differences
according to the latent profile belongingness. Unlike the previous view of SES simply
being an observed index of all its indicators or as being a latent continuous construct, this
measurement approach conceptualizes SES by forming distinct categories or typologies of
it. The latent class approach takes into consideration the measurement error in the response
patterns of the SES indicators, and bases the categorizations on the prior and posterior
probability distributions under the conditioned maximum likelihood estimation. This
analysis will firstly be applied to Swedish PIRLS 2006 data and the empirical findings will
be verified by Norwegian data. SES indicators of individuals are from Student and Home
Questionnaires in PIRLS 2006 and the school SES composition is measured, in addition to
the individual level SES indicators, by variables from School Questionnaire.
Keywords: SES; PIRLS; latent class analysis; school composition; reading achievement
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