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  • 1.
    Laukaityte, Inga
    Umeå universitet, Samhällsvetenskapliga fakulteten, Handelshögskolan vid Umeå universitet, Statistik.
    Single Imputation from a Conditional Distribution vs Multiple Imputation for Data with a Non-monotone Missing PatternManuskript (preprint) (Övrigt vetenskapligt)
    Abstract [en]

    Missing information is common in real data studies. When missingness is large, it should not be ignored and, instead, a missing data imputation method should be considered. The choice of the imputation method depends on the type or the pattern of missing information, as well as the nature of data. For instance, observations in large-scale educational assessments are incomplete by missing some components and based on usually positively correlated results within students. In all types of analysis of such data, the correlation has to be considered in a reliable calculation of properties of estimates. The aim of this paper is to compare a single imputation from a conditional distribution (with or without weights) and multiple imputation for data with a non-monotone missing pattern and high positive correlation between variables. For this purpose, such estimates as mean and variance are compared. The simulation results showed that expectation and variance are estimated more reliably when the imputation from a conditional distribution (without and with weights) or a complete-data set are used, compared to multiple imputation.

  • 2.
    Laukaityte, Inga
    Umeå universitet, Samhällsvetenskapliga fakulteten, Handelshögskolan vid Umeå universitet, Statistik.
    Statistical modeling in international large-scale assessments2016Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    This thesis contributes to the area of research based on large-scale educational assessments, focusing on the application of multilevel models. The role of sampling weights, plausible values (response variable imputed multiple times) and imputation methods are demonstrated by simulations and applications to TIMSS (Trends in International Mathematics and Science Study) and PISA (Programme for International Student Assessment) data.

    The large-scale assessments use multistage sampling design, which means that the units such as schools, classrooms, or students at some or all stages are selected with unequal probabilities. In order to make valid estimates and inferences sampling weights should be used. Thus, in the first paper, we examine different approaches and give recommendations concerning handling sampling weights in multilevel models when analyzing large-scale assessments.

    Due to limitations in time and the number of students, the complex surveys use matrix sampling of items. This means that a response variable, i.e. students' performance, contains a large amount of information that is missing by design. Therefore, in order to estimate students' proficiency, TIMSS and PISA use the plausible values approach, which results in a set of five plausible values – proficiencies, computed for each student. In the second paper, different user strategies concerning plausible values for multilevel models as well as means and variances are examined with both real and simulated data. Missing information that is present because of the matrix sampling design for instance like the one used in PISA, can be arranged into a non-monotone missing data pattern, where all variables are incomplete and highly positively correlated. In the third paper, we compare a few imputation methods: a single imputation from a conditional distribution (with and without weights) and multiple imputation, for data with a non-monotone missing pattern (with no complete variables) and high positive correlation between variables.

    In several of the recent international large-scale assessments, students in Sweden demonstrate a decreasing performance. Some previous research has shown that changes in performance depend on students’ performance levels. In the fourth paper, we studied the relationship between student performance and the between-school variance and tried to identify factors associated with student performance in mathematics in PISA in low-, medium-, and high- performing schools in the Nordic countries.

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  • 3.
    Laukaityte, Inga
    et al.
    Umeå universitet, Samhällsvetenskapliga fakulteten, Handelshögskolan vid Umeå universitet, Statistik.
    Rolfsman, Ewa
    Umeå universitet, Samhällsvetenskapliga fakulteten, Institutionen för tillämpad utbildningsvetenskap, Beteendevetenskapliga mätningar (BVM).
    Low-, Medium-, and High-performing Schools in the Nordic Countries: student Performance at PISA Mathematics 2003-2012Manuskript (preprint) (Övrigt vetenskapligt)
    Abstract [en]

    Decreasing performance among students in Sweden on international comparative studies and increasing segregation of schools, has led to a debate concerning strategies for improving student performance. The aim of this study is to analyse the between school variance and to identify factors associated with student performance in PISA in mathematics at different performance levels in the Nordic countries. In order to separate the effect of school-level variables, from the effect of student’s background factors and to take care of the multistage sampling design used in PISA, multilevel analysis was used. The results show that no evidence regarding the relationship between the average student performance in mathematics and the between-school variance was found which, is in contrast to previous studies conducted on science performance in PISA. Regarding school-level factors, our results overall have shown that few school-level factors (having a positive or a negative effect) seemed to be associated with performance. School-level factors associated with performance have mainly been identified among low- and medium-performing schools, and to a less extent among students at high-performing schools (only in Sweden and Denmark). This is a result which is in line with other studies showing the educational system’s incapacity to provide support for high-performing students and to enhance their learning.

  • 4.
    Laukaityte, Inga
    et al.
    Umeå universitet, Samhällsvetenskapliga fakulteten, Institutionen för tillämpad utbildningsvetenskap.
    Rolfsman, Ewa
    Umeå universitet, Samhällsvetenskapliga fakulteten, Institutionen för tillämpad utbildningsvetenskap.
    Low, medium, and high-performing schools in the Nordic countries. Student performance at PISA Mathematics 2003-20122020Ingår i: Education Inquiry, ISSN 2000-4508, E-ISSN 2000-4508Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Decreasing performance in several core subjects among students in Sweden and the increasing segregation of schools are urgent issues in relation to equity in education, which has been a long-term goal in Sweden. The aim of this study is to identify factors in the school environment associated with student performance in PISA in mathematics at different performance levels in the Nordic countries. In order to separate the effects of school-level variables from the effects of student background factors, and to deal with the multistage sampling design used in PISA, multilevel analysis was used in this exploratory study. Based on data from PISA 2003 and 2012, which are the most recent assessments with a focus on mathematics, results have shown that a few school-level factors seemed to be associated with student success, and then mainly among low and medium-performing schools. Overall, school-level factors associated with success (or lack of it) partially differ between countries and over the years. These results have implications for educational professionals since some of the school-level factors identified inhibit potential for change.

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  • 5.
    Laukaityte, Inga
    et al.
    Umeå universitet, Samhällsvetenskapliga fakulteten, Handelshögskolan vid Umeå universitet, Statistik.
    Wiberg, Marie
    Umeå universitet, Samhällsvetenskapliga fakulteten, Handelshögskolan vid Umeå universitet, Statistik.
    Importance of sampling weights in multilevel modeling of international large-scale assessment data2018Ingår i: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 47, nr 20, s. 4991-5012Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Multilevel modeling is an important tool for analyzing large-scale assessment data. However, the standard multilevel modeling will typically give biased results for such complex survey data. This bias can be eliminated by introducing design weights which must be used carefully as they can affect the results. The aim of this paper is to examine different approaches and to give recommendations concerning handling design weights in multilevel models when analyzing large-scale assessments such as TIMSS (The Trends in International Mathematics and Science Study). To achieve the goal of the paper, we examined real data from two countries and included a simulation study. The analyses in the empirical study showed that using no weights or only level 1 weights sometimes could lead to misleading conclusions. The simulation study only showed small differences in estimation of the weighted and unweighted models when informative design weights were used. The use of unscaled or not rescaled weights however caused significant differences in some parameter estimates.

  • 6.
    Laukaityte, Inga
    et al.
    Umeå universitet, Samhällsvetenskapliga fakulteten, Handelshögskolan vid Umeå universitet, Statistik.
    Wiberg, Marie
    Umeå universitet, Samhällsvetenskapliga fakulteten, Handelshögskolan vid Umeå universitet, Statistik.
    Using plausible values in secondary analysis in large–scale assessments2017Ingår i: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 46, nr 22, s. 11341-11357Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Plausible values are typically used in large–scale assessment studies, in particular in the Trends in International Mathematics and Science Study and the Programme for International Student Assessment. Despite its large spread there are still some questions regarding the use of plausible values and how such use affects statistical analyses. The aim of this paper is to demonstrate the role of plausible values in large–scale assessment surveys when multilevel modelling is used. Different user strategies concerning plausible values for multilevel models as well as means and variances are examined. The results show that some commonly used user strategies give incorrect results while others give reasonable estimates but incorrect standard errors. These findings are important for anyone wishing to make secondary analyses of large–scale assessment data, especially those interested in using multilevel models to analyze the data.

  • 7.
    Lyrén, Per-Erik
    et al.
    Umeå universitet, Samhällsvetenskapliga fakulteten, Institutionen för tillämpad utbildningsvetenskap, Beteendevetenskapliga mätningar (BVM).
    Laukaityte, Inga
    Umeå universitet, Samhällsvetenskapliga fakulteten, Institutionen för tillämpad utbildningsvetenskap, Beteendevetenskapliga mätningar (BVM).
    Högskoleprovet våren och hösten 2018: provtagargruppens sammansättning och resultat2019Rapport (Övrigt vetenskapligt)
    Abstract [sv]

    Högskoleprovet har sedan 1977 fungerat som urvalsinstrument för antagningtill universitets- och högskolestudier. Avsikten med föreligganderapport är att beskriva provtagargrupperna våren och hösten 2018 medavseende på sammansättning och resultat. Resultaten presenteras förprovtagare med olika kön, ålder och utbildning. Vidare beskrivs hurnormeringen av provresultaten genomförs och utfallet av normeringen, samtupprepat provtagande.

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  • 8.
    Rolfsman, Ewa
    et al.
    Umeå universitet, Samhällsvetenskapliga fakulteten, Institutionen för tillämpad utbildningsvetenskap.
    Wiberg, Marie
    Umeå universitet, Samhällsvetenskapliga fakulteten, Handelshögskolan vid Umeå universitet, Statistik.
    Laukaityte, Inga
    Umeå universitet, Samhällsvetenskapliga fakulteten, Handelshögskolan vid Umeå universitet, Statistik.
    School effectiveness in the Nordic countries in relation to PISA and TIMSS2013Konferensbidrag (Refereegranskat)
    Abstract [en]

    In a Nordic perspective, the finish students overall achieve the highest score on PISA (Programme for International Student Assessment), while the Swedish students exhibit declining results. The results of the Swedish students have drawn attention to the quality of education and the role of the educational professionals and the efficiency of the school. It is therefore of vital importance to investigate whether these results can be related to school level factors in a Nordic perspective. However, TIMSS (Trends in International Mathematics and Science Study) and PISA exhibit similarities as well as differences as they target different subjects. In addition, the results on TIMSS and PISA differ between countries. The aim of this study is to investigate whether school level factors can contribute to the explanation of the results for the Nordic countries participating in PISA 2009 and, if so, identify factors that can be influenced in order to enhance students’ achievement. We focus on school effectiveness in relation to PISA, since all Nordic countries participate in PISA. However, the results are contrasted to results from TIMSS for Sweden and Norway. In order to separate the effect of school level variables from the effect of student’s home environment and to take care of the sampling design used in TIMSS and PISA, multilevel analysis was used. The results show that only a few school level factors were significant, and only in Sweden and Finland. Furthermore, school level factors in Sweden and Norway on PISA differ from school level factors based on TIMSS data.

  • 9.
    Wiberg, Marie
    et al.
    Umeå universitet, Samhällsvetenskapliga fakulteten, Handelshögskolan vid Umeå universitet, Statistik.
    Rolfsman, Ewa
    Umeå universitet, Samhällsvetenskapliga fakulteten, Institutionen för tillämpad utbildningsvetenskap, Beteendevetenskapliga mätningar (BVM).
    Laukaityte, Inga
    Umeå universitet, Samhällsvetenskapliga fakulteten, Handelshögskolan vid Umeå universitet, Statistik.
    School effectiveness in mathematics in Sweden and Norway 2003, 2007 and 20112013Konferensbidrag (Refereegranskat)
    Abstract [en]

    International studies like TIMSS (Trends in International Mathematics and Science Study) usually highlight students’ study result changes between two assessments. The aim of this study is to identify factors that contribute to the explanation why some schools is effective but others are less effective in terms of the students’  academic achievement in mathematics on TIMSS. We focus on the school efficiency in Sweden and Norway at the time points 2003, 2007 and 2011. In the core subject mathematics, Sweden’s result declined in TIMSS between 2003 and 2011 while Norway’s result inclined. Since Sweden and Norway have similar educational systems it is of interest to examine the incline in Norway as opposed to the decline in Sweden between 2003 and 2011. We used multilevel analysis in order to separate the effect of school level variables from the effect of student’s home environment and to take care of the sampling design used in TIMSS. The results show that Norway and Sweden exhibit different trends. In Norway it was possible to identify school level factors which affect the students’ achievements. In Sweden this could not be obtained. Note, in both countries at all three timepoints most of the student home background factors were significant suggesting that the students’ background play a large role in mathematics achievement.

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